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Publisher: Elsevier   (Total: 3157 journals)

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Showing 1 - 200 of 3160 Journals sorted alphabetically
A Practical Logic of Cognitive Systems     Full-text available via subscription   (Followers: 9)
AASRI Procedia     Open Access   (Followers: 15)
Academic Pediatrics     Hybrid Journal   (Followers: 35, SJR: 1.655, CiteScore: 2)
Academic Radiology     Hybrid Journal   (Followers: 24, SJR: 1.015, CiteScore: 2)
Accident Analysis & Prevention     Partially Free   (Followers: 96, SJR: 1.462, CiteScore: 3)
Accounting Forum     Hybrid Journal   (Followers: 27, SJR: 0.932, CiteScore: 2)
Accounting, Organizations and Society     Hybrid Journal   (Followers: 37, SJR: 1.771, CiteScore: 3)
Achievements in the Life Sciences     Open Access   (Followers: 5)
Acta Anaesthesiologica Taiwanica     Open Access   (Followers: 7)
Acta Astronautica     Hybrid Journal   (Followers: 417, SJR: 0.758, CiteScore: 2)
Acta Automatica Sinica     Full-text available via subscription   (Followers: 2)
Acta Biomaterialia     Hybrid Journal   (Followers: 27, SJR: 1.967, CiteScore: 7)
Acta Colombiana de Cuidado Intensivo     Full-text available via subscription   (Followers: 2)
Acta de Investigación Psicológica     Open Access   (Followers: 3)
Acta Ecologica Sinica     Open Access   (Followers: 10, SJR: 0.18, CiteScore: 1)
Acta Haematologica Polonica     Free   (Followers: 1, SJR: 0.128, CiteScore: 0)
Acta Histochemica     Hybrid Journal   (Followers: 3, SJR: 0.661, CiteScore: 2)
Acta Materialia     Hybrid Journal   (Followers: 262, SJR: 3.263, CiteScore: 6)
Acta Mathematica Scientia     Full-text available via subscription   (Followers: 5, SJR: 0.504, CiteScore: 1)
Acta Mechanica Solida Sinica     Full-text available via subscription   (Followers: 9, SJR: 0.542, CiteScore: 1)
Acta Oecologica     Hybrid Journal   (Followers: 12, SJR: 0.834, CiteScore: 2)
Acta Otorrinolaringologica (English Edition)     Full-text available via subscription  
Acta Otorrinolaringológica Española     Full-text available via subscription   (Followers: 3, SJR: 0.307, CiteScore: 0)
Acta Pharmaceutica Sinica B     Open Access   (Followers: 1, SJR: 1.793, CiteScore: 6)
Acta Poética     Open Access   (Followers: 4, SJR: 0.101, CiteScore: 0)
Acta Psychologica     Hybrid Journal   (Followers: 27, SJR: 1.331, CiteScore: 2)
Acta Sociológica     Open Access   (Followers: 1)
Acta Tropica     Hybrid Journal   (Followers: 6, SJR: 1.052, CiteScore: 2)
Acta Urológica Portuguesa     Open Access  
Actas Dermo-Sifiliograficas     Full-text available via subscription   (Followers: 3, SJR: 0.374, CiteScore: 1)
Actas Dermo-Sifiliográficas (English Edition)     Full-text available via subscription   (Followers: 2)
Actas Urológicas Españolas     Full-text available via subscription   (Followers: 3, SJR: 0.344, CiteScore: 1)
Actas Urológicas Españolas (English Edition)     Full-text available via subscription   (Followers: 1)
Actualites Pharmaceutiques     Full-text available via subscription   (Followers: 6, SJR: 0.19, CiteScore: 0)
Actualites Pharmaceutiques Hospitalieres     Full-text available via subscription   (Followers: 3)
Acupuncture and Related Therapies     Hybrid Journal   (Followers: 7)
Acute Pain     Full-text available via subscription   (Followers: 14, SJR: 2.671, CiteScore: 5)
Ad Hoc Networks     Hybrid Journal   (Followers: 11, SJR: 0.53, CiteScore: 4)
Addictive Behaviors     Hybrid Journal   (Followers: 17, SJR: 1.29, CiteScore: 3)
Addictive Behaviors Reports     Open Access   (Followers: 8, SJR: 0.755, CiteScore: 2)
Additive Manufacturing     Hybrid Journal   (Followers: 10, SJR: 2.611, CiteScore: 8)
Additives for Polymers     Full-text available via subscription   (Followers: 23)
Advanced Drug Delivery Reviews     Hybrid Journal   (Followers: 160, SJR: 4.09, CiteScore: 13)
Advanced Engineering Informatics     Hybrid Journal   (Followers: 11, SJR: 1.167, CiteScore: 4)
Advanced Powder Technology     Hybrid Journal   (Followers: 17, SJR: 0.694, CiteScore: 3)
Advances in Accounting     Hybrid Journal   (Followers: 8, SJR: 0.277, CiteScore: 1)
Advances in Agronomy     Full-text available via subscription   (Followers: 15, SJR: 2.384, CiteScore: 5)
Advances in Anesthesia     Full-text available via subscription   (Followers: 28, SJR: 0.126, CiteScore: 0)
Advances in Antiviral Drug Design     Full-text available via subscription   (Followers: 2)
Advances in Applied Mathematics     Full-text available via subscription   (Followers: 10, SJR: 0.992, CiteScore: 1)
Advances in Applied Mechanics     Full-text available via subscription   (Followers: 11, SJR: 1.551, CiteScore: 4)
Advances in Applied Microbiology     Full-text available via subscription   (Followers: 24, SJR: 2.089, CiteScore: 5)
Advances In Atomic, Molecular, and Optical Physics     Full-text available via subscription   (Followers: 14, SJR: 0.572, CiteScore: 2)
Advances in Biological Regulation     Hybrid Journal   (Followers: 4, SJR: 2.61, CiteScore: 7)
Advances in Botanical Research     Full-text available via subscription   (Followers: 2, SJR: 0.686, CiteScore: 2)
Advances in Cancer Research     Full-text available via subscription   (Followers: 33, SJR: 3.043, CiteScore: 6)
Advances in Carbohydrate Chemistry and Biochemistry     Full-text available via subscription   (Followers: 9, SJR: 1.453, CiteScore: 2)
Advances in Catalysis     Full-text available via subscription   (Followers: 5, SJR: 1.992, CiteScore: 5)
Advances in Cell Aging and Gerontology     Full-text available via subscription   (Followers: 4)
Advances in Cellular and Molecular Biology of Membranes and Organelles     Full-text available via subscription   (Followers: 12)
Advances in Chemical Engineering     Full-text available via subscription   (Followers: 27, SJR: 0.156, CiteScore: 1)
Advances in Child Development and Behavior     Full-text available via subscription   (Followers: 10, SJR: 0.713, CiteScore: 1)
Advances in Chronic Kidney Disease     Full-text available via subscription   (Followers: 9, SJR: 1.316, CiteScore: 2)
Advances in Clinical Chemistry     Full-text available via subscription   (Followers: 26, SJR: 1.562, CiteScore: 3)
Advances in Colloid and Interface Science     Full-text available via subscription   (Followers: 19, SJR: 1.977, CiteScore: 8)
Advances in Computers     Full-text available via subscription   (Followers: 14, SJR: 0.205, CiteScore: 1)
Advances in Dermatology     Full-text available via subscription   (Followers: 15)
Advances in Developmental Biology     Full-text available via subscription   (Followers: 12)
Advances in Digestive Medicine     Open Access   (Followers: 9)
Advances in DNA Sequence-Specific Agents     Full-text available via subscription   (Followers: 5)
Advances in Drug Research     Full-text available via subscription   (Followers: 25)
Advances in Ecological Research     Full-text available via subscription   (Followers: 44, SJR: 2.524, CiteScore: 4)
Advances in Engineering Software     Hybrid Journal   (Followers: 28, SJR: 1.159, CiteScore: 4)
Advances in Experimental Biology     Full-text available via subscription   (Followers: 8)
Advances in Experimental Social Psychology     Full-text available via subscription   (Followers: 46, SJR: 5.39, CiteScore: 8)
Advances in Exploration Geophysics     Full-text available via subscription   (Followers: 1)
Advances in Fluorine Science     Full-text available via subscription   (Followers: 9)
Advances in Food and Nutrition Research     Full-text available via subscription   (Followers: 59, SJR: 0.591, CiteScore: 2)
Advances in Fuel Cells     Full-text available via subscription   (Followers: 16)
Advances in Genetics     Full-text available via subscription   (Followers: 16, SJR: 1.354, CiteScore: 4)
Advances in Genome Biology     Full-text available via subscription   (Followers: 8, SJR: 12.74, CiteScore: 13)
Advances in Geophysics     Full-text available via subscription   (Followers: 6, SJR: 1.193, CiteScore: 3)
Advances in Heat Transfer     Full-text available via subscription   (Followers: 23, SJR: 0.368, CiteScore: 1)
Advances in Heterocyclic Chemistry     Full-text available via subscription   (Followers: 12, SJR: 0.749, CiteScore: 3)
Advances in Human Factors/Ergonomics     Full-text available via subscription   (Followers: 23)
Advances in Imaging and Electron Physics     Full-text available via subscription   (Followers: 2, SJR: 0.193, CiteScore: 0)
Advances in Immunology     Full-text available via subscription   (Followers: 36, SJR: 4.433, CiteScore: 6)
Advances in Inorganic Chemistry     Full-text available via subscription   (Followers: 8, SJR: 1.163, CiteScore: 2)
Advances in Insect Physiology     Full-text available via subscription   (Followers: 2, SJR: 1.938, CiteScore: 3)
Advances in Integrative Medicine     Hybrid Journal   (Followers: 6, SJR: 0.176, CiteScore: 0)
Advances in Intl. Accounting     Full-text available via subscription   (Followers: 3)
Advances in Life Course Research     Hybrid Journal   (Followers: 8, SJR: 0.682, CiteScore: 2)
Advances in Lipobiology     Full-text available via subscription   (Followers: 1)
Advances in Magnetic and Optical Resonance     Full-text available via subscription   (Followers: 8)
Advances in Marine Biology     Full-text available via subscription   (Followers: 18, SJR: 0.88, CiteScore: 2)
Advances in Mathematics     Full-text available via subscription   (Followers: 11, SJR: 3.027, CiteScore: 2)
Advances in Medical Sciences     Hybrid Journal   (Followers: 7, SJR: 0.694, CiteScore: 2)
Advances in Medicinal Chemistry     Full-text available via subscription   (Followers: 5)
Advances in Microbial Physiology     Full-text available via subscription   (Followers: 4, SJR: 1.158, CiteScore: 3)
Advances in Molecular and Cell Biology     Full-text available via subscription   (Followers: 23)
Advances in Molecular and Cellular Endocrinology     Full-text available via subscription   (Followers: 8)
Advances in Molecular Toxicology     Full-text available via subscription   (Followers: 7, SJR: 0.182, CiteScore: 0)
Advances in Nanoporous Materials     Full-text available via subscription   (Followers: 3)
Advances in Oncobiology     Full-text available via subscription   (Followers: 2)
Advances in Organ Biology     Full-text available via subscription   (Followers: 1)
Advances in Organometallic Chemistry     Full-text available via subscription   (Followers: 17, SJR: 1.875, CiteScore: 4)
Advances in Parallel Computing     Full-text available via subscription   (Followers: 7, SJR: 0.174, CiteScore: 0)
Advances in Parasitology     Full-text available via subscription   (Followers: 5, SJR: 1.579, CiteScore: 4)
Advances in Pediatrics     Full-text available via subscription   (Followers: 24, SJR: 0.461, CiteScore: 1)
Advances in Pharmaceutical Sciences     Full-text available via subscription   (Followers: 12)
Advances in Pharmacology     Full-text available via subscription   (Followers: 16, SJR: 1.536, CiteScore: 3)
Advances in Physical Organic Chemistry     Full-text available via subscription   (Followers: 8, SJR: 0.574, CiteScore: 1)
Advances in Phytomedicine     Full-text available via subscription  
Advances in Planar Lipid Bilayers and Liposomes     Full-text available via subscription   (Followers: 3, SJR: 0.109, CiteScore: 1)
Advances in Plant Biochemistry and Molecular Biology     Full-text available via subscription   (Followers: 10)
Advances in Plant Pathology     Full-text available via subscription   (Followers: 5)
Advances in Porous Media     Full-text available via subscription   (Followers: 5)
Advances in Protein Chemistry     Full-text available via subscription   (Followers: 19)
Advances in Protein Chemistry and Structural Biology     Full-text available via subscription   (Followers: 20, SJR: 0.791, CiteScore: 2)
Advances in Psychology     Full-text available via subscription   (Followers: 64)
Advances in Quantum Chemistry     Full-text available via subscription   (Followers: 6, SJR: 0.371, CiteScore: 1)
Advances in Radiation Oncology     Open Access   (SJR: 0.263, CiteScore: 1)
Advances in Small Animal Medicine and Surgery     Hybrid Journal   (Followers: 3, SJR: 0.101, CiteScore: 0)
Advances in Space Biology and Medicine     Full-text available via subscription   (Followers: 6)
Advances in Space Research     Full-text available via subscription   (Followers: 404, SJR: 0.569, CiteScore: 2)
Advances in Structural Biology     Full-text available via subscription   (Followers: 5)
Advances in Surgery     Full-text available via subscription   (Followers: 11, SJR: 0.555, CiteScore: 2)
Advances in the Study of Behavior     Full-text available via subscription   (Followers: 34, SJR: 2.208, CiteScore: 4)
Advances in Veterinary Medicine     Full-text available via subscription   (Followers: 18)
Advances in Veterinary Science and Comparative Medicine     Full-text available via subscription   (Followers: 14)
Advances in Virus Research     Full-text available via subscription   (Followers: 5, SJR: 2.262, CiteScore: 5)
Advances in Water Resources     Hybrid Journal   (Followers: 47, SJR: 1.551, CiteScore: 3)
Aeolian Research     Hybrid Journal   (Followers: 6, SJR: 1.117, CiteScore: 3)
Aerospace Science and Technology     Hybrid Journal   (Followers: 352, SJR: 0.796, CiteScore: 3)
AEU - Intl. J. of Electronics and Communications     Hybrid Journal   (Followers: 8, SJR: 0.42, CiteScore: 2)
African J. of Emergency Medicine     Open Access   (Followers: 6, SJR: 0.296, CiteScore: 0)
Ageing Research Reviews     Hybrid Journal   (Followers: 11, SJR: 3.671, CiteScore: 9)
Aggression and Violent Behavior     Hybrid Journal   (Followers: 457, SJR: 1.238, CiteScore: 3)
Agri Gene     Hybrid Journal   (Followers: 1, SJR: 0.13, CiteScore: 0)
Agricultural and Forest Meteorology     Hybrid Journal   (Followers: 17, SJR: 1.818, CiteScore: 5)
Agricultural Systems     Hybrid Journal   (Followers: 31, SJR: 1.156, CiteScore: 4)
Agricultural Water Management     Hybrid Journal   (Followers: 42, SJR: 1.272, CiteScore: 3)
Agriculture and Agricultural Science Procedia     Open Access   (Followers: 4)
Agriculture and Natural Resources     Open Access   (Followers: 3)
Agriculture, Ecosystems & Environment     Hybrid Journal   (Followers: 57, SJR: 1.747, CiteScore: 4)
Ain Shams Engineering J.     Open Access   (Followers: 5, SJR: 0.589, CiteScore: 3)
Air Medical J.     Hybrid Journal   (Followers: 6, SJR: 0.26, CiteScore: 0)
AKCE Intl. J. of Graphs and Combinatorics     Open Access   (SJR: 0.19, CiteScore: 0)
Alcohol     Hybrid Journal   (Followers: 12, SJR: 1.153, CiteScore: 3)
Alcoholism and Drug Addiction     Open Access   (Followers: 11)
Alergologia Polska : Polish J. of Allergology     Full-text available via subscription   (Followers: 1)
Alexandria Engineering J.     Open Access   (Followers: 1, SJR: 0.604, CiteScore: 3)
Alexandria J. of Medicine     Open Access   (Followers: 1, SJR: 0.191, CiteScore: 1)
Algal Research     Partially Free   (Followers: 10, SJR: 1.142, CiteScore: 4)
Alkaloids: Chemical and Biological Perspectives     Full-text available via subscription   (Followers: 2)
Allergologia et Immunopathologia     Full-text available via subscription   (Followers: 1, SJR: 0.504, CiteScore: 1)
Allergology Intl.     Open Access   (Followers: 5, SJR: 1.148, CiteScore: 2)
Alpha Omegan     Full-text available via subscription   (SJR: 3.521, CiteScore: 6)
ALTER - European J. of Disability Research / Revue Européenne de Recherche sur le Handicap     Full-text available via subscription   (Followers: 10, SJR: 0.201, CiteScore: 1)
Alzheimer's & Dementia     Hybrid Journal   (Followers: 51, SJR: 4.66, CiteScore: 10)
Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring     Open Access   (Followers: 4, SJR: 1.796, CiteScore: 4)
Alzheimer's & Dementia: Translational Research & Clinical Interventions     Open Access   (Followers: 4, SJR: 1.108, CiteScore: 3)
Ambulatory Pediatrics     Hybrid Journal   (Followers: 6)
American Heart J.     Hybrid Journal   (Followers: 53, SJR: 3.267, CiteScore: 4)
American J. of Cardiology     Hybrid Journal   (Followers: 56, SJR: 1.93, CiteScore: 3)
American J. of Emergency Medicine     Hybrid Journal   (Followers: 44, SJR: 0.604, CiteScore: 1)
American J. of Geriatric Pharmacotherapy     Full-text available via subscription   (Followers: 10)
American J. of Geriatric Psychiatry     Hybrid Journal   (Followers: 14, SJR: 1.524, CiteScore: 3)
American J. of Human Genetics     Hybrid Journal   (Followers: 34, SJR: 7.45, CiteScore: 8)
American J. of Infection Control     Hybrid Journal   (Followers: 29, SJR: 1.062, CiteScore: 2)
American J. of Kidney Diseases     Hybrid Journal   (Followers: 35, SJR: 2.973, CiteScore: 4)
American J. of Medicine     Hybrid Journal   (Followers: 47)
American J. of Medicine Supplements     Full-text available via subscription   (Followers: 3, SJR: 1.967, CiteScore: 2)
American J. of Obstetrics and Gynecology     Hybrid Journal   (Followers: 223, SJR: 2.7, CiteScore: 4)
American J. of Ophthalmology     Hybrid Journal   (Followers: 66, SJR: 3.184, CiteScore: 4)
American J. of Ophthalmology Case Reports     Open Access   (Followers: 5, SJR: 0.265, CiteScore: 0)
American J. of Orthodontics and Dentofacial Orthopedics     Full-text available via subscription   (Followers: 6, SJR: 1.289, CiteScore: 1)
American J. of Otolaryngology     Hybrid Journal   (Followers: 25, SJR: 0.59, CiteScore: 1)
American J. of Pathology     Hybrid Journal   (Followers: 28, SJR: 2.139, CiteScore: 4)
American J. of Preventive Medicine     Hybrid Journal   (Followers: 28, SJR: 2.164, CiteScore: 4)
American J. of Surgery     Hybrid Journal   (Followers: 38, SJR: 1.141, CiteScore: 2)
American J. of the Medical Sciences     Hybrid Journal   (Followers: 12, SJR: 0.767, CiteScore: 1)
Ampersand : An Intl. J. of General and Applied Linguistics     Open Access   (Followers: 7)
Anaerobe     Hybrid Journal   (Followers: 4, SJR: 1.144, CiteScore: 3)
Anaesthesia & Intensive Care Medicine     Full-text available via subscription   (Followers: 63, SJR: 0.138, CiteScore: 0)
Anaesthesia Critical Care & Pain Medicine     Full-text available via subscription   (Followers: 18, SJR: 0.411, CiteScore: 1)
Anales de Cirugia Vascular     Full-text available via subscription  
Anales de Pediatría     Full-text available via subscription   (Followers: 3, SJR: 0.277, CiteScore: 0)
Anales de Pediatría (English Edition)     Full-text available via subscription  
Anales de Pediatría Continuada     Full-text available via subscription  
Analytic Methods in Accident Research     Hybrid Journal   (Followers: 5, SJR: 4.849, CiteScore: 10)
Analytica Chimica Acta     Hybrid Journal   (Followers: 42, SJR: 1.512, CiteScore: 5)
Analytical Biochemistry     Hybrid Journal   (Followers: 185, SJR: 0.633, CiteScore: 2)
Analytical Chemistry Research     Open Access   (Followers: 12, SJR: 0.411, CiteScore: 2)
Analytical Spectroscopy Library     Full-text available via subscription   (Followers: 12)
Anesthésie & Réanimation     Full-text available via subscription   (Followers: 2)
Anesthesiology Clinics     Full-text available via subscription   (Followers: 23, SJR: 0.683, CiteScore: 2)
Angiología     Full-text available via subscription   (SJR: 0.121, CiteScore: 0)
Angiologia e Cirurgia Vascular     Open Access   (Followers: 1, SJR: 0.111, CiteScore: 0)
Animal Behaviour     Hybrid Journal   (Followers: 205, SJR: 1.58, CiteScore: 3)

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Journal Cover
Neurocomputing
Journal Prestige (SJR): 1.073
Citation Impact (citeScore): 4
Number of Followers: 6  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 0925-2312
Published by Elsevier Homepage  [3157 journals]
  • Matrix Recovery with Implicitly Low-Rank Data
    • Abstract: Publication date: Available online 17 January 2019Source: NeurocomputingAuthor(s): Xingyu Xie, Jianlong Wu, Guangcan Liu, Jun Wang In this paper, we study the problem of matrix recovery, which aims to restore a target matrix of authentic samples from grossly corrupted observations. Most of the existing methods, such as the well-known Robust Principal Component Analysis (RPCA), assume that the target matrix we wish to recover is low-rank. However, the underlying data structure is often non-linear in practice, therefore the low-rankness assumption could be violated. To tackle this issue, we propose a novel method for matrix recovery in this paper, which could well handle the case where the target matrix is low-rank in an implicit feature space but high-rank or even full-rank in its original form. Namely, our method pursues the low-rank structure of the target matrix in an implicit feature space. By making use of the specifics of an accelerated proximal gradient based optimization algorithm, the proposed method could recover the target matrix with non-linear structures from its corrupted version. Comprehensive experiments on both synthetic and real datasets demonstrate the superiority of our method.
       
  • Multi-Task Learning Model based on Recurrent Convolutional Neural Networks
           for Citation Sentiment and Purpose Classification
    • Abstract: Publication date: Available online 17 January 2019Source: NeurocomputingAuthor(s): Abdallah Yousif, Zhendong Niu, James Chambua, Zahid Younas Khan Automated citation analysis is a method of identifying sentiment and purpose of citations in the citing works. Most of the existing approaches use machine learning techniques to boost the performance of citation sentiment classification (CSC) and citation purpose classification (CPC), which are the main tasks of automated citation analysis. However, such approaches address CPC and CSC by learning them separately, which often suffer from inadequate training data and time-consuming for feature engineering. To alleviate these problems, we propose a multitask learning model based on convolutional and recurrent neural networks. The proposed model benefits from jointly learning CSC and CPC by modeling the citation context with task-specific information and shared layers for citation sentiment and purpose classification. The network architecture of the proposed model is useful to represent the citation context and extracts the features automatically. We conduct experiments on two public datasets to evaluate the performance of the proposed model using standard metrics such as precision, recall, and F-score. The results of CSC and CPC tasks show improvements relative to classical machine learning algorithms such as SVM and NB as well as single-task deep learning models.
       
  • A novel deep learning driven low-cost mobility prediction approach for 5G
           cellular networks: The case of the Control/Data Separation Architecture
           (CDSA)
    • Abstract: Publication date: Available online 17 January 2019Source: NeurocomputingAuthor(s): Metin Ozturk, Mandar Gogate, Oluwakayode Onireti, Ahsan Adeel, Amir Hussain, Muhammad A. Imran One of the fundamental goals of mobile networks is to enable uninterrupted access to wireless services without compromising the expected quality of service (QoS). This paper proposes a novel analytical model for a holistic handover (HO) cost evaluation, that integrates signaling overhead, latency, call dropping, and radio resource wastage. The developed mathematical model is applicable to several cellular architectures, but the focus here is on the Control/Data Separation Architecture (CDSA). Furthermore, HO prediction is proposed and evaluated as part of the holistic cost for the first time, including through the novel application of a recurrent deep learning architecture, specifically, a stacked long-short-term memory (LSTM) model. Simulation results and preliminary analysis reveal different cases where non-predictive and predictive deep neural networks can be utilized, complying with the low cost and effective HO management requirement. Both analytical and machine learning models are evaluated with real-world human behaviors and interactions modeling data set. Numerical and comparative simulation results demonstrate the potential of our proposed framework in designing an enhanced, deep-learning driven HO management.
       
  • Sensor fault detection and diagnosis in the presence of outliers
    • Abstract: Publication date: Available online 17 January 2019Source: NeurocomputingAuthor(s): Chen Xu, Shunyi Zhao, Fei Liu In this paper, a sensor fault detection and diagnosis (FDD) method is proposed for linear state-space models in the presence of outliers. The t-distribution with unknown scale matrix and degrees of freedom (dof) parameter is used to describe the measurement noise. By using the variational Bayesian inference, the states, the scale matrix, and the dof parameter are estimated simultaneously. Since the noise distribution is no longer the Gaussian, a modified residual evaluation is proposed to detect the fault. After that, the cause of fault can be determined by observing the changes on measurement noise covariance. Two continuous stirred tank reactor (CSTR) process is conducted to demonstrate that the proposed method can provide more reliable FDD results than the existing methods when measurements contain outliers.
       
  • Motion planning and adaptive neural sliding mode tracking control for
           positioning of uncertain planar underactuated manipulator
    • Abstract: Publication date: Available online 17 January 2019Source: NeurocomputingAuthor(s): Pan Zhang, Xuzhi Lai, Yawu Wang, Min Wu The research on the robust control of uncertain planar underactuated manipulators is almost nonexistent due to the lack of actuator and the uncontrollability at equilibrium points. Taking an uncertain planar three-link passive-active-active underactuated manipulator as an example, this paper develops a robust control scheme including motion planning and adaptive tracking control to realize its position control. According to the constraints between the passive link and active links and the target position of the manipulator, differential evolution algorithm is used to solve the target angles and ratio between the angular velocities. Then, a set of motion trajectories is planned based on the target values. Considering the uncertain parameter perturbation and external disturbance exist, we use RBF neural network to online approximate the uncertainty. Meanwhile, we develop a set of fast terminal sliding mode controllers to track the planned trajectories, and design adaptive laws to guarantee the stability and convergence of the tracking system. Next, an online iteration algorithm is presented to correct the deviations of all link angles caused by the parameter perturbation, which makes the manipulator gradually approach to its target position. Finally, the simulation results verify the effectiveness of the proposed method.
       
  • A Feature Enriching Object Detection Framework with Weak Segmentation Loss
    • Abstract: Publication date: Available online 17 January 2019Source: NeurocomputingAuthor(s): Tianqi Zhang, Li-Ying Hao, Ge Guo This paper proposes an object detection model named Feature Enriching Object Detection Framework with Weak Segmentation Loss (FEOD) based on convolutional neural networks, which is a improvement of You Only Look Once (YOLO). To overcome the shortcoming of insensitivity to small objects, a novel feature enriching module is proposed to augment the semantic information of the feature in the shallow layer within a typical deep detector. Meanwhile, Focal Loss is also introduced to our model to further improve the algorithm performence. To obtain features more suitable for object detection, a more powerful feature extractor – DetNet is used as the backbone. An mAP of 83.8 in VOC2007 test and an mAP of 82.1 in VOC2012 test are achieved with a FPS of 11.3 with a NVIDIA GTX1080Ti GPU. For a lower resolution version, we achieve an mAP of 81.9 and 80.9 respectively in VOC2007 and VOC2012 tests with FPS of 17. Comparisons with other object detection algorithms have shown that our method works well and achieves a trade-off of accuracy and speed.
       
  • Local Adaptive Joint Sparse Representation for Hyperspectral Image
           Classification
    • Abstract: Publication date: Available online 16 January 2019Source: NeurocomputingAuthor(s): Jiangtao Peng, Xue Jiang, Na Chen, Huijing Fu In this paper, a local adaptive joint sparse representation (LAJSR) model is proposed for the classification of hyperspectral remote sensing images. It improves the original joint sparse representation (JSR) method in both the signal and dictionary construction phase and sparse representation phase. Given a testing pixel, a similar signal set is constructed by picking a few of the most similar pixels from its spatial neighborhood. The original training dictionary consists of training samples from different classes and is extended by adding spatial neighbors of each training sample. A local adaptive dictionary is built by selecting the most representative atoms from the extended dictionary that are correlated to the similar signal set. In the LAJSR framework, the selected similar signals are simultaneously represented by the local adaptive dictionary, and the obtained sparse representation coefficients are further weighted by a sparsity concentration index vector which aims to concentrate and highlight the coefficients on the expected class. Experimental results on two benchmark hyperspectral data sets have demonstrated that the proposed LAJSR method is much more effective than existing JSR and SVM methods, especially in the case of small sample sizes.
       
  • Robust Unsupervised Feature Selection by Nonnegative Sparse Subspace
           Learning
    • Abstract: Publication date: Available online 14 January 2019Source: NeurocomputingAuthor(s): Wei Zheng, Hui Yan, Jian Yang Sparse subspace learning has been demonstrated to be effective in data mining and machine learning. In this paper, we cast the unsupervised feature selection scenario as a matrix factorization problem from the viewpoint of sparse subspace learning. By minimizing the reconstruction residual, the learned feature weight matrix with the l2,1-norm and the non-negative constraints not only removes the irrelevant features, but also captures the underlying low dimensional structure of the data points. Meanwhile in order to enhance the model's robustness, l1-norm error function is used to resistant to outliers and sparse noise. An efficient iterative algorithm is introduced to optimize this non-convex and non-smooth objective function and the proof of its convergence is given. Although, there is a subtraction item in our multiplicative update rule, we validate its non-negativity. The superiority of our model is demonstrated by comparative experiments on various original datasets with and without malicious pollution.
       
  • A new multi-objective wrapper method for feature selection – Accuracy
           and stability analysis for BCI
    • Abstract: Publication date: Available online 14 January 2019Source: NeurocomputingAuthor(s): Jesús González, Julio Ortega, Miguel Damas, Pedro Martín-Smith, John Q. Gan Feature selection is an important step in building classifiers for high-dimensional data problems, such as EEG classification for BCI applications. This paper proposes a new wrapper method for feature selection, based on a multi-objective evolutionary algorithm, where the representation of the individuals or potential solutions, along with the breeding operators and objective functions, have been carefully designed to select a small subset of features that has good generalization capability, trying to avoid the over-fitting problems that wrapper methods usually suffer. A novel feature ranking procedure is also proposed in order to analyze the stability of the proposed wrapper method.Four different classification schemes have been applied within the proposed wrapper method in order to evaluate its accuracy and stability for feature selection on a real motor imagery dataset. Experimental results show that the wrapper method presented in this paper is able to obtain very small subsets of features, which are quite stable and also achieve high classification accuracy, regardless of the classifiers used.
       
  • Visibility Restoration of Single Foggy Images under Local Surface Analysis
    • Abstract: Publication date: Available online 12 January 2019Source: NeurocomputingAuthor(s): Lin-Yuan He, Liu Kun, Ji-Zhong Zhao, Du-Yan Bi A variety of empirical methods, which are represented by dark channel prior, have been proved effective for haze removal. However, undesirable artifacts and color distortion are still left on some of dehazing results, which directly determines the performance of computer vision tasks. Different from traditional statistical methods, we apply Multi-dimensional theory that quickly predicts haze free images. To this purpose, the local manifold similarity is employed to reduce the error of initial estimation. Moreover, contrast-based gaussian curvature is also introduced in order to obtain the smoothness transmission map. Compared with conventional methods, quantitative and qualitative comparisons have shown our approach improvement visual results.
       
  • Least squares support vector machine with self-organizing multiple kernel
           learning and sparsity
    • Abstract: Publication date: 28 February 2019Source: Neurocomputing, Volume 331Author(s): Chang Liu, Lixin Tang, Jiyin Liu In recent years, least squares support vector machines (LSSVMs) with various kernel functions have been widely used in the field of machine learning. However, the selection of kernel functions is often ignored in practice. In this paper, an improved LSSVM method based on self-organizing multiple kernel learning is proposed for black-box problems. To strengthen the generalization ability of the LSSVM, some appropriate kernel functions are selected and the corresponding model parameters are optimized using a differential evolution algorithm based on an improved mutation strategy. Due to the large computation cost, a sparse selection strategy is developed to extract useful data and remove redundant data without loss of accuracy. To demonstrate the effectiveness of the proposed method, some benchmark problems from the UCI machine learning repository are tested. The results show that the proposed method performs better than other state-of-the-art methods. In addition, to verify the practicability of the proposed method, it is applied to a real-world converter steelmaking process. The results illustrate that the proposed model can precisely predict the molten steel quality and satisfy the actual production demand.
       
  • A novel recurrent neural network and its finite-time solution to
           time-varying complex matrix inversion
    • Abstract: Publication date: 28 February 2019Source: Neurocomputing, Volume 331Author(s): Lin Xiao, Yongsheng Zhang, Kenli Li, Bolin Liao, Zhiguo Tan A complex-valued nonlinear recurrent neural network is designed and researched for time-varying matrix inversion solving in complex field. Unlike the design methods of the conventional gradient neural network (CGNN) and the previous Zhang neural network (ZNN), the proposed complex-valued nonlinear recurrent neural network (CVNRNN) model is established on basis of a nonlinear evolution formula and possesses a better finite-time convergence Besides, the detailed theoretical analysis provides a guarantee for the finite-time convergence achievement of the CVNRNN model. In addition, the theoretical analysis is also verified by numerical simulations, which comparatively show that the proposed CVNRNN model is faster and more accurate than the ZNN model and the CGNN model in solving time-varying complex matrix inversion.
       
  • Sparse fully convolutional network for face labeling
    • Abstract: Publication date: 28 February 2019Source: Neurocomputing, Volume 331Author(s): Minghui Dong, Shiping Wen, Zhigang Zeng, Zheng Yan, Tingwen Huang This paper proposes a sparse fully convolutional network (FCN) for face labeling. FCN has demonstrated strong capabilities in learning representations for semantic segmentation. However, it often suffers from heavy redundancy in parameters and connections. To ease this problem, group Lasso regularization and intra-group Lasso regularization are utilized to sparsify the convolutional layers of the FCN. Based on this framework, parameters that correspond to the same output channel are grouped into one group, and these parameters are simultaneously zeroed out during training. For the parameters in groups that are not zeroed out, intra-group Lasso provides further regularization. The essence of the regularization framework lies in its ability to offer better feature selection and higher sparsity. Moreover, a fully connected conditional random fields (CRF) model is used to refine the output of the sparse FCN. The proposed approach is evaluated on the LFW face dataset with the state-of-the-art performance. Compared with a non-regularized FCN, the sparse FCN reduces the number of parameters by 91.55% while increasing the segmentation performance by 11% relative error reduction.
       
  • Persistent hidden states and nonlinear transformation for long short-term
           memory
    • Abstract: Publication date: 28 February 2019Source: Neurocomputing, Volume 331Author(s): Heeyoul Choi Recurrent neural networks (RNNs) have been drawing much attention with great success in many applications like speech recognition and neural machine translation. Long short-term memory (LSTM) is one of the most popular RNN units in deep learning applications. LSTM transforms the input and the previous hidden states to the next states with the affine transformation, multiplication operations and a nonlinear activation function, which makes a good data representation for a given task. The affine transformation includes rotation and reflection, which change the semantic or syntactic information of dimensions in the hidden states. However, considering that a model interprets the output sequence of LSTM over the whole input sequence, the dimensions of the states need to keep the same type of semantic or syntactic information regardless of the location in the sequence. In this paper, we propose a simple variant of the LSTM unit, persistent recurrent unit (PRU), where each dimension of hidden states keeps persistent information across time, so that the space keeps the same meaning over the whole sequence. In addition, to improve the nonlinear transformation power, we add a feedforward layer in the PRU structure. In the experiment, we evaluate our proposed methods with three different tasks, and the results confirm that our methods have better performance than the conventional LSTM.
       
  • Hybrid Hierarchical Reinforcement Learning for online guidance and
           navigation with partial observability
    • Abstract: Publication date: 28 February 2019Source: Neurocomputing, Volume 331Author(s): Ye Zhou, Erik-Jan van Kampen, Qiping Chu Autonomous guidance and navigation problems often have high-dimensional spaces, multiple objectives, and consequently a large number of states and actions, which is known as the ‘curse of dimensionality’. Furthermore, systems often have partial observability instead of a perfect perception of their environment. Recent research has sought to deal with these problems by using Hierarchical Reinforcement Learning, which often uses same or similar reinforcement learning methods within one application so that multiple objectives can be combined. However, there is not a single learning method that can benefit all targets. To acquire optimal decision-making most efficiently, this paper proposes a hybrid Hierarchical Reinforcement Learning method consisting of several levels, where each level uses various methods to optimize the learning with different types of information and objectives. An algorithm is provided using the proposed method and applied to an online guidance and navigation task. The navigation environments are complex, partially observable, and a priori unknown. Simulation results indicate that the proposed hybrid Hierarchical Reinforcement Learning method, compared to flat or non-hybrid methods, can help to accelerate learning, to alleviate the ‘curse of dimensionality’ in complex decision-making tasks. In addition, the mixture of relative micro states and absolute macro states can help to reduce the uncertainty or ambiguity at high levels, to transfer the learned results within and across tasks efficiently, and to apply to non-stationary environments. This proposed method can yield a hierarchical optimal policy for autonomous guidance and navigation without a priori knowledge of the system or the environment.
       
  • Delay-dependent L2–L∞ state estimation for neural networks with state
           and measurement time-varying delays
    • Abstract: Publication date: 28 February 2019Source: Neurocomputing, Volume 331Author(s): Wei Qian, Yujie Li, Yonggang Chen, Yi Yang This paper deals with the L2–L∞ state estimation for neural networks with time-varying delays. Considering the limited channel capacity or the long transmission time during signal transmission, a new system model with different state and measurement time-varying delays is established. Then, a new Lyapunov–Krasovskii functional (LKF) taking advantage of two types of delay information is constructed, Jensen integral inequality, Wirtinger-based integral inequality and convex combination approach are used to estimate the derivative of functional. Meantime, a novel L2–L∞ performance analysis method making full use of delay information is proposed, as a result, the delay-dependent conditions with less conservatism are obtained, under which the estimation error system is asymptotically stable with a prescribed L2–L∞ performance level. Numerical examples are given to show the effectiveness and the advantage of the proposed method.
       
  • Fast and robust dynamic hand gesture recognition via key frames extraction
           and feature fusion
    • Abstract: Publication date: 28 February 2019Source: Neurocomputing, Volume 331Author(s): Hao Tang, Hong Liu, Wei Xiao, Nicu Sebe Gesture recognition is a hot topic in computer vision and pattern recognition, which plays a vitally important role in natural human-computer interface. Although great progress has been made recently, fast and robust hand gesture recognition remains an open problem, since the existing methods have not well balanced the performance and the efficiency simultaneously. To bridge it, this work combines image entropy and density clustering to exploit the key frames from hand gesture video for further feature extraction, which can improve the efficiency of recognition. Moreover, a feature fusion strategy is also proposed to further improve feature representation, which elevates the performance of recognition. To validate our approach in a “wild” environment, we also introduce two new datasets called HandGesture and Action3D datasets. Experiments consistently demonstrate that our strategy achieves competitive results on Northwestern University, Cambridge, HandGesture and Action3D hand gesture datasets. Our code and datasets will release at https://github.com/Ha0Tang/HandGestureRecognition.
       
  • Low-rank Bayesian tensor factorization for hyperspectral image denoising
    • Abstract: Publication date: 28 February 2019Source: Neurocomputing, Volume 331Author(s): Kaixuan Wei, Ying Fu In this paper, we present a low-rank Bayesian tensor factorization approach for hyperspectral image (HSI) denoising problem, where zero-mean white and homogeneous Gaussian additive noise is removed from a given HSI. The approach is based on two intrinsic properties underlying a HSI, i.e., the global correlation along spectrum (GCS) and nonlocal self-similarity across space (NSS). We first adaptively construct the patch-based tensor representation for the HSI to extract the NSS knowledge while preserving the property of GCS. Then, we employ the low rank property in this representation to design a hierarchical probabilistic model based on Bayesian tensor factorization to capture the inherent spatial-spectral correlation of HSI, which can be effectively solved under the variational Bayesian framework. Furthermore, through incorporating these two procedures in an iterative manner, we build an effective HSI denoising model to recover HSI from its corruption. This leads to a state-of-the-art denoising performance, consistently surpassing recently published leading HSI denoising methods in terms of both comprehensive quantitative assessments and subjective visual quality.
       
  • Event-triggered passive synchronization for Markov jump neural networks
           subject to randomly occurring gain variations
    • Abstract: Publication date: 28 February 2019Source: Neurocomputing, Volume 331Author(s): Mingcheng Dai, Jianwei Xia, Huang Xia, Hao Shen This paper concentrates on the passive synchronization issue for Markov jump neural networks subject to randomly occurring gain variations, in which the event-triggered mechanism is employed to save the limited communication resource. Moreover, the gain variations of the controller are considered to occur in a random way, which is modeled by a Bernoulli parameter. The goal is to build a controller which ensures that the synchronization error system is stochastically stable and satisfies a passive property. By utilizing the stochastic analysis theory and convex optimization technique, some results with less conservatism are derived. Ultimately, the effectiveness and validity of the design method are illustrated by a numerical example.
       
  • Stability analysis of fractional Quaternion-Valued Leaky Integrator Echo
           State Neural Networks with multiple time-varying delays
    • Abstract: Publication date: 28 February 2019Source: Neurocomputing, Volume 331Author(s): Seyed Mehdi Abedi Pahnehkolaei, Alireza Alfi, J.A. Tenreiro Machado This paper studies the stability problem of fractional order continuous-time Quaternion-Valued Leaky Integrator Echo State Neural Networks (NN) with multiple time-varying delays. The NN is decomposed into four real-valued systems without considering the non-commutativity of quaternion multiplication resulting in Hamilton rules. Then, the delay-independent stability analysis of the NN equilibrium point with Lipschitz continuous activation function is derived in the real space. The existence and uniqueness of the equilibrium point are also given using the contraction map in the real space. Simulations results show the feasibility of the method.
       
  • Target heat-map network: An end-to-end deep network for target detection
           in remote sensing images
    • Abstract: Publication date: 28 February 2019Source: Neurocomputing, Volume 331Author(s): Huai Chen, Libao Zhang, Jie Ma, Jue Zhang In this paper, we focus on approaching fast and accurate target detection in high-resolution remote sensing images (RSIs). Recently, machine-learning based target detection systems have drawn increasing attention and some excellent target detection frameworks have been proposed for RSIs. However, huge storage and time consumption are still key flaws of those methods in practical applications. Especially, the employment of transfer learning would produce tremendous growth in parameters. A new target detection framework named target heat-map network (THNet) is proposed in this paper to address these problems. This framework consists of three parts: shallow features-extracting network, decoder network, and the location method. Firstly, we introduce the transfer-compression learning to train a shallow network under the supervision of a deep pre-trained network. Secondly, a decoder network is constructed to predict heat-map layers. Finally, a location method based on thresholding is proposed to identify positions of target instances. Compared with existing state-of-the-art methods including Faster-R-CNN, YOLOv2 and SSD, THNet with transfer learning has better performance, and THNet with transfer-compression learning also has superior performance in quantitative evaluation as well as significantly reducing time and saving storage, which makes it a great choice for applications with critical requirements for storage cost and running time.
       
  • An effective EM algorithm for mixtures of Gaussian processes via the MCMC
           sampling and approximation
    • Abstract: Publication date: 28 February 2019Source: Neurocomputing, Volume 331Author(s): Di Wu, Jinwen Ma The Mixture of Gaussian Processes (MGP) is a powerful statistical model for characterizing multimodal data, but its conventional Expectation-Maximization (EM) algorithm (Dempster et al., 1977) is computationally intractable because of its time complexity. To solve this problem, some approximation techniques have been proposed in the conventional EM algorithm. However, these approximate EM algorithms are ineffective or limited in some situations. To implement the EM algorithm more effectively, we approximate the EM algorithm with simulated samples of latent variable via the Monte Carlo Markov Chain (MCMC) sampling, and design an MCMC EM algorithm. Experiments on both synthetic and real-world data sets demonstrate that our MCMC EM algorithm is more effective than the state-of-the-art EM algorithms on classification and prediction problems.
       
  • Topical Co-Attention Networks for hashtag recommendation on microblogs
    • Abstract: Publication date: 28 February 2019Source: Neurocomputing, Volume 331Author(s): Yang Li, Ting Liu, Jingwen Hu, Jing Jiang Hashtags provide a simple and natural way of organizing content in microblog services. Along with the fast growing of microblog services, the task of recommending hashtags for microblogs has been given increasing attention in recent years. However, much of the research depends on hand-crafted features. Motivated by the successful use of neural models for many natural language processing tasks, in this paper, we adopt an attention based neural network to learn the representation of a microblog post. Unlike previous works, which only focus on content attention of microblogs, we propose a novel Topical Co-Attention Network (TCAN) that jointly models content attention and topic attention simultaneously, in the sense that the content representation(s) are used to guide the topic attention and the topic representation is used to guide content attention. We conduct experiments and test with different settings of TCAN on a large real-world dataset. Experimental results show that our model significantly outperforms various competitive baseline methods. Furthermore, the incorporation of topical co-attention mechanism gives more than 13.6% improvement in F1 score compared with the standard LSTM based methods.
       
  • Synchronization analysis of network systems applying sampled-data
           controller with time-delay via the Bessel–Legendre inequality
    • Abstract: Publication date: 28 February 2019Source: Neurocomputing, Volume 331Author(s): Hongru Ren, Junlin Xiong, Renquan Lu, Yuanqing Wu In this paper, taking advantage of aperiodic sampled data control technique, the synchronization issue of network systems is brought into focus. During the transmission of sampled data, time-varying delays are concerned. Applying input delay method, the sampled data network systems can be rebuilt via continuous systems with another new delay term in the distributed controller. Unlike the utilization of Jensen and Wirtinger-based inequalities in most literature, the Bessel–Legendre integral inequality is introduced, which can relax conservative further. The characteristics of this integral inequality are adequately merged with the establishment of augmented Lyapunov functional. Two sufficient conditions for synchronizability of network systems are established. In the end, a simulation example is illustrated to verify the efficacy and advantage of the designed approach.
       
  • Interval-valued data prediction via regularized artificial neural network
    • Abstract: Publication date: 28 February 2019Source: Neurocomputing, Volume 331Author(s): Zebin Yang, Dennis K.J. Lin, Aijun Zhang The prediction of interval-valued data is a challenging task as the predicted lower bounds of intervals should not cross over the corresponding upper bounds. In this paper, a regularized artificial neural network (RANN) is proposed to address this difficult problem. It provides a flexible trade-off between prediction accuracy and interval crossing. Compared to existing hard-constrained methods, the RANN has the advantage that it does not necessarily reduce the prediction accuracy while preventing interval crossing. Extensive experiments are conducted based on both simulation and real-life datasets, with comparison to multiple traditional models, including the linear constrained center and range method, the least absolute shrinkage and selection operator-based interval-valued regression, the nonlinear interval kernel regression, the interval multi-layer perceptron and the multi-output support vector regression. Experimental results show that the proposed RANN model is an effective tool for interval-valued data prediction tasks with high prediction accuracy.
       
  • Five-instant type discrete-time ZND solving discrete time-varying linear
           system, division and quadratic programming
    • Abstract: Publication date: 28 February 2019Source: Neurocomputing, Volume 331Author(s): Jian Li, Yunong Zhang, Mingzhi Mao Discrete time-varying linear system (LS) is a fundamental topic in science and engineering. However, conventional methods essentially designed for time-invariant LS generally assume that LS is time-invariant during a small time interval (i.e., sampling gap) for solving time-varying LS. This assumption quite limits their precision because of the existing of lagging errors. Discarding this assumption, Zhang neural dynamics (ZND) method improves the precision for LS solving, which is a great alternative for the solving of discrete time-varying problems. Note that precision solutions to discrete time-varying problems depend on discretization formulas. In this paper, we propose a new ZND model to solve the discrete time-varying LS. The discrete time-varying division is a special case of discrete time-varying LS with the solution being a scalar while it is usually studied alone. Considering the above inner connection, we further propose a special model for solving the discrete time-varying division. Moreover, as an application of discrete time-varying LS, the discrete time-varying quadratic programming (QP) subject to LS is also studied. The convergence and precision of proposed models are guaranteed by theoretical analyses and substantiated by numerous numerical experiments.
       
  • Robust tracking control strategy for a quadrotor using RPD-SMC and RISE
    • Abstract: Publication date: 28 February 2019Source: Neurocomputing, Volume 331Author(s): Zhi Li, Xin Ma, Yibin Li This paper proposes a robust hierarchical controller using adaptive radical basis function neural networks (RBFNNs) based proportional derivative-sliding mode control (RPD-SMC) method and robust integral of the signum of error (RISE) approach for an under-actuated quadrotor in the presence of disturbances and parametric uncertainties. The quadrotor system is decoupled into two parts: the outer loop for position control and the inner loop for attitude control. The RPD-SMC is designed for the outer loop to ensure robust position tracking. The PRD-SMC combines the advantages of simplicity of PD control, the strong robustness of SMC and the approximation ability of arbitrary functions of RBFNNs. The RISE method is applied in the inner loop to guarantee fast convergence of the attitude angles to their desired values with continuous control signals. The capabilities of online approximating and null steady-state tracking are proved using Lyapunov stability theory. The effectiveness of the proposed control strategy is validated by comparing with the performances achieved by PD, PID, PD-SMC and RBFNNs based controllers via numerical simulations.
       
  • Gradient-aware blind face inpainting for deep face verification
    • Abstract: Publication date: 28 February 2019Source: Neurocomputing, Volume 331Author(s): Fuzhang Wu, Yan Kong, Weiming Dong, Yanjun Wu ID face photos are widely used for identity verification in many business authentication situations. To avoid any infringement and misuse, the ID photos provided by the relevant government agencies and business organizations are always corrupted with designed watermarks, such as random wave lines or meshes. These corrupted images are further compressed with JPEG algorithm to reduce their storage size. The artifacts caused by the random meshes and JPEG compression seriously destroy the original image information and quality, which makes the face verification between the corrupted ID faces and daily life images extremely difficult. To tackle these issues, a preprocessing step called blind inpainting is needed to recover the corrupted ID faces. In this paper, we present a new framework to address this blind face inpainting problem. We use an improved Deep Recursive Residual Network (IDRRN) to learn an effective non-linear mapping between the corrupted and clean ID image pairs. To train the IDRRN model, a unified Euclidean loss function considering both 0- and 1st-order pixel residuals is proposed to enhance the image pixel as well as gradient reconstruction. In addition, we collect a dataset of clean ID images and develop a simulation procedure to generate corresponding corrupted ID face images. Final experiments demonstrate that the recovered ID face images inferred from our IDRRN model achieve the best performance in terms of perceptual error and verification accuracy.
       
  • An ensemble approach for supporting the respiratory isolation of presumed
           tuberculosis inpatients
    • Abstract: Publication date: 28 February 2019Source: Neurocomputing, Volume 331Author(s): E.d.S. Alves, João B.O. Souza Filho, A.L. Kritski Tuberculosis remains a global health challenge, especially in low and middle-income countries. New diagnostic tools can allow earlier diagnosis, reducing both the mortality and transmission in the community. In hospitals, the decision making relative to the allocation of presumed pulmonary tuberculosis inpatients in airborne rooms is critical, since no standard criterion has been established. In this paper, we propose a novel technique for developing a committee of classifiers aiming at supporting the decision making relative to inpatient respiratory isolation. The proposed approach is agnostic on the classification model adopted, exploiting tailored strategies for optimally integrating a small and diverse set of compact classifiers, resulting in highly accurate committees. The results confirm that the resulting committees have outperformed several recently proposed single-models and ensemble solutions, including deep learning techniques. As a practical benefit, the adoption of such decision support tool can reduce in almost one half the percentage of inpatients unnecessarily isolated at a university hospital.
       
  • Machine learning on sequential data using a recurrent weighted average
    • Abstract: Publication date: 28 February 2019Source: Neurocomputing, Volume 331Author(s): Jared Ostmeyer, Lindsay Cowell Recurrent Neural Networks (RNN) are a type of statistical model designed to handle sequential data. The model reads a sequence one symbol at a time. Each symbol is processed based on information collected from the previous symbols. With existing RNN architectures, each symbol is processed using only information from the previous processing step. To overcome this limitation, we propose a new kind of RNN model that computes a recurrent weighted average (RWA) over every past processing step. Because the RWA can be computed as a running average, the computational overhead scales like that of any other RNN architecture. The approach essentially reformulates the attention mechanism into a stand-alone model. The performance of the RWA model is assessed on the variable copy problem, the adding problem, classification of artificial grammar, classification of sequences by length, and classification of the MNIST images (where the pixels are read sequentially one at a time). On almost every task, the RWA model is found to fit the data significantly faster than a standard LSTM model.
       
  • Adaptive neural network force tracking impedance control for uncertain
           robotic manipulator based on nonlinear velocity observer
    • Abstract: Publication date: 28 February 2019Source: Neurocomputing, Volume 331Author(s): Zeqi Yang, Jinzhu Peng, Yanhong Liu In this paper, an adaptive neural network force tracking impedance control scheme based on a nonlinear observer is proposed to control robotic system with uncertainties and external disturbances. It is supposed that the joint positions and interaction force of the robotic system can be measured, while the joint velocities are unknown and unmeasured. Then, a nonlinear velocity observer is designed to estimate the joint velocities of the manipulator, and the stability of the observer is analyzed using the Lyapunov stability theory. Based on the estimated joint velocities, an adaptive radial basis function neural network (RBFNN) impedance controller is developed to track the desired contact force of the end-effector and the desired trajectories of the manipulator, where the adaptive RBFNN is used to compensate the system uncertainties so that the accuracy of the joint positions and force tracking can be then improved. Based on the Lyapunov stability theorem, it is proved that the proposed adaptive RBFNN impedance control system is stable and the signals in closed-loop system are all bounded. Finally, simulation examples on a two-link robotic manipulator are presented to show the efficiency of the proposed method.
       
  • Evolution of trading strategies with flexible structures: A configuration
           comparison
    • Abstract: Publication date: 28 February 2019Source: Neurocomputing, Volume 331Author(s): Carlos Martín, David Quintana, Pedro Isasi Evolutionary Computation is often used in the domain of automated discovery of trading rules. Within this area, both Genetic Programming and Grammatical Evolution offer solutions with similar structures that have two key advantages in common: they are both interpretable and flexible in terms of their structure. The core algorithms can be extended to use automatically defined functions or mechanisms aimed to promote parsimony. The number of references on this topic is ample, but most of the studies focus on a specific setup. This means that it is not clear which is the best alternative. This work intends to fill that gap in the literature presenting a comprehensive set of experiments using both techniques with similar variations, and measuring their sensitivity to an increase in population size and composition of the terminal set. The experimental work, based on three S&P 500 data sets, suggest that Grammatical Evolution generates strategies that are more profitable, more robust and simpler, especially when a parsimony control technique was applied. As for the use of automatically defined function, it improved the performance in some experiments, but the results were inconclusive.
       
  • Deep memory and prediction neural network for video prediction
    • Abstract: Publication date: 28 February 2019Source: Neurocomputing, Volume 331Author(s): Zhipeng Liu, Xiujuan Chai, Xilin Chen Inspired by the concept of memory mechanism and predictive coding from the cognitive neuroscience, this paper presents a deep memory and prediction neural network (DMPNet) for video prediction. Correspondingly, memory and error propagation units are designed in DMPNet to capture the previous spatial-temporal information and compute current predictive error which is forwarded to the prediction unit for correcting the subsequent video prediction. Subsequently, prediction unit takes the information stored in memory unit and predictive error of previous frame as input to predict the next frame. We evaluate our method on two public real-world datasets and demonstrate that the proposed DMPNet outperforms some state-of-the-art methods quantitatively and qualitatively.
       
  • Interneuronal gamma oscillations in hippocampus via adaptive exponential
           integrate-and-fire neurons
    • Abstract: Publication date: 28 February 2019Source: Neurocomputing, Volume 331Author(s): LieJune Shiau, Laure Buhry Fast neuronal oscillations in gamma frequencies are observed in neocortex and hippocampus during essential arousal behaviors. Through a four-variable Hodgkin–Huxley type model, Wang and Buzsáki have numerically demonstrated that such rhythmic activity can emerge from a random network of GABAergic interneurons via minimum synaptic inputs. In this case, the intrinsic neuronal characteristics and network structure act as the main drive of the rhythm. We investigate inhibitory network synchrony with a low complexity, two-variable adaptive exponential integrate-and-fire (AdEx) model, whose parameters possess strong physiological relevances, and provide a comparison with the two-variable Izhikevich model and Morris–Lecar model. Despite the simplicity of these three models, AdEx model shares two important results with the previous biophysically detailed Hodgkin–Huxley type model: the minimum number of synaptic input necessary to initiate network gamma-band rhythms remains the same, and this number is weakly dependent on the network size. Meanwhile, Izhikevich and Morris–Lecar neurons demonstrate different results in this study. We further investigate the necessary neuronal, synaptic and connectivity properties, including gap junctions and shunting inhibitions, for AdEx model leading to sparse and random network synchrony in gamma rhythms and nested theta gamma rhythms. These findings suggest a computationally more tractable framework for studying synchronized networks in inducing cerebral gamma band activities.
       
  • Multi-label learning method based on ML-RBF and laplacian ELM
    • Abstract: Publication date: 28 February 2019Source: Neurocomputing, Volume 331Author(s): Xinzheng Xu, Dong Shan, Shan Li, Tongfeng Sun, Pengcheng Xiao, Jianping Fan Multi-label data widely exist in the real world, and the multi-label learning deals with the problem in which samples contain many labels. The main task of the multi-label learning is to train a model which adapts to the multi-label data such that the label of unknown label data can be predicted. Multi-label radial basis function neural network (ML-RBF) is an effective multi-label learning model, which combines K-means clustering and RBF neural network. The laplacian extreme learning machine (Lap-ELM) improved the traditional ELM by considering the structural relationship between low-dimension data and high-dimension data. As a kind of single-hidden layer feed-forward neural network (SLFN), ELM has the characteristics of fast training and good generalization ability compared to RBF. Affinity Propagation (AP) clustering algorithm can automatically determine the number of clusters. In this paper, a novel multi-label learning method named ML-AP-RBF-Lap-ELM is proposed which integrates AP clustering algorithm, ML-RBF and Lap-ELM. In this new model, the ML-RBF is used to map in the input layer. The number of hidden nodes and the center of the RBF function can be automatically determined by the AP clustering algorithm. The weights from the hidden layer to the output layer are solved by Lap-ELM. The simulation results show that ML-AP-RBF-Lap-ELM performs well on the three common data sets, including Natural Scene, Yeast Gene and 20NG (20 New Groups).
       
  • Multi-scale semantic image inpainting with residual learning and GAN
    • Abstract: Publication date: 28 February 2019Source: Neurocomputing, Volume 331Author(s): Libin Jiao, Hao Wu, Haodi Wang, Rongfang Bie Image inpainting aims to fill the corrupt area semantically and recover the semantic and detailed information; however, concurrent methods suffer from convergence crash and arbitrarily paste the inference of the missing area into the corrupt context. In this paper, we study a combination of an encoder–decoder generator for image semantic inpainting and a multi-layer convolutional net for image seamless fusion, which is capable of restoring image effectively and seamlessly. Specifically, the encoder–decoder generator learns and extracts the latent compressed representations of missing areas from the context of a corrupt image, and further predicts a semantically correct estimation of the missing area based on the latent representations. The consecutive convolutional net smooths the discrepancy between the original image context and the estimation and seamlessly merges predictions and original images. The skip connections between the encoder and the decoder bridge the backward propagation of gradients, therefore boost the learning ability of the generator and stabilize the convergence of reconstruction loss. The performance and superiority of our method are illustrated and demonstrated on the real-world dataset qualitatively and quantitatively, and the experiments manifest acceptable semantic inpainting results, which significantly illustrates the effectiveness of our model.
       
  • Blind image quality assessment based on joint log-contrast statistics
    • Abstract: Publication date: 28 February 2019Source: Neurocomputing, Volume 331Author(s): Qiaohong Li, Weisi Lin, Ke Gu, Yabin Zhang, Yuming Fang During recent years, quality-aware features extracted from natural scene statistics (NSS) models have been used in development of blind image quality assessment (BIQA) algorithms. Generally, the univariate distributions of bandpass coefficients are used to fit a parametric probabilistic model and the model parameters serve as the quality-aware features. However, the inter-location, inter-direction and inter-scale correlations of natural images cannot be well exploited by such NSS models, as it is hard to capture such dependencies using univariate marginal distributions. In this paper, we build a novel NSS model of joint log-contrast distribution to take into account the across space and direction correlations of natural images (inter-scale correlation to be explored as the next step). Furthermore, we provide a new efficient approach to extract quality-aware features as the gradient of log-likelihood on the NSS model, instead of using model parameters directly. Finally, we develop an effective joint-NSS model based BIQA metric called BJLC (BIQA based on joint log-contrast statistics). Extensive experiments on four public large-scale image databases have validated that objective quality scores predicted by the proposed BIQA method are in higher accordance with subjective ratings generated by human observers compared with existing methods.
       
  • T–S fuzzy model-based adaptive repetitive consensus control for
           second-order multi-agent systems with imprecise communication topology
           structure
    • Abstract: Publication date: 28 February 2019Source: Neurocomputing, Volume 331Author(s): Jiaxi Chen, Junmin Li, Ruirui Duan This paper investigates the consensus problem of the second-order nonlinear multi-agent systems (MAS) with unknown periodic time-varying parameters (UPTVP) and imprecise communication topology structure (ICTS). T–S fuzzy models is used to express the ICTS, and the concept of fuzzy union connected (FUC) is defined. Under the condition that the ICTS is FUC, an adaptive repetitive learning control protocol is presented such that all the followers can track the leader asymptotically. With the information of leader agent is known to a small portion of following agents, an auxiliary control term was presented for each follower agent to handle leaders dynamics. The distributed MAS consensus is analyzed based on the Lyapunov stability theory. Furthermore, the proposed protocol is further promoted to solve the formation control problem. Finally, simulation results are shown to demonstrate the validity of the proposed design methods in this paper.
       
  • Multi-modal semantic autoencoder for cross-modal retrieval
    • Abstract: Publication date: 28 February 2019Source: Neurocomputing, Volume 331Author(s): Yiling Wu, Shuhui Wang, Qingming Huang Cross-modal retrieval has gained much attention in recent years. As the research mainstream, most of existing approaches learn projections for data from different modalities into a common space where data can be compared directly. However, they neglect the preservation of feature and semantic information, so they are unable to obtain satisfactory results as expected. In this paper, we propose a two-stage learning method to learn multi-modal mappings that project multi-modal data to low dimensional embeddings that preserve both feature and semantic information. In the first stage, we combine both low-level feature and high-level semantic information to learn feature-aware semantic code vectors. In the second stage, we use encoder–decoder paradigm to learn projections. The encoder projects feature vectors to code vectors, and the decoder projects code vectors back to feature vectors. The encoder-decoder paradigm guarantees the embeddings to preserve both feature and semantic information. An alternating minimization procedure is developed to solve the multi-modal semantic autoencoder optimization problem. Extensive experiments on three benchmark datasets demonstrate that the proposed method outperforms state-of-the-art cross-modal retrieval methods.
       
  • Segmented convex-hull algorithms for near-separable NMF and NTF
    • Abstract: Publication date: 28 February 2019Source: Neurocomputing, Volume 331Author(s): Rafał Zdunek, Tomasz Sadowski Many computational problems in machine learning can be represented with near-separable matrix factorization models. In a geometric approach, linear separability means that the entire set of data points can be modeled as a conical combination of a few data points, referred to as the extreme rays that express meaningful features. In this study, we propose segmented convex-hull algorithms for estimating the extreme rays of the simplicial cone generated by observations in the near-separable and inconsistent non-negative matrix factorization (NMF) and non-negative tensor factorization (NTF) models. The segmentation is based on the concept of hierarchical convex-hull NMF. The proposed algorithms are used to solve near-separable noisy blind source separation problems and classification problems. For the former, the experimental results demonstrate that they significantly outperform the state-of-the-art geometry-based NMF algorithms and the basic hierarchical alternating least squares NTF, if observations are noisy with signal-to-noise ratio lower than 100 dB. For the latter, the classification results also provide strong evidence for the effectiveness of the proposed approach with respect to existing geometry-based NMF algorithms.
       
  • A deep manifold learning approach for spatial-spectral classification with
           limited labeled training samples
    • Abstract: Publication date: 28 February 2019Source: Neurocomputing, Volume 331Author(s): Xichuan Zhou, Nian Liu, Fang Tang, Yingjun Zhao, Kai Qin, Lei Zhang, Dong Li One major challenge of designing deep learning systems for hyperspectral data classification is the lack of labeled training samples. Inspired by recent manifold learning researches, this paper presents a novel Locality Preserving Convolutional Network to address this challenge. The proposed method invents a semi-supervised locality-preserving regularization operation, and inserts a new layer in the three-dimensional convolutional neural network for end-to-end spatial-spectral classification. The benefits are three-fold. First, by using unlabeled training samples which are more easily available, the proposed method reduces the number of labeled samples required for training a deep learning model; Second, the proposed method incorporates the intrinsic geographical correlation among nearby samples into the extracted features, which prevents it from losing accuracy when only limited labeled samples are available; Third, with a three-dimensional architecture, the proposed method can extract the spatial and spectral features simultaneously from the hyperspectral data for classification. A gradient-decent based approach is deployed to train the whole network in a unified way. Experiments over different benchmarks show that, the proposed method relieves the Hughes phenomenon for deep learning, and achieves competitively high classification accuracy compared to other state-of-the-art approaches.
       
  • Evolutionary fuzzification of RIPPER for regression: Case study of stock
           prediction
    • Abstract: Publication date: 28 February 2019Source: Neurocomputing, Volume 331Author(s): Shahrokh Asadi This paper presents a novel approach for extracting the knowledge base (KB) of a Mamdani fuzzy rule based system (FRBS) for stock market prediction. The KB, which is the most important component of the FRBS, has two main components: rule base (RB) and data base (DB). In the proposed model, the RB is learned using repeated incremental pruning to produce error reduction (RIPPER), which is state-of-the-art in classification rule induction. The RIPPER algorithm is applied to classification problems. In order to extend it for regression problems, unsupervised discretization of the target attribute and supervised discretization of continuous input attributes are used. Next, the classification rules obtained from RIPPER are fuzzified and the initial Mamdani FRBS is formed. The DB is tuned using a Genetic Algorithm (GA). The proposed model in this paper is the first model utilizing directly capabilities and benefits of rule-based classification systems in regression. The accuracy of the proposed model is tested in the context of stock market prediction, which is a complex and difficult area in regression problems. The proposed model is implemented using several indices from different stock markets such as the Taiwan Stock Exchange Index (TSE), Tehran Price Index (TEPIX). Other indices including the Industry Index, Top 50 Companies Index and Financial Group Index from Tehran Stock Exchange are also considered. Furthermore, the daily stock prices of multiple large companies such as Apple, DELL, IBM, British Airlines and Ryanair Airlines are incorporated. As shown by the mean absolute percentage error (MAPE) and non-parametric statistical tests, the proposed model offers superior performance compared to other models.
       
  • Rhythmic control of oscillatory sequential dynamics in heteroclinic motifs
    • Abstract: Publication date: 28 February 2019Source: Neurocomputing, Volume 331Author(s): Roberto Latorre, Pablo Varona, Mikhail I. Rabinovich Cognitive/behavioral brain functions are implemented through temporary correlated sequential activity of many brain elements that form universal anatomical and functional motifs, i.e., characteristic functional interactions among brain nodes, at different levels of the neural hierarchy. Such motif dynamics is determined by both the interconnections among nodes and their intrinsic oscillations. This paper focuses on heteroclinic motifs, i.e., those built in networks of oscillatory nodes that interact through asymmetric inhibitory coupling in a winnerless competitive way. We introduce a basic rate-phase motif model – based on a generalization of the well-known ecological Lotka–Volterra model – for the analysis and prediction of control processes that emerge in interacting heteroclinic motifs under periodic stimulation. This approach describes both intensity and phase in each node. We study how a rhythmic signal, which can be linked to internal or external sources, can functionally change the heteroclinic network and produce a rich gallery of motifs in the form of coordinated sequential activations. In computer simulations of the model in a “master-slave” approximation, we report phenomena such as dynamical filtering, encoding enhancement and transition to chaos. Our results are relevant in the context of several experimental protocols related to the role of brain rhythms and/or the use of external rhythmic stimulation, in particular in the context of transcranial control and evoked potentials, to assess cognitive functions and their associated pathologies.
       
  • Digging into it: Community detection via hidden attributes analysis
    • Abstract: Publication date: 28 February 2019Source: Neurocomputing, Volume 331Author(s): Rui Li, Fanghua Ye, Shaoan Xie, Chuan Chen, Zibin Zheng Identifying community structure in complex systems is essential for characterizing and understanding their functions and properties. Over the past decades, considerable efforts have been devoted to analyzing the community structure of networks and numerous community detection methods have consequently been developed. Among the proposed methods, none of them has explored the community membership in depth, which may provide useful information about the nodes and the communities. In this paper, we name the information contained in the community membership as hidden attributes of nodes and communities, and design a delicate nonnegative matrix factorization (a widely used framework for both disjoint and overlapping community detection) based model to extract the hidden attributes and use these hidden attributes to modify the community detection results on unannotated networks. To test our model’s expansibility, we also extend it on annotated networks by adding observed nodes’ attributes into it. Experiment results on both unannotated and annotated real-world networks show superior performance of our model over state-of-the-art approaches.
       
  • Dual graph regularized compact feature representation for unsupervised
           feature selection
    • Abstract: Publication date: 28 February 2019Source: Neurocomputing, Volume 331Author(s): Shaoyong Li, Chang Tang, Xinwang Liu, Yaping Liu, Jiajia Chen As one of the dimensionality reduction methods, unsupervised feature selection aims to select a subset of original features without the labels of data instances. It becomes more and more important since large amounts of unlabelled high dimensional data need to be processed in many machine learning and data mining tasks. In this paper, we propose a novel unsupervised feature selection method via compact feature representation, with the local geometrical structure of data being preserved by using a dual Laplacian graph regularization term. In detail, different to many previous representation based methods which use the original data as representation dictionary, we propose to learn a feature dictionary subspace for compact and robust feature representation. During the dictionary learning process, the l2,1-norm is imposed on the combination coefficient matrix to select discriminate features for constructing the latent feature dictionary. Meanwhile, the local geometrical structure of original data is preserved from the perspectives of subspace learning and feature representation. In general, our method conducts dictionary learning and unsupervised feature selection simultaneously. We develop an efficient optimization algorithm based on Alternating Direction Method of Multipliers to solve the proposed optimization problem and experiments on various types of real world datasets are conducted to demonstrate the effectiveness of the proposed method.
       
  • Numerical solution for ruin probability of continuous time model based on
           neural network algorithm
    • Abstract: Publication date: 28 February 2019Source: Neurocomputing, Volume 331Author(s): Tao Zhou, Xia Liu, Muzhou Hou, Chunhui Liu In the classical risk model, the ruin probability satisfies the renewal Integro-differential equation, which only has an analytic solution when the claim distribution obeys the exponential distribution. In this paper, due to the characteristics of the existence of initial conditions for the equations, by using the modern artificial intelligence and machine learning theory, we construct a neural network model, in which trigonometric function serves as the activation function. Then, based on the thinking of ELM algorithm, at the same time, different from the classic ELM algorithm, the initial condition of the equation are added to the solver model, and the improved ELM algorithm (IELM) are designed. Finally, through some numerical experiments by using Matlab programming, the numerical solutions of the integral Integro-differential equation under arbitrary claims distribution at any time have been obtained. Through the comparison of numerical solutions with the analytical solutions and traditional numerical solutions, the feasibility and superiority of the proposed IELM algorithm are clearly proved.
       
  • Diagnosis and location of the open-circuit fault in modular multilevel
           converters: An improved machine learning method
    • Abstract: Publication date: 28 February 2019Source: Neurocomputing, Volume 331Author(s): Cui Li, Zhenxing Liu, Yong Zhang, Li Chai, Bo Xu In this paper, the fault diagnosis and location (FDL) problem of the open circuit fault for modular multilevel converter (MMC) is investigated. A mixed kernel support tensor machine (MKSTM) is provided, and it’s employed to improve the support tensor machine which is an important algorithm of machine learning. By extracting the characteristic data of ac current and internal circulation current in either normal operation or open-circuit fault, then training and classifying the obtained samples with MKSTM, FDL of MMC can be realized with the supplied algorithm. Finally, experimental results show that the classification accuracy of MKSTM algorithm is improved observably than single kernel function STM such as linear, Radial Basis function (RBF), sigmoid and polynomial types. Synchronously, the open-circuit fault can be effectively diagnosed and located with the proposed method.
       
  • A spatially constrained shifted asymmetric Laplace mixture model for the
           grayscale image segmentation
    • Abstract: Publication date: 28 February 2019Source: Neurocomputing, Volume 331Author(s): Hao Sun, Xianqiang Yang, Huijun Gao In this paper, the grayscale image segmentation problem is investigated and a new mixture model with shifted asymmetric Laplace distribution component is proposed. Instead of the conventional Gaussian model, the shifted asymmetric Laplace distribution model is adopted to model the pixels. The spatial constraint on neighboring pixels is introduced into the proposed shifted asymmetric Laplace mixture model, which makes the model be robust to noise and outliers of the images. The unknown model parameters are estimated via the expectation-maximization (EM) algorithm, which can guarantee convergence to a local minimum. The experimental verification is performed on both synthesized images and images of real chip to prove the effectiveness of our image segmentation method.
       
  • A robust loss function for classification with imbalanced datasets
    • Abstract: Publication date: 28 February 2019Source: Neurocomputing, Volume 331Author(s): Yidan Wang, Liming Yang Based on minimizing misclassification cost, a new robust loss function is designed in this paper to deal with the imbalanced classification problem under noise environment. It is nonconvex but maintains Fisher consistency. Applying the proposed loss function into support vector machine (SVM), a robust SVM framework is presented which results in a Bayes optimal classifier. However, nonconvexity makes the model difficult to optimize. We develop an alternative iterative algorithm to solve the proposed model. What’s more, we analyze the robustness of the proposed model theoretically from a re-weighted SVM viewpoint and the obtained optimal solution is consistent with Bayesian optimal decision rule. Furthermore, numerical experiments are carried out on databases that are drawn from UCI Machine Learning Repository and a practical application. With two different types of noise environments, one with label noise and one with feature noise, experiment results show that on these two databases the proposed method achieves better generalization results compared to other SVM methods.
       
  • Cooperative output regulation of linear discrete-time time-delay
           multi-agent systems by adaptive distributed observers
    • Abstract: Publication date: 28 February 2019Source: Neurocomputing, Volume 331Author(s): Yamin Yan, Zhiyong Chen In this paper, we study the cooperative output regulation problem for heterogeneous linear discrete-time time-delay multi-agent systems by adaptive distributed observers. An adaptive distributed observer-based approach was recently proposed for estimating both a leader’s state and its system dynamics, to each follower. This approach is applied to discrete-time multi-agent systems with input-delays to relax the assumption that all the followers know the leader’s system dynamics. Based on the adaptive distributed observer, we further generalize the adaptive dynamic solver for solving the regulator equations associated with each follower from delay-free case to time-delay case. Finally, the problem is solved by a distributed dynamic output feedback controller.
       
  • An imprecise extension of SVM-based machine learning models
    • Abstract: Publication date: 28 February 2019Source: Neurocomputing, Volume 331Author(s): Lev V. Utkin A general approach for incorporating imprecise prior knowledge and for robustifying the machine learning SVM-based models is proposed in the paper. The main idea underlying the approach is to use a double duality representation in the framework of the minimax strategy of decision making. This idea allows us to get simple extensions of SVMs including additional constraints for optimization variables (the Lagrange multipliers) formalizing the incorporated imprecise information. The approach is applied to regression, binary classification and one-class classification SVM-based problems. Moreover, it is adopted to set-valued or interval-valued training data. For every problem, numerical examples are provided which illustrate how imprecise information may improve the machine learning algorithm performance.
       
  • Fault diagnosis observer for descriptor Takagi-Sugeno systems
    • Abstract: Publication date: 28 February 2019Source: Neurocomputing, Volume 331Author(s): F.R. López-Estrada, D. Theilliol, C.M. Astorga-Zaragoza, J.C. Ponsart, G. Valencia-Palomo, J. Camas-Anzueto This paper proposes a methodology to design a robust observer for Takagi–Sugeno descriptor (TS-D) systems with unmeasurable premise variables and its application to sensor fault detection and isolation. A robust H∞ approach is considered to minimize the effect of uncertainties given by the unmeasurable premise variables, disturbances and sensor noise. As a result, a set of relaxed linear matrix inequalities (LMI) are derived, which provides sufficient conditions to guarantee the convergence of the proposed state observer. Finally, a conventional fault detection scheme is considered by means of residual generation and evaluation. An academic example is given to illustrate the effectiveness of the proposed method.
       
  • Exponential stability criterion of the switched neural networks with
           time-varying delay
    • Abstract: Publication date: 28 February 2019Source: Neurocomputing, Volume 331Author(s): Hui-Ting Wang, Zhen-Tao Liu, Yong He In this paper, the delay-dependent stability problem of the switched neural networks with time-varying delay is considered. By taking advantage of the average dwell time method and Lyapunov–Krasovskii functional (LKF) method, and using free-matrix-based integral inequality and the extended reciprocally convex matrix inequality, a less conservative delay-dependent exponential stability criterion in linear matrix inequalities (LMIs) is developed. Two numerical examples are given to demonstrate the benefits of the proposed criterion.
       
  • Bio-inspired fault detection circuits based on synapse and spiking neuron
           models
    • Abstract: Publication date: 28 February 2019Source: Neurocomputing, Volume 331Author(s): Junxiu Liu, Yongchuang Huang, Yuling Luo, Jim Harkin, Liam McDaid Recent studies have shown that the electronic hardware devices can be compromised by the faults and fault tolerance is a crucial capability. This paper addresses the challenge of fault detection in the CMOS circuits, using two bio-inspired structures based on the HP lab's memristor and the BSIM3v3.2.2 transistor models. The first fault detection circuit (FDC) includes the memristor-based synapses and a modified leaky integrate-and-fire (LIF)-based neuron. The memristor-based synapse circuits can be further optimized which is the proposed second fault detection method (O-FDC), and it has a lower hardware overhead and power consumption compared to the former. Experimental results demonstrate that the proposed structures can detect the circuit faults under the inputs of direct current (DC), alternating current (AC) voltage sources, and pulse signals. Under the input of DC, the fault detection times for the two proposed structures are about 0.16 ms and 1.2 ms, respectively; when the input source is AC, the corresponding fault detection times are about 0.206 ms and 0.758 ms; and it takes only 6.47us for fault detection under the input of pulse signals. This work provides an alternative solution to enhance the fault-tolerant capability of the hardware systems.
       
  • Design of adaptive backstepping dynamic surface control method with RBF
           neural network for uncertain nonlinear system
    • Abstract: Publication date: 22 February 2019Source: Neurocomputing, Volume 330Author(s): Xiaoyu Shi, Yuhua Cheng, Chun Yin, Xuegang Huang, Shou-ming Zhong This paper develops an adaptive backstepping dynamic surface control method with RBF Neural Network for a class of nonlinear system under extra disturbances. The considered RBF Neural Network based on adaptive control is applied to approximate the unknown smooth function arbitrarily. The “explosion of the complexity” is eliminated by utilizing the dynamic surface control technique. The Lyapunov function is employed to verify the globally asymptotically stability of the control nonlinear system. Four examples were given to show that the novel control method can not only tracking the expected trajectory very well but also has a better approximation capability for various complex unknown smooth function under disturbances.
       
  • One-layer neural network for solving least absolute deviation problem with
           box and equality constraints
    • Abstract: Publication date: 22 February 2019Source: Neurocomputing, Volume 330Author(s): Cuiping Li, Xingbao Gao This paper presents a neural network for solving least absolute deviation problems with equality and box constraints. Compared with some existing models, the proposed neural network has fewer state variables and only one-layer structure. The proposed model is proved to be Lyapunov stable and converge to an exact optimal solution of the original problem. Some simulation results show the validity and transient behavior of the proposed neural network.
       
  • Localizing salient body motion in multi-person scenes using convolutional
           neural networks
    • Abstract: Publication date: 22 February 2019Source: Neurocomputing, Volume 330Author(s): Florian Letsch, Doreen Jirak, Stefan Wermter With modern computer vision techniques being successfully developed for a variety of tasks, extracting meaningful knowledge from complex scenes with multiple people still poses problems. Consequently, experiments with application-specific motion, such as gesture recognition scenarios, are often constrained to single person scenes in the literature. Therefore, in this paper we address the challenging task of detecting salient body motion in scenes with more than one person. We propose a neural architecture that only reacts to a specific kind of motion in the scene: A limited set of body gestures. The model is trained end-to-end, thereby avoiding hand-crafted features and the strong reliance on pre-processing as it is prevalent in similar studies. The presented model implements a saliency mechanism that reacts to body motion cues which have not been included in previous computational saliency systems. Our architecture consists of a 3D Convolutional Neural Network that receives a frame sequence as its input and localizes active gesture movement. To train our network with a large data variety, we introduce an approach to combine Kinect recordings of one person into artificial scenes with multiple people, yielding a large diversity of scene configurations in our dataset. We performed experiments using these sequences and show that the proposed model is able to localize the salient body motion of our gesture set. We found that 3D convolutions and a baseline model with 2D convolutions perform surprisingly similar on our task. Our experiments revealed the influence of gesture characteristics on how well they can be learned by our model. Given a distinct gesture set and computational restrictions, we conclude that using 2D convolutions might often perform equally well.
       
  • A novel design of memristor-based bidirectional associative memory
           circuits using Verilog-AMS
    • Abstract: Publication date: 22 February 2019Source: Neurocomputing, Volume 330Author(s): Bo Li, Yonglei Zhao, Guoyong Shi Recently, memristor as an emerging device has been used as the basic synapse realization component for the circuit implementation of artificial neural networks. Following this trend, this paper studies the circuit realization of a memristor-based bidirectional associative memory (BAM) system that extends the memristor crossbar array structure for bidirectional synaptic weighting operation. The neuron cell in BAM is represented by digital circuit element JK flip-flop for hardware cost-saving. Meanwhile a novel memristor programming strategy is also considered and examined to ease the on-chip learning. The design of such memristive BAM circuit system is conducted by hardware description language Verilog-AMS and has been validated in a commercial circuit simulation environment via a case study. Test results show that a set of binary character patterns can be memorized and recalled successfully by the trained memristive BAM system.
       
  • A highly scalable parallel spike-based digital neuromorphic architecture
           for high-order fir filters using LMS adaptive algorithm
    • Abstract: Publication date: 22 February 2019Source: Neurocomputing, Volume 330Author(s): Giovanny Sanchez, Carlos Diaz, Juan-Gerardo Avalos, Luis Garcia, Angel Vazquez, Karina Toscano, Juan-Carlos Sanchez, Hector Perez This brief presents a highly scalable parallel neuromorphic architecture to efficiently compute high-order adaptive FIR filters using a least mean square (LMS) algorithm. This has been achieved by eliminating critical paths because they critically affect the scalability of advanced parallel architectures. Here, the scalability is defined in terms of number of taps and bit-length. On the computation of high-order adaptive FIR filters, the multiplication is the most demanding operation. Therefore, we have made intensive efforts to create a compact new neural multiplier by improving the design and hardware implementation of an existing neural multiplier. The resulting multiplier requires 50 % fewer synapses, 40 % fewer neurons, 30 % fewer area resources and 26 % fewer clock cycles compared with the existing neural multiplier, respectively. The efficient implementation of the proposed multiplier has allowed us to eliminate critical paths significantly and thus the bit-length can be easily increased to guarantee the convergence performance when high-order adaptive filters are processed. To demonstrate its effectiveness, the proposed multiplier was included in the neuromorphic architecture to support high-order adaptive FIR filters. In addition, we employ the time multiplexing technique to maximize the utilization of the proposed neural multiplier by performing filter processing and the adaptive process because multiplication is involved in both. We mainly use this strategy to eliminate critical paths and reduce the area consumption by implementing a large number of taps. The proposed neuromorphic architecture was implemented on the Kintex-7 Field Programmable Gate Array (FPGA) development kit to validate its performance. Our results demonstrate that the neuromorphic architecture is capable of processing higher adaptive FIR filters compared with previously reported solutions. This potentially allow its practical use in many advanced digital signal processing applications such as acoustic echo cancellers, active noise control, channel equalization and system identification.
       
  • Semi-supervised feature selection analysis with structured multi-view
           sparse regularization
    • Abstract: Publication date: 22 February 2019Source: Neurocomputing, Volume 330Author(s): Caijuan Shi, Changyu Duan, Zhibin Gu, Qi Tian, Gaoyun An, Ruizhen Zhao Facing abundant and various multi-view data, how to effectively combine the multi-view data information has become an important research topic in feature selection analysis. However, existing feature selection methods usually consider each view features as a whole without fully considering the individual feature in each view. In this paper, we construct a structured multi-view sparse regularization and then propose a novel semi-supervise feature selection framework, namely Structured Multi-view Hessian sparse Feature Selection (SMHFS)1. With the structured multi-view sparse regularization, SMHFS can simultaneously learn the importance of each view features and the importance of individual feature in each view. In addition, SMHFS utilizes multi-view Hessian regularization to enhance the semi-supervised learning performance. An iterative algorithm is introduced and its convergence is proven. Finally, SMHFS is applied into image annotation task and extensive experiments are conducted. The experimental results show SMHFS can effectively combine the multi-view data information to achieve better feature selection performance compared to other methods.
       
  • Sparse augmented Lagrangian algorithm for system identification
    • Abstract: Publication date: 22 February 2019Source: Neurocomputing, Volume 330Author(s): Xiaoquan Tang, Long Zhang, Xiaolin Wang A huge class of nonlinear dynamic systems can be approximated by the Nonlinear AutoRegressive with eXogenous inputs (NARX) models. This paper proposes a novel method, Sparse Augmented Lagrangian (SAL), for NARX model variable selection and parameter estimation. Firstly, Split Augmented Lagrangian Shrinkage Algorithm (SALSA) is applied to produce some intermediate models with subsampling technique, and then only the model terms with high selecting probability are chosen into the final model, followed by the model parameter estimation via SALSA. The model sparsity and algorithm convergence can be guaranteed through theoretical analysis. Two nonlinear examples and one real-world application from the process industry are used to demonstrate the effectiveness and advantages of the proposed method in comparison to several popular methods.
       
  • Simplex basis function based sparse least squares support vector
           regression
    • Abstract: Publication date: 22 February 2019Source: Neurocomputing, Volume 330Author(s): Xia Hong, Richard Mitchell, Giuseppe Di Fatta In this paper, a novel sparse least squares support vector regression algorithm, referred to as LSSVR-SBF, is introduced which uses a new low rank kernel based on simplex basis function, which has a set of nonlinear parameters. It is shown that the proposed model can be represented as a sparse linear regression model based on simplex basis functions. We propose a fast algorithm for least squares support vector regression solution at the cost of O(N) by avoiding direct kernel matrix inversion. An iterative estimation algorithm has been proposed to optimize the nonlinear parameters associated with the simplex basis functions with the aim of minimizing model mean square errors using the gradient descent algorithm. The proposed fast least square solution and the gradient descent algorithm are alternatively applied. Finally it is shown that the model has a dual representation as a piecewise linear model with respect to the system input. Numerical experiments are carried out to demonstrate the effectiveness of the proposed approaches.
       
  • A hybrid particle swarm optimization algorithm for load balancing of MDS
           on heterogeneous computing systems
    • Abstract: Publication date: 22 February 2019Source: Neurocomputing, Volume 330Author(s): Dapu Li, Kenli Li, Jie Liang, Aijia Ouyang An efficient hybrid genetic algorithm and particle swarm optimization algorithm (HGAPSO)is studied in this work for load balancing of molecular dynamics simulations (MDS) on heterogeneous supercomputers by combining the genetic algorithm (GA) and the particle swarm optimization (PSO) algorithm. A hybrid CPU-GPU platform is applied to enabling large-scale MDS that emulates the metal solidification. Applied to task scheduling of the parallel algorithm, the approach obtains excellent results. The experimental results show that the proposed algorithm can improve the efficiency of parallel computing and the precision of physical simulation.
       
  • Anomaly detection via adaptive greedy model
    • Abstract: Publication date: 22 February 2019Source: Neurocomputing, Volume 330Author(s): Dongdong Hou, Yang Cong, Gan Sun, Ji Liu, Xiaowei Xu Anomaly detection is one of the fundamental problems within diverse research areas and application domains. In comparison with most sparse representation based anomaly detection methods adopting a relaxation term of sparsity via ℓ1 norm, we propose an unsupervised anomaly detection method optimized via an adaptive greedy model based on ℓ0 norm constraint, which is more accurate, robust and sparse in theory. Firstly for feature representation, a concise feature space is learned in an unsupervised way via stacked autoencoder network. We propose a dictionary selection model based on ℓ2, 0 norm constraint to select an optimal small subset of the training data to construct a condense dictionary, which can improve accuracy and reduce computational burden simultaneously. Finally, each testing sample is reconstructed by ℓ0 norm constraint based sparse representation, and anomalies are determined depending on the sparse reconstruction scores accordingly. For model optimization, an adaptive forward-backward greedy model is utilized to optimize this nonconvex problem with the theoretical guarantee. Our proposed method is evaluated with our real industrial dataset and benchmark datasets, and various experimental results demonstrate that our proposed method is comparable with conventional supervised methods and performs better than most comparative unsupervised methods.
       
  • Event-triggered exponential synchronization of complex dynamical networks
           with cooperatively directed spanning tree topology
    • Abstract: Publication date: 22 February 2019Source: Neurocomputing, Volume 330Author(s): Hao Dai, Jinping Jia, Li Yan, Fakui Wang, Weisheng Chen This paper focuses on the exponential synchronization problem of complex dynamical networks (CDNs) with time-varying network topology via event-triggered communication strategy. The definition of a new time-varying network topology, called cooperatively directed spanning tree topology, is first given based on the integral of time-varying Laplacian matrix. This topology does not require the network constantly connected. In other words, the network topology is allowed to be disconnected at all times, but only the integral of the Laplacian matrix of the network graph is required to contain directed spanning tree over a period of time. Moreover, in order to achieve the exponential synchronization for CDNs under the cooperatively directed spanning tree topology, a sufficient condition is derived by the virtues of algebraic graph theory, event-triggered communication strategy, matrix inequality and the special Lyapunov stability analysis method. Additionally, event-triggered communication strategy can avoid continuous communication, which can reduce the communication load and energy consumption. The Zeno behavior is excluded as well by the strictly positive sampling intervals based on the upper right-hand Dini derivative, and thus to avoid infinite triggers. Finally, simulation examples are given to show the effectiveness of the proposed exponential synchronization criteria.
       
  • Boosted Convolutional Neural Network for object recognition at large scale
    • Abstract: Publication date: 22 February 2019Source: Neurocomputing, Volume 330Author(s): Sourour Brahimi, Najib Ben Aoun, Chokri Ben Amar During the last few years, object recognition has received a big of interest in an attempt to make use of the large scale image datasets. Object recognition allows understanding image based on the objects that it contains. The success of the deep learning based methods in recognizing objects has encouraged recent works to follow this approach. In this paper, we propose a Boosted Convolutional Neural Network approach for object recognition. Our approach uses a very deep convolutional neural network reinforced by adding Boosted Blocks. These Boosted Blocks consist of a succession of convolutional layers boosted by using a Multi-Bias Nonlinear Activation function, that enriches the expressive power of the network, as well as a Concatenated Rectified Linear Unit function which ensures the preservation of all the information after the convolutional layer. Besides, inspired by the visual system of the human brain, our Boosted Convolutional Layer is designed following a recurrent structure. In addition, rather than using the classical max-pooling, our Boosted Convolutional Neural Network is improved by applying Generalizing pooling which allows pooling to adapt to complex and variable patterns. Furthermore, the Spatial Pyramid Pooling after the last Boosted Block has been conducted after the Boosted Block in order to remove the fixed-size constraint of input image. Our approach is evaluated on four different object recognition benchmarks: Pascal VOC 2007, Pascal VOC 2012, CIFAR-10 and the larger dataset ILSVRC-2012. These datasets are widely used by the recent object recognition methods. The experimental results validate the efficiency of our method in comparison with other methods from the literature.
       
  • Robust balancing scheme-based approach for tensor completion
    • Abstract: Publication date: 22 February 2019Source: Neurocomputing, Volume 330Author(s): Shangqi Gao, Qibin Fan Firstly, we propose a balancing scheme called BS to obtain new matricizations for an unbalanced tensor, and develop an efficient algorithm, which employs the idea of Orthogonal-Matching-Pursuit, to implement the BS. Then, we propose a new model for tensor completion based on the BS, and develop an algorithm called BS-TMac, which is rooted from a well known algorithm TMac, to solve the proposed model. Finally, we test our algorithms on synthetic and real world data to show the robustness of the BS-based model in reconstruction for unbalanced tensors. The numerical experiments show that BS-TMac outperforms compared methods in recovery quality.
       
  • Big graph classification frameworks based on Extreme Learning Machine
    • Abstract: Publication date: 22 February 2019Source: Neurocomputing, Volume 330Author(s): Yongjiao Sun, Boyang Li, Ye Yuan, Xin Bi, Xiangguo Zhao, Guoren Wang Graph data analysis is a hot topic in recent research area. Graph classification is one of the most important graph data analysis problems, which choose the most probable class labels of graphs using models based on the training dataset. It has wildly applications in protein group identification, chemical compounds classification and so on. Many existing research of graph learning suffer from high computation cost as large scale graph data are dramatically increased. In order to realize big graph classification with real-time learning ability and good scalability, efficient feature extraction approaches and ELM variants are utilized in this paper. To be specific, we present three frameworks of big graph classification based on ELMs: (1) a framework with a compression-based frequent subgraph mining method to reduce graph size; (2) an incremental framework to handle dynamic graphs; (3) a distributed framework with distributed ELMs to provide good scalability and easy implementation on cloud platforms. Extensive experiments are conducted on clusters with large real-world graph datasets. The experimental results demonstrate that our frameworks are efficient in big graph classification applications, and well suitable for dynamic networks. The results also validate that ELM and its variants have good classification performance on large-scale graphs.
       
  • Mood-aware visual question answering
    • Abstract: Publication date: 22 February 2019Source: Neurocomputing, Volume 330Author(s): Nelson Ruwa, Qirong Mao, Liangjun Wang, Jianping Gou, Ming Dong The concept of Visual Question Answering (VQA) has recently attracted the attention of many researchers in the field of machine learning. Different attention models have been proposed in VQA for the purpose of addressing the need to focus on local regions of an image. This paper proposes the concept of Mood-Aware Visual Question Answering (MAVQA) using novel long short term memory (LSTM) and convolutional neural network (CNN) attention models that combine the local image features, the question and the mood detected from the particular region of the image to produce a mood-based answer using a pre-processed image dataset. The attention mechanisms serve to enable the VQA model to only focus on parts of the image that are relevant to both the detected mood and the key words in the question. The irrelevant parts of the image are ignored, thus improving classification accuracy by reducing the chances of predicting wrong answers. Whereas previous efforts have utilized CNN mostly for the embedding of images and text, we formulate a CNN attention algorithm for the image, question and mood. The more direct convolutional attention operation is more efficient and effective, when the number of views and kernel length are optimized, than the winding recurrent LSTM attention operation. The experimental results prove that MAVQA is effectively mood-aware, and the accuracy levels of our LSTM attention model are well within the range of previous conventional VQA benchmarks, while our novel CNN attention model outperforms the previous baselines in several instances. The additional attention on the mood does not only improve classification accuracy but also substantially contributes towards the analysis and comprehension of image features, a key development in modern artificial intelligence.
       
  • Problems of encoder-decoder frameworks for high-resolution remote sensing
           image segmentation: Structural stereotype and insufficient learning
    • Abstract: Publication date: 22 February 2019Source: Neurocomputing, Volume 330Author(s): Yi Sun, Yan Tian, Yiping Xu This work explores the use of deep convolutional neural networks for high resolution remote sensing imagery segmentation. Encoder-decoder frameworks are popular in semantic image segmentation. However, encoder-decoder models face two main problems. The one is structural stereotype which is receptive fields imbalance rooted in this kind of frameworks. The other is insufficient learning that deeper neural networks tend to encounter the notorious problem of vanishing gradients. Structural stereotype leads to unfair learning and inhomogeneous reasoning. We are the first to reveal the problem and propose ensemble training and inference strategies to suppress the adverse consequences of structural stereotype as far as possible. To alleviate the problem of insufficient learning, we propose a novel residual architecture for encoder-decoder models. The proposed method yields state-of-the-art performances on the ISPRS 2D semantic labeling contest benchmark.
       
  • Distributed optimization for deep learning with gossip exchange
    • Abstract: Publication date: 22 February 2019Source: Neurocomputing, Volume 330Author(s): Michael Blot, David Picard, Nicolas Thome, Matthieu Cord We address the issue of speeding up the training of convolutional neural networks by studying a distributed method adapted to stochastic gradient descent. Our parallel optimization setup uses several threads, each applying individual gradient descents on a local variable. We propose a new way of sharing information between different threads based on gossip algorithms that show good consensus convergence properties. Our method called GoSGD has the advantage to be fully asynchronous and decentralized.
       
  • DCT–CNN-based classification method for the Gongbi and Xieyi techniques
           of Chinese ink-wash paintings
    • Abstract: Publication date: 22 February 2019Source: Neurocomputing, Volume 330Author(s): Wei Jiang, Zheng Wang, Jesse S. Jin, Yahong Han, Meijun Sun Different from the western paintings, Chinese ink-wash paintings (IWPs) have own distinctive art styles. Furthermore, Chinese IWPs can be divided into two classes, Gongbi (traditional Chinese realistic painting) and Xieyi (freehand style). The extraction of Chinese IWP features with good classification results is challenging because of similar content. This paper presents a novel framework by combining a discrete cosine transformation (DCT) and convolutional neural networks (CNNs). In this framework, a CNN automatically extracts Chinese IWP features from a small subset of the DCT coefficients of an image instead of raw pixels commonly because of its good performance. We evaluate the proposed framework on a dataset including 1400 Chinese IWPs. Experimental results show that the proposed framework achieves competitive classification performance compared to existing benchmark methods.
       
  • Noisy low-tubal-rank tensor completion
    • Abstract: Publication date: 22 February 2019Source: Neurocomputing, Volume 330Author(s): Andong Wang, Zhihui Lai, Zhong Jin In many applications of multi-dimensional signal processing, noisy tensor completion arises often where the acquired data suffers from miss values and noise. Recently, models based on the tubal nuclear norm (TNN) have been applied successfully in a number of tensor completion tasks. However, statistical analysis of these models is still insufficient. In this paper, a TNN-based estimator is proposed to estimate a tensor from its partial noisy observations. Statistically, a non-asymptotic upper bound on the estimation error is established and proved to be optimal (up to a logarithm factor) in a minimax sense. Algorithmically, an ADMM-based algorithm and a composite gradient descent (CGD) algorithm are proposed to compute the estimator. The CGD algorithm is further proved to enjoy globally geometric rates of convergence up to the statistical error. The sharpness of the proposed upper bound and the geometric convergence rate of the proposed CGD algorithm are verified in two experiments on synthetic datasets, respectively. The superiority of the proposed algorithms over several state-of-the-art convex algorithms is demonstrated in experiments on color images and a dataset from environment perception for unmanned ground vehicle.
       
  • Semantic image segmentation via guidance of image classification
    • Abstract: Publication date: 22 February 2019Source: Neurocomputing, Volume 330Author(s): Falong Shen, Gang Zeng This paper describes a joint segmentation and classification approach that exploits global image features to validate the predictions from local appearance descriptors and to ensure their consistent labeling. The in-between interplay is encoded by a parameter-learning process of a unified deep learning model embedding a fully convolution network portion. Although FCN has a relatively large recept field, the integration of the image content as a whole makes the prediction more reasonable and logical, since coincidences in local neighborhoods are more likely to be depressed given global structures. We also propose a content-sensitive co-occurrence priori for label compatibility, which provides additional constraints for CRF based segmentation.
       
  • Vector-kernel convolutional neural networks
    • Abstract: Publication date: 22 February 2019Source: Neurocomputing, Volume 330Author(s): Jun Ou, Yujian Li In computer vision, convolutional neural networks (CNNs) obtain extremely striking recognition performance. However, in many CNNs there exists a great deal of parameter redundancy because of matrix kernels. To address this problem, we propose a novel model, namely, vector-kernel convolutional neural network (VeckerNet). In a VeckerNet, each convolutional layer can only use vector kernels of either size k × 1 or 1 × k. Compared to the popular models, e.g., AlexNet, VGG, ResNet and DenseNet, the VeckerNets obtain up to 20.8% relative performance improvement with the parameter reduction by 3 to 97%. Impressively, compared to the ResNets with the same depth, e.g., 44, 56, 101 and 110 layers, the VeckerNets obtain 0.57 to 1.4% relative performance improvement with a decrease of parameters by up to more than two-thirds. The experimental results indicate that the VeckerNets can retain good recognition performance while effectively reducing network parameters.
       
  • Toward practical remote iris recognition: A boosting based framework
    • Abstract: Publication date: 22 February 2019Source: Neurocomputing, Volume 330Author(s): Man Zhang, Zhaofeng He, Hui Zhang, Tieniu Tan, Zhenan Sun In this paper, we present a generalized boosting framework to tackle some challenging problems in practical remote iris recognition, namely, iris detection, iris mislocalization detection, iris spoof detection as well as iris recognition. This solution takes advantages of a set of carefully designed features and well-tuned boosting algorithms. Basically, there are two major contributions. The first one is an exploration into the intrinsic properties of remote iris recognition as well as the carefully designed robust features for specific problems. For example, the randomness of iris texture is explored, and ordinal measures are adopted as features for iris representation. The second major contribution is the methodology on how to tune Adaboost learning for specific problems. For instance, an effective similarity oriented boosting algorithm is proposed for iris recognition inspired by the similarity property of the training samples. Other specific contributions include: an efficient topological model of Haar-like features for robust iris detection, a texture and Adaboost based method for efficient iris spoof detection and iris mislocalization detection, a novel Gaussian model for adaptive decision making, etc. Extensive experiments on challenging iris image databases are conducted to evaluate the usefulness of the proposed methods, and the results show that state-of-the-art performance is achieved.
       
  • Constraint-based clustering by fast search and find of density peaks
    • Abstract: Publication date: 22 February 2019Source: Neurocomputing, Volume 330Author(s): Ruhui Liu, Weiping Huang, Zhengshun Fei, Kai Wang, Jun Liang Clustering by fast search and find of density peaks (CFDP) algorithm first proposed on Science is based on assumptions that the cluster center has the highest density among its neighbors and keeps a distance from other cluster centers. In CFDP algorithm, a local density metric and a minimal distance vector are first calculated for constructing a decision graph to select cluster centers. However, CFDP's performance is quite sensitive to parameter selection and relies on other prior knowledge. To solve the problem, this paper proposed a new clustering algorithm named constraint-based clustering by fast search and find of density peaks (CCFDP). In the proposed algorithm, several potential cluster centers are automatically formed and the structural information from constraints could be made full use of. CCFDP adopts a new method to obtain the density metric and the decision graph. After that, the decision graph is analyzed from different perspectives to help complete the final clustering. CCFDP is a semi-supervised robust clustering algorithm, combining semi-supervised constraints, density clustering and hierarchical clustering. Three synthetic and seven open datasets are used for testing its performance and robustness. The final results show that CCFDP outperforms other well-known constraint-based clustering algorithms.
       
  • Fast and efficient algorithm for matrix completion via closed-form
           2/3-thresholding operator
    • Abstract: Publication date: 22 February 2019Source: Neurocomputing, Volume 330Author(s): Zhi Wang, Wendong Wang, Jianjun Wang, Siqi Chen Matrix completion is arguably one of the most studied problems in machine learning and data analysis. Inspired by the closed-form formulas for L2/3 regularization, we employ the Schatten 2/3 quasi-norm to approximate the rank of a matrix, which can provide a better approximation than traditional ways. We also establish a necessary optimal condition and propose a fixed point iterative scheme for solving L2/3 regularization problem. Through analysing the monotonicity and the accumulation point of L2/3 regularization problem, the convergence of this iteration is analysed. By discussing the optimal selection of the regularization parameter together with a fast Monte Carlo algorithm and an approximate singular value decomposition (SVD) procedure, we build a fast and efficient algorithm that solves the induced optimization problem well. Extensive experiments have been conducted and the results show that the proposed algorithm is fast, efficient and robust. Specifically, we compare the proposed algorithm with state-of-the-art matrix completion algorithms on many synthetic data and large recommendation datasets. Our proposed algorithm is able to achieve similar or better prediction performance, while being faster and more efficient than alternatives. Furthermore, we demonstrate the effectiveness of our proposed algorithm by solving image inpainting problems.
       
  • Spiking pattern recognition using informative signal of image and
           unsupervised biologically plausible learning
    • Abstract: Publication date: 22 February 2019Source: Neurocomputing, Volume 330Author(s): Soheila Nazari, Karim faez The recent progress of low-power neuromorphic hardware provides exceptional conditions for applications where their focus is more on saving power. However, the design of spiking neural networks (SNN) to recognize real-world patterns on such hardware remains a major challenge ahead of the researchers. In this paper, SNN inspired by the model of local cortical population as a biological neuro-computing resource for digit recognition was presented. SNN was equipped with spike-based unsupervised weight optimization based on the dynamical behavior of the excitatory (AMPA) and inhibitory (GABA) synapses using Spike Timing Dependent Plasticity (STDP). This biologically plausible learning enables neurons to make decisions and learns the structure of the input examples. There are two main reasons why this structure is state of the art compared to previous works: learning process is compatible with many experimental observations on induction of long-term potentiation and long-term depression, image to signal mapping created an informative signal of the image based on sequences of prolate spheroidal wave functions (PSWFs). The proposed image mapping translates the pixels attributes to the frequency, phase, and amplitude of a sinusoidal signal. This mapping enables the SNN to generalize better to the realistic sized images and significantly decreases the size of the input layer. Cortical SNN compared to earlier related studies recognized MNIST digits more accurate and achieved 96.1% classification accuracy with unsupervised learning based on sparse spike activity.
       
  • Trajectory tracking of constrained robotic systems via a hybrid control
           strategy
    • Abstract: Publication date: 22 February 2019Source: Neurocomputing, Volume 330Author(s): Weiwei Sun, You Wu, Liping Wang In this paper, a novel hybrid coordinated control scheme is proposed for robotic systems with full-state constraints. Asymmetric barrier Lyapunov functions (ABLFs) in backstepping design procedure are employed and corresponding backstepping controller is presented to prevent full-state constraints violation. Energy-based Hamilton control is utilized to provide Hamilton controller. Hybrid control method, which includes both backstepping and Hamilton control, is considered for improving asymptotic position tracking performance. Asymptotically stability of the closed-loop system is analyzed in Lyapunov sense. It is shown that proposed hybrid controller can effectively enhance response speed and tracking accuracy while ensuring that full-state constraints are not violated. Simulation example is provided to illustrate the feasibility and advantage of control algorithm.
       
  • Four actor-critic structures and algorithms for nonlinear multi-input
           multi-output system
    • Abstract: Publication date: 22 February 2019Source: Neurocomputing, Volume 330Author(s): Zhijian Huang, Yanyan Zhang, Yihua Liu, Guichen Zhang The action-dependent heuristic approximate dynamic (ADHDP) for nonlinear multi-input multi-output (MIMO) system needs different forms to adapt to variable practical objects. Due to some inappropriate network structure or training algorithm, unsuccessful designs or undesirable control effect is common in reality. Thus, at first, this paper addresses the chain rule problem of the compound derivative in training the nonlinear MIMO ADHDP. Then, this paper researches and proposes four actor-critic algorithms systematically according to four typical nonlinear systems. That is, the action-network extension, the sub-network, the cascaded action-network and the combined method. To illustrate the four methods, their detailed structures, derivation procedures and training algorithms are derived. The Lyapunov stability for the nonlinear MIMO ADHDP is proved as well. Through examples of an idling engine and aircraft controlling, the simulation results show the effectiveness of these methods. Besides, the property, advantages, disadvantages and the applicability of these methods are compared and highlighted. The four methods can be used to meet the design requirement of almost all the nonlinear MIMO ADHDP control systems. For incoming scholars in search of a nonlinear MIMO ADHDP to achieve the best control effect, the four actor-critic structures and algorithms can be a reference.
       
  • Label aided deep ranking for the automatic diagnosis of Parkinsonian
           syndromes
    • Abstract: Publication date: 22 February 2019Source: Neurocomputing, Volume 330Author(s): Andrés Ortiz, Francisco J. Martínez Murcia, Jorge Munilla, Juan M. Górriz, Javier Ramírez Parkinsonism is the second most common neurodegenerative disease in the world. Its diagnosis usually relies on visual analysis of Emission Computed Tomography (SPECT) images acquired using 123I−ioflupane radiotracer. This aims to detect a deficit of dopamine transporters at the striatum. The use of Computer Aided tools for diagnosis based on statistical data processing and machine learning methods have significantly improved the diagnosis accuracy. In this paper we propose a classification method based on Deep Ranking which learns an embedding function that projects the source images into a new space in which samples belonging to the same class are closer to each other, while samples from different classes are moved apart. Moreover, the proposed approach introduces a new cost-sensitive loss function to avoid overfitting due to class imbalance (an usual issue in practical biomedical applications), along with label information to produce sparser embedding spaces. The experiments carried out in this work demonstrate the superiority of the proposed method, improving the diagnosis accuracy achieved by previous methodologies and validate our approach as an efficient way to construct linear classifiers.
       
  • Brightness guided preprocessing for automatic cold steel weapon detection
           in surveillance videos with deep learning
    • Abstract: Publication date: 22 February 2019Source: Neurocomputing, Volume 330Author(s): Alberto Castillo, Siham Tabik, Francisco Pérez, Roberto Olmos, Francisco Herrera The automatic detection of cold steel weapons handled by one or multiple persons in surveillance videos can help reducing crimes. However, the detection of these metallic objects in videos faces an important problem: their surface reflectance under medium to high illumination conditions blurs their shapes in the image and hence makes their detection impossible. The objective of this work is two-fold: (i) To develop an automatic cold steel weapon detection model for video surveillance using Convolutional Neural Networks(CNN) and (ii) strengthen its robustness to light conditions by proposing a brightness guided preprocessing procedure called DaCoLT (Darkening and Contrast at Learning and Test stages). The obtained detection model provides excellent results as cold steel weapon detector and as automatic alarm system in video surveillance.
       
  • Re-KISSME: A robust resampling scheme for distance metric learning in the
           presence of label noise
    • Abstract: Publication date: 22 February 2019Source: Neurocomputing, Volume 330Author(s): Fanxia Zeng, Wensheng Zhang, Siheng Zhang, Nan Zheng Distance metric learning aims to learn a metric with the similarity of samples. However, the increasing scalability and complexity of dataset or complex application brings about inevitable label noise, which frustrates the distance metric learning. In this paper, we propose a resampling scheme robust to label noise, Re-KISSME, based on Keep It Simple and Straightforward Metric (KISSME) learning method. Specifically, we consider the data structure and the priors of labels as two resampling factors to correct the observed distribution. By introducing the true similarity as latent variable, these two factors are integrated into a maximum likelihood estimation model. As a result, Re-KISSME can reason the underlying similarity of each pair and reduce the influence of label noise to estimate the metric matrix. Our model is solved by iterative algorithm with low computational cost. With synthetic label noise, the experiments on UCI datasets and two application datasets of person re-identification confirm the effectiveness of our proposal.
       
  • A jointly learned deep embedding for person re-identification
    • Abstract: Publication date: 22 February 2019Source: Neurocomputing, Volume 330Author(s): Caihong Yuan, Jingjuan Guo, Ping Feng, Zhiqiang Zhao, Chunyan Xu, Tianjiang Wang, Gwangmin Choe, Kui Duan Person re-identification (ReID) is an importance study issue in the modern video surveillance area. However, it is very challenging due to the large variations of intra-class and the small variations of inter-class. To solve the problem, most deep ReID methods usually learn a feature embedding based on verification or identification. However, both learning approaches are complementary and neither of them can well address this issue. Motivated by this, we propose a deep joint learning framework based on verification-identification to achieve a discriminative deep feature embedding. Specifically, for verification, a triplet network is adopted and an improved triplet loss is proposed by adding a novel penalty item in the framework. For identification, an identity classifier with the softmax loss is attached to the top of the triplet network at the same time. Thus, driven by the improved triplet loss and the softmax loss simultaneously, a more discriminative and compact feature embedding will be learned for ReID. Extensive experiments over several popular benchmarks achieve state-of-the-art results which well demonstrate the effectiveness of the proposed method for ReID.
       
  • Genetic intuitionistic weighted fuzzy k-modes algorithm for
           categorical data
    • Abstract: Publication date: 22 February 2019Source: Neurocomputing, Volume 330Author(s): R.J. Kuo, Thi Phuong Quyen Nguyen Data clustering with categorical attributes has been widely used in many real-world applications. Most of the existing clustering algorithms proposed for the categorical data face two major drawbacks of termination at a local optimal solution and considering all attributes equally. Thus, this study proposes a novel clustering method, named genetic intuitionistic weighted fuzzy k-modes (GIWFKM) algorithm, based on the conventional fuzzy k-modes and genetic algorithm (GA). The proposed algorithm firstly introduces the intuitionistic weighted fuzzy k-modes (IWFKM) algorithm which employs the intuitionistic fuzzy set in the clustering process and the new similarity measure for categorical data based on frequency probability-based distance metric. Then, the GIWFKM algorithm, which integrates the IWFKM algorithm and GA, is proposed to employ the global optimal solution. Moreover, the GIWFKM algorithm performs the unsupervised feature selection based on the correlation coefficient to remove some redundant features which can both improve the clustering performance and reduce the computational time. To evaluate the clustering result, a series of experiments in different categorical datasets are conducted to compare the performance of the proposed algorithms with that of other benchmark algorithms including fuzzy k-modes, weighted fuzzy k-modes, genetic fuzzy k-modes, space structure-based clustering, and many-objective fuzzy centroids clustering algorithms. The experimental results conducted on the datasets collected from UCI machine learning repository exhibit that the GIWFKM algorithm outperforms the other benchmark algorithms in terms of Adjusted Rank Index (ARI) and clustering accuracy (CA).
       
  • Affine formation control for heterogeneous multi-agent systems with
           directed interaction networks
    • Abstract: Publication date: 22 February 2019Source: Neurocomputing, Volume 330Author(s): Yang Xu, Delin Luo, Dongyu Li, Yancheng You, Haibin Duan In this paper, affine formation control problems of heterogeneous multi-agent systems with linear dynamics are studied. As a novel method, the affine formation control has the ability to handle various movement constraints based on the affine transformation. A proportional-integral (PI)-type control scheme is proposed to ensure the effective affine formation control of the leader-following group with directed interaction graphs and successfully eliminate the steady-state errors. Sufficient conditions for the selection of control parameters are given and proved. Considering that the followers are subjected to the external bounded time-varying disturbances, the designed protocol shows good robustness and can guarantee uniformly ultimate boundedness of affine formation errors. Numerical simulations are carried out to verify the theoretical results.
       
  • Indoor object recognition in RGBD images with complex-valued neural
           networks for visually-impaired people
    • Abstract: Publication date: 22 February 2019Source: Neurocomputing, Volume 330Author(s): Rim Trabelsi, Issam Jabri, Farid Melgani, Fethi Smach, Nicola Conci, Ammar Bouallegue We present a new multi-modal technique for assisting visually-impaired people in recognizing objects in public indoor environment. Unlike common methods which aim to solve the problem of multi-class object recognition in a traditional single-label strategy, a comprehensive approach is developed here allowing samples to take more than one label at a time. We jointly use appearance and depth cues, specifically RGBD images, to overcome issues of traditional vision systems using a new complex-valued representation. Inspired by complex-valued neural networks (CVNNs) and multi-label learning techniques, we propose two methods in order to associate each input RGBD image to a set of labels corresponding to the object categories recognized at once. The first one, ML-CVNN, is formalized as a ranking strategy where we make use of a fully complex-valued RBF network and extend it to be able to solve multi-label problems using an adaptive clustering method. The second method, L-CVNNs, deals with problem transformation strategy where instead of using a single network to formalize the classification problem as a ranking solution for the whole label set, we propose to construct one CVNN for each label where the predicted labels will be later aggregated to construct the resulting multi-label vector. Extensive experiments have been carried on two newly collected multi-labeled RGBD datasets prove the efficiency of the proposed techniques.
       
  • Anti-synchronization analysis and pinning control of multi-weighted
           coupled neural networks with and without reaction-diffusion terms
    • Abstract: Publication date: 22 February 2019Source: Neurocomputing, Volume 330Author(s): Jie Hou, Yanli Huang, Shunyan Ren In this paper, the multi-weighted coupled neural networks (MWCNN) models with and without reaction-diffusion terms are studied, respectively. Firstly, we analyze the anti-synchronization of MWCNN with and without coupling delays by means of Lyapunov functional approach and some inequality techniques, and put forward some anti-synchronization conditions for the considered networks. Additionally, it is well known that pinning control is a very efficient tool to achieve the anti-synchronization of networks by adopting appropriate pinning controllers to a small fraction of nodes in networks. Therefore, we further investigate the pinning anti-synchronization of the considered networks, and derive some sufficient conditions which ensure that these networks are pinning anti-synchronized. Similarly, anti-synchronization analysis and pinning control for multi-weighted coupled reaction-diffusion neural networks (MWCRDNN) with and without coupling delays are considered, and several anti-synchronization and pinning anti-synchronization criteria for MWCRDNN are established. Lastly, four examples are used to confirm the effectiveness of the derived results.
       
  • Self-triggered leader-following consensus of multi-agent systems with
           input time delay
    • Abstract: Publication date: 22 February 2019Source: Neurocomputing, Volume 330Author(s): Xin Wang, Housheng Su In this paper, a self-triggered algorithm for leader-following consensus of general linear multi-agent systems with input time delay is proposed. It is shown that the leader-following consensus can be reached asymptotically and the Zeno-behaviour of triggering time sequences can be excluded. Under this algorithm, the next exact triggering time instant of each agent can be computed by the local information at the previous triggering time instant. Continuous communication, monitoring of measurement error and updating of controller input are thus avoided.
       
  • Complex Zhang neural networks for complex-variable dynamic quadratic
           programming
    • Abstract: Publication date: 22 February 2019Source: Neurocomputing, Volume 330Author(s): Qiang Ma, Sitian Qin, Tao Jin In this paper, a neurodynamic approach concerning complex Zhang neural networks (ZNNs) is presented to solve a complex-variable dynamic quadratic programming (QP). The proposed complex ZNNs are activated by various complex activation functions respectively, such as linear function, sign function and Li function. It is proved that ZNNs with different activation functions have different convergence rates involving super exponential convergency and finite time convergency. Furthermore, by introducing an integral term, a noise-tolerated ZNN is proposed to ensure the convergency under constant noise. Compared with existing works, the presented complex ZNNs in this paper have less amount of neurons and superior convergence rate. Some numerical examples are provided to illustrate the effectiveness and validity of the results. In addition, the complex ZNNs also apply well to LCMP beamforming problem.
       
  • A feature learning approach for face recognition with robustness to noisy
           label based on top-N prediction
    • Abstract: Publication date: 22 February 2019Source: Neurocomputing, Volume 330Author(s): Menglong Yang, Feihu Huang, Xuebin Lv Collecting a vast amount of face data with identity labels to train a convolutional neural network is an effective mean to learn a discriminative feature representation for face recognition. However, the datasets with larger scale often contain more noisy labels, that directly affects the ultimate performance of the learned model. This paper proposes an end-to-end feature learning method with robustness to noisy label. First, a data filtering method is proposed to automatically online filter out the data with false label, by checking the consistency between the annotated label and the results of top-N prediction. Then the loss functions of softmax and center loss are simply revised to only supervise the reserved feature. Finally, we use MS-Celeb-1M dataset, which contains massive noisy labels, to train a 128-D feature representation without any pre-train or data pre-clean. A single learned model gets an accuracy of 99.43% on LFW test set, that is very close to the model trained using the clean data.
       
  • BP-STDP: Approximating backpropagation using spike timing dependent
           plasticity
    • Abstract: Publication date: 22 February 2019Source: Neurocomputing, Volume 330Author(s): Amirhossein Tavanaei, Anthony Maida The problem of training spiking neural networks (SNNs) is a necessary precondition to understanding computations within the brain, a field still in its infancy. Previous work has shown that supervised learning in multi-layer SNNs enables bio-inspired networks to recognize patterns of stimuli through hierarchical feature acquisition. Although gradient descent has shown impressive performance in multi-layer (and deep) SNNs, it is generally not considered biologically plausible and is also computationally expensive. This paper proposes a novel supervised learning approach based on an event-based spike-timing-dependent plasticity (STDP) rule embedded in a network of integrate-and-fire (IF) neurons. The proposed temporally local learning rule follows the backpropagation weight change updates applied at each time step. This approach enjoys benefits of both accurate gradient descent and temporally local, efficient STDP. The experimental results on the XOR problem, the Iris data, and the MNIST dataset demonstrate that the proposed SNN performs as successfully as the traditional NNs. Our approach also compares favorably with the state-of-the-art multi-layer SNNs. Thus, this method can be applied to develop deep SNNs with end-to-end STDP-based learning rules in future studies.
       
  • Finite-time consensus control of heterogeneous nonlinear MASs with
           uncertainties bounded by positive functions
    • Abstract: Publication date: 22 February 2019Source: Neurocomputing, Volume 330Author(s): Wei Liu, Qian Ma, Qiang Wang, Hongyan Feng In this paper, we investigate the finite-time consensus control problem for heterogeneous nonlinear multi-agent systems (MASs) subject to nonlinear uncertainties. In the case of a connected and undirected communication graph, a new finite-time consensus control algorithm for leaderless MASs is presented. The designed consensus protocol can guarantee that the steady state error of every two agents converge to zero in a finite-time. Compared with some existing studies, in which the upper bounds of uncertainties are assumed to be some positive constants, the upper bound assumption conditions of uncertainties are relaxed to be some positive functions. Finally, a numerical example and a practical model are presented to demonstrate the effectiveness of the proposed consensus control method.
       
  • 3G structure for image caption generation
    • Abstract: Publication date: 22 February 2019Source: Neurocomputing, Volume 330Author(s): Aihong Yuan, Xuelong Li, Xiaoqiang Lu It is a big challenge of computer vision to make machine automatically describe the content of an image with a natural language sentence. Previous works have made great progress on this task, but they only use the global or local image feature, which may lose some important subtle or global information of an image. In this paper, we propose a model with 3-gated model which fuses the global and local image features together for the task of image caption generation. The model mainly has three gated structures. (1) Gate for the global image feature, which can adaptively decide when and how much the global image feature should be imported into the sentence generator. (2) The gated recurrent neural network (RNN) is used as the sentence generator. (3) The gated feedback method for stacking RNN is employed to increase the capability of nonlinearity fitting. More specially, the global and local image features are combined together in this paper, which makes full use of the image information. The global image feature is controlled by the first gate and the local image feature is selected by the attention mechanism. With the latter two gates, the relationship between image and text can be well explored, which improves the performance of the language part as well as the multi-modal embedding part. Experimental results show that our proposed method outperforms the state-of-the-art for image caption generation.
       
  • Novel circuit designs of memristor synapse and neuron
    • Abstract: Publication date: 22 February 2019Source: Neurocomputing, Volume 330Author(s): Qinghui Hong, Liang Zhao, Xiaoping Wang In this work, novel circuits based on memristors for implementing electronic synapse and artificial neuron are designed. First, two simple synaptic circuits for implementing weighting calculations of voltage and current modes using twin memristors are proposed. A synaptic weighting operation is defined as a difference function between the twin memristors, which can be adjusted in reverse by applying programmed signals and conducting positive, zero, and negative synaptic weights. Second, two neuron circuits using the proposed memristor synapses, in which parallel computing and programming can be achieved, are designed. Finally, performances of the proposed memristor synapses and neuron circuits, such as weight programming, neuron computing, and parallel operation, are analyzed through PSpice simulations.
       
  • Adaptive course control based on trajectory linearization control for
           unmanned surface vehicle with unmodeled dynamics and input saturation
    • Abstract: Publication date: 22 February 2019Source: Neurocomputing, Volume 330Author(s): Dongdong Mu, Guofeng Wang, Yunsheng Fan, Bingbing Qiu, Xiaojie Sun This paper presents a novel course control strategy for unmanned surface vehicles (USV) subject to unmodeled dynamics and time-varying external disturbance. A practical adaptive course controller is proposed by trajectory linearization control (TLC) technology, neural network minimum learning parameter method (MLP), disturbance observer (DOB) and auxiliary design system. MLP and DOB are introduced to compensate for unmodeled dynamics and time-varying external disturbance, respectively. In addition, auxiliary design system is hired to deal with input saturation issue. By Lyapunov stability theory, it is proved that all the error signals in the course control system are uniform ultimate bounded. The advantages of the developed control strategy are that first, from the author’s point of view, TLC technology is applied to the field of USV motion control for the first time, which opens a new research direction of this algorithm; second, MLP with a smaller computational burden is used to replace radial basis function (RBF) neural network, which is more convenient for engineering implementation; third, the proposed scheme has strong anti-interference ability, which can compensate for the inherent defect that the USV with a smaller volume is susceptible to external disturbance. Finally, numerical simulations prove the effectiveness and correctness of the proposed control strategy.
       
  • Hyperspectral image denoising via minimizing the partial sum of singular
           values and superpixel segmentation
    • Abstract: Publication date: 22 February 2019Source: Neurocomputing, Volume 330Author(s): Yang Liu, Caifeng Shan, Quanxue Gao, Xinbo Gao, Jungong Han, Rongmei Cui Hyperspectral images (HSIs) are often corrupted by noise during the acquisition process, thus degrading the HSI’s discriminative capability significantly. Therefore, HSI denoising becomes an essential preprocess step before application. This paper proposes a new HSI denoising approach connecting Partial Sum of Singular Values (PSSV) and superpixels segmentation named as SS-PSSV, which can remove the noise effectively. Based on the fact that there is a high correlation between different bands of the same signal, it is easy to know the property of low rank between distinct bands. To this end, PSSV is utilized, and in order to better tap the low-rank attribute of pixels, we introduce the superpixels segmentation method, which allows pixels in HSI with high similarity to be grouped in the same sub-block as much as possible. Extensive experiments display that the proposed algorithm outperforms the state-of-the-art.
       
 
 
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