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Publisher: Elsevier   (Total: 3182 journals)

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Showing 1 - 200 of 3182 Journals sorted alphabetically
Academic Pediatrics     Hybrid Journal   (Followers: 39, SJR: 1.655, CiteScore: 2)
Academic Radiology     Hybrid Journal   (Followers: 26, SJR: 1.015, CiteScore: 2)
Accident Analysis & Prevention     Partially Free   (Followers: 105, SJR: 1.462, CiteScore: 3)
Accounting Forum     Hybrid Journal   (Followers: 28, SJR: 0.932, CiteScore: 2)
Accounting, Organizations and Society     Hybrid Journal   (Followers: 42, SJR: 1.771, CiteScore: 3)
Achievements in the Life Sciences     Open Access   (Followers: 7)
Acta Anaesthesiologica Taiwanica     Open Access   (Followers: 6)
Acta Astronautica     Hybrid Journal   (Followers: 441, SJR: 0.758, CiteScore: 2)
Acta Automatica Sinica     Full-text available via subscription   (Followers: 2)
Acta Biomaterialia     Hybrid Journal   (Followers: 29, SJR: 1.967, CiteScore: 7)
Acta Colombiana de Cuidado Intensivo     Full-text available via subscription   (Followers: 3)
Acta de Investigación Psicológica     Open Access   (Followers: 3)
Acta Ecologica Sinica     Open Access   (Followers: 11, SJR: 0.18, CiteScore: 1)
Acta Histochemica     Hybrid Journal   (Followers: 5, SJR: 0.661, CiteScore: 2)
Acta Materialia     Hybrid Journal   (Followers: 318, 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: 2, SJR: 0.307, CiteScore: 0)
Acta Pharmaceutica Sinica B     Open Access   (Followers: 2, SJR: 1.793, CiteScore: 6)
Acta Poética     Open Access   (Followers: 4, SJR: 0.101, CiteScore: 0)
Acta Psychologica     Hybrid Journal   (Followers: 26, 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: 7, SJR: 0.19, CiteScore: 0)
Actualites Pharmaceutiques Hospitalieres     Full-text available via subscription   (Followers: 3)
Acupuncture and Related Therapies     Hybrid Journal   (Followers: 8)
Acute Pain     Full-text available via subscription   (Followers: 15, SJR: 2.671, CiteScore: 5)
Ad Hoc Networks     Hybrid Journal   (Followers: 11, SJR: 0.53, CiteScore: 4)
Addictive Behaviors     Hybrid Journal   (Followers: 18, SJR: 1.29, CiteScore: 3)
Addictive Behaviors Reports     Open Access   (Followers: 9, SJR: 0.755, CiteScore: 2)
Additive Manufacturing     Hybrid Journal   (Followers: 11, SJR: 2.611, CiteScore: 8)
Additives for Polymers     Full-text available via subscription   (Followers: 23)
Advanced Drug Delivery Reviews     Hybrid Journal   (Followers: 187, SJR: 4.09, CiteScore: 13)
Advanced Engineering Informatics     Hybrid Journal   (Followers: 12, SJR: 1.167, CiteScore: 4)
Advanced Powder Technology     Hybrid Journal   (Followers: 17, SJR: 0.694, CiteScore: 3)
Advances in Accounting     Hybrid Journal   (Followers: 9, SJR: 0.277, CiteScore: 1)
Advances in Agronomy     Full-text available via subscription   (Followers: 17, SJR: 2.384, CiteScore: 5)
Advances in Anesthesia     Full-text available via subscription   (Followers: 30, 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: 12, SJR: 0.992, CiteScore: 1)
Advances in Applied Mechanics     Full-text available via subscription   (Followers: 12, 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: 15, 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: 34, 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: 5)
Advances in Cellular and Molecular Biology of Membranes and Organelles     Full-text available via subscription   (Followers: 14)
Advances in Chemical Engineering     Full-text available via subscription   (Followers: 29, SJR: 0.156, CiteScore: 1)
Advances in Child Development and Behavior     Full-text available via subscription   (Followers: 11, SJR: 0.713, CiteScore: 1)
Advances in Chronic Kidney Disease     Full-text available via subscription   (Followers: 10, 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: 20, 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: 13)
Advances in Digestive Medicine     Open Access   (Followers: 12)
Advances in DNA Sequence-Specific Agents     Full-text available via subscription   (Followers: 7)
Advances in Drug Research     Full-text available via subscription   (Followers: 26)
Advances in Ecological Research     Full-text available via subscription   (Followers: 44, SJR: 2.524, CiteScore: 4)
Advances in Engineering Software     Hybrid Journal   (Followers: 29, 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: 52, 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: 67, SJR: 0.591, CiteScore: 2)
Advances in Fuel Cells     Full-text available via subscription   (Followers: 17)
Advances in Genetics     Full-text available via subscription   (Followers: 21, SJR: 1.354, CiteScore: 4)
Advances in Genome Biology     Full-text available via subscription   (Followers: 11, SJR: 12.74, CiteScore: 13)
Advances in Geophysics     Full-text available via subscription   (Followers: 7, SJR: 1.193, CiteScore: 3)
Advances in Heat Transfer     Full-text available via subscription   (Followers: 26, SJR: 0.368, CiteScore: 1)
Advances in Heterocyclic Chemistry     Full-text available via subscription   (Followers: 11, SJR: 0.749, CiteScore: 3)
Advances in Human Factors/Ergonomics     Full-text available via subscription   (Followers: 26)
Advances in Imaging and Electron Physics     Full-text available via subscription   (Followers: 3, SJR: 0.193, CiteScore: 0)
Advances in Immunology     Full-text available via subscription   (Followers: 37, SJR: 4.433, CiteScore: 6)
Advances in Inorganic Chemistry     Full-text available via subscription   (Followers: 10, 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: 9, 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: 21, SJR: 0.88, CiteScore: 2)
Advances in Mathematics     Full-text available via subscription   (Followers: 15, SJR: 3.027, CiteScore: 2)
Advances in Medical Sciences     Hybrid Journal   (Followers: 8, SJR: 0.694, CiteScore: 2)
Advances in Medicinal Chemistry     Full-text available via subscription   (Followers: 6)
Advances in Microbial Physiology     Full-text available via subscription   (Followers: 5, SJR: 1.158, CiteScore: 3)
Advances in Molecular and Cell Biology     Full-text available via subscription   (Followers: 25)
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: 5)
Advances in Oncobiology     Full-text available via subscription   (Followers: 2)
Advances in Organ Biology     Full-text available via subscription   (Followers: 2)
Advances in Organometallic Chemistry     Full-text available via subscription   (Followers: 18, 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: 27, SJR: 0.461, CiteScore: 1)
Advances in Pharmaceutical Sciences     Full-text available via subscription   (Followers: 19)
Advances in Pharmacology     Full-text available via subscription   (Followers: 17, SJR: 1.536, CiteScore: 3)
Advances in Physical Organic Chemistry     Full-text available via subscription   (Followers: 9, 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: 6)
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: 68)
Advances in Quantum Chemistry     Full-text available via subscription   (Followers: 6, SJR: 0.371, CiteScore: 1)
Advances in Radiation Oncology     Open Access   (Followers: 2, 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: 424, 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: 13, SJR: 0.555, CiteScore: 2)
Advances in the Study of Behavior     Full-text available via subscription   (Followers: 38, SJR: 2.208, CiteScore: 4)
Advances in Veterinary Medicine     Full-text available via subscription   (Followers: 20)
Advances in Veterinary Science and Comparative Medicine     Full-text available via subscription   (Followers: 15)
Advances in Virus Research     Full-text available via subscription   (Followers: 6, SJR: 2.262, CiteScore: 5)
Advances in Water Resources     Hybrid Journal   (Followers: 54, SJR: 1.551, CiteScore: 3)
Aeolian Research     Hybrid Journal   (Followers: 6, SJR: 1.117, CiteScore: 3)
Aerospace Science and Technology     Hybrid Journal   (Followers: 387, 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: 12, SJR: 3.671, CiteScore: 9)
Aggression and Violent Behavior     Hybrid Journal   (Followers: 480, SJR: 1.238, CiteScore: 3)
Agri Gene     Hybrid Journal   (Followers: 1, SJR: 0.13, CiteScore: 0)
Agricultural and Forest Meteorology     Hybrid Journal   (Followers: 18, SJR: 1.818, CiteScore: 5)
Agricultural Systems     Hybrid Journal   (Followers: 31, SJR: 1.156, CiteScore: 4)
Agricultural Water Management     Hybrid Journal   (Followers: 44, 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: 58, SJR: 1.747, CiteScore: 4)
Ain Shams Engineering J.     Open Access   (Followers: 5, SJR: 0.589, CiteScore: 3)
Air Medical J.     Hybrid Journal   (Followers: 8, 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: 12)
Alergologia Polska : Polish J. of Allergology     Full-text available via subscription   (Followers: 1)
Alexandria Engineering J.     Open Access   (Followers: 2, SJR: 0.604, CiteScore: 3)
Alexandria J. of Medicine     Open Access   (Followers: 1, SJR: 0.191, CiteScore: 1)
Algal Research     Partially Free   (Followers: 11, 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: 11, SJR: 0.201, CiteScore: 1)
Alzheimer's & Dementia     Hybrid Journal   (Followers: 53, SJR: 4.66, CiteScore: 10)
Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring     Open Access   (Followers: 6, SJR: 1.796, CiteScore: 4)
Alzheimer's & Dementia: Translational Research & Clinical Interventions     Open Access   (Followers: 6, SJR: 1.108, CiteScore: 3)
Ambulatory Pediatrics     Hybrid Journal   (Followers: 5)
American Heart J.     Hybrid Journal   (Followers: 58, SJR: 3.267, CiteScore: 4)
American J. of Cardiology     Hybrid Journal   (Followers: 66, SJR: 1.93, CiteScore: 3)
American J. of Emergency Medicine     Hybrid Journal   (Followers: 47, SJR: 0.604, CiteScore: 1)
American J. of Geriatric Pharmacotherapy     Full-text available via subscription   (Followers: 13)
American J. of Geriatric Psychiatry     Hybrid Journal   (Followers: 14, SJR: 1.524, CiteScore: 3)
American J. of Human Genetics     Hybrid Journal   (Followers: 37, 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: 36, SJR: 2.973, CiteScore: 4)
American J. of Medicine     Hybrid Journal   (Followers: 50)
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: 264, 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: 32, 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: 39, 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: 67, SJR: 0.138, CiteScore: 0)
Anaesthesia Critical Care & Pain Medicine     Full-text available via subscription   (Followers: 25, SJR: 0.411, CiteScore: 1)
Anales de Cirugia Vascular     Full-text available via subscription   (Followers: 1)
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: 44, SJR: 1.512, CiteScore: 5)
Analytica Chimica Acta : X     Open Access  
Analytical Biochemistry     Hybrid Journal   (Followers: 211, SJR: 0.633, CiteScore: 2)
Analytical Chemistry Research     Open Access   (Followers: 13, SJR: 0.411, CiteScore: 2)
Analytical Spectroscopy Library     Full-text available via subscription   (Followers: 14)
Anesthésie & Réanimation     Full-text available via subscription   (Followers: 2)
Anesthesiology Clinics     Full-text available via subscription   (Followers: 25, 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: 227, SJR: 1.58, CiteScore: 3)
Animal Feed Science and Technology     Hybrid Journal   (Followers: 7, SJR: 0.937, CiteScore: 2)
Animal Reproduction Science     Hybrid Journal   (Followers: 7, SJR: 0.704, CiteScore: 2)

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Similar Journals
Journal Cover
Advanced Engineering Informatics
Journal Prestige (SJR): 1.167
Citation Impact (citeScore): 4
Number of Followers: 12  
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1474-0346
Published by Elsevier Homepage  [3182 journals]
  • Production service system enabled by cloud-based smart resource hierarchy
           for a highly dynamic synchronized production process
    • Abstract: Publication date: October 2019Source: Advanced Engineering Informatics, Volume 42Author(s): Kai Zhang, Ming Wan, Ting Qu, Hongfei Jiang, Peize Li, Zefeng Chen, Jinjie Xiang, Xiaodong He, Congdong Li, George Q. Huang The rapidly changing market demands entail a modern production system to cope with ever more diversified production orders. Not only the parameters and flow of the production process, sometimes the production resources and the corresponding application systems which make production decision and control even need to be reconfigurable. Due to the inability to provide dynamically reconfigurable manufacturing resources, systems and application systems, enterprises have to sacrifice profits to narrow their business scope or take risks to purchase a large number of production resources. In any case, it has brought operational burden to enterprises. This paper systematically analyses the production management requirements of a large-scale production system in terms of both hardware (production equipment) and software (application system) which is oriented to dynamic production demands, and then proposes a production service system enabled by cloud-based smart resource hierarchy (PnSS-CSRH). The platform is based on an open resource management system and inherits the general cloud structure and the AUTOM framework. PnSS-CSRH provides industrial users with integrated and synchronization services for the software and hardware resources involved in the production process in the PnSS mode. Using the integrated service mode, PnSS-CSRH not only helps resource providers to increase the frequency of resource leases, provides customers with targeted and systemic hardware and software overall solutions, and increases the platform usage rate, which brings more benefits to the stakeholders of PnSS-CSRH. At last, the PnSS-CSRH is used to provide services to the case company to validate the effectiveness.
  • An Internet of Things-enabled BIM platform for modular integrated
           construction: A case study in Hong Kong
    • Abstract: Publication date: October 2019Source: Advanced Engineering Informatics, Volume 42Author(s): Yue Zhai, Ke Chen, Jason X. Zhou, Jin Cao, Zhongyuan Lyu, Xin Jin, Geoffrey Q.P. Shen, Weisheng Lu, George Q. Huang In recent years, Building Information Modelling (BIM) has been acting an important role in the delivery of Modular Integrated Construction (MiC) project. However, the full potential of BIM to MiC project cannot be realized without accurate information collection, timely information exchange, and automatic decision support throughout the project life cycle. In order to fulfil such requirements, this paper aims to develop an Internet of Things-enabled BIM platform (IBIMP) for the MiC project. A real-life project located in Hong Kong were deeply explored for developing the platform. The IBIMP consists of smart construction objects (SCOs) equipped with smart trinity tag (STT) and GPS sensor, smart gateway system, data source management service, location-based service, rule-based progress control service, as well as decision support services for prefabrication production, transportation, and on-site assembly processes. With the combination of advanced Internet of Things (IoT) technology and BIM technology, the barriers that hamper the possible functions of BIM can be overcame. By using application scenarios of a subsided sale flats MiC project in Hong Kong as examples, this study demonstrates how problems encountered by independent stakeholders such as inconvenient data collection, lack of automatic decision support, and incomplete information can be addressed by the IBIMP.
  • A high performance hybrid SSVEP based BCI speller system
    • Abstract: Publication date: October 2019Source: Advanced Engineering Informatics, Volume 42Author(s): D. Saravanakumar, M. Ramasubba Reddy The existing EEG based keyboard/speller systems have a tradeoff between the target detection time and classification accuracy. This study focuses on increasing the accuracy and probability of target classification rates in the SSVEP based speller system. We proposed two different types of hybrid SSVEP system by combining SSVEP with vision based eye gaze tracker (VET) and electro-oculogram (EOG). Thirty six targets were randomly chosen for this study and their corresponding visual stimulus was presented with unique frequencies. The visual stimuli were segregated into three groups and each group were arranged into different regions (left/middle/right) of the keyboard/speller layout for improving the probability of target detection rate. The VET/ EOG data were utilized to identify the regions that belong to the selected target. The region/group determination decreases the issue of misclassification of SSVEP frequencies. The averaged spelling accuracies of SSVEP-VET and SSVEP-EOG system for all the subjects is 91.2% and 91.39% respectively. Later, a visual feedback was added to the SSVEP-EOG system (SSVEP-EOG-VF) for improving the target detection rate. In this case, an average classification accuracy of 98.33% was obtained with the information transfer rate (ITR) of 69.21 bits/min for all the subjects. An accuracy of 100% was obtained for five subjects with the ITR of 74.1 bits/min in this system.
  • Crowdsourced reliable labeling of safety-rule violations on images of
           complex construction scenes for advanced vision-based workplace safety
    • Abstract: Publication date: October 2019Source: Advanced Engineering Informatics, Volume 42Author(s): Yanyu Wang, Pin-Chao Liao, Cheng Zhang, Yi Ren, Xinlu Sun, Pingbo Tang Construction workplace hazard detection requires engineers to analyze scenes manually against many safety rules, which is time-consuming, labor-intensive, and error-prone. Computer vision algorithms are yet to achieve reliable discrimination of anomalous and benign object relations underpinning safety violation detections. Recently developed deep learning-based computer vision algorithms need tens of thousands of images, including labels of the safety rules violated, in order to train deep-learning networks for acquiring spatiotemporal reasoning capacity in complex workplaces. Such training processes need human experts to label images and indicate whether the relationship between the worker, resource, and equipment in the scenes violate spatiotemporal arrangement rules for safe and productive operations. False alarms in those manual labels (labeling no-violation images as having violations) can significantly mislead the machine learning process and result in computer vision models that produce inaccurate hazard detections. Compared with false alarms, another type of mislabels, false negatives (labeling images having violations as “no violations”), seem to have fewer impacts on the reliability of the trained computer vision models.This paper examines a new crowdsourcing approach that achieves above 95% accuracy in labeling images of complex construction scenes having safety-rule violations, with a focus on minimizing false alarms while keeping acceptable rates of false negatives. The development and testing of this new crowdsourcing approach examine two fundamental questions: (1) How to characterize the impacts of a short safety-rule training process on the labeling accuracy of non-professional image annotators? And (2) How to properly aggregate the image labels contributed by ordinary people to filter out false alarms while keeping an acceptable false negative rate? In designing short training sessions for online image annotators, the research team split a large number of safety rules into smaller sets of six. An online image annotator learns six safety rules randomly assigned to him or her, and then labels workplace images as “no violation” or ‘violation” of certain rules among the six learned by him or her. About one hundred and twenty anonymous image annotators participated in the data collection. Finally, a Bayesian-network-based crowd consensus model aggregated these labels from annotators to obtain safety-rule violation labeling results. Experiment results show that the proposed model can achieve close to 0% false alarm rates while keeping the false negative rate below 10%. Such image labeling performance outdoes existing crowdsourcing approaches that use majority votes for aggregating crowdsourced labels. Given these findings, the presented crowdsourcing approach sheds lights on effective construction safety surveillance by integrating human risk recognition capabilities into advanced computer vision.Graphical abstractGraphical abstract for this article
  • A method of automatic extraction of parameters of multi-LoD BIM models for
           typical components in wooden architectural-heritage structures
    • Abstract: Publication date: October 2019Source: Advanced Engineering Informatics, Volume 42Author(s): Haoyu Liu, Linlin Xie, Liwen Shi, Miaole Hou, Aiqun Li, Yungang Hu A building-information-modeling (BIM) model, which is established based on high-fidelity point-cloud data, can be well used to preserve architectural heritage. Two related critical issues for this conservation are multiple-level-of-detail (multi-LoD) parametric models that emphasize different protection requirements for typical components, and a method for automatically extracting the corresponding parameters from a high-fidelity point cloud. Taking typical Chinese wooden architectural-heritage structures as an example, multi-LoD principles for typical components without damage are proposed according to the different requirements. Then, a framework of multi-LoD parametric models was developed and implemented in BIM. Based on this, a method for automatically extracting the abovementioned parameters is developed and the critical parameters of this method are recommended. To validate the reliability and efficiency of this method, the parameters of multi-LoD models of typical components are extracted. The results indicate that the relative and absolute errors of values of such parameters are mostly less than 2% and 0.5 mm, respectively. Moreover, this method is capable of extracting parameters from millions of point-cloud data within 7 min, thus validating the high efficiency and reliability of the proposed method.
  • Classification of brain signal (EEG) induced by shape-analogous letter
    • Abstract: Publication date: October 2019Source: Advanced Engineering Informatics, Volume 42Author(s): Rohit Bose, Sim Kuan Goh, Kian F. Wong, Nitish Thakor, Anastasios Bezerianos, Junhua Li Visual perception of English letters involves different underlying brain processes including brain activity alteration in multiple frequency bands. However, shape analogous letters elicit brain activities which are not obviously distinct and it is therefore difficult to differentiate those activities. In order to address discriminative feasibility and classification performance of the perception of shape-analogous letters, we performed an experiment in where EEG signals were obtained from 20 subjects while they were perceiving shape analogous letters (i.e., ‘p’, ‘q’, ‘b’, and ‘d’). Spectral power densities from five typical frequency bands (i.e., delta, theta, alpha, beta and gamma) were extracted as features, which were then classified by either individual widely-used classifiers, namely k-Nearest Neighbors (kNN), Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), Random Forest (RF) and AdaBoost (ADA), or an ensemble of some of them. The F-score was employed to select most discriminative features so that the dimension of features was reduced. The results showed that the RF achieved the highest accuracy of 74.1% in the case of multi-class classification. In the case of binary classification, the best performance (Accuracy 86.39%) was achieved by the RF classifier in terms of average accuracy across all possible pairs of the letters. In addition, we employed decision fusion strategy to exert complementary strengths of different classifiers. The results demonstrated that the performance was elevated from 74.10% to 76.63% for the multi-class classification and from 86.39% to 88.08% for the binary class classification.
  • Smart product-service systems in interoperable logistics: Design and
           implementation prospects
    • Abstract: Publication date: October 2019Source: Advanced Engineering Informatics, Volume 42Author(s): Shenle Pan, Ray Y. Zhong, Ting Qu To deal with the increasing complexity of customer demands, supply chain (SC) and logistics organisation and management have been constantly moving towards collaboration, intelligence, and service-orientation. The importance of service-oriented design for SC and logistics systems has been stressed, especially with regards to interoperability and sustainability. In this context, the recent intelligent interoperable logistics paradigm has been increasingly studied and the Smart Product-Service System (PSS) concept seems interesting for the paradigm. Smart PSS are characterised by their ability to collect and process information autonomously and subsequently make decisions and self-act/evolve. Interested in the potential for tackling complex logistics systems, this paper investigates how smart PSS could be considered and designed for service-oriented, intelligent interoperable logistics. A recent breakthrough logistics paradigm called the Physical Internet (PI) is taken as a practical example in this research. We present and discuss key design issues and innovative business models associated with smart PSS in PI. The results clearly indicate the promising potential of smart PSS in PI and the need for further research. Consequently, new research avenues leading to a new era of intelligent interoperable logistics are outlined. This paper intends to contribute to two main areas of research: the design and implementation of smart PSS in PI, and functional and conceptual research on PI and intelligent interoperable logistics.
  • High-quality as-is 3D thermal modeling in MEP systems using a deep
           convolutional network
    • Abstract: Publication date: October 2019Source: Advanced Engineering Informatics, Volume 42Author(s): Hyojoo Son, Changwan Kim, Hyunchul Choi With the growing need for automated condition monitoring and analysis in existing buildings, significant effort has been spent on the development of three-dimensional (3D) thermal models. However, little attention has been paid to ensuring the quality of these 3D thermal models, which can directly impact the accuracy of condition monitoring and analysis results. This study aims to propose a method to generate a high-quality 3D thermal model for mechanical, electrical, and plumbing (MEP) systems by bridging the quality discrepancy between high-resolution laser scan data and low-resolution thermal images using a deep convolutional neural network. The proposed method consists of two main parts: (1) improving the resolution of thermal images based on a deep convolutional network and (2) generating a high-quality 3D thermal model by mapping improved thermal images. The performance of the thermal image resolution improvement was validated using a dataset consisting of 312 thermal images. The results demonstrated that the quality of the improved thermal images based on a deep convolutional network was higher than conventional bicubic interpolation in terms of root mean square error (RMSE), peak signal-to-noise ratio (PSNR), and structural similarity (SSIM). Qualitative analysis of a 3D thermal model utilizing the resolution-improved thermal images was also conducted. This was further qualitatively analyzed to have resulted in improved overall quality of the 3D thermal model. The ability to generate a high-quality 3D thermal model can help auditors to perform automated condition monitoring and analysis in buildings based on objective and accurate data.
  • Unsupervised extraction of patterns and trends within highway systems
           condition attributes data
    • Abstract: Publication date: October 2019Source: Advanced Engineering Informatics, Volume 42Author(s): Leslie Titus-Glover Highway agencies combine expert opinions and basic regression modeling techniques to process vast amounts of time series condition attributes data to define highway network health. The health rating exhibit high variability and lack adequate detail for executive-level maintenance planning and resource allocation. This paper presents a new methodology for data abstraction, analysis, and clustering for pattern recognition of highway network health. The methodology describes mathematical and statistical data abstraction algorithms for data preprocessing (smoothening (unweighted moving average), scaling (normalization), and weights derivation (entropy) to compute a composite health index (CHI)), and salient features extraction. Data analysis involved cluster analysis to identify patterns in asset current health and future outlook. The outcome is a characterization of highway network health for executive-level decision making. The algorithms included in this methodology have been successfully applied in the fields of biology, finance, econometrics, bioinformatics, marketing, and social science for pattern recognition. The accuracy of the new methodology is illustrated with an experiment using 463 in-service pavement assets and internal/external metrics (including the degree to which methodology performance classification outcomes conform to national expert opinion). The results from the experiment confirm an accurate and computationally inexpensive methodology, which provides outcomes that compare to real-world pavement condition rating metrics.
  • Smart robotic mobile fulfillment system with dynamic conflict-free
           strategies considering cyber-physical integration
    • Abstract: Publication date: October 2019Source: Advanced Engineering Informatics, Volume 42Author(s): C.K.M. Lee, Bingbing Lin, K.K.H. Ng, Yaqiong Lv, W.C. Tai Smart mobile robots are deployed to the warehouse environments to improve the efficiency, because of its characteristics of high automation and flexibility characteristics. However, the trajectory planning is a great challenge especially when a number of mobile robotics operates in the warehouse simultaneously. This paper proposes a cyber-physical system model for smart robotic warehouse to implement the workflow data collection and procedure monitor. A decoupled method is presented to find a conflict-free path for the mobile vehicles in the warehouses, after distributing destinations to mobile robots to minimize the total travel distance. The improved A* algorithm is applied to find paths from the source node to the destination node for single mobile vehicle in the domain of smart logistics. Collisions are detected by comparing the occupying time window of each mobile vehicle. Three collision avoidance strategies are developed to solve the conflicts and the candidate path with the minimal completion time is selected as the final determined route. The contribution of the paper is to propose a CPS-enabled robotic warehouse with dynamic conflict-free strategy to self-configure the path to optimize warehouse operation efficiency.
  • Cloud based cyber-physical systems: Network evaluation study
    • Abstract: Publication date: October 2019Source: Advanced Engineering Informatics, Volume 42Author(s): Erik Kajati, Peter Papcun, Chao Liu, Ray Y. Zhong, Jiri Koziorek, Iveta Zolotova In recent years, the Industry 4.0 concept brings new demands and trends in different areas; one of them is distributing computational power to the cloud. This concept also introduced the Reference Architectural Model for Industry 4.0 (RAMI 4.0). The efficiency of data communications within the RAMI 4.0 model is a critical issue. Aiming to evaluate the efficiency of data communication in the Cloud Based Cyber-Physical Systems (CB-CPS), this study analyzes the periods and data amount required to communicate with individual hierarchy levels of the RAMI 4.0 model. The evaluation of the network properties of the communication protocols eligible for CB-CPS is presented. The network properties to different cloud providers and data centers’ locations have been measured and interpreted. To test the findings, an architecture for cloud control of laboratory model was proposed. It was found that the time of the day; the day of the week; and data center utilization have a negligible impact on latency. The most significant impact lies in the data center distance and the speed of the communication channel. Moreover, the communication protocol also has impact on the latency. The feasibility of controlling each level of RAMI 4.0 through cloud services was investigated. Experimental results showed that control is possible in many solutions, but these solutions mostly cannot depend just on cloud services. The intelligence on the edge of the network will play a significant role. The main contribution is a thorough evaluation of different cloud providers, locations, and communication protocols to provide recommendations sufficient for different levels of the RAMI 4.0 architecture.
  • Construction automation and robotics for high-rise buildings over the past
           decades: A comprehensive review
    • Abstract: Publication date: October 2019Source: Advanced Engineering Informatics, Volume 42Author(s): Shiyao Cai, Zhiliang Ma, Miroslaw J. Skibniewski, Song Bao Automation and robotics technology is expected to improve the productivity of the construction industry as well as to solve problems such as labor shortage and safety risks, especially for high-rise buildings. Substantial research efforts have been devoted to the field over the past decades, while the application rate at the construction sites is still limited. Although various reviews have summarized the research topics and future trends in this field, few research efforts have been made on a consideration of both academic research and practical application in the industry. Focusing on high-rise building construction, this study explores the development of both academic research and practical application of automation and robotics based on literature and market review. Scientometric and critical literature reviews were conducted to identify and analyze the development of key research areas based on academic publications from the 1980s to present. In the meantime, the development of basic technologies was summarized. The market review surveyed on existing products and developers of construction automation and robotics. By comparing the results of the literature review and market review, four development patterns of academic research and product application were identified, i.e., simultaneous development led by the same party, development at a similar pace with the two sides taking the lead in different aspects, academic research providing basic technologies for product development, and available technologies in academic research with no products found. Then three gaps in this field, i.e., the gap between academic research and products, the gap between products and application, and the gap between the construction industry and the robotics industry, were discussed with corresponding suggestions to narrow the gaps, followed by an outlook for future directions. This study contributes to the knowledge body by identifying and analyzing the key research areas and the development gaps systematically.
  • Proactive mental fatigue detection of traffic control operators using
           bagged trees and gaze-bin analysis
    • Abstract: Publication date: October 2019Source: Advanced Engineering Informatics, Volume 42Author(s): Fan Li, Chun-Hsien Chen, Gangyan Xu, Li Pheng Khoo, Yisi Liu Most of existing eye movement-based fatigue detectors utilize statistical analysis of fixations, saccades, and blinks as inputs. Nevertheless, these parameters require long recording time and heavily depend on eye trackers. In an effort to facilitate proactive detection of mental fatigue, we introduced a complemental fatigue indicator, named gaze-bin analysis, which simply presents the eye-tracking data with histograms. A method which engaged the gaze-bin analysis as inputs of semisupervised bagged trees was developed. A case study in a vessel traffic service center demonstrated that this approach can alleviate the burden of manual labeling as well as improve the performance of fatigue detection model. In addition, the results show that the approach can achieve an excellent accuracy of 89%, which outperformed other methods. In general, this study provided a complemental indicator for detecting mental fatigue as well as enabled the application of a low sampling rate eye tracker in the traffic control center.
  • A framework for brain learning-based control of smart structures
    • Abstract: Publication date: October 2019Source: Advanced Engineering Informatics, Volume 42Author(s): Hamid Radmard Rahmani, Geoffrey Chase, Marco Wiering, Carsten Könke A novel framework for intelligent structural control is proposed using reinforcement learning. In this approach, a deep neural network learns how to improve structural responses using feedback control. The effectiveness of the framework is demonstrated in a case study for a moment frame subjected to earthquake excitations. The performance of the learning method was improved by proposing a state-selector function that prevented the neural network from forgetting key states. Results show that the controller significantly improves structural responses not only to earthquake records on which it was trained but also to earthquake records new to the controller. The controller also has stable performance under environmental uncertainties. This capability distinguishes the proposed approach and makes it more appropriate for the situations in which it is likely that the controller will be exposed to unpredictable external excitations and high degrees of uncertainties.
  • Template-based configuration and execution of decision workflows in design
           of complex engineered systems
    • Abstract: Publication date: October 2019Source: Advanced Engineering Informatics, Volume 42Author(s): Zhenjun Ming, Gehendra Sharma, Janet K. Allen, Farrokh Mistree The research reported in the paper is from a decision-based design perspective wherein the principal role (but not only) role of a designer is to make decisions. Decision workflows are the processes by which the solutions pertaining to the design of complex systems are generated. Decision workflows are core to design processes, in which a set of decisions are connected (or interconnected) to generate shared and desired design outputs. Careful configuration of decision workflows is very important to ensure the generation of designs using available resources. Configuration of decision workflows is a process that requires a designer to use the basic elements to compose feasible workflows and then select an appropriate one for implementation in designing a product or a system. In this paper, we propose a template-based method for the design and execution of decision workflows associated with designing engineered systems. The value of the method is anchored in that it facilitates designers rapidly planning the processes, namely, the decision workflows, for designing products or systems. Moreover, due to the fact that these decision workflows are modeled in a computational manner, designers are able to execute decision workflows to explore the solution space and identify satisficing design solutions in early design stages. A gearbox with connected gears and shafts is a typical complex engineered system that can be partitioned into multiple levels of interacting subsystems. We illustrate the method and the decision workflows using a gear and shaft (within a gearbox) design example.
  • Edge-cloud orchestration driven industrial smart product-service systems
           solution design based on CPS and IIoT
    • Abstract: Publication date: October 2019Source: Advanced Engineering Informatics, Volume 42Author(s): Bufan Liu, Yingfeng Zhang, Geng Zhang, Pai Zheng The rapid booming of advanced information and communication technologies (ICT) has promoted an encouraging smart, connected product (SCP) market that further triggers the development of manufacturing towards the servitization proposition, viz. smart product-service systems (PSS). Smart PSS aims to provide a solution (product-service) with high satisfaction and less environmental influence by leveraging SCP as the media tool. Its solution design should not just focus on the physical world nor only be enabled by the cloud side, while the cyber world and the edge side must be included in the Industry 4.0. However, only few current researches investigate about the smart PSS, let alone an overall cyber-physical and edge-cloud discussion to support its solution design. In order to fill this gap, this work proposes an edge-cloud orchestration driven solution design based on the cyber-physical systems (CPS) and industrial Internet of Things (IIoT). To make our ideas concrete, a real-life milling process was conducted as an illustrative example. It is hoped that this study can furnish industrial enterprises with meaningful sights in the process of servitization and value co-creation.
  • A novel data-driven graph-based requirement elicitation framework in the
           smart product-service system context
    • Abstract: Publication date: October 2019Source: Advanced Engineering Informatics, Volume 42Author(s): Zuoxu Wang, Chun-Hsien Chen, Pai Zheng, Xinyu Li, Li Pheng Khoo Nowadays, industrial companies are facing ever-increasing challenges to generate new value-in-use and maintain their high competitiveness in the market. With the rapid development of Information and Communication Technology (ICT), IT is embedded in the products themselves, i.e. smart, connected products (SCPs) to generate values. Hence, an emerging value proposition paradigm, smart product-service system (Smart PSS) was introduced, by leveraging both SCPs and its generated services as a solution bundle to meet individual customer needs. Unlike other types of PSS, in Smart PSS, massive user-generated data and product-sensed data are collected during the usage phase, where potential requirements can be elicited readily in a value co-creation manner with context-awareness. Nevertheless, only few scholars discuss any systematic manner to support requirement elicitation in such context. To fill the gaps, this research proposes a novel data-driven graph-based requirement elicitation framework in the Smart PSS, so as to assist engineering/designers make better design improvement or new design concept generation in a closed-loop manner. It underlines the informatics-based approach by integrating heterogeneous data sources into a holistic consideration. Moreover, by leveraging graph-based approach, context-product-service information can be linked by the edges and nodes in-between them to derive reliable requirements. To validate its feasibility and advantages, an illustrative example of smart bicycle design improvement is further adopted. As an explorative study, it is hoped that this work provides useful insights for the requirement elicitation process in today’s smart connected environment.
  • Deep convolutional learning for general early design stage prediction
    • Abstract: Publication date: October 2019Source: Advanced Engineering Informatics, Volume 42Author(s): Sundaravelpandian Singaravel, Johan Suykens, Philipp Geyer Designers rely on performance predictions to direct the design toward appropriate requirements. Machine learning (ML) models exhibit the potential for rapid and accurate predictions. Developing conventional ML models that can be generalized well in unseen design cases requires an effective feature engineering and selection. Identifying generalizable features calls for good domain knowledge by the ML model developer. Therefore, developing ML models for all design performance parameters with conventional ML will be a time-consuming and expensive process. Automation in terms of feature engineering and selection will accelerate the use of ML models in design.Deep learning models extract features from data, which aid in model generalization. In this study, we (1) evaluate the deep learning model’s capability to predict the heating and cooling demand on unseen design cases and (2) obtain an understanding of extracted features. Results indicate that deep learning model generalization is similar to or better than that of a simple neural network with appropriate features. The reason for the satisfactory generalization using the deep learning model is its ability to identify similar design options within the data distribution. The results also indicate that deep learning models can filter out irrelevant features, reducing the need for feature selection.
  • Recognizing people’s identity in construction sites with computer
           vision: A spatial and temporal attention pooling network
    • Abstract: Publication date: October 2019Source: Advanced Engineering Informatics, Volume 42Author(s): Ran Wei, Peter E.D. Love, Weili Fang, Hanbin Luo, Shuangjie Xu Several prototype vision-based approaches have been developed to capture and recognize unsafe behavior in construction automatically. Vision-based approaches have been difficult to use due to their inability to identify individuals who commit unsafe acts when captured using digital images/video. To address this problem, we applied a novel deep learning approach that utilizes a Spatial and Temporal Attention Pooling Network to remove redundant information contained in a video to enable a person’s identity to be automatically determined. The deep learning approach we have adopted focuses on: (1) extracting spatial feature maps using the spatial attention network; (2) extracting temporal information using the temporal attention networks; and (3) recognizing a person’s identity by computing the distance between features. To validate the feasibility and effectiveness of the adopted deep learning approach, we created a database of videos that contained people performing their work on construction sites, conducted an experiment, and then performed k-fold cross-validation. The results demonstrated that the approach could accurately identify a person’s identity from videos captured from construction sites. We suggest that our computer-vision approach can potentially be used by site managers to automatically recognize those individuals that engage in unsafe behavior and therefore be used to provide instantaneous feedback about their actions and possible consequences.
  • A user-centric smart product-service system development approach: A case
           study on medication management for the elderly
    • Abstract: Publication date: October 2019Source: Advanced Engineering Informatics, Volume 42Author(s): Danni Chang, Zhenyu Gu, Fan Li, Rong Jiang With the advancement in Internet implementations, computational intelligence and network technologies, smart product-service system (SPSS) has become an important research area. A lot of research effort has been devoted to construct the conceptual framework, identify the important elements and evaluate the effectiveness of SPSS. However, there is still no SPSS development approach from user-centric perspective. Therefore, this article aims to provide a novel understanding of user-centric SPSS (UC-SPSS), outline the conceptual framework of UC-SPSS and contribute a UC-SPSS development approach. Specifically, a multimodal user analysis module with S-E-T (society-economy-technology) analysis, user behavioral analysis and user segmentation is deployed. According to the user needs identified, a provider identification and integration network is established in the dimensions of material, data and value flows. Jointly considering the user needs and provider capability, the BCE (benefit-cost-expectation) model and Product Function Architecture are applied to assist in the realization of the smart, connected service. To illustrate, a UC-SPSS on medication management for the elderly was developed, and it has been evaluated from user experience and sustainable value aspects. The results showed that the developed medication service is interesting and helpful for the elderly to take their medication. However, the service is not simple enough, especially in data visualization. In terms of sustainable value, the developed service can achieve better performance in economic, material and energy costs, and can support the further regulation of medical industry. Based on the case illustration, the proposed approach appears effective to help with SPSS development.
  • Agent evaluation based on multi-source heterogeneous information table
           using TOPSIS
    • Abstract: Publication date: October 2019Source: Advanced Engineering Informatics, Volume 42Author(s): Libo Zhang, Tianxing Wang, Huaxiong Li, Bing Huang, Xianzhong Zhou In agent evaluation, a specific role-playing may need more than one capabilities or the task execution process can be divided into several stages. The diverse perspectives to assess candidate agents are denoted as attributes, which is more practical than treating experts as attributes in many other works. In the evaluation table, the attribute values may come from different sources and the data types may not be the same. Therefore, we consider evaluation issues in a Multi-Source Heterogeneous Information System (MSHIS). Considering that grading, voting and marking are three common evaluation scenarios, linguistic variable, Intuitionistic Fuzzy Value (IFV) and real number are utilized to describe the corresponding evaluation results. To evaluate agents in MSHIS, a TOPSIS-based evaluation method is adopted in this work. In the proposed method, the range is utilized to nondimensionalize the distance between agents in each attribute. Then, a weighted Euclidean distance metric is adopted to measure the comprehensive distance. The relative closeness to the ideal agents reflects the agent’s capability on the concerned task. Finally, the illustrative example and comparative experiments are presented to illustrate the effectiveness of our method.
  • A survey of smart product-service systems: Key aspects, challenges and
           future perspectives
    • Abstract: Publication date: October 2019Source: Advanced Engineering Informatics, Volume 42Author(s): Pai Zheng, Zuoxu Wang, Chun-Hsien Chen, Li Pheng Khoo The rapid development of information and communication technologies (ICT) has enabled the prevailing digital transformation (i.e. digitalization), where physical products can be readily digitized in the virtual space and seamlessly interconnected. Meanwhile, industries are ever increasingly adopting service business models (i.e. servitization), so as to offer not only physical products but also services as a solution bundle to meet individual customer needs. Such convergence of both digitalization and servitization (i.e. digital servitization) has triggered an emerging IT-driven business paradigm, smart product-service systems (Smart PSS). As a novel paradigm coined in 2014, to the authors’ knowledge, only 2 conference papers have provided some literature review to date, and many issues remain uncovered or not comprehensively investigated. Aiming to fill this gap, this paper has conducted a systematic review of Smart PSS or related papers published ever since its first brought up to date (30/06/2019), and selected 97 representative items together with other 37 supplementary works to summarize the tendency towards Smart PSS, its business and technical aspects, current challenges, and future perspectives. From the survey, it is found that several hybrid concerns are the key challenges faced, and self-adaptiveness with sustainability, advanced IT infrastructure, human-centric perspectives, and circular lifecycle management are the core future perspectives to explore. It is hoped that this work can attract more open discussions and provide useful insights to both academics and industries in their exploration and implementation of Smart PSS.
  • Three-dimensional (3D) reconstruction of structures and landscapes: A new
           point-and-line fusion method
    • Abstract: Publication date: October 2019Source: Advanced Engineering Informatics, Volume 42Author(s): Ying Zhou, Lingling Wang, Peter E.D. Love, Lieyun Ding, Cheng Zhou The technique of three-dimensional (3D) reconstruction is widely used to develop infrastructure and landscape models to manage cities and assets better. Accurately reconstructing 3D structures (e.g., planes or lines) is a core step in rebuilding a model, especially within a built environment, where piece-wise planar/linear structures predominately prevail. As high-resolution images of large areas have become increasingly accessible, this paper develops an improved 3D reconstruction pipeline using the combination of point and line features. By introducing a dense reconstruction algorithm, which is an improved patch based stereo matching algorithm, this paper presents a robust approach that can be used to overcome the inaccuracies, integrity and reconstruction inefficiencies associated with point clouds. A 3D line extraction method is added to reconstruct accurate edges of buildings. The experimental results demonstrate that the proposed method visually improves the reconstruction effect of a 3D structure and a model's visualization.
  • A service-oriented multi-player maintenance grouping strategy for complex
           multi-component system based on game theory
    • Abstract: Publication date: October 2019Source: Advanced Engineering Informatics, Volume 42Author(s): Fengtian Chang, Guanghui Zhou, Wei Cheng, Chao Zhang, Changle Tian The development of smart product service system (PSS) urges the emergence of service-oriented maintenance grouping strategy for complex multi-component system. This strategy is designed from the data-driven performance-based service manner where the proactive original equipment manufacturer (OEM) and service providers are both involved. In this situation, the traditional maintenance grouping methods are incapable to determine the optimal grouping service time for each exactor due to the little consideration from their interaction relations in the grouping process. Thus, this paper proposes one OEM and multiple service provider’s multi-player maintenance grouping strategy. It is constructed from the multi-objective Stackelberg-Nash game model where the OEM is the upper-level leader and all the involved service providers are the lower-level followers. The serviced components, service time and paid prices are considered firstly by the leader. After that, the followers could compete to select the remaining serviced components and service time. In order to obtain the Stackelberg-Nash equilibrium solution, the bi-level nested parallel solution algorithm with the improved multi-fitness functions is also developed. Finally, a numerical example from wind turbine is studied. The evaluation and comparison results show that our method could provide a feasible and effective maintenance grouping strategy for each player under smart PSS.
  • Intelligent fault diagnosis for rotating machinery using deep Q-network
    • Abstract: Publication date: October 2019Source: Advanced Engineering Informatics, Volume 42Author(s): Yu Ding, Liang Ma, Jian Ma, Mingliang Suo, Laifa Tao, Yujie Cheng, Chen Lu Fault diagnosis methods for rotating machinery have always been a hot research topic, and artificial intelligence-based approaches have attracted increasing attention from both researchers and engineers. Among those related studies and methods, artificial neural networks, especially deep learning-based methods, are widely used to extract fault features or classify fault features obtained by other signal processing techniques. Although such methods could solve the fault diagnosis problems of rotating machinery, there are still two deficiencies. (1) Unable to establish direct linear or non-linear mapping between raw data and the corresponding fault modes, the performance of such fault diagnosis methods highly depends on the quality of the extracted features. (2) The optimization of neural network architecture and parameters, especially for deep neural networks, requires considerable manual modification and expert experience, which limits the applicability and generalization of such methods. As a remarkable breakthrough in artificial intelligence, AlphaGo, a representative achievement of deep reinforcement learning, provides inspiration and direction for the aforementioned shortcomings. Combining the advantages of deep learning and reinforcement learning, deep reinforcement learning is able to build an end-to-end fault diagnosis architecture that can directly map raw fault data to the corresponding fault modes. Thus, based on deep reinforcement learning, a novel intelligent diagnosis method is proposed that is able to overcome the shortcomings of the aforementioned diagnosis methods. Validation tests of the proposed method are carried out using datasets of two types of rotating machinery, rolling bearings and hydraulic pumps, which contain a large number of measured raw vibration signals under different health states and working conditions. The diagnosis results show that the proposed method is able to obtain intelligent fault diagnosis agents that can mine the relationships between the raw vibration signals and fault modes autonomously and effectively. Considering that the learning process of the proposed method depends only on the replayed memories of the agent and the overall rewards, which represent much weaker feedback than that obtained by the supervised learning-based method, the proposed method is promising in establishing a general fault diagnosis architecture for rotating machinery.
  • Spatial prediction of shallow landslide using Bat algorithm optimized
           machine learning approach: A case study in Lang Son Province, Vietnam
    • Abstract: Publication date: October 2019Source: Advanced Engineering Informatics, Volume 42Author(s): Dieu Tien Bui, Nhat-Duc Hoang, Hieu Nguyen, Xuan-Linh Tran This study develops a machine learning method that hybridizes the Least Squares Support Vector Classification (LSSVC) and Bat Algorithm (BA), named as BA-LSSVC, for spatial prediction of shallow landslide. To construct and verify the hybrid method, a Geographic Information System (GIS) database for the study area of Lang Son province (Vietnam) has been employed. LSSVC is used to separate data samples in the GIS database into two categories of non-landslide (negative class) and landslide (positive class). The BA metaheuristic is employed to assist the LSSVC model selection process by fine-tuning its hyper-parameters: the regularization coefficient and the kernel function parameter. Experimental results point out that the hybrid BA-LSSVC can help to achieve a desired prediction with an accuracy rate of more than 90%. The performance of BA-LSSVC is also better than those of benchmark methods, including the Convolutional Neural Network, Relevance Vector Machine, Artificial Neural Network, and Logistic Regression. Hence, the newly developed model is a capable tool to assist local authority in landslide hazard mitigation and management.Graphical abstractGraphical abstract for this article
  • Estimation of the degree of hydration of concrete through automated
           machine learning based microstructure analysis – A study on effect of
           image magnification
    • Abstract: Publication date: October 2019Source: Advanced Engineering Informatics, Volume 42Author(s): Srikanth Sagar Bangaru, Chao Wang, Marwa Hassan, Hyun Woo Jeon, Tarun Ayiluri The scanning electron microscopy (SEM) images are commonly used to understand the microstructure of the concrete. With the advancements in the field of computer vision, many researchers have adopted the image processing technique for the microstructure analysis. Most of the previous methods are not adaptable, non-reproducible, semi-automated, and most importantly all these methods are highly influenced by image magnification. Therefore, to overcome these challenges, this paper presents a machine learning based image segmentation method for microstructure analysis and degree of hydration measurement using SEM images. In addition, the authors looked into the impact of magnification of SEM images on the model accuracy and classifier training for the degree of hydration measurement considering two scenarios. First, the image segmentation was performed using a classifier of specific magnification, and then a common classifier is trained using the image of different magnification. The results show that the Random Forest classifier algorithm is suitable for microstructure analysis using SEM images. Through the statistical analysis, it has been proved that there is no significant effect of magnification on model training and accuracy for the degree of hydration measurement. So, a single classifier can be used to process the images of different magnification of a specimen which reduces the effort of training and computational time. The proposed method can generate highly accurate and reliable results in a shorter time and lower cost. Moreover, the findings in this research can be useful for researchers to determine the optimum magnification required for the microstructure analysis.
  • Detecting, locating, and characterizing voids in disaster rubble for
           search and rescue
    • Abstract: Publication date: October 2019Source: Advanced Engineering Informatics, Volume 42Author(s): Da Hu, Shuai Li, Junjie Chen, Vineet R. Kamat After natural and man-made disasters such as earthquakes, hurricanes, and explosions, victims may survive in voids that are formed naturally in collapsed structures. First responders need to identify and locate these critical voids for rapid search and rescue operations. Due to the complex and unstructured occlusions in disaster areas, visual and manual search is time-consuming and error-prone. In this study, we proposed a novel method to automatically detect, locate, and characterize voids buried in disaster rubble using ground penetrating radar (GPR). After preprocessing the collected radargrams, the boundaries of potential voids are segmented based on radar signal patterns, and the 95% confidence intervals are constructed around the segmented boundaries to account for uncertainties. To improve the detection accuracy, the geometric relations of the detected boundaries and their signal characteristics are examined to confirm the void existence. Then, the void location and dimension are estimated based on calibrated velocity of radar wave and its travel time. The effectiveness and efficiency of the proposed method were manifested by its performance in laboratory and field experiments. The contribution of this study is twofold. First, the feasibility of using GPR to detect, locate, and characterize voids in collapsed structures is experimentally tested, innovatively extending the application of GPR to search and rescue operations. Second, algorithms are developed to process non-intuitive radargrams to provide first responders actionable information.
  • A methodological framework with rough-entropy-ELECTRE TRI to classify
           failure modes for co-implementation of smart PSS
    • Abstract: Publication date: October 2019Source: Advanced Engineering Informatics, Volume 42Author(s): Zhiwen Liu, Xinguo Ming Smart PSS (product-service system) has been transcending the scope of traditional PSS through ICTs (information and communication technologies). The co-implementation is a specific way of co-creation to generate value for customer and supplier in smart PSS. Meanwhile, the service failures in co-implementation can bring about service paradox. As such, three key research questions, including the construction of co-implementation of smart PSS, the extension of risk factors in FMECA and the category of identified failure modes, are put forward and conquered by a proposed methodological framework. First, in order to construct the co-implementation of smart PSS involving customer and supplier, the PCN (process-chain-network) diagram with direct interaction, surrogate interaction and independent handling is employed to reflect the nature of interaction. Second, since that the highly complexity of co-implementation of smart PSS can lead to more huge risk, an extended-FMECA (failure mode effect and criticality analysis) sheet is formulated by considering the adaptation of autonomy. Third, to handle the uncertainty of expert judgments more reasonably and determine the weights of risk factors without additional information, a hybrid approach integrating rough-entropy-ELECTRE TRI is proposed to classify failure modes into ordinal classes, so that decision-makers can swiftly access them. This proposed methodological framework also is illustrated by the co-implementation of smart fridge-service system, in which the feasibility and superiority are verified by analysing comparative results.
  • An augmented self-adaptive parameter control in evolutionary computation:
           A case study for the berth scheduling problem
    • Abstract: Publication date: October 2019Source: Advanced Engineering Informatics, Volume 42Author(s): Masoud Kavoosi, Maxim A. Dulebenets, Olumide F. Abioye, Junayed Pasha, Hui Wang, Hongmei Chi The demand for international seaborne trade has substantially increased over the last three decades and is predicted to continue increasing during the upcoming years. A marine container terminal, as an important node in supply chains, should be able to successfully cope with increasing demand volumes. Berth scheduling can significantly influence the general throughput of marine container terminals. In this study, a mixed-integer linear programming mathematical model is proposed for the berth scheduling problem, aiming to minimize the summation of waiting costs, handling costs, and late departure costs of the vessels that are to be served at a marine container terminal. An innovative Evolutionary Algorithm is designed to solve the developed mathematical model. The proposed solution algorithm relies on the augmented self-adaptive parameter control strategy, which is developed in order to effectively change the algorithmic parameters throughout the search process. Performance of the designed algorithm is evaluated against nine alternative state-of-the-art metaheuristic-based algorithms, which have been frequently used for berth scheduling in the marine container terminal operations literature. The results demonstrate that all the developed algorithms have a high level of stability and return competitive solutions at convergence. The computational experiments also prove superiority of the designed augmented self-adaptive Evolutionary Algorithm over the alternative algorithms in terms of different performance indicators.
  • Risk-aware supply chain intelligence: AI-enabled supply chain and
           logistics management considering risk mitigation
    • Abstract: Publication date: October 2019Source: Advanced Engineering Informatics, Volume 42Author(s): Wei Yan, Junliang He, Amy J.C. Trappey
  • Dynamic modelling of customer preferences for product design using DENFIS
           and opinion mining
    • Abstract: Publication date: October 2019Source: Advanced Engineering Informatics, Volume 42Author(s): Huimin Jiang, C.K. Kwong, G.E. Okudan Kremer, W.-Y. Park Previous studies mainly employed customer surveys to collect survey data for understanding customer preferences on products and developing customer preference models. In reality, customer preferences on products could change over time. Thus, the time series data of customer preferences under different time periods should be collected for the modelling of customer preferences. However, it is difficult to obtain the time series data based on customer surveys because of long survey time and substantial resources involved. In recent years, a large number of online customer reviews of products can be found on various websites, from which the time series data of customer preferences can be extracted easily. Some previous studies have attempted to analyse customer preferences on products based on online customer reviews. However, two issues were not addressed in previous studies which are the fuzziness of the sentiment expressed by customers existing in online reviews and the modelling of customer preferences based on the time series data obtained from online reviews. In this paper, a new methodology for dynamic modelling of customer preferences based on online customer reviews is proposed to address the two issues which mainly involves opinion mining and dynamic evolving neural-fuzzy inference system (DENFIS). Opinion mining is adopted to analyze online reviews and perform sentiment analysis on the reviews under different time periods. With the mined time series data and the product attribute settings of reviewed products, a DENFIS approach is introduced to perform the dynamic modelling of customer preferences. A case study is used to illustrate the proposed methodology. The results of validation tests indicate that the proposed DENFIS approach outperforms various adaptive neuro-fuzzy inference system (ANFIS) approaches in the dynamic modelling of customer preferences in terms of the mean relative error and variance of errors. In addition, the proposed DENFIS approach can provide both crisp and fuzzy outputs that cannot be realized by using existing ANFIS and conventional DENFIS approaches.
  • Differential received signal strength based RFID positioning for
           construction equipment tracking
    • Abstract: Publication date: October 2019Source: Advanced Engineering Informatics, Volume 42Author(s): Changzhi Wu, Xiangyu Wang, Mengcheng Chen, Mi Jeong Kim A novel differential received signal strength (RSS) positioning algorithm is proposed in the paper. Different from traditional methods to find the relationship between RSS and distance, this new positioning approach is based on linear regression of angle and differential received signal strengths. The advantages of this positioning algorithm is robust to heterogeneity of RFID tags as well as direction between tag and reader. Several experiments are firstly carried out in open environment and then a further experiment is conducted in a LNG training centre to validate our proposed algorithm. The results show that our proposed algorithm can achieve better accuracy than existing RFID positioning approaches for equipment tracking.
  • BIM reconstruction from 3D point clouds: A semantic registration approach
           based on multimodal optimization and architectural design knowledge
    • Abstract: Publication date: October 2019Source: Advanced Engineering Informatics, Volume 42Author(s): Fan Xue, Weisheng Lu, Ke Chen, Christopher J. Webster Reconstructing semantically rich building information model (BIM) from 2D images or 3D point clouds represents a research realm that is gaining increasing popularity in architecture, engineering, and construction. Researchers have found that architectural design knowledge, such as symmetry, planarity, parallelism, and orthogonality, can be utilized to improve the effectiveness of such BIM reconstruction. Following this line of enquiry, this paper aims to develop a novel semantic registration approach for complicated scenes with repetitive, irregular-shaped objects. The approach first formulates the architectural repetition as the multimodality in mathematics. Thus, the reconstruction of repetitive objects becomes a multimodal optimization (MMO) problem of registering BIM components which have accurate geometries and rich semantics. Then, the topological information about repetition and symmetry in the reconstructed BIM is recognized and regularized for BIM semantic enrichment. A university lecture hall case, consisting of 1.9 million noisy points of 293 chairs, was selected for an experiment to validate the proposed approach. Experimental results showed that a BIM was satisfactorily created (achieving about 90% precision and recall) automatically in 926.6 s; and an even more satisfactory BIM achieved 99.3% precision and 98.0% recall with detected semantic and topological information under the minimal effort of human intervention in 228.4 s. The multimodality model of repetitive objects, the repetition detection and regularization for BIM, and satisfactory reconstruction results in the presented approach can contribute to methodologies and practices in multiple disciplines related to BIM and smart city.
  • Onsite video mining for construction hazards identification with visual
    • Abstract: Publication date: October 2019Source: Advanced Engineering Informatics, Volume 42Author(s): Ruoxin Xiong, Yuanbin Song, Heng Li, Yuxuan Wang Widely-used video monitoring systems provide a large corpus of unstructured image data on construction sites. Although previous developed vision-based approaches can be used for hazards recognition in terms of detecting dangerous objects or unsafe operations, such detection capacity is often limited due to lack of semantic representation of visual relationships between/among the components or crews in the workplace. Accordingly, the formal representation of textural criteria for checking improper relationships should also be improved. In this regard, an Automated Hazards Identification System (AHIS) is developed to evaluate the operation descriptions generated from site videos against the safety guidelines extracted from the textual documents with the assistance of the ontology of construction safety. In particular, visual relationships are modeled as a connector between site components/operators. Moreover, both visual descriptions of site operations and semantic representations of safety guidelines are coded in the three-tuple format and then automatically converted into Horn clauses for reasoning out the potential risks. A preliminary implementation of the system was tested on two separate onsite video clips. The results showed that two types of crucial hazards, i.e., failure to wear a helmet and walking beneath the cane, were successfully identified with three rules from Safety Handbook for Construction Site Workers. In addition, the high-performance results of Recall@50 and Recall@100 demonstrated that the proposed visual relationship detection method is promising in enriching the semantic representation of operation facts extracted from site videos, which may lead to better automation in the detection of construction hazards.
  • A genuine smile is indeed in the eyes – The computer aided non-invasive
    • Abstract: Publication date: October 2019Source: Advanced Engineering Informatics, Volume 42Author(s): Hassan Ugail, Ahmad Al-dahoud Understanding the detailed differences between posed and spontaneous smiles is an important topic with a range of applications such as in human-computer interaction, automatic facial emotion analysis and in awareness systems. During the past decade or so, there have been very promising solutions for accurate automatic recognition and detailed facial emotion analysis. To this end, many methods and techniques have been proposed for distinguishing between spontaneous and posed smiles. Our aim here is to go beyond the present state of the art in this field. Hence, in this work, we are concerned with understanding the exact distribution of a smile – both spontaneous and posed – across the face. To do this, we utilise a lightweight computational framework which we have developed to analyse the dynamics of human facial expressions. We utilise this framework to undertake a detailed study of the smile expression. Based on computing the optical flow across the face – especially across key parts of the face such as the mouth, the cheeks and around the eyes – we are able to accurately map the dynamic weight distribution of the smile expression. To validate our computational model, we utilise two publicly available datasets, namely the CK + dataset in which the subjects express posed smiles and the MUG dataset in which the subjects express genuine smiles. Our results not only confirm what already exists in the literature – i.e. that the spontaneous genuine smile is truly in the eyes – but it also gives further insight into the exact distribution of the smile across the face.
  • Automatic generation of fabrication drawings for façade mullions and
           transoms through BIM models
    • Abstract: Publication date: October 2019Source: Advanced Engineering Informatics, Volume 42Author(s): Min Deng, Vincent J.L. Gan, Yi Tan, Ajay Joneja, Jack C.P. Cheng Fabrication drawings are essential for manufacturing, design evaluation and inspection of building components, especially for building façade structural components. In order to clearly represent the physical characteristics of the façade structural components, a large number of section views need to be produced, which is very time-consuming and labor intensive. Therefore, automatic generation of fabrication drawings for building façade components (such as mullions and transoms) is of paramount importance. In this paper, attempts have been made to develop an efficient framework in order to automatically generate fabrication drawings for building façade structural components, including mullions and transoms. To represent the complex physical characteristics (such as holes and notches) on mullions and transoms using minimum number of drawing views, a computational algorithm based on graph theory is developed to eliminate duplicated section views. Another methodology regarding the generation of breaks for top views is also proposed to further improve the quality of drawing layouts. The obtained drawing views are then automatically arranged using a developed approach. In addition, primary dimensions of the drawing views focusing on the physical features are also generated. Furthermore, in order to maintain the consistency of drawing formats across multiple drawings, a methodology is proposed to determine the scaling factors of the drawings by using clustering technique. In an illustrative example, the proposed framework is used to generate the fabrication drawings for a typical BIM model containing façade structural components, and saving in time is observed.
  • State of the art in big data applications in microgrid: A review
    • Abstract: Publication date: October 2019Source: Advanced Engineering Informatics, Volume 42Author(s): Karim Moharm The prospering Big data era is emerging in the power grid. Multiple world-wide studies are emphasizing the big data applications in the microgrid due to the huge amount of produced data. Big data analytics can impact the design and applications towards safer, better, more profitable, and effective power grid. This paper presents the recognition and challenges of the big data and the microgrid. The construction of big data analytics is introduced. The data sources, big data opportunities, and enhancement areas in the microgrid like stability improvement, asset management, renewable energy prediction, and decision-making support are summarized. Diverse case studies are presented including different planning, operation control, decision making, load forecasting, data attacks detection, and maintenance aspects of the microgrid. Finally, the open challenges of big data in the microgrid are discussed.
  • An asymmetric and optimized encryption method to protect the
           confidentiality of 3D mesh model
    • Abstract: Publication date: October 2019Source: Advanced Engineering Informatics, Volume 42Author(s): Yaqian Liang, Fazhi He, Haoran Li 3D models are widely used in computer graphics, design and manufacture engineering, art animation and entertainment. With the universal of acquisition equipment and sensors, a huge number of 3D models are generated, which are becoming the major source of engineering data. How to preserve the privacy of the 3D models is a challenge issue. In this paper, an asymmetric and optimized encryption method is presented to protect the 3D mesh models. Firstly, we propose an asymmetric encryption method for 3D mesh models to overcome the drawbacks of traditional symmetric encryption. The primary benefit is that our approach can enhance the security of the key. Secondly, we extend the typically asymmetric encryption algorithm from integer domain to float domain. In our method, we present a normalization function to map the float DC (Discrete Cosine) coefficients to integer domain. Thirdly, considering that the shape error and encryption/decryption computation cost are contradictory in the normalization mapping, we formulate the contradiction as a multi-objective optimization problem. And then, we propose a multi-objective solution to find an optimized mapping range for encryption/decryption efficiently. Furthermore, benefiting from the proposed asymmetric encryption framework, we continue to put forward a method to check the integrity of the encrypted 3D mesh model, in which the digest is encrypted twice to generate digital signature more safely. The proposed method has been tested on 3D mesh models from Stanford university and other sources to demonstrate the effect of the proposed encryption method and optimization mechanism.
  • Parametric modelling and evolutionary optimization for cost-optimal and
           low-carbon design of high-rise reinforced concrete buildings
    • Abstract: Publication date: October 2019Source: Advanced Engineering Informatics, Volume 42Author(s): Vincent J.L. Gan, C.L. Wong, K.T. Tse, Jack C.P. Cheng, Irene M.C. Lo, C.M. Chan Design optimization of reinforced concrete structures helps reducing the global carbon emissions and the construction cost in buildings. Previous studies mainly targeted at the optimization of individual structural elements in low-rise buildings. High-rise reinforced concrete buildings have complicated structural designs and consume tremendous amounts of resources, but the corresponding optimization techniques were not fully explored in literature. Furthermore, the relationship between the optimization of individual structural elements and the topological arrangement of the entire structure is highly interactive, which calls for new optimization methods. Therefore, this study aims to develop a novel optimization approach for cost-optimal and low-carbon design of high-rise reinforced concrete structures, considering both the structural topology and individual element optimizations. Parametric modelling is applied to define the relationship between individual structural members and the behavior of the entire building structure. A novel evolutionary optimization technique using the genetic algorithm is proposed to optimize concrete building structures, by first establishing the optimal structural topology and then optimizing individual member sizes. In an illustrative example, a high-rise reinforced concrete building is used to examine the proposed optimization approach, which can systematically explore alternative structural designs and identify the optimal solution. It is shown that the carbon emissions and material cost are both reduced by 18–24% after performing optimization. The proposed approach can be extended to optimize other types of buildings (such as steel framework) with a similar problem nature, thereby improving the cost efficiency and environmental sustainability of the built environment.
  • Topological semantics for lumped parameter systems modeling
    • Abstract: Publication date: October 2019Source: Advanced Engineering Informatics, Volume 42Author(s): Randi Wang, Vadim Shapiro Behaviors of many engineering systems are described by lumped parameter models that encapsulate the spatially distributed nature of the system into networks of lumped elements; the dynamics of such a network is governed by a system of ordinary differential and algebraic equations. Languages and simulation tools for modeling such systems differ in syntax, informal semantics, and in the methods by which such systems of equations are generated and simulated, leading to numerous interoperability challenges. Logical extensions of SysML aim specifically at unifying a subset of the underlying concepts in such languages.We propose to unify semantics of all such systems using standard notions from algebraic topology. In particular, Tonti diagrams classify all physical theories in terms of physical laws (topological and constitutive) defined over a pair of dual cochain complexes and may be used to describe different types of lumped parameter systems. We show that all possible methods for generating the corresponding state equations within each physical domain correspond to paths over Tonti diagrams. We further propose a generalization of Tonti diagram that captures the behavior and supports canonical generation of state equations for multi-domain lumped parameter systems.The unified semantics provides a basis for greater interoperability in systems modeling, supporting automated translation, integration, reuse, and numerical simulation of models created in different authoring systems and applications. Notably, the proposed algebraic topological semantics is also compatible with spatially and temporally distributed models that are at the core of modern CAD and CAE systems.
  • An automatic literature knowledge graph and reasoning network modeling
           framework based on ontology and natural language processing
    • Abstract: Publication date: October 2019Source: Advanced Engineering Informatics, Volume 42Author(s): Hainan Chen, Xiaowei Luo With the advancement of scientific and engineering research, a huge number of academic literature are accumulated. Manually reviewing the existing literature is the main way to explore embedded knowledge, and the process is quite time-consuming and labor intensive. As the quantity of literature is increasing exponentially, it would be more difficult to cover all aspects of the literature using the traditional manual review approach. To overcome this drawback, bibliometric analysis is used to analyze the current situation and trend of a specific research field. In the bibliometric analysis, only a few key phrases (e.g., authors, publishers, journals, and citations) are usually used as the inputs for analysis. Information other than those phrases is not extracted for analysis, while that neglected information (e.g., abstract) might provide more detailed knowledge in the article. To tackle with this problem, this study proposed an automatic literature knowledge graph and reasoning network modeling framework based on ontology and Natural Language Processing (NLP), to facilitate the efficient knowledge exploration from literature abstract. In this framework, a representation ontology is proposed to characterize the literature abstract data into four knowledge elements (background, objectives, solutions, and findings), and NLP technology is used to extract the ontology instances from the abstract automatically. Based on the representation ontology, a four-space integrated knowledge graph is built using NLP technology. Then, reasoning network is generated according to the reasoning mechanism defined in the proposed ontology model. To validate the proposed framework, a case study is conducted to analyze the literature in the field of construction management. The case study proves that the proposed ontology model can be used to represent the knowledge embedded in the literatures’ abstracts, and the ontology elements can be automatically extracted by NLP models. The proposed framework can be an enhancement for the bibliometric analysis to explore more knowledge from the literature.
  • Intelligent collaborative patent mining using excessive topic generation
    • Abstract: Publication date: October 2019Source: Advanced Engineering Informatics, Volume 42Author(s): Usharani Hareesh Govindarajan, Amy J.C. Trappey, Charles V. Trappey An inevitable consequence of the technology-driven economy has led to the increased importance of intellectual property protection through patents. Recent global pro-patenting shifts have further resulted in high technology overlaps. Technology components are now spread across a huge corpus of patent documents making its interpretation a knowledge-intensive engineering activity. Intelligent collaborative patent mining facilitates the integration of inputs from patented technology components held by diverse stakeholders. Topic generative models are powerful natural language tools used to decompose data corpus topics and associated word bag distributions. This research develops and validates a superior text mining methodology, called Excessive Topic Generation (ETG), as a preprocessing framework for topic analysis and visualization. The presented ETG methodology adapts the topic generation characteristics from Latent Dirichlet Allocation (LDA) with added capability to generate word distance relationships among key terms. The novel ETG approach is used as the core process for intelligent collaborative patent mining. A case study of 741 global Industrial Immersive Technology (IIT) patents covering inventive and novel concepts of Virtual Reality (VR), Augmented Reality (AR), and Brain Machine Interface (BMI) are systematically processed and analyzed using the proposed methodology. Based on the discovered topics of the IIT patents, patent classification (IPC/CPC) predictions are analyzed to validate the superior ETG results.
  • Untangling parameters: A formalized framework for identifying overlapping
           design parameters between two disciplines for creating an
           interdisciplinary parametric model
    • Abstract: Publication date: October 2019Source: Advanced Engineering Informatics, Volume 42Author(s): Niloufar Emami Employing an interdisciplinary approach in design is an important part of the future of architecture. Therefore, taking a step toward better understanding the overlaps between disciplines, and formalizing the process of integration between disciplines accelerates progress in the field. In examining an interdisciplinary design approach using computational design and simulation tools, while considering shell structures as a special case for spanning large-span roofs, structural and daylighting discipline are considered. The aim is to understand what are the design parameters that co-exist in the structural and daylighting design disciplines, and how may these parameters be implemented in a parametric model created by designers. The parametric model that includes discipline specific parameters can later be used for interdisciplinary performance-based design. Implementing design parameters calls for an understanding of the ways in which parameters affect design and performance. This research considers the application of parametric design methods at the early stages of design for designing high-performance buildings.
  • Research on multi-objective decision-making under cloud platform based on
           quality function deployment and uncertain linguistic variables
    • Abstract: Publication date: October 2019Source: Advanced Engineering Informatics, Volume 42Author(s): Jiashuang Fan, Suihuai Yu, Jianjie Chu, Dengkai Chen, Mingjiu Yu, Tong Wu, Jian Chen, Fangmin Cheng, Chuan Zhao With developments in cloud computing and big data, the term “cloud” has become a household name owing to its characteristics of smartness and connectedness. Cloud-based platforms are associated with people. However, multi-objective decision-making problem in cloud platforms has not been extensively studied. There is no consensus on the reasonable and effective implementation of the decision-making model to select the optimum design scheme in a cloud platform and its establishment to objectively evaluate its significance. In this study, to efficiently and accurately realize the optimal selection of design scheme, a fuzzy evaluation mechanism in handling the vagueness and uncertainty is established, with the advantage of quality function deployment and strength Pareto evolutionary algorithm in decision analysis. A case study is provided to validate the proposed approach in cloud platforms. To reveal the advantages of the proposed method, it was compared with other methods such as the requirement-scenario-experience evaluation framework and a rough number-based integrated method. MATLAB programming was used for this comparison and accuracy certification. The result shows that the proposed model is more efficient and dynamic and provided users with multidimensional evaluation based on open and distributed resources system.
  • A novelty detection patent mining approach for analyzing technological
    • Abstract: Publication date: October 2019Source: Advanced Engineering Informatics, Volume 42Author(s): Juite Wang, Yi-Jing Chen Early opportunity identification is critical for technology-based firms seeking to develop technology or product strategies for competitive advantage in the future. This research develops a patent mining approach based on the novelty detection statistical technique to identify unusual patents that may provide a fresh idea for potential opportunities. A natural language processing technique, latent semantic analysis, is applied to extract hidden relations between words in patent documents for alleviating the vocabulary mismatch problem and reducing the cumbersome efforts of keyword selection by experts. The angle-based outlier detection method, a novelty detection statistical technique, is used to determine outlier patents that are distinct from the majority of collected patent documents in a high-dimensional data space. Finally, visualization tools are developed to analyze the identified outlier patents for exploring potential technological opportunities. The developed methodology is applied in the telehealth industry and research findings can help telehealth firms formulate their technology strategies.
  • Times-series data augmentation and deep learning for construction
           equipment activity recognition
    • Abstract: Publication date: October 2019Source: Advanced Engineering Informatics, Volume 42Author(s): Khandakar M. Rashid, Joseph Louis Automated, real-time, and reliable equipment activity recognition on construction sites can help to minimize idle time, improve operational efficiency, and reduce emissions. Previous efforts in activity recognition of construction equipment have explored different classification algorithms anm accelerometers and gyroscopes. These studies utilized pattern recognition approaches such as statistical models (e.g., hidden-Markov models); shallow neural networks (e.g., Artificial Neural Networks); and distance algorithms (e.g., K-nearest neighbor) to classify the time-series data collected from sensors mounted on the equipment. Such methods necessitate the segmentation of continuous operational data with fixed or dynamic windows to extract statistical features. This heuristic and manual feature extraction process is limited by human knowledge and can only extract human-specified shallow features. However, recent developments in deep neural networks, specifically recurrent neural network (RNN), presents new opportunities to classify sequential time-series data with recurrent lateral connections. RNN can automatically learn high-level representative features through the network instead of being manually designed, making it more suitable for complex activity recognition. However, the application of RNN requires a large training dataset which poses a practical challenge to obtain from real construction sites. Thus, this study presents a data-augmentation framework for generating synthetic time-series training data for an RNN-based deep learning network to accurately and reliably recognize equipment activities. The proposed methodology is validated by generating synthetic data from sample datasets, that were collected from two earthmoving operations in the real world. The synthetic data along with the collected data were used to train a long short-term memory (LSTM)-based RNN. The trained model was evaluated by comparing its performance with traditionally used classification algorithms for construction equipment activity recognition. The deep learning framework presented in this study outperformed the traditionally used machine learning classification algorithms for activity recognition regarding model accuracy and generalization.
  • Detection of correlation characteristics between financial time series
           based on multi-resolution analysis
    • Abstract: Publication date: October 2019Source: Advanced Engineering Informatics, Volume 42Author(s): Xiang-Xin Wang, Ling-Yu Xu, Jie Yu, Huai-Yu Xu, Xuan Yu Interactions between financial time series are complex and changeable in both time and frequency domains. To reveal the evolution characteristics of the time-varying relations between bivariate time series from a multi-resolution perspective, this study introduces an approach combining wavelet analysis and complex networks. In addition, to reduce the influence the phase lag between the time series has on the correlations, we propose dynamic time-warping (DTW) correlation coefficients to reflect the correlation degree between bivariate time series. Unlike previous studies that symbolized the time series only based on the correlation strength, the second-level symbol is set according to the correlation length during the coarse-graining process. This study presents a novel method to analyze bivariate time series and provides more information for investors and decision makers when investing in the stock market. We choose the closing prices of two stocks in China’s market as the sample and explore the evolutionary behavior of correlation modes from different resolutions. Furthermore, we perform experiments to discover the critical correlation modes between the bull market and the bear market on the high-resolution scale, the clustering effect during the financial crisis on the middle-resolution scale, and the potential pseudo period on the low-resolution scale. The experimental results exactly match reality, which provides powerful evidence to prove that our method is effective in financial time series analysis.
  • Coordinating patient preferences through automated negotiation: A
           multiagent systems model for diagnostic services scheduling
    • Abstract: Publication date: October 2019Source: Advanced Engineering Informatics, Volume 42Author(s): Jie Gao, Terrence Wong, Chun Wang This paper presents a multiagent systems model for patient diagnostic services scheduling. We assume a decentralized environment in which patients are modeled as self-interested agents who behave strategically to advance their own benefits rather than the system wide performance. The objective is to improve the utilization of diagnostic imaging resources by coordinating patient individual preferences through automated negotiation. The negotiation process consists of two stages, namely patient selection and preference scheduling. The contract-net protocol and simulated annealing based meta-heuristics are used to design negotiation protocols at the two stages respectively. In terms of game theoretic properties, we show that the proposed protocols are individually rational and incentive compatible. The performance of the preference scheduling protocol is evaluated by a computational study. The average percentage gap analysis of various configurations of the protocol shows that the results obtained from the protocol are close to the optimal ones. In addition, we present the algorithmic properties of the preference scheduling protocol through the validation of a set of eight hypotheses.
  • Deep topology network: A framework based on feedback adjustment learning
           rate for image classification
    • Abstract: Publication date: October 2019Source: Advanced Engineering Informatics, Volume 42Author(s): Long Fan, Tao Zhang, Xin Zhao, Hao Wang, Mingming Zheng Convolutional Neural Network (CNN) has demonstrated its superior ability to achieve amazing accuracy in computer vision field. However, due to the limitation of network depth and computational complexity, it is still difficult to obtain the best classification results for the specific image classification tasks. In order to improve classification performance without increasing network depth, a new Deep Topology Network (DTN) framework is proposed. The key idea of DTN is based on the iteration of multiple learning rate feedback. The framework consists of multiple sub-networks and each sub-network has its own learning rate. After the determined iteration period, these learning rates can be adjusted according to the feedback of training accuracy, in the feature learning process, the optimal learning rate is updated iteratively to optimize the loss function. In practice, the proposed DTN framework is applied to several state-of-the-art deep networks, and its performance is tested by extensive experiments and comprehensive evaluations of CIFAR-10 and MNIST benchmarks. Experimental results show that most deep networks can benefit from the DTN framework with an accuracy of 99.5% on MINIST dataset, which is 5.9% higher than that on the CIFAR-10 benchmark.
  • A multi-objective reliability-based decision support system for
           incorporating decision maker utilities in the design of infrastructure
    • Abstract: Publication date: October 2019Source: Advanced Engineering Informatics, Volume 42Author(s): Yasaman Shahtaheri, Madeleine M. Flint, Jesús M. de la Garza Infrastructure comprises the most fundamental facilities and systems serving society. Because infrastructure exists in economic, social, and environmental contexts, all lifecycle phases of such facilities should maximize utility for society, occupants, and designers. However, due to uncertainties associated with the nature of the built environment, the economic, social, and environmental (i.e., triple bottom line) impacts of infrastructure assets must be described as probabilistic. For this reason, optimization models should aim to maximize decision maker utilities with respect to multiple and potentially conflicting probabilistic decision criteria. Although stochastic optimization and multi-objective optimization are well developed in the field of operations research, their intersection (multi-objective optimization under uncertainty) is much less developed and computationally expensive. This article presents a computationally efficient, adaptable, multi-objective decision support system for finding optimal infrastructure design configurations with respect to multiple probabilistic decision criteria and decision maker requirements (utilities). The proposed model utilizes the First Order Reliability Method (FORM) in a systems reliability approach to assess the reliability of alternative infrastructure design configurations with regard to the probabilistic decision criteria and decision maker defined utilities, and prioritizes the decision criteria that require improvement. A pilot implementation is undertaken on a nine-story office building in Los Angeles, California to illustrate the capabilities of the framework. The results of the pilot implementation revealed that “high-performing” design configurations (with higher initial costs and lower failure costs) had a higher probability of meeting the decision maker’s preferences than more traditional, low initial cost configurations. The proposed framework can identify low-impact designs that also maximize decision maker utilities.
  • Method for digital evaluation of existing production systems adequacy to
           changes in product engineering in the context of the automotive industry
    • Abstract: Publication date: October 2019Source: Advanced Engineering Informatics, Volume 42Author(s): Jaqueline Sebastiany Iaksch, Milton Borsato Current industry practices during the Product Development Process (PDP) still points to the isolation of knowledge domains even with the increase of digitalization. Considering manufacturing process constrains from the beginning of the PDP avoids problems in later stages during the whole product life cycle. Through the application of concepts of the Digital Thread approach, the opportunity to intelligently integrate knowledge into product development is presented, creating a “digital fabric” capable of directing and supporting all stages of the product life cycle. Through these concepts, this research proposes the elaboration of an ontological model and application method capable of evaluating, in real time, the adequacy of the existing production systems, integrating the project and process information. The methodological framework used for the development of this method was Design Science Research. In this way, six steps were performed: (i) problem identification and motivation; (ii) definition of the objectives of the solution; (iii) artifact design and development; (iv) demonstration; (v) evaluation; and, (vi) results report. Through the description of manufacturing systems, the solution contributes to the digital evaluation and facilitates the decision making regarding productive systems, as well as data recovery, reutilization and management. In order to do an initial framework validation, it was performed the application of adequacy principles of a specific production line in automotive sector. However, this choice of a complex industry sector translates a clear possibility of framework adaptation to another industrial segment.
  • A hypernetwork-based approach to collaborative retrieval and reasoning of
           engineering design knowledge
    • Abstract: Publication date: October 2019Source: Advanced Engineering Informatics, Volume 42Author(s): Gongzhuang Peng, Hongwei Wang, Heming Zhang, Keke Huang Complex product development increasingly entails creation and sharing of design knowledge in a collaborative and integrated working environment. In this context, it has become a central issue to address the multifaceted feature of design knowledge for such a collaborative knowledge sharing scheme. This paper proposes a hypernetwork-based approach to explicitly capturing the relationships between various elements in a multifaceted knowledge representation. Specifically, a knowledge hypernetwork model is constructed, which is composed of a designer network, a product network, an issue network and a knowledge unit network. The relationships between various nodes from different networks are identified and defined according to specific node properties. In addition, topological characteristics of the hypernetwork structure are analyzed together with the statistical indicators. Based on this model, the Bayesian approach is adopted to conduct the collaborative reasoning process whereby knowledge elements relevant to the current design task are recommended according to the issues to be resolved and the current design context. A case study conducted in this work shows that the proposed approach is effective in capturing the complex relationships between multi-faceted knowledge elements and enables collaborative retrieval and reasoning of knowledge records.
  • Hybrid data-driven vigilance model in traffic control center using
           eye-tracking data and context data
    • Abstract: Publication date: October 2019Source: Advanced Engineering Informatics, Volume 42Author(s): Fan Li, Ching-Hung Lee, Chun-Hsien Chen, Li Pheng Khoo Vigilance decrement of traffic controllers would greatly threaten public safety. Hence, extensive studies have been conducted to establish the physiological data-based vigilance model for objectively monitoring or detecting vigilance decrement. Nevertheless, most of them using intrusive devices to collect physiological data and failed to consider context information. Consequently, these models can be used in a laboratory environment while cannot adapt to dynamic working conditions of traffic controllers. The goal of this research is to develop an adaptive vigilance model for monitoring vigilance objectively and non-intrusively. In recent years, with advanced information and communication technology, a massive amount of data can be collected from connected daily use items. Hence, we proposed a hybrid data-driven approach based on connected objects for establishing vigilance model in the traffic control center and provide an elaborated case study to illustrate the method. Specifically, eye movements are selected as the primary inputs of the proposed vigilance model; Bagged trees technique is adapted to generate the vigilance model. The results of case study indicated that (1) eye metrics would be correlated with the vigilance performance subjected to the mental fatigue levels, (2) the bagged trees with the fusion features as inputs achieved a relatively stable performance under the condition of data loss, (3) the proposed method could achieve better performance than the other classic machine learning methods.
  • Introducing article numbering to Advanced Engineering Informatics
    • Abstract: Publication date: August 2019Source: Advanced Engineering Informatics, Volume 41Author(s):
  • Developing a conceptual framework of smart work packaging for constraints
           management in prefabrication housing production
    • Abstract: Publication date: October 2019Source: Advanced Engineering Informatics, Volume 42Author(s): Xiao Li, Geoffrey Qiping Shen, Peng Wu, Fan Xue, Hung-lin Chi, Clyde Zhengdao Li Constraints management is the process of satisfying bottlenecks to facilitate tasks assigned to crews being successfully executed. However, managing constraints is inherently challenging in prefabrication housing production (PHP), due to the fragmentation of processes and information during project delivery. Enlightened by the broadly accepted work packaging method and the smart construction objects (SCOs) model, this study aims to define and implement smart work packaging (SWP) for constraints management in PHP. Firstly, the framework of SWP-enabled constraints management (SWP-CM) with three primary functions, including constraints modeling, constraints optimization, and constraints monitoring, is established. In addition, this study develops a layered abstract model as a prototype representation to elaborate on the implementation of SWP for practitioners. Finally, a laboratory-based test is applied to validate the framework. It can prove that SWP indeed opens new avenues for smart constraints management for PHP.
  • Road pothole extraction and safety evaluation by integration of point
           cloud and images derived from mobile mapping sensors
    • Abstract: Publication date: October 2019Source: Advanced Engineering Informatics, Volume 42Author(s): Hangbin Wu, Lianbi Yao, Zeran Xu, Yayun Li, Xinran Ao, Qichao Chen, Zhengning Li, Bin Meng The automatic detection and extraction of road pothole distress is an important issue regarding healthy road structures, monitoring, and maintenance. In this paper, a new algorithm that integrates the mobile point cloud and images is proposed for the detection of road potholes. The algorithm includes three steps: 2D candidate pothole extraction from the images using a deep learning method, 3D candidate pothole extraction via a point cloud, and pothole determination by depth analysis. Because the texture features of the pothole and asphalt or concrete patches greatly differ from those of a normal road, pothole or patch distress images are used to establish a training set and train and test the deep learning system. Subsequently, the 2D candidate pothole is extracted from the images and labeled via the trained DeepLabv3+, a state-of-the-art pixel-wise classification (semantic segmentation) network. The edge of the candidate pothole in the image is then used to establish the relationship between the mobile point cloud and images. The original road point cloud around the edge of the candidate pothole is categorized into two groups, that is, interior and exterior points, according to the relationship between the point cloud and images. The exterior points are used to fit the road plane and calculate the accurate 3D shape of the candidate potholes. Finally, the interior points of a candidate pothole are used to analyze the depth distribution to determine if the candidate pothole is a pothole or patch. To verify the proposed method, two cases, including real and simulation cases, are selected. The real case is an expressway in Shanghai with a length of 26.4 km. Based on the proposed method, 77 candidate potholes are extracted by the DeepLabv3+ system; 49 potholes and 28 patches are finally filtered. The affected lanes and pothole locations are analyzed. The simulation case is selected to verify the geometric accuracy of the detected potholes. The results show that the mean accuracy of the detected potholes is ∼1.5–2.8 cm.
  • Registering georeferenced photos to a building information model to
           extract structures of interest
    • Abstract: Publication date: October 2019Source: Advanced Engineering Informatics, Volume 42Author(s): Junjie Chen, Donghai Liu, Shuai Li, Da Hu Vision-based techniques are being used to inspect structures such as buildings and infrastructure. Due to various backgrounds in the acquired images, conventional vision-based techniques rely heavily on manual processing to extract relevant structures of interest for subsequent analysis in many applications, such as distress detection. This practice is laborious, time-consuming, and error-prone. To address the challenge, this study proposes a new method that automatically matches a georeferenced real-life photo with a building information model-rendered synthetic image to allow the extraction of relevant structure of interest. Field experiments were conducted to validate and evaluate the proposed method. The average accuracy of this method is 79.21% and the processing speed is 140 s per image. The proposed method has the potential to reduce the workload of image processing for vision-based structural inspection.
  • One class based feature learning approach for defect detection using deep
    • Abstract: Publication date: October 2019Source: Advanced Engineering Informatics, Volume 42Author(s): Abdul Mujeeb, Wenting Dai, Marius Erdt, Alexei Sourin Detecting defects is an integral part of any manufacturing process. Most works still utilize traditional image processing algorithms to detect defects owing to the complexity and variety of products and manufacturing environments. In this paper, we propose an approach based on deep learning which uses autoencoders for extraction of discriminative features. It can detect different defects without using any defect samples during training. This method, where samples of only one class (i.e. defect-free samples) are available for training, is called One Class Classification (OCC). This OCC method can also be used for training a neural network when only one golden sample is available by generating many copies of the reference image by data augmentation. The trained model is then able to generate a descriptor—a unique feature vector of an input image. A test image captured by an Automatic Optical Inspection (AOI) camera is sent to the trained model to generate a test descriptor, which is compared with a reference descriptor to obtain a similarity score. After comparing the results of this method with a popular traditional similarity matching method SIFT, we find that in the most cases this approach is more effective and more flexible than the traditional image processing-based methods, and it can be used to detect different types of defects with minimum customization.
  • A CNN-based 3D patch registration approach for integrating sequential
           models in support of progress monitoring
    • Abstract: Publication date: August 2019Source: Advanced Engineering Informatics, Volume 41Author(s): Lei Lei, Ying Zhou, Hanbin Luo, Peter E.D. Love Significant advancements in three-dimensional (3D) imaging technologies have enabled the ability to effectively monitor and manage the progress of works in construction. Traditionally, 3D point clouds have been used in conjunction with building information models to visualize the progress of works. The discrepancies between ‘as-planned’ and ‘actual’ models are unable to be automatically identified using the existing approaches due the absence of an effective registration algorithm. To ensure the registration accuracy of multi-scanned point clouds, an automated method based on a data-driven Convolutional Neural Network (CNN) deep learning algorithm is proposed. In this instance, 3D Point cloud patches are aligned with spatial datasets that are scanned from different locations using range cameras. The registration results are used to automatically detect spatial changes when compared with different point clouds. The quantified changes are utilized to determine the percentage of work that has been completed at fixed intervals. The developed registration approach is tested and validated using a series of experiments. It is demonstrated that discrepancies between ‘as-planned’ and ‘actual’ models can be identified with a higher level of accuracy, which can enable the baseline for monitoring construction to be undertaken in real-time.
  • A lattice-based approach for navigating design configuration spaces
    • Abstract: Publication date: October 2019Source: Advanced Engineering Informatics, Volume 42Author(s): Alison McKay, Hau Hing Chau, Christopher F. Earl, Amar Kumar Behera, Alan de Pennington, David C. Hogg Design configurations, such as Bills of Materials (BoMs), are indispensable parts of any product development process and integral to the design descriptions stored in proprietary Computer Aided Design and Product Lifecycle Management systems. Engineers use BoMs and other design configurations as lenses to repurpose design descriptions for specific purposes. For this reason, multiple BoMs typically occur in any given product development process. For example, an engineering BoM may be used to define a configuration that best supports a design activity whereas a manufacturing BoM may be used to define the configuration of parts that best supports a manufacturing process. Current practice for the definition of BoMs involves the use of indented parts lists and dendograms that are prone to error because it is easy to create discrepancies across BoMs that, in essence, are defined through collections of part identifiers such as names and part numbers. Such errors have a significant detrimental effect on the performance of product development processes by creating the need for rework, adding costs and increasing time to market.This paper introduces a design description capability that ensures consistency across BoMs for a given design. A boolean hypercube lattice is used to define a design configuration space that includes all possible configurations for a given design description. Valid operations within the space are governed by the mathematics of hypercube lattices. The design description capability is demonstrated through an early engineering design configuration software tool that offers significant benefits by ensuring consistency across the BoMs for a given design. The software uses and generates design descriptions that are exported from and imported to commercially available design systems through a standard (ISO 10303-214) interface format. In this way, potential for early impact on industry practice is high.
  • A novel inverse data driven modelling approach to performance-based
           building design during early stages
    • Abstract: Publication date: August 2019Source: Advanced Engineering Informatics, Volume 41Author(s): Roya Rezaee, Jason Brown, John Haymaker, Godfried Augenbroe Energy analysis at the early stage of building design is a critical, yet difficult task in performance-based design. The difficulty arises from the complex, iterative, and uncertain nature of building design and the challenges of integration with well-posed energy assessment tools. The purpose of this article is to first review characteristics of performance-based design and establish requirements for a methodology that includes generating promising design alternatives, assessing the energy performance in tandem with the generation of alternatives, and choosing an alternative design solution with confidence. The study then proposes a novel systematic data-driven method, based on linear inverse modeling that generates plausible ranges for design parameters given a preferred energy target. The energy performance in this method is described as a linear function of the design parameters for a particular scenario of design. The application of the proposed method in a case study shows that it is capable of helping designers make informed decisions regarding energy performance iteratively and confidently at the early stages of building design.
  • Improving reconstruction of tunnel lining defects from ground-penetrating
           radar profiles by multi-scale inversion and bi-parametric full-waveform
    • Abstract: Publication date: August 2019Source: Advanced Engineering Informatics, Volume 41Author(s): Deshan Feng, Xun Wang, Bin Zhang Complex irregular defects of tunnel linings under complicated geological conditions cannot be accurately reconstructed with the traditional full-waveform inversion (FWI) method due to their irregular geometrical characteristics and complex dielectric properties. Because of extensive inversion calculations and high memory requirements, the traditional FWI method is very sensitive to the initial model and plunge into a local minimum or cycle skipping. To solve this problem, a novel ground-penetrating radar (GPR) FWI method involving two parameters (i.e. permittivity and conductivity) is proposed for improving reconstruction accuracy of lining defects using the total variation (TV) regularization. First, the Delaunay unstructured triangular mesh in finite element time-domain (FETD) method is employed to perform GPR forward modeling, and then the total-variation model constraint and multi-scale inversion strategy are implemented during execution of the conjugate gradient (CG) algorithm, which facilitates the quick search for the global optimal minimum value, thus guaranteeing the avoidance of the ill-posed problem during the inversion process. Accordingly, the detailed features of lining defects can be characterized and reconstructed even for those complicated geological conditions, more specifically, the results show that, with fewer iterations (up to 59 times less), the proposed method present a lower reconstruction error both on permittivity (up to 12.05% lower) and conductivity (up to 7.35% lower). From a comparison of the inversion results and the model, it can be concluded that the proposed FWI algorithm can effectively eliminate the non-physical oscillation and artifacts in the image reconstruction, which may significantly improving the accuracy of defects interpretation and assessing the severity of complex defects.
  • A metamodel for cyber-physical systems
    • Abstract: Publication date: August 2019Source: Advanced Engineering Informatics, Volume 41Author(s): Theresa Fitz, Michael Theiler, Kay Smarsly With the advent of the Internet of Things and Industry 4.0 concepts, cyber-physical systems in civil engineering experience an increasing impact on structural health monitoring (SHM) and control applications. Designing, optimizing, and documenting cyber-physical system on a formal basis require platform-independent and technology-independent metamodels. This study, with emphasis on communication in cyber-physical systems, presents a metamodel for describing cyber-physical systems. First, metamodeling concepts commonly used in computing in civil engineering are reviewed and possibilities and limitations of describing communication-related information are discussed. Next, communication-related properties and behavior of distributed cyber-physical systems applied for SHM and control are explained, and system components relevant to communication are specified. Then, the metamodel to formally describe cyber-physical systems is proposed and mapped into the Industry Foundation Classes (IFC), an open international standard for building information modeling (BIM). Finally, the IFC-based approach is verified using software of the official IFC certification program, and it is validated by BIM-based example modeling of a prototype cyber-physical system, which is physically implemented in the laboratory. As a result, cyber-physical systems applied for SHM and control are described and the information is stored, documented, and exchanged on the formal basis of IFC, facilitating design, optimization, and documentation of cyber-physical systems.
  • A shared ontology for integrated highway planning
    • Abstract: Publication date: August 2019Source: Advanced Engineering Informatics, Volume 41Author(s): Jojo France-Mensah, William J. O'Brien Many highway agencies have several functional groups responsible for planning for safety, maintenance and rehabilitation (M&R), mobility, and other functions. The functional nature of State Highway Agencies (SHAs) can result in a siloed approach to planning. Such efforts are further challenged by functional groups utilizing legacy systems which lack interoperability. In practice, this leads to redundant planning efforts and potential spatial-temporal conflicts in the projects proposed by the different groups over a planning period. There is a need for an integrated approach to planning supported by information systems. However, the existing literature on formalized knowledge representation fails to adequately account for the level of information needed for cross-functional planning of projects scheduled for the same network. Hence, this study presents an ontology for integrating information to support the cross-functional and spatial-temporal planning of highway projects. The Integrated Highway Planning Ontology (IHP-Onto) is a shared representation of knowledge about pavement assets, M&R planning, and inter-project coordination. Sources of the knowledge acquired included expert interviews, a review of nation-wide studies, and previously published ontologies. The implementation phase included a case study demonstration of the ontology by answering relevant competency questions via SPARQL queries. Based on the data-driven evaluation of the ontology, the precision and recall rates obtained were 97% and 92% respectively. Based on the results of the evaluation approaches, IHP-Onto was demonstrated as being sufficient to represent domain knowledge capable of supporting integrated highway planning.
  • Development of an IoT-based big data platform for day-ahead prediction of
           building heating and cooling demands
    • Abstract: Publication date: August 2019Source: Advanced Engineering Informatics, Volume 41Author(s): X.J. Luo, Lukumon O. Oyedele, Anuoluwapo O. Ajayi, Chukwuka G. Monyei, Olugbenga O. Akinade, Lukman A. Akanbi The emerging technologies of the Internet of Things (IoT) and big data can be utilised to derive knowledge and support applications for energy-efficient buildings. Effective prediction of heating and cooling demands is fundamental in building energy management. In this study, a 4-layer IoT-based big data platform is developed for day-ahead prediction of building energy demands, while the core part is the hybrid machine learning-based predictive model. The proposed energy demand predictive model is based on the hybrids of k-means clustering and artificial neural network (ANN). Due to different temperatures of walls, windows, grounds, roofs and indoor air, various IoT sensors are installed at different locations of the building. To determine the input variables to the hybrid machine learning-based predictive model, correlation analysis is adopted. Through clustering analysis, the characteristic patterns of daily weather profile are identified. Thus, the annual profile is classified into several featuring groups. Each group of weather profile, along with IoT sensor readings, building operating schedules as well as heating and cooling demands, is used to train the sub-ANN predictive models. Due to the involvement of IoT sensors, the overall prediction accuracy can be improved. It is found that the mean absolute percentage error of energy demands prediction is 3% and 8% in training and testing cases, respectively.Graphical abstractGraphical abstract for this article
  • Fostering the transfer of empirical engineering knowledge under
           technological paradigm shift: An experimental study in conceptual design
    • Abstract: Publication date: August 2019Source: Advanced Engineering Informatics, Volume 41Author(s): Xinyu Li, Zuhua Jiang, Yeqin Guan, Geng Li, Fuhua Wang Knowledge transfer is a frequently-used method for novice engineers to rapidly adapt themselves to a related new technological paradigm. However, due to different types in knowledge transfer, it remains divergent about the mechanism and motivation, leading to the chaos in tackling the low-quality and low-efficiency transfer process. This study concentrated on the individual-level transfer of empirical engineering knowledge (EEK) in product designers’ mind triggered by technological paradigm shift, trying to accelerate the process and improve the result. Based on concept-knowledge model and eye-tracking data, this paper proposed a two-phase protocol and implements it in a practical case of conceptual design. Three factors of designers, prior cognitive level, utilization of the stimulus materials, and self-directed learning willingness, were evaluated and investigated. Results of 82 engineering students and young scholars showed that all three factors significantly impacted the validity and integrity of transferred EEK, and the efficiency of EEK transfer process. Specifically, key concepts in the stimulus materials, and creativity and initiative in learning willingness improved the validity and integrity. Non-essential words and too complex drawings lowered the efficiency. High cognitive level had side-effects on the transfer. These results inferred that the transfer of EEK could be fostered by wisely reusing past experience, selectively utilizing stimulus materials, and actively promoting learning willingness, which helps the designers to perform better in product design innovation.
  • An integrated highly synchronous, high resolution, real time eye tracking
           system for dynamic flight movement
    • Abstract: Publication date: August 2019Source: Advanced Engineering Informatics, Volume 41Author(s): Hong Jie Wee, Sun Woh Lye, Jean-Philippe Pinheiro Electronic surveillance systems are being used rapidly today, ranging from a simple video camera to a complex biometric surveillance system for facial patterns and intelligent computer vision based surveillance systems, which are applied in many fields such as home monitoring, security surveillance of important places and mission critical tasks like air traffic control surveillance. Such systems normally involve a computer system and a human surveillance operator, who looks at the dynamic display to perform his surveillance tasks. Exploitation of shared information between these physical heterogeneous data capture systems with human operated functions is one emerging aspect in electronic surveillance that has yet to be addressed deeply. Hence, an innovative interaction interface for such knowledge extraction and representation is required. Such an interface should establish a data activity register frame which captures information depicting various surveillance activities at a specified spatial and time reference.This paper presents a real time eye tracking system, which integrates two sets of activity data in a highly dynamic changing and synchronous manner in real-time with respect to both spatial and time frames, through the “Dynamic Data Alignment and Timestamp Synchronisation Model”. This model matches the timestamps of the two data streams, aligns them to the same spatial reference frame before fusing them together into a data activity register frame. The Air Traffic Control (ATC) domain is used to illustrate this model, where experiments are conducted under simulated radar traffic situations with participants and their radar input data. Test results revealed that this model is able to synchronise the timestamp of the eye and dynamic display data, align both of these data spatially, while taking into account dynamic changes in space and time on a simulated radar display. This system can also distinguish and show variations in the monitoring behaviour of participants. As such, new knowledge can be extracted and represented through this innovative interface, which can then be applied to other applications in the field of electronic surveillance to unearth monitoring behaviour of the human surveillance operator.
  • Inferring workplace safety hazards from the spatial patterns of
           workers’ wearable data
    • Abstract: Publication date: August 2019Source: Advanced Engineering Informatics, Volume 41Author(s): Kanghyeok Yang, Changbum R. Ahn Hazard identification in construction typically requires safety managers to manually inspect an area. However, current approach is very limited due to the dynamic nature of construction sites and the subjective nature of human perception. Using wearable inertial measurement units (WIMU), previous literatures revealed the relationship between a worker’s abnormal gait patterns and the existence of slip, trip and fall (STF) hazards. Though the prior work demonstrated the strong correlation between STF hazards and abnormal gait patterns, automated hazard identification is a challenging issue due to the lack of knowledge on decision threshold on identifying hazards under different construction environments. To fill the research gap, this study developed an approach that can automatically identify the STF hazards without knowledge about thresholds by investigating the spatial associations of workers’ abnormal gait occurrences. An experiment simulating a brick installation was performed with different types of STF hazards (e.g., poor housekeeping), and results demonstrate the feasibility of STF hazards identification with the developed approach. The results highlight the opportunities of revealing potential accident hotspots via an efficient and semi-automated methodology, which overcomes many of the limitations in current practice.
  • Corrigendum to “A structural service innovation approach for designing
           smart product service systems: Case study of smart beauty service” [Adv.
           Eng. Inform. 40 (2019) 154–167]
    • Abstract: Publication date: August 2019Source: Advanced Engineering Informatics, Volume 41Author(s): Ching-Hung Lee, Chun-Hsien Chen, Amy J.C. Trappey
  • Xgboost application on bridge management systems for proactive damage
    • Abstract: Publication date: August 2019Source: Advanced Engineering Informatics, Volume 41Author(s): Soram Lim, Seokho Chi Bridge inspection is one of the most fundamental tasks in bridge management practices. Because of limited professional manpower and budget constraints, providing prior information about possible damage can reduce inspection errors and time. The purpose of this study was to estimate the condition of bridges at a damage level, considering various influencing factors for seven different damage types by six different main structure types, using data from the Korean Bridge Management System. The extreme gradient boosting (XGBoost) method was used because it has the advantage of not assuming determinacy and independence, and it clearly can handle the numerous variables that affect damage to bridges. As a result, out of the 38 decision trees that were generated, 36 trees were derived with significant performance measures. The influence of the variables was calculated by the Shapley Additive Explanation (SHAP) value. Age, average daily truck traffic, vehicle weight limit, total length, and effective width were found to be the major factors that influenced damage to bridges. This study confirmed that more detailed structural factors were significant contributors to severe damage to complex structural designs and the use of multiple kinds of materials, such as the cross-sectional properties of girders for the concrete deck of bridges with steel girders compared to the properties of the decks for bridges made of a simple slab of reinforced concrete. The research findings emphasized the benefits of artificial intelligence in the analysis of the conditions of bridges and showed its potential for use in network-level decision making for preventive maintenance.
  • A directed failure causality network (DFCN) based method for function
           components risk prioritization under interval type-2 fuzzy environment
    • Abstract: Publication date: August 2019Source: Advanced Engineering Informatics, Volume 41Author(s): Hongzhan Ma, Xuening Chu, Weizhong Wang, Xinwang Liu, Deyi Xue Failure risk prioritization of function components plays a key role in the process to redesign a mechanical product. However, the failure causality relationships (FCRs) among failure modes of components are often ignored in the existing design risk assessment methods, leading to inaccurate risk prioritization results. A failure mode in one component can be the cause of a failure mode in another component, and a failure mode with low chance of failure may result in another failure mode with high chance of failure through propagation among failure modes. Thus, the ultimate effects of each failure mode should be determined by considering the effects of failure propagations. In this research, a directed failure causality network (DFCN) model considering FCRs is proposed to describe the FCRs and to predict risks of the designed product. In addition, uncertainties of linguistic terms in evaluation are also considered in the developed model, because linguistic terms are more suitable and natural than quantitative numbers for design engineers to assess design risks based on their knowledge. To describe these uncertainties, interval type-2 fuzzy set is employed to model the designers’ subjective linguistic terms for determining the weights of edges and weights of vertices in the DFCN. A case study for failure risk prioritization of components in redesign of a large tonnage crawler crane (LTCC) is implemented to demonstrate the effectiveness of the proposed method.
  • A novel approach for sand liquefaction prediction via local mean-based
           pseudo nearest neighbor algorithm and its engineering application
    • Abstract: Publication date: August 2019Source: Advanced Engineering Informatics, Volume 41Author(s): Shuai Huang, Mingming Huang, Yuejun Lyu The prediction method plays crucial roles in accurate prediction of sand liquefaction. Recently, machine learning has been widely used for prediction of sand liquefaction, and the Local Mean-based Pseudo Nearest Neighbor (LMPNN) algorithm, one of machine learning techniques, showed good performance in pattern recognition. In this study, we propose a sand liquefaction prediction model based on the LMPNN algorithm, which is the first work of applying the LMPNN algorithm to sand liquefaction prediction. Then, our proposed prediction model is used for evaluation of site liquefaction grade in Tongzhou District of China. And the comparison between our proposed prediction model with the liquefaction evaluation method in the Chinese code is made, which will provide an important approach to predicting the sand liquefaction grades for the major construction project sites. Extensive experiments on grade prediction demonstrate that the effectiveness of our proposed prediction model based on the LMPNN algorithm. In addition, shaking table test of an engineering site model is conducted for evaluating whether this engineering site model is liquefaction and non-liquefaction or not. And the experiment result of the shaking table test is the same as that of our proposed prediction model based on LMPNN algorithm, which further demonstrates the effectiveness of our proposed prediction model. Consequently, our proposed prediction model is proved to have a good prospect of engineering application in the liquefaction prediction.
  • Automated classification of building information modeling (BIM) case
           studies by BIM use based on natural language processing (NLP) and
           unsupervised learning
    • Abstract: Publication date: Available online 30 April 2019Source: Advanced Engineering InformaticsAuthor(s): Namcheol Jung, Ghang Lee This paper comparatively analyzes a method to automatically classify case studies of building information modeling (BIM) in construction projects by BIM use. It generally takes a minimum of thirty minutes to hours of collection and review and an average of four information sources to identify a project that has used BIM in a manner that is of interest. To automate and expedite the analysis tasks, this study deployed natural language processing (NLP) and commonly used unsupervised learning for text classification, namely latent semantic analysis (LSA) and latent Dirichlet allocation (LDA). The results were validated against one of representative supervised learning methods for text classification—support vector machine (SVM). When LSA and LDA detected phrases in a BIM case study that had higher similarity values to the definition of each BIM use than the threshold values, the system determined that the project had deployed BIM in the detected approach. For the classification of BIM use, the BIM uses specified by Pennsylvania State University were utilized. The approach was validated using 240 BIM case studies (512,892 features). When BIM uses were employed in a project, the project was labeled as “1”; when they were not, the project was labeled as “0.” The performance was analyzed by changing parameters: namely, document segmentation, feature weighting, dimensionality reduction coefficient (k-value), the number of topics, and the number of iterations. LDA yielded the highest F1 score, 80.75% on average. LDA and LSA yielded high recall and low precision in most cases. Conversely, SVM yielded high precision and low recall in most cases and fluctuations in F1 scores.
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