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Publisher: Elsevier   (Total: 3043 journals)

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Showing 1 - 200 of 3043 Journals sorted alphabetically
AASRI Procedia     Open Access   (Followers: 15)
Academic Pediatrics     Hybrid Journal   (Followers: 20, SJR: 1.402, h-index: 51)
Academic Radiology     Hybrid Journal   (Followers: 18, SJR: 1.008, h-index: 75)
Accident Analysis & Prevention     Partially Free   (Followers: 83, SJR: 1.109, h-index: 94)
Accounting Forum     Hybrid Journal   (Followers: 23, SJR: 0.612, h-index: 27)
Accounting, Organizations and Society     Hybrid Journal   (Followers: 27, SJR: 2.515, h-index: 90)
Achievements in the Life Sciences     Open Access   (Followers: 4)
Acta Anaesthesiologica Taiwanica     Open Access   (Followers: 5, SJR: 0.338, h-index: 19)
Acta Astronautica     Hybrid Journal   (Followers: 331, SJR: 0.726, h-index: 43)
Acta Automatica Sinica     Full-text available via subscription   (Followers: 3)
Acta Biomaterialia     Hybrid Journal   (Followers: 25, SJR: 2.02, h-index: 104)
Acta Colombiana de Cuidado Intensivo     Full-text available via subscription   (Followers: 1)
Acta de Investigación Psicológica     Open Access   (Followers: 2)
Acta Ecologica Sinica     Open Access   (Followers: 8, SJR: 0.172, h-index: 29)
Acta Haematologica Polonica     Free   (SJR: 0.123, h-index: 8)
Acta Histochemica     Hybrid Journal   (Followers: 3, SJR: 0.604, h-index: 38)
Acta Materialia     Hybrid Journal   (Followers: 211, SJR: 3.683, h-index: 202)
Acta Mathematica Scientia     Full-text available via subscription   (Followers: 5, SJR: 0.615, h-index: 21)
Acta Mechanica Solida Sinica     Full-text available via subscription   (Followers: 9, SJR: 0.442, h-index: 21)
Acta Oecologica     Hybrid Journal   (Followers: 9, SJR: 0.915, h-index: 53)
Acta Otorrinolaringologica (English Edition)     Full-text available via subscription   (Followers: 1)
Acta Otorrinolaringológica Española     Full-text available via subscription   (Followers: 3, SJR: 0.311, h-index: 16)
Acta Pharmaceutica Sinica B     Open Access   (Followers: 2)
Acta Poética     Open Access   (Followers: 4)
Acta Psychologica     Hybrid Journal   (Followers: 23, SJR: 1.365, h-index: 73)
Acta Sociológica     Open Access  
Acta Tropica     Hybrid Journal   (Followers: 6, SJR: 1.059, h-index: 77)
Acta Urológica Portuguesa     Open Access  
Actas Dermo-Sifiliograficas     Full-text available via subscription   (Followers: 4)
Actas Dermo-Sifiliográficas (English Edition)     Full-text available via subscription   (Followers: 3)
Actas Urológicas Españolas     Full-text available via subscription   (Followers: 4, SJR: 0.383, h-index: 19)
Actas Urológicas Españolas (English Edition)     Full-text available via subscription   (Followers: 2)
Actualites Pharmaceutiques     Full-text available via subscription   (Followers: 5, SJR: 0.141, h-index: 3)
Actualites Pharmaceutiques Hospitalieres     Full-text available via subscription   (Followers: 4, SJR: 0.112, h-index: 2)
Acupuncture and Related Therapies     Hybrid Journal   (Followers: 3)
Ad Hoc Networks     Hybrid Journal   (Followers: 11, SJR: 0.967, h-index: 57)
Addictive Behaviors     Hybrid Journal   (Followers: 15, SJR: 1.514, h-index: 92)
Addictive Behaviors Reports     Open Access   (Followers: 5)
Additive Manufacturing     Hybrid Journal   (Followers: 8, SJR: 1.039, h-index: 5)
Additives for Polymers     Full-text available via subscription   (Followers: 20)
Advanced Drug Delivery Reviews     Hybrid Journal   (Followers: 128, SJR: 5.2, h-index: 222)
Advanced Engineering Informatics     Hybrid Journal   (Followers: 11, SJR: 1.265, h-index: 53)
Advanced Powder Technology     Hybrid Journal   (Followers: 16, SJR: 0.739, h-index: 33)
Advances in Accounting     Hybrid Journal   (Followers: 9, SJR: 0.299, h-index: 15)
Advances in Agronomy     Full-text available via subscription   (Followers: 15, SJR: 2.071, h-index: 82)
Advances in Anesthesia     Full-text available via subscription   (Followers: 25, SJR: 0.169, h-index: 4)
Advances in Antiviral Drug Design     Full-text available via subscription   (Followers: 3)
Advances in Applied Mathematics     Full-text available via subscription   (Followers: 6, SJR: 1.054, h-index: 35)
Advances in Applied Mechanics     Full-text available via subscription   (Followers: 10, SJR: 0.801, h-index: 26)
Advances in Applied Microbiology     Full-text available via subscription   (Followers: 22, SJR: 1.286, h-index: 49)
Advances In Atomic, Molecular, and Optical Physics     Full-text available via subscription   (Followers: 16, SJR: 3.31, h-index: 42)
Advances in Biological Regulation     Hybrid Journal   (Followers: 4, SJR: 2.277, h-index: 43)
Advances in Botanical Research     Full-text available via subscription   (Followers: 3, SJR: 0.619, h-index: 48)
Advances in Cancer Research     Full-text available via subscription   (Followers: 25, SJR: 2.215, h-index: 78)
Advances in Carbohydrate Chemistry and Biochemistry     Full-text available via subscription   (Followers: 9, SJR: 0.9, h-index: 30)
Advances in Catalysis     Full-text available via subscription   (Followers: 5, SJR: 2.139, h-index: 42)
Advances in Cellular and Molecular Biology of Membranes and Organelles     Full-text available via subscription   (Followers: 12)
Advances in Chemical Engineering     Full-text available via subscription   (Followers: 24, SJR: 0.183, h-index: 23)
Advances in Child Development and Behavior     Full-text available via subscription   (Followers: 10, SJR: 0.665, h-index: 29)
Advances in Chronic Kidney Disease     Full-text available via subscription   (Followers: 10, SJR: 1.268, h-index: 45)
Advances in Clinical Chemistry     Full-text available via subscription   (Followers: 28, SJR: 0.938, h-index: 33)
Advances in Colloid and Interface Science     Full-text available via subscription   (Followers: 18, SJR: 2.314, h-index: 130)
Advances in Computers     Full-text available via subscription   (Followers: 16, SJR: 0.223, h-index: 22)
Advances in Developmental Biology     Full-text available via subscription   (Followers: 11)
Advances in Digestive Medicine     Open Access   (Followers: 4)
Advances in DNA Sequence-Specific Agents     Full-text available via subscription   (Followers: 5)
Advances in Drug Research     Full-text available via subscription   (Followers: 22)
Advances in Ecological Research     Full-text available via subscription   (Followers: 41, SJR: 3.25, h-index: 43)
Advances in Engineering Software     Hybrid Journal   (Followers: 25, SJR: 0.486, h-index: 10)
Advances in Experimental Biology     Full-text available via subscription   (Followers: 7)
Advances in Experimental Social Psychology     Full-text available via subscription   (Followers: 40, SJR: 5.465, h-index: 64)
Advances in Exploration Geophysics     Full-text available via subscription   (Followers: 3)
Advances in Fluorine Science     Full-text available via subscription   (Followers: 8)
Advances in Food and Nutrition Research     Full-text available via subscription   (Followers: 47, SJR: 0.674, h-index: 38)
Advances in Fuel Cells     Full-text available via subscription   (Followers: 15)
Advances in Genetics     Full-text available via subscription   (Followers: 15, SJR: 2.558, h-index: 54)
Advances in Genome Biology     Full-text available via subscription   (Followers: 11)
Advances in Geophysics     Full-text available via subscription   (Followers: 6, SJR: 2.325, h-index: 20)
Advances in Heat Transfer     Full-text available via subscription   (Followers: 21, SJR: 0.906, h-index: 24)
Advances in Heterocyclic Chemistry     Full-text available via subscription   (Followers: 8, SJR: 0.497, h-index: 31)
Advances in Human Factors/Ergonomics     Full-text available via subscription   (Followers: 25)
Advances in Imaging and Electron Physics     Full-text available via subscription   (Followers: 2, SJR: 0.396, h-index: 27)
Advances in Immunology     Full-text available via subscription   (Followers: 35, SJR: 4.152, h-index: 85)
Advances in Inorganic Chemistry     Full-text available via subscription   (Followers: 9, SJR: 1.132, h-index: 42)
Advances in Insect Physiology     Full-text available via subscription   (Followers: 3, SJR: 1.274, h-index: 27)
Advances in Integrative Medicine     Hybrid Journal   (Followers: 5)
Advances in Intl. Accounting     Full-text available via subscription   (Followers: 4)
Advances in Life Course Research     Hybrid Journal   (Followers: 8, SJR: 0.764, h-index: 15)
Advances in Lipobiology     Full-text available via subscription   (Followers: 2)
Advances in Magnetic and Optical Resonance     Full-text available via subscription   (Followers: 9)
Advances in Marine Biology     Full-text available via subscription   (Followers: 16, SJR: 1.645, h-index: 45)
Advances in Mathematics     Full-text available via subscription   (Followers: 10, SJR: 3.261, h-index: 65)
Advances in Medical Sciences     Hybrid Journal   (Followers: 6, SJR: 0.489, h-index: 25)
Advances in Medicinal Chemistry     Full-text available via subscription   (Followers: 5)
Advances in Microbial Physiology     Full-text available via subscription   (Followers: 4, SJR: 1.44, h-index: 51)
Advances in Molecular and Cell Biology     Full-text available via subscription   (Followers: 22)
Advances in Molecular and Cellular Endocrinology     Full-text available via subscription   (Followers: 10)
Advances in Molecular Toxicology     Full-text available via subscription   (Followers: 7, SJR: 0.324, h-index: 8)
Advances in Nanoporous Materials     Full-text available via subscription   (Followers: 4)
Advances in Oncobiology     Full-text available via subscription   (Followers: 3)
Advances in Organometallic Chemistry     Full-text available via subscription   (Followers: 15, SJR: 2.885, h-index: 45)
Advances in Parallel Computing     Full-text available via subscription   (Followers: 7, SJR: 0.148, h-index: 11)
Advances in Parasitology     Full-text available via subscription   (Followers: 7, SJR: 2.37, h-index: 73)
Advances in Pediatrics     Full-text available via subscription   (Followers: 24, SJR: 0.4, h-index: 28)
Advances in Pharmaceutical Sciences     Full-text available via subscription   (Followers: 13)
Advances in Pharmacology     Full-text available via subscription   (Followers: 15, SJR: 1.718, h-index: 58)
Advances in Physical Organic Chemistry     Full-text available via subscription   (Followers: 7, SJR: 0.384, h-index: 26)
Advances in Phytomedicine     Full-text available via subscription  
Advances in Planar Lipid Bilayers and Liposomes     Full-text available via subscription   (Followers: 3, SJR: 0.248, h-index: 11)
Advances in Plant Biochemistry and Molecular Biology     Full-text available via subscription   (Followers: 8)
Advances in Plant Pathology     Full-text available via subscription   (Followers: 5)
Advances in Porous Media     Full-text available via subscription   (Followers: 4)
Advances in Protein Chemistry     Full-text available via subscription   (Followers: 18)
Advances in Protein Chemistry and Structural Biology     Full-text available via subscription   (Followers: 19, SJR: 1.5, h-index: 62)
Advances in Psychology     Full-text available via subscription   (Followers: 60)
Advances in Quantum Chemistry     Full-text available via subscription   (Followers: 5, SJR: 0.478, h-index: 32)
Advances in Radiation Oncology     Open Access  
Advances in Small Animal Medicine and Surgery     Hybrid Journal   (Followers: 2, SJR: 0.1, h-index: 2)
Advances in Space Research     Full-text available via subscription   (Followers: 343, SJR: 0.606, h-index: 65)
Advances in Structural Biology     Full-text available via subscription   (Followers: 8)
Advances in Surgery     Full-text available via subscription   (Followers: 7, SJR: 0.823, h-index: 27)
Advances in the Study of Behavior     Full-text available via subscription   (Followers: 30, SJR: 1.321, h-index: 56)
Advances in Veterinary Medicine     Full-text available via subscription   (Followers: 15)
Advances in Veterinary Science and Comparative Medicine     Full-text available via subscription   (Followers: 13)
Advances in Virus Research     Full-text available via subscription   (Followers: 5, SJR: 1.878, h-index: 68)
Advances in Water Resources     Hybrid Journal   (Followers: 43, SJR: 2.408, h-index: 94)
Aeolian Research     Hybrid Journal   (Followers: 5, SJR: 0.973, h-index: 22)
Aerospace Science and Technology     Hybrid Journal   (Followers: 307, SJR: 0.816, h-index: 49)
AEU - Intl. J. of Electronics and Communications     Hybrid Journal   (Followers: 8, SJR: 0.318, h-index: 36)
African J. of Emergency Medicine     Open Access   (Followers: 5, SJR: 0.344, h-index: 6)
Ageing Research Reviews     Hybrid Journal   (Followers: 8, SJR: 3.289, h-index: 78)
Aggression and Violent Behavior     Hybrid Journal   (Followers: 405, SJR: 1.385, h-index: 72)
Agri Gene     Hybrid Journal  
Agricultural and Forest Meteorology     Hybrid Journal   (Followers: 15, SJR: 2.18, h-index: 116)
Agricultural Systems     Hybrid Journal   (Followers: 30, SJR: 1.275, h-index: 74)
Agricultural Water Management     Hybrid Journal   (Followers: 38, SJR: 1.546, h-index: 79)
Agriculture and Agricultural Science Procedia     Open Access  
Agriculture and Natural Resources     Open Access   (Followers: 1)
Agriculture, Ecosystems & Environment     Hybrid Journal   (Followers: 53, SJR: 1.879, h-index: 120)
Ain Shams Engineering J.     Open Access   (Followers: 5, SJR: 0.434, h-index: 14)
Air Medical J.     Hybrid Journal   (Followers: 5, SJR: 0.234, h-index: 18)
AKCE Intl. J. of Graphs and Combinatorics     Open Access   (SJR: 0.285, h-index: 3)
Alcohol     Hybrid Journal   (Followers: 9, SJR: 0.922, h-index: 66)
Alcoholism and Drug Addiction     Open Access   (Followers: 6)
Alergologia Polska : Polish J. of Allergology     Full-text available via subscription   (Followers: 1)
Alexandria Engineering J.     Open Access   (Followers: 1, SJR: 0.436, h-index: 12)
Alexandria J. of Medicine     Open Access  
Algal Research     Partially Free   (Followers: 8, SJR: 2.05, h-index: 20)
Alkaloids: Chemical and Biological Perspectives     Full-text available via subscription   (Followers: 3)
Allergologia et Immunopathologia     Full-text available via subscription   (Followers: 1, SJR: 0.46, h-index: 29)
Allergology Intl.     Open Access   (Followers: 4, SJR: 0.776, h-index: 35)
ALTER - European J. of Disability Research / Revue Européenne de Recherche sur le Handicap     Full-text available via subscription   (Followers: 7, SJR: 0.158, h-index: 9)
Alzheimer's & Dementia     Hybrid Journal   (Followers: 48, SJR: 4.289, h-index: 64)
Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring     Open Access   (Followers: 5)
Alzheimer's & Dementia: Translational Research & Clinical Interventions     Open Access   (Followers: 3)
American Heart J.     Hybrid Journal   (Followers: 48, SJR: 3.157, h-index: 153)
American J. of Cardiology     Hybrid Journal   (Followers: 45, SJR: 2.063, h-index: 186)
American J. of Emergency Medicine     Hybrid Journal   (Followers: 38, SJR: 0.574, h-index: 65)
American J. of Geriatric Pharmacotherapy     Full-text available via subscription   (Followers: 6, SJR: 1.091, h-index: 45)
American J. of Geriatric Psychiatry     Hybrid Journal   (Followers: 16, SJR: 1.653, h-index: 93)
American J. of Human Genetics     Hybrid Journal   (Followers: 31, SJR: 8.769, h-index: 256)
American J. of Infection Control     Hybrid Journal   (Followers: 24, SJR: 1.259, h-index: 81)
American J. of Kidney Diseases     Hybrid Journal   (Followers: 33, SJR: 2.313, h-index: 172)
American J. of Medicine     Hybrid Journal   (Followers: 46, SJR: 2.023, h-index: 189)
American J. of Medicine Supplements     Full-text available via subscription   (Followers: 3)
American J. of Obstetrics and Gynecology     Hybrid Journal   (Followers: 191, SJR: 2.255, h-index: 171)
American J. of Ophthalmology     Hybrid Journal   (Followers: 54, SJR: 2.803, h-index: 148)
American J. of Ophthalmology Case Reports     Open Access   (Followers: 3)
American J. of Orthodontics and Dentofacial Orthopedics     Full-text available via subscription   (Followers: 6, SJR: 1.249, h-index: 88)
American J. of Otolaryngology     Hybrid Journal   (Followers: 23, SJR: 0.59, h-index: 45)
American J. of Pathology     Hybrid Journal   (Followers: 26, SJR: 2.653, h-index: 228)
American J. of Preventive Medicine     Hybrid Journal   (Followers: 21, SJR: 2.764, h-index: 154)
American J. of Surgery     Hybrid Journal   (Followers: 34, SJR: 1.286, h-index: 125)
American J. of the Medical Sciences     Hybrid Journal   (Followers: 12, SJR: 0.653, h-index: 70)
Ampersand : An Intl. J. of General and Applied Linguistics     Open Access   (Followers: 5)
Anaerobe     Hybrid Journal   (Followers: 4, SJR: 1.066, h-index: 51)
Anaesthesia & Intensive Care Medicine     Full-text available via subscription   (Followers: 55, SJR: 0.124, h-index: 9)
Anaesthesia Critical Care & Pain Medicine     Full-text available via subscription   (Followers: 10)
Anales de Cirugia Vascular     Full-text available via subscription  
Anales de Pediatría     Full-text available via subscription   (Followers: 2, SJR: 0.209, h-index: 27)
Anales de Pediatría (English Edition)     Full-text available via subscription  
Anales de Pediatría Continuada     Full-text available via subscription   (SJR: 0.104, h-index: 3)
Analytic Methods in Accident Research     Hybrid Journal   (Followers: 2, SJR: 2.577, h-index: 7)
Analytica Chimica Acta     Hybrid Journal   (Followers: 38, SJR: 1.548, h-index: 152)
Analytical Biochemistry     Hybrid Journal   (Followers: 162, SJR: 0.725, h-index: 154)
Analytical Chemistry Research     Open Access   (Followers: 8, SJR: 0.18, h-index: 2)
Analytical Spectroscopy Library     Full-text available via subscription   (Followers: 11)
Anesthésie & Réanimation     Full-text available via subscription   (Followers: 1)
Anesthesiology Clinics     Full-text available via subscription   (Followers: 22, SJR: 0.421, h-index: 40)
Angiología     Full-text available via subscription   (SJR: 0.124, h-index: 9)
Angiologia e Cirurgia Vascular     Open Access  
Animal Behaviour     Hybrid Journal   (Followers: 157, SJR: 1.907, h-index: 126)
Animal Feed Science and Technology     Hybrid Journal   (Followers: 5, SJR: 1.151, h-index: 83)
Animal Reproduction Science     Hybrid Journal   (Followers: 5, SJR: 0.711, h-index: 78)
Annales d'Endocrinologie     Full-text available via subscription   (Followers: 1, SJR: 0.394, h-index: 30)
Annales d'Urologie     Full-text available via subscription  
Annales de Cardiologie et d'Angéiologie     Full-text available via subscription   (SJR: 0.177, h-index: 13)
Annales de Chirurgie de la Main et du Membre Supérieur     Full-text available via subscription  
Annales de Chirurgie Plastique Esthétique     Full-text available via subscription   (Followers: 2, SJR: 0.354, h-index: 22)
Annales de Chirurgie Vasculaire     Full-text available via subscription   (Followers: 1)

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Journal Cover Advanced Engineering Informatics
  [SJR: 1.265]   [H-I: 53]   [11 followers]  Follow
   Hybrid Journal Hybrid journal (It can contain Open Access articles)
   ISSN (Print) 1474-0346
   Published by Elsevier Homepage  [3043 journals]
  • Robust normal estimation and region growing segmentation of infrastructure
           3D point cloud models
    • Authors: Ali Khaloo; David Lattanzi
      Pages: 1 - 16
      Abstract: Publication date: October 2017
      Source:Advanced Engineering Informatics, Volume 34
      Author(s): Ali Khaloo, David Lattanzi
      Modern remote sensing technologies such as three-dimensional (3D) laser scanners and image-based 3D scene reconstruction are in increasing demand for applications in civil infrastructure design, maintenance, operation, and as-built construction verification. The complex nature of the 3D point clouds these technologies generate, as well as the often massive scale of the 3D data, make it inefficient and time consuming to manually analyze and manipulate point clouds, and highlights the need for automated analysis techniques. This paper presents one such technique, a new region growing algorithm for the automated segmentation of both planar and non-planar surfaces in point clouds. A core component of the algorithm is a new point normal estimation method, an essential task for many point cloud processing algorithms. The newly developed estimation method utilizes robust multivariate statistical outlier analysis for reliable normal estimation in complex 3D models, considering that these models often contain regions of varying surface roughness, a mixture of high curvature and low curvature regions, and sharp features. An adaptation of Mahalanobis distance, in which the mean vector and covariance matrix are derived from a high-breakdown multivariate location and scale estimator called Deterministic MM-estimator (DetMM) is used to find and discard outlier points prior to estimating the best local tangent plane around any point in a cloud. This approach is capable of more accurately estimating point normals located in highly curved regions or near sharp features. Thereafter, the estimated point normals serve a region growing segmentation algorithm that only requires a single input parameter, an improvement over existing methods which typically require two control parameters. The reliability and robustness of the normal estimation subroutine was compared against well-known normal estimation methods including the Minimum Volume Ellipsoid (MVE) and Minimum Covariance Determinant (MCD) estimators, along with Maximum Likelihood Sample Consensus (MLESAC). The overall region growing segmentation algorithm was then experimentally validated on several challenging 3D point clouds of real-world infrastructure systems. The results indicate that the developed approach performs more accurately and robustly in comparison with conventional region growing methods, particularly in the presence of sharp features, outliers and noise.

      PubDate: 2017-08-02T14:59:24Z
      DOI: 10.1016/j.aei.2017.07.002
      Issue No: Vol. 34 (2017)
  • A functional feature modeling method
    • Authors: Zhengrong Cheng; Yongsheng Ma
      Pages: 1 - 15
      Abstract: Publication date: August 2017
      Source:Advanced Engineering Informatics, Volume 33
      Author(s): Zhengrong Cheng, Yongsheng Ma
      With the advances in CAD technology, it has been increasingly convenient to model product shapes digitally. For example, in a feature-based parametric CAD system, the product shape could be parameterized and thus altered with the change of parameters. However, without a consistent and systematic CAD modeling method, CAD models are not robust enough to capture functional design knowledge and cope with design changes, especially functional changes. A poorly constructed CAD model could result in erroneous or inconsistent design that requires a lot of expertise, manpower and repetitive computation to rebuild a valid and consistent model. The situation can be worse if the model is complex. The gap between functional design considerations and procedural CAD modeling demands an integrated CAD modeling approach. This paper proposes a functional feature-based CAD modeling method to guide designers building CAD models that are valid and yet agile to represent functional design considerations. A case study is presented to demonstrate the feasibility of the proposed research.

      PubDate: 2017-05-01T12:40:24Z
      DOI: 10.1016/j.aei.2017.04.003
      Issue No: Vol. 33 (2017)
  • A user requirement driven framework for collaborative design knowledge
    • Authors: Yahui Wang; Suihuai Yu; Ting Xu
      Pages: 16 - 28
      Abstract: Publication date: August 2017
      Source:Advanced Engineering Informatics, Volume 33
      Author(s): Yahui Wang, Suihuai Yu, Ting Xu
      In today’s competitive global marketplace and knowledge-based economy, user requirement becomes an important input information for enterprises to develop new product and a critical factor to drive product collaborative design evolution. Meanwhile, there remains no consensus on how best to support knowledge activities and significant challenges to establishing design information management facing to rapid collaborative product development with dynamic user requirement. This paper introduces solutions for designer to deal with dynamic user requirement information through requirement evaluation and prediction method. In this study, we propose a user requirements-oriented knowledge management concept that is based on a four level hierarchy map model with special regard to knowledge collaboration and information communication. Furthermore, a novel distributed concurrent and interactive user requirement database was constructed, and the framework driven by user requirement was put forward to support collaborative design knowledge management. Finally, the service robot design project of a start-up company is used as a case study to explain the implementation of proposed framework.

      PubDate: 2017-05-07T12:50:23Z
      DOI: 10.1016/j.aei.2017.04.002
      Issue No: Vol. 33 (2017)
  • A two-phase approach to mine short-period high-utility itemsets in
           transactional databases
    • Authors: Jerry Chun-Wei Lin; Jiexiong Zhang; Philippe Fournier-Viger; Tzung-Pei Hong; Ji Zhang
      Pages: 29 - 43
      Abstract: Publication date: August 2017
      Source:Advanced Engineering Informatics, Volume 33
      Author(s): Jerry Chun-Wei Lin, Jiexiong Zhang, Philippe Fournier-Viger, Tzung-Pei Hong, Ji Zhang
      The discovery of high-utility itemsets (HUIs) in transactional databases has attracted much interest from researchers in recent years since it can uncover hidden information that is useful for decision making, and it is widely used in many domains. Nonetheless, traditional methods for high-utility itemset mining (HUIM) utilize the utility measure as sole criterion to determine which item/sets should be presented to the user. These methods ignore the timestamps of transactions and do not consider the period constraint. Hence, these algorithms often finds HUIs that are profitable but that seldom occur in transactions. In this paper, we address this limitation of previous methods by pushing the period constraint in the HUI mining process. A new framework called short-period high-utility itemset mining (SPHUIM) is designed to identify patterns in a transactional database that appear regularly, are profitable, and also yield a high utility under the period constraint. The aim of discovering short-period high-utility itemsets (SPHUI) is hence to identify patterns that are interesting both in terms of period and utility. The paper proposes a baseline two-phase short-period high-utility itemset (SPHUIT P) mining algorithm to mine SPHUIs in a level-wise manner. Then, to reduce the search space of the SPHUITP algorithm and speed up the discovery of SPHUIs, two pruning strategies are developed and integrated in the baseline algorithm. The resulting algorithms are denoted as SPHUIMT and SPHUITID, respectively. Substantial experiments both on real-life and synthetic datasets show that the three proposed algorithms can efficiently and effectively discover the complete set of SPHUIs, and that considering the short-period constraint and the utility measure can greatly reduce the number of patterns found.

      PubDate: 2017-05-12T02:23:07Z
      DOI: 10.1016/j.aei.2017.04.007
      Issue No: Vol. 33 (2017)
  • Analyzing engineering change of aircraft assembly tooling considering both
           duration and resource consumption
    • Authors: Leilei Yin; Dunbing Tang; Inayat Ullah; Qi Wang; Haitao Zhang; Haihua Zhu
      Pages: 44 - 59
      Abstract: Publication date: August 2017
      Source:Advanced Engineering Informatics, Volume 33
      Author(s): Leilei Yin, Dunbing Tang, Inayat Ullah, Qi Wang, Haitao Zhang, Haihua Zhu
      Aircraft assembly tooling is developed according to the constraints of geometric information and technical requirements of aircraft, and frequent aircraft changes can cause assembly tooling tasks to change frequently. Assembly tooling parts are large in amount and complex in structure. Due to the complex dependencies among the tasks of assembly tooling, change in one task can cause changes to many other tasks, which may require much time and resources to completely resolve them. However, long cycle and mass resource consumption for the engineering change would normally lead to high risk, high cost, high rework, and so on. The primary result of this work is the provision of a development support to find the optimal solution of assembly tooling change by examining the combined effects of duration and resource consumption. In this paper, engineering change progression of assembly tooling is modeled as a decrease of impact on affected tasks, which implies that the duration of certain changed task reduces gradually. Besides, a deterministic simulation model is developed to analyze the change propagation schemes. The model explores the combined effects of task parallelism, resource constraints and change propagation during the engineering change process of assembly tooling. Finally, a case study of an assembly tooling for the reinforced frame module is implemented and the analysis results suggest that the proposed method offers a valuable basis for providing targeted guidance on how to obtain the optimal engineering change scheme of assembly tooling.

      PubDate: 2017-05-22T08:21:22Z
      DOI: 10.1016/j.aei.2017.04.006
      Issue No: Vol. 33 (2017)
  • A machine learning approach for characterizing soil contamination in the
           presence of physical site discontinuities and aggregated samples
    • Authors: Alyssa Ngu-Oanh Quach; Lucie Tabor; Dany Dumont; Benoit Courcelles; James-A. Goulet
      Pages: 60 - 67
      Abstract: Publication date: August 2017
      Source:Advanced Engineering Informatics, Volume 33
      Author(s): Alyssa Ngu-Oanh Quach, Lucie Tabor, Dany Dumont, Benoit Courcelles, James-A. Goulet
      Rehabilitation of contaminated soils in urban areas is in high demand because of the appreciation of land value associated with the increased urbanization. Moreover, there are financial incentives to minimize soil characterization uncertainties. Minimizing uncertainty is achieved by providing models that are better representation of the true site characteristics. In this paper, we propose two new probabilistic formulations compatible with Gaussian Process Regression (GPR) and enabling (1) to model the experimental conditions where contaminant concentration is quantified from aggregated soil samples and (2) to model the effect of physical site discontinuities. The performance of approaches proposed in this paper are compared using a Leave One Out Cross-Validation procedure (LOO-CV). Results indicate that the two new probabilistic formulations proposed outperform the standard Gaussian Process Regression.

      PubDate: 2017-05-22T08:21:22Z
      DOI: 10.1016/j.aei.2017.05.002
      Issue No: Vol. 33 (2017)
  • A performance benchmark over semantic rule checking approaches in
           construction industry
    • Authors: Pieter Pauwels; Tarcisio Mendes de Farias; Chi Zhang; Ana Roxin; Jakob Beetz; Jos De Roo; Christophe Nicolle
      Pages: 68 - 88
      Abstract: Publication date: August 2017
      Source:Advanced Engineering Informatics, Volume 33
      Author(s): Pieter Pauwels, Tarcisio Mendes de Farias, Chi Zhang, Ana Roxin, Jakob Beetz, Jos De Roo, Christophe Nicolle
      As more and more architectural design and construction data is represented using the Resource Description Framework (RDF) data model, it makes sense to take advantage of the logical basis of RDF and implement a semantic rule checking process as it is currently not available in the architectural design and construction industry. The argument for such a semantic rule checking process has been made a number of times by now. However, there are a number of strategies and approaches that can be followed regarding the realization of such a rule checking process, even when limiting to the use of semantic web technologies. In this article, we compare three reference rule checking approaches that have been reported earlier for semantic rule checking in the domain of architecture, engineering and construction (AEC). Each of these approaches has its advantages and disadvantages. A criterion that is tremendously important to allow adoption and uptake of such semantic rule checking approaches, is performance. Hence, this article provides an overview of our collaborative test results in order to obtain a performance benchmark for these approaches. In addition to the benchmark, a documentation of the actual rule checking approaches is discussed. Furthermore, we give an indication of the main features and decisions that impact performance for each of these three approaches, so that system developers in the construction industry can make an informed choice when deciding for one of the documented rule checking approaches.

      PubDate: 2017-05-22T08:21:22Z
      DOI: 10.1016/j.aei.2017.05.001
      Issue No: Vol. 33 (2017)
  • A novel approach for precipitation forecast via improved K-nearest
           neighbor algorithm
    • Authors: Mingming Huang; Runsheng Lin; Shuai Huang; Tengfei Xing
      Pages: 89 - 95
      Abstract: Publication date: August 2017
      Source:Advanced Engineering Informatics, Volume 33
      Author(s): Mingming Huang, Runsheng Lin, Shuai Huang, Tengfei Xing
      The prediction method plays crucial roles in accurate precipitation forecasts. Recently, machine learning has been widely used for forecasting precipitation, and the K-nearest neighbor (KNN) algorithm, one of machine learning techniques, showed good performance. In this paper, we propose an improved KNN algorithm, which offers robustness against different choices of the neighborhood size k, particularly in the case of the irregular class distribution of the precipitation dataset. Then, based our improved KNN algorithm, a new precipitation forecast approach is put forward. Extensive experimental results demonstrate that the effectiveness of our proposed precipitation forecast approach based on improved KNN algorithm.

      PubDate: 2017-06-01T03:04:40Z
      DOI: 10.1016/j.aei.2017.05.003
      Issue No: Vol. 33 (2017)
  • Restoration of the distorted color to detect the discoloration status of a
           steel bridge coating using digital image measurements
    • Authors: Kuo-Wei Liao; Di-Rong Cheng
      Pages: 96 - 111
      Abstract: Publication date: August 2017
      Source:Advanced Engineering Informatics, Volume 33
      Author(s): Kuo-Wei Liao, Di-Rong Cheng
      Discoloration, representing the degradation of the coating, is often a trigger of a progressive damage process for a steel bridge. The detection of discoloration status often relies on a routine visual inspection in Taiwan, resulting in subjective results. To minimize human error in such inspection, this study provides an alternative approach, in which the digital image measurement is adopted. The process of acquiring images in any means is, in fact, a duplicate of the cross-media color process and often results in distorted colors. Consequently, to determine the discoloration between fading and its authentic color, one first must perform the color restoration for the acquired faded images. To reduce error in the restoration process, this study uses the least-square support vector machine (LS-SVM), spectral power distribution, spectral reflectance and matrix restoration to form an integrated algorithm. The average color difference between distorted and undistorted fading color of the proposed approach is approximately 5 (NBS value), which is applicable in the industry, requiring a 4th level of color difference for steel bridge coating inspections. A smartphone application is developed based on the algorithm established to facilitate the application for detecting color differences in steel bridges.

      PubDate: 2017-06-11T02:06:27Z
      DOI: 10.1016/j.aei.2017.04.005
      Issue No: Vol. 33 (2017)
  • Current research trends and application areas of fuzzy and hybrid methods
           to the risk assessment of construction projects
    • Authors: Muhammad Saiful Islam; Madhav Prasad Nepal; Martin Skitmore; Meghdad Attarzadeh
      Pages: 112 - 131
      Abstract: Publication date: August 2017
      Source:Advanced Engineering Informatics, Volume 33
      Author(s): Muhammad Saiful Islam, Madhav Prasad Nepal, Martin Skitmore, Meghdad Attarzadeh
      Fuzzy and hybrid methods have been increasingly used in construction risk management research and this study aims to compile and analyse the basic concepts and methods applied in this field to date. A content analysis is made of a comprehensive literature review of publications during 2005–2017. It is found that the nature of complex projects is such that most risks are interdependent of each other. Therefore, a fuzzy structured method such as the fuzzy analytical network process (FANP) has frequently been used for different complex projects. However, the application of FANP is limited because of the tedious and lengthy calculations required for the pairwise comparisons needed and an inability to incorporate new information into the risk structure. To overcome this constraint, a fuzzy Bayesian belief network (FBBN) has been increasingly used for risk assessment. Further project-specific studies based on FBBN are recommended to justify its broader application. Beyond fuzzy methods, the Credal network – an extended form of Bayesian network- is found to have potential for risk assessment under uncertainty.

      PubDate: 2017-06-11T02:06:27Z
      DOI: 10.1016/j.aei.2017.06.001
      Issue No: Vol. 33 (2017)
  • A novel change feature-based approach to predict the impact of current
           proposed engineering change
    • Authors: Jinping Chen; Shusheng Zhang; Mingwei Wang; Changhong Xu
      Pages: 132 - 143
      Abstract: Publication date: August 2017
      Source:Advanced Engineering Informatics, Volume 33
      Author(s): Jinping Chen, Shusheng Zhang, Mingwei Wang, Changhong Xu
      The past engineering change (EC) knowledge can be reused to evaluate the impact of current proposed EC, which is gradually accepted as an effective strategy for engineers to handle EC businesses in enterprises. However, the existing approaches to evaluate EC impact are still time-consuming and complex. So this paper proposes a novel change feature-based approach to predict the impact of current proposed EC. Firstly, the related concepts of change feature are defined. Secondly, the working flow of proposed approach is introduced. Afterwards, a mathematical model is constructed for the prediction of EC impact. Finally, an application case verifies the feasibility of the proposed approach, and the evaluation against two state-of-the-art approaches (namely Mehta’s approach and k-Nearest Neighbor approach) has been done. The results of evaluation show that our approach is better than the two approaches in terms of three indexes: (a) the success rate of prediction, (b) the time of prediction, and (c) the loss function.

      PubDate: 2017-06-28T11:56:03Z
      DOI: 10.1016/j.aei.2017.06.002
      Issue No: Vol. 33 (2017)
  • Dynamic neural network method-based improved PSO and BR algorithms for
           transient probabilistic analysis of flexible mechanism
    • Authors: Lu-Kai Song; Cheng-Wei Fei; Guang-Chen Bai; Lin-Chong Yu
      Pages: 144 - 153
      Abstract: Publication date: August 2017
      Source:Advanced Engineering Informatics, Volume 33
      Author(s): Lu-Kai Song, Cheng-Wei Fei, Guang-Chen Bai, Lin-Chong Yu
      To improve the computing efficiency and precision of transient probabilistic analysis of flexible mechanism, dynamic neural network method (DNNM)-based improved particle swarm optimization (PSO)/Bayesian regularization (BR) (called as PSO/BR-DNNM) is proposed based on the developed DNNM with the integration of extremum response surface method (ERSM) and artificial neural network (ANN). The mathematical model of DNNM is established based on ANN on the foundation of investigating ERSM. Aiming at the high nonlinearity and strong coupling characteristics of limit state function of flexible mechanism, accurate weights and thresholds of PSO/BR-DNNM function are discussed by searching initial weights and thresholds based on the improved PSO and training final weights and thresholds by the BR-based training performance function. The probabilistic analysis of two-link flexible robot manipulator (TFRM) was investigated with the proposed method. Reliability degree, distribution characteristics and major factors (section sizes of link-2) of TFRM are obtained, which provides a useful reference for a more effective TFRM design. Through the comparison of three methods (Monte Carlo method, DNNM, PSO/BR-DNNM), it is demonstrated that PSO/BR-DNNM reshapes the probability of flexible mechanism probabilistic analysis and improves the computing efficiency while keeping acceptable computational precision. Moreover, the proposed method offers a useful insight for reliability-based design optimization of flexible mechanism and thereby also enriches the theory and method of mechanical reliability design.

      PubDate: 2017-07-09T06:26:20Z
      DOI: 10.1016/j.aei.2017.05.005
      Issue No: Vol. 33 (2017)
  • A method for clustering unlabeled BIM objects using entropy and TF-IDF
           with RDF encoding
    • Authors: Mostafa Ali; Yasser Mohamed
      Pages: 154 - 163
      Abstract: Publication date: August 2017
      Source:Advanced Engineering Informatics, Volume 33
      Author(s): Mostafa Ali, Yasser Mohamed
      Oil and gas projects involve different construction disciplines such as mechanical, structural, and electrical. The current practice in these projects involves creating separate building information models for the different disciplines and compiling them into one model to check for collisions or conflicts. Due to intellectual property, contractual requirements, unfinished design, or technical issues during final model compilation, the final merged model lacks essential data for contractors such as the trade of each object in the model. Nonetheless, the model is issued to contractors who utilize it in different pre-construction planning tasks. However, due to data loss, incompleteness, or inconsistency, the model usability can become limited and the contractor has to review the model manually to extract information from it. This is a lengthy and costly task that becomes more challenging in fast-tracked projects that involve periodically issuing updated Building Information Models. One type of information that contractors need for different planning and estimation purposes is the scope of work for different construction trades in different areas of the project. In many cases, models lack explicit attributes of 3D objects that make it possible to perform an automated query of these objects by trade type. This research suggests a state of the art solution to automate the extraction of this information in such cases. In this paper, we describe a method that utilizes Resource Description Framework (RDF) encoding of BIM data together with Term Frequency-Inverse Document Frequency (TF-IDF) and entropy-based algorithms to automatically group 3D objects based on their trade. The proposed methodology is tested using three actual cases of oil and gas projects with more than four million objects in total. The results show that the proposed method can achieve a 91% purity in the generated groups.

      PubDate: 2017-07-09T06:26:20Z
      DOI: 10.1016/j.aei.2017.06.005
      Issue No: Vol. 33 (2017)
  • Model-based space planning for temporary structures using simulation-based
           multi-objective programming
    • Authors: Haifeng Jin; Mohammad Nahangi; Paul M. Goodrum; Yongbo Yuan
      Pages: 164 - 180
      Abstract: Publication date: August 2017
      Source:Advanced Engineering Informatics, Volume 33
      Author(s): Haifeng Jin, Mohammad Nahangi, Paul M. Goodrum, Yongbo Yuan
      Construction trades need to share temporary structures to increase the output of direct work while controlling the labor input of indirect work. The purpose of this research is to develop a framework to determine the optimal location of temporary structures in a computerized practical manner for piping construction projects. Based on the spatial relationship between work envelope and scaffolding placement requirements, this paper presents the optimization model in two phases: the simulation-based optimization model and a multi-attribute utility (MAU) based alternative selection model. A multi-objective optimization model is established to improve scaffolding availability among multiple activities while maximizing piping crew productivity. The multi-attribute utility model is employed to handle the uncertainty of the assessment weights on the attributes to illustrate the preference of decision makers among different scaffolding placement alternatives obtained from the first phase. The approach was validated in a piping module, which provided superintendents and space planners with an effective decision-making tool among possible scaffolding alternatives in piping construction. The proposed optimization technique is an alternative methodology for solving the productivity-tasks-scaffolding trade-off problem, which further revolutionizes the spatial coordination process of workspace management and temporary structure planning.

      PubDate: 2017-07-23T07:14:39Z
      DOI: 10.1016/j.aei.2017.07.001
      Issue No: Vol. 33 (2017)
  • Information Quality Assessment for Facility Management
    • Authors: Puyan A. Zadeh; Guan Wang; Hasan B. Cavka; Sheryl Staub-French; Rachel Pottinger
      Pages: 181 - 205
      Abstract: Publication date: August 2017
      Source:Advanced Engineering Informatics, Volume 33
      Author(s): Puyan A. Zadeh, Guan Wang, Hasan B. Cavka, Sheryl Staub-French, Rachel Pottinger
      Assessing the quality of building information models (BIMs) is an important yet challenging task within the construction industry as projects are increasingly being delivered with BIM. This is particularly essential for facility management (FM) users as downstream information consumers that depend on the quality of models developed in the previous project phases. The research presented in this paper addresses this challenge by introducing a framework for information quality assessment (IQA) of BIMs for FM uses. The IQA framework is the outcome of an extensive study of two large owner organizations involving numerous BIM projects. The framework is structured based on the essential FM subjects: assets, spaces, and systems, and the model characteristics: objects, attributes, relationships, and spatial information. The framework is then operationalized through the development and evaluation of information quality (IQ) tests using BIM model checking tools across three projects with different levels of detail and complexity. The proposed IQA framework and associated tests advance the state of knowledge about BIM quality in terms of methods to represent and evaluate conformance to owner requirements.

      PubDate: 2017-07-23T07:14:39Z
      DOI: 10.1016/j.aei.2017.06.003
      Issue No: Vol. 33 (2017)
  • A variable fidelity information fusion method based on radial basis
    • Authors: Qi Zhou; Ping Jiang; Xinyu Shao; Jiexiang Hu; Longchao Cao; Li Wan
      Pages: 26 - 39
      Abstract: Publication date: April 2017
      Source:Advanced Engineering Informatics, Volume 32
      Author(s): Qi Zhou, Ping Jiang, Xinyu Shao, Jiexiang Hu, Longchao Cao, Li Wan
      Radial basis function (RBF) model has been widely used in complex engineering design process to replace the computational-intensive simulation models. This paper proposes a variable-fidelity metamodeling (VFM) approach based on RBF, in which different levels fidelity information can be integrated and fully exploited. In the proposed VFM approach, a RBF metamodel is constructed for the low-fidelity (LF) model as a start. Then by taking the constructed LF metamodel as a prior-knowledge and mapping the output space of the LF metamodel to that of the studied high-fidelity (HF) model, a variable fidelity (VF) metamodel is created to approximate the relationships between the design variables and corresponding output responses. A numerical illustrative example is adopted to make a detailed comparison between the VFM approach developed in this research and three existing scaling function based VFM approaches, considering different sample sizes and sample noises. Results illustrate that the proposed VFM approach outperforms the scaling function based VFM approaches both in global and local accuracy. Then the proposed VFM approach is applied to two engineering problems, modeling aerodynamic data for a three-dimensional aircraft and the prediction of weld bead profile in laser welding, to illustrate its ability in support of complex engineering design.

      PubDate: 2017-01-06T09:02:10Z
      DOI: 10.1016/j.aei.2016.12.005
      Issue No: Vol. 32 (2017)
  • Measurement system design for civil infrastructure using expected utility
    • Authors: Romain Pasquier; James-A. Goulet; Ian F.C. Smith
      Pages: 40 - 51
      Abstract: Publication date: April 2017
      Source:Advanced Engineering Informatics, Volume 32
      Author(s): Romain Pasquier, James-A. Goulet, Ian F.C. Smith
      For system identification, most sensor-placement strategies are based on the minimization of the model-parameter uncertainty. However, reducing the uncertainty in remaining-life prognosis of structures is often more relevant. This paper proposes an optimization strategy using utility theory and probabilistic behavior prognoses based on model falsification to support decisions related to monitoring interventions. This approach, illustrated by the full-scale case study of a bridge, allows quantification of the expected utility of measurement systems while also indicating the profitability of monitoring actions. In addition, this approach is able to determine when the expected performance of monitoring configurations is reduced due to over-instrumentation. The use of model falsification for system identification allows for explicit inclusion of engineering heuristics in this knowledge intensive task while also offering robustness to effects of systematic modeling errors that are associated with idealization of complex civil structures.

      PubDate: 2017-01-06T09:02:10Z
      DOI: 10.1016/j.aei.2016.12.002
      Issue No: Vol. 32 (2017)
  • A quantitative approach to design alternative evaluation based on
           data-driven performance prediction
    • Authors: Zi-jian Zhang; Lin Gong; Yan Jin; Jian Xie; Jia Hao
      Pages: 52 - 65
      Abstract: Publication date: April 2017
      Source:Advanced Engineering Informatics, Volume 32
      Author(s): Zi-jian Zhang, Lin Gong, Yan Jin, Jian Xie, Jia Hao
      Design alternative evaluation in the early stages of engineering design plays an important role in determining the success of new product development, as it influences considerably the subsequent design activities. However, existing approaches to design alternative evaluation are overly reliant on experts’ ambiguous and subjective judgments and qualitative descriptions. To reduce subjectivity and improve efficiency of the evaluation process, this paper proposes a quantitative evaluation approach through data-driven performance predictions. In this approach, the weights of performance characteristics are determined based on quantitative assessment of expert judgments, and the ranking of design alternatives is achieved by predicting performance values based on historical product design data. The experts’ subjective and often vague judgments are captured quantitatively through a rough number based Decision-Making Trial and Evaluation Laboratory (DEMATEL) method. In order to facilitate performance based quantitative ranking of alternatives at the early stages of design where no performance calculation is possible, a particle swarm optimization based support vector machine (PSO-SVM) is applied for historical data based performance prediction. The final ranking of alternatives given the predicted values of multiple performance characteristics is achieved through Višekriterijumska Optimizacija I kompromisno Rešenje (VIKOR). A case study is carried out to demonstrate the validity of the proposed approach.

      PubDate: 2017-01-14T09:16:24Z
      DOI: 10.1016/j.aei.2016.12.009
      Issue No: Vol. 32 (2017)
  • A rule-based servicescape design support system from the design patterns
           of theme parks
    • Authors: Deedee Aram Min; Kyung Hoon Hyun; Sun-Joong Kim; Ji-Hyun Lee
      Pages: 77 - 91
      Abstract: Publication date: April 2017
      Source:Advanced Engineering Informatics, Volume 32
      Author(s): Deedee Aram Min, Kyung Hoon Hyun, Sun-Joong Kim, Ji-Hyun Lee
      Servicescape design of leisure spaces such as theme parks influence the level of visitor satisfaction and the re-visitation rate. However, only a few large theme park operators have a separate theme of specialists with heuristic knowledge for servicescaping. In response, small companies turn to the specialists paying high fees and royalties usually leading to outdated servicescape designs. In order to help small companies to design servicescape that provide satisfying experience to the visitors, a systematical understanding of the multi-layered and complex service environment of theme parks is necessary. The purpose of this research is to integrate the concept of precedent-based design into a rule-based system by identifying and organizing the design patterns of servicescape into reusable knowledge, propose a servicescape design support system, and confirm whether if the system helps to increase designers’ credence regarding their design in terms of its market success. To accomplish these research aims, six steps were carried out: (1) selection of globally accepted facilities for being successful, (2) analysis of precedents for the discovery of repeating servicescape design patterns, (3) knowledge acquisition and organization for each design patterns, (4) formulation of servicescape rules using the acquired and organized knowledge, (5) integration of the rules into a general design process of a facility as a system, and (6) application of the system in practice. The resulting system was presented to an expert in the field of theme park designs to confirm whether the design process is applicable and how it will perform in practice. From this interview, we received positive feedbacks and well as feedbacks for improvements in the aspects of the system’s applicability in practice, its functionality, and usability. After confirming the applicability of the overall process of the system, we conducted an experiment to experts in practice from various design and planning fields and asked to go through two sets of scenarios in which the first is to design a given site without the system and the second is to design the same site with the system. After each scenario, the subjects were asked to fill out a revised After-Scenario Questionnaire (ASQ). From this experiment, the results indicate that the subjects felt an increase in their credence toward their designs in terms of its market success while the satisfaction with the easiness and the time it took decreased. If our system is revised in the aspects of usability and functionality, our system which uses precedent design patterns could be used to help small company designers to produce servicescape concept designs with more design credence.

      PubDate: 2017-01-28T08:47:07Z
      DOI: 10.1016/j.aei.2017.01.005
      Issue No: Vol. 32 (2017)
  • Research on a knowledge modelling methodology for fault diagnosis of
           machine tools based on formal semantics
    • Authors: Qiang Zhou; Ping Yan; Yang Xin
      Pages: 92 - 112
      Abstract: Publication date: April 2017
      Source:Advanced Engineering Informatics, Volume 32
      Author(s): Qiang Zhou, Ping Yan, Yang Xin
      Fault diagnosis is a critical activity in PHM (Prognostics and Health Management) of machine tools due to its great significance in such efforts as prolonging lifespan, improving production efficiency, and reducing production costs. An efficient knowledge model is necessary to build an intelligent fault diagnosis system. There have been several achievements in knowledge representation and modelling. However, due to their various purposes and depths, the established knowledge models are less compatible, reusable or transplantable, which restricts knowledge sharing and integration. A knowledge modelling methodology for fault diagnosis of machine tools based on formal semantics (KMM-MTFD) is proposed in this paper to build an open, shared, and scalable ontology-based knowledge model of fault diagnosis of various machine tools (OKM-MTFD). First, the proposed predicate-logic-based analysis method of fault elements is adopted to study the fault diagnosis domain and extract the common domain knowledge, which enables the establishment of the core ontology of OKM-MTFD to assure formal semantics. Next, using the proposed two-stage classification method of fault elements and external ontology reference methods, the core ontology can be extended into OKM-MTFD for a type or a specific machine tool. The knowledge reasoning and querying methods based on OWL axioms, SWRL rules, special fault attributes and SPARQL are provided to utilize the knowledge base efficiently. Finally, an ontology-based knowledge model and knowledge base of a hobbing machine tool is presented to exemplify the validity of the proposed KMM-MTFD.

      PubDate: 2017-02-04T09:00:27Z
      DOI: 10.1016/j.aei.2017.01.002
      Issue No: Vol. 32 (2017)
  • Private local automation clouds built by CPS: Potential and challenges for
           distributed reasoning
    • Authors: Borja Ramis Ferrer; Jose Luis Martinez Lastra
      Pages: 113 - 125
      Abstract: Publication date: April 2017
      Source:Advanced Engineering Informatics, Volume 32
      Author(s): Borja Ramis Ferrer, Jose Luis Martinez Lastra
      The employment of cyber-physical systems allows the control of processes in modern production lines. On the other hand, several research works have recently presented how ontology-based knowledge representation can be a suitable method for modelling industrial systems. However, system models are located far away from where the data is generated which adds complexity for cross-domain communications and resource management. Current embedded devices can encapsulate ontological models that can be accessed as local resources. This article presents the integration of interconnected devices as the computational nodes of a cloud which is private and local. In this way, functionalities, such as knowledge management and process control can be performed closer to the industrial equipment. Moreover, this research work discusses the potential and challenges for performing distributed reasoning in the private local automation cloud. In addition, the article describes main aspects of the system architecture and the behaviour of the networked embedded devices in the cloud. The research work results will be used as a high-level roadmap for further system implementation.

      PubDate: 2017-02-10T09:06:50Z
      DOI: 10.1016/j.aei.2017.01.007
      Issue No: Vol. 32 (2017)
  • Self-corrective knowledge-based hybrid tracking system using BIM and
           multimodal sensors
    • Authors: JeeWoong Park; Jingdao Chen; Yong K. Cho
      Pages: 126 - 138
      Abstract: Publication date: April 2017
      Source:Advanced Engineering Informatics, Volume 32
      Author(s): JeeWoong Park, Jingdao Chen, Yong K. Cho
      Researchers have recently devoted considerable attention to acquiring location awareness of assets. They have explored various technologies, such as video cameras, radio signal strength indicator-based sensors, and motion sensors, in the development of tracking systems. However, each system presents unique drawbacks especially when applied in complex indoor construction environments; this paper classifies them into two categories: absolute tracking and relative tracking. By understanding the nature of problems in each tracking category, this research develops a novel tracking methodology that uses knowledge of the strengths and weaknesses of various components used in the proposed tracking system. This paper presents the development of a hybrid-tracking system that integrates Bluetooth Low Energy (BLE) technology, motion sensors, and Building Information Model (BIM). The hypothesis tested through this integration was whether such knowledge-based integration could provide a method that can correct errors found in each of the used sensing technologies and thereby improve the reliability of the tracking system. Field experimental trials were conducted in a full-scale indoor construction site to assess the performance of individual components and the integrated system. The results indicated that the addition of map knowledge from a BIM model showed the capability of correcting improbable movements. Furthermore, the knowledge-based decision making process demonstrated its capability to make positive interaction by reducing the positioning errors by 42% on average. In sum, the proposed hybrid-tracking system presented a novel method to compensate for the weakness of each system component and thus achieve a more accurate and precise tracking in dynamic and complex indoor construction sites.

      PubDate: 2017-02-15T10:55:53Z
      DOI: 10.1016/j.aei.2017.02.001
      Issue No: Vol. 32 (2017)
  • Intelligent fault diagnosis of rolling bearing using hierarchical
           convolutional network based health state classification
    • Authors: Chen Lu; Zhenya Wang; Bo Zhou
      Pages: 139 - 151
      Abstract: Publication date: April 2017
      Source:Advanced Engineering Informatics, Volume 32
      Author(s): Chen Lu, Zhenya Wang, Bo Zhou
      Rolling bearing tips are often the most susceptible to electro-mechanical system failure due to high-speed and complex working conditions, and recent studies on diagnosing bearing health using vibration data have developed an assortment of feature extraction and fault classification methods. Due to the strong non-linear and non-stationary characteristics, an effective and reliable deep learning method based on a convolutional neural network (CNN) is investigated in this paper making use of cognitive computing theory, which introduces the advantages of image recognition and visual perception to bearing fault diagnosis by simulating the cognition process of the cerebral cortex. The novel feature representation method for bearing data is first discussed using supervised deep learning with the goal of identifying more robust and salient feature representations to reduce information loss. Next, the deep hierarchical structure is trained in a robust manner that is established using a transmitting rule of greedy training layer by layer. Convolution computation, rectified linear units, and sub-sampling are applied for weight replication and reducing the number of parameters that need to be learned to improve the general feed-forward back propagation training. The CNN model could thus reduce learning computation requirements in the temporal dimension, and an invariance level of working condition fluctuation and ambient noise is provided by identifying the elementary features of bearings. A top classifier followed by a back propagation process is used for fault classification. Contrast experiments and analyses have been undertaken to delineate the effectiveness of the CNN model for fault classification of rolling bearings.

      PubDate: 2017-03-01T21:30:59Z
      DOI: 10.1016/j.aei.2017.02.005
      Issue No: Vol. 32 (2017)
  • An error correction framework for sequences resulting from known
           state-transition models in Non-Intrusive Load Monitoring
    • Authors: Suman Giri; Mario Bergés
      Pages: 152 - 162
      Abstract: Publication date: April 2017
      Source:Advanced Engineering Informatics, Volume 32
      Author(s): Suman Giri, Mario Bergés
      Non-Intrusive Load Monitoring (NILM), the set of techniques used for disaggregating total electricity consumption in a building into its constituent electrical loads, has recently received renewed interest in the research community, partly due to the roll-out of smart metering technology worldwide. Event-based NILM approaches (i.e., those that are based on first segmenting the power time-series and associating each segment with the operation of electrical appliances) are a commonly implemented solution but are prone to the propagation of errors through the data processing pipeline. Thus, during energy estimation (the final step in the process), many corrections need to be made to account for errors incurred during segmentation, feature extraction and classification (the other steps typically present in event-based approaches). A robust framework for energy estimation should use the labels from classification to (1) model the different state transitions that can occur in an appliance; (2) account for any misclassifications by correcting event labels that violate the extracted model; and (3) accurately estimate the energy consumed by that appliance over a period of time. In this paper, we address the second problem by proposing an error-correcting algorithm which looks at sequences generated by Finite State Machines (FSMs) and corrects for errors in the sequence; errors are defined as state transitions that violate the said FSM. We evaluate our framework on simulated data and find that it improves energy estimation errors. We further test it on data from 43 appliances collected from 19 houses and find that the framework significantly improves errors in energy estimates when compared to the case with no correction in 19 appliances, leaves 17 appliances unchanged, and has a slightly negative impact on 6 appliances.

      PubDate: 2017-03-01T21:30:59Z
      DOI: 10.1016/j.aei.2017.01.006
      Issue No: Vol. 32 (2017)
  • The documentation of product configuration systems: A framework and an IT
    • Authors: Sara Shafiee; Lars Hvam; Anders Haug; Michael Dam; Katrin Kristjansdottir
      Pages: 163 - 175
      Abstract: Publication date: April 2017
      Source:Advanced Engineering Informatics, Volume 32
      Author(s): Sara Shafiee, Lars Hvam, Anders Haug, Michael Dam, Katrin Kristjansdottir
      When designing and maintaining a product configuration system (PCS), complete and up-to-date documentation of the system is needed in the form of a product model that outlines the structures, attributes, and constraints of the PCS. Furthermore, up-to-date documentation for the PCS is crucial for maintenance, further development, system quality and communication with domain experts. Product models are the main communication and documentation tools used in PCS projects. Recent studies have shown that up-to-date documentation for the PCS is often lacking due to the significant amount of work required to maintain product models. To address these challenges, this paper proposes an approach for documenting the PCS that is based on the structure, attributes, and constraints modelled within the PCS, in which the product model is generated directly from the PCS. The suggested approach avoids knowledge duplication, as knowledge needs to be maintained within the PCS only. It involves two steps: the first is the building of the initial product model, which is used for the programming of the PCS. In the second step, the product model is generated directly from the PCS and is based on the structure, attributes, and constraints inside the PCS. The product model does not need to be maintained, therefore, outside the PCS. This approach meets the demand for agile documentation and efficient communication with domain experts, and uses the fewest resources possible. Furthermore, to support the framework, an IT documentation system is proposed that is capable of retrieving knowledge from the PCS and thus generating the product model. Our framework and IT documentation system were developed and tested at a case company on five different projects. The results confirm that benefits can be achieved by using the proposed IT documentation system, as time and resources are saved, while the quality of the PCS is improved.
      Graphical abstract image

      PubDate: 2017-03-08T17:15:30Z
      DOI: 10.1016/j.aei.2017.02.004
      Issue No: Vol. 32 (2017)
  • Heuristic approach for solving employee bus routes in a large-scale
           industrial factory
    • Authors: Komgrit Leksakul; Uttapol Smutkupt; Raweeroj Jintawiwat; Suriya Phongmoo
      Pages: 176 - 187
      Abstract: Publication date: April 2017
      Source:Advanced Engineering Informatics, Volume 32
      Author(s): Komgrit Leksakul, Uttapol Smutkupt, Raweeroj Jintawiwat, Suriya Phongmoo
      This paper compares different methods for solving a location-routing problem (LRP), using real-world data from the bus transport service for employees of a large-scale industrial factory in Thailand. We tested four AI (artificial intelligence) techniques Maximin, K-means, Fuzzy C-means, and Competitive Learning and two hybrids of these four K-means with Competitive Learning and K-means with Maximin to allocate the bus stops. The efficiency of the algorithms was compared, in terms of the quality of the solutions. The K-means with Maximin provided the best solution, as it minimized number of bus stop locations and employees’ total traveling distance while satisfied employee at maximum radius 1.73km, compared to K-means with Competitive Learning, as the same number of bus stop it provided higher total traveling distance and maximum radius. The other non-hybrid techniques provided higher number of bus stop locations. We then used ant colony optimization (ACO) to determine the optimal routing between the 300–700 bus stops as allocated by K-means with Maximin. The optimal bus routing to transport the factory’s 5000 plus employees required 134 buses (134 independent routes) covering 500 bus stops and traveling nearly 5000km. While optimal, this routing was costly and created monitoring difficulties. To address these concerns, we constrained the number of bus routes; while this dramatically increased the total distance, it provided a more practical solution for the factory.

      PubDate: 2017-03-08T17:15:30Z
      DOI: 10.1016/j.aei.2017.02.006
      Issue No: Vol. 32 (2017)
  • Inspection equipment study for subway tunnel defects by grey-scale image
    • Authors: Hongwei Huang; Yan Sun; Yadong Xue; Fei Wang
      Pages: 188 - 201
      Abstract: Publication date: April 2017
      Source:Advanced Engineering Informatics, Volume 32
      Author(s): Hongwei Huang, Yan Sun, Yadong Xue, Fei Wang
      In recent years, much attention has been paid to Machine Vision-Based (MVB) technology for tunnel main defect (leakage and crack) inspection as an innovative technology. Based on the principle of MVB technology, various researchers have developed tunnel inspection equipment, but most of them need either a trailer or an external power supply, which cannot meet the demand of subway tunnel inspection in China. The limited inspection time, high demand for precision, rigid requirements of operational management and high cost of the equipment restrict the application of this method in China. MTI-100 (Moving Tunnel Inspection) was developed under these circumstances. To capture stable, high-quality images of the lining surface as the raw data of inspection, an image capture system is well designed based on CCD (Charge-coupled Device) camera scanning. Additionally, equipment optimization design of the mechanism and electricity requirements for the inspection accuracy of subway tunnel inspection is investigated. The maximal size and weight of equipment elements determined the convenience of inspection, which is primarily conditioned by these designs. The effects of lighting and vibration have been considered. A method to calculate the image shift caused by vibration is proposed. The software network is another core component of the equipment, which connects the image acquisition, image storage and defect recognition. The famous Otsu method is used for leakage recognition. A new algorithm based on the features of the local image grid is developed to recognize cracks. A comparative study shows its high accuracy for crack recognition. Finally, a simulative tunnel test and field inspection are undertaken to verify the performance of the non-destructive subway tunnel inspection equipment. Through these tests, the accuracy, stability, repeatability, labor intensity and efficiency of the equipment have been verified. A real project test certified that the developed MTI-100 is quite suitable for practical tunnel inspection.

      PubDate: 2017-03-27T04:17:29Z
      DOI: 10.1016/j.aei.2017.03.003
      Issue No: Vol. 32 (2017)
  • 3D parametric human face modeling for personalized product design:
           Eyeglasses frame design case
    • Authors: Chih-Hsing Chu; I-Jan Wang; Jeng-Bang Wang; Yuan-Ping Luh
      Pages: 202 - 223
      Abstract: Publication date: April 2017
      Source:Advanced Engineering Informatics, Volume 32
      Author(s): Chih-Hsing Chu, I-Jan Wang, Jeng-Bang Wang, Yuan-Ping Luh
      Personalized design enhances the values added by a product or service by satisfying individual customer requirements. It has nowadays become a trend in consumer product development. This paper proposes a computational framework for personalized design of the eyeglasses frame based on parametric face modeling. A large amount of three-dimensional facial models is collected by non-contact scanning as training data. Applying principal component analysis reduces the data complexity while preserving sufficient data variance. The reduced models are modified using cross-parameterization so that they have the same mesh connectivity. Kriging characterizes the correlation between the mesh point coordinates of a face model and a set of feature parameters. The Kriging result synthesizes 3D facial geometry approximating to individual users with given parameter values. Rendering the synthesized geometry with facial images of real persons generates realistic face models. These models not only allow adjusting the frame design in real-time, but also evaluating whether or how the design style fits individual face characteristics. This study enhances the practical values of 3D anthropometric data by realizing the concept of human-centric design.

      PubDate: 2017-03-27T04:17:29Z
      DOI: 10.1016/j.aei.2017.03.001
      Issue No: Vol. 32 (2017)
  • Selection of target LEED credits based on project information and climatic
           factors using data mining techniques
    • Authors: M.A. Jun; Jack C.P. Cheng
      Pages: 224 - 236
      Abstract: Publication date: April 2017
      Source:Advanced Engineering Informatics, Volume 32
      Author(s): M.A. Jun, Jack C.P. Cheng
      Developed by the United States Green Building Council, Leadership in Energy and Environmental Design (LEED) is a credit-based rating system that provides third-party verification for green buildings. Selection of target credits is important yet challenging for LEED managers because various factors such as target certification grade level and building features need to be considered on a case-by-case basis. Local climatic factors could affect the selection of green building technologies and hence the target credits, but currently there is no research suggesting target LEED credits based on climatic factors. This paper presents a methodology for the selection of target LEED credits based on project information and climatic factors. This study focuses on projects certified with LEED for Existing Buildings (LEED-EB). Information of 912 projects and their surrounding climatic circumstances was collected and studied. 55 classification models for 47 LEED-EB credits were then constructed and optimized using three classification algorithms - Random Forests, AdaBoost Decision Tree, and Support Vector Machine (SVM). The results showed that Random Forests performed the best in most of the 55 classification models. With a combination of the three algorithms, the trained classification models were used to develop a web-based decision support system for LEED credit selection. The system was tested using 20 recently certified LEED projects, and the results showed that our system had an accuracy of 82.56%.

      PubDate: 2017-03-27T04:17:29Z
      DOI: 10.1016/j.aei.2017.03.004
      Issue No: Vol. 32 (2017)
  • A function-based computational method for design concept evaluation
    • Authors: Jia Hao; Qiangfu Zhao; Yan Yan
      Pages: 237 - 247
      Abstract: Publication date: April 2017
      Source:Advanced Engineering Informatics, Volume 32
      Author(s): Jia Hao, Qiangfu Zhao, Yan Yan
      Concept generation is an indispensable step of innovation design. However, the limited knowledge and design thinking fixation of designers often impede the generation of novel design concepts. Computational tools can be a necessary supplement for designers. They can generate a big number of design concepts based on an existing knowledge base. For filtering these design concepts, this work presents a computational measurement of novelty, feasibility and diversity based on 500,000 granted patents. First, about 1700 functional terms (terminologies) are mapped to high dimensional vectors (100 dimensional space) by word embedding technique. The resulted database is knowledge base-I (KB-I). Then, we adopt circular convolution to convert patents into high dimensional vectors. The resulted database is KB-II. Based on the two knowledge bases, the computational definitions of novelty, feasibility and diversity are developed. We conduct six experiments based on KB-II, a random dataset and a real product dataset, and the results show that these metrics can be used to roughly filter a big number of design concepts, and then expert-based method can be further used. This work provides a computational framework for measuring the novelty, feasibility and diversity of design concept.

      PubDate: 2017-04-03T15:24:46Z
      DOI: 10.1016/j.aei.2017.03.002
      Issue No: Vol. 32 (2017)
  • Signage visibility analysis and optimization system using BIM-enabled
           virtual reality (VR) environments
    • Authors: Ali Motamedi; Zhe Wang; Nobuyoshi Yabuki; Tomohiro Fukuda; Takashi Michikawa
      Pages: 248 - 262
      Abstract: Publication date: April 2017
      Source:Advanced Engineering Informatics, Volume 32
      Author(s): Ali Motamedi, Zhe Wang, Nobuyoshi Yabuki, Tomohiro Fukuda, Takashi Michikawa
      The proper placement of signage greatly influences pathfinding and information provision in public spaces. Clear visibility, easy comprehension, and efficient placement are all important for successful signage. We propose a signage visibility analysis and optimization system, utilizing an updated Building Information Model (BIM) and a game engine software application to simulate the movement of pedestrians. BIM can provide an up-to-date digital representation of a building and its assets, while computer simulation environments have the potential to simulate the movement of pedestrians. Combining these two technologies provides an opportunity to create a tool that analyzes the efficiency of installed signage and visualizes them in VR environments. The proposed tool contains algorithms, functions and predefined scenarios to calculate the coverage and the visibility of a building’s signage system. This system assists building managers to analyze (visually or by using statistics) the visibility of signboards, to assess their proper placement, and to optimize their placement. The applicability of the method has been validated in case studies performed in subway stations in Japan.

      PubDate: 2017-04-03T15:24:46Z
      DOI: 10.1016/j.aei.2017.03.005
      Issue No: Vol. 32 (2017)
  • Multi-class US traffic signs 3D recognition and localization via
           image-based point cloud model using color candidate extraction and
           texture-based recognition
    • Authors: Vahid Balali; Arash Jahangiri; Sahar Ghanipoor Machiani
      Pages: 263 - 274
      Abstract: Publication date: April 2017
      Source:Advanced Engineering Informatics, Volume 32
      Author(s): Vahid Balali, Arash Jahangiri, Sahar Ghanipoor Machiani
      Continuous condition monitoring and inspection of traffic signs are essential to ensure that safety and performance criteria are met. The use of 3D point cloud modeling by the construction industry has been significantly increased in recent years especially for recording the as-is conditions of facilities. The high-precision and dense 3D point clouds generated by photogrammetry can facilitate the process of asset condition assessment. This paper presents an automated computer-vision based method that detects, classifies, and localizes traffic signs via street-level image-based 3D point cloud models. The proposed pipeline integrates 3D object detection algorithm. An improved Structure-from-Motion (SfM) procedure is developed to create a 3D point cloud of roadway assets from the street level imagery. In order to assist with accurate 3D recognition and localization by color and texture features extraction, an automated process of point cloud cleaning and noise removal is proposed. Using camera pose information from SfM, the points within the bounding box of detected traffic signs are then projected into the cleaned point cloud by using the triangulation method (linear and non-linear) and the 3D points corresponding to the traffic sign in question are labeled and visualized in 3D. The proposed framework is validated using real-life data, which represent the most common types of traffic signs. The robustness of the proposed pipeline is evaluated by analyzing the accuracy in detection of traffic signs as well as the accuracy in localization in 3D point cloud model. The results promise to better and more accurate visualize the location of the traffic signs with respect to other roadway assets in 3D environment.

      PubDate: 2017-04-03T15:24:46Z
      DOI: 10.1016/j.aei.2017.03.006
      Issue No: Vol. 32 (2017)
  • Crowd-based velocimetry for surface flows
    • Authors: Yao-Yu Yang; Shih-Chung Kang
      Pages: 275 - 286
      Abstract: Publication date: April 2017
      Source:Advanced Engineering Informatics, Volume 32
      Author(s): Yao-Yu Yang, Shih-Chung Kang
      Flow velocity measurement is important in hydrology. Recently, owing to the popularity of sensors and processors, image-based flow velocity measurement methods have become an important research direction. Particle image velocimetry (PIV) is a key example. However, due to the uncertainty of the features, PIV sometimes provides very inaccurate results and always requires customized setups. In this research, we take advantage of the human perception system, that is, the strong abilities related to feature identification and tracking, in order to estimate the surface flow velocity of a river. We developed a method called crowd-based velocimetry (CBV) to incorporate the human perception capacity in the estimation of the flow velocity. CBV includes three main steps: (1) video processing, (2) crowd processing, and (3) statistical processing. We validated CBV by measuring a fast, steady, and uniform river surface flow in an artificial canal. The results show that compared to radar measurements from the center of the flow, CBV measured the surface flow velocity with a deviation ranging between +12.1% and +17.3% from the radar measurement, while PIV resulted in a −1.7% to −24.3% deviation. With rapidly improving mobile devices, CBV allows enormous numbers of people to engage in flow measurement, making CBV more reliable, more efficient, and more economical.

      PubDate: 2017-04-10T15:44:52Z
      DOI: 10.1016/j.aei.2017.03.007
      Issue No: Vol. 32 (2017)
  • Fast convergence optimization model for single and multi-purposes
           reservoirs using hybrid algorithm
    • Authors: Mohammad Ehteram; Sayed-Farhad Mousavi; Hojat Karami; Saeed Farzin; Mohammad Emami; Faridah Binti Othman; Zahra Amini; Ozgur Kisi; Ahmed El-Shafie
      Pages: 287 - 298
      Abstract: Publication date: April 2017
      Source:Advanced Engineering Informatics, Volume 32
      Author(s): Mohammad Ehteram, Sayed-Farhad Mousavi, Hojat Karami, Saeed Farzin, Mohammad Emami, Faridah Binti Othman, Zahra Amini, Ozgur Kisi, Ahmed El-Shafie
      Developing optimal operation policy for single or multi-purposes dams and reservoirs is a complex engineering application. The main reasons for such complexity are the stochastic nature of the system input and slow convergence of the optimization method. Furthermore, searching optimal operation for multi-purposes or chain reservoir systems, becomes even more complex because of interfering operations between successive dams. In this study, a new hybrid algorithm has been introduced by merging the genetic algorithm (GA) with the krill algorithm. In fact, the proposed hybrid algorithm amalgamates the advantages of both algorithms, first, the ability to converge fast for global optimum and, second, considering the effect of stochastic nature of the system. Three benchmark functions were used to evaluate the performance of this proposed optimization model. In addition, the proposed hybrid algorithm was examined for Karun-4 reservoir in Iran as an example for a hydro-power generation dam. Two benchmark problems of hydropower operations for multi-purposes reservoir systems, namely four-reservoir and ten-reservoir systems were considered in the study. Results showed that the proposed hybrid algorithm outperformed the well-developed traditional nonlinear programming solvers, such as Lingo 8 software.

      PubDate: 2017-04-10T15:44:52Z
      DOI: 10.1016/j.aei.2017.04.001
      Issue No: Vol. 32 (2017)
  • A descriptive semantics of modelling process catering for whole product
    • Authors: Xiao-Bo Ge; Xiao-Dong Shao; Shen Li; Dou Wang
      Pages: 299 - 311
      Abstract: Publication date: April 2017
      Source:Advanced Engineering Informatics, Volume 32
      Author(s): Xiao-Bo Ge, Xiao-Dong Shao, Shen Li, Dou Wang
      To date, the entire product parametric technology has made great progress to significantly improve product design efficiency. However, the technology uses an established modelling process, which makes it very difficult to adapt to changing product requirements. A method that can be customised for the whole machine is discussed in this paper; this method is intended to increase the adaptability of the entire product parametric technology. First, a frame model concept is proposed based on an analysis of a large number of product modelling processes. Second, a semantic model for describing the modelling process is proposed, and its instantiation is studied. Next, according to the semantics of the modelling process, a product parametric modelling system is established. Finally, based on an examination of the electronic module modelling, it was found that modelling efficiency increased significantly.

      PubDate: 2017-04-24T16:28:01Z
      DOI: 10.1016/j.aei.2017.04.004
      Issue No: Vol. 32 (2017)
  • Hypergraphs and extremal optimization in 3D integrated circuit design
    • Authors: Katarzyna Grzesiak-Kopeć; Piotr Oramus; Maciej Ogorzałek
      Abstract: Publication date: Available online 8 July 2017
      Source:Advanced Engineering Informatics
      Author(s): Katarzyna Grzesiak-Kopeć, Piotr Oramus, Maciej Ogorzałek
      The circuit design task poses an extremely difficult intellectual challenge. The solution has to meet a number of specific requirements and satisfy a variety of constraints. Efficient search of huge and discontinuous spaces requires new non-deterministic and heuristic algorithms. The goal of the research is to minimize the total wire-length of interconnects between sub-circuits. The paper presents a knowledge intensive 3D ICs layout hypergraph representation together with the elaborated neighborhood optimization heuristics. The results of the Extremal Optimization (EO) implementation applied to the MCNC set of benchmark circuits are reported.

      PubDate: 2017-07-09T06:26:20Z
      DOI: 10.1016/j.aei.2017.06.004
  • An IFC schema extension and binary serialization format to efficiently
           integrate point cloud data into building models
    • Authors: Thomas Krijnen; Jakob Beetz
      Abstract: Publication date: Available online 3 April 2017
      Source:Advanced Engineering Informatics
      Author(s): Thomas Krijnen, Jakob Beetz
      In this paper we suggest an extension to the Industry Foundation Classes (IFC) model to integrate point cloud datasets. The proposal includes a schema extension to the core model allowing the storage of points, either as Cartesian coordinates, points in parametric space of associated building element surfaces or as discrete height fields projected as grids onto building elements. To handle the considerable amounts of data generated in the process of scanning building structures, we present intelligent compression approaches combined with the Hierarchical Data Format (HDF) as an efficient serialization and an alternative to clear text encoded ISO 10303 part 21 files. Based on prototypical implementations we show results of various serialization options and their impacts on storage efficiency. In this proposal the deepened semantic relationships have been favoured over compression ratios. Nevertheless, with various near-lossless layers of compression and binary serialization applied, a compression ratio of up to 67.7% is obtained for a building model with integrated point clouds, compared to the raw source data. The binary serialization is able to handle hundreds of millions of points, out of which specific spatial and semantic subsets can rapidly be extracted due to the containerized hierarchical storage model and association to building elements. The authors advocate the use of binary storage for sizeable point cloud scans, but also show how especially the grid discretization can result into usable points cloud segments embedded into text-based IFC models.

      PubDate: 2017-04-10T15:44:52Z
      DOI: 10.1016/j.aei.2017.03.008
  • A framework to design a human-centred adaptive manufacturing system for
           aging workers
    • Authors: Margherita Peruzzini; Marcello Pellicciari
      Abstract: Publication date: Available online 22 February 2017
      Source:Advanced Engineering Informatics
      Author(s): Margherita Peruzzini, Marcello Pellicciari
      The so-called smart manufacturing systems (SMS) combine smart manufacturing technologies, cyber-physical infrastructures, and data control to realize predictive and adaptive behaviours. In this context, industrial research focused mainly on improving the manufacturing system performance, almost neglecting human factors (HF) and their relation to the production systems. However, in order to create an effective smart factory context, human performance should be included to drive smart system adaptation in efficient and effective way, also by exploiting the linkages between tangible and intangible entities offered by Industry 4.0. Furthermore, modern companies are facing another interesting trend: aging workers. The age of workers is generally growing up and, consequently, the percentage of working 45–64years old population with different needs, capabilities, and reactions, is increasing. This research focuses on the design of human-centred adaptive manufacturing systems (AMS) for the modern companies, where aging workers are more and more common. In particular, it defines a methodology to design AMS able to adapt to the aging workers’ needs considering their reduced workability, due to both physical and cognitive functional decrease, with the final aim to improve the human-machine interaction and the workers’ wellbeing. The paper finally presents an industrial case study focusing on the woodworking sector, where an existing machine has been re-designed to define a new human-centred AMS. The new machine has been engineered and prototyped by adopting cyber-physical systems (CPS) and pervasive technologies to smartly adapt the machine behaviour to the working conditions and the specific workers’ skills, tasks, and cognitive-physical abilities, with the final aim to support aging workers. The achieved benefits were expressed in terms of system usability, focusing on human-interaction quality.

      PubDate: 2017-02-22T21:18:34Z
      DOI: 10.1016/j.aei.2017.02.003
  • The generation of hierarchic structures via robust 3D topology
    • Authors: Hèrm Hofmeyer; Mattias Schevenels; Sjonnie Boonstra
      Abstract: Publication date: Available online 21 February 2017
      Source:Advanced Engineering Informatics
      Author(s): Hèrm Hofmeyer, Mattias Schevenels, Sjonnie Boonstra
      Commonly used building structures often show a hierarchic layout of structural elements. It can be questioned whether such a layout originates from practical considerations, e.g. related to its construction, or that it is (relatively) optimal from a structural point of view. This paper investigates this question by using topology optimisation in an attempt to generate hierarchical structures. As an arbitrarily standard design case, the principle of a traditional timber floor that spans in one direction is used. The optimisation problem is first solved using classical sensitivity and density filtering. This leads indeed to solutions with a hierarchic layout, but they are practically unusable as the floor boarding is absent. A Heaviside projection is therefore considered next, but this does not solve the problem. Finally, a robust approach is followed, and this does result in a design similar to floor boarding supported by timber joists. The robust approach is then followed to study a floor with an opening, two floors that span in two directions, and an eight-level concrete building. It can be concluded that a hierarchic layout of structural elements likely originates from being optimal from a structural point of view. Also clear is that this conclusion cannot be obtained by means of standard topology optimisation based on sensitivity or density filtering (as often found in commercial finite element codes); robust 3D optimisation is required to obtain a usable, constructible (or in the future: 3D printable) structural design, with a crisp black-and-white density distribution.

      PubDate: 2017-02-22T21:18:34Z
      DOI: 10.1016/j.aei.2017.02.002
  • Are you a human or a humanoid: Predictive user modelling through
           behavioural analysis of online gameplay data
    • Authors: Chen Gao; Kaiqi Jin; Haifeng Shen; Muhammed Ali Babar
      Abstract: Publication date: Available online 7 February 2017
      Source:Advanced Engineering Informatics
      Author(s): Chen Gao, Kaiqi Jin, Haifeng Shen, Muhammed Ali Babar
      Intelligent agents are widely used in robotics, gaming and simulation. A key issue is modelling human behaviours so that intelligent agents can use a human’s behavioural model to imitate them and predict their next moves. In this article, we use Internet-based multiplayer online gaming (MOG) as a case study to present our approach to predictive user modelling through behavioural analysis of online gameplay data. As latency is an inherited bottleneck of the Internet and is likely to remain so into a foreseeable future, a lot of efforts have been made to address the resulting issues. Most of the existing latency handling techniques are based on the assumption that latency is within an acceptable threshold so that they can alleviate or even completely hide its negative impact on players’ quality of experience (QoE) that directly determines consumers’ satisfaction of the provided MOG services. While this assumption is mostly valid, it is worth noting that a player’s Internet connection latency always fluctuates (known as jitter), possibly to the extent of exceeding a MOG’s designated threshold in which case none of the techniques can handle properly but disconnecting the player from the gameplay session. Forcing a player to quit prematurely simply due to a spike of unusual high latency has a significant negative impact both on the gameplay’s fairness and on the player’s QoE. To improve customer satisfaction of a MOG service, we propose a more tolerant approach by temporarily substituting a player with a humanoid bot in the event of latency hikes so that the player always remains in the gameplay session. The challenge in this approach is to create a personalised humanoid bot that can imitate the playing pattern of the individual human player being substituted. Our solution is to first extract key variables that have impact on the human player’s decision-makings through behavioural analysis of the player’s historical gameplay data, then model the relationships among these variables, and finally creates the player’s humanoid bot with the model. In this paper, we use a multiplayer online pong game as a case study to explain behavioural variables, modelling techniques, processes, outcomes, and performance studies.

      PubDate: 2017-02-10T09:06:50Z
      DOI: 10.1016/j.aei.2017.01.004
  • Advanced design, analysis, and implementation of pervasive and smart
           collaborative systems enabled with knowledge modelling and big data
    • Authors: Amy J.C. Trappey; Fredrik Elgh; Timo Hartmann; Anne James; Josip Stjepandic; Charles V. Trappey; Nel Wognum
      Abstract: Publication date: Available online 4 February 2017
      Source:Advanced Engineering Informatics
      Author(s): Amy J.C. Trappey, Fredrik Elgh, Timo Hartmann, Anne James, Josip Stjepandic, Charles V. Trappey, Nel Wognum

      PubDate: 2017-02-10T09:06:50Z
      DOI: 10.1016/j.aei.2017.01.001
  • Recognition of process patterns for BIM-based construction schedules
    • Authors: Kateryna Sigalov; Markus König
      Abstract: Publication date: Available online 18 January 2017
      Source:Advanced Engineering Informatics
      Author(s): Kateryna Sigalov, Markus König
      Construction scheduling is a very demanding and time intensive process. Building information modeling (BIM) is becoming increasingly important for planning and scheduling, as it provides significant support for this difficult assignment. Further improvements can be achieved by applying predefined process templates for BIM-based schedules. It can reduce the planning time and thus increase the productivity. However, a manual definition of proper and application-specific process templates is very challenging. The automatic detection of recurring similar configurations of construction processes, called process patterns, would greatly support this complex task. Identified process patterns can be subsequently generalized, supporting the design of process templates. This contribution presents an overall concept for process pattern recognition in BIM-based construction schedules by applying graph-based methods. Due to the fact that graph matching algorithms are in general very time- and resource-consuming, an indexing technique based on features is used to solve this problem more efficiently. The paper focuses on the estimation of similarity in construction schedules, describing feature-based methods and similarity measure definitions in detail. Another emphasis is the preparation of schedules for the recognition of process patterns, including decomposition of schedules into smaller parts, referred to as subschedules, and normalization of features. The potential of this concept is demonstrated by two different case studies. The proper results of the evaluation show that the proposed method and similarity metrics are sufficient for the recognition of process patterns in construction schedules.

      PubDate: 2017-01-22T08:30:34Z
      DOI: 10.1016/j.aei.2016.12.003
  • Leveraging existing occupancy-related data for optimal control of
           commercial office buildings: A review
    • Authors: Weiming Shen; Guy Newsham; Burak Gunay
      Abstract: Publication date: Available online 18 January 2017
      Source:Advanced Engineering Informatics
      Author(s): Weiming Shen, Guy Newsham, Burak Gunay
      A primary strategy for the energy-efficient operation of commercial office buildings is to deliver building services, including lighting, heating, ventilating, and air conditioning (HVAC), only when and where they are needed, in the amount that they are needed. Since such building services are usually delivered to provide occupants with satisfactory indoor conditions, it is important to accurately determine the occupancy of building spaces in real time as an input to optimal control. This paper first discusses the concepts of building occupancy resolution and accuracy and briefly reviews conventional (explicit) occupancy detection approaches. The focus of this paper is to review and classify emerging, potentially low-cost approaches to leveraging existing data streams that may be related to occupancy, usually referred to as implicit/ambient/soft sensing approaches. Based on a review and a comparison of related projects/systems (in terms of occupancy sensing type, resolution, accuracy, ground truth data collection method, demonstration scale, data fusion and control strategies) the paper presents the state-of-the-art of leveraging existing occupancy-related data for optimal control of commercial office buildings. It also briefly discusses technology trends, challenges, and future research directions.

      PubDate: 2017-01-22T08:30:34Z
      DOI: 10.1016/j.aei.2016.12.008
  • Knowledge-based design for assembly in agile manufacturing by using Data
           Mining methods
    • Authors: R. Kretschmer; A. Pfouga; S. Rulhoff; J. Stjepandić
      Abstract: Publication date: Available online 10 January 2017
      Source:Advanced Engineering Informatics
      Author(s): R. Kretschmer, A. Pfouga, S. Rulhoff, J. Stjepandić
      Decision making in early production planning phases is typically based on a rough estimation due to lack of a comprehensive, reliable knowledge base. Virtual planning has been prevailed as a method used to evaluate risks and costs before the concrete realization of production processes. The process of product assembly, which yields a high share in total production costs, gets its particular importance. This paper introduces a new approach and its initial implementation for knowledge-based design for assembly in agile manufacturing by using data mining (DM) methods in the field of series production with high variance. The approach adopts the usage of bulk data with old, successful designs in order to extrapolate its scope for assembly processes. Especially linked product and process data allow the innovative usage of DM methods in order to facilitate the front loading in the product development. The concept presents an affordable assistance potential for development of new products variants along the product emergence process (PEP). With this approach an early cost estimation of assembly processes in series production can be conducted using advanced DM methods as shown in an industrial use case. Furthermore, design and planning processes can be supported effectively.

      PubDate: 2017-01-14T09:16:24Z
      DOI: 10.1016/j.aei.2016.12.006
  • A collaborative system for capturing and reusing in-context design
           knowledge with an integrated representation model
    • Authors: Gongzhuang Peng; Hongwei Wang; Heming Zhang; Yanwei Zhao; Aylmer L. Johnson
      Abstract: Publication date: Available online 5 January 2017
      Source:Advanced Engineering Informatics
      Author(s): Gongzhuang Peng, Hongwei Wang, Heming Zhang, Yanwei Zhao, Aylmer L. Johnson
      Current research on design knowledge capture and reuse has predominantly focused on either the codification view of knowledge or the personalisation view of knowledge, resulting in a failure to address designers’ knowledge needs caused by a lack of context of information and insufficient computational support. Precisely motivated by this gap, this work aims to address the integration of these two views into a complete, contextual and trustworthy knowledge management scheme enabled by the emerging collaborative technologies. Specifically, a knowledge model is developed to represent an integrated knowledge space, which can combine geometric model, knowledge-based analysis codes and problem-solving strategies and processes. On this basis, a smart collaborative system is also designed and developed to streamline the design process as well as to facilitate knowledge capture, retrieval and reuse as users with different roles are working on various tasks within this process. An engineering case study is undertaken to demonstrate the idea of collaborative knowledge creation and sharing and evaluate the effectiveness of the knowledge representation model and the collaborative technologies employed. As evidenced in the development and evaluation, the methods proposed are effective for capturing an integrated knowledge space and the collaborative knowledge management system not only facilitates problem-solving using knowledge-based analysis but also supplies in-context tacit knowledge captured from the communications between users throughout the design process.

      PubDate: 2017-01-06T09:02:10Z
      DOI: 10.1016/j.aei.2016.12.007
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