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Showing 1 - 200 of 3162 Journals sorted alphabetically
A Practical Logic of Cognitive Systems     Full-text available via subscription   (Followers: 9)
AASRI Procedia     Open Access   (Followers: 14)
Academic Pediatrics     Hybrid Journal   (Followers: 30, SJR: 1.402, h-index: 51)
Academic Radiology     Hybrid Journal   (Followers: 22, SJR: 1.008, h-index: 75)
Accident Analysis & Prevention     Partially Free   (Followers: 88, SJR: 1.109, h-index: 94)
Accounting Forum     Hybrid Journal   (Followers: 25, SJR: 0.612, h-index: 27)
Accounting, Organizations and Society     Hybrid Journal   (Followers: 35, SJR: 2.515, h-index: 90)
Achievements in the Life Sciences     Open Access   (Followers: 5)
Acta Anaesthesiologica Taiwanica     Open Access   (Followers: 7, SJR: 0.338, h-index: 19)
Acta Astronautica     Hybrid Journal   (Followers: 396, SJR: 0.726, h-index: 43)
Acta Automatica Sinica     Full-text available via subscription   (Followers: 2)
Acta Biomaterialia     Hybrid Journal   (Followers: 27, SJR: 2.02, h-index: 104)
Acta Colombiana de Cuidado Intensivo     Full-text available via subscription   (Followers: 2)
Acta de Investigación Psicológica     Open Access   (Followers: 3)
Acta Ecologica Sinica     Open Access   (Followers: 8, SJR: 0.172, h-index: 29)
Acta Haematologica Polonica     Free   (Followers: 1, SJR: 0.123, h-index: 8)
Acta Histochemica     Hybrid Journal   (Followers: 3, SJR: 0.604, h-index: 38)
Acta Materialia     Hybrid Journal   (Followers: 245, 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: 10, SJR: 0.915, h-index: 53)
Acta Otorrinolaringologica (English Edition)     Full-text available via subscription  
Acta Otorrinolaringológica Española     Full-text available via subscription   (Followers: 2, SJR: 0.311, h-index: 16)
Acta Pharmaceutica Sinica B     Open Access   (Followers: 1)
Acta Poética     Open Access   (Followers: 4)
Acta Psychologica     Hybrid Journal   (Followers: 27, 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: 3)
Actas Dermo-Sifiliográficas (English Edition)     Full-text available via subscription   (Followers: 2)
Actas Urológicas Españolas     Full-text available via subscription   (Followers: 3, SJR: 0.383, h-index: 19)
Actas Urológicas Españolas (English Edition)     Full-text available via subscription   (Followers: 1)
Actualites Pharmaceutiques     Full-text available via subscription   (Followers: 6, SJR: 0.141, h-index: 3)
Actualites Pharmaceutiques Hospitalieres     Full-text available via subscription   (Followers: 3, SJR: 0.112, h-index: 2)
Acupuncture and Related Therapies     Hybrid Journal   (Followers: 6)
Acute Pain     Full-text available via subscription   (Followers: 15)
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: 8)
Additive Manufacturing     Hybrid Journal   (Followers: 9, SJR: 1.039, h-index: 5)
Additives for Polymers     Full-text available via subscription   (Followers: 22)
Advanced Cement Based Materials     Full-text available via subscription   (Followers: 3)
Advanced Drug Delivery Reviews     Hybrid Journal   (Followers: 137, 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: 8, SJR: 0.299, h-index: 15)
Advances in Agronomy     Full-text available via subscription   (Followers: 12, SJR: 2.071, h-index: 82)
Advances in Anesthesia     Full-text available via subscription   (Followers: 28, SJR: 0.169, h-index: 4)
Advances in Antiviral Drug Design     Full-text available via subscription   (Followers: 2)
Advances in Applied Mathematics     Full-text available via subscription   (Followers: 10, 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: 14, 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: 2, SJR: 0.619, h-index: 48)
Advances in Cancer Research     Full-text available via subscription   (Followers: 29, SJR: 2.215, h-index: 78)
Advances in Carbohydrate Chemistry and Biochemistry     Full-text available via subscription   (Followers: 7, SJR: 0.9, h-index: 30)
Advances in Catalysis     Full-text available via subscription   (Followers: 5, SJR: 2.139, h-index: 42)
Advances in Cell Aging and Gerontology     Full-text available via subscription   (Followers: 3)
Advances in Cellular and Molecular Biology of Membranes and Organelles     Full-text available via subscription   (Followers: 12)
Advances in Chemical Engineering     Full-text available via subscription   (Followers: 27, SJR: 0.183, 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: 19, SJR: 2.314, h-index: 130)
Advances in Computers     Full-text available via subscription   (Followers: 14, SJR: 0.223, h-index: 22)
Advances in Dermatology     Full-text available via subscription   (Followers: 15)
Advances in Developmental Biology     Full-text available via subscription   (Followers: 11)
Advances in Digestive Medicine     Open Access   (Followers: 8)
Advances in DNA Sequence-Specific Agents     Full-text available via subscription   (Followers: 5)
Advances in Drug Research     Full-text available via subscription   (Followers: 23)
Advances in Ecological Research     Full-text available via subscription   (Followers: 42, SJR: 3.25, h-index: 43)
Advances in Engineering Software     Hybrid Journal   (Followers: 27, 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: 43, SJR: 5.465, h-index: 64)
Advances in Exploration Geophysics     Full-text available via subscription   (Followers: 1)
Advances in Fluorine Science     Full-text available via subscription   (Followers: 9)
Advances in Food and Nutrition Research     Full-text available via subscription   (Followers: 53, SJR: 0.674, h-index: 38)
Advances in Fuel Cells     Full-text available via subscription   (Followers: 17)
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: 8)
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: 11, SJR: 0.497, h-index: 31)
Advances in Human Factors/Ergonomics     Full-text available via subscription   (Followers: 22)
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: 37, SJR: 4.152, h-index: 85)
Advances in Inorganic Chemistry     Full-text available via subscription   (Followers: 8, SJR: 1.132, h-index: 42)
Advances in Insect Physiology     Full-text available via subscription   (Followers: 2, SJR: 1.274, h-index: 27)
Advances in Integrative Medicine     Hybrid Journal   (Followers: 6)
Advances in Intl. Accounting     Full-text available via subscription   (Followers: 3)
Advances in Life Course Research     Hybrid Journal   (Followers: 8, SJR: 0.764, h-index: 15)
Advances in Lipobiology     Full-text available via subscription   (Followers: 1)
Advances in Magnetic and Optical Resonance     Full-text available via subscription   (Followers: 9)
Advances in Marine Biology     Full-text available via subscription   (Followers: 14, SJR: 1.645, h-index: 45)
Advances in Mathematics     Full-text available via subscription   (Followers: 11, 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: 21)
Advances in Molecular and Cellular Endocrinology     Full-text available via subscription   (Followers: 8)
Advances in Molecular Toxicology     Full-text available via subscription   (Followers: 7, SJR: 0.324, h-index: 8)
Advances in Nanoporous Materials     Full-text available via subscription   (Followers: 3)
Advances in Oncobiology     Full-text available via subscription   (Followers: 1)
Advances in Organ Biology     Full-text available via subscription   (Followers: 1)
Advances in Organometallic Chemistry     Full-text available via subscription   (Followers: 16, SJR: 2.885, h-index: 45)
Advances in Parallel Computing     Full-text available via subscription   (Followers: 6, SJR: 0.148, h-index: 11)
Advances in Parasitology     Full-text available via subscription   (Followers: 5, 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: 10)
Advances in Pharmacology     Full-text available via subscription   (Followers: 16, SJR: 1.718, h-index: 58)
Advances in Physical Organic Chemistry     Full-text available via subscription   (Followers: 8, 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: 5)
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: 59)
Advances in Quantum Chemistry     Full-text available via subscription   (Followers: 6, SJR: 0.478, h-index: 32)
Advances in Radiation Oncology     Open Access  
Advances in Small Animal Medicine and Surgery     Hybrid Journal   (Followers: 3, SJR: 0.1, h-index: 2)
Advances in Space Biology and Medicine     Full-text available via subscription   (Followers: 5)
Advances in Space Research     Full-text available via subscription   (Followers: 388, SJR: 0.606, h-index: 65)
Advances in Structural Biology     Full-text available via subscription   (Followers: 5)
Advances in Surgery     Full-text available via subscription   (Followers: 9, SJR: 0.823, h-index: 27)
Advances in the Study of Behavior     Full-text available via subscription   (Followers: 29, SJR: 1.321, h-index: 56)
Advances in Veterinary Medicine     Full-text available via subscription   (Followers: 17)
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: 46, SJR: 2.408, h-index: 94)
Aeolian Research     Hybrid Journal   (Followers: 6, SJR: 0.973, h-index: 22)
Aerospace Science and Technology     Hybrid Journal   (Followers: 336, 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: 6, SJR: 0.344, h-index: 6)
Ageing Research Reviews     Hybrid Journal   (Followers: 10, SJR: 3.289, h-index: 78)
Aggression and Violent Behavior     Hybrid Journal   (Followers: 437, 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: 31, SJR: 1.275, h-index: 74)
Agricultural Water Management     Hybrid Journal   (Followers: 43, SJR: 1.546, h-index: 79)
Agriculture and Agricultural Science Procedia     Open Access   (Followers: 1)
Agriculture and Natural Resources     Open Access   (Followers: 2)
Agriculture, Ecosystems & Environment     Hybrid Journal   (Followers: 56, 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: 6, 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: 11, SJR: 0.922, h-index: 66)
Alcoholism and Drug Addiction     Open Access   (Followers: 9)
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   (Followers: 1)
Algal Research     Partially Free   (Followers: 10, SJR: 2.05, h-index: 20)
Alkaloids: Chemical and Biological Perspectives     Full-text available via subscription   (Followers: 2)
Allergologia et Immunopathologia     Full-text available via subscription   (Followers: 1, SJR: 0.46, h-index: 29)
Allergology Intl.     Open Access   (Followers: 5, SJR: 0.776, h-index: 35)
Alpha Omegan     Full-text available via subscription   (SJR: 0.121, h-index: 9)
ALTER - European J. of Disability Research / Revue Européenne de Recherche sur le Handicap     Full-text available via subscription   (Followers: 9, SJR: 0.158, h-index: 9)
Alzheimer's & Dementia     Hybrid Journal   (Followers: 49, SJR: 4.289, h-index: 64)
Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring     Open Access   (Followers: 4)
Alzheimer's & Dementia: Translational Research & Clinical Interventions     Open Access   (Followers: 4)
Ambulatory Pediatrics     Hybrid Journal   (Followers: 6)
American Heart J.     Hybrid Journal   (Followers: 50, SJR: 3.157, h-index: 153)
American J. of Cardiology     Hybrid Journal   (Followers: 51, SJR: 2.063, h-index: 186)
American J. of Emergency Medicine     Hybrid Journal   (Followers: 44, SJR: 0.574, h-index: 65)
American J. of Geriatric Pharmacotherapy     Full-text available via subscription   (Followers: 10, SJR: 1.091, h-index: 45)
American J. of Geriatric Psychiatry     Hybrid Journal   (Followers: 14, 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: 26, SJR: 1.259, h-index: 81)
American J. of Kidney Diseases     Hybrid Journal   (Followers: 34, SJR: 2.313, h-index: 172)
American J. of Medicine     Hybrid Journal   (Followers: 43, 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: 202, SJR: 2.255, h-index: 171)
American J. of Ophthalmology     Hybrid Journal   (Followers: 62, SJR: 2.803, h-index: 148)
American J. of Ophthalmology Case Reports     Open Access   (Followers: 6)
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: 25, SJR: 0.59, h-index: 45)
American J. of Pathology     Hybrid Journal   (Followers: 27, SJR: 2.653, h-index: 228)
American J. of Preventive Medicine     Hybrid Journal   (Followers: 27, SJR: 2.764, h-index: 154)
American J. of Surgery     Hybrid Journal   (Followers: 37, 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: 6)
Anaerobe     Hybrid Journal   (Followers: 4, SJR: 1.066, h-index: 51)
Anaesthesia & Intensive Care Medicine     Full-text available via subscription   (Followers: 63, SJR: 0.124, h-index: 9)
Anaesthesia Critical Care & Pain Medicine     Full-text available via subscription   (Followers: 15)
Anales de Cirugia Vascular     Full-text available via subscription  
Anales de Pediatría     Full-text available via subscription   (Followers: 3, 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: 5, SJR: 2.577, h-index: 7)
Analytica Chimica Acta     Hybrid Journal   (Followers: 39, SJR: 1.548, h-index: 152)
Analytical Biochemistry     Hybrid Journal   (Followers: 175, SJR: 0.725, h-index: 154)
Analytical Chemistry Research     Open Access   (Followers: 10, SJR: 0.18, h-index: 2)
Analytical Spectroscopy Library     Full-text available via subscription   (Followers: 11)
Anesthésie & Réanimation     Full-text available via subscription   (Followers: 2)
Anesthesiology Clinics     Full-text available via subscription   (Followers: 23, SJR: 0.421, h-index: 40)
Angiología     Full-text available via subscription   (SJR: 0.124, h-index: 9)
Angiologia e Cirurgia Vascular     Open Access   (Followers: 1)

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Journal Cover Advanced Engineering Informatics
  Journal Prestige (SJR): 1.265
  Citation Impact (citeScore): 53
  Number of Followers: 11  
   Hybrid Journal Hybrid journal (It can contain Open Access articles)
   ISSN (Print) 1474-0346
   Published by Elsevier Homepage  [3162 journals]
  • Decentralized damage detection of seismically-excited buildings using
           multiple banks of Kalman estimators
    • Authors: Jau-Yu Chou; Chia-Ming Chang
      Pages: 1 - 13
      Abstract: Publication date: October 2018
      Source:Advanced Engineering Informatics, Volume 38
      Author(s): Jau-Yu Chou, Chia-Ming Chang
      Natural hazards result in ill-conditioned structures with unfavorable damage. To early recognize damage existence, structures can be screened by damage detection methods after a critical hazard event. These damage detection methods are often developed based on a centralized acquiring and computing system that challenges the feasibility of deployment in a large-scale structure. Decentralized damage detection methods alter a single system to multiple subsystems that allow spatially distributing in a structure and yield comparable performance with the centralized approach. In this study, a decentralized damage detection method based on modal prediction errors via multiple banks of Kalman estimators is proposed. First, a sensor network is comprised of multiple subsystems over a structure of which the subsystems have overlapped sensing nodes. These subsystems are individually identified by an input–output frequency-domain system identification method under ambient vibrations. The identified models are then converted into several banks of Kalman estimators, and the estimators generate the estimation of structural modal responses. The prediction errors are calculated from the differentiation between measured and estimated modal responses, and the accumulated standard deviations of modal prediction errors serve as the damage indices for recognizing the damage occurrence, locations, and levels. A numerical example is introduced to demonstrate the proposed method as well as to evaluate the detection effectiveness. Moreover, the proposed method is also experimentally verified by a scaled twin-tower building using shake table testing. The experimental results indicate that the proposed method is quite effective to inform damage of structures in terms of damage occurrence, locations, and levels.

      PubDate: 2018-06-07T09:35:11Z
      DOI: 10.1016/j.aei.2018.05.009
      Issue No: Vol. 38 (2018)
  • Understanding building occupant activities at scale: An integrated
           knowledge-based and data-driven approach
    • Authors: Andrew J. Sonta; Perry E. Simmons; Rishee K. Jain
      Pages: 1 - 13
      Abstract: Publication date: August 2018
      Source:Advanced Engineering Informatics, Volume 37
      Author(s): Andrew J. Sonta, Perry E. Simmons, Rishee K. Jain
      Buildings are our homes and our workplaces. They directly affect our well-being, and they impact the natural global environment primarily through the energy they consume. Understanding the behavior of occupants in buildings has vital implications for improving the energy efficiency of building systems and for providing knowledge to designers about how occupants will utilize the spaces they create. However, current methods for inferring building occupant activity patterns are limited in two primary areas: First, they lack adaptability to new spaces and scalability to larger spaces due to the time and cost intensity of collecting ground truth data for training the embedded algorithms. Second, they do not incorporate explicit knowledge about occupant dynamics in their implementation, limiting their ability to uncover deep insights about activity patterns in the data. In this paper, we develop a methodology for classifying occupant activity patterns from plug load sensor data at the desk level. Our method makes us of a common unsupervised learning algorithm—the Gaussian mixture model—and, in addition, it incorporates explicit knowledge about occupant presence and absence in order to preserve adaptability and effectiveness. We validate our method using a pilot study in an academic office building and demonstrate its potential for scalability through a case study of an open-office building in San Francisco, CA. Our method offers key insights into spatially and temporally granular occupancy states and space utilization that could not otherwise be obtained.
      Graphical abstract image

      PubDate: 2018-05-29T15:27:55Z
      DOI: 10.1016/j.aei.2018.04.009
      Issue No: Vol. 37 (2018)
  • A large-scale evaluation of automated metadata inference approaches on
           sensors from air handling units
    • Authors: Jingkun Gao; Mario Bergés
      Pages: 14 - 30
      Abstract: Publication date: August 2018
      Source:Advanced Engineering Informatics, Volume 37
      Author(s): Jingkun Gao, Mario Bergés
      Building automation systems provide abundant sensor data to enable the potential of using data analytics to, among other things, improve the energy efficiency of the building. However, deployment of these applications for buildings, such as, fault detection and diagnosis (FDD) on multiple buildings remains a challenge due to the non-trivial efforts of organizing, managing and extracting metadata associated with sensors (e.g., information about their location, function, etc.), which is required by applications. One of the reasons leading to the problem is that varying conventions, acronyms, and standards are used to define this metadata. To better understand the nature of the problem, as well as the performance and scalability of existing solutions, we implement and test 6 different time-series based metadata inference approaches on sensors from 614 air handling units (AHU) instrumented in 35 building sites accounting for more than 400 buildings distributed across United States of America. We infer 12 types of sensors and actuators in AHUs required by a rule-based FDD application: AHU performance and assessment rules (APAR). Our results show that: (1) the average performance of these approaches in terms of accuracy is similar across building sites, though there is significant variance; (2) the expected accuracy of classifying the type of points required by APAR for a new unseen building is, on average, 75%; (3) the performance of the model does not decrease as long as training data and testing data are extracted from adjacent months.
      Graphical abstract image

      PubDate: 2018-05-29T15:27:55Z
      DOI: 10.1016/j.aei.2018.04.010
      Issue No: Vol. 37 (2018)
  • Personalized method for self-management of trunk postural ergonomic
           hazards in construction rebar ironwork
    • Authors: Xuzhong Yan; Heng Li; Hong Zhang; Timothy M. Rose
      Pages: 31 - 41
      Abstract: Publication date: August 2018
      Source:Advanced Engineering Informatics, Volume 37
      Author(s): Xuzhong Yan, Heng Li, Hong Zhang, Timothy M. Rose
      Construction rebar workers face postural ergonomic hazards that can lead to work-related Lower Back Disorders (LBDs), primarily due to their prolonged awkward working postures required by the job. In a previous study, Wearable Inertial Measurement Units (WIMUs)-based Personal Protective Equipment (PPE) was developed to alert workers when their trunk inclination holding time exceeded acceptable thresholds as defined in ISO standard 11226:2000. However, subsequent field testing identified PPE was ineffective for some workers because the adopted ISO thresholds were not personalized and did not consider differences in individual’s response to postural ergonomic hazards. To address this problem, this paper introduces a worker-centric method to assist in the self-management of work-related ergonomic hazards, based on data-driven personalized healthcare intervention. Firstly, personalized information is gathered by providing each rebar ironworker a WIMU-based personalized mobile health (mHealth) system to capture their trunk inclination angle and holding time data. Then, the captured individual trunk inclination holding times are analyzed by a Gaussian-like probability density function, where abnormal holding time thresholds can be generated and updated in response to incoming trunk inclination records of an individual during work time. These abnormal holding time thresholds are then adapted to be used as personalized trunk inclination holding time recommendations for an individual worker to self-manage their working postures, based on their own trunk inclination records. The proposed worker-centric method to assist in the self-management of ergonomic postural hazards leading to LBDs was field tested on a construction site over a three-month duration. The results of the paired t-tests indicate that posture scores evaluated by the Ovako Working Posture Analysis System (OWAS) significantly decrease when the personalized recommendation is applied, while increase again when the personalized recommendation is removed. Based on data-driven personalized healthcare intervention, the results demonstrate the significant potential of the proposed worker-centric self-management method for rebar workers in preventing and controlling postural ergonomic hazards during construction rebar ironwork.

      PubDate: 2018-05-29T15:27:55Z
      DOI: 10.1016/j.aei.2018.04.013
      Issue No: Vol. 37 (2018)
  • A BIM-based visualization and warning system for fire rescue
    • Authors: Xiu-Shan Chen; Chi-Chang Liu; I-Chen Wu
      Pages: 42 - 53
      Abstract: Publication date: August 2018
      Source:Advanced Engineering Informatics, Volume 37
      Author(s): Xiu-Shan Chen, Chi-Chang Liu, I-Chen Wu
      Structural fires are common disasters. In Taiwan, about 100 firefighters die during fire rescues each year, primarily because they are unaware of the causes of the fire and unfamiliar with the location’s environment. Meanwhile, evacuees often die in the panic of evacuation. To solve these problems, this research proposes a Building Information Modeling (BIM)-based visualization and warning system for fire rescue. A fire dynamics simulator (FDS) simulates various conditions of structural fires in conjunction with the visualization and integration properties of BIM, and the simulation results for temperature, carbon monoxide, and visibility can be integrated and presented in the BIM model for briefing purposes before rescue operations begin. In addition, this research integrates Internet of Things (IoT) technology, which allows real-time situation monitoring. In the event of a fire, the BIM model will immediately display the situation of the fire scene and control LED escape route pointers according to the actual situation. The primary objective of this system is to provide useful information to firefighters such that they can be aware of the fire’s environment and create an effective rescue plan. Moreover, the automated LED escape route pointer may assist the building’s occupants to escape, provide the firefighters with valuable information, and allow them quickly to discover hazards so that the number of casualties can be minimized.

      PubDate: 2018-05-29T15:27:55Z
      DOI: 10.1016/j.aei.2018.04.015
      Issue No: Vol. 37 (2018)
  • IFC Monitor – An IFC schema extension for modeling structural health
           monitoring systems
    • Authors: Michael Theiler; Kay Smarsly
      Pages: 54 - 65
      Abstract: Publication date: August 2018
      Source:Advanced Engineering Informatics, Volume 37
      Author(s): Michael Theiler, Kay Smarsly
      The pervasive emergence of sensing technologies for structural health monitoring (SHM) and the digitalization ubiquitous in engineering (“Industry 4.0”) pose increasing demands on information modeling concepts in civil engineering. While in building information modeling (BIM) conventional building information (such as geometry, material, or cost) can precisely be described using current modeling standards, information about SHM systems, referred to as “monitoring-related information”, cannot be fully described on a well-defined, formal basis. In this paper, a BIM-based approach towards describing monitoring-related information is proposed, using the Industry Foundation Classes (IFC), an open BIM standard facilitating the interoperability of BIM models, as a formal basis. First, possibilities and constraints of describing monitoring-related information with the IFC schema are discussed. Then, information necessary to describe SHM systems is integrated into a semantic model serving as a technology-independent metamodel. Next, the IFC schema is extended to enable BIM-based descriptions of SHM systems in compliance with IFC modeling capabilities, which is referred to as “IFC Monitor” schema. The IFC Monitor schema is verified with test software used in the official IFC certification program. For validation, a prototype SHM system is formally described using the IFC Monitor schema. The validation aims at checking if the IFC Monitor schema is capable of precisely describing monitoring-related information. As will be shown in this paper, the description of the prototype SHM system meets the requirements of a well-defined IFC model as specified in the official IFC certification program. As a result, the IFC Monitor schema proposed in this study advances BIM-based descriptions of SHM systems in association with structural systems being monitored on a well-defined, formal basis.

      PubDate: 2018-05-29T15:27:55Z
      DOI: 10.1016/j.aei.2018.04.011
      Issue No: Vol. 37 (2018)
  • An adaptive clustering-based genetic algorithm for the dual-gantry
           pick-and-place machine optimization
    • Authors: Tian He; Debiao Li; Sang Won Yoon
      Pages: 66 - 78
      Abstract: Publication date: August 2018
      Source:Advanced Engineering Informatics, Volume 37
      Author(s): Tian He, Debiao Li, Sang Won Yoon
      This research proposes an adaptive clustering-based genetic algorithm (ACGA) to optimize the pick-and-place operation of a dual-gantry component placement machine, which has two independent gantries that alternately place components onto a printed circuit board (PCB). The proposed optimization problem consists of several highly interrelated sub-problems, such as component allocation, nozzle and feeder setups, pick-and-place sequences, etc. In the proposed ACGA, the nozzle and component allocation decisions are made before the evolutionary search of a genetic algorithm to improve the algorithm efficiency. First, the nozzle allocation problem is modeled as a nonlinear integer programming problem and solved by a search-based heuristic that minimizes the total number of the dual-gantry cycles. Then, an adaptive clustering approach is developed to allocate components to each gantry cycle by evaluating the gantry traveling distances over the PCB and the component feeders. Numerical experiments compare the proposed ACGA to another clustering-based genetic algorithm LCO and a heuristic algorithm mPhase in the literature using 30 industrial PCB samples. The experiment results show that the proposed ACGA algorithm reduces the total gantry moving distance by 5.71% and 4.07% on average compared to the LCO and mPhase algorithms, respectively.

      PubDate: 2018-05-29T15:27:55Z
      DOI: 10.1016/j.aei.2018.04.007
      Issue No: Vol. 37 (2018)
  • Distress classification of class-imbalanced inspection data via
           correlation-maximizing weighted extreme learning machine
    • Authors: Keisuke Maeda; Sho Takahashi; Takahiro Ogawa; Miki Haseyama
      Pages: 79 - 87
      Abstract: Publication date: August 2018
      Source:Advanced Engineering Informatics, Volume 37
      Author(s): Keisuke Maeda, Sho Takahashi, Takahiro Ogawa, Miki Haseyama
      This paper presents distress classification of class-imbalanced inspection data via correlation-maximizing weighted extreme learning machine (CMWELM). For distress classification, it is necessary to extract semantic features that can effectively distinguish multiple kinds of distress from a small amount of class-imbalanced data. In recent machine learning techniques such as general deep learning methods, since effective feature transformation from visual features to semantic features can be realized by using multiple hidden layers, a large amount of training data are required. However, since the amount of training data of civil structures becomes small, it becomes difficult to perform successful transformation by using these multiple hidden layers. On the other hand, CMWELM consists of two hidden layers. The first hidden layer performs feature transformation, which can directly extract the semantic features from visual features, and the second hidden layer performs classification with solving the class-imbalanced problem. Specifically, in the first hidden layer, the feature transformation is realized by using projections obtained by maximizing the canonical correlation between visual and text features as weight parameters of the hidden layer without designing multiple hidden layers. Furthermore, the second hidden layer enables successful training of our classifier by using weighting factors concerning the class-imbalanced problem. Consequently, CMWELM realizes accurate distress classification from a small amount of class-imbalanced data.

      PubDate: 2018-05-29T15:27:55Z
      DOI: 10.1016/j.aei.2018.04.014
      Issue No: Vol. 37 (2018)
  • Integrating multi-granularity model and similarity measurement for
           transforming process data into different granularity knowledge
    • Authors: Yubin Fan; Chuang Liu; Junbiao Wang
      Pages: 88 - 102
      Abstract: Publication date: August 2018
      Source:Advanced Engineering Informatics, Volume 37
      Author(s): Yubin Fan, Chuang Liu, Junbiao Wang
      The core of intelligent manufacturing is to incorporate the expert knowledge in manufacturing process, and knowledge transformation is the key to knowledge accumulation and application. In this paper, the research carried on transformation for different granularity knowledge from the cases of sheet metal parts in process planning. First of all, this paper analyzes the difference of organization structure between process data and knowledge in the base. The multi-granularity model of process knowledge is established in the form of tuple, which helps to clarify the hierarchy structure and internal relations. Thereafter, the concrete process is presented to transform single granularity process data into multi-granularity process knowledge, i.e., process data extraction, state determination and knowledge construction. With respect to state determination, similarity measure methods for different granularity knowledge are established to reduce the redundancy in the transformation process. As a novel approach, sequence alignment based on edit distance is proposed to calculate similarity exactly between two process flows. Finally, the knowledge transformation tool for different granularity knowledge is developed to enhance knowledge acquisition and improve the strength of knowledge reuse in fabrication order design for sheet metal parts through application of the above method. Also an example is given to illustrate the usefulness of the proposed method.

      PubDate: 2018-05-29T15:27:55Z
      DOI: 10.1016/j.aei.2018.04.012
      Issue No: Vol. 37 (2018)
  • Crowd simulation-based knowledge mining supporting building evacuation
    • Authors: Calin Boje; Haijiang Li
      Pages: 103 - 118
      Abstract: Publication date: August 2018
      Source:Advanced Engineering Informatics, Volume 37
      Author(s): Calin Boje, Haijiang Li
      Assessing building evacuation performance designs in emergency situations requires complex scenarios which need to be prepared and analysed using crowd simulation tools, requiring significant manual input. With current procedures, every design iteration requires several simulation scenarios, leading to a complicated and time-consuming process. This study aims to investigate the level of integration between digital building models and crowd simulation, within the scope of design automation. A methodology is presented in which existing ontology tools facilitate knowledge representation and mining throughout the process. Several information models are used to integrate, automate and provide feedback to the design decision-making processes. The proposed concept thus reduces the effort required to create valid simulation scenarios by applying represented knowledge, and provides feedback based on results and design objectives. To apply and test the methodology a system was developed, which is introduced here. The context of building performance during evacuation scenarios is considered, but additional design perspectives can be included. The system development section expands on the essential theoretical concepts required and the case study section shows a practical implementation of the system.

      PubDate: 2018-05-29T15:27:55Z
      DOI: 10.1016/j.aei.2018.05.002
      Issue No: Vol. 37 (2018)
  • Scan-to-BIM for ‘secondary’ building components
    • Authors: Antonio Adán; Blanca Quintana; Samuel A. Prieto; Frédéric Bosché
      Pages: 119 - 138
      Abstract: Publication date: August 2018
      Source:Advanced Engineering Informatics, Volume 37
      Author(s): Antonio Adán, Blanca Quintana, Samuel A. Prieto, Frédéric Bosché
      Works dealing with Scan-to-BIM have, to date, principally focused on 'structural' components such as floors, ceilings and walls (with doors and windows). But the control of new facilities and the production of their corresponding as-is BIM models requires the identification and inspection of numerous other building components and objects, e.g. MEP components, such as plugs, switches, ducts, and signs. In this paper, we present a new 6D-based (XYZ + RGB) approach that processes dense coloured 3D points provided by terrestrial laser scanners in order to recognize the aforementioned smaller objects that are commonly located on walls. This paper focuses on the recognition of objects such as sockets, switches, signs, extinguishers and others. After segmenting the point clouds corresponding to the walls of a building, a set of candidate objects are detected independently in the colour and geometric spaces, and an original consensus procedure integrates both results in order to infer recognition. Finally, the recognized object is positioned and inserted in the as-is semantically-rich 3D model, or BIM model. The assessment of the method has been carried out in simulated scenarios under virtual scanning providing high recognition rates and precise positioning results. Experimental tests in real indoors using our MoPAD (Mobile Platform for Autonomous Digitization) platform have also yielded promising results.

      PubDate: 2018-05-29T15:27:55Z
      DOI: 10.1016/j.aei.2018.05.001
      Issue No: Vol. 37 (2018)
  • Automated detection of workers and heavy equipment on construction sites:
           A convolutional neural network approach
    • Authors: Weili Fang; Lieyun Ding; Botao Zhong; Peter E.D. Love; Hanbin Luo
      Pages: 139 - 149
      Abstract: Publication date: August 2018
      Source:Advanced Engineering Informatics, Volume 37
      Author(s): Weili Fang, Lieyun Ding, Botao Zhong, Peter E.D. Love, Hanbin Luo
      Detecting the presence of workers, plant, equipment, and materials (i.e. objects) on sites to improve safety and productivity has formed an integral part of computer vision-based research in construction. Such research has tended to focus on the use of computer vision and pattern recognition approaches that are overly reliant on the manual extraction of features and small datasets (<10k images/label), which can limit inter and intra-class variability. As a result, this hinders their ability to accurately detect objects on construction sites and generalization to different datasets. To address this limitation, an Improved Faster Regions with Convolutional Neural Network Features (IFaster R-CNN) approach is used to automatically detect the presence of objects in real-time is developed, which comprises: (1) the establishment dataset of workers and heavy equipment to train the CNN; (2) extraction of feature maps from images using deep model; (3) extraction of a region proposal from feature maps; and (4) object recognition. To validate the model’s ability to detect objects in real-time, a specific dataset is established to train the IFaster R-CNN models to detect workers and plant (e.g. excavator). The results reveal that the IFaster R-CNN is able to detect the presence of workers and excavators at a high level of accuracy (91% and 95%). The accuracy of the proposed deep learning method exceeds that of current state-of-the-art descriptor methods in detecting target objects on images.

      PubDate: 2018-05-29T15:27:55Z
      DOI: 10.1016/j.aei.2018.05.003
      Issue No: Vol. 37 (2018)
  • Detecting healthy concrete surfaces
    • Authors: Philipp Hüthwohl; Ioannis Brilakis
      Pages: 150 - 162
      Abstract: Publication date: August 2018
      Source:Advanced Engineering Informatics, Volume 37
      Author(s): Philipp Hüthwohl, Ioannis Brilakis
      Teams of engineers visually inspect more than half a million bridges per year in the US and EU. There is clear evidence to suggest that they are not able to meet all bridge inspection guideline requirements due to a combination of the level of detail expected, the limited time available and the large area of bridge surfaces to be inspected. Methods have been proposed to address this problem through damage detection in visual data, yet the inspection load remains high. This paper proposes a method to tackle this problem by detecting (and disregarding) healthy concrete areas that comprise over 80–90% of the total area. The originality of this work lies in the method’s slicing and merging to enable the sequential processing of high resolution bridge surface textures with a state of the art classifier to distinguish between healthy and potentially unhealthy surface texture. Morphological operators are then used to generate an outline mask to highlight the classification results in the surface texture. The training and validation set consists of 1028 images taken from multiple Department of Transportation bridge inspection databases and data collection from ten highway bridges around Cambridge. The presented method achieves a search space reduction for an inspector of 90.1% with a risk of missing a defect patch of 8.2%. This work is of great significance for bridge inspectors as they are now able to spend more time on assessing potentially unhealthy surface regions instead of searching for these needles in a mainly healthy concrete surface haystack.

      PubDate: 2018-05-29T15:27:55Z
      DOI: 10.1016/j.aei.2018.05.004
      Issue No: Vol. 37 (2018)
  • A review of 3D reconstruction techniques in civil engineering and their
    • Authors: Zhiliang Ma; Shilong Liu
      Pages: 163 - 174
      Abstract: Publication date: August 2018
      Source:Advanced Engineering Informatics, Volume 37
      Author(s): Zhiliang Ma, Shilong Liu
      Three-dimensional (3D) reconstruction techniques have been used to obtain the 3D representations of objects in civil engineering in the form of point cloud models, mesh models and geometric models more often than ever, among which, point cloud models are the basis. In order to clarify the status quo of the research and application of the techniques in civil engineering, literature retrieval is implemented by using major literature databases in the world and the result is summarized by analyzing the abstracts or the full papers when required. First, the research methodology is introduced, and the framework of 3D reconstruction techniques is established. Second, 3D reconstruction techniques for generating point clouds and processing point clouds along with the corresponding algorithms and methods are reviewed respectively. Third, their applications in reconstructing and managing construction sites and reconstructing pipelines of Mechanical, Electrical and Plumbing (MEP) systems, are presented as typical examples, and the achievements are highlighted. Finally, the challenges are discussed and the key research directions to be addressed in the future are proposed. This paper contributes to the knowledge body of 3D reconstruction in two aspects, i.e. summarizing systematically the up-to-date achievements and challenges for the applications of 3D reconstruction techniques in civil engineering, and proposing key future research directions to be addressed in the field.

      PubDate: 2018-05-29T15:27:55Z
      DOI: 10.1016/j.aei.2018.05.005
      Issue No: Vol. 37 (2018)
  • Topological mapping and assessment of multiple settlement time series in
           deep excavation: A complex network perspective
    • Authors: Cheng Zhou; Lieyun Ding; Ying Zhou; Hanbin Luo
      Pages: 1 - 19
      Abstract: Publication date: April 2018
      Source:Advanced Engineering Informatics, Volume 36
      Author(s): Cheng Zhou, Lieyun Ding, Ying Zhou, Hanbin Luo
      This study proposed a novel methodology that integrates complex network theory and multiple time series to enhance the systematic understanding of the daily settlement behavior in deep excavation. The original time series of ground surface, surrounding buildings, and structure settlement instrumentation data over an excavation time period were measured into a similarity matrix with correlation coefficients. A threshold was then determined and binarized into adjacent matrix to identify the optimal topology and structure of the complex network. The reconstructed settlement network has nodes corresponding to multiple settlement time series individually and edges regarded as nonlinear relationships between them. A deep excavation case study of the metro station project in the Wuhan Metro network, China, was applied to validate the feasibility and potential value of the proposed approach. Results of the topological analysis corroborate a small-world phenomenon with highly compacted interactions and provide the assessment of the significance among multiple settlement time series. This approach, which provides a new way to assess the safety monitoring data in underground construction, can be implemented as a tool for extracting macro- and micro-level decision information from multiple settlement time series in deep excavation from complex system perspectives.

      PubDate: 2018-04-15T06:35:53Z
      DOI: 10.1016/j.aei.2018.02.005
      Issue No: Vol. 36 (2018)
  • A new wind power prediction method based on ridgelet transforms, hybrid
           feature selection and closed-loop forecasting
    • Authors: Hua Leng; Xinran Li; Jiran Zhu; Haiguo Tang; Zhidan Zhang; Noradin Ghadimi
      Pages: 20 - 30
      Abstract: Publication date: April 2018
      Source:Advanced Engineering Informatics, Volume 36
      Author(s): Hua Leng, Xinran Li, Jiran Zhu, Haiguo Tang, Zhidan Zhang, Noradin Ghadimi
      To reduce network integration and boost energy trading, wind power forecasting can play an important role in power systems. Furthermore, the uncertain and nonconvex behavior of wind signals make its prediction complex. For this purpose, accurate prediction tools are needed. In this paper, a ridgelet transform is applied to a wind signal to decompose it into sub-signals. The output of ridgelet transform is considered as input of new feature selection to identify the best candidates to be used as the forecast engine input. Finally, a new hybrid closed loop forecast engine is proposed based on a neural network and an intelligent algorithm to predict the wind signal. The effectiveness of the proposed forecast model is extensively evaluated on a real-world electricity market through a comparison with well-known forecasting methods. The obtained numerical results demonstrate the validity of proposed method.

      PubDate: 2018-04-15T06:35:53Z
      DOI: 10.1016/j.aei.2018.02.006
      Issue No: Vol. 36 (2018)
  • Comparison of multi-objective evolutionary algorithms in hybrid Kansei
           engineering system for product form design
    • Authors: Meng-Dar Shieh; Yongfeng Li; Chih-Chieh Yang
      Pages: 31 - 42
      Abstract: Publication date: April 2018
      Source:Advanced Engineering Informatics, Volume 36
      Author(s): Meng-Dar Shieh, Yongfeng Li, Chih-Chieh Yang
      Understanding the affective needs of customers is crucial to the success of product design. Hybrid Kansei engineering system (HKES) is an expert system capable of generating products in accordance with the affective responses. HKES consists of two subsystems: forward Kansei engineering system (FKES) and backward Kansei engineering system (BKES). In previous studies, HKES was based primarily on single-objective optimization, such that only one optimal design was obtained in a given simulation run. The use of multi-objective evolutionary algorithm (MOEA) in HKES was only attempted using the non-dominated sorting genetic algorithm-II (NSGA-II), such that very little work has been conducted to compare different MOEAs. In this paper, we propose an approach to HKES combining the methodologies of support vector regression (SVR) and MOEAs. In BKES, we constructed predictive models using SVR. In FKES, optimal design alternatives were generated using MOEAs. Representative designs were obtained using fuzzy c-means algorithm for clustering the Pareto front into groups. To enable comparison, we employed three typical MOEAs: NSGA-II, the Pareto envelope-based selection algorithm-II (PESA-II), and the strength Pareto evolutionary algorithm-2 (SPEA2). A case study of vase form design was provided to demonstrate the proposed approach. Our results suggest that NSGA-II has good convergence performance and hybrid performance; in contrast, SPEA2 provides the strong diversity required by designers. The proposed HKES is applicable to a wide variety of product design problems, while providing creative design ideas through the exploration of numerous Pareto optimal solutions.

      PubDate: 2018-04-15T06:35:53Z
      DOI: 10.1016/j.aei.2018.02.002
      Issue No: Vol. 36 (2018)
  • Sine-square embedded fuzzy sets versus type-2 fuzzy sets
    • Authors: Mehmet Karakose; Hasan Yetis; Erhan Akin
      Pages: 43 - 54
      Abstract: Publication date: April 2018
      Source:Advanced Engineering Informatics, Volume 36
      Author(s): Mehmet Karakose, Hasan Yetis, Erhan Akin
      The uncertainty is an inherent part of real-world applications. Type-2 fuzzy sets minimize the effects of uncertainties that cannot be modeled using type-1 fuzzy sets. However, the computational complexity of the type-2 fuzzy sets is very high and it is more difficult than type-1 fuzzy sets to use and understand. This paper proposes sine-square embedded fuzzy sets and gives a comparison with type-2 and nonstationary fuzzy sets. The sine-square embedded fuzzy sets consist of type-1 fuzzy sets and the sine function. The footprint of uncertainty in the type-2 fuzzy sets is provided with amplitude and frequency of sine-square function in the proposed algorithm. The proposed sine-square embedded fuzzy sets are much simpler than the type-2 fuzzy sets and the nonstationary fuzzy sets. Two control applications that are chosen as position control of a dc motor and simulation of human lifting motion using five-segment human model are carried out to demonstrate the effectiveness of the proposed approach.

      PubDate: 2018-04-15T06:35:53Z
      DOI: 10.1016/j.aei.2018.02.007
      Issue No: Vol. 36 (2018)
  • Robust sensor placement for pipeline monitoring: Mixed integer and greedy
    • Authors: Lina Sela; Saurabh Amin
      Pages: 55 - 63
      Abstract: Publication date: April 2018
      Source:Advanced Engineering Informatics, Volume 36
      Author(s): Lina Sela, Saurabh Amin
      Intelligent water systems – aided by sensing technologies – have been identified as an important mechanism towards ensuring the resilience of urban systems. In this work, we study the problem of sensor placement that is robust to intermittent failures of sensors, i.e. sensor interruptions. We propose robust mixed integer optimization (RMIO) and robust greedy approximation (RGA) solution approaches. The underlying idea of both approaches is to promote solutions that achieve multiple detectability of events, such that these events remain detectable even when some sensors are interrupted. Additionally, we apply a previously proposed greedy approximation approach for solving the robust submodular function optimization (RSFO) problem. We compare scalability of these approaches and the quality of the solutions using a set of real water-networks. Our results demonstrate that considering sensor interruptions in the design stage improves sensor network performance. Importantly, we find that although the detection performances of RMIO and RGA approaches are comparable, RMIO generally has better performance than RGA, and is scalable to large-scale networks. Furthermore, the results demonstrate that RMIO and RGA approaches tend to outperform the RSFO approach.

      PubDate: 2018-04-15T06:35:53Z
      DOI: 10.1016/j.aei.2018.02.004
      Issue No: Vol. 36 (2018)
  • Guess your size: A hybrid model for footwear size recommendation
    • Authors: Shan Huang; Zhi Wang; Yong Jiang
      Pages: 64 - 75
      Abstract: Publication date: April 2018
      Source:Advanced Engineering Informatics, Volume 36
      Author(s): Shan Huang, Zhi Wang, Yong Jiang
      In recent years, online shopping for footwear has rapidly increased. However, the user experience has not been satisfactory because of the size mismatch problem, i.e., customers often fail to choose the right size online. Traditional size selection schemes, including those suggesting that users select footwear sizes according to their past experiences or those based on simple measurements, usually result in a high return rate of up to 35 % . The limitation of the traditional size selection schemes is that they fail to consider (1) the characteristics of foot shapes and (2) the preferences of individual customers. In this paper, we propose a size recommendation framework that is jointly based on 3D (foot and last) features and user preference. First, we report measurement studies of foot shape characteristics based on foot data for 10 K individuals. Our findings reveal that users have diverse foot shapes and different personal preferences regarding size matching. Second, based on our measurement insights, we design a size recommendation model that jointly considers 3D foot models, shoe characteristics and user preferences. We also provide a predictive model that predicts comfort levels for particular parts of the foot based on the given size recommendation. Finally, our data-driven experiments show that the proposed size recommendation improves the size selection accuracy to 92 % , which is a 22 % improvement compared to conventional solutions.

      PubDate: 2018-04-15T06:35:53Z
      DOI: 10.1016/j.aei.2018.02.003
      Issue No: Vol. 36 (2018)
  • Quantitative lifecycle risk analysis of the development of a just-in-time
           transportation network system
    • Authors: John P.T. Mo; Matthew Cook
      Pages: 76 - 85
      Abstract: Publication date: April 2018
      Source:Advanced Engineering Informatics, Volume 36
      Author(s): John P.T. Mo, Matthew Cook
      The automotive manufacturing industry is under financial pressure due to massive cost structure, relatively small scale operation and strong global competition. In order to improve their operational cost efficiency, companies have adopt lean principles in all their manufacturing activities, in particular, just-in-time supply chain. However, a consequence of this policy makes the transportation network from the local supply chain time critical. This paper uses an enterprise system model integrated with a quantitative method to study a manufacturing company’s logistics system re-development project. The quantitative risk analysis examines the project’s systems engineering management plan to see if it is sufficiently to mitigate risks in design, monitoring and validation of the project’s lifecycle processes. The computed risk profile shows a trend of decreasing risk and suggests areas of improvement in the systems engineering plan to ensure greater probability of success. The research assumes a single risk profile for the supply chain. Research is continuing in expanding to more accurate risk profile of the project when partners of the supply chain have individual profiles.

      PubDate: 2018-04-15T06:35:53Z
      DOI: 10.1016/j.aei.2018.03.002
      Issue No: Vol. 36 (2018)
  • Toolbox for super-structured and super-structure free multi-disciplinary
           building spatial design optimisation
    • Authors: Sjonnie Boonstra; Koen van der Blom; Hèrm Hofmeyer; Michael T.M. Emmerich; Jos van Schijndel; Pieter de Wilde
      Pages: 86 - 100
      Abstract: Publication date: April 2018
      Source:Advanced Engineering Informatics, Volume 36
      Author(s): Sjonnie Boonstra, Koen van der Blom, Hèrm Hofmeyer, Michael T.M. Emmerich, Jos van Schijndel, Pieter de Wilde
      Multi-disciplinary optimisation of building spatial designs is characterised by large solution spaces. Here two approaches are introduced, one being super-structured and the other super-structure free. Both are different in nature and perform differently for large solution spaces and each requires its own representation of a building spatial design, which are also presented here. A method to combine the two approaches is proposed, because the two are prospected to supplement each other. Accordingly a toolbox is presented, which can evaluate the structural and thermal performances of a building spatial design to provide a user with the means to define optimisation procedures. A demonstration of the toolbox is given where the toolbox has been used for an elementary implementation of a simulation of co-evolutionary design processes. The optimisation approaches and the toolbox that are presented in this paper will be used in future efforts for research into- and development of optimisation methods for multi-disciplinary building spatial design optimisation.

      PubDate: 2018-04-15T06:35:53Z
      DOI: 10.1016/j.aei.2018.01.003
      Issue No: Vol. 36 (2018)
  • A simulation methodology for a system of product life cycle systems
    • Authors: Hideki Kobayashi; Takuya Matsumoto; Shinichi Fukushige
      Pages: 101 - 111
      Abstract: Publication date: April 2018
      Source:Advanced Engineering Informatics, Volume 36
      Author(s): Hideki Kobayashi, Takuya Matsumoto, Shinichi Fukushige
      To realize environmental sustainability, the flow of natural resources into industrial systems must be reduced and stabilized at a suitable level. One way to reduce resource flows in society is to establish resource-circulating manufacturing systems. To foster the circulation of resources in industry, life cycle simulation (LCS) technologies, which are based on discrete-event modeling, have been developed to dynamically evaluate the life cycles of products from resource extraction to end of life from both environmental and economic aspects. In reality, various industrial products interact with each other in unanticipated ways, and then these interactions affect the material flows in product life cycles. This type of complex system is called a system of systems (SoS). Focusing on this issue, we expand the evaluation's system boundary to include a system of multiple product life cycle systems. To handle an SoS quantitatively, we introduce typical types of interactions between product life cycle systems. The purpose of this study was to propose a new LCS methodology, called “LCS4SoS,” that focuses on an SoS consisting of different kinds of product life cycle systems. A prototype system of LCS4SoS was implemented based on this proposed methodology. Through a case study, it was found that the proposed methodology is useful for evaluating an SoS consisting of multi-product life cycle systems.

      PubDate: 2018-04-15T06:35:53Z
      DOI: 10.1016/j.aei.2018.03.001
      Issue No: Vol. 36 (2018)
  • Soft sensor based on stacked auto-encoder deep neural network for air
           preheater rotor deformation prediction
    • Authors: Xiao Wang; Han Liu
      Pages: 112 - 119
      Abstract: Publication date: April 2018
      Source:Advanced Engineering Informatics, Volume 36
      Author(s): Xiao Wang, Han Liu
      Soft sensors have been widely used in industrial processes over the past two decades because they use easy-to-measure process variables to predict difficult-to-measure ones. Some success has been achieved by the dominant traditional methods of modeling soft sensors based on statistics, such as principal components analysis (PCA) and partial least square (PLS), but such sensors usually become inaccurate and inefficient when processing strong nonlinear data. In this paper, a new soft sensor modeling approach is proposed based on a deep learning network. First, stacked auto-encoders (SAEs) are employed to extract high-level feature representations of the input data. In the process of training each layer of a SAE, the Limited-memory Broyden-Fletcher-Goldfarb-Shanno algorithm (L-BFGS) is adopted to optimize the weights parameters. Then, a support vector regression (SVR) is added to predict the target value on the basis of the features obtained from the SAE. To improve the model performance, Genetic Algorithm (GA) is used to obtain the optimal parameters of the SVR. To evaluate the proposed method, a soft sensor model for estimating the rotor deformation of air preheaters in a thermal power plant boiler is studied. The experimental results demonstrate that the soft sensor based on the SAE-SVR algorithm is more effective than the existing methods are.

      PubDate: 2018-04-15T06:35:53Z
      DOI: 10.1016/j.aei.2018.03.003
      Issue No: Vol. 36 (2018)
  • Consumer driven product technology function deployment using social media
           and patent mining
    • Authors: Amy J.C. Trappey; Charles V. Trappey; Chin-Yuan Fan; Ian J.Y. Lee
      Pages: 120 - 129
      Abstract: Publication date: April 2018
      Source:Advanced Engineering Informatics, Volume 36
      Author(s): Amy J.C. Trappey, Charles V. Trappey, Chin-Yuan Fan, Ian J.Y. Lee
      The capability of identifying real-time customer needs is critical for manufacturers that provide short life cycle consumer products such as smart phones. Companies need to form research and development (R&D) strategies to improve key functional features for short lifespan products to reflect the adoption of innovative technologies and changing customer expectations. With the pervasive use of the Internet, this research crawls and analyzes the online voice of customers (VoC), overcoming the time lag of offline surveys, to identify and prioritize product functions for deployment using extended quality function deployment (eQFD) models. In this research, the novel analytics of the manufacturer’s patent portfolio is added as an additional eQFD dimension to map ranked functional improvements to a manufacturer’s R&D capabilities. Thus, a computer supported eQFD system is developed to perform the unique mappings and gap analyses between the VoC, the prioritized product functions, and the manufacturer’s patent portfolio. The newly developed eQFD methodology and its novel discoveries are demonstrated in detail using a case study of three smart phones launched during the same time frame. The products include the Samsung Galaxy S7, the Huawei Honor 5X, and the ASUS Zenfone 3. The newly developed methodology is generally applicable to support VoC-centric product function deployment and R&D strategic planning in other domains.

      PubDate: 2018-04-15T06:35:53Z
      DOI: 10.1016/j.aei.2018.03.004
      Issue No: Vol. 36 (2018)
  • Integrated BIM, game engine and VR technologies for healthcare design: A
           case study in cancer hospital
    • Authors: Yu-Cheng Lin; Yen-Pei Chen; Huey-Wen Yien; Chao-Yung Huang; Yu-Chih Su
      Pages: 130 - 145
      Abstract: Publication date: April 2018
      Source:Advanced Engineering Informatics, Volume 36
      Author(s): Yu-Cheng Lin, Yen-Pei Chen, Huey-Wen Yien, Chao-Yung Huang, Yu-Chih Su
      The results of healthcare design should meet the requirements of design teams as well as healthcare stakeholders. However, misunderstandings that occur between the design teams and healthcare stakeholders when using 2D illustrations leads to the need for re-design and rework during the design phase. To overcome this problem, this study develops a Database-supported VR/BIM-based Communication and Simulation (DVBCS) system integrated with BIM, game engine and VR technologies for healthcare design special in the Semi-immersed VR environment. The DVBCS system is applied in a case study of a design project of a cancer center in Taiwan to verify the system and demonstrate its effectiveness in practice. The results demonstrate that a DVBCS system is an effective visual communication and simulation platform for healthcare design. The advantage of the DVBCS system lies not only in improving the communication efficiency between the design teams and healthcare stakeholders, but also in facilitating visual interactions and easing the decision-making process while communicating in the 3D VR/BIM environment. The effective use of the proposed DVBCS system will assist design teams and stakeholders significantly in systematically handling healthcare design work in future healthcare design.

      PubDate: 2018-04-15T06:35:53Z
      DOI: 10.1016/j.aei.2018.03.005
      Issue No: Vol. 36 (2018)
  • Research on static service BOM transformation for complex products
    • Authors: Chunliu Zhou; Xiaobing Liu; Fanghong Xue; Hongguang Bo; Kai Li
      Pages: 146 - 162
      Abstract: Publication date: April 2018
      Source:Advanced Engineering Informatics, Volume 36
      Author(s): Chunliu Zhou, Xiaobing Liu, Fanghong Xue, Hongguang Bo, Kai Li
      With the trend toward servitization and the development of big data, host manufacturers of complex products are exploring methods to effectively organize product data for providing satisfactory maintenance, repair and overhaul (MRO) service. With the motivation, this research develops a service bill-of-material (SBOM) from a product life cycle perspective. We propose a generic-SBOM (G-SBOM) to manage common information of one product type or batched products and an individual-SBOM (I-SBOM) to administrate instance products, together constituting a static SBOM. The transformation process from engineering BOM, manufacturing resume and purchased part information to SBOM are explained in detail and described in a mathematical model. The compound SBOM reduces data redundancy and meets the basic requirements of MRO service, such as position management and alternative parts management. At the same time, its formation process reflects product lifecycle data integration for complex products. Finally, this method is verified by a product example and realized in a prototype XBOM system for a high-speed train manufacturer enterprise in China.

      PubDate: 2018-04-15T06:35:53Z
      DOI: 10.1016/j.aei.2018.02.008
      Issue No: Vol. 36 (2018)
  • Systematic design space exploration using a template-based ontological
    • Authors: Ru Wang; Anand Balu Nellippallil; Guoxin Wang; Yan Yan; Janet K. Allen; Farrokh Mistree
      Pages: 163 - 177
      Abstract: Publication date: April 2018
      Source:Advanced Engineering Informatics, Volume 36
      Author(s): Ru Wang, Anand Balu Nellippallil, Guoxin Wang, Yan Yan, Janet K. Allen, Farrokh Mistree
      The realization of complex engineered systems using models that are typically incomplete, inaccurate and not of equal fidelity requires the understanding and prediction of process behavior in design. This necessitates the need for extending designer’s abilities in making design decisions that are robust, flexible and modifiable particularly in the early stages of design. To address this requirement, we propose in this paper, an ontology for design space exploration and a template-based ontological method that supports systematic design space exploration ensuring the determination of the right combination of design information that meets the different goals and requirements set for a process chain. Using the proposed method, a designer is able to (1) systematically adjust the design space in due time to manage the risks of errors accumulating and propagating during the design of different stages of the process chain, (2) improve the ability to communicate and understand the interactions between design information in the process chain. We achieve the said through (1) procedure for design space exploration is identified to determine the sequence of activities needed for the systematic exploration of design space under uncertainty; (2) the decision-based design information flow is archived using the design space exploration process template and represented by utilizing frame-based ontology to facilitate the management of re-usable information. We demonstrate the efficacy of this template-based ontological method for design space exploration by carrying out the design of a multi-stage hot rod rolling system in steel manufacturing process chain.

      PubDate: 2018-04-15T06:35:53Z
      DOI: 10.1016/j.aei.2018.03.006
      Issue No: Vol. 36 (2018)
  • Developing final as-built BIM model management system for owners during
           project closeout: A case study
    • Authors: Yu-Cheng Lin; Cheng-Ping Lin; Hsin-Tzu Hu; Yu-Chih Su
      Pages: 178 - 193
      Abstract: Publication date: April 2018
      Source:Advanced Engineering Informatics, Volume 36
      Author(s): Yu-Cheng Lin, Cheng-Ping Lin, Hsin-Tzu Hu, Yu-Chih Su
      To apply final as-built BIM models to facility management (FM) during the operation phase, it is important for owners to obtain an accurate, final as-built model from the general contractors (GCs) following project closeout. Confirming the accuracy of the final as-built BIM model is one of the most important works executed by owners to meet the accuracy requirement of final as-built models for FM. However, many practical problems arise relating to the management of final as-built models such as final as-built model mismatch, the lack of available final as-built models, and the entry of incorrect non-geometric information into the final as-built models. To solve these practical problems, this study develops a Final As-built BIM Model Management (FABMM) system for owners to handle final as-built BIM model inspection, modification, and confirmation (BMIMC) work beyond project closeout. The proposed approach and system can be used to manage the status and results of BMIMC management work for the final as-built BIM model to be performed. The proposed approach and system were applied in a case study in a selected building in Taiwan to verify and demonstrate its practical effectiveness. This study identifies the benefits, limitations, and conclusions of the FABMM system, and presents suggestions for its further application.

      PubDate: 2018-04-15T06:35:53Z
      DOI: 10.1016/j.aei.2018.04.001
      Issue No: Vol. 36 (2018)
  • A subspace learning-based feature fusion and open-set fault diagnosis
           approach for machinery components
    • Authors: Ye Tian; Zili Wang; Lipin Zhang; Chen Lu; Jian Ma
      Pages: 194 - 206
      Abstract: Publication date: April 2018
      Source:Advanced Engineering Informatics, Volume 36
      Author(s): Ye Tian, Zili Wang, Lipin Zhang, Chen Lu, Jian Ma
      Open-set fault diagnosis is an important but often neglected issue in machinery components, as in practical industrial applications, the failure data are in most cases unavailable or incomplete at the training stage, leading to the failure of most closed-set methods based on fault classifiers. Thus, based on the subspace learning methods, this paper proposes an open-set fault diagnosis approach with self-adaptive ability. First, for feature fusion, without using traditional dimensionality reduction methods, a data visualization method based on t-distributed stochastic neighbor embedding is employed for its ability in mining and enhancing the fault feature separability, which is the key in fault recognition. Then, for open-set fault diagnosis, to detect unknown fault classes and recognize known health states in only one model, the kernel null Foley-Sammon transform is applied to build a null space. To reduce the misjudgment rate and increase the detection accuracy, a self-adaptive threshold is automatically set according to the testing data. Moreover, the final recognition results are described as distances, which helps the operators to make maintenance decision. Case studies based on vibration datasets of a plunger pump, a centrifugal pump and a gearbox demonstrate the effectiveness of the proposed approach.
      Graphical abstract image

      PubDate: 2018-04-15T06:35:53Z
      DOI: 10.1016/j.aei.2018.04.006
      Issue No: Vol. 36 (2018)
  • Automatic classification of fine-grained soils using CPT measurements and
           Artificial Neural Networks
    • Authors: Cormac Reale; Kenneth Gavin; Lovorka Librić; Danijela Jurić-Kaćunić
      Pages: 207 - 215
      Abstract: Publication date: April 2018
      Source:Advanced Engineering Informatics, Volume 36
      Author(s): Cormac Reale, Kenneth Gavin, Lovorka Librić, Danijela Jurić-Kaćunić
      Soil classification is a means of grouping soils into categories according to a shared set of properties or characteristics that will exhibit similar engineering behaviour under loading. Correctly classifying site conditions is an important, costly, and time-consuming process which needs to be carried out at every building site prior to the commencement of construction or the design of foundation systems. This paper presents a means of automating classification for fine-grained soils, using a feed-forward ANN (Artificial Neural Networks) and CPT (Cone Penetration Test) measurements. Thus representing a significant saving of both time and money streamlining the construction process. 216 pairs of laboratory results and CPT tests were gathered from five locations across Northern Croatia and were used to train, test, and validate the ANN models. The resultant Neural Networks were saved and were subjected to a further external verification using CPT data from the Veliki vrh landslide. A test site, which the model had not previously been exposed to. The neural network approach proved extremely adept at predicting both ESCS (European Soil Classification System) and USCS (Unified Soil Classification System) soil classifications, correctly classifying almost 90% of soils. While the soils that were incorrectly classified were only partially misclassified. The model was compared to a previously published model, which was compiled using accepted industry standard soil parameter correlations and was shown to be a substantial improvement, in terms of correlation coefficient, absolute average error, and the accuracy of soil classification according to both USCS and ESCS guidelines. The study confirms the functional link between CPT results, the percentage of fine particles FC, the liquid limit w L and the plasticity index I P. As the training database grows in size, the approach should make soil classification cheaper, faster and less labour intensive.

      PubDate: 2018-04-15T06:35:53Z
      DOI: 10.1016/j.aei.2018.04.003
      Issue No: Vol. 36 (2018)
  • Tender calls search using a procurement product named entity recogniser
    • Authors: Ahmad Mehrbod; António Grilo
      Pages: 216 - 228
      Abstract: Publication date: April 2018
      Source:Advanced Engineering Informatics, Volume 36
      Author(s): Ahmad Mehrbod, António Grilo
      A product search service in an e-Procurement Marketplace can help the suppliers to find the best suitable tenders according to their products. Various possible ways to define and specify a product by different companies make it difficult to match a tender as a product request with the similar products offered by the suppliers. Semantic search engines try to overcome this problem by understanding the intent and contextual meaning of the words within a search domain. A fundamental part of such search engines can be a named entity recogniser that extracts desired searchable elements from the search context. This paper develops a recogniser that can extract “Procurement Product” mentions from tenders and other procurement documents. A self-learning approach has been adopted in order to train the model for extracting product mentions. The proposed approach uses already known product mentions in tenders as the training data to train the model and then use the trained model to recognize the product mentions from other tenders. The accuracy of the model has been tested evaluated using tenders that have been published in public procurement e-marketplaces. The results show that the proposed approach achieved high values of precision and recall in different test datasets. The recogniser can be used as the search element extractor for semantic search in procurement e-marketplaces. Therefore, the improvement of search performance by using the recogniser is also tested in finding tenders from different public procurement resources. The results show the semantic search process which uses the recogniser improves the search precision by about 25%.

      PubDate: 2018-05-29T15:27:55Z
      DOI: 10.1016/j.aei.2018.04.005
      Issue No: Vol. 36 (2018)
  • Automated residential building detection from airborne LiDAR data with
           deep neural networks
    • Authors: Zixiang Zhou; Jie Gong
      Pages: 229 - 241
      Abstract: Publication date: April 2018
      Source:Advanced Engineering Informatics, Volume 36
      Author(s): Zixiang Zhou, Jie Gong
      Detection of building objects in airborne LiDAR data is an essential task in many types of geospatial data applications such as urban reconstruction and damage assessment. Traditional approaches used in building detection often rely on shape primitives that can be detected by 2D/3D computer vision techniques. These approaches require carefully engineered features which tend to be specific to building types. Furthermore, these approaches are often computationally expensive with the increase of data size. In this paper, we propose a novel approach that employs a deep neural network to recognize and extract residential building objects in airborne LiDAR data. This proposed approach does not require any pre-defined geometric or texture features, and it is applicable to airborne LiDAR data sets with varied point densities and with damaged building objects. The latter makes our approach particularly useful in damage assessment applications. The research results show that the proposed approach is capable of achieving the state-of-the-art accuracy in building detection in these different types of point cloud data sets.

      PubDate: 2018-05-29T15:27:55Z
      DOI: 10.1016/j.aei.2018.04.002
      Issue No: Vol. 36 (2018)
  • Strategic information revelation in collaborative design
    • Authors: Adam Dachowicz; Siva Chaitanya Chaduvula; Mikhail J. Atallah; Ilias Bilionis; Jitesh H. Panchal
      Pages: 242 - 253
      Abstract: Publication date: April 2018
      Source:Advanced Engineering Informatics, Volume 36
      Author(s): Adam Dachowicz, Siva Chaitanya Chaduvula, Mikhail J. Atallah, Ilias Bilionis, Jitesh H. Panchal
      Confidentiality preservation is of high concern in collaboration, which may involve the flow of sensitive information between collaborators. This concern is a potential barrier to forming collaborations that may otherwise enhance each collaborator’s individual contribution, and raises the need to study the trade-off between value gain and confidentiality loss from information exchange. In this paper, we analyze this trade-off by considering different revelation strategies in a collaborative design scenario. We propose a framework that provides a guideline for designers to evaluate their respective revelation strategies and thus make better decisions when choosing a particular revelation strategy in a design iteration. This framework utilizes concepts from Bayesian updating and quantifies the confidentiality lost and value gained for a particular revelation, providing a mathematical abstraction of the collaborative design process as a sequence of information revelation decisions. We illustrate the use of our proposed framework in an automobile suspension design scenario, and show the changes in performance (Alice’s and Bob’s objective function responses) and confidentiality (KL divergence) for each.

      PubDate: 2018-05-29T15:27:55Z
      DOI: 10.1016/j.aei.2018.04.004
      Issue No: Vol. 36 (2018)
  • Benchmarking thermoception in virtual environments to physical
           environments for understanding human-building interactions
    • Authors: Gokce Ozcelik; Burcin Becerik-Gerber
      Pages: 254 - 263
      Abstract: Publication date: April 2018
      Source:Advanced Engineering Informatics, Volume 36
      Author(s): Gokce Ozcelik, Burcin Becerik-Gerber
      Thermal comfort influences occupant satisfaction, well-being and productivity in built environments. Several decisions during the design stage (e.g., heating, ventilation, air conditioning design, color and placement of furniture, etc.) impact the building occupants’ thermoception (i.e., the sense by which animals perceive the temperature of the environment and their body). However, understanding the influence of design decisions on occupant behavior is not always feasible due to the resources needed for creating physical testbeds and the need for controlling several contributing factors to comfort and satisfaction. Virtual environments (environments created with virtual reality technology) are novel venues for studying human behavior. However, in order to use virtual environments in the thermoception domain, validation of these environments as adequate representations of physical environments (built environments) is imperative. As the first step towards this goal, we benchmarked virtual environments to physical environments under different thermal stimuli (i.e., hot and cold indoor air temperature). We identified perceived thermal comfort and satisfaction, perceived indoor air temperature, number and type of interactions as markers for the thermoceptive comparison of virtual and physical offices. We conducted an experiment with 56 participants and pursued a systematic statistical analysis. The results show that virtual environments are adequate representations of physical environments in the thermoception domain, especially for subjective perceived thermal comfort and satisfaction assessment. We also found that the type of first adaptive interactions could be used as the markers of thermoception in virtual environments.

      PubDate: 2018-05-29T15:27:55Z
      DOI: 10.1016/j.aei.2018.04.008
      Issue No: Vol. 36 (2018)
  • A deep learning-based method for detecting non-certified work on
           construction sites
    • Authors: Qi Fang; Heng Li; Xiaochun Luo; Lieyun Ding; Timothy M. Rose; Wangpeng An; Yantao Yu
      Pages: 56 - 68
      Abstract: Publication date: January 2018
      Source:Advanced Engineering Informatics, Volume 35
      Author(s): Qi Fang, Heng Li, Xiaochun Luo, Lieyun Ding, Timothy M. Rose, Wangpeng An, Yantao Yu
      The construction industry is a high hazard industry. Accidents frequently occur, and part of them are closely relate to workers who are not certified to carry out specific work. Although workers without a trade certificate are restricted entry to construction sites, few ad-hoc approaches have been commonly employed to check if a worker is carrying out the work for which they are certificated. This paper proposes a novel framework to check whether a site worker is working within the constraints of their certification. Our framework comprises key video clips extraction, trade recognition and worker competency evaluation. Trade recognition is a new proposed method through analyzing the dynamic spatiotemporal relevance between workers and non-worker objects. We also improved the identification results by analyzing, comparing, and matching multiple face images of each worker obtained from videos. The experimental results demonstrate the reliability and accuracy of our deep learning-based method to detect workers who are carrying out work for which they are not certified to facilitate safety inspection and supervision.

      PubDate: 2018-04-15T06:35:53Z
      DOI: 10.1016/j.aei.2018.01.001
      Issue No: Vol. 35 (2018)
  • Semantic BMS: Allowing usage of building automation data in facility
    • Authors: Adam Kučera; Tomáš Pitner
      Pages: 69 - 84
      Abstract: Publication date: January 2018
      Source:Advanced Engineering Informatics, Volume 35
      Author(s): Adam Kučera, Tomáš Pitner
      Facility benchmarking and evaluation of facility performance are the crucial tasks in reaching efficient, economical and sustainable facility operation. Modern buildings are equipped with building automation systems (BAS) that contain vast numbers of various sensors that can be utilised in performance assessment. However, such systems lack convenient tools for data inspection, which limits their use in building performance and efficiency analysis and benchmarking especially on large sites. The paper presents a middleware layer designed to enrich BAS data with additional semantic information. As a semantic model, an adaptation of the Semantic Sensor Network (SSN) ontology for the field of building operation analysis is used. The middleware provides convenient interfaces for querying the model. The proposed system provides the facility managers with a convenient way to use the BAS data for benchmarking and decision support.

      PubDate: 2018-02-04T22:06:52Z
      DOI: 10.1016/j.aei.2018.01.002
      Issue No: Vol. 35 (2018)
  • Special issue on EG-ICE 2016
    • Authors: Barbara Strug; Grażyna Ślusarczyk
      Abstract: Publication date: Available online 17 February 2018
      Source:Advanced Engineering Informatics
      Author(s): Barbara Strug, Grażyna Ślusarczyk

      PubDate: 2018-02-25T16:37:12Z
      DOI: 10.1016/j.aei.2018.02.001
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