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Publisher: Elsevier   (Total: 3175 journals)

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Showing 1 - 200 of 3175 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: 28, SJR: 1.402, h-index: 51)
Academic Radiology     Hybrid Journal   (Followers: 22, SJR: 1.008, h-index: 75)
Accident Analysis & Prevention     Partially Free   (Followers: 90, 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: 33, SJR: 2.515, h-index: 90)
Achievements in the Life Sciences     Open Access   (Followers: 5)
Acta Anaesthesiologica Taiwanica     Open Access   (Followers: 6, SJR: 0.338, h-index: 19)
Acta Astronautica     Hybrid Journal   (Followers: 376, 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: 236, 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: 25, 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: 14)
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: 7)
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: 132, 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: 27, 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: 28, 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: 14)
Advances in Developmental Biology     Full-text available via subscription   (Followers: 10)
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: 21)
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: 6)
Advances in Experimental Social Psychology     Full-text available via subscription   (Followers: 42, 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: 7)
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: 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: 7)
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: 9, SJR: 0.497, h-index: 31)
Advances in Human Factors/Ergonomics     Full-text available via subscription   (Followers: 23)
Advances in Imaging and Electron Physics     Full-text available via subscription   (Followers: 2, SJR: 0.396, h-index: 27)
Advances in Immunology     Full-text available via subscription   (Followers: 36, 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: 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: 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: 15, 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: 15, 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: 7)
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: 17)
Advances in Protein Chemistry and Structural Biology     Full-text available via subscription   (Followers: 18, 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: 375, 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: 333, 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: 9, SJR: 3.289, h-index: 78)
Aggression and Violent Behavior     Hybrid Journal   (Followers: 429, 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: 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: 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: 9, 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: 48, 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: 50, SJR: 2.063, h-index: 186)
American J. of Emergency Medicine     Hybrid Journal   (Followers: 42, 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: 32, SJR: 2.313, h-index: 172)
American J. of Medicine     Hybrid Journal   (Followers: 42, 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: 189, 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: 61, SJR: 0.124, h-index: 9)
Anaesthesia Critical Care & Pain Medicine     Full-text available via subscription   (Followers: 14)
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: 4, SJR: 2.577, h-index: 7)
Analytica Chimica Acta     Hybrid Journal   (Followers: 39, SJR: 1.548, h-index: 152)
Analytical Biochemistry     Hybrid Journal   (Followers: 164, 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: 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   (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  [3175 journals]
  • 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
  • Short-term electricity demand forecasting with MARS, SVR and ARIMA models
           using aggregated demand data in Queensland, Australia
    • Authors: Mohanad S. Al-Musaylh; Ravinesh C. Deo; Jan F. Adamowski; Yan Li
      Pages: 1 - 16
      Abstract: Publication date: January 2018
      Source:Advanced Engineering Informatics, Volume 35
      Author(s): Mohanad S. Al-Musaylh, Ravinesh C. Deo, Jan F. Adamowski, Yan Li
      Accurate and reliable forecasting models for electricity demand (G) are critical in engineering applications. They assist renewable and conventional energy engineers, electricity providers, end-users, and government entities in addressing energy sustainability challenges for the National Electricity Market (NEM) in Australia, including the expansion of distribution networks, energy pricing, and policy development. In this study, data-driven techniques for forecasting short-term (24-h) G-data are adopted using 0.5 h, 1.0 h, and 24 h forecasting horizons. These techniques are based on the Multivariate Adaptive Regression Spline (MARS), Support Vector Regression (SVR), and Autoregressive Integrated Moving Average (ARIMA) models. This study is focused in Queensland, Australia’s second largest state, where end-user demand for energy continues to increase. To determine the MARS and SVR model inputs, the partial autocorrelation function is applied to historical (area aggregated) G data in the training period to discriminate the significant (lagged) inputs. On the other hand, single input G data is used to develop the univariate ARIMA model. The predictors are based on statistically significant lagged inputs and partitioned into training (80%) and testing (20%) subsets to construct the forecasting models. The accuracy of the G forecasts, with respect to the measured G data, is assessed using statistical metrics such as the Pearson Product-Moment Correlation coefficient (r), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE). Normalized model assessment metrics based on RMSE and MAE relative to observed means ( RMSE G ¯ and MAE G ¯ ), Willmott’s Index (WI), Legates and McCabe Index ( E LM ) , and Nash–Sutcliffe coefficients ( E NS ) are also utilised to assess the models’ preciseness. For the 0.5 h and 1.0 h short-term forecasting horizons, the MARS model outperforms the SVR and ARIMA models displaying the largest WI (0.993 and 0.990) and lowest MAE (45.363 and 86.502 MW), respectively. In contrast, the SVR model is superior to the MARS and ARIMA models for the daily (24 h) forecasting horizon demonstrating a greater WI (0.890) and MAE (162.363 MW). Therefore, the MARS and SVR models can be considered more suitable for short-term G forecasting in Queensland, Australia, when compared to the ARIMA model. Accordingly, they are useful scientific tools for further exploration of real-time electricity demand data forecasting.

      PubDate: 2017-12-12T15:36:37Z
      DOI: 10.1016/j.aei.2017.11.002
      Issue No: Vol. 35 (2017)
  • Change propagation analysis for system modeling using Semantic Web
    • Authors: Haoqi Wang; Vincent Thomson; Chengtong Tang
      Pages: 17 - 29
      Abstract: Publication date: January 2018
      Source:Advanced Engineering Informatics, Volume 35
      Author(s): Haoqi Wang, Vincent Thomson, Chengtong Tang
      Change propagation potentially affects many aspects of a SysML-based system model during the iterative process of Model-Based Systems Engineering (MBSE). However, few authors have addressed the implication of engineering change and its impact. To address having a successful change process, this article analyzes and explicitly represents different scenarios of how a system model is changed from a formal perspective, i.e., how a system model should be changed, and how model elements should be added, deleted or modified in response to design changes. A workflow is introduced to guide the change process taking change propagation into account. Second, change impact relationships among requirements, behaviors, and structures of the system model are formalized by an ontology to make the semantics both human-understandable and machine-readable. Reasoning rules are defined as well in order to improve automation of the change process. Finally, an experiment using a water distiller system showed that the identification of change impact information could help designers complete the change in less time and with higher quality.

      PubDate: 2017-12-12T15:36:37Z
      DOI: 10.1016/j.aei.2017.11.004
      Issue No: Vol. 35 (2017)
  • Agent-based evacuation modeling with multiple exits using NeuroEvolution
           of Augmenting Topologies
    • Authors: Mehmet Erkan Yuksel
      Pages: 30 - 55
      Abstract: Publication date: January 2018
      Source:Advanced Engineering Informatics, Volume 35
      Author(s): Mehmet Erkan Yuksel
      Evacuation modeling offers challenging research topics to solve problems related to the development of emergency planning strategies. In this paper, we built an agent-based evacuation simulation model to study the pedestrian dynamics and learning process by applying the NeuroEvolution of Augmenting Topologies (NEAT) which is a powerful method to evolve artificial neural networks (ANNs) through genetic algorithms (GAs). The NEAT method strengthens the analogy between GAs and biological evolution by both optimizing and complexifying the solutions simultaneously. We set our main goal to develop a model by identifying the most appropriate fitness function for the agents that can learn how to change and improve their behaviors in a simulation environment such as moving towards the visible targets, producing efficient locomotion, communicating with each other, and avoiding obstacles while reaching targets. The fitness function we chose captured the learning process effectively and our NEAT-based implementation evolved suitable structures for the ANNs autonomously. According to our experiments and observations in the simulated environment, the agents accomplished their tasks successfully and found their ways to the exits.

      PubDate: 2017-12-12T15:36:37Z
      DOI: 10.1016/j.aei.2017.11.003
      Issue No: Vol. 35 (2017)
  • 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)
  • Long-term knowledge evolution modeling for empirical engineering knowledge
    • Authors: Xinyu Li; Zuhua Jiang; Bo Song; Lijun Liu
      Pages: 17 - 35
      Abstract: Publication date: October 2017
      Source:Advanced Engineering Informatics, Volume 34
      Author(s): Xinyu Li, Zuhua Jiang, Bo Song, Lijun Liu
      In this era of knowledge economy, appropriate management of the rapidly evolving knowledge is a real and urgent issue for factories and enterprises, in order to maintain the competitive edges. However, facing the onerous analysis required for understanding the long-term knowledge evolution, especially the evolving of empirical knowledge in the engineering field, effective and comprehensive modeling methods for knowledge evolution are absent. In this paper, a novel knowledge evolution modeling method is proposed for portraying the long-term evolution of empirical engineering knowledge (EEK) and assisting engineers in comprehending the evolving history. Three phases, EEK elicitation and formalization, EEK networks foundation, and family-tree evolution model construction, are included in the modeling method. This method is developed using natural language processing, semantic similarity calculation, fuzzy neural network prediction, clustering algorithm, and latent topic extraction techniques. To evaluate the performance of the proposed modeling method, an evolution model of empirical knowledge in computer-aided design (CAD) is constructed and then verified. Experimental results show that the proposed method outperforms the former approaches in feasibility and effectiveness, and hence opens up a better way of further understanding the long-term evolution course of EEK.

      PubDate: 2017-08-28T04:45:21Z
      DOI: 10.1016/j.aei.2017.08.001
      Issue No: Vol. 34 (2017)
  • Location-based measurement and visualization for interdependence network
           on construction sites
    • Authors: Xincong Yang; Xiaowei Luo; Heng Li; Xiaochun Luo; Hongling Guo
      Pages: 36 - 45
      Abstract: Publication date: October 2017
      Source:Advanced Engineering Informatics, Volume 34
      Author(s): Xincong Yang, Xiaowei Luo, Heng Li, Xiaochun Luo, Hongling Guo
      Appropriately assigning workers to tasks is vitally important in project management. To do this, project managers need to objectively and effectively measure and visualize the spatiotemporal orders of real construction process as well as coordination structure of the workforce. However, currently there is no method/tool available to project managers to represent spatiotemporal orders of construction processes. To address this issue, this paper presents a novel approach to measuring the real spatiotemporal order of onsite tasks as well as the task interdependence by an interdependence network. This approach extracts the distance of workspace distributions as a key interdependence indicator from historical location tracks across different construction stages according to the area-restricted nature of construction activities. It then integrates generated interdependence into a network over time, to imply the cooperation patterns in stages and a task delivery across stages with a holistic view. To validate the approach, location data were collected from 31 workers working in a high-rise housing construction project for one week to construct the interdependence network of this project, which was used to quantitatively evaluate the performance of construction schedule, assignments and cooperation. Results show that the interdependence network is able to provide insightful information on how workers perform individual tasks onsite and it is also an effective tool to identify and display the interactions among site workers.

      PubDate: 2017-09-18T09:42:59Z
      DOI: 10.1016/j.aei.2017.09.003
      Issue No: Vol. 34 (2017)
  • Data-driven approaches for measurement interpretation: analysing
           integrated thermal and vehicular response in bridge structural health
    • Authors: Rolands Kromanis; Prakash Kripakaran
      Pages: 46 - 59
      Abstract: Publication date: October 2017
      Source:Advanced Engineering Informatics, Volume 34
      Author(s): Rolands Kromanis, Prakash Kripakaran
      A comprehensive evaluation of a structure’s performance based on quasi-static measurements requires consideration of the response due to all applied loads. For the majority of short- and medium-span bridges, temperature and vehicular loads are the main drivers of structural deformations. This paper therefore evaluates the following two hypotheses: (i) knowledge of loads and their positions, and temperature distributions can be used to accurately predict structural response, and (ii) the difference between predicted and measured response at various sensor locations can form the basis of anomaly detection techniques. It introduces a measurement interpretation approach that merges the regression-based thermal response prediction methodology that was proposed previously by the authors with a novel methodology for predicting traffic-induced response. The approach first removes both environmentally (temperature) and operationally (traffic) induced trends from measurement time series of structural response. The resulting time series is then analysed using anomaly detection techniques. Experimental data collected from a laboratory truss is used for the evaluation of this approach. Results show that (i) traffic-induced response is recognized once thermal effects are removed, and (ii) information of the location and weight of a vehicle can be used to generate regression models that predict traffic-induced response. Asa whole, the approach is shown to be capable of detecting damage by analysing measurements that include both vehicular and thermal response.

      PubDate: 2017-09-18T09:42:59Z
      DOI: 10.1016/j.aei.2017.09.002
      Issue No: Vol. 34 (2017)
  • Dynamic rolling strategy for multi-vessel quay crane scheduling
    • Authors: Daofang Chang; Ting Fang; Yiqun Fan
      Pages: 60 - 69
      Abstract: Publication date: October 2017
      Source:Advanced Engineering Informatics, Volume 34
      Author(s): Daofang Chang, Ting Fang, Yiqun Fan
      This paper focuses on the container loading and unloading problem with dynamic ship arrival times. Using a determined berth plan, in combination with the reality of a container terminal production scheduling environment, this paper proposes a scheduling method for quay cranes that can be used for multiple vessels in a container terminal, based on a dynamic rolling-horizon strategy. The goal of this method is to minimize the operation time of all ships at port and obtain operation equilibrium of quay cranes by establishing a mathematical model and using a genetic algorithm to solve the model. Numerical simulations are applied to calculate the optimal loading and unloading order and the completion time of container tasks on a ship. By comparing this result with the traditional method of quay crane loading and unloading, the paper verifies that the quay crane scheduling method for multiple vessels based on a dynamic rolling-horizon strategy can provide a positive contribution to improve the efficiency of container terminal quay crane loading and unloading and reduce resource wastage.

      PubDate: 2017-09-30T13:54:03Z
      DOI: 10.1016/j.aei.2017.09.001
      Issue No: Vol. 34 (2017)
  • Stream flow predictions using nature-inspired Firefly Algorithms and a
           Multiple Model strategy – Directions of innovation towards next
           generation practices
    • Authors: R. Khatibi; M.A. Ghorbani; F. Akhoni Pourhosseini
      Pages: 80 - 89
      Abstract: Publication date: October 2017
      Source:Advanced Engineering Informatics, Volume 34
      Author(s): R. Khatibi, M.A. Ghorbani, F. Akhoni Pourhosseini
      Stream flow prediction is studied by Artificial Intelligence (AI) in this paper using Artificial Neural Network (ANN) as a hybrid of Multi-Layer Perceptron (MLP) with the Levenberg–Marquardt (LM) backpropagation learning algorithm (MLP-LM) and (ii) MLP integrated with the Fire-Fly Algorithm (MLP-FFA). Monthly stream flow records used in this prediction problem comprise two stations at Bear River, the U.S.A., for the period of 1961–2012. Six different model structures are investigated for both MLP-LM and MLP-FFA models and their results were analysed using a number of performance measures including Correlation Coefficients (CC) and the Taylor diagram. The results indicate a significant improvement is likely in predicting downstream flows by MLP-FFA over that by MLP-LM, attributed to identifying the global minimum. In addition, an emerging multiple model (ensemble) strategy is employed to treat the outputs of the two MLP-LM and MLP-FFA models as inputs to an ANN model. The results show yet another further possible improvement. These two avenues for improvements identify possible directions towards next generation research activities.
      Graphical abstract image

      PubDate: 2017-10-14T16:26:47Z
      DOI: 10.1016/j.aei.2017.10.002
      Issue No: Vol. 34 (2017)
  • Mobile augmented reality for teaching structural analysis
    • Authors: Yelda Turkan; Rafael Radkowski; Aliye Karabulut-Ilgu; Amir H. Behzadan; An Chen
      Pages: 90 - 100
      Abstract: Publication date: October 2017
      Source:Advanced Engineering Informatics, Volume 34
      Author(s): Yelda Turkan, Rafael Radkowski, Aliye Karabulut-Ilgu, Amir H. Behzadan, An Chen
      Structural analysis is an introductory core course that is taught in every civil engineering program as well as in most architectural and construction engineering programs. Previous research unveils students' deficits in understanding the behavior of structural elements in a three-dimensional (3D) context due to the shortcomings of traditional lecturing approaches, which put too much emphasis on the analysis of individual structural members, thereby falling short in providing a solid, easy-to-follow, and holistic approach to analyzing complex structures with a large number of interconnected elements. In this paper, the authors introduce a new pedagogy for teaching structural analysis that incorporates mobile augmented reality (AR) and interactive 3D visualization technology. The goal of this study is to enhance the contents used in structural analysis textbooks and on worksheets by visualizing discrete structural members employing AR along with interactive 3D models in order to illustrate how the structures behave under different loading conditions. Students can interactively change the load and observe the reaction resulting from this change with the instant feedback provided by the AR interface. The feasibility of AR concepts and interaction metaphors, as well as the potential of using AR for teaching structural analysis are investigated, specifically by focusing on challenges regarding content integration and interaction. An AR application is designed and developed, and a pilot study is conducted in a junior level structural analysis class to assess the pedagogical impact and the design concepts employed by the AR tool. Control and test groups are deployed, and students’ performance is measured using pre- and post-tests. The results of the pilot study indicate that the utilized AR design concepts have potential to contribute to students’ learning by providing interactive and 3D visualization features, which support constructive engagement and retention of information in students.

      PubDate: 2017-10-21T13:06:45Z
      DOI: 10.1016/j.aei.2017.09.005
      Issue No: Vol. 34 (2017)
  • Semantic as-built 3D modeling of structural elements of buildings based on
           local concavity and convexity
    • Authors: Hyojoo Son; Changwan Kim
      Pages: 114 - 124
      Abstract: Publication date: October 2017
      Source:Advanced Engineering Informatics, Volume 34
      Author(s): Hyojoo Son, Changwan Kim
      The aim of this study is to propose a method for generating as-built BIMs from laser-scan data obtained during the construction phase, particularly during ongoing structural works. The proposed method consists of three steps: region-of-interest detection to distinguish the 3D points that are part of the structural elements to be modeled, scene segmentation to partition the 3D points into meaningful parts comprising different types of elements (e.g., floors, columns, walls, girders, beams, and slabs) using local concave and convex properties between structural elements, and volumetric representation. The proposed method was tested in field experiments by acquiring and processing laser-scan data from construction sites. The performance of the proposed method was evaluated by quantitatively measuring how accurately each of the structural elements was recognized as its functional semantics. Overall, 139 elements of the 141 structural elements (99%) in the two construction sites combined were recognized and modeled according to their actual functional semantics. As the experimental results imply, the proposed method can be used for as-built BIMs without any prior information from as-planned models.

      PubDate: 2017-11-05T03:47:10Z
      DOI: 10.1016/j.aei.2017.10.001
      Issue No: Vol. 34 (2017)
  • Wood defects classification using laws texture energy measures and
           supervised learning approach
    • Authors: K. Kamal; R. Qayyum; S. Mathavan; T. Zafar
      Pages: 125 - 135
      Abstract: Publication date: October 2017
      Source:Advanced Engineering Informatics, Volume 34
      Author(s): K. Kamal, R. Qayyum, S. Mathavan, T. Zafar
      Machine vision based inspection systems are in great focus nowadays for quality control applications. The proposed work presents a novel approach for classification of wood knot defects for an automated inspection. The proposed technique utilizes gray level co-occurrence matrix and laws texture energy measures as texture feature extractors and feed-forward back-propagation neural network as classifier. The proposed work involves the comparison of gray level co-occurrence matrix based features with laws texture energy measures based features. Firstly it takes contrast, correlation, energy and homogeneity as input parameters to a feed-forward back propagation neural network to predict wood defects and then it take energy calculated from laws texture energy measures based energy maps as input feature to a feed-forward back propagation neural network. Mean Square Error (MSE) for training data is found to be 0.0718 and 90.5% overall average classification accuracy is achieved when laws texture energy measures based features are used as input to the neural network as compared to gray level co-occurrence matrix based input features where MSE for training data is found to be 0.10728 and 84.3% overall average classification accuracy is achieved. The proposed technique shows promising results to classify wood defects using a feed forward back-propagation neural network.

      PubDate: 2017-11-11T16:19:26Z
      DOI: 10.1016/j.aei.2017.09.007
      Issue No: Vol. 34 (2017)
  • A study of patent analysis of LED bicycle light by using modified DEMATEL
           and life span
    • Authors: Zone-Ching Lin; Guo-En Hong; Po-Fan Cheng
      Pages: 136 - 151
      Abstract: Publication date: October 2017
      Source:Advanced Engineering Informatics, Volume 34
      Author(s): Zone-Ching Lin, Guo-En Hong, Po-Fan Cheng
      The present study proposed a modified decision-making trial and evaluation laboratory (DEMATEL) method. This innovation method involves collecting the repeated or identically defined technical keywords of patent techniques related to light-emitting diode (LED) bicycle light to determine the ratios of the normalized numerical values of these technical keywords by using one technical domain as the primary domain and another as a variable. The values obtained are then converted to mutual influence levels on a scale of 0 to 4, replacing the conventional expert questionnaire. In this study, in accordance with the operational steps of the decision-making trial and evaluation laboratory method, a general relational influence matrix, direct and indirect relationships diagram, and values of centrality (D + R) and causality (D − R) were obtained. A causal diagram was therefore created. The causal diagram was drawn using values of (D + R) and (D − R) as the two axes and facilitated determining the levels of mutual influence between technical domains. In accordance with the proposed modified decision-making trial and evaluation laboratory method, this study collected patents related to LED bicycle light; moreover, the normalized numerical values of key technical, part/component, and function words that appeared in these patents were calculated. Furthermore, clusters of technical and part or component words were defined in accordance with the first-layer technical category. The second-layer technical categories and functional categories were subsequently defined under the first-layer technical categories to establish the technique–function matrix, thereby dividing the techniques related to LED bicycle light into seven main technical domains. This study then analyzed patent life span. Patent life span was calculated using the announcement date of related patents. Finally, this study investigated the development potential of each technical domain of LED bicycle light by conducting a combined analysis of causal diagram obtained by modified DEMATEL method, activity trend chart of techniques, and patent life spans. The proposed patent analysis method and results can serve companies and engineers as references to facilitate developing new patents.

      PubDate: 2017-11-11T16:19:26Z
      DOI: 10.1016/j.aei.2017.09.004
      Issue No: Vol. 34 (2017)
  • Development of ergonomic posture recognition technique based on 2D
           ordinary camera for construction hazard prevention through view-invariant
           features in 2D skeleton motion
    • Authors: Xuzhong Yan; Heng Li; Chen Wang; JoonOh Seo; Hong Zhang; Hongwei Wang
      Pages: 152 - 163
      Abstract: Publication date: October 2017
      Source:Advanced Engineering Informatics, Volume 34
      Author(s): Xuzhong Yan, Heng Li, Chen Wang, JoonOh Seo, Hong Zhang, Hongwei Wang
      Outdoor tasks operated by construction workers are physically demanding, requiring awkward postures leading to pain, injury, accident, or permanent disability. Ergonomic posture recognition (EPR) technique could be a novel solution for ergonomic hazard monitoring and assessment, yet non-intrusiveness and applicability in complex outdoor environment are always critical considerations for device selection in construction site. Thus, we choose RGB camera to capture skeleton motions, which is non-intrusive for workers compared with wearable sensors. It is also stable and widely used in an outdoor construction site considering various light conditions and complex working areas. This study aims to develop an ergonomic posture recognition technique based on 2D ordinary camera for construction hazard prevention through view-invariant features in 2D skeleton motion. Based on captured 2D skeleton motion samples in the test-run, view-invariant features as classifier inputs were extracted to ensure the learned classifier not sensitive to various camera viewpoints and distances to a worker. Three posture classifiers regarding human back, arms, and legs were employed to ensure three postures to be recognized simultaneously in one video frame. The average accuracies of three classifiers in 5-fold cross validation were as high as 95.0%, 96.5%, and 97.6%, respectively, and the overall accuracies tested by three new activities regarding ergonomic assessment scores captured from different camera heights and viewpoints were 89.2%, 88.3%, and 87.6%, respectively. The developed EPR-aided construction accident auto-prevention technique demonstrated robust accuracy to support on-site postural ergonomic assessment for construction workers’ safety and health assurance.

      PubDate: 2017-11-24T18:12:43Z
      DOI: 10.1016/j.aei.2017.11.001
      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 review of essential standards and patent landscapes for the Internet of
           Things: A key enabler for Industry 4.0
    • Authors: Amy J.C. Trappey; Charles V. Trappey; Usharani Hareesh Govindarajan; Allen C. Chuang; John J. Sun
      Pages: 208 - 229
      Abstract: Publication date: August 2017
      Source:Advanced Engineering Informatics, Volume 33
      Author(s): Amy J.C. Trappey, Charles V. Trappey, Usharani Hareesh Govindarajan, Allen C. Chuang, John J. Sun
      This paper is a formal overview of standards and patents for Internet of Things (IoT) as a key enabler for the next generation advanced manufacturing, referred as Industry 4.0 (I 4.0). IoT at the fundamental level is a means of connecting physical objects to the Internet as a ubiquitous network that enables objects to collect and exchange information. The manufacturing industry is seeking versatile manufacturing service provisions to overcome shortened product life cycles, increased labor costs, and fluctuating customer needs for competitive marketplaces. This paper depicts a systematic approach to review IoT technology standards and patents. The thorough analysis and overview include the essential standard landscape and the patent landscape based on the governing standards organizations for America, Europe and China where most global manufacturing bases are located. The literature of emerging IoT standards from the International Organization for Standardization (ISO), the International Electrotechnical Commission (IEC) and the Guobiao standards (GB), and global patents issued in US, Europe, China and World Intellectual Property Organization (WIPO) are systematically presented in this study.

      PubDate: 2017-09-18T09:42:59Z
      DOI: 10.1016/j.aei.2016.11.007
      Issue No: Vol. 33 (2017)
  • Platform as a service gateway for the Fog of Things
    • Authors: Nandor Verba; Kuo-Ming Chao; Anne James; Daniel Goldsmith; Xiang Fei; Sergiu-Dan Stan
      Pages: 243 - 257
      Abstract: Publication date: August 2017
      Source:Advanced Engineering Informatics, Volume 33
      Author(s): Nandor Verba, Kuo-Ming Chao, Anne James, Daniel Goldsmith, Xiang Fei, Sergiu-Dan Stan
      Internet of Things (IoT), one of the key research topics in recent years, together with concepts from Fog Computing, brings rapid advancements in Smart City, Monitoring Systems, industrial control, transportation and other fields. These applications require a reconfigurable sensor architecture that can span multiple scenarios, devices and use cases that allow storage, networking and computational resources to be efficiently used on the edge of the network. There are a number of platforms and gateway architectures that have been proposed to manage these components and enable application deployment. These approaches lack horizontal integration between multiple providers as well as higher order functionalities like load balancing and clustering. This is partly due to the strongly coupled nature of the deployed applications, a lack of abstraction of device communication layers as well as a lock-in for communication protocols. This limitation is a major obstacle for the development of a protocol agnostic application environment that allows for single application to be migrated and to work with multiple peripheral devices with varying protocols from different local gateways. This research looks at existing platforms and their shortcomings as well as proposes a messaging based modular gateway platform that enables clustering of gateways and the abstraction of peripheral communication protocol details. These novelties allow applications to send and receive messages regardless of their deployment location and destination device protocol, creating a more uniform development environment. Furthermore, it results in a more streamlined application development and testing while providing more efficient use of the gateway’s resources. Our evaluation of a prototype for the system shows the need for the migration of resources and the QoS advantages of such a system. The examined use case scenarios show that clustering proves to be an advantage in certain use cases as well as presenting the deployment of a larger testing and control environment through the platform.

      PubDate: 2017-09-18T09:42:59Z
      DOI: 10.1016/j.aei.2016.11.003
      Issue No: Vol. 33 (2017)
  • Evaluation of quality of service provisioning in large-scale pervasive and
           smart collaborative wireless sensor and actor networks
    • Authors: Goran Horvat; Drago Zagar; Jelena Vlaovic
      Pages: 258 - 273
      Abstract: Publication date: August 2017
      Source:Advanced Engineering Informatics, Volume 33
      Author(s): Goran Horvat, Drago Zagar, Jelena Vlaovic
      With the production of low cost sensors, classical concept of Wireless Sensor Networks (WSNs) evolved into large-scale concept hosting thousands of nodes within a network and generating abundant quantities of data. As these networks are being continuously developed a new class of WSNs are proposed: Wireless Sensor and Actor Networks (WSANs). These networks introduce the actuating component, alongside with the sensing component, where QoS is becoming a very significant factor. The authors of this paper approach the problem of QoS support in large-scale WSAN from a physical layer, where the deployment parameters effects on QoS metrics are demystified. The analysis is formulated on two scenarios: worst case scenario (all nodes transmit data towards the network sink) and best case scenario (a single node transmits a stream of data towards a network sink). For both scenarios two routing protocols were compared, a simple flooding algorithm and a simple distance vector protocol. Also, a new relation between hop count and latency based on transmission power is observed, not reported in the available literature, resulting in a new proposed empirical latency model.

      PubDate: 2017-09-18T09:42:59Z
      DOI: 10.1016/j.aei.2016.10.003
      Issue No: Vol. 33 (2017)
  • Metamodeling of Smart Environments: from design to implementation
    • Authors: Franco Cicirelli; Giancarlo Fortino; Antonio Guerrieri; Giandomenico Spezzano; Andrea Vinci
      Pages: 274 - 284
      Abstract: Publication date: August 2017
      Source:Advanced Engineering Informatics, Volume 33
      Author(s): Franco Cicirelli, Giancarlo Fortino, Antonio Guerrieri, Giandomenico Spezzano, Andrea Vinci
      A smart environment is a physical environment enriched with sensing, actuation, communication and computation capabilities aiming at acquiring and exploiting knowledge about the environment so as to adapt itself to its inhabitants’ preferences and requirements. In this domain, there is the need of tools supporting the design and analysis of applications. In this paper, the Smart Environment Metamodel (SEM) framework is proposed. The framework allows to model applications by exploiting concepts specific to the smart environment domain. SEM approaches the modeling from two different points of view, namely the functional and data perspectives. The application of the framework is supported by a set of general guidelines to drive the analysis, the design and the implementation of smart environments. The effectiveness of the framework is shown by applying it to the modeling of a real smart office scenario that has been developed, deployed and analyzed.

      PubDate: 2017-09-18T09:42:59Z
      DOI: 10.1016/j.aei.2016.11.005
      Issue No: Vol. 33 (2017)
  • Knowledge-based design for assembly in agile manufacturing by using Data
           Mining methods
    • Authors: R. Kretschmer; A. Pfouga; S. Rulhoff; J. Stjepandić
      Pages: 285 - 299
      Abstract: Publication date: August 2017
      Source:Advanced Engineering Informatics, Volume 33
      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: 2017-09-18T09:42:59Z
      DOI: 10.1016/j.aei.2016.12.006
      Issue No: Vol. 33 (2017)
  • Modularized design-oriented systematic inventive thinking approach
           supporting collaborative service innovations
    • Authors: Yu-Hui Wang; Ching-Hung Lee; Amy J.C. Trappey
      Pages: 300 - 313
      Abstract: Publication date: August 2017
      Source:Advanced Engineering Informatics, Volume 33
      Author(s): Yu-Hui Wang, Ching-Hung Lee, Amy J.C. Trappey
      The rapid evolution of new service systems raises crucial challenges for service design and requires effective methods. This study depicts a conceptual service design framework, called design-oriented systematic inventive thinking (DSIT) approach, which can be applied in different problem contexts. DSIT is presented as a new systematic and collaborative intelligence approach for creating and evaluating complex service systems using multi-criteria data analytics. DSIT synthesizes the current field of TRIZ service-design knowledge system and the emerging area of non-TRIZ service-design knowledge system. DSIT enables integrated development of service offerings at four dimensions and provides the matching integrated service design approach for each dimension. Four types of service design approaches are conceptualized as “human-independent service engineering,” “problem-clarified service engineering,” “solution-converged service engineering,” and “designing for service.” A new service computer-aided design system (service CAD) named DSIT explorer is developed consisting of customization, compatibility, and extensiveness of DSIT modules. A pervasive and smart collaborative service system (i.e., the smart MOS burger service solution) designed using DSIT explorer is illustrated. DSIT is a holistic, interdisciplinary, and collaborative service design concept, which is incorporated into a collaborative and intelligent service CAD framework to enable systematic inventive thinking throughout phases of service design lifecycle from problem definition, problem resolution, to solution evaluation.

      PubDate: 2017-09-18T09:42:59Z
      DOI: 10.1016/j.aei.2016.11.006
      Issue No: Vol. 33 (2017)
  • 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
      Pages: 314 - 329
      Abstract: Publication date: August 2017
      Source:Advanced Engineering Informatics, Volume 33
      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: 2017-09-18T09:42:59Z
      DOI: 10.1016/j.aei.2016.12.007
      Issue No: Vol. 33 (2017)
  • Integrating affective features with engineering features to seek the
           optimal product varieties with respect to the niche segments
    • Authors: Chih-Hsuan Wang; Hsin-Tze Chin
      Pages: 350 - 359
      Abstract: Publication date: August 2017
      Source:Advanced Engineering Informatics, Volume 33
      Author(s): Chih-Hsuan Wang, Hsin-Tze Chin
      In recent years, the popularity of smart phones substantially leads to poor sales of the low-end digital cameras. One of the most astounding industry news is Kodak’s bankruptcy in 2011 although Kodak was a pioneer in the field of digital still cameras. In reality, not only functional capability but also affective design can influence user purchase intentions on consumer electronics. In this paper, both affective features (AFs), and engineering features (EFs) are considered to achieve successful product planning. In particular, two critical issues are addressed: (1) market partitioning and (2) product differentiation. Initially, Kansei engineering is employed to capture user attitude toward AFs. Then, a classification tree is constructed to carry out effective market partitioning. Secondly, correspondence analysis is applied to capture user perceptions of EFs for identifying the core features that best characterize distinct market segments. Finally, VIKOR (VlseKriterijumska Optimizacija I Kompromisno Resenje) ranking is conducted to prioritize various product portfolios to accomplish product differentiation. In summary, the presented framework can help industrial practitioners transform diverse customer requirements into attractive alternatives while keep controllable manufacturing costs.
      Graphical abstract image

      PubDate: 2017-09-18T09:42:59Z
      DOI: 10.1016/j.aei.2016.10.002
      Issue No: Vol. 33 (2017)
  • A collaborative web-based platform for the prescription of Custom-Made
    • Authors: Marco Mandolini; Agnese Brunzini; Michele Germani
      Pages: 360 - 373
      Abstract: Publication date: August 2017
      Source:Advanced Engineering Informatics, Volume 33
      Author(s): Marco Mandolini, Agnese Brunzini, Michele Germani
      Many foot pathologies are prevented or treated with Custom Made Insoles (CMIs). Although a strong computerization has characterized the shoe development process during the last decade, the CMI sector still lacks a software platform integrating the design and diagnosis tools used by the stakeholders of this area. Moreover, the prescription of CMIs is only based on the experience of skilled podiatrists rather than on a common and shared knowledge (e.g. guidelines, best practices, rules, etc.). This paper presents a multi-users and knowledge-based platform, called Smart Prescription Platform (SPP), covering the whole CMI development phases, from foot diagnosis to the production, involving clinicians, patients, manufacturers and controllers. The web-based platform is fully integrated with the technologies available in the orthopaedic sector, which are 3D/4D scanners, baropodometric platforms, footwear virtual catalogues, plantar pressure simulators, Augmented Reality devices and 3D CAD systems. The use of standard file formats (e.g. .stl, .bmp, .xml) allows an electronic dataflow among the tools. The main module of the platform, called Prescription System (PS), is used for prescribing custom-made insoles for patients with different health conditions, satisfying the needs of all actors and optimizing the data exchange. PS is a knowledge-based prescription system integrating the best practices related to the prescription of CMIs. The PS output is a XML file representing the electronic order, used to exchange data with the other tools of the SPP. The proposed platform has been tested with a twofold aim: to validate the usability of the Prescription System and the inter-operability of the platform tools. The positive results gathered during the validation, led the experts to start using the web platform for their daily work.

      PubDate: 2017-09-18T09:42:59Z
      DOI: 10.1016/j.aei.2016.10.004
      Issue No: Vol. 33 (2017)
  • An encryption approach for product assembly models
    • Authors: X.T. Cai; S. Wang; X. Lu; W.D. Li
      Pages: 374 - 387
      Abstract: Publication date: August 2017
      Source:Advanced Engineering Informatics, Volume 33
      Author(s): X.T. Cai, S. Wang, X. Lu, W.D. Li
      In a collaboration environment, it is a challenge how to effectively share the information needed for collaboration while protecting other confidential information in a product assembly model. In this paper, an innovative encryption approach for assembly models to support collaboration is presented. This approach is content based encryption and effective for the secure sharing of feature-based assembly models. In the approach, a classification algorithm for features in an assembly model to be shared or protected during collaboration has been first developed. An encryption algorithm for a feature has been then designed to ensure the parameterization, topological and geometrical validity, and self-adaptability of the encrypted feature. An algorithm for parts with multiple encryption features has been developed. Based on the above algorithms, parts are finally assembled and the geometry and topology of the assembling structure are kept un-changed to enhance collaborators’ interoperability. The characteristics and innovations of the approach include: (1) the approach is feature based, integrative into the main-stream commercial Computer Aided Design (CAD) systems, and flexible to meet various users’ needs for encrypting features selected by users during collaboration, (2) in the approach, the topological and geometrical validity of an assembly model after encryption is maintained to ensure effective collaboration on the assembly, and (3) the approach is parametrically controlled through adjusting position and size parameters so as to ensure the user friendliness of using the approach. A case study with complex geometries and assembly structures has been used to validate the effectiveness and robustness of the approach in industrial applications.

      PubDate: 2017-09-18T09:42:59Z
      DOI: 10.1016/j.aei.2016.12.001
      Issue No: Vol. 33 (2017)
  • Extracting failure time data from industrial maintenance records using
           text mining
    • Authors: Kazi Arif-Uz-Zaman; Michael E. Cholette; Lin Ma; Azharul Karim
      Pages: 388 - 396
      Abstract: Publication date: August 2017
      Source:Advanced Engineering Informatics, Volume 33
      Author(s): Kazi Arif-Uz-Zaman, Michael E. Cholette, Lin Ma, Azharul Karim
      Reliability modelling requires accurate failure time of an asset. In real industrial cases, such data are often buried in different historical databases which were set up for purposes other than reliability modelling. In particular, two data sets are commonly available: work orders (WOs), which detail maintenance activities on the asset, and downtime data (DD), which details when the asset was taken offline. Each is incomplete from a failure perspective, where one wishes to know whether each downtime event was due to failure or scheduled activities. In this paper, a text mining approach is proposed to extract accurate failure time data from WOs and DD. A keyword dictionary is constructed using WO text descriptions and classifiers are constructed and applied to attribute each of the DD events to one of two classes: failure or nonfailure. The proposed method thus identifies downtime events whose descriptions are consistent with urgent unplanned WOs. The applicability of the methodology is demonstrated on maintenance data sets from an Australian electricity and sugar processing companies. Analysis of the text of the identified failure events seems to confirm the accurate identification of failures in DD. The results are expected to be immediately useful in improving the estimation of failure times (and thus the reliability models) for real-world assets.

      PubDate: 2017-09-18T09:42:59Z
      DOI: 10.1016/j.aei.2016.11.004
      Issue No: Vol. 33 (2017)
  • A string-wise CRDT algorithm for smart and large-scale collaborative
           editing systems
    • Authors: Xiao Lv; Fazhi He; Weiwei Cai; Yuan Cheng
      Pages: 397 - 409
      Abstract: Publication date: August 2017
      Source:Advanced Engineering Informatics, Volume 33
      Author(s): Xiao Lv, Fazhi He, Weiwei Cai, Yuan Cheng
      With the development of big data and cloud computing, real-time collaborative editing systems have to face new challenges. How to support string-wise operations for smart and large-scale collaborations is one of the key issues in next generation of collaborative editing systems, which is both the core topic of collaborative computing area and the fundamental research of many collaborative systems in science and engineering. However, string-wise operations have troubled the existing collaborative editing algorithms, including Operational Transformation (OT) and Commutative Replicated Data Type (CRDT), for many years. This paper proposes a novel and efficient CRDT algorithm that integrates string-wise operations for smart and massive-scale collaborations. Firstly, the proposed algorithm ensures the convergence and maintains operation intentions of collaborative users under an integrated string-wise framework. Secondly, formal proofs are provided to prove both the correctness of the proposed algorithm and the intentions preserving of string-wise operations. Thirdly, the time complexity of the proposed algorithm has been analyzed in theory to be lower than that of the state of the art OT algorithm and CRDT algorithm. Fourthly, experiment evaluations show that the proposed algorithm outperforms the state of the art OT algorithm and CRDT algorithm.

      PubDate: 2017-09-18T09:42:59Z
      DOI: 10.1016/j.aei.2016.10.005
      Issue No: Vol. 33 (2017)
  • Inside Front Cover - Editorial Board Page
    • Abstract: Publication date: October 2017
      Source:Advanced Engineering Informatics, Volume 34

      PubDate: 2017-11-24T18:12:43Z
  • Storage yard management based on flexible yard template in container
    • Authors: Caimao Tan; Junliang Wang
      Abstract: Publication date: October 2017
      Source:Advanced Engineering Informatics, Volume 34
      Author(s): Caimao Tan, Junliang He, Yu Wang
      With the bottleneck of port operation moving from the quay side to the yard area, storage yard management is becoming increasingly important in the container terminal. This paper studies on storage yard management in container terminal, a flexible yard template strategy is proposed instead of the fixed yard template strategy. Based on the strategy, an integrated optimization model simultaneously considering space allocation and yard crane deployment for the tactical storage yard management is formulated. Besides, Numerical experiments are conduced to verify the effectiveness of the proposed strategy and mathematical model.

      PubDate: 2017-10-21T13:06:45Z
  • An assessment model for RFID impacts on prevention and visibility of
           inventory inaccuracy presence
    • Authors: Qin Ray; Zhong H.Y. Dai Z.L. Zhuang
      Abstract: Publication date: October 2017
      Source:Advanced Engineering Informatics, Volume 34
      Author(s): W. Qin, Ray Y. Zhong, H.Y. Dai, Z.L. Zhuang
      Bullwhip effect has been considered as one of major research topics in supply chain management. Most of the studies disregarded the mismatch between the recorded inventory and the reality. However, it is shown that the inventory inaccuracy under uncertainty is a widespread phenomenon in both retail and distribution centers. Due to the propagation of information distortion along the supply chain, the financial impacts of inventory inaccuracy include not only the cost of direct inventory loss but also the increasing holding and shortage cost at each stage. The emergence of RFID technology offers a possible solution to alleviate the growing cost of inventory inaccuracy. By making full use of RFID technology, this paper attempts to compare the inventory inaccuracy impact on bullwhip effect in terms of order variance amplification and supply chain performance under two scenarios: (1) all members are aware of the inaccuracy and optimize their operations; (2) all members deploy RFID technology to reduce inventory inaccuracy. Informed order policy is used as benchmark to capture the true RFID value and differentiate two types of RFID impacts, prevention and visibility, to provide more manageable insight. In particular, the incentive of sharing information in supply chain is also provided by comparing the cost of two supply chain settings.

      PubDate: 2017-10-08T14:36:19Z
  • Inside Front Cover - Editorial Board Page
    • Abstract: Publication date: August 2017
      Source:Advanced Engineering Informatics, Volume 33

      PubDate: 2017-09-18T09:42:59Z
  • 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
  • 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
  • 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
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