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Leveraging graph convolutional networks for semi-supervised fault diagnosis of HVAC systems in data-scarce contexts

Building Simulation 2023年第8期16卷 1499-1517页

作者： Cheng Fan Yiwen Lin Marco Savino Piscitelli Roberto Chiosa Huilong Wang Alfonso Capozzoli Yuanyuan Ma Key Laboratory for Resilient Infrastructures of Coastal Cities Ministry of EducationShenzhen UniversityShenzhenChina Sino-Australia Joint Research Center in BIM and Smart Construction Shenzhen UniversityShenzhenChina College of Civil and Transportation Engineering Shenzhen UniversityShenzhenChina Department of Energy TEBE research groupBAEDA LabPolitecnico di TorinoTorinoItaly

The continuous accumulation of operational data has provided an ideal platform to devise and implement customized data analytics for smart HVAC fault detection and *** practice,the potentials of advanced supervised learning algorithms have not been fully realized due to the lack of sufficient labeled *** tackle such data challenges,this study proposes a graph neural network-based approach to effectively utilizing both labeled and unlabeled operational data for optimum *** specifically,a graph generation method is proposed to transform tabular building operational data into association graphs,based on which graph convolutions are performed to derive useful insights for fault *** experiments have been designed to evaluate the values of the methods *** datasets on HVAC air-side operations have been used to ensure the generalizability of results *** data scenarios,which vary in training data amounts and imbalance ratios,have been created to comprehensively quantify behavioral patterns of representative graph convolution networks and their *** research results indicate that graph neural networks can effectively leverage associations among labeled and unlabeled data samples to achieve an increase of 2.86%–7.30%in fault classification accuracies,providing a novel and promising solution for smart building management.

关键词： fault detection and diagnosis graph convolutional networks semi-supervised learning HVAC systems machine learning

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Information Flow based Semantic Integration: Analyzing the Relationship between the Initial Knowledge and the Final Result

Information Flow based Semantic Integration: Analyzing the R...

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11th International Conference on Informatics and Semiotics in Organisations

作者： Wang, Yang Feng, Junkang Database Research Group School of Computing University of the West SC United Kingdom Department of Civil Engineering University of Strathclyde United Kingdom Donghua University Shanghai China Beijing Union University Beijing China

ISBN: (纸本)9780980605723

In today's business world, it is common for enterprises to deploy different but coexisting information systems. Systems integration is extremely difficult since semantic information residing in these systems is unavoidably inconsistent. An information flow based semantic integration approach helps alleviate semantic heterogeneities when a global ontology over the component systems is derived. Integration results may vary due to different semantic interpretations of a problem domain. In this paper, we emphasize the role of initial semantic correspondences, which may trigger an integration process, and explore how these correspondences would affect integration results.

关键词： Semantic Integration Ontology Information Flow Knowledge Sharing

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Laboratory analyses of nutrient release processes from Haihe River sediment

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International Journal of Sediment research 2012年第1期27卷 61-72页

作者： Min WU Xue-ming SUN Sui-liang HUANG Xian-qiang TANG Miklas SCHOLZ Key Laboratory of Pollution Processes and Environmental Criteria of Ministry of Education Nankai University Numerical Simulation Group for Water Environment Tianjin Key Laboratory of Environmental Remediation and Pollution ControlCollege of Environmental Science and EngineeringNankai University Civil Engineering Research Centre School of ComputingScience and EngineeringThe University of Salford

Sediment samples were collected from the heavily contaminated Haihe River to conduct static nutrient release experiments with tap water,and to evaluate the effect of dredging depths,salinity and light on the corresponding release *** study is significant because it helps decision-makers to assess the likely impact of dredging(***) on contaminated freshwater and brackish *** concentrations of total nitrogen and inorganic phosphorus were approximately 950 mg/kg and 760 mg/kg,*** typical organic matter concentration was 8.6%.Data obtained from the nutrient leaching experiments indicated that 5cm-dredging of sediment reduced the water column total phosphorus,total dissolved phosphorus and ammonia-nitrogen concentrations *** contrast,dredging 10cm surface sediment could improve the water *** salinity values benefited the release of nitrogen and phosphorus from *** contrast to indoor experiments, concentrations of total phosphorus,total dissolved phosphorus and ammonia-nitrogen were higher for the outdoor experiments.

关键词： Aquatic sediment and water interaction Water quality management Nitrogen Phosphorus Salinity Dredging Water depth Light Haihe River China

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Introduction to the Special Issue on Computational Intelligent Systems for Solving Complex engineering Problems: Principles and Applications

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Computer Modeling in engineering & Sciences 2024年第3期138卷 2023-2027页

作者： Danial Jahed Armaghani Ahmed SalihMohammed Ramesh Murlidhar Bhatawdekar Pouyan Fakharian Ashutosh Kainthola Wael Imad Mahmood School of Civil and Environmental Engineering University of Technology SydneyUltimoNSW 2007Australia College of Engineering University of SulaimaniKurdistan46001Iraq Engineering Department American University of IraqSulaimaniKurdistan46001Iraq Centre of Tropical Geoengineering(GEOTROPIK) Institute of Smart Infrastructure and Innovative Engineering(ISIIC)Faculty of Civil EngineeringUniversiti Teknologi MalaysiaJohor Bahru81310Malaysia Faculty of Civil Engineering Semnan UniversitySemnanIran Geo-Engineering and Computing Laboratory Banaras Hindu UniversityVaranasi221005India Scientific Research Centre Soran UniversitySoranErbilKurdistan-Region44008Iraq

Computational Intelligent(CI)systems represent a pivotal intersection of cutting-edge technologies and complex engineering challenges aimed at solving real-world *** comprehensive body of work delves into the realm of CI,which is designed to tackle intricate and multifaceted engineering problems through advanced computational *** history of CI systems is a fascinating journey that spans several decades and has its roots in the development of artificial intelligence and machine learning *** a wide array of practical examples and case studies,this special issue bridges the gap between theoretical concepts and practical implementation,shedding light on how CI systems can optimize processes,design solutions,and inform decisions in complex engineering *** compilation stands as an essential resource for both novice learners and seasoned practitioners,offering a holistic perspective on the potential of CI in reshaping the future of engineering problem-solving.

关键词： offering bridges landscape

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Applications of back propagation neural networks in predicting nutrient effluent concentrations from permeable pavements

Applications of back propagation neural networks in predicti...

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11th International Conference on computing and Control for the Water Industry, CCWI 2011

作者： Tota-Maharaj, Kiran Devi Prasad, T. Scholz, Miklas Sustainable Water Management Group Civil Engineering Research Centre School of Computing Science and Engineering University of Salford Newton Building Greater Manchester M5 4 WT United Kingdom

ISBN: (纸本)0953914089

Artificial intelligence techniques such as neural networks are modelling tools that can be applied to analyse urban runoff water quality issues. Artificial neural networks are frequently used to model various highly variable and non-linear physical phenomena in the water and environmental engineering fields. The application of neural networks for analysing the performance of permeable pavements is timely and novel. Artificial neural networks are a promising tool for environmental process assessment and modelling. Feed-forward neural networks are the most widely adopted methodology for the prediction and forecasting of water quality and quantity variables. This paper presents the application of back-propagation neural networks and the testing of the Levenberg-Marquardt, Quasi-Newton and Bayesian Regularization algorithms. The back-propagation neural network models incorporating these algorithms performed classification and regression tasks without knowledge of the underlying physical processes occurring throughout the pavement system. The neural networks were statistically assessed for their goodness of prediction of outflow water quality with respect to ammonia-nitrogen, nitrate-nitrogen, and ortho-phosphate-phosphorus by numerical computation of the mean absolute error, root square mean error, mean absolute relative error and the coefficient of correlation for the prediction versus measured dataset. These performance indices compared the measured and estimated water quality parameters. The neural network models were functions of the readily available water quality parameters. Three neural network models were assessed for their efficiency in accurately simulating effluent water quality parameters from various experimental pavement systems. The models predicted all key parameters with high correlation coefficients and low minimum statistical errors. The back-propagation and feed-forward neural network models performed optimally as pollutant removal predictors with re

关键词： Neural network models

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Virtual sensors to improve on-line hydraulic model calibration

Virtual sensors to improve on-line hydraulic model calibrati...

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12th Annual International Conference on Water Distribution Systems Analysis 2010, WDSA 2010

作者： Goldsmith, Daniel Preis, Ami Allen, Michael Whittle, Andrew J. Cogent Computing Applied Research Centre Coventry University United Kingdom Center for Environmental Sensing and Modeling MIT SMART Centre Singapore Singapore Department of Civil and Environmental Engineering MIT Cambridge MA United States

ISBN: (纸本)9780784412039

A new approach for monitoring a water distribution system involving Virtual Sensors is presented. In this approach, wireless sensor nodes are permanently deployed within the distribution system, providing continuous, on-line hydraulic data that can be assimilated into hydraulic models. In addition, temporary nodes are deployed for short periods (one week) around the distribution network. A Virtual Sensor is implemented using a data imputation technique called Gaussian Process Regression, which combines the historical data collected by the temporary node with correlated data from a subset of permanent sensor nodes. Use of spatially-correlated data accounts for new trends in the data that do not appear in the historical data collected by the temporary node. An increase in the number of sensors (a combination of real and virtual) is important for reducing the ill-conditioned state of the hydraulic model calibration procedure. The technique is demonstrated as a proof-of-concept using data collected from the WaterWiSe@SG testbed in Singapore, and is shown to predict pressure data trends with an accuracy of 0.76 PSI RMSE after a six-week test. © 2011 ASCE.

关键词： Water distribution systems

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A categorical approach towards metamodeling cyber-physical systems 11

A categorical approach towards metamodeling cyber-physical s...

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11th International Workshop on Structural Health Monitoring 2017: Real-Time Material State Awareness and Data-Driven Safety Assurance, IWSHM 2017

作者： Legatiuk, Dmitrii Theiler, Michael Dragos, Kosmas Smarsly, Kay Department of Applied Mathematics Bauhaus University Weimar Germany Department of Computing in Civil Engineering Bauhaus University Weimar Germany DFG Research Training Group 1462 Bauhaus University Weimar Germany

ISBN: (纸本)9781605953304

Cyber-physical systems, orchestrating computing, networking, and physical processes, are coupled, hybrid systems characterized by heterogeneous, interconnected subsystems. In civil engineering, the most common applications of cyber-physical systems are associated with structural health monitoring (SHM) and structural control. Given the complexity and heterogeneity of cyber-physical systems designed for structural health monitoring and control applications, it is hardly possible to reliably assess the quality of a cyber-physical system (CPS), which is needed for increased availability and for identifying potential sources of faults in a CPS. This conceptual paper proposes a metamodeling approach that can be used as a basis to model cyber-physical systems in order to assess the CPS quality based on category theory. Category theory is a mathematical theory that allows formalizing concepts of high-level abstractions on a meta level. Using the CPS metamodeling approach presented in this paper, each subsystem of a CPS can be modeled and assessed individually, entailing a holistic quality assessment of a CPS and its subsystems. However, since this paper is conceptual, it is intended to devote additional future research efforts to metamodeling and assessing cyber-physical systems, achieving a complete conceptual modeling framework for cyber-physical systems applied in structural health monitoring and control.

关键词： Cyber Physical System

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Immersive virtual reality experiments for emergency evacuation response in deep underground space

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Tunnelling and Underground Space Technology 2025年 163卷

作者： Wang, Chong Li, Chao Zhou, Tiejun Wang, Dachuan An, Xiaoxiao Lv, Jiayue Wang, Jie School of Architecture and Urban Planning Chongqing University Chongqing400045 China School of Civil Engineering Key Laboratory of New Technology for Construction of Cities in Mountain Area Chongqing University Chongqing400045 China Smart Evacuation and Urban Safety Research Center Chongqing University Chongqing400045 China School of Civil Engineering and Architecture Southwest University of Science and Technology Mianyang621010 China China Railway Siyuan Survey and Design Institute Group Co. Ltd Wuhan430063 China Chongqing Planning & Design Institute Chongqing401147 China

Safe evacuation of crowds from deep underground spaces is challenging owing to large building sizes, enclosed indoor spaces, and long upward-evacuation distances. A virtual-reality (VR) scenario was created to study human behavior during fire emergencies in deep underground space, and a series of immersive VR experiments for evacuation was conducted. The experiment investigated evacuee pre-movement time (also called pre-evacuation time or response delay) and its distribution characteristics in deep underground space, in addition to their exit-selection ratio under five factors: left–right habituation, crowd effect, visibility, spatial familiarity, and luminance. A post-experiment questionnaire also examined participant impressions of aspects such as the realism and emotion of the VR experience. The results show that pre-movement time was shorter in deep underground space than in aboveground buildings and shallow underground space. Individual pre-movement time was significantly influenced both by their real experience with fire emergencies and whether the student participants majored in architecture. Spatial familiarity, crowd effect, and visibility had a more significant impact on human decision-making overall. Left-right habituation and luminance are less influential;crowd effect reinforces under-risk behavior and a sense of grouping, when crowd effect and visibility reduction work together. The study conclusions are likely to influence future VR-evacuation experiments and fire-safety design. The empirical data can also be input into computer simulations that model building evacuations. Quantifying experimental data on pedestrian behavior is essential for developing performance-based evacuation safety designs in deep underground spaces. © 2025 Elsevier Ltd

关键词： Pedestrian safety

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Concept and research Framework for Coordinated Situation Awareness and Active Defense of Cyber-physical Power Systems Against Cyber-attacks

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Journal of Modern Power Systems and Clean Energy 2021年第3期9卷 477-484页

作者： Ming Ni Manli Li Jun’e Li Yingjun Wu Qi Wang the NARI Group Corporation(State Grid Electric Power Research Institute) NARI Technology Co.Ltd.and the State Key Laboratory of Smart Grid Protection and ControlNanjing 211106China the School of Cyber Science and Engineering Wuhan Universityand the Key Laboratory of Aerospace Information Security and Trusted Computing of the Ministry of EducationWuhan 430072China the College of Energy and Electrical Engineering Hohai UniversityNanjing 211100China the School of Electrical Engineering Southeast UniversityNanjing 210096China

Due to the tight coupling between the cyber and physical sides of a cyber-physical power system(CPPS),the safe and reliable operation of CPPSs is being increasingly impacted by cyber *** situation poses a challenge to traditional security defense systems,which considers the threat from only one side,i.e.,cyber or *** cope with cyberattacks,this paper reaches beyond the traditional one-side security defense systems and proposes the concept of cyber-physical coordinated situation awareness and active defense to improve the ability of *** example of a regional frequency control system is used to show the validness and potential of this ***,the research framework is presented for studying and implementing this ***,key technologies for cyber-physical coordinated situation awareness and active defense against cyber-attacks are introduced.

关键词： Cyber-physical power system(CPPS) cyber security cyber-attack situation awareness active defense

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Machine learning assisted screening of two dimensional chalcogenide ferromagnetic materials with Dzyaloshinskii Moriya interaction

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npj Computational Materials 2024年第1期10卷 761-769页

作者： Peng Han Jingtong Zhang Shengbin Shi Yunhong Zhao Yajun Zhang Jie Wang Department of Engineering Mechanics and Key Laboratory of Soft Machines and Smart Devices of Zhejiang Province Zhejiang University38 Zheda RoadHangzhou310027China Research Center for New Materials Computing Zhejiang LaboratoryHangzhou311100 ZhejiangChina Key Laboratory of Mechanics on Disaster and Environment in Western China Ministry of Education of ChinaLanzhou UniversityLanzhouGansu730000China Department of Mechanics and Engineering Sciences College of Civil Engineering and MechanicsLanzhou UniversityLanzhouGansu730000China

Magnetic skyrmions are potential candidates for high-density storage and logic devices because of their inherent topological stability and nanoscale ***-dimensional(2D)Janus transition metal chalcogenides(TMDs)are widely used to induce skyrmions due to the breaking of inversion ***,the experimental synthesis of Janus TMDs is rare,which indicates that the Janus configuration might not be the most stable MXY ***,through machine-learning-assisted high-throughput first-principles calculations,we demonstrate that not all MXY compounds can be stabilized in Janus layered structure and a large proportion prefer to form other configurations with lower energy than the Janus ***,these new configurations exhibit a strong Dzyaloshinskii–Moriya interaction(DMI),which can generate and stabilize skyrmions even under a strong magnetic *** work provides not only an efficient method for obtaining ferromagnetic materials with strong DMI but also a theoretical guidance for the synthesis of TMDs via experiments.

关键词： structure field ferromagnetic

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