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International Conference on control, Automation and systems ( ICCAS)

作者： Sitthiphat Promchaiwattana David Banjerdpongchai Department of Electrical Engineering Faculty of Engineering Intelligent Control Automation of Process Systems Research Unit Chulalongkorn University 254 Phayathai Road Pathumwan Bangkok Thailand

This paper presents the design of supervisory model predictive control for HVAC systems with consideration of peak demand shaving and thermal comfort. In this work, we analyze the operating points with the thermal comfort requirement. Afterwards, we linearize the nonlinear dynamic model to a linear time-varying model. In particular, we specify that the operating point of the open-loop HVAC system satisfies the thermal comfort condition. This makes the predicted mean vote (PMV) equal to zero or nearly to zero. Subsequently, we calculate the zone temperature, the zone humidity ratio, and the volumetric flow rate. When considering the time-varying disturbance profiles, it appears that the operating points are time-varying. We observe that the coefficient of performance (COP) and the operating points are time-varying which lead to time-varying model. Moreover, we design supervisory control (SC) and model predictive control (MPC) for linear time-varying model. In the SC design, we aim to find the optimal reference zone temperature which is used as the reference signal for MPC. In the MPC design, the objectives are twofold. First, the zone temperature tracks the reference signal. Second, both electrical energy cost and deviation of desired thermal comfort are minimized. The computer simulation shows that MPC of time-varying HVAC system yields the best reference tracking. In addition, it reduces total electrical energy cost the most. We conclude that the optimal temperature reference can be effectively applied to the building HVAC systems while the occupants feel comfortable and the total electrical energy cost is the lowest.

关键词： HVAC Costs Input variables Buildings Predictive models Supervisory control Thermal analysis

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Physics-informed neural networks for parameter learning of wildfire spreading

arXiv

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arXiv 2024年

作者： Vogiatzoglou, Konstantinos Papadimitriou, C. Bontozoglou, V. Ampountolas, K. System Dynamics Laboratory Department of Mechanical Engineering University of Thessaly Volos38334 Greece Transport Processes & Process Equipment Laboratory Department of Mechanical Engineering University of Thessaly Volos38334 Greece Automatic Control & Autonomous Systems Laboratory Department of Mechanical Engineering University of Thessaly Volos38334 Greece

Wildland fires pose a terrifying natural hazard, underscoring the urgent need to develop datadriven and physics-informed digital twins for wildfire prevention, monitoring, intervention, and response. In this direction of research, this work introduces a physics-informed neural network (PiNN) designed to learn the unknown parameters of an interpretable wildfire spreading model. The considered modeling approach integrates fundamental physical laws articulated by key model parameters essential for capturing the complex behavior of wildfires. The proposed machine learning framework leverages the theory of artificial neural networks with the physical constraints governing wildfire dynamics, including the first principles of mass and energy conservation. Training of the PiNN for physics-informed parameter identification is realized using synthetic data on the spatiotemporal evolution of one- and two-dimensional firefronts, derived from a high-fidelity simulator, as well as empirical data (ground surface thermal images) from the Troy Fire that occurred on June 19, 2002, in California. The parameter learning results demonstrate the predictive ability of the proposed PiNN in uncovering the unknown coefficients of the wildfire model in one- and two-dimensional fire spreading scenarios as well as the Troy Fire. Additionally, this methodology exhibits robustness by identifying the same parameters even in the presence of noisy data. By integrating this PiNN approach into a comprehensive framework, the envisioned physics-informed digital twin will enhance intelligent wildfire management and risk assessment, providing a powerful tool for proactive and reactive strategies. Copyright © 2024, The Authors. All rights reserved.

关键词： Risk assessment

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What Influences Intermodal Choices: Metro-Centric, Bus-Centric, Hybrid? Insights from Machine Learning Approaches

SSRN

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SSRN 2024年

作者： Ma, Xinwei Tian, Xiaolin Cui, Hongjun He, Mingjia Wang, Jianbiao Cheng, Long School of Civil and Transportation Engineering Hebei University of Technology Tianjin300401 China Institute for Dynamic Systems and Control Department of Mechanical and Process Engineering ETH Zurich Switzerland Institute of Materials and Systems for Sustainability Nagoya University Nagoya Japan Jiangsu Key Laboratory of Urban ITS School of Transportation Southeast University Nanjing China

Three types of intermodals are defined: bus-centric, metro-centric, and hybrid, each representing combinations of bus, metro, and a mix of metro and bus with other travel modes for a trip, respectively. Using the household survey from Nanjing, China, comprising 162,191 trips, we applied the multiple models to reveal the nonlinear effects of socio-demographic and travel-related attributes on intermodal travel choices. Results show that bus-centric intermodal choice accounts for 65.82% of the total among the three types. The optimal model, random forest, indicates the relative importance of travel-related, individual, and household attributes, contributing 46.46%, 31.35%, and 22.18% respectively. Non-public transit travel time demonstrates an inverted V-shaped association with bus-centric intermodal choice, with a peak at around 5 minutes. Older individuals prefer bus-centric intermodal, while younger lean towards metro-centric and hybrid intermodal. Compared to car ownership and motorcycle ownership, bike ownership and E-bike ownership exhibit relatively high impact on intermodal travel choices. © 2024, The Authors. All rights reserved.

关键词： Buses

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SAD-TIME: a Spatiotemporal-fused network for depression detection with Automated multi-scale Depth-wise and TIME-interval-related common feature extractor

arXiv

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arXiv 2024年

作者： Wang, Han-Guang Hou, Hui-Rang Jin, Li-Cheng Xu, Chen-Yang Zhang, Zhong-Yi Meng, Qing-Hao Zhang, Yong-Hui Tianjin Key Laboratory of Process Measurement and Control Institute of Robotics and Autonomous Systems School of Electrical and Information Engineering Tianjin University Tianjin300072 China Department of Psychiatry Tianjin Anding Hospital Mental Health Center Tianjin Medical University China

Background and Objective: Depression is a severe mental disorder, and accurate diagnosis is pivotal to the cure and rehabilitation of people with depression. However, the current questionnaire-based diagnostic methods could bring subjective biases and may be denied by subjects. In search of a more objective means of diagnosis, researchers have begun to experiment with deep learning-based methods for identifying depressive disorders in recent years. Methods: In this study, a novel Spatiotemporal-fused network with Automated multi-scale Depth-wise and TIME-interval-related common feature extractor (SAD-TIME) is proposed. SAD-TIME incorporates an automated nodes’ common features extractor (CFE), a spatial sector (SpS), a modified temporal sector (TeS), and a domain adversarial learner (DAL). The CFE includes a multiscale depth-wise 1D-convolutional neural network and a time-interval embedding generator, where the unique information of each channel is preserved. The SpS fuses the functional connectivity with the distance-based connectivity containing spatial position of EEG electrodes. A multi-head-attention graph convolutional network is also applied in the SpS to fuse the features from different EEG channels. The TeS is based on long short-term memory and graph transformer networks, where the temporal information of different time-windows is fused. Moreover, the DAL is used after the SpS to obtain the domain-invariant feature. Results: Experimental results under tenfold cross-validation show that the proposed SAD-TIME method achieves 92.00% and 94.00% depression classification accuracies on two datasets, respectively, in cross-subject mode. Conclusion: SAD-TIME is a robust depression detection model, where the automatedly-generated features, the SpS and the TeS assist the classification performance with the fusion of the innate spatiotemporal information in the EEG signals. Copyright © 2024, The Authors. All rights reserved.

关键词： Convolutional neural networks

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AMG: A Depression Detection Model with Autoencoder and Multi-Head Graph Convolutional Network

AMG: A Depression Detection Model with Autoencoder and Multi...

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Chinese control Conference (CCC)

作者： Han-Guang Wang Qing-Hao Meng Li-Cheng Jin Jian-Bang Wang Hui-Rang Hou Tianjin Key Laboratory of Process Measurement and Control Institute of Robotics and Autonomous Systems School of Electrical and Information Engineering Tianjin University Tianjin China China Southern Power Grid Company Limited Guangzhou China

Depression is a common chronic mental disorder characterized by high prevalence, recurrence, suicide rate, disability rate and heavy disease burden. Depression assessment based on questionnaires is subjective, therefore, a novel framework to is hereby designed to detect depression automatically and obj ectively. The framework first utilizes a two-way autoencoder to extract the hidden features from the original differential entropy features. The new features are used to generate the channel connectivity graph, using the multi-head attention mechanism. Next, the new features and graphs are input into the graph convolutional network (GCN) to aggregate spatial information. Finally, the output from GCN is passed to a fully connected layer and a soft layer to obtain the predicted label. Experiments are performed on Multimodal Open Dataset for Mental-disorder Analysis dataset. A final accuracy of 88.68% and a cross entropy loss of 2.4532 are obtained, when using ten-fold cross validation, outperforming other traditional machine learning methods including SVM, KNN, decision tree.

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Demand Response Optimization for the Enhancement of the Distribution System's Operation

Demand Response Optimization for the Enhancement of the Dist...

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International Conference on Smart Energy systems and Technologies (SEST)

作者： Maria Fotopoulou George Tsekouras Dimitrios Rakopoulos Vassiliki Kontargyri Department of Electrical and Electronics Engineering University of West Attica Athens Greece Chemical Process and Energy Resources Institute Centre for Research and Technology Hellas Athens Greece Control Systems and Industrial Automation Laboratory National and Kapodistrian University of Athens Athens Greece

ISBN: (数字)9798350386493

ISBN: (纸本)9798350386509

Following the latest energy market trends, active distribution systems incorporate an ascending mix of flexible loads, which can participate in Demand Response (DR) strategies. The purpose of this research paper is to present a DR strategy for the Distribution System Operator (DSO) in order to enhance the system's operation and mitigate power quality issues, such as voltage drops, and congestion. The proposed algorithm aims at smoothing the load curve, taking into consideration the available flexible load and the limitations of the system. The algorithm is demonstrated on a model of the distribution system of Skiathos island, Greece, in the context of ENFLATE, a Horizon Europe project, and future scenarios with photovoltaic (PV) penetration are studied as well. According to the results, the proposed DR algorithm manages to reduce the voltage drop, eliminate the number of events where the voltage drop exceeds the limits and reduce the losses of the distribution system up to 8%-9%, depending on the PV penetration.

关键词： Electric potential Smoothing methods Power quality Europe Voltage Artificial neural networks Transformers Demand response Load modeling Context modeling

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The role of green HRM in driving a firm's green competitive advantage: the mediating role of green organizational identity

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SN Business and Economics 2021年第11期1卷 1-19页

作者： Muisyo, Paul Kivinda Qin, Su Ho, Thu Hau School of Management Xi’an Jiaotong University Xi’an China State Key Laboratory for Manufacturing Systems Engineering Xi’an China The Key Laboratory of the Ministry of Education for Process Control and Efficiency Engineering Xi’an China School of Management Northwest University Xi’an China

Despite the significant contribution made by green human resource management (GHRM) towards the firm's green performance, scholars have not given adequate attention to how GHRM contributes to the green competitiveness of a firm. This situation, therefore, presents a major gap for scholars in green management to fill. We draw on the Ability Motivation Opportunity theory to examine how GHRM drives GCA in a firm. Grounded on this theory, we develop a hypothetical model that is tested by survey data collected from the Chinese manufacturing sector. The results reveal that firms can capitalize on GHRM to build a green competitive advantage (GCA). The findings further illuminate the necessary conditions of green organizational identity (GOI) and green leadership (GL). We believe that the utility and originality of this paper provides a nuanced understanding of how firms can derive GCA from GHRM. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2021.

关键词： Green competitive advantage Green human resource management Green leadership Green organizational identity

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Tracking performance limitations of MIMO discrete-time networked control systems with multiple constraints

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Science China(Information Sciences) 2023年第8期66卷 267-268页

作者： Xiaowei JIANG Bin ZHANG Xiangyong CHEN Huaicheng YAN School of Automation China University of Geosciences Key Laboratory of Industrial Internet of Things & Networked Control Ministry of Education Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education School of Automation and Electrical Engineering Linyi University Key Laboratory of Smart Manufacturing in Energy Chemical Process of the Ministry of Education

Dear editor,Networked control systems(NCSs) have attracted widespread attention in some fields because they offer the advantages of reduced cabling, greater resource sharing,and ease of installation and maintenance [1]. However, in an unreliable network environment, network constraints can significantly limit the performance of NCSs.

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Iterative Learning control for Ramp Metering on Service Station On-ramps

arXiv

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arXiv 2024年

作者： Xiang, Hongxi Cenedese, Carlo Balta, Efe C. Lygeros, John Department of Mechanical and Process Engineering ETH Zürich Zürich Switzerland Department of Information Technology and Electrical Engineering ETH Zürich Zürich Switzerland Delft Center for Systems and Control Delft University of Technology Delft Netherlands Inspire AG Zürich Switzerland

Congestion on highways has become a significant social problem due to the increasing number of vehicles, leading to considerable waste of time and pollution. Regulating the outflow from the Service Station can help alleviate this congestion. Notably, traffic flows follow recurring patterns over days and weeks, allowing for the application of Iterative Learning control (ILC). Building on these insights, we propose an ILC approach based on the Cell Transmission Model with service stations (CTM-s). It is shown that ILC can effectively compensate for potential inaccuracies in model parameter estimates by leveraging historical data. Copyright © 2024, The Authors. All rights reserved.

关键词： Traffic congestion

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Eco-driving of fuel cell electric trucks: optimal speed planning combining dynamic programming and Pontryagin’s minimum principle

Eco-driving of fuel cell electric trucks: optimal speed plan...

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IEEE Conference on Vehicular Technology (VTC)

作者： Alessandro Ferrara Christoph Hametner Division of Process Control and Automation TU Wien Institute of Mechanics and Mechatronics Vienna Austria Christian Doppler Laboratory for Innovative Control and Monitoring of Automotive Powertrain Systems TU Wien Vienna Austria

ISBN: (纸本)9781665454698

Eco-driving is an energy-aware driving style that improves the overall vehicle efficiency to reduce operating costs and extend the driving range. This paper focuses on the eco-driving of fuel cell electric trucks, optimizing the speed plan based on the route elevation, and studying the trade-off between driving time and range. In particular, the optimal speed plan is created by combining two methods from optimal control theory (dynamic programming and Pontryagin’s minimum principle) to include an energy management strategy for fuel consumption optimization. The speed plan created with the proposed method increases the vehicle range by 8% compared with a constant speed plan. Moreover, the study shows that the vehicle range can be extended up to 50% if the driving time is increased, which might be an essential solution to cope with the undeveloped hydrogen refueling infrastructure. The conclusion of this work indicates several directions to continue the research on the topic: for example, addressing robustness to traffic conditions and including component degradation mitigation targets in the optimization (e.g. SoC operating range and fuel cell voltage degradation).

关键词： Degradation Vehicular and wireless technologies Hydrogen Fuel cells Voltage Robustness Planning

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