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Collective Entity Alignment for Knowledge Fusion of Power Grid Dispatching Knowledge Graphs

IEEE/CAA Journal of Automatica Sinica 2022年第11期9卷 1990-2004页

作者： Linyao Yang Chen Lv Xiao Wang Ji Qiao Weiping Ding Jun Zhang Fei-Yue Wang the State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190 the School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China the China Electric Power Research Institute Beijing 100192China IEEE the Qingdao Academy of Intelligent Industries Qingdao 256200China the School of Information Science and Technology Nantong UniversityNantong 226019China the School of Electrical Engineering and Automation Wuhan UniversityWuhan 430072China

Knowledge graphs(KGs)have been widely accepted as powerful tools for modeling the complex relationships between concepts and developing knowledge-based *** recent years,researchers in the field of power systems have explored KGs to develop intelligent dispatching systems for increasingly large power *** multiple power grid dispatching knowledge graphs(PDKGs)constructed by different agencies,the knowledge fusion of different PDKGs is useful for providing more accurate decision *** achieve this,entity alignment that aims at connecting different KGs by identifying equivalent entities is a critical *** entity alignment methods cannot integrate useful structural,attribute,and relational information while calculating entities’similarities and are prone to making many-to-one alignments,thus can hardly achieve the best *** address these issues,this paper proposes a collective entity alignment model that integrates three kinds of available information and makes collective counterpart *** model proposes a novel knowledge graph attention network(KGAT)to learn the embeddings of entities and relations explicitly and calculates entities’similarities by adaptively incorporating the structural,attribute,and relational ***,we formulate the counterpart assignment task as an integer programming(IP)problem to obtain one-to-one *** not only conduct experiments on a pair of PDKGs but also evaluate o ur model on three commonly used cross-lingual *** comparisons indicate that our model outperforms other methods and provides an effective tool for the knowledge fusion of PDKGs.

关键词： Entity alignment integer programming(IP) knowledge fusion knowledge graph embedding power dispatch

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SegDQ: Segmentation assisted multi-object tracking with dynamic query-based transformers

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NEUROCOMPUTING 2022年第0期481卷 91-101页

作者： Liu, Yating Bai, Tianxiang Tian, Yonglin Wang, Yutong Wang, Jiangong Wang, Xiao Wang, Fei-Yue Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Univ Sci & Technol China Dept Automat Hefei 230027 Peoples R China Qingdao Acad Intelligent Ind Qingdao 266000 Peoples R China

Multi-Object Tracking (MOT) has been one of the most important topics in computer vision. The tradi-tional tracking-by-detection framework of MOT is severely suffered from the poor detection results. In this paper, based on Transformer, we introduce the tracking-by-query MOT framework, and propose to apply semantic segmentation as an auxiliary task to optimize the training of MOT trackers, which addresses more on extracted foreground features. In addition, a feature-dependent dynamic object query (DOQ), instead of a fixed-learned object query (LOQ), is put forward to retrieve the new detections, improving the flexibility and constringency of the framework. We tested our SegDQ method on various scenarios including MOTChallenge 15, 16 and 17 datasets. The experimental results show that it obvi-ously improves the MOTA and IDF1 indexes of tracking results. (c) 2022 Published by Elsevier B.V.

关键词： Multi-object tracking Transformer Semantic task Dynamic query

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ASCL: Adversarial supervised contrastive learning for defense against word substitution attacks

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NEUROCOMPUTING 2022年 510卷 59-68页

作者： Shi, Jiahui Li, Linjing Zeng, Daniel Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 101408 Peoples R China Tianjin Zhongke Intelligent Recognit Co Ltd Tianjin 300450 Peoples R China Chinese Acad Sci Inst Automat Beijing Peoples R China

Attacks with adversarial examples can tremendously worsen the performance of deep neural networks (DNNs). Hence, defending against such adversarial attacks is crucial for nearly all DNN-based applica-tions. Adversarial training is an effective and extensively adopted approach for increasing the robustness of DNNs in which benign examples and their adversarial counterparts are considered together in the training stage. However, this may result in a decrease in accuracy on benign examples because it does not account for the inter-class distance of benign examples. To overcome the aforementioned dilemma, we devise a novel defense approach named adversarial supervised contrastive learning (ASCL), which combines adversarial training with supervised contrastive learning to enhance the robustness of DNN-based models while maintaining their clean accuracy. We validate the effectiveness of the proposed ASCL approach in the scenario of defending against word substitution attacks by means of extensive experiments on benchmark tasks and datasets. The experimental results show that ASCL reduces the attack success rate to 20% while maintaining the accuracy for clean inputs within a 2% margin. (c) 2022 Elsevier B.V. All rights reserved.

关键词： Adversarial example Adversarial training Model robustness Contrastive learning Natural language processing

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Acting as a Decision Maker: Traffic-Condition-Aware Ensemble Learning for Traffic Flow Prediction

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IEEE TRANSACTIONS ON intelligent TRANSPORTATION systems 2022年第4期23卷 3190-3200页

作者： Chen, Yuanyuan Chen, Hongyu Ye, Peijun Lv, Yisheng Wang, Fei-Yue Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Harbin Univ Sci & Technol Sch Automat Harbin 150080 Peoples R China Qingdao Acad Intelligent Ind Qingdao 266109 Peoples R China Macau Univ Sci & Technol Inst Engn Taipa Macao Peoples R China

Accurate traffic prediction under various conditions is an important but challenging task. Due to the complicated non-stationary temporal dynamics in traffic flow time series and spatial dependencies on roadway networks, there is no particular method that is clearly superior to all others. Here, we focus on investigating ensemble learning that benefits from multiple base models, and propose a traffic-condition-aware ensemble approach that acts as a decision maker by stacking multiple predictions based on dynamic traffic conditions. To sense traffic conditions, we apply the Convolutional Neural Network (CNN) model to capture the spatiotemporal patterns embedded in traffic flow. Then, the high-level features extracted by CNN are used to generate weights to ensemble multiple predictions of different models. Extensive experiments are performed with a real traffic dataset from the Caltrans Performance Measurement System. We compare the proposed approach with competitive models, including Gradient Boosting Regression Tree (GBRT) model, Weight Regression model, Support Vector Regression (SVR) model, Long Short-term Memory (LSTM) model, Historical Average (HA) model and CNN model. Experimental results demonstrate that our method can effectively improve the performances of traffic flow prediction.

关键词： Traffic flow prediction ensemble learning deep learning

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Event-Triggered Optimal Parallel Tracking control for Discrete-Time Nonlinear systems

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IEEE TRANSACTIONS ON systems MAN CYBERNETICS-systems 2022年第6期52卷 3772-3784页

作者： Lu, Jingwei Wei, Qinglai Liu, Yujia Zhou, Tianmin Wang, Fei-Yue Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Macau Univ Sci & Technol Inst Syst Engn Macau Peoples R China Qingdao Acad Intelligent Ind Qingdao 266109 Peoples R China

A novel event-triggered optimal tracking control (ETOTC) method is developed for discrete-time nonlinear systems in this study. For the time-invariant desired trajectory, we prove that the tracking error is asymptotically stable, and an upper bound of the real performance index can be predetermined by a design parameter. For the time-varying desired trajectory, the developed triggering condition reduces communication costs by relaxing the restriction of the asymptotic stability of the closed-loop system, and we prove that the tracking error is uniformly ultimately bounded (UUB). The developed ETOTC method entails obtaining the next state of the real system. Therefore, a parallel control approach is proposed to predict the next state by constructing a parallel system for the real system. Neural networks (NNs) and adaptive dynamic programming (ADP) techniques are utilized in the parallel control approach. Moreover, the stability analysis of the closed-loop system is shown, and the tracking error and NN weight estimation errors are proved to be UUB using the Lyapunov approach. Finally, we validate the developed ETOTC method through two simulations.

关键词： Nonlinear systems Trajectory Optimal control control systems Mathematical model Steady-state Dynamic programming Adaptive dynamic programming (ADP) event-triggered neural network (NN) nonlinear optimal control parallel control parallel system tracking control

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Cooperative Localization in Transportation 5.0

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES

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IEEE TRANSACTIONS ON intelligent VEHICLES 2024年第3期9卷 4259-4264页

作者： Gao, Letian Xia, Xin Zheng, Zhaoliang Xiang, Hao Meng, Zonglin Han, Xu Zhou, Zewei He, Yi Wang, Yutong Li, Zhaojian Zhang, Yubiao Ma, Jiaqi Univ Calif Los Angeles UCLA UCLA Mobil Lab Los Angeles CA 90095 USA Univ Calif Los Angeles UCLA FHWA Ctr Excellence New Mobil & Automated Vehicle Los Angeles CA 90095 USA Wuhan Univ Technol Intelligent Transportat Syst Res Ctr Wuhan 430063 Peoples R China Wuhan Univ Technol Engn Res Ctr Transportat Safety Minist Educ Wuhan 430063 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Michigan State Univ Dept Mech Engn E Lansing MI 48824 USA Gen Motors Control & Learning Syst Grp Global R&D Warren MI 48090 USA

In the era of future mobility within Transportation 5.0, autonomy and cooperation across all road users and smart infrastructure stand as the key features to enhance transportation safety, efficiency, and sustainability, supported by cooperative perception, decision-making and planning, and control. An accurate and robust localization system plays a vital role in enabling these modules for future mobility and is constrained by environmental uncertainties and sensing limitations. To achieve precise and resilient localization in this new era, this letter introduces emerging technologies including edge computing, hybrid data-driven and physical model approaches, foundation models as well as parallel intelligence, that are beneficial for next-generation localization systems. On top of these key technologies, by integrating real-world testing and digital twin technology, we further put forward a Decentralized Autonomous Service (DAS)-based cooperative localization framework for future mobility systems to enhance the resilience, robustness, and safety of transportation systems.

关键词： Location awareness Transportation Roads Robot sensing systems Safety Digital twins Computational modeling Cooperative driving cooperative localization localization service smart infrastructure transportation 5.0

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Deep reinforcement learning-based optimal bidding strategy for real-time multi-participant electricity market with short-term load

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ELECTRIC POWER systems RESEARCH 2024年 233卷

作者： Liu, Chuwei Rao, Xuan Zhao, Bo Liu, Derong Wei, Qinglai Wang, Yonghua Guangdong Univ Technol Sch Automat Guangzhou 510006 Peoples R China Beijing Normal Univ Sch Syst Sci Beijing 100875 Peoples R China Southern Univ Sci & Technol Sch Syst Design & Intelligent Mfg Shenzhen 518055 Peoples R China Univ Illinois Dept Elect & Comp Engn Chicago IL 60607 USA Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China

This paper aims to address the bidding strategy optimization in the real-time multi-participant electricity market with short-term load dynamics. In order to avoid the sub-optimal solution and the dependence on the complete information in traditional mathematical programming methods, an electricity market bidding strategy optimization algorithm based on deep reinforcement learning (DRL) is developed. While conventional reinforcement learning algorithms (e.g., Q-learning and deep Q-learning) are only capable of handling simple problems in discrete state spaces, the proximal policy optimization (PPO) algorithm is implemented in the bidding strategy optimization since it can optimize the bidding strategy in the continuous action and state spaces. In order to substantiate the aforementioned perspective, this paper conducts a two-part experimental study. First, experiments which consider the fixed demand load of market participants show that the developed method can reach the Nash equilibrium just like the bi-level optimization, and higher profits can be achieved by adjusting hyperparameters. Then, complex experiments which consider the time-varying demand load verify the DRL-based electricity market bidding strategy performs better than bi-level optimization-based methods and increases the profits of generators.

关键词： Deep reinforcement learning Proximal policy optimization algorithm Bi-level optimization Electricity market bidding strategy Nash equilibrium

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Fault Diagnosis for Sucker Rod Pumping systems: A Parallel System Approach

IEEE JOURNAL OF RADIO FREQUENCY IDENTIFICATION

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IEEE JOURNAL OF RADIO FREQUENCY IDENTIFICATION 2022年 6卷 743-747页

作者： Wang, Xingxia Cheng, Xiang Lu, Jingwei Liu, Shuliang Zhang, Zhixue Liu, Zhaohai Wang, Fei-Yue Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Macau Univ Sci & Technol Macau Inst Syst Engn Macau Peoples R China Beijing Mot G Intelligent Technol Co Ltd Prod Management Dept Beijing 100102 Peoples R China Tangshan Jidong Petr Machinery Co Ltd Tangshan 063299 Hebei Peoples R China

To solve the problem of insufficient physical faulty data in sucker rod pumping systems, the parallel system theory is utilized to investigate fault diagnosis of sucker rod pumping systems. Moreover, a novel framework of fault diagnosis, parallel fault diagnosis (PFD), is proposed. Firstly, to achieve PFD, the parallel sucker rod pumping system, named Bo-Yi, is proposed in this paper. Then, based on the proposed Bo-Yi, parallel fault diagnosis is investigated through the parallel learning theory, which can generate more useful systematic data and facilitate analyzing sucker rod pumping systems more thoroughly. Finally, the framework of parallel fault diagnosis for the parallel sucker rod pumping system is given to clearly demonstrate the related operational process.

关键词： Parallel systems sucker rod pumping systems (SRPs) parallel fault diagnosis (PFD) parallel learning parallel sucker rod pumping systems (Bo-Yi)

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TADNet: Temporal Attention Decomposition Networks for Probabilistic Energy Forecasting

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IEEE TRANSACTIONS ON POWER systems 2024年第6期39卷 7190-7202页

作者： Ye, Jiarui Zhao, Bo Liu, Derong Wei, Qinglai Wang, Yonghua Guangdong Univ Technol Sch Automat Guangzhou 510006 Peoples R China Beijing Normal Univ Sch Syst Sci Beijing 100875 Peoples R China Southern Univ Sci & Technol Sch Syst Design & Intelligent Mfg Shenzhen 518055 Peoples R China Univ Illinois Dept Elect & Comp Engn Chicago IL 60607 USA Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China

Many decision-making processes in the energy industry depend on probabilistic forecasting to quantify the future uncertainties. However, existing methods are difficult to provide reliable probabilistic forecasting for the energy industry. Hence, we focus on solving the multi-horizon probabilistic forecasting problem of energy time series. In this paper, the time series is considered as the superposition of a regular changing component and a residual component. On this basis, we develop a novel deep learning-based temporal attention decomposition network (TADNet). TADNet predicts the regular change of the time series, and then quantifies the fluctuation intervals of the residual component, which enables TADNet to achieve reliable probabilistic forecasting. To take both short- and long-term patterns into account, TADNet combines convolution with attention mechanism to extract the temporal feature from the time series. To enable efficient information extraction, the gating components are utilized to suppress unnecessary features. Through extensive empirical evaluation of several real-world energy datasets, TADNet achieves better prediction accuracy than the compared models.

关键词： Forecasting Time series analysis Probabilistic logic Predictive models Reliability Long short term memory Load modeling Deep learning neural networks attention mechanism time series forecasting probabilistic energy forecasting

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Nearly optimal stabilization of unknown continuous-time nonlinear systems: A new parallel control approach

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NEUROCOMPUTING 2024年 578卷

作者： Lu, Jingwei Wang, Xingxia Wei, Qinglai Wang, Fei-Yue Tsinghua Univ Dept Ind Engn Beijing 100084 Peoples R China Qingdao Acad Intelligent Ind Qingdao 266114 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

This paper develops a novel online nearly optimal control (ONOC) method for unknown continuous -time (CT) nonaffine nonlinear systems without recovering unknown systems. First, a dynamic control law is proposed for CT nonaffine nonlinear systems using parallel control. To achieve the proposed dynamic control law, an affine augmented system (AAS) is constructed according to the original system, and an augmented performance index (API) is constructed on the basis of the original performance index (OPI). Then, the stability relationship between the original system and the AAS is provided, and it is proven that, by selecting a suitable parameter in the API, optimal control of the AAS with the API is equivalent to near -optimal control of the original system with the OPI. Subsequently, based on the proposed dynamic control law, we extend integral reinforcement learning (IRL) to completely unknown CT nonaffine systems, and it is further proved that closed -loop signals are uniformly ultimately bounded (UUB) without the assumption that the input dynamics are bounded. Furthermore, the OPI can be set to an arbitrary positive -definite form, and the UUB bound for the state vector can be predetermined. Lastly, simulations are offered to exhibit the correctness of the developed ONOC method. Source code of this paper is available at: https://***/lujingweihh/Adaptive-dynamic-programmingalgorithms/tree/main/model_free_integral_reinforcement_learning.

关键词： Adaptive dynamic programming (ADP) Integral reinforcement learning (IRL) Nearly optimal control Nonaffine nonlinearity Parallel control Unknown nonlinear systems

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