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28th IEEE International Conference on Emerging Technologies and Factory automation, ETFA 2023

作者： Ţiru, Andreea Elena Vasiliev, Iulian Diaconu, Larisa Vilanova, Ramon Voipan, Daniel Ratnaweera, Harsha Dunǎrea de Jos University of Galaţi Department of Automatic Control and Electrical Engineering Galaţi Romania Autonomous University of Barcelona Department of Automation Systems and Advanced Control Research Barcelona Spain Dunǎrea de Jos University of Galaţi Department of Computer Science and Information Technology Galaţi Romania Ås Norway

ISBN: (纸本)9798350339918

The management of wastewater is a significant global concern that calls for innovative solutions to lessen its negative effects on the environment. Conventional techniques of treating wastewater need improvement in order to deal with newly discovered contaminants, which highlights the importance of providing precise estimates of process performance and resource requirements. The worsening water shortage situation requires a paradigm shift in which wastewater is viewed as a useful resource. It is possible to create an economy that is both sustainable and circular by treating and recycling wastewater, putting less pressure on freshwater supplies, and leaving as little of an environmental footprint as possible. This study investigates the use of Artificial Neural Networks (ANNs) as software estimators in the treatment of wastewater, with a particular emphasis on predicting ammonium concentrations in effluent. In order to deal with imbalanced time-series data, the research introduces innovative data pretreatment strategies. These techniques include a Sliding Window protocol, Data Normalization, and a K-Fold training scheme. This illustrates the potential of ANNs to revolutionize wastewater treatment procedures and drive developments in this field. The suggested method demonstrates higher performance when estimating pollutant concentrations, showing the ability of ANNs to do so. © 2023 IEEE.

关键词： Neural networks

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Monocular Visual-Inertial and Robotic-Arm Calibration in a Unifying Framework

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IEEE/CAA Journal of Automatica Sinica 2022年第1期9卷 146-159页

作者： Yinlong Zhang Wei Liang Mingze Yuan Hongsheng He Jindong Tan Zhibo Pang Key Laboratory of Networked Control Systems Shenyang Institute of AutomationChinese Academy of SciencesShenyang 110016 State Key Laboratory of Robotics Shenyang Institute of AutomationChinese Academy of SciencesShenyang 110016 Institutes for Robotics and Intelligent Manufacturing Chinese Academy of SciencesShenyang 110169China School of Computing Wichita State UniversityWichita 67260 USA Department of Mechanical Aerospace and Biomedical EngineeringUniversity of TennesseeTN 37996 USA Department of Automation Technology ABB Corporate Research SwedenVasteras 72178Sweden Department of Intelligent Systems Royal Institute of Technology(KTH)Stockholm 11428Sweden

Reliable and accurate calibration for camera,inertial measurement unit(IMU)and robot is a critical prerequisite for visual-inertial based robot pose estimation and surrounding environment ***,traditional calibrations suffer inaccuracy and *** address these problems,this paper proposes a monocular visual-inertial and robotic-arm calibration in a unifying *** our method,the spatial relationship is geometrically correlated between the sensing units and robotic *** decoupled estimations on rotation and translation could reduce the coupled errors during the ***,the robotic calibration moving trajectory has been designed in a spiral pattern that enables full excitations on 6 DOF motions repeatably and *** calibration has been evaluated on our developed *** the experiments,the calibration achieves the accuracy with rotation and translation RMSEs less than 0.7°and 0.01 m,*** comparisons with state-of-the-art results prove our calibration consistency,accuracy and effectiveness.

关键词： Calibration inertial measurement unit(IMU) monocular camera robotic arm spiral moving trajectory

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Design Optimization of High Frequency Transformer for Resonant DC/DC Converter 6

Design Optimization of High Frequency Transformer for Resona...

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6th IEEE International Electrical and Energy Conference, CIEEC 2023

作者： Yu, Xiangdong Zhang, Zheng Wang, Xiaozhou Xu, Jian Zhao, Shuang Ding, Lijian Shen, Min Yin, Chenxu School of Electrical Engineering and Automation Hefei University of Technology Hefei China School of Intelligent Manufacturing and Control Engineering Qilu Institute of Technology Jinan China State Grid Anhui Electric Power CO. Ltd Corporate Management Department Hefei China

ISBN: (纸本)9798350346671

Resonant DC/DC converter is extensively applied in the industry due to their superiorities such as zero voltage switching which enables it to operate under higher frequency. As the core component of resonant DC/DC converter, the high-frequency transformer (HFT) is critical to the performance of the converter in regard of power density, efficiency, and reliability. However, as the frequency and power rating increase, the power loss on the wire and core increase as well. It is desired to propose the optimal design process for integrated HFT which considers the tradeoff between volume and power. This paper comprehensively investigates the impact of air gap length, location and winding structure on the transformer. Based on the analytical model, the design flow of a magnetics integrated transformer which incorporates intrinsic magnetizing inductance and leakage inductance for the resonant tank is presented. Finally, the analytical model and the optimization design of HFT are validated with experimental results. © 2023 IEEE.

关键词： High frequency transformers

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Online Vehicle Forensics Method of Responsible Party for Accidents Based on LSTM-BiDBN External Intrusion Detection

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Journal of Shanghai Jiaotong university(Science) 2024年第6期29卷 1161-1168页

作者： LIU Wen XU Jianxin YANG Genke CHEN Yuanfang Ningbo Industrial Internet Institute Ningbo 315000ZhejiangChina Ningbo Artificial Intelligence Institute of Shanghai Jiao Tong University Ningbo 315000ZhejiangChina Department of Automation Shanghai Jiao Tong UniversityShanghai 200240China College of Control Science and Engineering Zhejiang UniversityHangzhou 310027China School of Cyberspace Hangzhou Dianzi UniversityHangzhou 310018China

Vehicle data is one of the important sources of traffic accident digital *** propose a novel method using long short-term memory-deep belief network by binary encoding(LSTM-BiDBN)controller area network identifier(CAN ID)to extract the event sequence of CAN IDs and the semantic of CAN IDs *** of detecting attacks only aimed at a specific CAN ID,the proposed method fully considers the potential interaction between electronic control *** this means,we can detect whether the vehicle has been invaded by the outside,to online determine the responsible party of the *** use our LSTM-BiDBN to distinguish attack-free and abnormal situations on *** results show that our proposed method is more effective in identifying anomalies caused by denial of service attack,fuzzy attack and impersonation attack with an accuracy value of 97.02%,a false-positive rate of 6.09%,and a false-negative rate of 1.94%compared with traditional methods.

关键词： digital forensics deep belief network(DBN) long short-term memory(LSTM) binary encoding controller area network identifier(CAN ID) responsible party

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Distributed Least-Squares Optimization Solvers with Differential Privacy

arXiv

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arXiv 2024年

作者： Liu, Weijia Wang, Lei Guo, Fanghong Wu, Zhengguang Su, Hongye Institute of Cyber-Systems and Control College of Control Science and Engineering Zhejiang University Hangzhou310027 China Department of Automation Zhejiang University of Technology Hangzhou China

This paper studies the distributed least-squares optimization problem with differential privacy requirement of local cost functions, for which two differentially private distributed solvers are proposed. The first is established on the distributed gradient tracking algorithm, by appropriately perturbing the initial values and parameters that contain the privacy-sensitive data with Gaussian and truncated Laplacian noises, respectively. Rigorous proofs are established to show the achievable tradeoff between the (ǫ, δ)-differential privacy and the computation accuracy. The second solver is established on the combination of the distributed shuffling mechanism and the average consensus algorithm, which enables each agent to obtain a noisy version of parameters characterizing the global gradient. As a result, the least-squares optimization problem can be eventually solved by each agent locally in such a way that any given (ǫ, δ)-differential privacy requirement can be preserved while the solution may be computed with the accuracy independent of the network size, which makes the latter more suitable for large-scale distributed least-squares problems. Numerical simulations are presented to show the effectiveness of both solvers. © 2024, CC BY-NC-SA.

关键词： Parameter estimation

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Minimizing Epistemic Uncertainty for Predictive control of General Anesthesia

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IFAC-PapersOnLine 2024年第24期58卷 490-495页

作者： Clara M. Ionescu Robin De Keyser Dana Copot Erhan Yumuk Isabela R. Birs Erwin Hegedus Cristina I. Muresan Martine Neckebroek Ghent University Department of Electromechanics System and Metal Engineering Ghent Belgium Technical University Cluj-Napoca Department of Automation Memorandumului Street 20 Cluj Romania Istanbul Technical University Department of Control and Automation Engineering Maslak 34469 Istanbul Turkey Ghent University Hospital Department of Anesthesia Corneel Heymanslaan 10 Ghent 9000 Belgium

Epistemic uncertainty refers to the lack of information from knowledge and in medical field it is often paired with decision making situations and systems largely dependent on context and expertise. Our approach is uncertainty-centric and digitalizes knowledge with high certainty level, in order to improve the efficacy of knowledge-driven decision support system for anesthesia regulation. We propose to digitize some of surgical actions as part of minimizing uncertainty in closed loop control. Notable disturbances are those induced by the surgery actions, but they abide clinical protocols and semantic transformation enables their use in computers. We introduce a predictive control methodology to accommodate the augmented information of the system and evaluate its impact on the closed loop performance. Simulations of closed loop control of anesthesia for various surgery protocols indicate a high relevance and applicability of proposed approach. The proposed control structure allows the use of medical semantics in decision making process of anesthesia regulation.

关键词： closed loop control of anesthesia digitalized disturbance predictive control human in the loop medical semantics decision making systems epistemic uncertainty

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Mean policy-based proximal policy optimization for maneuvering decision in multi-UAV air combat

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Neural Computing and Applications 2024年第31期36卷 19667-19690页

作者： Zheng, Yifan Xin, Bin He, Bin Ding, Yulong Department of Control Science and Engineering Tongji University Shanghai China Frontiers Science Center for Intelligent Autonomous Systems Ministry of Education Shanghai China School of Automation Beijing Institute of Technology Beijing China National Key Lab of Autonomous Intelligent Unmanned Systems Beijing China Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of Technology Beijing China

Autonomous maneuvering decision-making is a crucial technology for Unmanned Aerial Vehicles (UAVs) to take the air domination in modern unmanned warfare. With the advantage of balancing exploration and exploitation, as well as the immediacy of end-to-end output by combining with deep neural network, multi-agent reinforcement learning (MARL) has made remarkable achievements in multi-UAV autonomous air combat maneuvering decision-making (MUAAMD). However, the implementation of effective cooperative policy learning remains a challenging issue for MARL methods with centralized training decentralized execution (CTDE) paradigm. This paper proposes a MARL-based method to improve the performance of cooperation in MUAAMD. Firstly, considering the constraints of dynamic and limited perception for UAVs in the realistic air combat scenario, the MUAAMD problem is formulated based on partially observable Markov game (POMG) model. Secondly, a novel efficient MARL algorithm named the mean policy-based proximal policy optimization (MP3O) is introduced. Specifically, a joint policy optimization mechanism is constructed by estimating the policies of neighboring agents in group as a mean-field approximation while training, which enables both centralized evaluation and improvement of cooperative policy under the CTDE paradigm. Thirdly, by combining with three improvement techniques, a cooperative decision-making framework for MUAAMD based on MP3O is proposed. Empirically, results of simulations and comparative experiments validate the effectiveness of proposed method in promoting cooperative policy learning in resolving MUAAMD problem. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Decision making

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Data-Driven Robust Optimization for Energy Chemical Processes under Uncertainties: A Review and Tutorial 4

Data-Driven Robust Optimization for Energy Chemical Processe...

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4th International Conference on Industrial Artificial Intelligence, IAI 2022

作者： Ning, Chao Li, Longyan Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Shanghai200240 China

ISBN: (数字)9781665451208

ISBN: (纸本)9781665451208

In recent years, data-driven robust optimization (DDRO) is becoming a popular and effective paradigm to address the challenging issue of uncertainty in energy chemical processes. This paper provides an overview of recent advances in the field of DDRO, with a primary focus on its methods and applications in process industries. Firstly, a brief introduction to various robust optimization model formulations and solution algorithms is presented. Secondly, research achievements of machine-learning enabled uncertainty sets, the corresponding DDRO, and variant techniques are summarized and analyzed in a systematic manner. Additionally, tutorial-like numerical examples are used to illustrate merits of DDRO compared with conventional robust optimization. Finally, fruitful applications of DDRO in energy chemical processes are encapsulated and categorized from domain perspectives. © 2022 IEEE.

关键词： Optimization

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Modeling and Calculation of Temperature Field in Slotless Motor Based on a New Lumped Parameter Thermal Network Method 26

Modeling and Calculation of Temperature Field in Slotless Mo...

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26th International Conference on Electrical Machines and Systems, ICEMS 2023

作者： Zhao, Jing Guo, Penglei Zheng, Yun Wang, You Yang, Lei Beijing Institute of Technology School of Automation Beijing China Beijing Aerospace Control Instrument Research Institute Department of Motor Power Supply Beijing China Beijing Institute of Control Engineering Beijing Key Laboratory of Long-life Technology of Precise Rotation and Transmission Mechanisms Beijing China

ISBN: (纸本)9798350317589

In order to achieve accurate analysis of the motor temperature field, the temperature field of the slotless motor is modeled and calculated based on the centralized parametric thermal network method. The distribution of the motor thermal network nodes is given, and the axial model of the motor is divided. By deriving the thermal resistance model of each component of the slotless motor, a full-domain thermal resistance network model of the motor is constructed, and the accuracy of the model is verified by comparing the results with the finite element simulation. © 2023 IEEE.

关键词： Finite element method

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Resource Allocation for Coupled Slices in Edge-Assisted Industrial IoT Systems

Resource Allocation for Coupled Slices in Edge-Assisted Indu...

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2023 IEEE/CIC International Conference on Communications in China, ICCC 2023

作者： Wen, Xiaojing Chen, Cailian Ren, Cheng Lyu, Ling Guan, Xinping Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Department of Automation Shanghai200240 China Dalian Maritime University School of Information Science and Technology Dalian116026 China

ISBN: (纸本)9798350345384

Multiple types of sensors are utilized for remote monitoring of single industrial process to compensate for the limited accuracy of a single type of sensor. Network slicing technology is considered promising for collecting data from heterogeneous sensors and meeting diverse Quality of Service (QoS) requirements. However, existing research on network slicing optimization mainly focuses on inter-slice resource constraints based on slice heterogeneity, neglecting slice coupling feature. In this regard, this paper proposes a novel network slicing framework for edge-assisted industrial IoT systems. The framework includes a slice coupling model and network resource allocation (transmission and computing) to address the problem of improving estimation performance while ensuring system convergence. The slice coupling model is primarily composed of the estimation error covariance of two slices, calculated using the Age of Task (AoT) as a bridge, which reflects the accuracy and freshness of the estimation. To reduce the complexity of the direct solution, the optimization problem is scaled down and decoupled. The iterative Particle Swarm Optimization (PSO) algorithm is proposed to determine the allocation rates of inter-slice transmission and computing resources. Simulation results demonstrate that the proposed algorithm achieves a flexible balance under heterogeneous estimation weights and exhibits near-optimal performance. © 2023 IEEE.

关键词： Resource allocation

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