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Innovations in Intelligent systems and Applications Conference

作者： Fehmi Can Ay Bengisu Kılınç Talha Enes Ercan Ahmet Kağızman Volkan Sezer Department of Mechatronics Engineering Istanbul Technical University Istanbul Turkey Department of Institute of Science Smart and Autonomous System Laboratory (SASlab) Istanbul Technical University Istanbul Turkey Department of Control and Automation Engineering Smart and Autonomous System Laboratory (SASlab) Istanbul Technical University Istanbul Turkey

ISBN: (数字)9798350379433

ISBN: (纸本)9798350379440

In recent years, the bicycle industry has undergone continuous transformation through the introduction of novel technologies and innovations. Automated Manual Transmission (AMT) system, which is widely used in motor vehicles and defined as automating the traditional manual gearbox through electronic components and actuators, has an important place in the automotive industry due to its advantages such as fuel efficiency, low cost, and driver comfort. However, a similar system is not yet available on bicycles. This article describes developing a new gear-shifting system that enables the manual gear-shifting mechanism in bicycles to be automated with an intelligence-based system. The developed system automatically performs the most appropriate gear change by processing the data received from the sensors, such as inclination, wheel, and pedal cadence, thus providing a more efficient and comfortable driving experience that does not require effort. The system has been tested by integrating it into a bicycle with manual transmission and it has been confirmed that automatic gear shifts were successful.

关键词： Industries Technological innovation Gears Wheels Manuals Bicycles Sensor systems Intelligent systems Intelligent sensors Vehicles

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Adaptive Deep Reinforcement Learning for Critical Boundary Scenario Generation 43

Adaptive Deep Reinforcement Learning for Critical Boundary S...

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43rd Chinese control Conference, CCC 2024

作者： Zhou, Junjie Wang, Lin Wang, Xiaofan Meng, Qiang Shanghai Jiao Tong University School of Electronic Information and Electrical Engineering Shanghai202400 China Ministry of Education of China Key Laboratory of System Control and Information Process Shanghai202400 China National University of Singapore Department of Civil and Environmental Engineering Singapore117576 Singapore Shanghai Institute of Technology School of Electrical and Electronic Engineering Shanghai202400 China

ISBN: (纸本)9789887581581

Critical boundary scenarios play a vital role in the comprehensive testing of autonomous vehicles, as they can significantly expedite testing processes and reduce economic and time costs. To tackle the intricate, multidimensional, and diverse nature of intelligent driving scenarios, this paper proposes an innovative bi-level adaptive deep reinforcement learning (BADRL) approach aimed at generating authentic and diverse critical boundary scenarios. Leveraging naturalistic driving data, background agents are trained to exhibit naturalistic and realistic driving behaviors using a neural-based naturalistic driving behavior model. To address the intricacies of multi-interaction, high-dimensional environments, the scenario complexity model is introduced to evaluate the relative complexity between the traffic environment and the tested autonomous vehicle in real time. By integrating the scenario complexity model, the naturalistic driving behavior learning, intelligent driving testing, and critical boundary scenario generation are concatenated together to form a closed loop. BADRL facilitates the upper-level neural network in learning to adaptively increase the complexity of test scenarios, which are then fed into lower-level models to optimize the behavior of principal traffic participants, thereby generating naturalistic and critical boundary test scenarios. Extensive simulations were carried out to verify the efficacy of the BADRL technique in complex intersection environments. Results indicate that the BADRL approach boosts the efficiency of critical boundary scenario generation by approximately 10% compared to the state-of-the-art method. © 2024 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Deep reinforcement learning

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Real-time Safety Helmet-wearing Detection Based on Improved YOLOv5

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Computer systems Science & engineering 2022年第12期43卷 1219-1230页

作者： Yanman Li Jun Zhang Yang Hu Yingnan Zhao Yi Cao NARI Nanjing Control System Ltd. Nanjing210032China School of Computer Nanjing University of Information Science&TechnologyNanjing210044China Department of Electrical and computer Engineering University of WindsorWindsorN9B 3P4Canada

Safety helmet-wearing detection is an essential part of the intelligentmonitoring system. To improve the speed and accuracy of detection, especiallysmall targets and occluded objects, it presents a novel and efficient detectormodel. The underlying core algorithm of this model adopts the YOLOv5 (YouOnly Look Once version 5) network with the best comprehensive detection performance. It is improved by adding an attention mechanism, a CIoU (CompleteIntersection Over Union) Loss function, and the Mish activation function. First,it applies the attention mechanism in the feature extraction. The network can learnthe weight of each channel independently and enhance the information dissemination between features. Second, it adopts CIoU loss function to achieve accuratebounding box regression. Third, it utilizes Mish activation function to improvedetection accuracy and generalization ability. It builds a safety helmet-wearingdetection data set containing more than 10,000 images collected from the Internetfor preprocessing. On the self-made helmet wearing test data set, the averageaccuracy of the helmet detection of the proposed algorithm is 96.7%, which is1.9% higher than that of the YOLOv5 algorithm. It meets the accuracy requirements of the helmet-wearing detection under construction scenarios.

关键词： Safety helmet wearing detection object detection deep learning YOLOv5 Attention Mechanism

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On the Equilibrium of a Class of Leader-Follower Games with Decision-Dependent Chance Constraints

arXiv

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arXiv 2024年

作者： Wang, Jingxiang Wang, Zhaojian Yang, Bo Liu, Feng Guan, Xinping The Key Laboratory of System Control and Information Processing Ministry of Education of China The Department of Automation Shanghai Jiao Tong University Shanghai China The State Key Laboratory of Power System The Department of Electrical Engineering Tsinghua University Beijing100084 China

In this paper, we study the existence of equilibrium in a single-leader-multiple-follower game with decision-dependent chance constraints (DDCCs), where decision-dependent uncertainties (DDUs) exist in the constraints of followers. DDUs refer to the uncertainties impacted by the leader’s strategy, while the leader cannot capture their exact probability distributions. To address such problems, we first use decision-dependent ambiguity sets under moment information and Cantelli’s inequality to transform DDCCs into second-order cone constraints. This simplifies the game model by eliminating the probability distributions. We further prove that there exists at least one equilibrium point for this game by applying Kakutani’s fixed-point theorem. Finally, a numerical example is provided to show the impact of DDUs on the equilibrium of such game models. Copyright © 2024, The Authors. All rights reserved.

关键词： Game theory

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Resilient Cooperative control of Multiple DC Microgrids With Interconnection Networks Against Cyber Attacks

IEEE Transactions on Industrial Cyber-Physical Systems

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IEEE Transactions on Industrial Cyber-Physical systems 2025年 3卷 116-126页

作者： Xiao, Feng Liu, Shiyu Wei, Bo Fang, Fang Qin, Jiahu North China Electric Power University State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources Beijing102206 China North China Electric Power University School of Control and Computer Engineering Beijing102206 China North China Electric Power University New Energy Generation National Engineering Research Center Beijing102206 China University of Science and Technology of China Department of Automation Hefei230027 China

The two control objectives of voltage regulation and precise current sharing are conflicting in multi-bus DC microgrids. In this paper, a distributed control strategy is proposed, and it can achieve accurate current sharing and regulate the voltage of a critical node to the rated value. However, such a distributed control system is susceptible to potential cyber attacks that could deteriorate microgrids performances by corrupting communication links or actuators. To overcome these difficulties, we design the interconnected communication networks to establish a resilient cooperative system of multiple microgrids against unknown bounded injection attacks. The proposed resilient control strategy can achieve the control objectives of maintaining voltage regulation and current sharing within each microgrids under different operating conditions, and the interconnected system consisting of multiple microgrids cooperates to mitigate the adverse effect of cyber attacks. The dynamics and stability of the overall close-loop system of the DC microgrids are analyzed, in the present or absent of cyber attacks. Simulation results validate the effectiveness of the proposed approach in two 4-bus DC microgrids. © 2023 IEEE.

关键词： Cyber attacks

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Enhancing Traffic Flow Forecasting with Spatio-Temporal Information: A Case Study on Highway Networks in Belgium 8

Enhancing Traffic Flow Forecasting with Spatio-Temporal Info...

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8th International Conference on Instrumentation, control, and Automation, ICA 2023

作者： Fathurrahman, Muhammad Farhan Catteeuw, Arne Gautama, Sidharta Aguirre, Angel L. Joelianto, Endra Sutarto, Herman Y. Ghent University Engineering and Product Design Department of Industrial Systems Ghent Belgium Department of Intelligent System Bandung Indonesia FlandersMake@UGent-Corelab ISyE Lommel Belgium Institut Teknologi Bandung Instrumentation and Control Research Group Faculty of Industrial Technology Bandung Indonesia

ISBN: (纸本)9798350301274

As the number of vehicles continues to increase every year, it is crucial to monitor traffic conditions effectively. Traffic forecasting serves as a valuable method in traffic management to support decision making. This study investigates the impact of utilizing spatio-Temporal data for traffic flow forecasting, as opposed to forecasting each location individually. We compare spatio-Temporal Long Short-Term Memory (LSTM), a machine-learning technique that utilizes spatially correlated sensor data in a network, and compare this to conventional LSTM and Seasonal ARIMA (SARIMA). Experiments have been conducted on counting loop data of a highway network in Belgium. We have investigated the performances of the forecasting model for different prediction horizons. The results show that spatial information improves the performance of the forecasting model and reduces the training time required. Further analysis show that the spatio-Temporal LSTM improves majorities of detectors while the minorities detectors are degraded which imply the importances of network selection. The computational load of the spatio-Temporal LSTM model is minimally impacted by the number of sensors in the network thus making it scalable. The results show that longer prediction horizons degrade the forecasting performance but the impact up to a 12 hour-horizon is not significant. © 2023 IEEE.

关键词： Long short-term memory

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Development of a Simple Dielectric Equivalent Circuit Model from Polarization and Depolarization Current Measurements

Development of a Simple Dielectric Equivalent Circuit Model ...

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IEEE/IAS Industrial and Commercial Power system Asia (I&CPS Asia)

作者： Peerawut Yutthagowith Phethai Nimsanong Yoshihiro Baba School of Engineering King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand Department of Power System Control Metropolitan Electricity Authority Power System Analysis Section Bangkok Thailand Graduate School of Science and Engineering Doshisha University Kyoto Japan

ISBN: (数字)9798350352290

ISBN: (纸本)9798350352306

Assessing insulation performance is vital for sustainable energy systems, which aim to minimize visual impact while ensuring reliability and resilience against weather-related disruptions. With increasing reliance on renewable energy sources, the importance of insulation performance becomes even more pronounced. Regular evaluations of insulation performance are critical for maintaining the safety, reliability, and resilience of electricity networks. By promptly identifying risks such as insulation degradation, utilities can improve maintenance practices, prioritize repairs, and enhance the integration of renewables, thereby boosting overall infrastructure performance. This paper introduces a straightforward and precise technique for determining the equivalent circuit parameters of insulation systems in high-voltage equipment. The proposed method facilitates the accurate determination of the dielectric loss factor from the equivalent circuit. To validate this approach, test cases were utilized to demonstrate its effectiveness. The results obtained through this method were then compared with those obtained using commercial software. The comparison reveals that the proposed approach results in a lower number of depolarization current branches compared to the commercial software. Also, it provides almost the same relative root mean square error and the lower root mean square relative error of the depolarization current. These findings underscore the appeal and effectiveness of the proposed method for determining the equivalent circuit of the dielectric model.

关键词： Insulation Renewable energy sources Current measurement Fitting Dielectric losses Software Integrated circuit modeling Root mean square Equivalent circuits Resilience

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H-/H∞UIO-Based Robust Fault Diagnosis Design in Finite Frequency Domain for Discrete-Time systems 41

H-/H∞UIO-Based Robust Fault Diagnosis Design in Finite Freq...

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41st Chinese control Conference, CCC 2022

作者： Gao, Sheng Wang, Jian Lu, Zhihui Ma, Guangfu Guo, Yanning Harbin Institute of Technology Department of Control Science and Engineering Harbin150001 China Beijing Institute of Remote Sensing Equipment Beijing100005 China Aerospace System Engineering Shanghai Shanghai201109 China

ISBN: (纸本)9789887581536

This study investigates the problem of robust fault diagnosis for discrete-time systems subjected to actuator faults and unknown input disturbances. A novel H-/L-{infty} unknown input observer (UIO) based on the generalized Kalman-Yakubovich-Popov (GKYP) lemma is presented by decoupling the partial unknown input disturbances and attenuating the unknown input disturbances that cannot be decoupled. Then, the H- performance index in the finite frequency domain is used to measure the fault sensitivity and the L-{infty} performance index is used to reduce the influence of unknown input disturbances on fault diagnosis results and ensure the robustness performance. A residual evaluation function and a time-varying threshold are presented based on the L-{alpha} performance index. In addition, sufficient conditions for designing the H-/L-{infty} UIO are proposed and transformed into linear matrix inequalities (LMIs) that can be easily solved. Finally, simulations of a simple discrete-time system are used to validate the effectiveness of the developed H-/L-{infty} UIO. © 2022 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Linear matrix inequalities

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Bearing-Based Target Entrapping control of Multiple Uncertain Agents With Arbitrary Maneuvers

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IFAC-PapersOnLine 2023年第2期56卷 8821-8826页

作者： Haifan Su Ziwen Yang Shanying Zhu Cailian Chen Wenbin Yu Department of Automation Shanghai Jiao Tong University China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management

This paper is concerned with bearing-based cooperative target entrapping control of multiple uncertain agents with arbitrary maneuvers including shape deformation, rotations, scalings, etc. A leader-follower structure is used, where the leaders move with the predesigned trajectories, and the followers are steered by an estimation-based control method, integrating a distance estimator using bearing measurements and a stress matrix-based formation controller. The signum functions are used to compensate for the uncertainties so that the agents’ accelerations can be piecewise continuous and bounded to track the desired dynamics. With proper design of the leaders’ trajectories and a geometric configuration, an affine matrix is determined so that the inter-agent relative bearings can be persistently exciting since the bearing rates are related to different weighted combinations of the affine matrix vectors. The asymptotic convergence of the estimation and control error is proved using Filipov properties and cascaded system theories. A sufficient condition for inter-agent collision avoidance is also proposed. Finally, simulation results are given to validate the effectiveness of the method.

关键词： Target entrapping control bearing measurement persistent excitation uncertainty

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Short-Term Photovoltaic Prediction Based on Transfer Learning and LSTM

Short-Term Photovoltaic Prediction Based on Transfer Learnin...

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IEEE Conference on Energy Internet and Energy system Integration (EI2)

作者： Tianyu Zhang Kedi Zheng Qixin Chen Department of Electrical Engineering State Key Laboratory of Power System Operation and Control Tsinghua University Beijing China

With the proposal of carbon peaking and carbon neutrality goals, the proportion of renewable energy generation continues to increase and the number of new photovoltaic power stations is growing rapidly. Accurate photovoltaic power prediction is of great significance for the safe, stable and operation of power system. In order to solve the problem of insufficient historical data of newly constructed PV power plants, this paper proposes a short-term PV power prediction model based on Long Short Term Memory and transfer learning. The model uses meteorological data, numerical weather forecast data and PV power as features. According to the transfer learning theory, the Long Short Term Memory model is pre-trained using the historical data of other previously-built PV power plants, and then a small amount of local operation data is used to fine-adjust the model. Case study based on Hebei open source photovoltaic and meteorological data set proves that the proposed transfer learning model can effectively improve the short-term PV power prediction accuracy, and the MAPE can be improved by up to 15%.

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