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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Shibei Zhu Rituraj Kaushik Samuel Kaski Ville Kyrki Department of Computer Science Department of Electrical Engineering and Automation Aalto University Finland Department of Computer Science University of Manchester United Kingdom

Learning policies from multiple demonstrators is often difficult because different individuals perform the same task differently due to hidden factors such as preferences. In the context of policy learning, this leads to multimodal policies. Existing policy learning methods often converge to a single solution mode, failing to capture the diversity in the solution space. In this paper, we introduce an imitation-guided reinforcement learning framework to solve the multimodal policy learning problem from a limited number of state-only demonstrations. Then, we propose LfBD (Learning from Behaviourally diverse Demonstration), an algorithm that builds a parameterised solution space to capture the variability in the behaviour space defined by demonstrations. To this end, we define a projection function based on the state density distributions from demonstrations to define such space. Our goal is not only to learn how to solve the task as the human demonstrator but also to extrapolate beyond the provided demonstrations. In addition, we show that with our method, we can perform a post-hoc policy search in the built solution space to recover policies that satisfy specific constraints or to find a policy that matches a given (state-only) behaviour.

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PDET: Progressive Diversity Expansion Transformer for Cross-Modality Visible-Infrared Person Re-identification 27th

PDET: Progressive Diversity Expansion Transformer for Cross...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Xiong, Mingfu Liang, Jingbang Guo, Yifei Lee, Ik Hyun Bakshi, Sambit Muhammad, Khan School of Computer Science and Artificial Intelligence Wuhan Textile University Hubei Wuhan430200 China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Hubei Wuhan430074 China Department of Mechatronics Engineering Tech University of Korea and IKLAB Inc. Seoul Korea Republic of Department of Computer Science and Engineering National Institute of Technology Rourkela Rourkela India School of Convergence Sungkyunkwan University Seoul03063 Korea Republic of

ISBN: (纸本)9783031783401

Visible-Infrared Person Re-identification (VI-ReID) would effectively improve the recognition performance in weak-lighting and nighttime scenes, which is an important research direction in pattern recognition and computer vision. However, existing methods usually focus on reducing the image differences between modalities (visible and infrared) to extract more reliable features, while neglecting the ability to discriminate the different identities with similar appearances. To address this problem, we propose a framework called "Progressive Diversity Expansion Transformer (PDET)", which includes a Diversity Distinguishing Vision Transformer Module (DDViTM) and a Cross-Modality Similarity Matching (CMSM) module for VI-ReID in this study. The DDViTM is proposed to implement the multiple embedded output vectors for a single input, learning feature representations of individual pedestrians in different modalities. The second module (CMSM) is used to improve the feature similarity between visible and infrared images, and dynamically adjust the image sequence weights of the two modalities to complete the training and optimization efficiency for the entire network. We conducted extensive experiments on the SYSU-MM01 and RegDB datasets, widely recognized public datasets for VR-ReID. The results demonstrate that the algorithm presented in this work has achieved promising performance compared to state-of-the-art methods. The code is available at https://***/jxsiaj/***. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Infrared imaging

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Prediction of Emission Characteristics of Spark Ignition (S.I.) Engines with Premium Level Gasoline-Ethanol-Alkane Blends Using Machine Learning 13th

Prediction of Emission Characteristics of Spark Ignition (S....

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13th International Advanced Computing Conference, IACC 2023

作者： Kumbhar, Sujit Khot, Sanjay Jujare, Varsha Patil, Vishal Husainy, Avesahemad Shedbalkar, Koustubha Department of Technology Shivaji University Maharashtra Kolhapur416004 India Department of Automation and Robotics Sharad Institute of Technology College of Engineering Yadrav Maharastra Inchalkaranji416121 India Sharad Institute of Technology College of Engineering Yadrav Maharastra Inchalkaranji416121 India Department of Computer Science and Engineering Sharad Institute of Technology College of Engineering Yadrav Maharastra Inchalkaranji416121 India Department of Mechanical Engineering Sharad Institute of Technology College of Engineering Yadrav Maharastra Inchalkaranji416121 India Department of Electrical Engineering Sharad Institute of Technology College of Engineering Yadrav Maharastra Inchalkaranji416121 India Department of Mechanical Engineering Sharad Institute of Technology College of Engineering Yadrav Maharastra Inchalkaranji416121 India

ISBN: (纸本)9783031566998

In the current research work, a single cylinder spark (S.I)ignition engine were used for investigations of premium level gasoline-ethanol-alkane experimentally with different operating conditions e.g. variation spark ignition timing. The Engine Lab and PE3 software were used for engine control and data acquisition system. The data obtained after experimentation were used to predict the engine emissions for different operating conditions. The engine emission characteristics were predicted using three machine learning algorithmsviz linear regression, decision tree and random forest. It was found that emissions characteristics such as carbon monoxide, unburnt hydrocarbon found to be minimum for 24°bTDC experimentally as well as predicted by machine learning algorithms with different operating conditions than other spark timing positions such as 15°, 18°, 21°, 27°, 30° bTDC. All three machine learning algorithms gave better results but the random forest algorithm were more accurate than linear regression and decision trees. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Paraffins

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Vessels Segmentation in Angiograms Using Convolutional Neural Network: A Deep Learning Based Approach

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computer Modeling in engineering & Sciences 2023年第7期136卷 241-255页

作者： Sanjiban Sekhar Roy Ching-Hsien Hsu Akash Samaran Ranjan Goyal Arindam Pande Valentina E.Balas School of Computer Science and Engineering Vellore Institute of TechnologyVellore632014India Department of Computer Science and Information Engineering Asia UniversityTaichung41354Taiwan Department of Medical Research China Medical University HospitalChina Medical UniversityTaichung406Taiwan Guangdong-Hong Kong-Macao Joint Laboratory for Intelligent Micro-Nano Optoelectronic Technology School of Mathematics and Big DataFoshan UniversityFoshan528000China Medica Superspecialty Hospital Kolkata700099India Automation and Applied Informatics Aurel Vlaicu University of AradArad310130Romania

Coronary arterydisease(CAD)has become a significant causeof heart attack,especially amongthose 40yearsoldor *** is a need to develop new technologies andmethods to deal with this *** researchers have proposed image processing-based solutions for CADdiagnosis,but achieving highly accurate results for angiogram segmentation is still a *** different types of angiograms are adopted for CAD *** paper proposes an approach for image segmentation using ConvolutionNeuralNetworks(CNN)for diagnosing coronary artery disease to achieve state-of-the-art *** have collected the 2D X-ray images from the hospital,and the proposed model has been applied to *** augmentation has been performed in this research as it’s the most significant task required to be initiated to increase the dataset’s ***,the images have been enhanced using noise removal techniques before being fed to the CNN model for segmentation to achieve high *** the output,different settings of the network architecture undoubtedly have achieved different accuracy,among which the highest accuracy of the model is 97.61%.Compared with the other models,these results have proven to be superior to this proposed method in achieving state-of-the-art results.

关键词： Angiogram convolution neural network coronary artery disease diagnosis of CAD image segmentation

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Data-Driven Robust Designs of Performance Prediction and Its Application in High-speed Trains 8

Data-Driven Robust Designs of Performance Prediction and Its...

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8th IEEE International Conference on Advanced Robotics and Mechatronics, ICARM 2023

作者： Chen, Hongtian Wang, Weijun Guo, Dongsheng Xu, Shuiqing Cheng, Chao Shanghai Jiao Tong University Department of Automation Shanghai200240 China School of Mathematics and Statistics Changchun University of Technology Changchun130000 China School of Information and Communication Engineering Hainan University Haikou570228 China School of Electrical Engineering and Automation Hefei University of Technology Hefei230009 China School of Computer Science and Engineering Changchun University of Technology Changchun130000 China

ISBN: (纸本)9798350300178

This paper presents a robust performance prediction scheme for traction systems in high-speed trains. Performance variables are latent indicators of the system running, which play an important role in automated monitoring and fault diagnosis. It can be inferred from manifest variables (measurements) instead of directly observed from traction systems. Recently, most studies explored the static method for predictions, which is not suitable for dynamic systems. Considering the tricky challenges in practice, the existing prediction methods need to be further improved. Motivated by this, the paper designs a data-driven prediction model for performance variables of traction systems in high-speed trains. Specifically, to remove the influence of disturbances or noises, a robust subspace identification skill is adopted, which is developed for the soft sensor. In addition, this study further reduces the process errors of estimations in the state-space model. This method is verified on a traction system platform and an actual traction motor experiment. The experimental results prove the effectiveness and superiority of the proposed scheme. © 2023 IEEE.

关键词： Vehicle performance

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Distributed optimization-based formation control: a dynamic event-triggered approach

Distributed optimization-based formation control: a dynamic ...

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第42届中国控制会议

作者： Hao Yu Junyi Yang Tongwen Chen School of Automation Beijing Institute of Technology Department of Electrical and Computer Engineering University of Alberta

This paper investigates an event-triggered approach for distributed optimization-based formation problems of multiagent systems(MASs) under directed graphs. A distributed optimization-based algorithm is proposed such that agents can rendezvous around an expected time-varying formation, and at the same time, optimize a global objective function. A novel dynamic event-triggering mechanism is proposed, where a buffer variable is introduced to store the historical local information including the average values of measurement errors over one transmission interval. Then, the closed-loop MAS is proved to be input-to-state exponentially stable with respect to external inputs. Zeno-freeness is guaranteed by a computable minimum interevent time. Moreover, the trade-off between network load and computation complexity is illustrated by comparing two kinds of event-triggered mechanisms based on different signals as inputs to the buffer variables. The effectiveness of the proposed method is verified by numerical examples.

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Multi-agent Deep Reinforcement Learning Based Multi-objective Charging Control for Electric Vehicle Charging Station 7th

Multi-agent Deep Reinforcement Learning Based Multi-objectiv...

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7th Chinese Conference on Swarm Intelligence and Cooperative Control, CCSICC 2023

作者： Gong, Jiabao Fu, Weiming Kang, Yu Qin, Jiahu Xiao, Feng Department of Automation University of Science and Technology of China Hefei230027 China State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources School of Control and Computer Engineering North China Electric Power University Beijing102206 China

ISBN: (纸本)9789819733279

This paper proposes a multi-agent deep reinforcement learning (MADRL) based algorithm for charging control of multiple electric vehicles (EVs) in an electric vehicle charging station (EVCS) with dynamic operations. By taking the uncertainties of EV users’ behaviors and charging prices into consideration, a Markov game with multiple objectives is first established, where the individual objective of reducing EV charging costs and the collective objective of mitigating charging load fluctuations are both studied. Then, a multi-agent actor-double-critic (MAADC) algorithm with a unified advantage actor-critic framework is proposed, where two advantage functions are designed as the local and global critics, and the integrated actor is employed to balance the learning towards the individual and collective objectives. The simulation results obtained in a delicately built EVCS environment show the effectiveness of MAADC. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Reinforcement learning

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Synchronization of Discrete-Time Chaotic Systems in Presence of the Cyber-Attack and its Application in Secure Message Recovery

SSRN

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SSRN 2023年

作者： Samimiat, Marzieh Ahmadi, Ali-Akbar Industrial Automation laboratory Electrical and Computer Engineering Department Kharazmi University Tehran Iran

The secure message recovery problem in presence of cyber-attack is considered in this paper. The message is modulated by a master chaotic system which may be vulnerable to a cyber-attack and then transmitted to a receiver. By designing a novel observer at the receiver side, the message is recovered correctly. All the design process is performed in discrete-time domain which is suitable for digital communications. Besides, there is no restrictions on the message and nonlinear term signature matrix which makes the proposed approach more suitable in the real applications. Numerical simulations are introduced to show the feasibility and superior performance of the proposed scheme in comparison with the conventional methods. © 2023, The Authors. All rights reserved.

关键词： Chaotic systems

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Set-Invariance for Discrete-Time Linear Systems with Time-Varying Delay: a Polyhedral Approach 63

Set-Invariance for Discrete-Time Linear Systems with Time-Va...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Dorea, Carlos E. T. Olaru, Sorin Niculescu, Silviu-Iulian Castelan, Eugenio B. Ufrn Department of Computer Engineering and Automation Natal Brazil Université Paris-Saclay Cnrs CentraleSupélec Laboratoire des Signaux et Systémes Gif-sur-Yvette91190 France DAS/POSAUTOMAçO/UFSC Florianόpolis Brazil

ISBN: (纸本)9798350316339

In this paper, we address the positive invariance property in linear discrete-time systems in the presence of time-varying delays in the states. We build the study on a recently proposed approach based on an appropriate model transformation which allows the derivation of delay-dependent invariance conditions. For polyhedral sets, we establish necessary and sufficient invariance conditions with respect to the transformed model and prove that such conditions imply the confinement of the state trajectories of the original system in the set for arbitrary realizations of the varying delay, as long as the initial states belong to a set which is positively invariant with respect to an augmented switching system without delay, and can be computed in a finite number of steps known in advance. © 2024 IEEE.

关键词： Linear time-invariant system

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Wind disturbance compensated path-following control for fixed-wing UAVs in arbitrarily strong winds

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Chinese Journal of Aeronautics 2024年第2期37卷 431-445页

作者： Hao LU Lan GAO Yunda YAN Mingzhe HOU Chenliang WANG Hangzhou Innovation Institute Beihang UniversityHangzhou 310051China School of Information Science and Technology Hangzhou Normal UniversityHangzhou 311121China Department of Computer Science University College LondonLondon WC1E 6BTUnited Kingdom Center for Control Theory and Guidance Technology Harbin Institute of TechnologyHarbin 150001China School of Automation Science and Electrical Engineering Beihang UniversityBeijing 100191China

Wind is the primary challenge for low-speed fixed-wing unmanned aerial vehicles to follow a predefined flight *** cope with various wind conditions,this paper proposes a wind disturbance compensated path following control strategy where the wind disturbance estimate is incorporated with the nominal guiding vector field to provide the desired airspeed direction for the *** the control input vector for the outer-loop kinematic subsystem needs to satisfy a magnitude constraint,a scaling mechanism is introduced to tune the proportions of the compensation and nominal ***,an optimization problem is formulated to pursue a maximum wind compensation in strong winds,which can be solved analytically to yield two scaling factors.A cascaded inner-loop tracking controller is also designed to fulfill the outer-loop wind disturbance compensated guiding vector ***-fidelity simulation results under sensor noises and realistic winds demonstrate that the proposed path following algorithm is less sensitive to sensor noises,achieves promising accuracy in normal winds,and mitigates the deviation from a desired path in wild winds.

关键词： Path following Guiding vector field Wind disturbance Disturbance rejection Fixed wings

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