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63rd IEEE Conference on Decision and control, CDC 2024

作者： Liu, Xinhui Cheng, Meiqi Shi, Dawei Shi, Ling Beijing Institute of Technology Miit Key Laboratory of Servo Motion System Drive and Control School of Automation Beijing100081 China Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Kowloon Hong Kong

ISBN: (纸本)9798350316339

In this paper, we investigate the issues of real-time sensor scheduling and state estimator design within large-scale sensor network systems. Specifically, data redundancy sometimes occurs in large-scale sensor arrays due to the excessive proximity of sensing units in the spatial domain, leading to high similarity in their measurement data. Currently, it is difficult to account for such redundancy in sensor scheduling algorithms found in existing literature, where the optimal subset of sensors is generally selected by optimizing objective functions formulated from certain performance criteria. To tackle this problem, we introduce an event-based sensor scheduling strategy, the triggering condition of which is designed founded on the similarity of sensor data, so as to identify the most informative subset of sensors for state estimation. To evaluate the impact of the sensor scheduling protocol on system observability, we propose a new notion of E(varepsilon)-observability, based on which an observability criterion is derived. In addition, we have designed a set-valued state estimation algorithm, which takes into account the intricate measurement information structure inherent within the sensor selection mechanism. The performance enhancement of the proposed estimator is also investigated. Finally, numerical experiments are conducted to validate the effectiveness of the proposed estimation algorithm and to verify the performance improvement. © 2024 IEEE.

关键词： Scheduling algorithms

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Electric Vehicles Modeling: A Review 15

Electric Vehicles Modeling: A Review

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15th IEEE International Conference on Industry Applications, INDUSCON 2023

作者： de Oliveira, Victor Manuel Riva dos Remédios, Vinícius Ferreira Donadel, Clainer Bravin dos Santos, Walbermark Marques Có, Márcio Almeida de Melo, Vinícius Secchin Nunes, Reginaldo Barbosa Electrical Engineering Department Federal Institute of Espírito Santo Vitória Brazil Control and Automation Eng Dept. Federal Institute of Espírito Serra Santo Brazil

ISBN: (纸本)9798350314182

The increasing concern about climate change has been leading researches in many areas to investigate solutions for this issue. An emerging subject related to it is electric vehicles (EV). Although it is an important topic, there is still a lack of review papers about EVs modeling. In this context, this paper aims to bring a review about EV modeling to fulfill this gap. A wide bibliography review is made to cover different kinds of EVs modeling, in many aspects. As a conclusion, in some areas, authors do not diverge from each other, while in others may exist a large range of different ways to model the EVs. In this case, researchers should choose the modeling that fits the best for their research, which may vary in simplicity and computational effort. © 2023 IEEE.

关键词： Battery Electric Vehicles Modeling Simulation

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Surrogate-Assisted Multiobjective Gene Selection for Cell Classification from Large-Scale Single-Cell RNA Sequencing Data

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IEEE Transactions on Evolutionary Computation 2025年第3期29卷 601-615页

作者： Lin, Jianqing He, Cheng Jiang, Hanjing Huang, Yabing Jin, Yaochu Huazhong University of Science and Technology Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China School of Artificial Intelligence and Automation Wuhan430074 China Huazhong University of Science and Technology State Key Laboratory of Advanced Electromagnetic Technology School of Electrical and Electronic Engineering Wuhan430074 China Renmin Hospital Department of Pathology Wuhan University Wuhan430060 China Westlake University Trustworth and General Artificial Intelligence Laboratory School of Engineering Hangzhou310030 China

Accurate cell classification is crucial but expensive for large-scale single-cell RNA sequencing (scRNA-seq) analysis. Gene selection (GS) emerges as a pivotal technique in identifying gene subsets of scRNA-seq for classification accuracy improvement and gene scale reduction. Nevertheless, the rising scale of scRNA-seq data presents challenges to existing GS methods regarding performance and computational time. Thus, we propose a surrogate-assisted evolutionary algorithm for multiobjective GS to address these deficiencies. An innovative two-phase initialization method is proposed to select sparse solutions to provide preliminary insights into gene contributions. Then, a binary competitive swarm optimizer is proposed for effective global search, where a local search method is embedded to eliminate irrelevant genes for efficiency consideration. Additionally, a surrogate model is adopted to forecast classification accuracy efficiently and substitutes part of the computationally expensive classification process. Experiments are conducted on eight large-scale scRNA-seq datasets with more than 20,000 genes. The effectiveness of the proposed GS method for scRNA-seq cell classification compared with eight state-of-the-art methods is validated. Gene expression analysis results of selected genes further validated the significance of the genes selected by the proposed method in the classification of scRNA-seq data. © 1997-2012 IEEE.

关键词： Gene expression

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Skeleton-based Action Recognition Using Two-stream Graph Convolutional Network with Pose Refinement 41

Skeleton-based Action Recognition Using Two-stream Graph Con...

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第41届中国控制会议

作者： Biao Zheng Luefeng Chen Min Wu Witold Pedrycz Kaoru Hirota School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Department of Electrical and Computer Engineering University of Alberta Tokyo Institute of Technology

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

With the development of science and technology,graph convolutional network has made great progress in improving the accuracy of action ***,there still exists some deficiencies in current ***,the human skeleton point coordinates entering into the network are barely refined,which may cause large ***,the second-order information(the length and direction of bones),which can reflect action characteristics discriminatively,is rarely *** solve the above issues,a two stream graph convolutional network with pose refinement for skeleton based action recognition is ***,we use an adaptive block to to help improve the *** test our method on Kinetics dataset and the experiment show it can get better results than some recent methods,which plays a positive role in future research.

关键词： Graph convolutional network Pose refinement Skeleton based action recognition Adaptive block

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A Comparison of Local Planner Algorithms for a ROS-based Omnidirectional Mobile Robot

A Comparison of Local Planner Algorithms for a ROS-based Omn...

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International Conference on Robotics and automation engineering (ICRAE)

作者： Mehmed Rafi Imamoglu Ecem Sumer Hakan Temeltas Department of Control and Automation Engineering Istanbul Technical University Istanbul Turkey

The topic of local planner algorithms applicable to omnidirectional mobile robots is yet to thoroughly investigated. This study investigates the performance of the most commonly implemented local planner algorithms for omnidirectional mobile robots by conducting a comparative analysis between the Dynamic Window Approach (DWA) and the Timed Elastic Band (TEB) algorithms. The evaluation was conducted within real-world settings, specifically targeting the challenges of parking maneuvers, dynamic obstacle avoidance and turning maneuvers. Performance metrics, tailored to each scenario, were employed to assess the algorithms. The results demonstrate that the TEB method outperforms the DWA in the tested conditions.

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control Barrier Function-Based Force Constrained Safety Compliance control for Manipulator 28

Control Barrier Function-Based Force Constrained Safety Comp...

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28th International Conference on automation and Computing, ICAC 2023

作者： Liang, Haiyan Wang, Xinming Mao, Jianliang Yang, Jun School of Automation Southeast University The Key Laboratory of Measurement and Control of Cse Ministry of Education Nanjing210096 China College of Automation Engineering Shanghai University of Electric Power Shanghai200090 China Loughborough University Department of Aeronautical and Automotive Engineering LoughboroughLE11 3TU United Kingdom

ISBN: (纸本)9798350335859

In this paper, a safety-critical compliance control with force constraint is proposed for the interaction task of a manipulator in the Cartesian space. Different from the conventional impedance control approach, the interaction force constraint is considered and interpreted by designing a new Force Constrained control Barrier Function (FCCBF) with exploiting the environment model. By formulating and solving a quadratic programming with modifying the baseline impedance controller under the FCCBF constraint, the proposed controller can not only make manipulator's interaction process satisfy expected compliance without exceeding permissible contact force range, but can also improve the dynamic characteristics of the system by eliminating force overshooting phenomena. Furthermore, with using the technique of nonlinear disturbance observer, a force sensor-less FCCBF approach is also provided, where the contact force is treated as an external disturbance. Finally, the effectiveness of the proposed algorithms is verified by both simulations and experimental tests on a 6 degree-of-freedom manipulator. © 2023 IEEE.

关键词： Manipulators

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Enhancing Scheduling Efficiency of Steel Logistics Parks by Optimized Storage Distribution

Enhancing Scheduling Efficiency of Steel Logistics Parks by ...

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2023 China automation Congress, CAC 2023

作者： Xu, Zhibin Zheng, Song Wang, Jinlong Xu, Zhezhuang Chen, Boyu Zhang, Qingdong College of Electrical Engineering and Automation Fuzhou University Key Laboratory of Industrial Automation Control Technology and Information Processing Education Department of Fujian Province Fuzhou350000 China Sansteel Minguang Co. Ltd. Sanming353000 China

ISBN: (纸本)9798350303759

The efficient management of storage has become a critical determinant of operational efficiency due to the growing and diverse demand for steel logistics parks. However, the current storage methods consistently result in significant imbalances in the loading of steel products and operating times across multiple yards. To address this challenge, we propose optimizing the distribution of steel products based on demand levels and retrieval efficiency. Firstly, we present an optimization problem that employs demand-driven storage principles to formulate the scheduling of steel product storage. Then, we introduce a novel algorithm called the Adaptive Preferred Evolutionary Heuristic (APEH) to tackle this problem. The results from our experiments indicate that this model effectively enhances the storage structure, and the proposed algorithm consistently yields optimal solutions within a reasonable timeframe. © 2023 IEEE.

关键词： Optimization

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Offline Reinforcement Learning via Sequence Modeling for Vision-Based Robotic Grasping 13

Offline Reinforcement Learning via Sequence Modeling for Vis...

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13th IEEE International Conference on CYBER Technology in automation, control, and Intelligent Systems, CYBER 2023

作者： Zeng, Sheng Sun, Xiao Chen, Kang Huang, Weibing Chen, Yi Chen, Dan Xu, Zhezhuang College of Electrical Engineering and Automation Key Laboratory of Industrial Automation Control Technology and Information Processing Fuzhou University Education Department of Fujian Province Fuzhou350000 China Evomotion Co. Ltd. Guangdong Shenzhen518000 China

ISBN: (纸本)9798350315196

High cost of environmental interaction and low data efficiency limit the development of reinforcement learning in robotic grasping. This paper proposes an end-to-end robotic grasping method based on offline reinforcement learning via sequence modeling. It considers the most recent n-step history to assist the agent in making decisions, where a predictive model learns to directly predict actions from raw image inputs. The experimental results show that our method can achieve higher grasping success rate with less training data than traditional reinforcement learning algorithms in offline setting. © 2023 IEEE.

关键词： Reinforcement learning

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Robust control of Feeding Speed in Coal Seam Drilling Process Considering Borehole Friction

Robust Control of Feeding Speed in Coal Seam Drilling Proces...

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2024 China automation Congress, CAC 2024

作者： Liu, Xiao Chen, Luefeng Lu, Chengda Fan, Haipeng Wu, Min Pedrycz, Witold School of Automation The Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems The Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education China University of Geosciences Wuhan430074 China Department of Electrical and Computer Engineering University of Alberta EdmontonABT6G 2R3 Canada Systems Research Institute Polish Academy of Sciences Warsaw00-901 Poland Department of Computer Engineering Istinye University Sariyer Istanbul34396 Turkey

ISBN: (纸本)9798350368604

During the coal seam drilling process, the drill string is subject to compressive deformation, compounded by unpredictable variations in formation hardness and borehole wall friction, leading to challenges in maintaining a stable feeding speed. This paper presents a novel approach by introducing uncertain parameters to describe the effects of formation hardness and borehole wall friction. Drill string axial movement model is modeled as a polyhedral system based on a lumped parameter representation. To meet industrial performance requirements, we design a robust H∞ controller to achieve consistent feeding speed control. Our simulation results demonstrate the controller’s effectiveness in ensuring system stability despite fluctuations in formation hardness and drill string friction. © 2024 IEEE.

关键词： Coal deposits

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Predicting the State of Crane Spreader for Steel Bar Delivery Based on Machine Learning 13

Predicting the State of Crane Spreader for Steel Bar Deliver...

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13th IEEE International Conference on CYBER Technology in automation, control, and Intelligent Systems, CYBER 2023

作者： Liu, Zibin Xu, Zhezhuang Chen, Jian Cui, Chen Chen, Boyu Zhang, Qingdong College of Electrical Engineering and Automation Key Laboratory of Industrial Automation Control Technology and Information Processing Education Fuzhou University Department of Fujian Province Fuzhou350000 China Sansteel Minguang Co. Ltd Fujian Sanming353000 China

ISBN: (纸本)9798350315196

Deducing the twisting pattern of spreader is very important for crane operators in steel warehouse, since crane spreaders have to be rotated repeatedly at steel bar transport sites. To improve the efficiency of the steel bar delivery, the state prediction for crane spreaders is necessary. In order to fulfill this requirement, the HTSK-LN-ReLU model prediction method based on Optuna optimization is applied in this paper. We deploy the IMU on the spreader surface, and then obtain the state data during the operation of the crane spreader by wireless transmission. The data are first denoised through fourier filtering, and then the Optuna framework is used to optimize the HTSK-LN-ReLU model's hyperparameters. The evaluation index of mean square error is used for model evaluation, and finally the optimal hyperparameter combination of the model is selected from 200 comparison experiments. Experimental results demonstrate superior performance of the HTSK-LN-ReLU model in predicting the state of crane spreader for steel bar delivery. © 2023 IEEE.

关键词： Forecasting

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