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4th IEEE International Conference on Power, Intelligent Computing and systems, ICPICS 2022

作者： Huang, Haoran Zhang, Tao Jia, Danping Zhang, Hualiang Yan, Hongyu Pei, Quanshen Ding, Fan Shenyang University of Technology School of Information Science and Engineering Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang Institute of Automation Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China School of Artificial Intelligence Shenyang University of Technology Shenyang China

ISBN: (数字)9781665467735

ISBN: (纸本)9781665467735

The X-Y precision plane motion platform is widely used in various precision motion control fields. In this paper, to improve the tracking accuracy of the X-Y precision plane motion table, a high-level iterative learning control with forgetting factor is proposed, and it is combined with a kind of PD-type iterative learning control with low-pass filter, and the method is applied to the Simulink model of the linear motor of the X-Y precision plane platform, thereby improving the trajectory accuracy of the X-Y precision plane motion platform. The traditional ILC has the disadvantage that the more number of iterations, the more divergent tracking error is. The new ILC proposed in this paper can not only effectively reduce the noise, reduce the tracking error, but also eliminate the problem of error divergence caused by too many iterations. At last, the experimental results of high-order PD iterative learning with forgetting factor and low-pass filter, PD iterative learning with low-pass filter, ordinary PD iterative learning are compared. The results show that the high-order PD iterative learning with forgetting factor and low-pass filter has better tracking effect than the first-order iterative learning and other several algorithms. It can make the movement trajectory of the X-Y precision plane motion platform more stable and improve the accuracy. © 2022 IEEE.

关键词： Low pass filters

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Improved Cascade Mask RCNN Single Character Detection Algorithm for Cast Pipe 4

Improved Cascade Mask RCNN Single Character Detection Algori...

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4th International Conference on Big Data and Artificial Intelligence and Software Engineering, ICBASE 2023

作者： Xu, Fuli Wang, Yu Jiang, Yan Wang, Huaizhen Cui, Yong Tao, Ye Wang, Yisheng Zhang, Qi Shenyang University of Technology School of Software Shenyang China Chinese Academy of Sciences Key Laboratory of Networked Control Systems Shenyang China Chinese Academy of Sciences Shenyang Institute of Automation Shenyang China Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Shenyang China Shanghai Ballsnow Intelligent Technology Co. Ltd Shanghai China Shenyang Zhongke Microcyber Technology Co. Ltd Shenyang China

ISBN: (纸本)9798350329490

Due to the interference of blurry background, character occlusion, and low background contrast in the Cast Pipe character images, the detection accuracy and real-time performance cannot meet the requirements of industrial scenes. To address these issues, this paper presents an improvement based on the Cascade Mask RCNN. Firstly, the Channel Attention Mechanism Module (Squeeze-and-Excitation, SE) is embedded into the ResNet structure. By constructing a bottleneck structure with two fully connected layers, the normalized weights are applied to each channel feature to enhance the network representation ability. Secondly, a bottom-up structure is introduced after the Feature Pyramid Networks (FPN) to transmit the strong localization information from lower-level features to higher-level semantic features. Thirdly, an adaptive pooling layer is added to allocate the candidate regions generated by the Region Proposal Network (RPN) to different scale feature maps. Additionally, skip connections are introduced between the bottom-level features and the highest-level features of each stage to reduce model parameters while retaining global representation capabilities. The improved algorithm is tested on a cast pipe dataset, achieving an average detection accuracy (mAP) of 98.7%, which is 2.7% higher than that of Cascade Mask RCNN. The accuracy indicates that the improved performance is superior to the original algorithm. © 2023 IEEE.

关键词： Semantics

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Research on cloud-side collaborative task migration strategy based on simulated annealing-Bee Colony Algorithm in Power IoT 2

Research on cloud-side collaborative task migration strategy...

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2nd International Conference on Consumer Electronics and Computer Engineering, ICCECE 2022

作者： Zhang, Bowen Zeng, Zeng Miao, Weiwei Chinese Academy of Sciences Key Laboratory of Networked Control Systems Shenyang China Chinese Academy of Sciences Shenyang Institute of Automation Shenyang China Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Shenyang China State Grid Jiangsu Electric Power Co. Ltd. Information Telecommunication Branch Nanjing China

ISBN: (纸本)9781665408868

In the power IoT, in order to make up for the shortcomings of insufficient computing capabilities of edge-side devices, cloud-side collaboration technology has attracted more and more attention. The cloud-side collaboration technology relies on the selection of task migration strategies. However, when the existing migration strategies face the complex edge-side terminal devices in the power IoT, the energy consumption and delay performance are not good enough, which greatly affects the user's power experience. Therefore, it is very valuable to study a cloud-side collaborative task migration strategy under the power IoT environment. This paper proposes a task migration strategy based on simulated annealing-Bee Colony Algorithm in the environment of power IoT, which can solve the problem of task migration. Experimental results show that compared with the existing popular methods, this method has advantages in terms of energy consumption and time delay. This research can meet the real-time requirements of the power IoT, while reducing energy consumption and improving the user's power experience, thereby promoting the further development of the power IoT. © 2022 IEEE.

关键词： Simulated annealing

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Weld Defects Detection by Neural Networks Based on Attention Mechanism

Weld Defects Detection by Neural Networks Based on Attention...

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IEEE Information Technology and Mechatronics Engineering Conference (ITOEC)

作者： Guang Yang Zhifeng Liu Jianjun Wang Shuping Cui State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China

Welding technology plays an indispensable role in ship construction, and its maturity has a direct impact on the quality of shipbuilding. The weld seam, being the weakest part of a welded structure, significantly affects the structural strength, service life, and safety reliability of ships. However, the current identification of weld defects in the ship industry relies on manual methods, which are subjective and prone to error. To address this issue, this paper presents a novel approach by collecting weld images from actual ship scenes, preprocessing them to eliminate noise and highlight defect features, and proposing a Transformer-based improved target detection model (T-OBJDETC) to enhance the recognition accuracy of the model.

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Actor-Based Learning Framework for the Co-Optimization of Modular Cpps Scheduling and control with Stochastic Processing Times

SSRN

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SSRN 2023年

作者： Wan, Guangxi Dong, Xiaoting Dong, Qingwei He, Yunpeng Zeng, Peng State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Key Laboratory of Networked Control Systems Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China University of Chinese Academy of Sciences Beijing100049 China

Scheduling with stochastic processing times is a kind of dynamic scheduling in production systems. The current scheduling optimization problem is formulated in the scheduling domain, which ignores the underlying synergies for optimization objectives between scheduling and control. To address this problem, we propose and formulate the problem of the co-optimization of scheduling and control with stochastic processing times (COSC-SPT) with a system respective view. To solve the COSC-SPT, we model the decision-making process as a Markov decision process and propose an actor-based learning framework to facilitate problem solving. To verify the feasibility of the proposed framework and the effectiveness of the formulated COSC-SPT, a real reducer assembly system is taken as a use case. The results of experiments demonstrate that the COSC-SPT provides better solutions than conventional scheduling problems. Keywords: Actor, cyber-physical production systems, optimization, scheduling, stochastic processing time. © 2023, The Authors. All rights reserved.

关键词： Markov processes

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Mixed Motivation Driven Social Multi-Agent Reinforcement Learning for Autonomous Driving

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IEEE/CAA Journal of Automatica Sinica 2025年第6期12卷 1272-1282页

作者： Long Chen Peng Deng Lingxi Li Xuemin Hu State Key Laboratory of Multimodal Artificial Intelligence Systems and the State Key Laboratory of Management and Control for Complex Systems Chinese Academy of Sciences Beijing WAYTOUS Inc. Beijing Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ) Shenzhen Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Xi'an China School of Computer Science and Information Engineering Hubei University Wuhan China Purdue School of Engineering and Technology Indiana University-Purdue University Indianapolis Indianapolis IN USA School of Artificial Intelligence Hubei University Wuhan Key Laboratory of Intelligent Sensing System and Security (Hubei University) Ministry of Education Wuhan China

Despite great achievement has been made in autonomous driving technologies, autonomous vehicles (AVs) still exhibit limitations in intelligence and lack social coordination, which is primarily attributed to their reliance on single-agent technologies, neglecting inter-AV interactions. Current research on multi-agent autonomous driving (MAAD) predominantly focuses on either distributed individual learning or centralized cooperative learning, ignoring the mixed-motive nature of MAAD systems, where each agent is not only self-interested in reaching its own destination but also needs to coordinate with other traffic participants to enhance efficiency and safety. Inspired by the mixed motivation of human driving behavior and their learning process, we propose a novel mixed motivation driven social multi-agent reinforcement learning method for autonomous driving. In our method, a multi-agent reinforcement learning (MARL) algorithm, called Social Learning Policy Optimization (SoLPO), which takes advantage of both the individual and social learning paradigms, is proposed to empower agents to rapidly acquire self-interested policies and effectively learn socially coordinated behavior. Based on the proposed SoLPO, we further develop a mixed-motive MARL method for autonomous driving combined with a social reward integration module that can model the mixed-motive nature of MAAD systems by integrating individual and neighbor rewards into a social learning objective for improved learning speed and effectiveness. Experiments conducted on the MetaDrive simulator show that our proposed method outperforms existing state-of-the-art MARL approaches in metrics including the success rate, safety, and efficiency. More-over, the AVs trained by our method form coordinated social norms and exhibit human-like driving behavior, demonstrating a high degree of social coordination.

关键词： Measurement Reinforcement learning Safety Driver behavior Autonomous vehicles Optimization

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I2EKF-LO: A Dual-Iteration Extended Kalman Filter Based LiDAR Odometry

arXiv

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arXiv 2024年

作者： Yu, Wenlu Xu, Jie Zhao, Chengwei Zhao, Lijun Nguyen, Thien-Minh Yuan, Shenghai Bai, Mingming Xie, Lihua State Key Laboratory of Robotics and Systems Harbin Institute of Technology Harbin150001 China The School of Electrical and Electronic Engineering Nanyang Technological University 639798 Singapore Hangzhou Guochen Robot Technology Company Limited Hangzhou311200 China State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou310000 China

LiDAR odometry is a pivotal technology in the fields of autonomous driving and autonomous mobile robotics. However, most of the current works focus on nonlinear optimization methods, and still existing many challenges in using the traditional Iterative Extended Kalman Filter (IEKF) framework to tackle the problem: IEKF only iterates over the observation equation, relying on a rough estimate of the initial state, which is insufficient to fully eliminate motion distortion in the input point cloud;the system process noise is difficult to be determined during state estimation of the complex motions;and the varying motion models across different sensor carriers. To address these issues, we propose the Dual-Iteration Extended Kalman Filter (I2EKF) and the LiDAR odometry based on I2EKF (I2EKF-LO). This approach not only iterates over the observation equation but also leverages state updates to iteratively mitigate motion distortion in LiDAR point clouds. Moreover, it dynamically adjusts process noise based on the confidence level of prior predictions during state estimation and establishes motion models for different sensor carriers to achieve accurate and efficient state estimation. Comprehensive experiments demonstrate that I2EKF-LO achieves outstanding levels of accuracy and computational efficiency in the realm of LiDAR odometry. Additionally, to foster community development, our code is open-sourced. Copyright © 2024, The Authors. All rights reserved.

关键词： State estimation

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A Charge Density Model of Silicon-nanowire GAA MOSFET Incoroperating the Source-drian Tunneling Effect for IC Design

A Charge Density Model of Silicon-nanowire GAA MOSFET Incoro...

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2021 International Symposium on Computer Technology and Information Science, ISCTIS 2021

作者： Cheng, He Zhang, Chao Liu, Tiefeng Xie, Chuang Yang, Zhjia Zhang, Zhipeng Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China

ISBN: (纸本)9781665414418

One numerical compact model for one-dimensional electron charge density of n-Type silicon ballistic nanowire gate-All-Around metal-oxide-semiconductor field-effect transistors with ultra-short channel incorporating the source-drain tunneling is proposed. When establishing the AC model in practical device applications, compact models describing transient analysis of the carrier behaviors in the device channel are needed. When focusing on the charge density of carriers within GAA MOSFET devices, carriers transporting along the channel direction induced by the source-drain tunneling effect should also be taken into account in the subthreshold region. In this study, transmission coefficients of the source-drain tunneling effect are evaluated through expressing energy level of the lowest subband with bringing in the exact boundary conditions and using the Wentzel-Kramers-Brillouin approximation. Then, the carrier density can be derived from the charge profile in the channel using quantum statistics theory. Finally, comparisons of the calculations of the carrier densities between the source-drain tunneling and the thermionic modes in ballistic transport are demonstrated. © 2021 IEEE.

关键词： MOSFET devices

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Modelling and control of a Parametric Wafer Manufacturing Process

Modelling and Control of a Parametric Wafer Manufacturing Pr...

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IEEE/IAS International Conference on Industry Applications (INDUSCON)

作者： Fotis N. Koumboulis Dimitrios G. Fragkoulis Dimitrios Mparkas Department of Digital Industry Technologies Robotics Automatic Control and Cyber-Physical Systems Laboratory School of Science National and Kapodistrian University of Athens Euripus Campus Greece Core Department National and Kapodistrian University of Athens Euripus Campus Greece

In the present paper the mathematical model of a wafer manufacturing process is presented using Discrete Event systems. The novelty of the proposed mathematical description is the parametric number of vacuum chambers and robotic manipulators, as well as that three vacuum chambers and the respective buffers are served by each robotic manipulator. The last robotic manipulator serves a parametric number of champers depending on the total number of vacuum chambers modulo 3. The desired behavior of the process is expressed in the form of a set of regular languages. The realization of each regular language in the form of a supervisor automaton is developed. The satisfactory performance of the controlled automaton is derived. The present result can be considered as the first attempt towards the study of wafer manufacturing processes, where each robotic manipulator serves a parametric number of vacuum chambers and buffers.

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An Innovative Smart Irrigation Using Embedded and Regression-Based Machine Learning Technologies for Improving Water Security and Sustainability

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IEEE Access 2025年 13卷 100731-100751页

作者： Abdennabi Morchid Abdennacer Elbasri Zahra Oughannou Hassan Qjidaa Rachid El Alami Badre Bossoufi Saleh Mobayen Paweł Skruch LIMAS Laboratory Faculty of Sciences Dhar El Mahraz Sidi Mohamed Ben Abdellah (SMBA) University Fes Morocco Department of Data Science Mundiapolis University of Casablanca Campus Nouaceur Casablanca Morocco Innovation in Mathematics and Intelligent Systems (IMIS) Laboratory Ibn Zohr University Agadir Morocco Systems and Sustainable Environment Laboratory (SED) Faculty of Engineering Sciences (FSI) Private University of Fez (UPF) Fes Morocco Graduate School of Intelligent Data Science National Yunlin University of Science and Technology Douliu Taiwan Energy Systems Research Center Khazar University Baku Azerbaijan Department of Automatic Control and Robotics AGH University of Science and Technology Kraków Poland

Global agriculture is facing major challenges such as food security, sustainable water management, and the preservation of natural resources. Water scarcity, exacerbated by climate change, requires adopting more efficient agricultural practices. Traditional irrigation systems, often imprecise, contribute to water wastage. The use of embedded systems and machine learning offers a solution for optimizing irrigation according to local conditions and actual crop needs while contributing to food security and environmental sustainability. This study proposes an innovative approach to irrigation management, integrating real-time data and predictive models to improve irrigation efficiency. This study proposes an irrigation system based on embedded systems, using sensors and algorithms to collect and analyze data in order to optimize water management. The system adjusts irrigation levels according to specific crop needs, thus contributing to more sustainable water management. Using ML algorithms like linear regression algorithms to model the relationships between environmental factors and crop water requirements, enabling accurate prediction of required irrigation levels based on data collected by sensors. The use of embedded systems such as the ESP32, combined with temperature, humidity, and water level sensors, has enabled the development of an autonomous and efficient system for collecting data in real-time and processing it for decision-making. The proposed model has an MAE of 0.8434, an RMSE of 0.8434, and a coefficient (R2 Score) of 0.4044, offering soil moisture prediction accuracy. Furthermore, the training time of our model is 0.00253 seconds, while the prediction time is 0.00117 seconds. These results show not only the performance of the proposed model in terms of accuracy but also its computational efficiency, outperforming some of the studies mentioned. The results of the study show a significant reduction in water consumption, with a marked improvement in water

关键词： Climate change Embedded systems Smart agriculture Irrigation Food security Optimization methods Machine learning Machine learning algorithms Sustainable development Water resources Water monitoring Environmental management

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