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2022 International Workshop on Automation, control, and Communication Engineering, IWACCE 2022

作者： Jing, Xiangyu Zang, Chuanzhi Liu, Yuqi Liu, Yu Xia, Changqing Shenyang University of Technology No. 111 Shenliao West Road Economic and Technological Development Zone Shenyang110870 China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang110016 China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China

ISBN: (纸本)9781510660915

Trajectory planning is one of the key technologies in the manipulator motion system, and the effectiveness of the manipulator as a whole is directly influenced by this technology. Thus, accurate and effective trajectory planning is crucial. In this study, a reinforcement learning system is used to plan a manipulator's trajectory. A trajectory planning of a manipulator based on the deep deterministic policy gradient (DDPG) method, which is easier to converge on than the Actor-Critic (AC) approach, is proposed in order to address the issue that the AC algorithm is difficult to converge on. A guiding reward function is provided in the reward function area to address the issue that the manipulator can't contact the target rapidly due to blind exploration. This can help the manipulator touch the target more quickly. In this paper, a twodegree of freedom manipulator is selected as the experimental object. The obstacle avoidance reward function based on the AC algorithm, the obstacle avoidance reward function based on the DDPG algorithm, and the obstacle avoidance reward function and guiding reward function based on the DDPG algorithm are compared. The findings demonstrate that the DDPG method converges easier than the AC algorithm, and that the directed reward function can help the manipulator avoid obstacles. © 2022 SPIE. All rights reserved.

关键词： Manipulators

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Intracranial Photodynamic Therapy Strategy Based Magnetic Continuum Robots for Glioblastoma Management

SSRN

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SSRN 2024年

作者： Wu, Junfeng Li, Na Lin, Daojing Zhou, Junjian Song, Yingqiu Liu, Lianqing Jiao, Niandong School of Electrical and Control Engineering Shenyang Jianzhu University Shenyang110168 China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110016 China University of Chinese Academy of Sciences Beijing100049 China Department of Radiotherapy Cancer Hospital of China Medical University Shenyang110042 China

Glioblastoma multiforme (GBM), a malignant brain tumor, poses a significant global health challenge because of its high invasiveness and resistance to treatment. Photodynamic therapy (PDT), with its high spatial-temporal selectivity, holds promise for GBM treatment, offering strong local selectivity, minimal systemic side effects, and low resistance. However, delivering photosensitizers across the blood-brain barrier and precisely targeting brain tumors through the skull with a laser present critical challenges to clinical application. Addressing these challenges, the soft, continuous structure and remote magnetic control of magnetic continuum robots (MCRs) offer new possibilities for precise delivery to GBM. Here, we propose an intracranial PDT strategy using MCRs, featuring a magnetic control model based on Gaussian Process Regression and a computer vision-based closed-loop control algorithm. This approach enables automatic delivery of nanomedicines and localized laser irradiation, characterized by simplicity, minimal data needs, and robustness. Additionally, under the combined effects of photodynamic nanoparticles (PNPs) and a laser, both U87 and GL261 cells exhibit high mortality rates, which increase with the concentration of PNPs, laser energy, and duration of exposure. In conclusion, this strategy overcomes PDT's limitations to superficial lesions, offers a novel GBM treatment approach, and constitutes a key milestone for using PDT in GBM treatment. © 2024, The Authors. All rights reserved.

关键词： Photodynamic therapy

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Temporally-Adaptive Models for Efficient Video Understanding

arXiv

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arXiv 2023年

作者： Huang, Ziyuan Zhang, Shiwei Pan, Liang Qing, Zhiwu Zhang, Yingya Liu, Ziwei Ang, Marcelo H. Advanced Robotics Centre National University of Singapore Singapore DAMO Academy Alibaba Group China S-Lab National Technological University Singapore Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science China

Spatial convolutions are extensively used in numerous deep video models. It fundamentally assumes spatio-temporal invariance, i.e., using shared weights for every location in different frames. This work presents Temporally-Adaptive Convolutions (TAdaConv) for video understanding, which shows that adaptive weight calibration along the temporal dimension is an efficient way to facilitate modeling complex temporal dynamics in videos. Specifically, TAdaConv empowers spatial convolutions with temporal modeling abilities by calibrating the convolution weights for each frame according to its local and global temporal context. Compared to existing operations for temporal modeling, TAdaConv is more efficient as it operates over the convolution kernels instead of the features, whose dimension is an order of magnitude smaller than the spatial resolutions. Further, kernel calibration brings an increased model capacity. Based on this readily plug-in operation TAdaConv as well as its extension, i.e., TAdaConvV2, we construct TAdaBlocks to empower ConvNeXt and Vision Transformer to have strong temporal modeling capabilities. Empirical results show TAdaConvNeXtV2 and TAdaFormer perform competitively against state-of-the-art convolutional and Transformer-based models in various video understanding benchmarks. Our codes and models are released at: https://***/alibaba-mmai-research/TAdaConv. Copyright © 2023, The Authors. All rights reserved.

关键词： Convolution

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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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A Multi-Model Fusion Approach for Transformer Fault Detection

A Multi-Model Fusion Approach for Transformer Fault Detectio...

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Information Technology,Big Data and Artificial Intelligence (ICIBA), International Conference on IEEE International Conference on

作者： Ke Zhou Wei Zhang Ligang Li Shaoming Pan Chengwei Huang Guangxi Power Grid Equipment Monitoring and Diagnosis Technology Innovation Center Guangxi Key Laboratory of Intelligent Control and Maintenance of Power Equipment Electric Power Research Institute of Guangxi Power Grid CO. LTD. Nanning 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 China Power Supply Bureau of Guangxi Hezhou Power Grid Co. Ltd.

ISBN: (数字)9798350363609

ISBN: (纸本)9798350363616

Addressing the weak generalization capability and suboptimal prediction performance of single mechanistic and data-driven models, this paper proposes a transformer fault detection method based on the fusion of mechanism and data models. Firstly, vibration and current data are collected on-site to train the SVM vibration model and the Adaboost current model. Secondly, current simulation data is obtained using laboratory simulation software. This simulation data, combined with the current data collected under the same conditions on-site, is used as input to train the GBDT error compensation model. Finally, these three models are used as inputs to the GRNN, completing the overall model design. This fusion of mechanistic and data-driven models not only addresses the parameter mismatch issue of the mechanistic model under complex working conditions but also deeply mines time-series data. Experimental results show that The mean squared error of the fusion model is 0.002 lower than that of the data model, and the coefficient of determination is improved by 5.07% compared to the mechanistic model, enhancing the overall predictive accuracy of the model.

关键词： Vibrations Support vector machines Training Accuracy Biological system modeling Fault detection Predictive models Transformers Prediction algorithms Data models

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Towards Autonomous Navigation of a Mobile Robot in a Steep Slope Vineyard

Towards Autonomous Navigation of a Mobile Robot in a Steep S...

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Proceedings of the International Convention MIPRO

作者： I. Hrabar J. Goričanec Z. Kovačić University of Zagreb Faculty of Electrical Engineering and Computing Laboratory for Robotics and Intelligent Control Systems (LARICS) Zagreb Croatia

This paper explores potential applications of an autonomous all-terrain mobile manipulator (AMM) in the agricultural vineyard environment. A robotic arm mounted on a mobile base performs a spraying task while moving past vines and a bud rubbing task while stopping next to vines. We discuss the challenges of AMM navigation when performing tasks in steep vineyard terrain. The presented navigation approach is based on the use of a heterogeneous robotic system consisting of an unmanned aerial vehicle (UAV) and AMM. The navigation information provided by the UAV gives the waypoints for the AMM. We also present preliminary results from navigation simulation, based on digital elevation model (DEM). The model uses real-world data, acquired by UAV photogrammetric measurement campaign. We conclude by highlighting some open problems and related future research directions.

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Environment-Driven Online LiDAR-Camera Extrinsic Calibration

arXiv

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arXiv 2025年

作者： Huang, Zhiwei Li, Jiaqi Zhong, Ping Fan, Rui The Department of Control Science & Engineering The College of Electronics & Information Engineering Tongji University Shanghai201804 China The School of Computer Science and Engineering Central South University Hunan Changsha410083 China The National Key Laboratory of Science and Technology on Automatic Target Recognition National University of Defense Technology Hunan Changsha410073 China The Department of Control Science & Engineering The College of Electronics & Information Engineering Shanghai Research Institute for Intelligent Autonomous Systems The State Key Laboratory of Intelligent Autonomous Systems Frontiers Science Center for Intelligent Autonomous Systems Tongji University Shanghai201804 China The National Key Laboratory of Human-Machine Hybrid Augmented Intelligence Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Shaanxi Xi’an710049 China

LiDAR-camera extrinsic calibration (LCEC) is the core for data fusion in computer vision. Existing methods typically rely on customized calibration targets or fixed scene types, lacking the flexibility to handle variations in sensor data and environmental contexts. This paper introduces EdO-LCEC, the first environment-driven, online calibration approach that achieves human-like adaptability. Inspired by the human perceptual system, EdO-LCEC incorporates a generalizable scene discriminator to actively interpret environmental conditions, creating multiple virtual cameras that capture detailed spatial and textural information. To overcome cross-modal feature matching challenges between LiDAR and camera, we propose dual-path correspondence matching (DPCM), which leverages both structural and textural consistency to achieve reliable 3D-2D correspondences. Our approach formulates the calibration process as a spatial-temporal joint optimization problem, utilizing global constraints from multiple views and scenes to improve accuracy, particularly in sparse or partially overlapping sensor views. Extensive experiments on real-world datasets demonstrate that EdO-LCEC achieves state-of-the-art performance, providing reliable and precise calibration across diverse, challenging environments. Copyright © 2025, The Authors. All rights reserved.

关键词： Spatio-temporal data

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Path-tracking of Mobile Robot Using PD-type Iterative Learning control with Forgetting Factor

Path-tracking of Mobile Robot Using PD-type Iterative Learni...

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第35届中国控制与决策会议

作者： Tao Zhang Jingzhe Fang Yun Teng Xintong Wang Yiqiang Zhang Xiyou Chen School of Electrical Engineering Dalian University of Technology Shenyang Institute of Automation Chinese Academy of Sciences Key Laboratory of Networked Control Systems Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences School of Electrical Engineering Shenyang University of Technology

In this paper,a discrete iterative learning control strategy is proposed for the path-tracking problem of mobile robot trajectory tracking with repeated *** algorithm adds the forgetting factor to the PD-type discrete iterative learning control and considers the correction of the initial state of the *** convergence of the algorithm is proved rigorously by using the paradigm theory,and the sufficient conditions of the paradigm form to make the algorithm converge are ***,the effectiveness of the proposed algorithm is verified by simulation.

关键词： Mobile robot discrete iterative learning control forgetting factor path-tracking

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Fast Realization of Robot 3D Simulation Based on WebGL

Fast Realization of Robot 3D Simulation Based on WebGL

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第35届中国控制与决策会议

作者： Tao Zhang Xintong Wang Yun Teng Jingzhe Fang Yiqiang Zhang Xiyou Chen School of Electrical Engineering Dalian University of Technology Shenyang Institute of Automation Chinese Academy of Sciences Key Laboratory of Networked Control Systems Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences School of Electrical Engineering Shenyang University of Technology

As science and technology have advanced,an increasing number of people are focusing their study on 3 D robot *** 3 D quick simulation of the mechanical arm can be achieved using 3 D modeling software and *** the third-party WebGL library *** to construct the simulation scenario,then interact with and render the 3 D scene and 3 D robot model directly on the Web,which is easier and faster than downloading *** essay briefly explains the benefits and applicability of *** in the creation of 3 D virtual simulations,then goes on to discuss the quick implementation process of 3 D robot simulations before summarizing the building technique.

关键词： webgl three.js 3D simulation robot

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Research on intrusion detection of industrial control system based on improved QPSO-SVM

Research on intrusion detection of industrial control system...

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2022 International Conference on Electronic Information Technology, EIT 2022

作者： Yu, Yajie Liu, Xianda Wei, Weixuan Chen, Hengfei Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang110016 China 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 College of Information Engineering Shenyang University of Chemical Technology Shenyang110142 China

ISBN: (纸本)9781510655096

Intrusion detection technology can effectively evaluate the security state of the system. However, the accuracy of traditional intrusion detection methods still needs to be improved. Therefore, this paper proposed a SVM model based on improved QPSO. QPSO is used to optimize the hyperparameters of SVM to improve its accuracy. In addition, the shrinkage expansion coefficient in QPSO is improved to make QPSO adaptive. Experiment shows that the proposed algorithm has good performance. © 2022 SPIE. All rights reserved.

关键词： Intrusion detection

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