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A Recurrent Neural Network Based Bicriteria Repetitive Motion Collision Avoidance Scheme for Motion Planning of Dual Redundant Manipulators 13

A Recurrent Neural Network Based Bicriteria Repetitive Motio...

引用

13th International conference on Intelligent control and Information Processing, ICICIP 2025

作者： Chen, Shirui Gao, Huanli Hu, Shaolin Zhang, Zhijun The School of Automation Science and Engineering South China University of Technology Guangzhou510640 China The School of Automation Guangdong University of Petrochemical Technology Maoming Guangzhou525000 China The Guangzhou Key Laboratory of Brain-Computer Interaction and Applications Guangzhou China The Key Library of Autonomous Systems and Network Control Ministry of Education The School of Automation Science and Engineering South China University of Technology Guangzhou510640 China Guangzhou510555 China The Jiangxi Thousand Talents Plan Nanchang University Nanchang330031 China The College of Computer Science and Engineering Jishou University Jishou416000 China Guangzhou510335 China The Shaanxi Provincial Key Laboratory of Industrial Automation School of Mechanical Engineering Shaanxi University of Technology Hanzhong723001 China The School of Information Science and Engineering Changsha Normal University Changsha410100 China The School of Automation Science and Engineering Maoming525000 China The Institute of Artificial Intelligence and Automation Guangdong University of Petrochemical Technology Maoming525000 China

ISBN: (纸本)9798331516147

To achieve low joint-angle drift and avoid mutual collision between dual redundant manipulators (DRMs) when they are doing collaboration works, a recurrent neural network based bicriteria repetitive motion collision avoidance (BRMCA) scheme is proposed for motion planning of DRMs. By designing a combined optimization objective encompassing both repetitive motion criterion and collision avoidance criterion, the BRMCA scheme has larger feasible region and less constraint contradiction issues compared with traditional inequality constraints based method. Furthermore, by incorporating velocity-level kinematic model and physical limits of DRMs into the scheme, a time varying quadratic programming (TVQP) problem is formulated. The linear variational inequality-based primal–dual neural network (LVI-PDNN) is used to solve the proposed scheme due to the high accuracy and low computation complexity. Finally, through comparison simulations of tracking Circle-shape and C-shape trajectories between BRMCA scheme and minimum velocity norm (MVN) scheme, it exhibits that the proposed BRMCA scheme can achieve low joint-angle drift, collision avoidance and high tracking accuracy when DRMs are doing cooperative tasks. © 2025 ieee.

关键词： Recurrent neural networks

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Carrier Phase Shifting control Method for Ultra-High Power Amplifier

Carrier Phase Shifting Control Method for Ultra-High Power A...

引用

Power Electronics, Computer applications (ICPECA), ieee International conference on

作者： Dapeng Li Rui Na Dezhi Zheng School of Instrumentation and Optoelectronic Engineering Beihang University Beijing China Advanced Research Institute of Multidisciplinary Sciences Beijing Institute of Technology Beijing China

ISBN: (数字)9798331533694

ISBN: (纸本)9798331533700

Digital control technology significantly enhances the performance of amplifiers, directly impacting the operational effectiveness of the system. However, for ultra-high power amplifiers, the interference from ultra-high voltage and current in complex environments presents challenges for precise digital control. To improve the stability and reliability of the system, this paper proposes a carrier phase shift control method for ultra-high power amplifiers. This method effectively mitigates the influence of internal circulating currents and enhances the quality of the system's output signal. Both simulation and experimental results demonstrate the advantages of the proposed control algorithm in improving key performance indicators of the system. Specifically, signal distortion across all spectral segments is kept below 1 %, and the dynamic range reaches 50 dB.

关键词： Digital control Heuristic algorithms Stability criteria Power amplifiers Interference Dynamic range Distortion Reliability Voltage control Standards

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Analysis of ALFC in Two Area Power System Using Golden Eagle Optimization

Analysis of ALFC in Two Area Power System Using Golden Eagle...

引用

Industrial Electronics: Developments & applications (ICIDeA), ieee International conference on-

作者： Ankan Mohanty Ajit Kumar Barisal School of Electrical Sciences Odisha University of Technology and Research Bhubaneswar India

ISBN: (数字)9798331533205

ISBN: (纸本)9798331533212

Automatic Load Frequency control (ALFC) is used in interconnected power systems to control tie-line power flows and keep the system frequency within allowable bounds. This paper simulates and analyzes ALFC in a two-area power system using the Golden Eagle Optimization technique to stabilize frequency deviations when a load disturbance occurs. The proposed method utilizes different iterations and population sizes to obtain the optimal PID controller values for stabilizing frequency in both areas.

关键词： Automatic frequency control Perturbation methods Power system stability control systems Stability analysis Robustness Optimization Tuning Load flow

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Non-linear Dynamic System Identification on Application of Industrial control

Non-linear Dynamic System Identification on Application of I...

引用

Industrial Electronics: Developments & applications (ICIDeA), ieee International conference on-

作者： Rakesh Kumar Pattanaik Debashis Dev Misra Mihir Narayan Mohanty DNR college of engineering & technology Bhimavaram AP India Assam down town University Khankar Gaon Assam India Siksha O’ Anusandhan Deemed to be University Bhubaneswar Odisha India

ISBN: (数字)9798331533205

ISBN: (纸本)9798331533212

System identification is a vital aspect of modern control and automation, offering a pathway to develop mathematical models from observed input-output data of dynamic systems. This paper explores methodologies, applications, and advancements in system identification. applications such as digital twin integration, industrial automation, and transportation systems highlight the practical relevance of these methodologies. A focus is given to integrating advanced machine learning techniques with traditional modelling approaches to enhance accuracy and scalability for nonlinear dynamic systems. Deep Random Vector Functional Neural Network is used for experimentation. Practical applications, including digital twin integration for real-time monitoring and control, are discussed to underline the relevance of system identification in Industry 4.0.

关键词： Scalability Industrial control Neural networks Transportation Vectors Mathematical models Real-time systems System identification Digital twins Nonlinear dynamical systems

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2025 International conference on Industrial technology, ICIT 2025 - Proceedings

2025 International Conference on Industrial Technology, ICIT...

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26th International conference on Industrial technology, ICIT 2025

ISBN: (纸本)9798331521950

The proceedings contain 148 papers. The topics discussed include: a helium bubble damage model based on population balance equation for focused helium ion beam processing;consensus control of singular stochastic multi-agent systems with state-dependent noise;predicting execution time of concurrent applications using performance counters;improved dynamic model identification of cobots based on a nonlinear friction model;an improved ADRC method based on time segment strategy and weighted observer fusion for UWM systems;finite-time equivalent-input-disturbance-based variable impedance control for robot manipulators;a LiDAR loop closure detection algorithm integrating multiple information of point cloud;and retrieval augmented generation for tables in textual documents based on graph representation learning.

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Research on the Application of Information Sharing technology in Distributed Photovoltaic Aggregation Optimization control Strategy

Research on the Application of Information Sharing Technolog...

引用

Power Electronics, Computer applications (ICPECA), ieee International conference on

作者： Daiwei Ling Kailang Huang Kai Wang Detian Kong Ran Zhou Wanning Power Supply Bureau of Hainan Power Grid Co. Ltd Wanning China

ISBN: (数字)9798331533694

ISBN: (纸本)9798331533700

This paper studies the application of information sharing technology in distributed photovoltaic aggregation optimization control, and proposes an optimization control strategy based on distributed gradient descent algorithm. This strategy realizes collaborative control between each substation through information sharing technology to reduce power fluctuations and improve the overall efficiency of photovoltaic power generation. This paper constructs a mathematical model of distributed photovoltaic system and designs a corresponding distributed gradient descent algorithm, which can realize global optimization control under the premise of exchanging only a small amount of information between each substation. The effectiveness of the optimization control strategy is verified by modeling and testing the photovoltaic system data of a certain actual substation. The simulation results show that after applying the distributed gradient descent algorithm, the information sharing between each substation improves the overall power generation efficiency of the system and reduces power fluctuations. Specific data show that without increasing additional hardware costs, the power generation efficiency of the system is increased by about 8% and the power fluctuation is reduced by about 12%. These results fully demonstrate that the proposed distributed photovoltaic optimization control strategy based on information sharing has significant advantages in practical applications.

关键词： Photovoltaic systems Substations Fluctuations Simulation Information sharing Collaboration Mathematical models Hardware Optimization Testing

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Conditional GAN for Enhancing Diffusion Models in Efficient and Authentic Global Gesture Generation from Audios

Conditional GAN for Enhancing Diffusion Models in Efficient ...

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2025 ieee/CVF Winter conference on applications of Computer Vision, WACV 2025

作者： Cheng, Yongkang Liang, Mingjiang Huang, Shaoli Han, Gaoge Ning, Jifeng Liu, Wei Tencent Ai Lab China University of Technology Sydney Australia Northwest A&f University China

ISBN: (纸本)9798331510831

Audio-driven simultaneous gesture generation is vital for human-computer communication, AI games, and film pro-duction. While previous research has shown promise, there are still limitations. Methods based on VAEs are accompa-nied by issues of local jitter and global instability, whereas methods based on diffusion models are hampered by low generation efficiency. This is because the denoising process of DDPM in the latter relies on the assumption that the noise added at each step is sampled from a unimodal distribution, and the noise values are small. DDIM bor-rows the idea from the Euler method for solving differential equations, disrupts the Markov chain process, and increases the noise step size to reduce the number of denoising steps, thereby accelerating generation. However, simply increasing the step size during the step-by-step denoising process causes the results to gradually deviate from the original data distribution, leading to a significant drop in the quality of the generated actions and the emergence of unnatural artifacts. In this paper, we break the assumptions of DDPM and achieves breakthrough progress in denoising speed and fidelity. Specifically, we introduce a conditional GAN to capture audio control signals and implicitly match the multimodal denoising distribution between the diffusion and denoising steps within the same sampling step, aiming to sample larger noise values and apply fewer denoising steps for high-speed generation. In addition, to enable the model to generate high-fidelity global gestures and avoid artifacts, we introduce an explicit motion geometric loss to enhance the quality and global stability of the generated gestures. Numerous qualitative and quantitative experiments show that compared to contemporary diffusion-based methods, our method offers faster generation speed and higher fidelity, and compared to non-diffusion methods, it provides a more stable global effect and a more natural user experience. © 2025 ieee.

关键词： Markov processes

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WeedsGalore: A Multispectral and Multitemporal UAV-Based Dataset for Crop and Weed Segmentation in Agricultural Maize Fields

WeedsGalore: A Multispectral and Multitemporal UAV-Based Dat...

引用

2025 ieee/CVF Winter conference on applications of Computer Vision, WACV 2025

作者： Celikkan, Ekin Kunzmann, Timo Yeskaliyev, Yertay Itzerott, Sibylle Klein, Nadja Herold, Martin Gfz German Research Centre for Geosciences Germany Humboldt-Universität zu Berlin Germany Scientific Computing Center Karlsruhe Institute of Technology Germany

ISBN: (纸本)9798331510831

Weeds are one of the major reasons for crop yield loss but current weeding practices fail to manage weeds in an efficient and targeted manner. Effective weed management is especially important for crops with high worldwide production such as maize, to maximize crop yield for meeting increasing global demands. Advances in near-sensing and computer vision enable the development of new tools for weed management. Specifically, state-of-the-art segmentation models, coupled with novel sensing technologies, can facilitate timely and accurate weeding and monitoring systems. However, learning-based approaches require annotated data and show a lack of generalization to aerial imaging for different crops. We present a novel dataset for semantic and instance segmentation of crops and weeds in agricultural maize fields. The multispectral UAV-based dataset contains images with RGB, red-edge, and nearinfrared bands, a large number of plant instances, dense annotations for maize and four weed classes, and is multitemporal. We provide extensive baseline results for both tasks, including probabilistic methods to quantify prediction uncertainty, improve model calibration, and demonstrate the approach's applicability to out-of-distribution data. The results show the effectiveness of the two additional bands compared to RGB only, and better performance in our target domain than models trained on existing datasets. We hope our dataset advances research on methods and operational systems for fine-grained weed identification, enhancing the robustness and applicability of UAVbased weed management. The dataset and code are available at https://***/GFZ/weedsgalore. © 2025 ieee.

关键词： Weed control

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Improved Model-Free Adaptive control and Line-of-Sight Guidance for Autonomous Vehicle Path Following

Improved Model-Free Adaptive Control and Line-of-Sight Guida...

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26th International conference on Industrial technology, ICIT 2025

作者： Xu, Zhanguo Yin, Yunfei Wei, Yuanlong Jiang, Wangyang School of Transportation Science and Engineering Harbin Institute of Technology Harbin China

ISBN: (纸本)9798331521950

Path tracking control is a fundamental technology in autonomous vehicle applications, but it faces significant challenges related to vehicle modeling and external disturbances. In this paper, an improved model-free adaptive control strategy, leveraging data-driven approaches and gradient descent parameter optimization, is proposed. First, an enhanced lineofsight method is used to generate the desired heading angle, simplifying the multi-dimensional path tracking problem into a more manageable heading angle tracking task. Then, the gradient descent model-free adaptive control strategy is applied, with the core idea of incorporating the gradient descent algorithm in the controller's time-varying parameter update process to achieve optimal control. The effectiveness of the proposed algorithm is validated through simulations, demonstrating its robustness in nonlinear systems and path tracking control scenarios. Comparative analysis with model-based methods highlights the superior stability and adaptability of the approach. © 2025 ieee.

关键词： Adaptive control systems

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Defensive Strategies Against CAN-Bus Infiltrations

Defensive Strategies Against CAN-Bus Infiltrations

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Industrial Electronics: Developments & applications (ICIDeA), ieee International conference on-

作者： Pratham Sharma Seema Prashant Singh Rana Mukesh Singh Amit Kumar Ashima Anand Department of Computer Science Thapar Inst. of Engg. And Technology Patiala Punjab India Department of E & I Engg. Thapar Inst. of Engg. And Technology Patiala Punjab India

ISBN: (数字)9798331533205

ISBN: (纸本)9798331533212

The Computerization of vehicles rapidly accelerated, and as vehicle networks link to external networks, automotive security becomes a critical issue. The controller Area Network (CAN) bus is the most widely used internal control network in automobiles, facing growing vulnerabilities. With the development of networked and self-driving technology, the sealing nature of automotive internal control networks is eroding, leaving the security standards inadequate by modern benchmarks. This paper aims to provide a brief overview of the defence strategies against CAN Bus infiltrations, identify current limitations to these existing strategies, and discuss the challenges of enhancing the security in automotive embedded systems.

关键词： Robust control Scalability Stability criteria Real-time systems Security controller area networks Automobiles Protection Standards Automotive engineering

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