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IEEE International Conference of intelligent Robotic and control Engineering (IRCE)

作者： Chunli Zhang Yuwei Xie Lei Yan Fucai Qian College of Automation and Information Engineering Xi’an University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi’an China

ISBN: (数字)9798350352399

ISBN: (纸本)9798350352405

In this paper, high-speed train is regarded as a single prime point, a new tracking control method combining adaptive control and Kalman filtering is proposed. This paper solves the noise interference problem in the tracking control process of high-speed train. The method first linearises the original system, then uses Kalman filtering to do model estimation, and the controller is designed for the predictive model through adaptive control. Finally, MATLAB is used to simulate and verify the effectiveness of this method. This proposed method successfully solves the problems of nonlinearity, noise interference and unknown parameters in system. The error caused by resistance in the high-speed train tracking control process can be dealt with effectively.

关键词： Resistance Filtering Noise Process control Interference Predictive models Mathematical models Kalman filters Adaptive control MATLAB

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Short-Term Load Forecasting Method Based on Multi-Branch Convolutional Network and Bayesian Optimization 43

Short-Term Load Forecasting Method Based on Multi-Branch Con...

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43rd Chinese control Conference, CCC 2024

作者： Lv, Ji Ding, Min Li, Quanhui Zhou, Sibei Fang, Zhijian China University of Geosciences School of Automation Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China

ISBN: (纸本)9789887581581

Accurate short-term power load forecasting is important to ensure the reliability and economy of the power grid. Convolutional neural networks are widely used, but they are difficult to capture the multi-scale temporal characteristics of load data. To address this problem, this study proposes a short-term load forecasting method based on a multi-branch CNN architecture and Bayesian optimisation. By using different sizes of convolutional kernels in different branches, different time-scale features of power load data are captured to improve the identification of rapid changes and potential long-term trends. In addition, the hyperparameters of the forecasting model are automatically adjusted by a Bayesian optimisation algorithm to achieve the best forecasting performance. In forecasting experiments on real load data, the method in this paper significantly improves the forecasting accuracy compared to baseline models. © 2024 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Convolutional neural networks

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Animatable Human Neural Radiance Field from Sparse Images Captured by a Single Camera 43

Animatable Human Neural Radiance Field from Sparse Images Ca...

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43rd Chinese control Conference, CCC 2024

作者： Yang, Qingru Xu, Chi China University of Geosciences School of Automation Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China

ISBN: (纸本)9789887581581

Recently, the use of neural radiance fields (NeRF) for three-dimensional reconstruction of humans has achieved high-quality view rendering. However, existing methods often rely on multiple or single videos as input, requiring dense images, and the rendering quality tends to be poor with sparse image inputs. To address this issue, this paper proposes a novel method that solely utilizes sparse images captured by a single camera to create an animatable human neural radiance field, enabling the rendering of high-fidelity human images under arbitrary novel viewpoints and poses. Specifically, our method utilizes body poses and multi-view feature to build a deformable neural radiance field, and employs frequency-segmented positional encoding to mitigate overfitting during training process. Extensive quantitative and qualitative experiments demonstrate that our proposed method outperforms prior works. © 2024 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Image reconstruction

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Integrated Prediction of Unfavorable Geology in Main Tunnel Considering the Geological Conditions of Parallel Tunnel

Integrated Prediction of Unfavorable Geology in Main Tunnel ...

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2024 China Automation Congress, CAC 2024

作者： Zhang, Meng Gan, Chao Cao, Weihua School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China

ISBN: (纸本)9798350368604

Currently, the advanced geological prediction for the main tunnel from the parallel tunnel often employs parallel deduction of detection conclusions, which faces issues such as a lack of diverse geological exploration information, a lack of analysis of geological structures and poor accuracy. In this paper, an integrated prediction method for unfavorable geology in main tunnel considering the geological conditions of parallel tunnel is proposed, which consists of three stages. In the first stage, based on the forecast mileage range and tunnel excavation conditions, the process is divided into four specific cases. In the second stage, it deduces the geology of the main tunnel along the orientation of rock strata occurrence or joint to parallel tunnel and selects the results of the rock strata occurrence or joint deduction according to the distance of the deduction. In the last stage, it deduces the geological conditions of parallel tunnel face and the integration of geophysical and drilling information for predictive conclusions based on the four specific cases. The proposed method offers higher prediction accuracy for rock mass integrity and surrounding rock grades compared to parallel deduction method. © 2024 IEEE.

关键词： Miocene

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3D Object Pose Estimation Based on End-to-End Differentiable Perspective Projection 43

3D Object Pose Estimation Based on End-to-End Differentiable...

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43rd Chinese control Conference, CCC 2024

作者： Cai, Yuheng Xu, Chi China University of Geosciences School of Automation Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China

ISBN: (纸本)9789887581581

Locating 3D objects from a single RGB image via perspective projection is a long-standing problem in computer vision. Inspired by differentiable geometry methods in the field of camera relocation, we apply this neural network combined with differentiable perspective projection to form an end-to-end architecture to the field of object position estimation through object 3D reconstruction methods. The proposed method uses a differentiable LM algorithm for end-to-end pose estimation to achieve higher accuracy, and the gradient is passed back to the neural network to guide its learning. At the same time, we propose a virtual view generation algorithm for augmenting the data to improve the algorithm's sensitivity to the size of the co-vision region. Through experiments, we prove the effectiveness of our method. © 2024 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Neural networks

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Fast Transient Voltage Stability Assessment for complex Receiving-End Power Grid 7

Fast Transient Voltage Stability Assessment for Complex Rece...

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7th International Conference on Power and Energy Applications, ICPEA 2024

作者： Liu, Xiyang Liu, Tianhao Liu, Yutian Li, Zhendong Shandong University Key Laboratory of Power System Intelligent Dispatch and Control Ministry of Education Jinan China Laiwu Power Supply Company State Grid Shandong Electric Power Company Laiwu China

ISBN: (纸本)9798350356113

The continuous increase of new energy generation and AC-DC interconnection make the transient voltage stability of the receiving-end power grid more and more complicated. In order to realize the rapid assessment of transient voltage stability, a one-dimensional Convolutional Neutral Network (CNN) for transient voltage stability assessment (TVSA) is constructed by selecting the features reasonably on the basis of using the Agglomerative Hierarchical Clustering (AHC) algorithm to partition the receiving-end grid. In order to enhance the assessment work, the Gradient-weighted Class Activation Mapping (Grad-CAM) algorithm is utilized to find out the concerns of the model to features, and the model knowledge of the neural network is analyzed on the basis of surveying on a large number of samples. Based on the transient voltage stability mechanism and the concerns of deep learning model, the factors that cause transient voltage instability are analyzed. The effectiveness of the proposed method is demonstrated by the case study of a provincial grid in eastern China. © 2024 IEEE.

关键词： Transient stability

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A Forecast Error Correction Method Based on Seq2Seq and Auto Encoder for Short-term Wind Power Forecast Enhancement

A Forecast Error Correction Method Based on Seq2Seq and Auto...

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2024 IEEE IAS Industrial and Commercial Power system Asia, I and CPS Asia 2024

作者： Ma, Haoyu Yang, Ming Han, Zifen Wang, Bo Che, Jianfeng Li, Menglin Control Shandong University Key Laboratory of Power System Intelligent Dispatch Jinan China State Grid Gansu Power Grid Corporation Lanzhou China China Electric Power Research Institute National Key Laboratory of Renewable Energy Grid-Integration Beijing China

ISBN: (纸本)9798350352290

Accurate wind power forecast (WPF) is highly significant for the safe and economic operation of power systems, and implementing error correction is a common and effective manner to further enhance the forecasting performance. Nonetheless, existing error correction methods fail to consider temporal relationship and may inadvertently increase the error under certain circumstances, thereby compromising the local precision of forecast. In order to address these issues, this paper proposes an error correction model based on Seq2Seq and auto encoder (AE) to improve forecasting accuracy. Firstly, the seq2seq with attention mechanism is introduced to infer the probable error sequence through learning the temporal relationship between the numerical weather prediction sequence and the actual error sequence. Secondly, the samples that the seq2seq providing a positive error inference are collected and the AE model is employed to extract the meteorological features at these moments. Finally, on the basis of the above two models, the error correction method is constructed consisting of preliminary error inference and posterior correction determination. Using operational data from Ningxia province, the proposed method is validated for its remarkable superiority based on the evaluation metrics. © 2024 IEEE.

关键词： Wind forecasting

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Multi-UAV Cooperative Path-Planning Under complex Threat Environment

Multi-UAV Cooperative Path-Planning Under Complex Threat Env...

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Chinese control Conference (CCC)

作者： Zhenchang Liu Yuting Liu Hongyin Zhao Hanchen Lu Mingrui Hao Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing

This paper proposes a cooperative path-planning method based on multi-Dubins path segments to meet the penetration requirements of UAVs in a complex threat environment. A pruning strategy is used in path-planning to sift path segments, and the diversity of threat avoidance paths is considered under a reasonable calculation amount. A threat intrusion strategy is designed for no safety path condition, which makes the cooperative path-planning method have excellent robustness. A cooperative path optimization method based on particle swarm optimization is adopted for the path length and collision avoidance requirements. The simulation results show that the UAV cooperative paths can avoid the threat area under any starting and ending points, and path lengths are close to the expected length.

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The Algorithm of Sequential Music Recommendation Based on BERT

The Algorithm of Sequential Music Recommendation Based on BE...

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2023 International Conference on Culture-Oriented Science and Technology, CoST 2023

作者： Li, Zhen Li, Kaiyue Yang, Qingqing Ministry of Culture and Tourism Key Laboratory of Acoustic Visual Technology and Intelligent Control System China Communication University of China Beijing Key Laboratory of Modern Entertainment Technology China Communication University of China Beijing China

ISBN: (纸本)9798350357998

Music recommendation algorithms aim to predict the songs that users might be interested in based on various factors, such as their listening history. The best existing method is Bert_Music, a sequential music recommendation model based on Bidirectional Encoder Representation from Transformer (BERT). This model leverages user sequence context information to improve recommendation performance. However, Bert_Music only considers the sequence information of songs and ignores their content characteristics. To address this limitation, we propose an improved version of Bert_Music, which is a sequential music recommendation model based on Bidirectional Encoder Representation from Transformer with tag information (BERT_MUSIC_T), uses song ID (Identity) and tag information to make more accurate recommendations. We compared the performance of Bert_Music_T with that of the Bert_Music model on the *** dataset and found that the Precision has increased by up to 2.85%. These results prove the validity of our method. © 2023 IEEE.

关键词： Recommender systems

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Semi-Autonomous Navigation Based on Local Semantic Map for Mobile Robot

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Journal of Shanghai Jiaotong university(Science) 2025年第1期30卷 27-33页

作者： ZHAO Yanfei XIAO Peng WANG Jingchuan GUO Rui Department of Automation Institute of Medical RoboticsShanghai Jiao Tong UniversityShanghai200240China State Key Laboratory of System Control and Information Processing Ministry of Education of ChinaShanghai200240China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240China State Grid Intelligence Technology Co. Ltd.Jinan250013China

Mobile robots represented by smart wheelchairs can assist elderly people with mobility *** paper proposes a multi-mode semi-autonomous navigation system based on a local semantic map for mobile robots,which can assist users to implement accurate navigation(e.g.,docking)in the environment without prior *** order to overcome the problem of repeated oscillations during the docking of traditional local path planning algorithms,this paper adopts a mode-switching method and uses feedback control to perform docking when approaching semantic *** last,comparative experiments were carried out in the real *** show that our method is superior in terms of safety,comfort and docking accuracy.

关键词： semi-autonomous navigation mobile robot semantic map voice interaction

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