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43rd Chinese control Conference, CCC 2024

作者： Chen, Ziyang 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 Ministry of Eduction Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan430074 China

ISBN: (纸本)9789887581581

Image feature matching is an important research field in computer vision that can be widely applied to advanced vision tasks, such as 3D reconstruction and visual tracking. Considering the low matching accuracy and poor robustness of traditional detector-based matching methods on weakly textured images, we propose a detector-free image matching method based on enhanced feature fusion. Given that weak texture images consist of a large number of similar or irregular pixels, we introduce a new model that enhances feature fusion to recover the underlying texture information of the image. First, we propose a bidirectional fusion model to extract multi-scale feature maps and add an initialization module to correlate the feature maps and enhance the representation ability of the backbone. Then, we perform feature transformation using the quadtree attention model and finally establish dense pixel-by-pixel matching at both coarse and fine scales. The quadtree attention model employs a hierarchical computational attention method, accurately capturing image details and remote dependencies while ensuring linear complexity. Experimental results demonstrate that the proposed method achieves higher matching accuracy and better generalization on large datasets such as MegaDepth and ScanNet, making it suitable for subsequent work. © 2024 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Image matching

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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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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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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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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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Adaptive Temporal Aggregation Network for Online Video Visual Relation Detection

SSRN

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SSRN 2023年

作者： He, Yuxuan Gan, Ming-Gang Ma, Qianzhao Wang, Chenyu State Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing100081 China

Nowadays, more and more researchers pay attention to scene perception of artificial robot. Video visual relation detection is an essential task for scene perception but existing methods are all offline methods which are unsuitable for real-time application. Thus, we first introduce online video visual relation detection task which aims at localizing and classifying relations in the current frame. Then, we propose Adaptive Temporal Aggregation Network for online video visual relation detection. In this network, we design content-based adaptive sampling method to sample historical frames which contain dynamic scene information. In order to extract history knowledge from these discrete features, we propose adaptive spatio-temporal activation module which consists of adaptive spatial activation unit and adaptive temporal aggregation unit to extract history knowledge from history memory. After obtaining history knowledge and current state, the proposed attention-based knowledge-state fusion module is used to fuse these dynamic and static visual information. We also construct spatial location features and semantic embedding features to improve final relation representations. Experiments show the effectiveness and efficiency of our method. Moreover, we implement our network on fetch robot and visualize the online video visual relation detecting process. © 2023, The Authors. All rights reserved.

关键词： Chemical activation

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Distributed robust fuzzy control for mixed-order multi-agent systems ensuring global consensus

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Science China Technological Sciences 2025年

作者： Xiaoli YANG Jiaxi CHEN Xinbo GAO Shuzhi Sam GE Key Laboratory of Intelligent Analysis and Decision on Complex Systems School of Mathematics and StatisticsChongqing University of Posts and Telecommunications School of Mathematics and Statistics Xidian University School of Computer Science and Technology Chongqing University of Posts and Telecommunications Department of Electrical and Computer Engineering National University of Singapore

This study investigates the global consensus problem of mixed-order multi-agent systems. Firstly, a control strategy based on switching functions is proposed to ensure the effective operation of fuzzy control in the fuzzy region. Secondly, the differential equation model satisfying the global Lipschitz condition is extended, and a more universal model framework is constructed. Furthermore, online learning laws with time-varying σ-modifications are introduced to effectively guarantee the global asymptotic convergence of the closed-loop multi-agent systems. Finally, the effectiveness of the proposed algorithm is validated through designed simulation experiments.

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Analysis of LoRa for Electronic Shelf Labels Based on Distributed Machine Learning

Analysis of LoRa for Electronic Shelf Labels Based on Distri...

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第42届中国控制会议

作者： Malak Abid Ali Khan Hongbin Ma Ying Jin Jingxiang Ma Zia Ur Rehman Mizanur Rahman School of Automation Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology

Deforestation is the primary source of global warming;traditional shelf labels use paper to display the price of the products,and human forces play a pivotal role in updating the tags where the pandemic has strictly limited its *** technologies provide connectivity and a fast-updating system to eliminate the paper-based *** is one of the contenders to design the system for electronic shelf labels(ESLs).In this paper,LoRa has been used to minify data losses and guarantee the successful decoding of the carrier *** data parallelism at the network server(NS) is used to distribute the data packets among the gateways(GWs) for concurrent transmissions to the end devices(EDs).The EDs are placed in different ranges using machine clustering to avoid intra-SF interference and *** data rate(DR) and spreading factors(SFs) have been proposed to improve the performance of pure and slotted ALOHA for the properly allocated *** orthogonality principles follow industrial,scientific,and medical regulations(ISM) to avoid data traffic *** under different duty cycles(DC) and bandwidth(BW) are examined to minify the network saturation and reduce the energy harvesting of the tags.

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Multiple–Model UKF/CKF State Estimation for Nonlinear systems

Multiple–Model UKF/CKF State Estimation for Nonlinear Syste...

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International Conference on Guidance, Navigation and control, ICGNC 2020

作者： Shi, Xiaodi Yan, Liping Xia, Yuanqing Xiao, Bo Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing100081 China School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing100876 China

ISBN: (纸本)9789811581540

In most control systems, modeling error and noise interference will always lead to the performance degradation and divergence of the UKF or the CKF. To settle a matter caused by model uncertainties, a new UKF/CKF frame combined with multiple model method is presented in this paper. Through probabilistic multiple model design method, this paper approximates the posterior densities by a finite number of probabilistically weighted points and uses these points to display the entire state space. Simulation results and comparison analysis demonstrate that the multiple-model UKF(MMUKF) and the multiple-model CKF(MMCKF) have higher precision and stronger robustness than the traditional UKF and CKF in case of model uncertainties. © 2022, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Uncertainty analysis

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Generating Waste Heat Energy from CPU Based on Thermoelectric Power Generator

Generating Waste Heat Energy from CPU Based on Thermoelectri...

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2021 China Automation Congress, CAC 2021

作者： Qian, Kun Cai, Yeyun Xu, Kun Deng, Fang Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing100081 China

ISBN: (纸本)9781665426473

Heat dissipation is extremely important for electronic component such as CPU or GPU of the computer. Now most researchers only design optimal heat sink for CPU for better heat dissipation, while few of them consider waste heat recycling of CPU. The CPU can be used as the heat source, while the original system has fan and heat sink as the cooling system. Thus, the original CPU working condition is very suitable for thermoelectric power generation and will bring great benefit to system itself. However, adding thermoelectric generator will increase the temperature of CPU, which will decrease the performance of the computer. It is necessary to reduce the hot side temperature while generating energy as much as possible. To achieve this, this paper designed a kind of intelligent waste heat generation system based on thermoelectric power generator. Three different shape heat sinks are designed, contrasted, and tested. By building experimental platform and establishing simulation model, the power of thermoelectric power generation and temperature change of the three heat sink structures are compared. The fin shaped heat sink is considered has the best output performance while keeping the hot side temperature within the acceptance range. The maximum output power is 10.67mW, and the maximum hot side temperature is 72◦C during the experiment. © 2021 IEEE

关键词： Waste heat

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