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A Lightweight Steel Strip Defect Detection Method Based on YOLO 43

A Lightweight Steel Strip Defect Detection Method Based on Y...

引用

43rd Chinese control Conference, CCC 2024

作者： Qian, Hao 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 Education Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan430074 China

ISBN: (纸本)9789887581581

As an essential component of modern industry, steel strips play an indispensable role in various fields and serve as a crucial raw material in industrial production. However, due to various factors such as production technology, the surface of steel strips often exhibits unavoidable defects like scratches and crazings. Therefore, implementing online surface quality inspection for steel strips is one effective way to enhance product quality and production efficiency. This paper proposes a detection method for surface defects in steel strips based on YOLO. By optimizing the network structure of YOLOv5s, the performance on the NEU-DET dataset has improved by nearly 4 % compared to the original network's mAP value. Experimental results indicate that the detection performance of this method is significantly superior to the current mainstream lightweight detection networks. © 2024 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Strip metal

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Alarm Prediction with Variational Output Dimension for Alarm Floods Using Transformer 43

Alarm Prediction with Variational Output Dimension for Alarm...

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43rd Chinese control Conference, CCC 2024

作者： Pang, Long Jiang, Wenbin Hu, Wenkai Wang, Zhuang Cao, Weihua 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 Education Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan430074 China

ISBN: (纸本)9789887581581

Industrial alarm systems play crucial roles in assessing the production status and ensuring process safety. However, the presence of alarm floods can make alarm systems partially or fully fail, and thus would cause serious consequences. Alarm prediction, as an advanced approach to address alarm floods, offers operators decision supports when an alarm flood occurs. Existing alarm prediction methods requires that the output alarm sequences are of a fixed dimension, making such methods unable to adapt to the changing alarm rate. Accordingly, this paper proposes an alarm prediction model based on Transformer. The proposed model can predict alarm sequences with variational dimensions and can also achieve remarkable accuracy. The effectiveness of the proposed method is demonstrated by a case study with data from the Vinyl Acetate Monomer (VAM) simulation model. © 2024 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Polyvinyl acetates

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Distributed Power Source Planning Based on Improved Particle Swarm Optimization 3

Distributed Power Source Planning Based on Improved Particle...

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3rd Asia Power and Electrical Technology Conference, APET 2024

作者： Wang, Jiahui Tan, Zhili 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)9798350367690

The multitude of advantages offered by distributed power generation units are recognized as crucial factors in enhancing the security of distribution networks. Ensuring the optimal size and placement of distributed generation (DG) units not only enhances system security but also increases profitability. Haphazard placement of DG units may lead to adverse impacts on the power system, resulting in increased power losses and diminished voltage distribution. Therefore, to ensure the optimal size and placement of DG units, this paper proposes a method to optimize DG scheduling and operation, thereby improving voltage distribution, reducing power losses, and enhancing the overall reliability of the entire grid. To reduce power loss (PL) and improve voltage distribution (VP), an adaptive chaotic particle swarm optimization (ACPSO) algorithm is employed to determine the optimal locations and sizes of DG units. By comparing this algorithm with traditional approaches, its efficacy in enhancing performance can be discerned. © 2024 IEEE.

关键词： Power distribution planning

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A Detector-Free Image Matching Method based on Enhanced Feature Fusion 43

A Detector-Free Image Matching Method based on Enhanced Feat...

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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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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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Tightly-coupled Lidar-GNSS-Inertial Fusion Odometry and Mapping

Tightly-coupled Lidar-GNSS-Inertial Fusion Odometry and Mapp...

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第43届中国控制会议

作者： Shuwei Yu Jing Li Tianwei Niu Junzheng Wang Key Laboratory of Intelligent Control and Decision of Complex Systems School of AutomationBeijing Institute of Technology

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

This paper proposes a tightly-coupled lidar-GNSS-inertial fusion system that achieves accurate state estimation and mapping for robot *** system effectively fuses lidar points,GNSS measurements,and IMU data using the iterated error state Kalman filter algorithm to obtain precise,drift-free,and real-time *** optimize computational efficiency,an incremental tree structure,ikdtree,is employed for managing a local map,and different Kalman gain formulas are utilized to process GNSS and lidar point observations separately to lower the computation ***,a factor graph is introduced to optimize the pose *** selectively introducing keyframes based on the odometry estimation,the graph incorporates odometry,GNSS measurements,and loop closure constraints to optimize all keyframe poses,resulting in a precise trajectory and global ***,extensive experiments are conducted using the KITTI dataset and several real-world scenarios to validate the proposed *** experimental results demonstrate that our method achieves precise localization and mapping across diverse environments.

关键词： State estimation Mapping Sensor fusion Robot navigation

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Finite-Time Synchronization of Flux-controlled complex-Valued Memristive Neural Networks

Finite-Time Synchronization of Flux-Controlled Complex-Value...

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2023 International Conference on Neuromorphic Computing, ICNC 2023

作者： Wang, Yonghuan Wang, Leimin Wu, Genping China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education School of Automation Wuhan430074 China Wuhan Second Ship Design and Research Institute Wuhan430064 China

ISBN: (纸本)9798350316889

In this paper, by using the flux-controlled memristor model, the finite-time synchronization problem of delayed complex-valued memristive neural networks (MCNNs) is studied. Firstly, according to the proposed memristor model, we model the MCNNs as continuous systems on voltage-flux-time (wp, varpi, t) domain. Then, we design a class of approaches to realize state synchronization between the drive and response systems, and the corresponding synchronization conditions are achieved. Finally, the effectiveness of results is illustrated with the simulations. © 2023 IEEE.

关键词： Memristors

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