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Conual Information Fusion for Small Object Detection 40

Conual Information Fusion for Small Object Detection

引用

40th Chinese control Conference, CCC 2021

作者： Chen, Jun Chen, Xiaoqiang Luo, Linbo Wang, Ganbei China University of Geosciences School of Automation Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education China

ISBN: (纸本)9789881563804

In view of the difficulty and low accuracy of small object detection for remote sensing images, this paper proposes a small object detection algorithm based on conual information fusion to solve the problem of real-time detection accuracy of small object. In this paper, we use bottom-up VGG16 network to realize multi-scale feature extraction to deal with the problem of insufficient image feature extraction. To direct at the problem that the feature information of each feature layer is single, the shallow feature layer and the deep feature layer are fused through the feature fusion module, which achieves the purpose that some feature layers have more abundant fusion features in the structure level. Aiming at the problem that the detection objects in remote sensing images are mainly small and medium-sized objects, this paper proposes to use the multivariate information of four different scale feature layers for classification prediction and regression prediction, so as to reduce the complexity of network model. The experimental results show that the proposed small object detection algorithm based on the fusion of four scale deep and shallow conual information can obtain good accuracy and real-time performance on the NWPU VHR-10 dataset, improve the detection accuracy on the basis of ensuring the real-time detection, and perform well in the small object detection task of remote sensing images. © 2021 Technical Committee on control Theory, Chinese Association of automation.

关键词： Remote sensing

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Disturbance Rejection in Lower-Limb Rehabilitation Robot with Parameter Uncertainties

Disturbance Rejection in Lower-Limb Rehabilitation Robot wit...

引用

intelligent Systems, International Workshop on

作者： He Wang Jinhua She Feng Wang Juan Zhao Seiichi Kawata School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China School of Engineering Tokyo University of Technology Hachioji Tokyo Japan

ISBN: (数字)9798350353303

ISBN: (纸本)9798350353310

This paper presents a method integrated equivalent-input-disturbance (EID) and repetitive control (RC) to mitigate the effects of non-linearity and uncertainty in a pedaling rehabilitation robot. The repetitive controller is designed to ensure accurate steady-state tracking of periodic signal. Meanwhile, the EID estimator, combined with a state observer, is responsible for estimating and compensating for the impacts of non-linearity, parameter uncertainties, and disturbances caused by patients with lower-limb impairments. We design a Lyapunov stability function to assess the stability of system. The parameters of the control gain are obtained by linear matrix inequality (LMI). The results confirm the effectiveness of the proposed method.

关键词： Uncertain systems Accuracy Uncertainty Simulation Observers Assistive robots Stability analysis Steady-state Robots Lyapunov methods

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Adaptive Sliding Mode control for Flexible Dual-manipulator with Friction Compensation

Adaptive Sliding Mode Control for Flexible Dual-manipulator ...

引用

Chinese automation Congress (CAC)

作者： Tao Song Shiqi Zheng Jiachun Sun Liangguang Li Qing Chang Zhipeng Li School of Automation China University of Geosciences Wuhan Campus Hubei Key Laboratory of Advanced Control and Intelligent Automation fo Complex Systems Wuhan China

This paper focuses on the coordinated tracking control scheme of dual-manipulator based on friction compensation. First, a new dual-manipulator model with flexible joints and friction is constructed; Second, a new adaptive neural control method is proposed for the dual-manipulators with flexible joints. Neural networks are used to approximate the unknown nonlinear dynamics in the model; Third, new state observers are constructed to estimate the joint friction. Based on the observers, the friction can be well compensated. The stability of the system is derived by using Lyapunov method. The simulation results verify the effectiveness of the practical method.

关键词： Adaptation models Uncertainty Torque Friction Simulation Neural networks Observers

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Improved BP Neural Network Utilizing Bayesian optimization Algorithm for Iron-making Process control

Improved BP Neural Network Utilizing Bayesian optimization A...

引用

IEEE International Symposium on Industrial Electronics (ISIE)

作者： Shaoran Wang Jinhua She Seiichi Kawata Feng Wang Juan Zhao Qifu Chen Zhenhong Liao School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China School of Engineering Tokyo University of Technology Hachioji Tokyo Japan

ISBN: (数字)9798350394085

ISBN: (纸本)9798350394092

The manufacturing sector covers a wide range of areas, focusing mainly on major equipment manufacturing and process industries, including new energy and new materials manufacturing. The ultimate goal is to realize intelligent manufacturing. Iron and steel metallurgy is an important pillar of the national economy, especially in the field of process industry. In the ironmaking process, a large number of process parameters are systematically collected, forming high-dimensional time series big data. These parameters contain hundreds of influencing factors. key production indicators such as yield, energy consumption and iron quality are closely related to a controlled intermediate variable in the smelting process, the furnace temperature. Silicon content [Si] and the mass percentage of Si in the molten iron is a good example. In order to predict and control the blast furnace ironmaking process more effectively, this paper introduces a BP neural network prediction model, and optimizes and compares the neural network model with genetic algorithm and Bayesian optimization technique.

关键词： Heuristic algorithms Neural networks Process control Predictive models Prediction algorithms Silicon Iron

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An Informer Based Alarm Early Prediction Method Over Consecutive Alarm Monitoring Periods

An Informer Based Alarm Early Prediction Method Over Consecu...

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IEEE International Conference on Industrial Informatics (INDIN)

作者： Wenbin Jiang Wenkai Hu Zhe Liu Fei Wang School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China SINOPEC-SK (WUHAN) Petrochemical Company Limited Wuhan China

ISBN: (数字)9798331527471

ISBN: (纸本)9798331527488

Alarm systems serve as the first layer of protection for modern process industries to monitor industrial processes and ensure operational safety. However, the presence of alarm floods is common in alarm systems and may distract operators from critical alarms. To provide decision supports to operators during alarm flood situations, alarm prediction becomes a potential effective solution. The difficulty of alarm prediction lies in how to achieve high prediction accuracy over long-term consecutive alarm monitoring periods, including situations with either high or low alarm rates. Consequently, this paper presents an industrial alarm prediction method for both alarm flood periods and non-alarm flood periods. Specifically, a strategy of combining fixed-length and fixed-time sliding windows is designed. Further, an alarm prediction model based on Informer is proposed for alarm prediction under different alarm rates. A case study is presented to prove the validity of the proposed alarm prediction method.

关键词： Industries Adaptation models Analytical models Accuracy Predictive models Alarm systems Floods Protection Informatics Monitoring

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Optimizing Bounding Box Regression in complex Backgrounds

Optimizing Bounding Box Regression in Complex Backgrounds

引用

Chinese automation Congress (CAC)

作者： Jun Chen Haiyan Zhang Anjun Yu Yiwei Wang 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 China University of Geosciences Wuhan China Jiangxi Ganyue Expressway Co. Ltd. Nanchang China School of Automation China University of Geosciences Wuhan China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

In the context of electrical power operation sites, the automated and accurate detection of whether workers are properly equipped with safety gear, such as safety clothing, helmets, and safety ropes for high-altitude operations, is crucial for ensuring the safety of personnel. Traditional object detection models often struggle to accurately identify these critical pieces of safety equipment in complex environments, due to variable object sizes, overlapping objects, and substantial background noise. To specifically address these challenges, we propose a novel loss function-Enhanced Alignment Intersection over Union (EAIoU) loss. This loss function is designed to enhance the model's ability to discriminate between closely spaced and overlapping safety gear in real-time, significantly improving robustness against scale variations and complex backgrounds. In experiments based on the YOLOv8s architecture, our model achieved a mean Average Precision (mAP) of 92.1 % on a dataset specific to electrical power operations, markedly outperforming traditional IoU loss functions. Additionally, EAIoU demonstrated improved performance on the COCO dataset, indicating its generalization capability across various complex scenarios. This research not only enhances the accuracy and real-time performance of safety equipment detection at electric power operation sites but also opens new avenues for advanced object detection technology in complex, multi-object environments.

关键词： Accuracy Gears Shape Object detection Robustness Real-time systems Safety Power systems Personnel Object recognition

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A Research on Attitude control of Quadrotor UAV Based on Equivalent-Input-Disturbance Approach and Model Predictive control

A Research on Attitude Control of Quadrotor UAV Based on Equ...

引用

intelligent Systems, International Workshop on

作者： Zihao Ning Jinhua She Seiichi Kawata School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China School of Engineering Tokyo University of Technology Hachioji Tokyo Japan

ISBN: (数字)9798350353303

ISBN: (纸本)9798350353310

This paper discusses the problem of attitude control of a quadrotor unmanned aerial vehicles (UAV). As external disturbances and system nonlinearities will affect the attitude control of quadcopter UAV, making it difficult for general control methods to achieve satisfactory results. To address this problem, this paper applies the equivalent-input-disturbance (EID) approach and the model predictive control (MPC) to the attitude control of a quadcopter UAV to ensure the reference tracking and suppress the influence of the total disturbances on the attitude of the quadcopter UAV, and the simulation results prove the effectiveness of the method.

关键词： Attitude control Simulation Conferences Autonomous aerial vehicles Prediction algorithms control systems intelligent systems Quadrotors Predictive control intelligent control

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Enhanced Gesture Recognition via Interpretable Temporal Convolutional Networks Utilizing Spatio-Temporal sEMG Features

Enhanced Gesture Recognition via Interpretable Temporal Conv...

引用

Pervasive and intelligent Computing (PICom), IEEE Conference on

作者： Xiaohu Ao Jinhua She Feng Wang Seiichi Kawata Edwardo Fumihiko Fukushima Jinseok Woo School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China School of Engineering Tokyo University of Technology Hachioji Tokyo Japan

ISBN: (数字)9798331522742

ISBN: (纸本)9798331522759

Rehabilitation robots and human-robot interaction systems are receiving increasing attention in modern healthcare and engineering. Motion intent recognition techniques based on surface electromyography (sEMG) signals provide important support for these systems. However, motion intent recognition models rarely focus on the spatiotemporal properties of sEMG signals simultaneously. There is a lack of interpretable analysis of these spatiotemporal properties. The purpose of this study is to perform gesture recognition of sEMG signals from the Ninapro database using a temporal convolutional network (TCN). The sEMG signals are converted into sEMG images, which are used as input to the TCN model. The TCN model is utilized to perform gesture recognition on these images. The results show that after fusing the spatial and temporal features of the sEMG signal, the recognition accuracy of the TCN model is significantly higher than that of models using only spatial features or only temporal features. To understand the decision-making process of the model, we used an interpretable method based on Shapley values to perform a detailed interpretive analysis of the recognition results. The experimental results show that the TCN model has significant accuracy and stability in the sEMG signal gesture recognition task, while the Shapley value-based interpretable method can effectively reveal the contribution of different features to the prediction results, thus improving the transparency and user trust in the model. This study not only provides a new technical tool for sEMG signal gesture recognition but also demonstrates the value of interpretable methods in deep learning models.

关键词： Accuracy Intent recognition Computational modeling Gesture recognition Predictive models Stability analysis Real-time systems Spatiotemporal phenomena Convolutional neural networks Time complexity

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CPO-VMD Method for Denoising sEMG Signals

CPO-VMD Method for Denoising sEMG Signals

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Pervasive and intelligent Computing (PICom), IEEE Conference on

作者： Jingwen Liu Juan Zhao Seiichi Kawata Feng Wang Jinhua She School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China School of Engineering Tokyo University of Technology Hachioji Tokyo Japan

ISBN: (数字)9798331522742

ISBN: (纸本)9798331522759

In the field of biomedical engineering, surface electromyography (sEMG) is a key tool for monitoring muscle activity and is widely used in various fields such as human- computer interfaces, muscle fatigue assessment, and rehabilitation training. However, sEMG signals are often affected by power line interference and other noise sources during the acquisition process, which may mask useful information. In this study, a new method combining the variational mode decomposition (VMD) and the crown porcupine optimization (CPO) algorithm, named CPO-VMD, is proposed, aiming to optimize the VMD parameters to improve the denoising effect of sEMG signals. By automatically adjusting the key parameters of the VMD through the intelligent algorithm, this method solves the problem of difficult parameter selection, furthermore, enhancing the denoising efficiency. In this paper, a simulated sEMG signal is constructed and the VMD parameters are optimized by CPO. The experimental results show that the method effectively improves the quality of sEMG signals and provides a more accurate idea for signal processing in muscle fatigue assessment and rehabilitation applications.

关键词： Training Noise reduction Signal processing algorithms Interference Muscles Fatigue Optimization Surface treatment Monitoring Signal to noise ratio

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Causality Analysis and Modeling of Fuel Ratio Influencing Factors in Blast Furnace Ironmaking Process

Causality Analysis and Modeling of Fuel Ratio Influencing Fa...

引用

Asian control Conference

作者： Binjie Qian Weihua Cao Qifu Chen Min Wu School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China School of Future Technology China University of Geosciences Wuhan China

ISBN: (数字)9789887581598

ISBN: (纸本)9798331540845

Blast furnace ironmaking is a key process in iron and steel production, and the fuel ratio has an important impact on the smelting efficiency, cost and environmental impact of the blast furnace. This study aims to provide new ideas and method-ological support for the optimized management and control of the blast furnace ironmaking process by exploring the effects of different factors on the fuel ratio. We firstly sorted out the factors affecting the fuel ratio based on the blast furnace process mechanism, and then deeply analyzed the causal relationship of each parameter on the fuel ratio based on the transfer entropy value, and screened out the parameters that did not have a significant effect on the fuel ratio. Taking the remaining key parameters as inputs, the LSTM and GRU models, which can capture time series information, are used to predict the mean and variance of the fuel ratio, respectively, and the two are combined to compute the final fuel ratio, which overcomes the problem of poor model generalization brought by small fluctuations in the training data of fuel ratio. The experimental results show that the convergence speed of the model is significantly improved after parameter screening, and the fuel ratio can be effectively predicted within a certain error range.

关键词： Computational modeling Time series analysis Smelting Process control Training data Predictive models Blast furnaces

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