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Research on Virtual USB Storage in Remote File Transfer 41

Research on Virtual USB Storage in Remote File Transfer

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第41届中国控制会议

作者： Jie Li Weihua Cao Zhicheng Zhao Han Peng School of Automation 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

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

Virtual USB storage technology is an application technology that integrates Universal Serial Bus(USB) virtualization,file system virtualization and reliable file data transfer,which is often applied to virtual file transfer in data *** the current traditional virtual USB storage technology relies on specific software and poor system compatibility,this paper proposes a hardware-based virtual USB storage solution that uses the built-in USB driver of each operating system to map the local USB storage device to the remote controlled server to achieve file reading and *** show that the scheme proposed in this paper has the advantages of simple implementation,good system compatibility,safe and stable *** technology has been successfully applied to the data room management of an enterprise.

关键词： USB storage virtualization file transfer remote mapping

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Image Semantic Segmentation Scheme based on XGBoost combination with Convolution Feature Extraction 41

Image Semantic Segmentation Scheme based on XGBoost combinat...

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第41届中国控制会议

作者： Zichen Dai Xuewen Liu Chi Xu School of Automation 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

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

Aiming at the problem that the existing image semantic segmentation schemes based on convolutional neural network require a large number of training data sets and high training cost,this paper presents a novel scheme for image semantic segmentation based on extreme gradient boosting(XGBoost),a scalable tree boosting system and the combination of convolution feature extraction(CFE).The scheme consists of three ***,convolution operation is performed on a small amount of RGB pixel information of the image processed by Gaussian filtering to acquire more multi-dimensional image feature ***,some preprocessing techniques of image feature information,including feature normalization and image label pixel number uniformity are performed to match the ***,the preprocessed image feature information and corresponding labels are input into XGBoost to train and predict the data of the test *** simulation and practical tests,compared with other commonly used few data classification algorithms like k-Nearest-Neighbor(kNN) and Support-VectorMachine(SVM),this scheme can achieve high precision pixel-level image semantic segmentation under the condition of lower training cost.

关键词： Image Semantic Segmentation Convolutional Feature Extraction XGBoost Gaussian Filtering Low Training Cost

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Particle Detection of complex Images Based on Convolutional Neural Network 41

Particle Detection of Complex Images Based on Convolutional ...

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第41届中国控制会议

作者： Yuting Ge Yi Liu Chi Xu School of Automation 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

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

Particle Detection plays important roles in industrial and medical image ***-learning based methods can only deal with the particle images with simple backgrounds and sparse *** most neural network based methods can also hardly to deal with overlapping objects and small particles,besides,the huge amount of parameters of these methods also has a great demand for annotated data,which is difficult to ***,we use bright pixels to represent the spatial position of particles,and artificially divid the sizes of particles into multiple intervals.A feature representation called Light-spots Maps is proposed to replace the bounding box and pixel classifications in our detection *** Light-weight networks based on ResNets is used to transform the particle images into Light-spots maps to detect the particle *** experiments,we prove the effectiveness of our method.

关键词： Objects Detection Particles Detection Convolutional Neural Network

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An Inverse Kinematic Optimization Method for the S-R-S Redundant Manipulator Considering Joint Limit and Singular Position Avoidance 41

An Inverse Kinematic Optimization Method for the S-R-S Redun...

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第41届中国控制会议

作者： Yisheng Mei Xin Chen School of Automation 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

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

Aiming at the problem of how to avoid the joint limit and singular position in continuous trajectory planning of the S-R-S redundant manipulator,a new inverse kinematics optimization algorithm is proposed in this ***,according to the special geometry of S-R-S manipulator,we take the arm angle as the redundant parameter to obtain the joint angle expressions under the given tip pose and arm *** we discuss the feasible arm angles under joint limit in nonsingular and singular cases,and give a complete method to obtain the effective arm ***,we introduce the concept of virtual force to represent the degree that the joint is far from the limit,and define the optimization objective function with the virtual force as the weight to avoid joint limit,the complex relationship between the arm angle and joint angles is linearized by the difference method to simplify the objective function to quadratic function,combine with the feasible arm angle range,we realize singularity avoidance and joint limit avoidance at the same *** simulation result shows that compared with the traditional algorithm,the algorithm proposed in this paper has better joint limit avoidance ability on the premise of avoiding singularity which reflects the superiority of the algorithm and can be used for the motion planning of the manipulator in practical engineering.

关键词： Arm angle Singular position avoidance Joint limit avoidance Inverse kinematic optimization

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Disturbance Rejection Based on Equivalent-Input-Disturbance Approach for Dynamic Positioning System of Offshore Drilling Platform 41

Disturbance Rejection Based on Equivalent-Input-Disturbance ...

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第41届中国控制会议

作者： Zhejiaqi Ma Chengda Lu Sheng Du Min Wu School of Automation 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

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

A dynamic positioning disturbance suppression method is presented for the offshore drilling platform based on the equivalent input disturbance(EID) approach in this ***,the offshore drilling platform's low-frequency and wavefrequency motion components are separated by the Luenberger *** a linear-quadratic regulator is designed to ensure the stability of the low-frequency motion closed-loop *** EID approach is used to estimate and compensate for the unknown time-varying disturbances of the environment,thereby improving the disturbance suppression ability of the *** results of a supply vessel demonstrate the validity of the method.

关键词： Dynamic positioning equivalent input disturbance offshore drilling platform unknown time-varying disturbances

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Finite-time H∞ state estimation for genetic regulatory networks with observations of two hidden-Markov modes 41

Finite-time H∞ state estimation for genetic regulatory netw...

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第41届中国控制会议

作者： You Wu Xiongbo Wan School of Automation 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

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

This paper investigates the problem of finite-time H state estimation for discrete time-delayed genetic regulatory networks（GRNs） with observations of two hidden-Markov modes.A new GRN model with two Markov jump delays is proposed,of which the mode observations are governed by two hidden-Markov *** sufficient conditions are derived,which guarantee the finite-time boundedness of the estimation error dynamics with prescribed H disturbance attenuation *** state estimator parameters are designed by solving several matrix inequalities.A numerical example is given to illustrate the validity of our results.

关键词： Genetic regulatory networks finite-time H_∞ state estimation hidden Markov model Markov jump delays

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Stability Analysis of Discrete-Time Delayed Neural Networks via A Cubic Function Negative-Determination Lemma 41

Stability Analysis of Discrete-Time Delayed Neural Networks ...

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第41届中国控制会议

作者： Wen-Hu Chen Fei Long Cui Zhu Chuan-Ke Zhang Yong He School of Automation 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

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

This paper is concerned with the stability analysis of discrete-time neural networks with a time-varying ***,an improved augmented Lyapunov-Krasovskii functional(LKF) is established by adding single/double-summation ***,by utilizing summation inequalities,the forward difference of the constructed functional is estimated as a cubic function with respect to the *** applying the cubic function negative-determination lemma,a less conservative stability criterion is obtained,whose advantages are demonstrated via a numerical example.

关键词： Discrete-time neural networks time-varying delay cubic function negative-determination lemma stability analysis

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Automatic Depression Detection Network Based on Facial Images and Facial Landmarks Feature Fusion

Automatic Depression Detection Network Based on Facial Image...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Hu, Min Xu, Lingxiang Liu, Lei Wang, Xiaohua Li, Hongbo Yang, Jiaoyun Hefei University of Technology Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine Hefei China Hefei University of Technology National Smart Eldercare International Science and Technology Cooperation Base Hefei China Hefei University of Technology Intelligent Interconnected Systems Laboratory of Anhui Province Hefei China

ISBN: (纸本)9798350386226

Artificial intelligence methods offer objectivity and convenience in automatic depression detection, however, current research often neglects the critical role of facial landmarks. This oversight results in insufficient spatial structure information and a lack of detailed local representation, which fails to capture the nuanced semantic information crucial for identifying depression-related clues. To address these issues, we introduce a novel dual-branch network model comprising the Landmark-Image-Landmark Net (LIL Net) and the Global Context Vision Transformer Net (GCVit Net). Through a dual-stream, multiscale, and cross-fusion strategy, LIL Net is designed to extract original facial image features alongside landmark features, prioritizing the detailed semantic information of potential depression clues. LIL Net employs an innovative LIL Attention approach to jointly learn multiscale features from facial landmarks and images, thereby enhancing the model's ability to capture fine-grained depression-related cues. Furthermore, the Multi-scale Feature Fusion (MSFF) module fuses the obtained multiscale features, augmenting the semantic expression of potential depression clues within facial landmarks via attention mechanisms. Meanwhile, the GCVit Net branch network supplements global information by extracting global facial features. Finally, the features from both branches are concatenated to enhance the accuracy of depression degree predictions. Experimental results demonstrate that our model has superior performance in detecting depression compared to existing methods. We release our code at https://***/xlx777/LIL-Net. © 2024 IEEE.

关键词： Artificial intelligence

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An Analysis Method for Influence of BF Operating Parameters on GUR Based on Convergent Cross Mapping Algorithm 41

An Analysis Method for Influence of BF Operating Parameters ...

引用

第41届中国控制会议

作者： Chenglin Yang Jianqi An Weijun Li Wenkai Hu Xin Chen School of Automation 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

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

A blast furnace(BF),which includes a variety of complex physical changes and chemical reactions,has many operating parameters that affect its smelting ***,the relationship between operation parameters and state parameters is complex and the influence of time delay between operation parameters and state parameters is difficult to be ***,this paper proposes a convergent cross mapping algorithm(CCM) incorporating delay analysis to solve this ***,using the CCM algorithm to calculate the correlation coefficients between operation parameters and state parameters,and judge the causal relationship between each operating parameter and state parameter by the convergence of the CCM ***,determine the specific delay time and time delay interval of BF operating variables to state variables through fusion time delay *** that the selection of embedding dimension has an important influence on the result of causality *** paper uses the cross-validation method to determine the optimal embedding ***,by comparing the mechanism analysis and the experimental results,the effectiveness of this method in determining causality and delay time of BF variables is verified.

关键词： Blast Furnace Operation Parameters causal relationship Convergence Cross Mapping Time Delay

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Design and Control of a Virtual Teacher with Individual Motor Signature 41

Design and Control of a Virtual Teacher with Individual Moto...

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第41届中国控制会议

作者： Siyuan Li Chao Zhai School of Automation 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

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

In this work,a virtual teacher is designed to play the mirror game with human *** individual motor signature is taken into account,which allows to produce diverse teaching *** teaching effect of virtual teacher is assessed by comparing and analyzing the original movement characteristics of human players and the movement characteristics after going through the teaching ***,stability analysis of the teaching model is ***,the feasibility of the model is verified by comparing the data of several groups of samples.

关键词： Virtual teacher Joint action Motor coordination Motor signature Control design

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