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Short-term Wind Power Prediction by Using Empirical Mode Decomposition Based GA-SVR 36

Short-term Wind Power Prediction by Using Empirical Mode Dec...

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第36届中国控制会议

作者： Hua Xie Min Ding Luefeng Chen Jianqi An Zhangbing Chen Min Wu School of Automation China University of Geosciences Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

ISBN: (纸本)9781538629185

Accurate short-term wind power prediction can improve the trade and the dispatch level of wind *** predict the short-term wind power,we investigated the empirical mode decomposition（EMD） of numerical weather prediction（NWP） and genetic algorithm（GA） optimization of support vector regression（SVR）.First,the wind speed data from NWP is decomposed into the EMD components,including multiple intrinsic mode functions（IMFs） and one ***,a genetic algorithmsupport vector regression model（GA-SVR） is used to build models of all ***,the prediction results of all components are integrated,which as input of wind-power curve for obtain the predicted value of wind *** order to validate the proposed method,one real-world dataset was used for model training and testing,and the results show that the proposed method is more accurate and effective than SVR and back propagation neural networks（BPNN）.

关键词： Wind Power Prediction Empirical Mode Decomposition Support Vector Regression Genetic Algorithm Fast Fourier Transform Wavelet Filter

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LRDE:Long-Range Dependency and Salient Feature Enhancement for Co-SOD 41

LRDE:Long-Range Dependency and Salient Feature Enhancement f...

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第41届中国控制会议

作者： LongSheng Wei SiYuan Guo GuanYu Zong Jiu Huang School of Automation China University of Geosciences Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

Different from other salient object detection(SOD) for the task of extracting salient obj ects in a image,collaborative saliency object detection(Co-SOD) targets a set of related image groups,and uses algorithms to find and locate the common collaborative salient objects in related image *** current SOD has achieved remarkable results,but most of the previous researches focus on how to obtain the consistency between images,while the contextual semantic information of images and the features of salient objects are less *** solve these problems,we propose a novel network to capture long-range dependencies on images and enhance(LRDE) the representation of salient ***,In LRDE,we capture the longrange dependencies by the Non-local module to obtain contextual semantic ***,in order to highlight the features of salient objects and enhance the representation of salient objects,we propose the Coordinate Attention module,which embeds the location-attention channel attention and encodes the feature maps as orientation-aware and position-aware attention maps,and It is applied complementary to the input feature map,thereby enhancing the representation of salient *** show that the method outperforms twelve improved methods on four publicly available datasets.

关键词： Co-SOD Long-Range Dependency Feature Enhancement Non-local Module Coordinate Attention Module

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An improved stability criterion for singular systems with time-varying delay via a relaxed integral inequality 36

An improved stability criterion for singular systems with ti...

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第36届中国控制会议

作者： ZHI Ya-Li HE Yong WU Min School of Automation China University of Geosciences Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

ISBN: (纸本)9781538629185

This paper concerns the stability problem of singular systems with time-varying delay. According to different delaypartition intervals, an augmented Lyapunov-Krasovskii functional with delay cross terms is constructed. Based on it, using the relaxed integral inequality technique is to obtain a less conservative stability criterion. As a result, two numerical examples are provided to demonstrate the effectiveness of the proposed method.

关键词： Stability Singular systems with time-varying delay Lyapunov-Krasovskii functional Relaxed integral inequality

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Multi-layer Performance Assessment Method for Sintering Production Process control System 36

Multi-layer Performance Assessment Method for Sintering Prod...

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第36届中国控制会议

作者： Wei Jiao Xin Chen Min Wu Wei Hua Cao School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

ISBN: (纸本)9781538629185

According to the hierarchical control system of sintering production process,a multi-layer performance assessment method is *** discuss the structure,function and technology used of multi-layer performance assessment strategy which is composed of sintering comprehensive coke ratio(CCR) assessment module,sintering ignition assessment module and fuzzy synthetic decision-making *** multi-layer performance assessment for sintering production process control system is realized based on sintering CCR prediction model,performance assessment method for sintering ignition control,and fuzzy synthetic evaluation *** proposed multi-layer performance assessment method is implemented on the simulation platform of the sintering production process in the laboratory,which effectively realized the performance assessment for sintering production process plant level control system,and then provides effective information for the staff to maintain the control system.

关键词： Hierarchical control system Performance assessment method Fuzzy synthetic evaluation Comprehensive coke ratio Sintering ignition control

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Face recognition using linear analysis techniques and neural networks based approaches

Face recognition using linear analysis techniques and neural...

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7th Annual Middle Eastern Simulation Multiconference, MESM 2005

作者： Aouicha, Ichraq Ben Tray, Torek Masmoudi, Dorra Sellami Research Unit on Intelligent Control Design and Optimization of Complex Systems Ecole Nation ale d'Ingénieurs de Sfax Tunisia

ISBN: (纸本)907738121X

In this papery we are concerned with face recognition techniques using principal component analysis (PCA), Fisher linear discriminant Analysis (FLDA) as linear approaches and Neural Networks. Known as one of the efficient techniques used in dimensionality reduction, PCA is based on information theory concepts, seeking a computational model that best describes a face, by extracting the most relevant information contained in that face. FLDA is a classification method that projects high-dimensional data onto a reduced space and performs classification in it. The projection maximizes the distance between the means of all classes while minimizing the variance within each class. Althoughf offering a good reduction in dimensionality, the reduced dimension remains too high for a decision making process. Neural Networks represents a good solution for decision making by achieving numeric classification. It is shown in this paper that hybrid solution associating dimension reduction techniques and Neural Networks reduces computational efforts required in Neural Networks image classifiers. Indeed, although giving satisfactory solutions, Neural Networks based approaches require high computations in face recognition classification problems. © 2005 EUROSIS-ETI.

关键词： Principal component analysis

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Position Attention Network for Object Tracking 41

Position Attention Network for Object Tracking

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第41届中国控制会议

作者： Mingzhong Du Longsheng Wei Shiyu Liu School of Automation China University of Geosciences Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

In recent years,Siamese-based trackers have achieved excellent performance,most of them usually calculate the similarity of each position on the search region to the object through the cross-correlation layer for tracking obj *** solve the problem that the above method neglects the correspondence of the local information between the object and the search region and cannot adapt to the object deformation well,we propose a Siamese network-based tracker with position attention network(SiamPA).First,we use Siamese backbone network to extract template and search region ***,we adopt the boxguided object feature selection strategy to avoid similarity calculations for background *** addition,we introduce the position attention network instead of the cross-correlation layer to learn the part-level relationship between the object and the search region ***,the classification-regression sub-network is used to decode the similarity respond map obtained by the position attention network and predict the position of the *** contribution,one is to propose a box-guided method for refining object features,and the other is to introduce a position attention network for information *** on three challenging benchmarks including GOT-10 k,UAV123 and OTB-100 demonstrate that our SiamPA achieves excellent tracking performance with a real-time speed.

关键词： Object tracking Siamese network Box-guided Position attention network

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Tea Bud Recognition Based on Machine Learning 41

Tea Bud Recognition Based on Machine Learning

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第41届中国控制会议

作者： Guangjun Wang Zhi Wang Yi Zhao Yangzheng Zhang School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

Due to the low efficiency and high cost of manual tea picking,as well as the development and application of machine vision and image recognition technology,the mechanization and intelligence of tea picking will become a *** of all,this paper analyzed the advantages and disadvantages of several common target detection methods by using Matlab ***,considering cost,accuracy and real-time performance,the method of combining K-means clustering and image morphology processing is finally selected to extract tea ***,the method is reproduced on STM32 single chip ***,the effect was verified in the actual tea garden,which laid a foundation for the subsequent intelligent picking.

关键词： Machine learning K-means clustering STM32

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Improved stabilization conditions for T-S fuzzy systems with interval time-varying delay 36

Improved stabilization conditions for T-S fuzzy systems with...

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第36届中国控制会议

作者： LIAN Zhi HE Yong ZHANG Chuanke WU Min School of Automation China University of Geosciences Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

ISBN: (纸本)9781538629185

This paper is concerned with the stabilization problem for T-S fuzzy system with interval time-varying delay. By constructing a novel augmented LKF and using a developed reciprocally convex matrix inequality proposed in this paper to bound the derivative of the LKF, a delay-dependent stabilization condition based on parallel distributed compensation scheme is worked out for the closed-loop fuzzy system. Two numerical examples and an application to control of a truck-trailer are given to illustrate the effectiveness of our method.

关键词： Fuzzy systems Delay-dependent Stabilization Reciprocally convex matrix inequality

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Research on Temperature Error Calibration Method of Fluxgate Sensor 11th

Research on Temperature Error Calibration Method of Fluxga...

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11th International Symposium on Computational Intelligence and Industrial Applications, ISCIIA 2024

作者： Yang, Ruiping Ge, Jian Luo, Wang Hu, Xiangyun She, Jinhua Chugo, Daisuke Dong, Haobin 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 Faculty of Engineering Kwansai Gakuin University Osaka669-1330 Japan School of Geophysics and Geomatics China University of Geosciences Wuhan430074 China Hubei Subsurface Multi-scale Imaging Key Laboratory Wuhan430074 China School of Engineering Tokyo University of Technology Hachioji Tokyo192-0982 Japan

ISBN: (纸本)9789819647521

The fluxgate sensor is the most widely used sensor in vector magnetic measurement. However, during long-term continuous observation, the fluxgate sensor will produce large measurement errors due to changes in ambient temperature. This paper proposes a temperature calibration method for the fluxgate sensor based on polynomial fitting to address the temperature error of fluxgate sensors. First, the effect of temperature on the performance indicators of the fluxgate sensor was analyzed. Second, according to the existing temperature-magnetic field data, a temperature calibration model of the fluxgate sensor was constructed. And compared it with other temperature calibration method, the result shows that the proposed temperature calibration method is relatively simple and can better achieve real-time calibration for sensor application scenarios. Finally, to verify the effectiveness of the proposed method, numerous laboratory experiments were implemented. The temperature drift was reduced from about 700 nT before calibration to about 1 nT, root mean square error RMSE=11.7189, indicating that the proposed method has a good temperature calibration effect on the data measured by the fluxgate sensor within the variable temperature background. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Curve Fitting Fluxgate Sensor Temperature Calibration

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Speaker-independent Speech Emotion Recognition Based on Random Forest Feature Selection Algorithm 36

Speaker-independent Speech Emotion Recognition Based on Rand...

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第36届中国控制会议

作者： Wei-Hua Cao Jian-Ping Xu Zhen-Tao Liu School of Automation China University of Geosciences Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

ISBN: (纸本)9781538629185

Feature selection is a crucial step in the development of a system for identifying emotions in *** to select high correlation features is an open *** paper focuses on feature selection method which aims to extract the most effective acoustic features to improve the performance of emotion *** feature selection of speakerindependent speech based on Random Forest is proposed,which can remove the redundant features that have high correlations with each *** on the speech emotion recognition based on Support Vector Machines(SVM) is performed,where the speaker-independent features selected by the proposal and Spearman correlation analysis are used for emotion recognition,*** results show that the proposal achieves 78.6%recognition rate on average,which is 2.2%higher than using Spearman.

关键词： Emotion Recognition Speech Speaker-independent Feature Selection Random Forest

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