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检索条件"机构=Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan"

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Position control of planar three-link underactuated manipulator based on wavelet neural network model

Position control of planar three-link underactuated manipula...

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第三十九届中国控制会议

作者： Yawu Wang Huiqing Yang Pan Zhang School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

This paper presents a control strategy for a planar three-link underactuated manipulator（UM） with a passive first link based on a wavelet neural network（WNN） model. Firstly, by using the particle swarm optimization（PSO） algorithm, the target angles of all links are calculated according to the established kinematic model and the given target position. Then, a WNN model is trained to describe the coupling relationship between the passive link and the second link. The difference between the current angle and the target angle of the passive link is converged to zero by repeatedly controlling the second link to rotate an angle which is calculated by the trained WNN model. Next, the active links are controlled to rotate to their target angles with low speeds. Finally, the effectiveness of the proposed control strategy is verified through experimental results.

关键词： planar underactuated manipulator position control wavelet neural network coupling relationship

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Delay-Dependent Stability Analysis of Delayed Discrete-Time systems via State-Connecting-Based Zero-Value Equations

Delay-Dependent Stability Analysis of Delayed Discrete-Time ...

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第三十九届中国控制会议

作者： Ke-You Xie Wen-Hu Chen Li Jin Chuan-Ke Zhang Yong He School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

This paper is concerned with the stability analysis of discrete time-delay system. Firstly, an improved augmented functional form is proposed and the positive definite condition of functional is derived. Then, the forward difference of functional is estimated by applying summation inequalities and a state-connecting-based zero-value equation. As a result, an improved stability criterion is established. Finally, a numerical example is given to show the efficiency and merit of the proposed method.

关键词： Discrete-time systems stability analysis time-varying delay zero-value equation

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Analysis of Bit-Rock Interaction Models Using Finite Element Simulation Data

Analysis of Bit-Rock Interaction Models Using Finite Element...

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第三十九届中国控制会议

作者： Hengyu Huang Min Wu Sike Ma Luefeng Chen Chengda Lu School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

The study of bit-rock interaction model is essential to describe the rock breaking process. In practice, it is difficult to get downhole measurement, and the downhole rock-breaking data is difficult to obtain. Therefore, this paper uses finite element simulation to obtain the kinetic data of bit-rock interaction, based on the analysis and comparison of existing models, an effective analysis method is provided for bit-rock interaction. Firstly, by using the Drucker-Prager rock criterion, actual bit and rock parameters, we develop the finite element bit-rock interaction experiments, and we obtain the data of rotating speed, rate-ofpenetration, weight-on-bit. Then, based on multiple nonlinear regression method, we identify the existing Young model, Jorden and Shirley model, Richard model, Ritto model parameters. Through the analysis and comparison of identification effects and characteristics of each model, we obtain the relationship among parameters of the bit-rock interaction.

关键词： Bit-rock interaction Weight on bit Finite element method Parameter identification

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Spectral Reflectance Reconstruction from Camera Responses by Adaptively Constructing Weight Matrix Combining Colorimetric and Lightness Similarities

Spectral Reflectance Reconstruction from Camera Responses by...

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第三十九届中国控制会议

作者： Xin Wang Hui Jiang Jing-Jing Zhang School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

An improved method for spectral reflectance reconstruction from digital camera raw RGB responses of pixels is proposed by adaptively weighting training samples considering colorimetric and lightness similarities. The proposed method was based on an adaptive local weighted linear regression model by using a Gaussian function in weighting matrix *** novelty of our method is designing the weighting matrix combining colorimetric and lightness similarities. The proposed method was tested using two different standard color charts, with a simulated digital camera based on the camera spectral sensitivity. Experimental results indicate that the proposed method exhibits considerable improvements in terms of the spectral reflectance and the colorimetric values in comparison with existing methods.

关键词： Spectral Reflectance Reconstruction Lightness Similarity Pseudo Inverse

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Cross-Corpus Speech Emotion Recognition Based on Hybrid Neural Networks

Cross-Corpus Speech Emotion Recognition Based on Hybrid Neur...

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第三十九届中国控制会议

作者： Abdul Rehman Zhen-Tao Liu Dan-Yun Li Bao-Han Wu School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

Speech emotion recognition helps enrich next-generation AI with emotional intelligence abilities by grasping the emotion from voice and words. At the current stage, speech emotion recognition（SER） is only used within experimental *** current challenge facing the SER research is the lack of robustness across cultures, languages and even minor differences such as age-gaps of speakers. To create a more adaptable SER in adversarial circumstances, we propose hybrid neural networks architecture that creates a holistic model by embedding the Mel Frequency Cepstrum Coefficients as one-hot inputs such that differences in coefficients in each emotional category are inflated according to their importance. We performed experiments on three different databases to test the cross-corpus effectiveness of the proposed model.

关键词： Emotion Recognition Speech Neural Networks Embedding Cross-corpus

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Wind power prediction based on multiple support vector machines

Wind power prediction based on multiple support vector machi...

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第三十九届中国控制会议

作者： Min Ding Zhe Chen School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

Wind power forecasting is of great significance in grid dispatching. This paper proposes a statistical model based on feature classification least squares support vector machine, which can predict short-term wind power. First of all, this paper analyzes the data of an actual power plant. After analyzing the data, it is found that there is uncertainty in the existence of multiple powers at the same wind speed. Then, in order to resolve this uncertainty, the wind speed and wind speed trend samples are density clustered according to the DBSCAN method. The clustering results are divided into several categories, and the samples of different categories are modeled by least squares support vector machines. Finally, the effectiveness of the proposed prediction model is compared with that of unclassified samples through the prediction model. Simulation results show that the designed model has higher prediction power accuracy.

关键词： Wind power prediction feature classification least squares support vector machine

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Feature Matching Algorithm Based on SURF and Lowes Algorithm

Feature Matching Algorithm Based on SURF and Lowes Algorithm

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第三十九届中国控制会议

作者： Xiaoqi Wang Weihua Cao Chaolong Yao Hang Yin School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

Feature extraction and matching of images is a key step in 3D reconstruction, and its accuracy directly affects the accuracy of 3D reconstruction. In this paper, aiming at the mismatch caused by the high similarity between screws, proposes a feature matching algorithm based on median filtering, Lowes algorithm and scale-invariant feature transform(SURF), called M-L-SURF algorithm. First, the median filtering is performed on the screw image to remove noise, then the SURF algorithm is used for feature extraction and matching, and finally, the Lowe's algorithm is used to filter the matching results. The results of experiments show that the M-L-SURF algorithm can achieve a 97.4% correct rate of screw image matching. The matching results obtained in this paper can be better applied to the subsequent work of 3D reconstruction.

关键词： Median filtering SURF Feature matching Lowes algorithm

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Novel Synchronization Criteria of Chaotic Lur’e systems With Time-varying Delayed Feedback PD control

Novel Synchronization Criteria of Chaotic Lur’e Systems Wit...

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第三十九届中国控制会议

作者： Run-Zhe Tao Ying Zhang Lei-Chao Pang Xing-Chen Shangguan Dan-Yun Li School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

This paper is concerned with the problem of asymptotical synchronization of chaotic Lur’e systems controlled via PD controller with time-varying delay. Firstly, a new Lyapunov-Krasovskii functional（LKF） with more information of time-varying delay is constructed. Then, by applying the Wirtinger-based integral inequality and the extended reciprocally convex combination lemma（RCCL）, a new synchronization criterion for time-varying delay is obtained, and a less conservatism corollary for the constant delay is established by weakening some terms of LKF. Finally, a numerical example is given to show the better performance of the proposed criteria.

关键词： Synchronization Chaotic Lur’e systems Time-varying delay PD feedback control

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An Improved Faster-RCNN Algorithm for Object Detection in Remote Sensing Images

An Improved Faster-RCNN Algorithm for Object Detection in Re...

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第三十九届中国控制会议

作者： Rui Liu Zhihua Yu Daili Mo Yunfei Cai School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

Compared with conventional object detection, remote sensing images are taken from the air. The angle of view is not fixed and the object direction, scale which compared with conventional object detection algorithm are quite different. These factors lead to the object detection in remote sensing images difficult. To solve the above problems, this paper proposes an improved remote sensing object detection method based on Faster-RCNN algorithm. Using online difficult example mining technology,feature pyramid structure, Soft-NMS technology, and RoI-Align technology to enhance the capabilities of Faster-RCNN in small object detection task in remote sensing images. The algorithm in this paper was evaluated on the RSOD-Dataset, compared with the original Faster-RCNN algorithm, the proposed algorithm improves the detection accuracy and training convergence speed,which shows that these improvements are of great significance to the object detection algorithm of remote sensing images.

关键词： Remote Sensing Images Faster-RCNN Small Object Detection

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A Composite Cost-Sensitive Neural Network for Imbalanced Classification

A Composite Cost-Sensitive Neural Network for Imbalanced Cla...

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第三十九届中国控制会议

作者： Lei Chen Yuan Zhu School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

Imbalanced data with skewed class distributions and different misclassification costs is common in many real-world applications. Traditional classification approach does not work well for imbalanced data, because they assume equal costs for each class. To deal with this problem, cost-sensitive approaches assign different misclassification costs for different classes without disrupting the true original distributions of samples. However, due to lack of prior knowledge, the misclassification costs are usually unknown and hard to choose in practice. Whats more, even instances in the same class may have different misclassification costs. As an extension of class-dependent costs, this paper presents a composite cost-sensitive deep neural network(CCS-DNN) for imbalanced classification. A specifically-designed cost-sensitive matrix, which is composed of exampledependent costs and class-dependent costs, is embedded into the loss function to improve the classification performance. And the parameters of both the cost-sensitive matrix and the network are jointly optimized during training. The results of comparative experiments on some benchmark datasets indicate that the CCS-DNN performs better than other baseline methods.

关键词： Cost-sensitive Learning Composite Costs Imbalanced Classification Deep Learning

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