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

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Single-Parameter Optimization Method for Extracting Coupling Matrix from Lossy Filters

Single-Parameter Optimization Method for Extracting Coupling...

引用

Chinese control Conference

作者： Wei-Hua Cao Can Liu Yan Yuan Xiao-Long Zhuang Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

This paper presents a novel single-parameter optimization method for extracting coupling matrix from either measured or electromagnetic simulated S-parameters of a narrow band coaxial-resonator filter with losses. Having had the polynomials of the S-parameters of a filter by the Cauchy method with removing phase shift, the rational polynomials can be obtained. Once the rational polynomials having been determined, a single-parameter optimization method is proposed to obtain ε and the coupling matrix with an assigned topology, which can be extracted using well established techniques. The measured Sparameters compared with the 5-parameters obtained from the coupling matrix, and the attenuation factor K is easily determined. The loss effects will be removed after obtaining the value of the attenuation factor K. The approach is useful and can be used in computer-aided tuning of microwave filters. Example is presented to illustrate the validity of the proposed method.

关键词： Scattering parameters Tuning Couplings Loss measurement Optimization methods Transmission line measurements Transmission line matrix methods

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Research on Digital Hearing Aid Speech Enhancement Algorithm

Research on Digital Hearing Aid Speech Enhancement Algorithm

引用

Chinese control Conference

作者： Junlei Song Yuan Meng Junming Cao Jin Fang Kaifeng Dong Fang Jin Wenqin Mo Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

The microphone array speech enhancement algorithm (MASEA), the minimum mean square error algorithm for short-time logarithmic spectrum estimation based on voice activity detection (VAD-LSA-MMSE), and the Wiener filtering algorithm based on voice activity detection (VAD-Wiener) are currently the three most commonly used speech enhancement algorithms. Among them, the MASEA algorithm has some disadvantages such as poor noise reduction effect. VAD-LSA-MMSE algorithm has some disadvantages of relying on high SNR and introducing music noise, thereby reducing the intelligibility. The VAD-Wiener algorithm has some disadvantages such as higher SNR requirements. Aiming at the shortcomings of these three speech enhancement algorithms, based on the VAD algorithm and the MASEA algorithm, this paper proposed a new speech enhancement algorithm by combining the characteristics of Wiener filtering algorithm and LSA-MMSE algorithm. The new speech enhancement algorithm is a VAD-based microphone array speech enhancement algorithm (VAD-MASEA). VAD-MASEA is better than the other three algorithms in noise reduction, speech enhancement and voice intelligibility, and has the characteristics of adapting to a lower SNR environment. This paper used MATLAB to carry out experimental research, including the new algorithm and the three existing algorithms were simulated and compared the signal waveforms of the four algorithms. Experimental results show that the proposed VAD-MASEA algorithm overcomes the high SNR requirement and can be used in low SNR environments and obtain highly intelligible enhanced signals.

关键词： Signal processing algorithms Speech enhancement Wiener filters Noise measurement Filtering algorithms Signal to noise ratio

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Staff lines Detection and Removal for Camera-Based Music Scores

Staff lines Detection and Removal for Camera-Based Music Sco...

引用

Chinese control Conference (CCC)

作者： Zhe Xiao Xin Chen Fang Wang Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

Musical staff lines detection and removal, the first step of most Optical Music Recognition (OMR) technology, aims to detect staff positions and segment score image by removing those staff lines. To deal with issues that the disappearance of thin staff lines in the captured score image, line repaired algorithm based on music rules is proposed in this paper. It first estimates two reference lengths, staff width and space between two adjacent lines. Then projection is used to obtain potential staves positions. At least the staves are finally determined based on line repaired algorithm, the missing lines repaired and the redundant lines deleted at the same time. The proposed technique is simple and effective, making full use of global information of the musical scores. Experimental results show that our method achieves impressive results on music score images captured from cameras.

关键词： Music Lenses Cameras Optical character recognition software Optical imaging Image segmentation Character recognition

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Adaboost-kNN for dynamic emotion recognition 8

Adaboost-kNN for dynamic emotion recognition

引用

8th International Symposium on Computational Intelligence and Industrial Applications and 12th China-Japan International Workshop on Information Technology and control Applications, ISCIIA and ITCA 2018

作者： Li, Min Su, Wanjuan Chen, Luefeng Wu, Min Hirota, Kaoru School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Tokyo Institute of Technology Yokohama226-8502 Japan

— K-nearest Neighbor based adaptive boosting (AdaBoost-KNN) is proposed for emotion understanding in human-robot interaction (HRI), where the real-time dynamic emotion is recognized according to facial expression. It can make robots capable of understanding human emotions and make HRI run smoothly. The Candide-3 model was adopted to extract the two-dimensional coordinate values of the key points of facial emotion, as the basis for emotion classification. The emotion classification is based on AdaBoost-KNN, which sets up seven basic KNN classifier. Then it iterates seven times to update the weight of samples, and combines the weak classifier with coefficient to establish the final classifier. Finally, the performance of the proposal is verified by using K-fold cross validation. Results show that the proposal achieves higher accuracy than that of a single KNN. © ISCIIA and ITCA 2018 - 8th International Symposium on Computational Intelligence and Industrial Applications and 12th China-Japan International Workshop on Information Technology and control Application.

关键词： Adaptive boosting

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A resonant cavity based on birdcage coil for overhauser geomagnetic sensor

A resonant cavity based on birdcage coil for overhauser geom...

引用

2018 IEEE International Instrumentation and Measurement Technology Conference, I2MTC 2018

作者： Bai, Bingjie Dong, Haobin Ge, Jian Liu, Huan Zhang, Cheng Wang, Luo Institute of Geophysics and Geomatics China University of Geosciences Hubei Wuhan China School of Automation China University of Geosciences Hubei Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Hubei Wuhan China Science and Technology on Near-Surface Detection Laboratory Jiangsu Wuxi China

ISBN: (纸本)9781538622223

The resonant cavity is an important component of Overhauser magnetometer sensor. Its function is to make the working substance generate dynamic nuclear polarization effect in the sensor. An alternative design of resonant cavity based on the birdcage coil structure was proposed to make the resonant frequency in accordance with the polarization frequency of the working substance. The structure and equivalent circuit of resonant cavity were analyzed. The model of resonant cavity was established and S-parameter is calculated to verify the frequency consistency of the resonant cavity and working substance. In addition, output signal experiment and simultaneous comparison testing of sensor prototype were conducted. Overall, this design has the advantages of high performance and strong anti-interference. © 2018 IEEE.

关键词： Equivalent circuits

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Fog Removal Method of Slope Monitoring Image Based On Vision Detection

Fog Removal Method of Slope Monitoring Image Based On Vision...

引用

Chinese control Conference

作者： Xianzhen Sang Yue Yang Xianhe Hou Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan

In the slope monitoring based on image detection, the main work is to process the acquired slope image. The landslide occurred mostly in the rain, fog and other complex weather conditions. If we can process effectively and fast fog images according to the fog horizon slope vision images. It would be helpful for subsequent image segmentation, object extraction, positioning, and improving the accuracy and efficiency of detection of slope deformation. Based on the visual technology in slope monitoring, we compared two kinds of defogging algorithm of slope images. One is a kind of image enhancement method of non-physical model, mainly including: equalization algorithm and homomorphic filtering algorithm, McCann Retinex algorithm and multi-scale Retinex algorithm and a global histogram. The other is the image restoration method based on physical model, including the dark channel prior bilateral filtering algorithm, and combined with the theory of dark channel prior to fog algorithm. The experimental results show that the histogram equalization method has the advantage of fast imaging quality in slope visual image processing, and is more suitable for slope monitoring.

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Hyperspectral Remote Sensing Image Classification Based on Convolutional Neural Network

Hyperspectral Remote Sensing Image Classification Based on C...

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Chinese control Conference

作者： Xiangyang Dai Wei Xue Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan

Remote sensing hyperspectral imaging can obtain rich spectral information of terrestrial objects, which allows the indistinguishable matter in the traditional wideband remote sensing to be distinguished in hyperspectral remote sensing. Hyperspectral image has the characteristics of "combining image with spectrum". Making full use of spectral information and spatial information in hyperspectral image is the premise of obtaining accurate classification results. At present, most of hyperspectral data feature extraction algorithms mainly utilize local spatial information in the same channel and spectral information in the same spatial location of different channels. However, these methods require a large amount of prior knowledge, it is difficult to fully grasp the hyperspectral data of all spatial and spectral information, and the model generalization ability is poor. With the development of deep learning, convolutional neural network shows superior performance in all kinds of visual tasks, especially in the two-dimensional image classification, and could get a high classification accuracy. In this paper, an image classification method based on three-dimensional convolution neural network is proposed based on the structural properties of hyperspectral data. In the proposed method, first the stereo image blocks of hyperspectral data are intercepted, then multi-layer convolution and pooling operation of extracted blocks by convolutional neural network are implemented to obtain the essential information of hyperspectral data, finally the classification of hyperspectral data is completed. The experimental results show the proposed method could provide better feature expression and classification accuracy for hyperspectral image.

关键词： Hyperspectral imaging Image classification Convolution Feature extraction Support vector machines

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An optimization method for coupling matrix elements of microwave filter

An optimization method for coupling matrix elements of micro...

引用

Chinese control Conference

作者： Xiao-Long Zhuang Wei-Hua Cao Yan Yuan Can Liu Yu-Sheng Guo China Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

This paper proposes a coupling matrix element optimization method for microwave filters. The traditional method is more complex and does not directly optimize the filter coupling matrix elements. The firefly algorithm optimization method used in this paper directly optimizes the elements of the N + 2 order coupling matrix. Compared with the traditional method, it has better speed and optimization effect. First, the elements of the specific coupling matrix are introduced into the optimization algorithm to be executed, then the matrix is iterated through the set objective function. Finally, when the optimized data is within the allowable range, the optimization of the elements of the coupling matrix is stopped and optimization is performed. The resulting coupling matrix outputs the response. To prove the effectiveness of the proposed method, three methods were used to optimize the coupling matrix elements of the fourth-order filter and compare the final optimization results.

关键词： Couplings Tuning Transmission line matrix methods Integrated circuit modeling Optimization methods Cats

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Bifurcation Analysis and control of Fractional-Order Quorum Sensing Network 37

Bifurcation Analysis and Control of Fractional-Order Quorum ...

引用

37th Chinese control Conference, CCC 2018

作者： Feng, Liu Xiangjuan, Dai Qicheng, Mei Guang, Ling Xinmei, Wang School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China School of Science Wuhan University of Technology Wuhan430070 China

ISBN: (纸本)9789881563941

In this paper, bifurcation analysis and control of fractional-order quorum sensing network regulated by sRNA is studied. The dynamics of fractional quorum sensing network with time-delay is analyzed. The stability criterion and bifurcation conditions of the fractional quorum sensing network are given. In order to control the dynamic properties of fractional order systems, a feedback controller is designed, and the complex dynamics of controlled fractional-order networks are further investigated. The simulation results show that this control method can achieve the ideal control effect. © 2018 Technical Committee on control Theory, Chinese Association of automation.

关键词： Stability criteria

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RBF Neural Network Based-PID control for Weight on Bit During Drilling Operation

RBF Neural Network Based-PID Control for Weight on Bit Durin...

引用

Chinese control Conference

作者： Sike Ma Min Wu Luefeng Chen Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan Hubei China

Deep drilling is a costly project and efficiency is of paramount importance. The weight on bit is one of the main operating parameters that influences the drilling efficiency and it was controlled by manual before. But after people saw the giant potential of an auto-drilling system in increasing the drilling efficiency, more and more studies on the feed back control of weight on bit have emerged. This paper mainly studied weight on bit dynamic under the variational formation based on a lumped parameter model and a self-tuning PID controller for weight on bit control. The parameters of the PID controller are tuned by using gradient descent method and RBF neural network identification.

关键词： Neural networks DC motors Feeds Suspensions Lifting equipment Torque Angular velocity

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