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system development for micro-electrode assembly of cochlear implant

System development for micro-electrode assembly of cochlear ...

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IEEE International Conference on Real-time Computing and Robotics (RCAR)

作者： Shaokui Weng Weibin Rong Zeyang Xia Hao Deng Yangzhou Gan Jing Xiong Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China Key Laboratory of Human-Machine Intelligence-Synergy Systems Chinese Academy of Sciences Shenzhen China State Key Laboratory of Robotics and System Harbin Institute of Technology

ISBN: (纸本)9781467389600

Cochlear Implant is an implantable medical device for severe deafness patients to restore hearing function. The micro-electrode in cochlear implant is directly contacted with the human cochlea for signals transmission, thus its assembly is an important step of cochlear implant production. However, currently micro-electrode is assembled manually and the assembly precision and efficiency are unable to meet the industrial standard. In this paper, an automatic assembly system of micro-electrode for Cochlear Implant was designed. In this system, several mechanical mechanism specially for the micron level parts transmission and assembly were designed, including a wire feeder, a ring transmission mechanism, and a welding mechanism. And combined with the human-computer interaction interface, the effectiveness of the overall performance of the assembly system was verified by experiments.

关键词： Wires Welding Cochlear implants Assembly systems Tungsten Needles Electrodes

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Multi-stage feature learning based object recognition and 3D pose estimation with kinect 6

Multi-stage feature learning based object recognition and 3D...

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6th International Conference on Information Science and Technology, ICIST 2016

作者： Zeng, Wei Liang, Guoyuan Guangdong, Can Wang Wu, Xinyu Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China Key Laboratory of Human-Machine-Intelligence Synergic Systems Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Shenzhen China Dept. Mechanical and Automation Engineering Chinese University of Hong Kong Shenzhen China

ISBN: (纸本)9781509012206

Impressive achievements have been achieved over the years in object recognition and 3D pose estimation while detecting objects and estimating their 3D poses reliably is still a very challenging problem, especially for texture-less objects in heavily cluttered scenes. In this paper, we introduce an improved approach based on multi-stage feature learning to identify object as well as their 3D orientation efficiently. Our approach is motivated by visual neuroscience models. Currently similar architectures have been successfully applied to pedestrian detection, traffic recognition, face representation, etc. We compute the feature descriptors by multi-stage convolutional neural networks (CNNs) which could map the object identity and 3D poses to the Euclidean distance space by evaluating the similarity between descriptors. By contrast with classical approaches, our method relies on fast and approximate Nearest Neighbor (NN) matching to solve the large scale problems instead of one classifier per object or multi-class classifiers whose complexity grows rapidly with the number of objects. The experiments of detecting poorly-textured objects in cluttered scenarios have been carried out to verify the effectiveness and efficacy of the proposed approach. © 2016 IEEE.

关键词： Object recognition

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A canonical correlation analysis based EMG classification algorithm for eliminating electrode shift effect

A canonical correlation analysis based EMG classification al...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Zhe Fan Zhong Wang Guanglin Li Ruomei Wang National Engineering Research Center of Digital Life Sun Yat-sen University Guangzhou China Key Laboratory of Human-Machine-Intelligence Synergic System Chinese Academy of Sciences Shenzhen China

ISBN: (纸本)9781457702198

Motion classification system based on surface Electromyography (sEMG) pattern recognition has achieved good results in experimental condition. But it is still a challenge for clinical implement and practical application. Many factors contribute to the difficulty of clinical use of the EMG based dexterous control. The most obvious and important is the noise in the EMG signal caused by electrode shift, muscle fatigue, motion artifact, inherent instability of signal and biological signals such as Electrocardiogram. In this paper, a novel method based on Canonical Correlation Analysis (CCA) was developed to eliminate the reduction of classification accuracy caused by electrode shift. The average classification accuracy of our method were above 95% for the healthy subjects. In the process, we validated the influence of electrode shift on motion classification accuracy and discovered the strong correlation with correlation coefficient of >0.9 between shift position data and normal position data.

关键词： Training Electrodes Correlation Electromyography Pattern recognition Prosthetics Feature extraction

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An Abstract Communication Service Interface of DPWS on Embedded Device in Industrial Automation

An Abstract Communication Service Interface of DPWS on Embed...

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第28届中国控制与决策会议

作者： Dandan Xu Bin Fang Huijie Li College of Electronic and Control Engineering Beijing University of Technology Engineering Research Center of Digital Community Ministry of Education Beijing Laboratory for Urban Mass Transit Beijing Key Laboratory of Computational Intelligence and Intelligent System

ISBN: (纸本)9781467397155

In view of the aging society,intelligent devices are more and more widely used in the field of industrial *** future factory will be dominated by Service-oriented Architecture(SOA).Devices can be abstracted by web services to enable their flexibility,dynamicity and interoperability with other *** paper presents an abstract communication service interface for web services that contributes to providing the flexible,loosely coupled system on embedded devices in an automatic industrial production line in which the implementations of the interface is constructed by the Devices Profile for Web Services(DPWS).It shows promising results in the remote data transmission without human intervention between the embedded server and enterprise information system.

关键词： SOA DPWS resource-constrained device Factory Automation

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Fault Detection of Chiller Based on Improved KPCA

Fault Detection of Chiller Based on Improved KPCA

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第28届中国控制与决策会议

作者： Nanhua Zhang Xuejin Gao Yafen Li Pu Wang College of Electronic and Control Engineering Beijing University of Technology Engineering Research Center of Digital Community Ministry of Education Beijing Laboratory for Urban Mass Transit Beijing Key Laboratory of Computational Intelligence and Intelligent System

ISBN: (纸本)9781467397155

Principal component analysis(PCA) is a common fault detection *** it is difficult to get high accuracy,if it is applied to complex nonlinear *** with complex system such as chiller,this paper proposes using kernel principal component analysis(KPCA) for fault ***,the selection of kernel parameters is a problem in the implement of KPCA *** algorithm(GA) is used to determine the kernel parameter through minimizing false alarm rate and maximizing detection *** method is verified by ASHRAE 1043-RP *** results show that it is better than *** it can improve the accuracy of fault detection.

关键词： chiller PCA GA KPCA fault detection

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Research on Subway Passenger Flow Combination Prediction Model Based on RBF Neural Networks and LSSVM

Research on Subway Passenger Flow Combination Prediction Mod...

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第28届中国控制与决策会议

作者： Pu Wang Cuixia Wu Xuejin Gao College of Electronic and Control Engineering Beijing University of Technology Engineering Research Center of Digital Community Ministry of Education Beijing Laboratory for Urban Mass Transit Beijing Key Laboratory of Computational Intelligence and Intelligent System

ISBN: (纸本)9781467397155

In view of the subway passenger flow's random problem,nonlinear problem and so on,in order to predict the subway passenger volume more accurately,this paper designs a kind of parallel variable coefficient weighted combination prediction model based on Radial Basis Function Neural Network and Least Squares Support Vector *** this method,firstly,the original data is ***,this paper respectively sets up RBF Neural Network and LSSVM prediction model for training and calculating the weighting coefficient with the results of the *** this paper separately does two kinds of models' prediction,and weights to get *** article uses 2012 passenger flow data of Beijing DONGZHIMEN Station for experiments,which shows that the result of combination prediction model is more accurate than the result of single prediction model.

关键词： Subway passenger flow Combination prediction Variable coefficient RBF neural network LSSVM

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A federated filtering personal navigation algorithm based on MEMS-INS/GPS integrated

A federated filtering personal navigation algorithm based on...

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第28届中国控制与决策会议

作者： Kai Pan Mingrong Ren Pu Wang Yanhong Liu College of Electronic and Control Engineering Beijing University of Technology Engineering Research Center of Digital Community Ministry of Education Beijing Laboratory for Urban Mass Transit Beijing Key Laboratory of Computational Intelligence and Intelligent System

ISBN: (纸本)9781467397155

Designed a zero velocity detection algorithm based on adaptive threshold in order to accurately detect the zero velocity moment,and through the kalman zero velocity correction algorithm timely correct the error of inertial navigation system(INS).Because at zero velocity correction time position and heading information is observed,by unscented kalman filter(UKF),INS and GPS are integrated navigation,and compared with extended kalman filter(EKF).Through the experiment under the condition of strong nonlinear,UKF has better filtering performance than EKF,and also better than the EKF on *** kalman zero velocity correction and UKF building federal filtering model can effectively inhibit the INS position drift *** the same time,in straight line phase,the use of GPS location information correct course *** GPS signal loss within a certain time,the integrated navigation system can get the right course ***,the positioning accuracy of the integrated navigation system is obviously improved.

关键词： Zero Velocity Correction EKF UKF Federated Filtering

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Fan Fault Signal Noise Reduction Based on Wavelet Threshold Function

Fan Fault Signal Noise Reduction Based on Wavelet Threshold ...

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第27届中国过程控制会议（CPCC2016）

作者： WANG Pu LI Tianyao GAO Xuejin WEN Zheng College of Electronic and Control Engineering Beijing University of Technology Beijing Laboratory For Urban Mass Transit Engineering Research Center of Digital Community Ministry of Education Beijing Key Laboratory of Computational Intelligence and Intelligent System

Fan fault signal acquisition is usually superimposed fault signal and noise signal in faultdiagnosis;it isnecessary to denoise the original *** purpose of this paper is to introduce a new wavelet threshold function asthe candidate of the current noise reduction threshold function standard for noise reduction of fan bearing *** disadvantage of hard threshold function is the discontinuity at the threshold would cause signal *** of soft threshold function is the large variance in result when wavelet coefficient is *** of this paper are to select appropriate parameters that would enable the threshold function to flexibly choosefrom hard and soft *** benefit of this new threshold function is the improvement in data validity *** advantage has been further proved by comparing the raw data of fan bearing false signal in differentmethods before and after noise reduction *** proposed method is better than aforementioned traditionalmethods.

关键词： Wavelet Fan Fault Signal Noise Reduction Based on Wavelet Threshold Function

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Nonlinear Fermentation Process Fault Monitoring Based on JITL Strategy

Nonlinear Fermentation Process Fault Monitoring Based on JIT...

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第28届中国控制与决策会议

作者： Yakun Zhang Xuejin Gao YaFen Li XiChang Wang Pu Wang College of Electronic Information and Control Engineering Beijing University of Technology Engineering Research Center of Digital Community Ministry of Education Beijing Laboratory for Urban Mass Transit Beijing Key Laboratory of Computational Intelligence and Intelligent System

ISBN: (纸本)9781467397155

PCA,as the representative of the traditional global fault monitoring algorithm,is a linear feature extraction *** global algorithm may cause a lot of false alarms in fault monitoring of fermentation process with nonlinear and multi-stage *** this paper,the Just In Time Learning(JITL) strategy is introduced into the Kernel Principal Component Analysis(KPCA) *** local model is established with the data similar to the current online *** the local model can represent the current state of the system,it is not necessary to identify the stages before *** the same time,the local KPCA model can be used to feature extract nonlinear *** data generated by the PenSimv2.0 simulation platform is used for verifying the *** results show that this method has a better effect than KPCA algorithms.

关键词： JITL KPCA Fermentation Fault Monitoring

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Fault Detection of Batch Process Based on MSICA-OCSVM

Fault Detection of Batch Process Based on MSICA-OCSVM

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第28届中国控制与决策会议

作者： Xuejin Gao Rong Ma College of Electronic Information and Control Engineering Beijing University of Technology Engineering Research Center of Digital Community Ministry of Education Beijing Laboratory for Urban Mass Transit Beijing Key Laboratory of Computational Intelligence and Intelligent System

ISBN: (纸本)9781467397155

When we use the traditional multi-scale independent component analysis method to extract independent component ICA on each scale,and then,using the ICA decomposition on the reconstructed data to construct monitoring statistics,however,data of the reconstruction on the nature was already independent component,it is meaningless to extract them by *** on the shortcoming,this paper proposes a MSICA-OCSVM method that was combined with Multi-scale Independent Component Analysis(MSICA) and One-class Support Vector machine(OCSVM) to monitor the ***,we can use the wavelet transform decomposition to monitor data at different *** then,the data was processing by threshold denoising,and was monitored on each scale extraction by using ICA independent principal ***,we can use the wavelet transform coefficients for each scale would scale back on the reconstruction of the new signal matrix(?) Finally,new OCSVM model was constructed by the reconstructed matrix *** can make the use of determined hyper-plane to construct a nonlinear statistic,and the appropriate control limits was determined by using kernel density *** is more,this method is applied to penicillin fermentation process simulation platform,the experimental results show that this method can effectively utilize the structure information data compared to traditional MSICA fault monitoring method,the failure rate of false positives,false negative rate was significantly reduced.

关键词： batch process one-class support vector machines(OCSVM) fault detection Multi-scale independent component analysis(MSICA)

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