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An online measurement method for noise-source impedance of electrical equipment

Progress In Electromagnetics Research C 2020年 100卷 95-103页

作者： Du, Mingxing Li, Yang Wang, Hongbin Ouyang, Ziwei Wei, Kexin Tianjin Key Laboratory of Control Theory & Applications in Complicated System Tianjin University of Technology Tianjin China State Key Laboratory of Reliability and Intelligence of Electrical Equipment Hebei University of Technology Tianjin China

The paper presents a method of extracting noise source impedance of electrical equipment under working condition. Firstly, based on the theory of two-port network, the measurement method of noise impedance is analyzed theoretically, and the injection probe and receiving probe are calibrated by two known resistors. No special calibration fixture is needed to calibrate the injection probe and receiving probe. Secondly, the port structure of the noise impedance measurement method is analyzed, and the noise source impedance is calculated by using the theory of microwave transmission. Compared with the traditional method, this method does not require calibration fixture and simplifies the experimental process. Finally, passive devices and active systems are tested respectively, and the experimental results show that the method is effective and feasible. © 2020, Electromagnetics Academy. All rights reserved.

关键词： Probes

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Specificity-preserving RGB-D saliency detection

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Computational Visual Media 2023年第2期9卷 297-317页

作者： Tao Zhou Deng-Ping Fan Geng Chen Yi Zhou Huazhu Fu School of Computer Science and Engineering Nanjing University of Science and TechnologyNanjing 210094China Key Laboratory of System Control and Information Processing Ministry of EducationShanghaiChina Computer Vision Lab ETH Z¨urichZ¨urichSwitzerland School of Computer Science and Engineering Northwestern Polytechnical UniversityXi’anChina School of Computer Science and Engineering Southeast UniversityNanjingChina Inception Institute of Artificial Intelligence Abu DhabiUnited Arab Emirates

Salient object detection(SOD)in RGB and depth images has attracted increasing research *** RGB-D SOD models usually adopt fusion strategies to learn a shared representation from RGB and depth modalities,while few methods explicitly consider how to preserve modality-specific *** this study,we propose a novel framework,the specificity-preserving network(SPNet),which improves SOD performance by exploring both the shared information and modality-specific ***,we use two modality-specific networks and a shared learning network to generate individual and shared saliency prediction *** effectively fuse cross-modal features in the shared learning network,we propose a cross-enhanced integration module(CIM)and propagate the fused feature to the next layer to integrate cross-level ***,to capture rich complementary multi-modal information to boost SOD performance,we use a multi-modal feature aggregation(MFA)module to integrate the modalityspecific features from each individual decoder into the shared *** using skip connections between encoder and decoder layers,hierarchical features can be fully *** experiments demonstrate that our SPNet outperforms cutting-edge approaches on six popular RGB-D SOD and three camouflaged object detection *** project is publicly available at https://***/taozh2017/SPNet.

关键词： salient object detection(SOD) RGB-D cross-enhanced integration module(CIM) multi-modal feature aggregation(MFA)

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Hopf bifurcation analysis and control of a bacteria-immunity system with delayed quorum sensing 8

Hopf bifurcation analysis and control of a bacteria-immunity...

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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： Liu, Feng Dai, Xiangjuan Mei, Qicheng Ling, Guang Sun, Fenglan School of Automation China University of Geosciences Wuhan430074 China Hubei key Laboratory of Advanced Control and Intelligence Automation for Complex System Wuhan430074 China School of Science Wuhan University of Technology Wuhan430070 China Research Center of System Theory and Application Chongqing University of Posts and Telecommunications Chongqing400065 China

A bacteria-immunity system model is formulated to describe the interaction between bacteria and immune cells with quorum sensing. Time delay is introduced to study this system. The stability and the conditions for the existence of Hopf bifurcation are obtained. Finally, numerical simulations are used to verify the theoretical conclusions. © 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： Bacteria

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An improved calculation method for static capacitance in inductor windings

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Progress In Electromagnetics Research C 2020年 104卷 25-36页

作者： Du, Mingxing Zhang, Yuxiao Wang, Hongbin Tian, Ye Ouyang, Ziwei Wei, Kexin Tianjin Key Laboratory of Control Theory & Applications in Complicated System Tianjin University of Technology Tianjin China State Key Laboratory of Reliability and Intelligence of Electrical Equipment Hebei University of Technology Tianjin China

This paper proposes an improved method for calculating static capacitance between two conductors with circular cross-sections in inductor windings. It considers the effects of electric field coupling and energy distribution on static capacitance. In this paper, the capacitance between two conductors in inductor windings is calculated by the improved calculation method and finite-element method (FEM), respectively. The relative error of the improved calculation method is between 0.11% and 4.51% compared to the FEM. In order to verify the effectiveness of this method for inductor winding, the orthogonal stacking winding and staggered stacking winding are chosen as calculation examples to accurately predict the static capacitance of multi-layer circular-section induction coils. Finite element models for the two types of windings are built to determine the capacitances for our 3 × 3 array arrangement winding. The results show that the improved calculation method proposed in this paper highly conforms to FEM. Finally, we adopt an air-cored cylindrical inductor winding for experimental verification, and the improved calculation method is proved to be correct. © 2020, Electromagnetics Academy. All rights reserved.

关键词： Capacitance

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Short-time Fourier transform covariance and selection, a feature extraction method for binary motor imagery classification

Short-time Fourier transform covariance and selection, a fea...

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2021 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2021

作者： Ma, Yue Zheng, Liangsheng Yi, Zhengkun Xiao, Yang Wang, Can Wu, Xinyu Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Chongqing University of Posts and Telecommunications

ISBN: (纸本)9781665436786

The brain-computer interfaces (BCI) technology is able to help dysfunctional people recover their motor functions. Electroencephalography (EEG) is an effective noninvasive method to construct BCI. Motor imagery (MI) paradigm can directly reflect the motor intention of user without additional stimulation equipment in EEG-BCI. The feature extraction methods are the key components to improve the accuracy of MI classification. Traditional feature extraction methods like CSP that only extract features in a single domain or two domains. In this study, we propose two novel feature selection method, short-time Fourier transform covariance and its selection method, which are aiming to extract spatial-time-frequency features simultaneously. In order to evaluate the proposed features, the BCI Competition IV Data Set IIb is employed to test the classification accuracy. By comparing the average accuracy of novel TSGSP method, the proposed method is more stable than TSGSP about 6% and the accuracy is just decrease about 0.5% at the same time. The average accuracy of 83.8% over all subjects is obtained. Superior classification performance results show that our proposed method has great potential, which is helpful for the further development and application of BCI technology for motor imaging in the field of neurorehabilitation. © 2021 IEEE.

关键词： Electroencephalography

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Prognostic Prediction for Non-small-Cell Lung Cancer Based on Deep Neural Network and Multimodal Data 17th

Prognostic Prediction for Non-small-Cell Lung Cancer Based o...

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17th International Conference on Intelligent Computing, ICIC 2021

作者： Zhang, Zhong-Si Xu, Fei Jiang, Han-Jing Chen, Zhan-Heng Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China Institute of Artificial Intelligence School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Institute of Machine Learning and Systems Biology School of Electronics and Information Engineering Tongji University No. 4800 Caoan Road Shanghai201804 China

ISBN: (纸本)9783030845315

Non-small-cell lung cancer (NSCLC) is the most common lung cancer with poor prognosis. Prognostic prediction is significant in improving the prognosis of NSCLC patients. Clinical information and multi-omics data including gene expression, miRNA, copy number variations, and DNA methylation are closely related to NSCLC prognosis. In this study, we propose a deep neural network to conduct prognostic prediction for NSCLC patients based on all five types of data. Given the high dimensional features of the omics data, past works reduce the feature dimension by regression analysis or correlation sorting algorithms. A shortcoming of these methods is that only a small number of features are considered. To overcome it, we propose a convolution neural network-based feature transformation method, which considers all features and extracts the abstract representations of the omics data. Based on the representations, we predict the five-year survival status of NSCLC patients. The results show that our method achieves a more precise prediction than previous work, with the areas under the curve improved from 0.8163 to 0.8508 and the accuracy improved from 0.7544 to 0.8096. © 2021, Springer Nature Switzerland AG.

关键词： Forecasting

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TactCapsNet: Tactile capsule network for object hardness recognition

TactCapsNet: Tactile capsule network for object hardness rec...

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2021 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2021

作者： Fang, Senlin Mi, Tingting Zhou, Zhenning Ye, Chaoxiang Liu, Chengliang Wu, Hancheng Yi, Zhengkun Wu, Xinyu Shenzhen Institute of Advanced Technology CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen518055 China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen518055 China Shenzhen Key Laboratory of Smart Sensing and Intelligent Systems Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China SIAT Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518055 China

ISBN: (纸本)9781665436786

Hardness is one of the most essential tactile clues for robots to recognize objects. However, methods for robots to recognize hardness are limited. In this paper, based on the Capsule Network (CapsNet), we propose a novel tactile capsule network (TactCapsNet) for object hardness recognition. Specifically, we collect a tactile dataset on the silicone samples with three different shapes, and the silicone samples of each shape have thirteen hardness levels ranging from 0A (Shore A scale) to 60A at 5A intervals. Furthermore, we construct the tactile image as the input of the CapsNet to make full use of the spatio-temporal information of the tactile hardness dataset. The experimental results prove that the proposed approach achieves higher accuracy and quadratic weighted kappa (QWK) than support vector machine (SVM), long short-term memory (LSTM), convolutional neural network (CNN), and CapsNet. © 2021 IEEE.

关键词： Hardness

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Switching fluctuation compensation of the multi-view optical positioning system under camera occlusion

Switching fluctuation compensation of the multi-view optical...

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2021 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2021

作者： Yu, Zhengjun Li, Shibo Hu, Ying Jin, Haiyang Yu, Gang Shenzhen518055 China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen Key Laboratory of Minimally Invasive Surgical Robotics and System Shenzhen518055 China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Shenzhen Key Laboratory of Minimally Invasive Surgical Robotics and System Shenzhen518055 China Shenzhen Broadcare Medical Robotics Co. Ltd

ISBN: (纸本)9781665436786

The advances of multi-view optical tracking technology led to the rise of image-guided robotic-assisted surgery. The positioning accuracy of multi-view system is extremely crucial in ensuring the safety of the surgery process. The calibration error and camera occlusion may lead to the system switching fluctuation problem, which affects the system stability and limits its usage in situations requiring high safety standards. In this paper, the direct linear transformation (DLT) method is applied to solve multi-view triangulation problem and the influence of the calibration parameter error on the measurement accuracy is analyzed in the semi-physical simulation environment. The results show that the system switching fluctuation has a great influence on the positioning accuracy of the system. In order to improve the measurement stability of the system under camera occlusion, this paper proposes an error compensation strategy based on neural network nonlinear fitting. The measurement data at different locations in the workspace are used as learning samples. The trained network model is used to predict the occlusion points of the system and the measurement results are compensated for errors. Finally, the effectiveness of the compensation method is verified by simulations and real experiments. © 2021 IEEE.

关键词： Cameras

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Design of a Novel Autonomous Docking system With a Supporting Robotic Fish

Design of a Novel Autonomous Docking System With a Supportin...

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2023 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2023

作者： Dong, Huijie Zhao, Chunyang Qiao, Tiezhu Wang, Jian Chen, Di Meng, Yan Yu, Junzhi Taiyuan University of Technology Key Laboratory of Advanced Transducers and Intelligent Control System Ministry of Education Taiyuan030024 China Chinese Academy of Sciences Laboratory of Cognitive and Decision Intelligence for Complex System Institute of Automation Beijing100190 China Peking University State Key Laboratory for Turbulence and Complex Systems Department of Advanced Manufacturing and Robotics College of Engineering Beijing100871 China Guangdong Ocean University College of Electronics and Information Engineering Zhanjiang524088 China

ISBN: (纸本)9798350327182

Underwater supporting robots serving as a relay of energy supplements and communication for other underwater equipment are promising for ocean exploration, development, and protection. This paper proposes a novel autonomous docking system centered on a designed supporting robotic fish named 'CourierFish'. Specifically, CourierFish is capable of docking with a surface dock station for supplying itself and docking with a seafloor platform for supporting equipment in the platform. A visual navigation scheme integrating LED and ArUco markers is presented for accurate localization. The control approach for docking motion is also illustrated. Simulations and aquatic experiments are performed to verify the feasibility of the proposed docking system. The obtained results lay a solid foundation for the construction of various underwater equipment and robot networks. © 2023 IEEE.

关键词： Fish

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A method for recognition of dynamic hand gestures based on wrist tendon sounds 22

A method for recognition of dynamic hand gestures based on w...

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22nd IEEE International Conference on E-Health Networking, Application and Services, HEALTHCOM 2020

作者： He, Bailin Wang, Can Wang, Hao Li, Mengyao Duan, Shengcai Wu, Xinyu Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China University of Chinese Academy of Sciences Beijing100049 China Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Hong Kong Samsung Electronics Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China

ISBN: (纸本)9781728162676

Hand gesture recognition are widely applied in game industry and rehabilitation engineering. Some recognition methods based on physiological data are attracting attention. In this paper, a novel method for dynamic hand gesture recognition based on sounds of wrist tendon is proposed. Bone conduction sensors which automatically filter the ambient noise is utilized to collect the wrist tendon sounds of three types of dynamic hand gestures. Continuous Wavelet Transform(CWT) is utilized extract the features of sounds and Support Vector machine(SVM) is utilized to train the classifier model. Five healthy subjects participated the experiment and the average test accuracy of three classes achieves 92.33%. The proposed method provides an effective perspective for hand gesture recognition. © 2021 IEEE.

关键词： Support vector machines

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