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IEEE International Conference on Cyborg and Bionic Systems (CBS)

作者： Pengyun Li Xiaoming Liu Xiaoqing Tang Masaru Kojima Jian Huang Qiang Huang Tatsuo Arai Beijing Advanced Innovation Center for Intelligent Robots and Systems and the Intelligent Robotics Institute School of Mechatronical Engineering Beijing Institute of Technology Beijing China Osaka University 1-3 Machikaneyama Toyonaka Osaka Japan Key Laboratory of Ministry of Education for Image Processing and Intelligent Control the School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9781728150734

ISBN: (纸本)9781728150741

It was reported that cancer cells could biologically respond and adapt to the confined environment during migration in extracellular matrix (ECM). But it is not clearly studied that how cancer cells respond and adapt to confined environment in mechanical way. Thus, in this study, we designed and fabricated a kind of one-layer micro channel to mimic confined environment for analyzing mechanical adaption of cell. This micro channel could apply sequential mechanical stresses on a number of cells with high throughput, and the time of cells passing through five rows of micro channels were used to evaluate their adaptability to the sequential and confined space. In experiments, each cell suffered 20 times of mechanical stresses after passing through 20 squeezing channels arranged in 5 rows. We observed the sequential squeezing processes under microscope and investigated the passing time using a high-speed camera. The preliminary results indicated that cancer cells could pass through and adapt to the confined environment faster than normal cells.

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A Wearable Finger Exoskeleton Based on the Crank-Rocker Mechanism for Rehabilitation 29

A Wearable Finger Exoskeleton Based on the Crank-Rocker Mech...

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29th International Symposium on Micro-NanoMechatronics and Human Science, MHS 2018

作者： Ke, Ang Huang, Jian Chen, Luyao He, Jiping Key Laboratory of Ministry of Education for Image Processing and Intelligent Control School of Automation Huazhong University of Science and Technology Luoyu Road 1037 Wuhan Hubei China Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of Technology 5 South Zhongguancun Street Beijing China

ISBN: (纸本)9781538667927

Rehabilitation of the grasp function of hands is critical for improving the quality of living for those who suffer from lost or weakened hand abilities due to neuromuscular disease. Current finger rehabilitation devices usually need a complex mechanical structure or a larger actuation system, which reduce the portability of the devices for daily living rehabilitation. In this work, we introduced the developing an ultra-light integrated modular finger exoskeleton that driven by a crank-rocker mechanism. The actuator for each joint is directly assembled at each joint by simplifying the structure design, which makes the device portable for patients. All of the parts are manufactured by 3D printing thus to reduce the initial moment of the whole device, the actuator we used is also the ultra-light server motor. The weight of the whole device is just about 60g (including 3 actuators). We analysised the kinematic of the exoskeleton, then a real-Time embedded position tracking control algorithm is implemented on an Arduino board, which is used as the controller. Experimental results show that our device is capable of both positive and active training for finger habilitation. © 2018 IEEE.

关键词： Actuators

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In Vitro Fertilization (IVF) cumulative pregnancy rate prediction from basic patient characteristics

arXiv

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arXiv 2019年

作者： Zhang, Bo Cui, Yuqi Wang, Meng Li, Jingjing Jin, Lei Wu, Dongrui Reproductive Medicine Center Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan Hubei430030 China Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan Hubei430074 China

Tens of millions of women suffer from infertility worldwide each year. In vitro fertilization (IVF) is the best choice for many such patients. However, IVF is expensive, time-consuming, and both physically and emotionally demanding. The first question that a patient usually asks before the IVF is how likely she will conceive, given her basic medical examination information. This paper proposes three approaches to predict the cumulative pregnancy rate after multiple oocyte pickup cycles. Experiments on 11,190 patients showed that first clustering the patients into different groups and then building a support vector machine model for each group can achieve the best overall performance. Our model could be a quick and economic approach for reliably estimating the cumulative pregnancy rate for a patient, given only her basic medical examination information, well before starting the actual IVF procedure. The predictions can help the patient make optimal decisions on whether to use her own oocyte or donor oocyte, how many oocyte pickup cycles she may need, whether to use embryo frozen, etc. They will also reduce the patient's cost and time to pregnancy, and improve her quality of life. Copyright © 2019, The Authors. All rights reserved.

关键词： Obstetrics

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$H_{\infty}$ consensus control of fractional-order multi-agent systems over the directed graph

$H_{\infty}$ consensus control of fractional-order multi-age...

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Chinese control Conference (CCC)

作者： Lin Chen Wen-Sen Wang Hao Wu Yan Lei Jiang-Wen Xiao Key Laboratory of Image Processing and Intelligent Control Ministry of Education State grid shanxi electric power research institute

In this paper, the H ∞ consensus of fractional-order multi-agent systems with directed communication graph is investigated. It's the first time to introduce the H ∞ control to investigate the consensus problem of the fractional-order multi-agent systems. In view of Mittag-Leffler stability theory and fractional Lyapunov directed method, a sufficient condition is presented to guarantee all the agents reach consensus with the desired H ∞ performance. Finally, the results are verified by several numerical simulations.

关键词： Multi-agent systems control systems Laplace equations Eigenvalues and eigenfunctions Power system stability Stability analysis Graph theory

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Spatial filtering for brain computer interfaces: A comparison between the common spatial pattern and its variant

arXiv

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arXiv 2018年

作者： He, He Wu, Dongrui Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control School of Automation Huazhong University of Science and Technology Wuhan China

The electroencephalogram (EEG) is the most popular form of input for brain computer interfaces (BCIs). However, it can be easily contaminated by various artifacts and noise, e.g., eye blink, muscle activities, powerline noise, etc. Therefore, the EEG signals are often filtered both spatially and temporally to increase the signal-to-noise ratio before they are fed into a machine learning algorithm for recognition. This paper considers spatial filtering, particularly, the common spatial pattern (CSP) filters for EEG classification. In binary classification, CSP seeks a set of filters to maximize the variance for one class while minimizing it for the other. We first introduce the traditional solution, and then a new solution based on a slightly different objective function. We performed comprehensive experiments on motor imagery to compare the two approaches, and found that generally the traditional CSP solution still gives better results. We also showed that adding regularization to the covariance matrices can improve the final classification performance, no matter which objective function is used. Copyright © 2018, The Authors. All rights reserved.

关键词： Brain computer interface

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MIME-KNN: Improve KNN Classifier Performance Include Classification Accuracy and Time Consumption

MIME-KNN: Improve KNN Classifier Performance Include Classif...

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2018 International Conference on Computer Science and Software Engineering (CSSE 2018)

作者： Taizhang Shang Xiang Xia Jun Zheng School of Automation Huazhong University of Science and Technology (HUST) Key Laboratory of Image Information Processing and Intelligent Control Ministry of Education

The K Nearest Neighbor(KNN) classifier has been widely used in the applications of data mining and machine learning, because of its simple implementation and distinguished performance. However, because the distance between all training samples and test samples have to be calculated, when there are too many samples or samples have huge features dimensionality, the time complexity and space complexity are high. The paper proposes a KNN algorithm with the minimum intra-class distance and the maximum extra-class distance(MIME-KNN). By finding a transformation matrix, the algorithm minimizes the intra-class distance and maximizes the distance between classes, which can improve the classification performance of traditional KNN algorithm. At the same time, the algorithm will also reduce the dimensionality of the samples to achieve the purpose of reducing time and space complexity. Experimental results show that the MIME-KNN work well in practical.

关键词： MIME-KNN Improve KNN Classifier Performance Include Classification Accuracy and Time Consumption

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Unsupervised Ensemble Learning for Class Imbalance Problems

Unsupervised Ensemble Learning for Class Imbalance Problems

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Chinese Automation Congress

作者： Zihan Liu Dongrui Wu Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control School of Automation Huazhong University of Science and Technology Wuhan China

Ensemble learning, which aggregates multiple base (weak) learners to obtain a strong learner, is an effective approach for improving the generalization performance of a machine learning model. Several completely unsupervised ensemble learning approaches have been proposed in the literature for binary classification. However, most of them only considered the case that the two classes are balanced, and hence their performances deteriorate when there is significant class imbalance, which often happens in practice. This paper proposes a spectral meta-learner for class imbalance (SMLCI) approach to explicitly consider the class imbalance. Experiments on 12 UCI datasets from various domains verified that SMLCI significantly outperformed the individual base classifiers, and also five existing unsupervised ensemble learning approaches, when the balanced classification accuracy is used as the performance measure.

关键词： Ensemble learning Unsupervised learning Class imbalance Binary classification

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A WOA-based algorithm for parameter optimization of support vector regression and its application to condition prognostics 36

A WOA-based algorithm for parameter optimization of support ...

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第36届中国控制会议

作者： LI Sai FANG Huajing School of Automation Key Laboratory of Image Processing and Intelligent ControlMinistry of EducationHuazhong University of Science and Technology

ISBN: (纸本)9781538629185

Condition monitoring is very important for system safety and condition-based *** series prediction capabilities of machine learning like support vector regression(SVR) can be utilized for ***,choosing optimal parameters for SVR is an important step in SVR model design,which heavily affects the performance of ***,a whale optimization algorithm(WOA) based algorithm is proposed for SVR parameters *** proposed algorithm has been evaluated through some benchmark ***,the proposed method with moving window technology is used to condition prognostics of the Tennessee Eastman *** and engineering application show that the SVR-WOA method is effective,by noting that the computation time is shortened in some application scenarios.

关键词： Support vector regression Whale optimization algorithm Parameter optimization Condition prognostics

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A battery management system with two-stage equalization 29

A battery management system with two-stage equalization

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第29届中国控制与决策会议

作者： Shengping Hong Jianhua Jiang Xi Li School of Automation Key Laboratory of Education Ministry for Image Processing and Intelligent ControlHuazhong University of Science & Technology

ISBN: (纸本)9781509046584

This paper presents a lithium-ion battery pack equalization system and method. The batteries are divided into several groups, which are connected in parallel with the bidirectional DC-DC converters respectively, and the output of DC-DC converters ends are connected in series with each other as a DC bus. The scheme divides equalization of the cells into two stages: intra-group equalization and inter-group equalization, and the two stages are respectively realized by battery time-sharing-access structure and stack energy-sharing structure. Then equalization strategy of the distributed battery energy storage system under two stages is proposed, especially the Single Cell Battery Access Timing Algorithm and MPC Algorithm. The simulation results show that the proposed battery management structure and control strategy can realize fast and accurate SOC equalization.

关键词： Battery management system(BMS) cell equalization battery time-sharing-access structure stack energy-sharing structure Single Cell Battery Access Timing Algorithm MPC Algorithm

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EEG-Based Brain-Computer Interfaces Are Vulnerable to Backdoor Attacks

arXiv

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arXiv 2020年

作者： Meng, Lubin Huang, Jian Zeng, Zhigang Jiang, Xue Yu, Shan Jung, Tzyy-Ping Lin, Chin-Teng Chavarriaga, Ricardo Wu, Dongrui Ministry of Education Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Brainnetome Center National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing China La Jolla CA United States Center for Advanced Neurological Engineering Institute of Engineering in Medicine Ucsd La Jolla CA United States Centre of Artificial Intelligence Faculty of Engineering and Information Technology University of Technology Sydney Australia Zhaw DataLab Zürich University of Applied Sciences Winterthur8401 Switzerland

Research and development of electroencephalogram (EEG) based brain-computer interfaces (BCIs) have advanced rapidly, partly due to deeper understanding of the brain and wide adoption of sophisticated machine learning approaches for decoding the EEG signals. However, recent studies have shown that machine learning algorithms are vulnerable to adversarial attacks. This article proposes to use narrow period pulse for poisoning attack of EEG-based BCIs, which is implementable in practice and has never been considered before. One can create dangerous backdoors in the machine learning model by injecting poisoning samples into the training set. Test samples with the backdoor key will then be classified into the target class specified by the attacker. What most distinguishes our approach from previous ones is that the backdoor key does not need to be synchronized with the EEG trials, making it very easy to implement. The effectiveness and robustness of the backdoor attack approach is demonstrated, highlighting a critical security concern for EEG-based BCIs and calling for urgent attention to address it. Copyright © 2020, The Authors. All rights reserved.

关键词： Electroencephalography

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