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IEEE International Conference on Mechatronics and Automation

作者： Chao Dong Bang Xie Xin Shao Complex System Control Theory and Application Key Laboratory Tianjin University of Technology(TJUT) 391 Binshui Xidao Xiqing District Tianjin China

ISBN: (数字)9781728164168

ISBN: (纸本)9781728164175

The defects of the existing commercial robot control system are analyzed and the feasibility of the open control system is studied. The robot control system was designed based on the CPAC development platform with the six-joint robot as the research object. Firstly, a kinematic model of the robot was established by the d-h linkage coordinate system method, and its motion control was described in detail from two aspects of forward and inverse kinematics. Secondly, the inverse kinematics of the robot was solved by using the RPY description method and PIPER criterion, and the correctness of the robot kinematics model was verified. Third, the robot control system software design. Finally, the function modules of the control system are verified through experiments. The experiments show that the robot runs with a small error of pose and posture, which can meet the requirements of the control system and meet the expected design objectives.

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Path Planning for Mobile Robots Based on Improved RRT Algorithm 17

Path Planning for Mobile Robots Based on Improved RRT Algori...

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17th IEEE International Conference on Mechatronics and Automation, ICMA 2020

作者： Shao, Lei Liu, Hanze Chen, Chao Du, Dongdong Li, Ji Liu, Hongli Tianjin University of Technology Binshui Xidao 391 Tianjin Key Laboratory for Control Theory Applications in Complicated System Tianjin300384 China Institute of Data Management and Application China Academy of Industrial Internet Beijing100020 China

ISBN: (纸本)9781728164151

Aiming at the shortcomings of the basic RRT algorithm, such as the random search ability is relatively strong, and the calculation result is usually not the shortest path, etc., an improved RRT path optimization algorithm is proposed, which uses the ant colony algorithm to obtain the basic RRT algorithm. The path is iterated and optimized, and eventually converges to a shortest path, improving the efficiency of planning the shortest path in advance, and conducting in-depth research on this algorithm. A large number of simulation results show that the improved algorithm has certain feasibility and effectiveness. In multiple iterations, it can eventually converge to a path with a better distance. © 2020 IEEE.

关键词： Motion planning

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Design of a Capsule Robot system for Gastric Hemorrhage Detection using Luminol

Design of a Capsule Robot System for Gastric Hemorrhage Dete...

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IEEE International Conference on Mechatronics and Automation

作者： Yuchen Mao Jian Guo Shuxiang Guo Qiang Fu Tianjin Key Laboratory for Control Theory & Application Complicated Systems and Intelligent Robot Laboratory School of Electrical Engineering and Automation Tianjin University of Technology Binshui Xidao Extension 391 Tianjin China Shenzhen Institute of Advanced Biomedical Robot Co. Ltd No.12 Ganli Sixth Road Jihua Street Longgang District Shenzhen China Key Laboratory of Convergence Medical Engineering System and Healthcare Technology The Ministry of Industry and Information Technology School of Life Science Beijing Institute of Technology No.5 Zhongguancun South Street Beijing China

Gastric hemorrhage in upper gastrointestinal bleeding is an obvious indicator of deteriorating gastric disease. This paper proposes a capsule robot system for detecting bleeding in the stomach. The patient ingests a capsule robot containing luminol reagent, which detects bleeding in the stomach via chemiluminescence. The MCU receives the optical sensor signal, and the data is transmitted via radio frequency. The external lower computer receives and forwards the data to the upper computer for processing and display. The data is used to determine the presence and amount of stomach bleeding. Chemiluminescence is an excellent sensor, requiring no external energy supply, and demonstrating excellent sensitivity and selectivity. Combining chemical and electronic sensors presents opportunities for further development. Capsule robot detection reduces the discomfort and risk of secondary injury associated with traditional endoscopy. This paper establishes an experimental platform using porcine blood to simulate human blood, demonstrating the feasibility of the system.

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UAV Remote Sensing Image Mosaic Technology Combined with Improved SPHP Algorithm

UAV Remote Sensing Image Mosaic Technology Combined with Imp...

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IEEE International Conference on Mechatronics and Automation

作者： Jianwei Leng Sining Wang Tianjin Key Laboratory of Complex Control Theory &Application Tianjin University of Technology Binshui Xidao 391 Tianjin China

ISBN: (数字)9781728164168

ISBN: (纸本)9781728164175

The paper proposes an improved algorithm, for the problem that SPHP algorithm is not suitable for the UAV Remote sensing image mosaic algorithm because it is susceptible to geomorphic factors, resulting in deformation or ghosting. The algorithm firstly extracts feature points by combining SURF algorithm and Harris algorithm. The obtained feature points are coarsely matched by KNN algorithm, then matched by PROSAC algorithm. Finally, the weight coefficient is introduced to calculate the spatial transformation model of the image overlap region. This model replaces the original spatial model of SPHP algorithm. The ghosting of the image overlap area is reduced, and the stitched image is caused to have a smaller deformation. The final result shows that this improved SPHP algorithm can effectively remove the ghost image of the stitched image and generate better stitching results.

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A convolutional neural network-based diagnostic method using resting-state electroencephalograph signals for major depressive and bipolar disorders

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Biomedical Signal Processing and control 2022年第PartB期72卷 103370-103370页

作者： Lei, Yu Belkacem, Abdelkader Nasreddine Wang, Xiaotian Sha, Sha Wang, Changming Chen, Chao Beijing Institute of Basic Medical Sciences Beijing China Department of Computer and Network Engineering College of Information Technology United Arab Emirates University Al Ain United Arab Emirates School of Artificial Intelligence Xidian University Xian China National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders Beijing Anding Hospital Capital Medical University Beijing China Brain-inspired Intelligence and Clinical Translational Research Center/Department of Neurosurgery Xuanwu Hospital Capitap Medical University Beijing China Key Laboratory of Complex System Control Theory and Application Tianjin University of Technology China Academy of Medical Engineering and Translational Medicine Tianjin University Tianjin China

Background: Early and accurate diagnosis of bipolar and major depressive disorders is important in clinical practice. However, no diagnostic biomarkers can discriminate bipolar from major depressive disorder with high accuracy at present. Methods: We propose a novel convolutional neural network architecture using multichannel raw resting-state electroencephalograph signals to differentiate bipolar disorder from major depressive disorder. This method has great potential in diagnosing mental disorders. In total, 101 patients with major depressive disorder, 82 patients with bipolar disorder, and 81 healthy controls were assessed. Clinical diagnosis was performed by psychiatrists based on the Diagnostic and Statistical Manual of Mental Disorders, fifth edition. Participants were instructed to fix their eyes on a cross on the monitor during the collection of resting-state electroencephalograph signals. Results: A classification mean accuracy of 96.88% was achieved using the resting-state electroencephalograph signals with the 10-fold cross-validation method. The results indicate that deep neural networks could learn efficient feature patterns automatically without manual feature selections. Both statistical analysis and feature visualization on different brain regions showed that the classification performance and the learned features of the proposed model were consistent with that obtained in neurobiological studies on bipolar and major depressive disorders. Conclusions: The combined use of deep neural networks and electroencephalograph signals is an effective approach for the computer-aided diagnosis of bipolar and major depressive disorders. © 2021 Elsevier Ltd

关键词： Deep neural networks

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Automatic Sleep Spindle Detection and Analysis in Patients with Sleep Disorders 1

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2nd International Workshop on Human Brain and Artificial Intelligence, HBAI 2020 held in Conjunction with IJCAI-PRICAI 2020

作者： Chen, Chao Zhu, Xuequan Belkacem, Abdelkader Nasreddine Lu, Lin Hao, Long You, Jia Shin, Duk Tan, Wenjun Huang, Zhaoyang Ming, Dong Academy of Medical Engineering and Translational Medicine Tianjin University Tianjin300072 China Key Laboratory of Complex System Control Theory and Application Tianjin University of Technology Tianjin300384 China Department of Computer and Network Engineering College of Information Technology UAE University Al Ain15551 United Arab Emirates Zhonghuan Information College Tianjin University of Technology Tianjin300380 China Department of Electronics and Mechatronics Tokyo Polytechnic University Atsugi243-0297 Japan School of Computer Science and Engineering Northeastern University Shenyang110189 China Department of Neurology Xuanwu Hospital Capital Medical University Beijing100088 China

ISBN: (数字)9789811612886

ISBN: (纸本)9789811612879

Nowadays, Sleep disorder is a common disease, and spindle spindles are important features of the second stage non-rapid eye movement (NREM) sleep. In this paper, we propose an improved automatic detection method of spindles based on wavelet transform. The spindles automatic detector is mainly composed of wavelet transform and clustering. We collected the electroencephalography (EEG) signals of six patients with sleep disorders all night for ten hours, and then preprocessed the data and other operations, and then used our improved method to detect the sleep EEG signals by spindles. By comparing with the previous automatic detection method not improved and another automatic detection method, the results show that the accuracy of sleep spindles detection can be effectively improved. The accuracy of the improved detector is 5.19% higher than before, and 9.7% higher than that of another method based on amplitude threshold. Finally, we made a simple comparison between people with sleep disorders and normal people. We found that there were significant differences in spindle density between people with sleep disorders and people without sleep disorders. The average spindle density in the normal population averaged 2.59 spindles per minute. People with sleep disorders had an average spindle density of 1.32 spindles per minute. In future research, our research direction is to improve the accuracy of spindles automatic detection by improving the spindles detector and study the difference of spindles between patients with sleep disorders and normal people in a large number of samples, so that the difference of spindles can be used as the basis for the diagnosis of sleep disorders. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Wavelet transforms

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Dual and reduced-order observer based attack-free protocols for consensus of multi-agent systems with Markovian switching topologies

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systems and control Letters 2025年 202卷

作者： Gao, Tian-Yu Li, Zhao-Yan Zhou, Bin Center for Control Theory and Guidance Technology Harbin Institute of Technology Harbin150001 China National Key Laboratory of Complex System Control and Intelligent Agent Cooperation Harbin150001 China School of Mathematics Harbin Institute of Technology Harbin150001 China Zhengzhou Research Institute of HIT Zhengzhou450000 China

This paper studies the output feedback consensus problem of linear multi-agent systems with Markovian switching topologies. To avoid reconstructing the Markovian switching topologies during designing the observers or adding the state of the observers to the information exchange loop, dual and reduced-order observer based attack-free consensus protocols are proposed, which only utilizes the relative outputs of neighboring agents. It is shown that, under certain mild conditions, a necessary and sufficient condition for the solvability of the mean square consensus problem is that the union of switching topologies has a spanning tree. Finally, numerical examples are given to illustrate the effectiveness of the proposed approaches. © 2025 Elsevier B.V.

关键词： Markov processes

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End-to-End AI-Based Point-of-Care Diagnosis system for Classifying Respiratory Illnesses and Early Detection of COVID-19

arXiv

引用

arXiv 2020年

作者： Belkacem, Abdelkader Nasreddine Ouhbi, Sofia Lakas, Abderrahmane Benkhelifa, Elhadj Chen, Chao Department of Computer and Network Engineering CIT UAEU UAE Department of Computer Science and Software Engineering CIT UAEU UAE Cloud Computing and Applications Reseach Lab Staffordshire University United Kingdom Key Laboratory of Complex System Control Theory and Application Tianjin University of Technology China

Respiratory symptoms can be a caused by different underlying conditions, and are often caused by viral infections, such as Influenza-like illnesses or other emerging viruses like the Coronavirus. These respiratory viruses, often, have common symptoms, including coughing, high temperature, congested nose, and difficulty breathing. However, early diagnosis of the type of the virus, can be crucial, especially in cases such as the recent COVID-19 pandemic. One of the factors that contributed to the spread of the pandemic, was the late diagnosis or confusing it with regular flu-like symptoms. Science has proved that one of the possible differentiators of the underlying causes of these different respiratory diseases is coughing, which comes in different types and forms. Therefore, a reliable lab-free tool for early and more accurate diagnosis that can differentiate between different respiratory diseases is very much needed. This paper proposes an end-to-end portable system that can record data from patients with symptom, including coughs (voluntary or involuntary) and translate them into health data for diagnosis, and with the aid of machine learning, classify them into different respiratory illnesses, including COVID-19. With the ongoing efforts to stop the spread of the COVID-19 disease everywhere today, and against similar diseases in the future, our proposed low cost and user-friendly solution can play an important part in the early diagnosis. Copyright © 2020, The Authors. All rights reserved.

关键词： Diagnosis

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Classification of Cognitive Ability of Healthy Elderly Individuals Using Resting-State Functional Connectivity Magnetic Resonance Imaging and An Extreme Learning Machine

Research Square

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Research Square 2021年

作者： Yang, Zekun Ge, Manling Abdelkader, Nasreddine Belkacem Fu, Xiaoxuan Zhang, Qirui Chen, Shenghua Xie, Chong Song, Zibo Chen, Chao State Key Laboratory of Reliability and Intelligence of Electrical Equipment Hebei University of Technology Tianjin300130 China Hebei Province Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability Hebei University of Technology Tianjin300130 China Department of Computer and Network Engineering College of Information Technology United Arab Emirates University Al Ain15551 United Arab Emirates Athinoula A. Martinos Center for Biomedical Imaging Department of Radiology Massachusetts General Hospital Harvard Medical School CharlestownMA02129 United States Department of Medical Imaging Jinling Hospital Nanjing University School of Medicine General Hospital of Eastern Theater Nanjing210002 China Key Laboratory of Complex System Control Theory and Application Tianjin University of Technology Tianjin300003 China

Purpose Quantitative determination of the correlation between cognitive ability and functional biomarkers in the elderly brain is essential. To identify biomarkers associated with cognitive performance in the elderly, this study combined an index model specific for resting-state functional connectivity (FC) magnetic resonance imaging (fcMRI) with a supervised machine learning method. Methods Performance scores on conventional cognitive test batteries and MRI data were obtained for 98 healthy elderly individuals and 90 healthy youth from two public databases. Based on the test scores, the elderly cohort was categorized into two groups: excellent and poor. An fcMRI index model was constructed for each elderly individual to determine the relative differences in FC among brain regions compared with that in the youth cohort. Brain areas sensitive to test scores could then be identified using the fcMRI indexes. To confirm the effectiveness of constructed model, the fcMRI indexes of these brain areas were used as feature matrix inputs for training an extreme learning machine. Classification accuracy was then tested in separate groups and confirmed by N-fold cross-validation. Results This learning study could effectively classify the cognitive status of healthy elderly individuals according to frontal lobe, temporal lobe, and parietal lobe FC values with a mean accuracy of 83.5%, which is substantially higher than that achieved using conventional correlation analysis. Conclusion This fcMRI classification study may facilitate early detection of age-related cognitive decline as well as help reveal the underlying pathological mechanisms. © 2021, CC BY.

关键词： Magnetic resonance imaging

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Association between Electroencephalographic microstates abnormalities and cognitive dysfunction in drug-naive MDD

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Brain Research 2025年 1860卷 149660-149660页

作者： Qin, Qin Liu, Xinyu Wang, Bin Wang, Xin Liang, Sixiang Chen, Chao Li, Meijia Han, Chuanliang Zhao, Xixi National Clinical Research Center for Mental Disorders & National Center for Mental Disorders Beijing Key Laboratory of Mental Disorders Beijing Anding Hospital Capital Medical University Beijing China Advanced Innovation Center for Human Brain Protection Capital Medical University Beijing China Department of Clinical Psychology Shandong Provincial Hospital affiliated to Shandong First Medical University Shandong China Key Laboratory of Complex System Control Theory and Application Tianjin University of Technology Tianjin 300384 China Faculty of Psychology and Center for Neuroscience Vrije Universiteit Brussel Brussels 1050 Belgium School of Biomedical Sciences and The Gerald Choa Neuroscience Institute The Chinese University of Hong Kong Hong Kong SAR China

Objectives: This study aims to investigate the link between Electroencephalography (EEG) microstate anomalies and cognitive impairments in individuals with drug-naive Major depressive disorder (MDD). Methods: We recruited 29 patients with drug-naive MDD and 30 healthy controls. The Hamilton Depression Rating Scale (HDRS-17) measured symptom severity, the MATRICS Consensus Cognitive Battery (MCCB) assessed neurocognitive function, and resting-state EEG data were collected using 64 scalp electrodes. Analysis of EEG microstates was conducted via the Microstate Analysis plugin for EEGLAB. Results: MDD group had lower scores in six neurocognitive MCCB domains. For EEG microstates, four similar ones (A − D) were found in both groups. Notably, microstate C duration was lower in MDD group (t = 4.549, P < 0.001), microstate D occurrence (t = 2.258, P = 0.028) and proportion (t = 3.733, P < 0.001) were lower in MDD group. There were significant differences in all 4 microstate transition probabilities between groups. For example, A − B, B − A etc. transitions were higher in MDD, while A − C, A − D etc. were lower. The proportion of microstate D was found positively correlated with Speed of processing (SOP) score (r = 0.499, df = 26, P = 0.007) and Working memory (WM) score (r = 0.451, df = 26, P = 0.016). The Occurrence of microstate D was found positively correlated with SOP score (r = 0.383, df = 26, P = 0.044) and WM score (r = 0.389, df = 26, P = 0.041). Conclusions: MDD patients show alterations in sub-second brain dynamics, characterized by a decreased proportion and occurrence of microstate D and shorter duration of microstate C, and significant shifts in microstate transition probabilities. These changes correlate with cognitive deficits across several domains, including processing speed and working memory. © 2025

关键词： Cognitive dysfunction Electroencephalographic microstates Major depressive disorder

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