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International Joint Conference on Neural Networks (IJCNN)

作者： Rong-Fei Gu Rui Li Wei-Long Zheng Bao-Liang Lu Center for Brain-Like Computing and Machine Intelligence Department of Computer Science and Engineering Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Brain Science and Technology Research Center Shanghai Jiao Tong University Shanghai China RuiJin-Mihoyo Laboratory Clinical Neuroscience Center RuiJin Hospital Shanghai Jiao Tong University School of Medicine Shanghai China

The study of the human decision-making process has long been a valuable field for both scientific research and practical application. Towards knowing and taking control of the decision-making process, evaluating the reliability of human decisions objectively plays an important role. Various studies have demonstrated that the confidence level of humans during the decision-making process is an important factor that reflects the correctness of decisions. In literature, several deep learning based methods have been developed to estimate decision confidence using Electroencephalography (EEG). Among these approaches, the spectral-spatial-temporal adaptive graph convolutional neural network (SST-AGCN) stands out. However, SST-AGCN focuses on specific subjects, and may lead to less efficiency in cross-subject situations, which are more common in application scenarios. In this paper, we propose a deep learning model called SST-AGCN with Domain Adaptation (SST-AGCN-DA) for cross-subject decision confidence estimation. To examine the effectiveness of our proposed model, we compare our SST-AGCN-DA with the original SST-AGCN, three typical domain adaption algorithms in the field, and the SST-AGCN with Domain Generalization (SST-AGCN-DG), which is another transfer learning model we developed in this paper. We conduct cross-subject confidence estimation experiments on an EEG dataset collected under a text-based decision-making task. The averaged results of leave-one-out cross-validation come out that the F1-scores of our proposed SST-AGCN-DA and SST-AGCN-DG are 79.45% and 77.04%, respectively, while the original SST-AGCN and the best of the existing domain adaptation algorithms are 74.15% and 74.25%, respectively.

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Towards Precise 3D Human Pose Estimation with Multi-Perspective Spatial-Temporal Relational Transformers

Towards Precise 3D Human Pose Estimation with Multi-Perspect...

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International Joint Conference on Neural Networks (IJCNN)

作者： Jianbin Jiao Xina Cheng Weijie Chen Xiaoting Yin Hao Shi Kailun Yang School of Artificial Intelligence Xidian University China State Key Laboratory of Extreme Photonics and Instrumentation Zhejiang University China School of Robotics Hunan University China National Engineering Research Center of Robot Visual Perception and Control Technology Hunan University China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

3D human pose estimation captures the human joint points in three-dimensional space while keeping the depth information and physical structure. That is essential for applications that require precise pose information, such as humancomputer interaction, scene understanding, and rehabilitation training. Due to the challenges in data collection, mainstream datasets of 3D human pose estimation are primarily composed of multi-view video data collected in laboratory environments, which contains rich spatial-temporal correlation information besides the image frame content. Given the remarkable selfattention mechanism of transformers, capable of capturing the spatial-temporal correlation from multi-view video datasets, we propose a multi-stage framework for 3D sequence-to-sequence (seq2seq) human pose detection. Firstly, the spatial module represents the human pose feature by intra-image content, while the frame-image relation module extracts temporal relationships and 3D spatial positional relationship features between the multiperspective images. Secondly, the self-attention mechanism is adopted to eliminate the interference from non-human body parts and reduce computing resources. Our method is evaluated on Human3.6M, a popular 3D human pose detection dataset. Experimental results demonstrate that our approach achieves stateof-the-art performance on this dataset. The source code will be available at https://***/WUJINHUAN/3D-human-pose.

关键词： Training Three-dimensional displays Correlation Computational modeling Source coding Pose estimation Video sequences

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Exploring Quasi-Global Solutions to Compound Lens Based Computational Imaging Systems

arXiv

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arXiv 2024年

作者： Gao, Yao Jiang, Qi Gao, Shaohua Sun, Lei Yang, Kailun Wang, Kaiwei State Key Laboratory of Extreme Photonics and Instrumentation Zhejiang University Hangzhou310027 China School of Robotics Hunan University Changsha410012 China National Engineering Research Center of Robot Visual Perception and Control Technology Hunan University Changsha410082 China

Recently, joint design approaches that simultaneously optimize optical systems and downstream algorithms through data-driven learning have demonstrated superior performance over traditional separate design approaches. However, current joint design approaches heavily rely on the manual identification of initial lenses, posing challenges and limitations, particularly for compound lens systems with multiple potential starting points. In this work, we present Quasi-Global Search Optics (QGSO) to automatically design compound lens based computational imaging systems through two parts: (i) Fused Optimization Method for Automatic Optical Design (OptiFusion), which searches for diverse initial optical systems under certain design specifications;and (ii) Efficient Physic-aware Joint Optimization (EPJO), which conducts parallel joint optimization of initial optical systems and image reconstruction networks with the consideration of physical constraints, culminating in the selection of the optimal solution in all search results. Extensive experimental results illustrate that QGSO serves as a transformative end-to-end lens design paradigm for superior global search ability, which automatically provides compound lens based computational imaging systems with higher imaging quality compared to existing paradigms. The source code will be made publicly available at https://***/LiGpy/QGSO. Copyright © 2024, The Authors. All rights reserved.

关键词： Imaging systems

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Erratum to:Aberrant dynamic functional connectivity of thalamocortical circuitry in major depressive disorder

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Journal of Zhejiang University-Science B(Biomedicine & Biotechnology) 2024年第10期25卷 801-802页

作者： Weihao ZHENG Qin ZHANG Ziyang ZHAO Pengfei ZHANG Leilei ZHAO Xiaomin WANG Songyu YANG Jing ZHANG Zhijun YAO Bin HU Gansu Provincial Key Laboratory of Wearable Computing School of Information Science and EngineeringLanzhou UniversityLanzhou730000China Second Clinical School Lanzhou UniversityLanzhou730030China Department of Magnetic Resonance Lanzhou University Second HospitalLanzhou730030China Gansu Province Clinical Research Center for Functional and Molecular Imaging Lanzhou730030China School of Medical Technology Beijing Institute of TechnologyBeijing100081China CAS Center for Excellence in Brain Science and Intelligence Technology Shanghai Institutes for Biological SciencesChinese Academy of SciencesShanghai200031China Joint Research Center for Cognitive Neurosensor Technology of Lanzhou University&Institute of Semiconductors Chinese Academy of SciencesLanzhou730000China

The article(Zheng et al.,2024)unfortunately contained a mistake:in Table 3,four"I"elements in the last"LF in thalamus"column are *** four arrows should be"↑"in the following correctversi... 详细信息

关键词： connectivity mistake dynamic

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MADNet: Maximizing Addressee Deduction Expectation for Multi-Party Conversation Generation

arXiv

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arXiv 2023年

作者： Gu, Jia-Chen Tan, Chao-Hong Chu, Caiyuan Ling, Zhen-Hua Tao, Chongyang Liu, Quan Liu, Cong National Engineering Research Center of Speech and Language Information Processing University of Science and Technology of China Hefei China iFLYTEK Research Hefei China Peking University Beijing China State Key Laboratory of Cognitive Intelligence China

Modeling multi-party conversations (MPCs) with graph neural networks has been proven effective at capturing complicated and graphical information flows. However, existing methods rely heavily on the necessary addressee labels and can only be applied to an ideal setting where each utterance must be tagged with an "@" or other equivalent addressee label. To study the scarcity of addressee labels which is a common issue in MPCs, we propose MADNet that maximizes addressee deduction expectation in heterogeneous graph neural networks for MPC generation. Given an MPC with a few addressee labels missing, existing methods fail to build a consecutively connected conversation graph, but only a few separate conversation fragments instead. To ensure message passing between these conversation fragments, four additional types of latent edges are designed to complete a fully-connected graph. Besides, to optimize the edge-type-dependent message passing for those utterances without addressee labels, an Expectation-Maximization-based method that iteratively generates silver addressee labels (E step), and optimizes the quality of generated responses (M step), is designed. Experimental results on two Ubuntu IRC channel benchmarks show that MADNet outperforms various baseline models on the task of MPC generation, especially under the more common and challenging setting where part of addressee labels are missing. Copyright © 2023, The Authors. All rights reserved.

关键词： Message passing

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A New Workspace and Structural Parameter Optimization Method of Parallel robot 18

A New Workspace and Structural Parameter Optimization Method...

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18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023

作者： Zhang, Haiyun Hu, Weijun Zhang, Renge Wang, Jin Lu, Guodong School of Robotics Ningbo University of Technology Ningbo315211 China Robotics Institute of Zhejiang University Hangzhou310027 China Zhejiang Qiantang Robot and Intelligent Equipment Reserch Co. Ltd Hangzhou310018 China State Key Laboratory of Fluid Power and Mechatronic Systems School of Mechanical Engineering Zhejiang University Hangzhou310027 China Robotics Research Center of Yuyao City Ningbo315400 China

ISBN: (纸本)9798350312201

The parallel robot workspace refers to the set of points and boundary that can be reached by the parallel mechanism. With the continuous development and industrial application of parallel robots, the workspace analysis and optimization becomes a critical link in this process. In this paper, a workspace solution approach based on seed space segmentation and principal component analysis is proposed. In order to improve the working performance of parallel robots, a flexible workspace evaluation method based on quadratic fourth moment is proposed, and a relationship model between flexible workspace and structural parameters is established. Furthermore, based on the multi-objective genetic algorithm, the structure parameters of the parallel robot are optimized by the workspace and flexible workspace indexes. Finally, the advantage and effectiveness of proposed method are verified by experiment and comparison with traditional methods. © 2023 IEEE.

关键词： Principal component analysis

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A kinematic calibration method for robots based on the PGTF error model and Levenberg-Marquardt algorithm 18

A kinematic calibration method for robots based on the PGTF ...

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18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023

作者： Zheng, Zhi Wang, Jin Zhu, Jun Liu, Luan Zhang, Haiyun Lu, Guodong State Key Laboratory of Fluid Power and Mechatronic Systems School of Mechanical Engineering Zhejiang University Hangzhou310027 China Robotics Research Center of Yuyao City Ningbo315400 China School of Robotics Ningbo University of Technology Ningbo315211 China Robotics Institute of Zhejiang University Hangzhou310027 China Zhejiang Qiantang Robot and Intelligent Equipment Research Co. Ltd Hangzhou310018 China

ISBN: (纸本)9798350312201

robots use motors for power output and realize joint motion through reducers and mechanical transmissions, which affect the absolute positioning accuracy. In view of this problem, an error model based on Preprocessing-Geometric-Transmission-Flexibility (PGTF) is proposed and solved by Levenberg-Marquardt algorithm. First, the theoretical kinematic model is established by MD-H model method. Second, the data was pre-processed in dimension upgrade and the robot error identification model was established for geometric error, transmission error and flexible error. Third, Levenberg-Marquardt algorithm was used to find the global convergence solution to realize the identification of error parameters, and the actual robot kinematic model was compensated. Finally, the experiment is carried out on the real robot machine. Through theoretical analysis and verification of real machine data, the average error of kenbei-5kg robot is reduced from 6.8787 mm to 0.1267 mm, the positioning accuracy is improved by 98.16%, the accuracy compensation algorithm is accurate, and the compensation effect is remarkable. © 2023 IEEE.

关键词： robots

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A Hand Motion Recognition System Combining Wearable Sensing Gloves and Template Matching Algorithm

A Hand Motion Recognition System Combining Wearable Sensing ...

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IEEE International Conference on Mechatronics and Automation

作者： Dapeng Wang Yupeng Fu Bo Yang Teng Liu Chuizhou Meng Shijie Guo State Key Laboratory of Reliability and Intelligence of Electrical Equipment Hebei University of Technology P. R. China Engineering Research Center of Ministry of Education for Intelligent Rehabilitation Device and Detection Technology Hebei University of Technology P. R. China Hebei Key Laboratory of Smart Sensing and Human-Robot Interaction Hebei University of Technology P. R. China School of Mechanical Engineering Hebei University of Technology P. R. China

ISBN: (数字)9798350388077

ISBN: (纸本)9798350388084

Hand motion recognition systems, especially those these using wearable electronic glove, have received growing attention in the field of human-computer interaction and medical rehabilitation. However, two problems still exist: (1) the sensors are usually single, which fails to acquire the multi-sensor information; and (2) Pattern recognition based on deep learning algorithms tend to require a large number of samples to ensure pattern recognition accuracy, whereas obtaining a large amount of hand movement information from a patient is difficult. To address the above problems, this study develops a hand motion recognition system through combining a wearable sensing glove and a new template matching algorithm. The wearable sensing glove employs five Flex sensors and one Inertial Measurement Unit (IMU) to obtain the gesture information, followed by realization of the human-computer interaction through the Unity3D software. Meanwhile, a simple template matching algorithm is constructed for the hand gesture recognition, which can recognize 10 numbers and 20 letters in American Sign Language (ASL) with an accuracy of 90.11% and 87.13%, respectively. The proposed hand motion recognition system can basically meet the needs of patients for hand movement recognition.

关键词： Human computer interaction Wrist Sign language Accuracy Software algorithms Software Sensors

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Experimental Study on Motion Controller based on NN-ARX and ARMAX Actuator Identification for 3-DoF Delta Parallel robot

Experimental Study on Motion Controller based on NN-ARX and ...

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RSI/ISM International Conference on robotics and Mechatronics (ICROM)

作者： Hasan Jalali Hossein Damavandi Ahmad Kalhor Mehdi Tale Masouleh Human and Robot Interaction Laboratory School of Electrical and Computer Engineering University of Tehran Tehran Iran Faculty of Computer Engineering and Information Technology Sadjad University Mashhad Iran Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering University of Tehran Tehran Iran

Today, system identification plays a pivotal role in control science and offering a myriad of applications. This paper places its focus on the identification of actuator models within real-world delta robots for informing controller design. Two identification methods, namely NN-ARX and ARMAX, have been employed to extract the dynamic characteristics of the robot actuators, resulting in dynamic models of these integral components being derived. These dynamic models have been utilized in simulations for controller design, and due to the disparities in the identification models, distinct controllers have been realized. Subsequently, these controllers have been practically implemented on delta robots, and their performances have been subjected to a comprehensive comparative analysis. The results demonstrate that controllers integrating the identified actuator models outperformed those designed without the incorporation of the identification models. In practice, the implementation of controllers based on NN-ARX yielded the most favorable results among all the tested controllers. This research not only underscores the importance of accurate actuator models in control system design but also highlights the superior performance of neural network-based controllers in real-world robotic applications. In particular, the practical results of NN-ARX-based controllers were able to achieve significantly lower RMSE of 2.3562, 1.9531, and 2.1185 for the three motors, respectively, as opposed to the 4.1369, 3.0125, and 3.0363 achieved by the ARMAX-based controllers.

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Imaging brain white matter function using resting-state functional MRI

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Science Bulletin 2025年第9期70卷 1384-1388页

作者： Gong-Jun Ji Zaixu Cui Ryan C.N.D’Arcy Wei Liao Bharat B.Biswal Qing Zhang Cheng Luo Yu-Feng Zang Zhaohua Ding Xi-Nian Zuo John C.Gore Kai Wang Department of Psychology and Sleep Medicine The Second Affiliated Hospital of Anhui Medical UniversityAnhui Medical UniversityHefei 230032China School of Mental Health and Psychological Sciences Anhui Medical UniversityHefei 230032China Chinese Institute for Brain Research Beijing 102206China BrainNET Health and Technology DistrictSimon Fraser UniversitySurreyBC V3V 0E8Canada School of Life Science and Technology University of Electronic Science and Technology of ChinaChengdu 611731China Department of Biomedical Engineering New Jersey Institute of TechnologyNewarkNJ 07102USA Center for Cognition and Brain Disorders The Affiliated Hospital of Hangzhou Normal UniversityHangzhou 310000China Vanderbilt University Institute of Imaging Science NashvilleTN 37232-2310USA State Key Laboratory of Cognitive Neuroscience and Learning Beijing Normal UniversityBeijing 100875China Department of Radiology and Radiological Sciences Vanderbilt University Medical CenterNashvilleTN 37212USA Department of Neurology The First Affiliated Hospital of Anhui Medical UniversityAnhui Medical UniversityHefei 230032China Anhui Institute of Translational Medicine Hefei 230032China Institute of Artificial Intelligence Hefei Comprehensive National Science CenterHefei 230032China Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders Hefei 230032China Collaborative Innovation Center of Neuropsychiatric Disorders and Mental Health Hefei 230032China

White matter(WM)comprises approximately half of the human brain volume and is primarily composed of bundles of axons and *** axons conduct nerve impulses between gray matter(GM)to support information transmission and coordination within brain networks or *** the overwhelming emphasis of human brain imaging on GM,few would deny the importance of the functional contributions of WM to human cognition and *** in vivo brain studies,diffusion magnetic resonance imaging(MRI)has been widely used to delineate WM fibers and measure their microstructural properties,but diffusion MRI reveals little about functional *** a long time,we have lacked an in vivo way to quantify functional metrics of *** contrast to the widespread application of functional MRI(fMRI)based on blood oxygenation-level-dependent(BOLD)signals to assess GM functions[1],BOLD effects in WM have been regarded as noise or nuisance variables in most neuroimaging studies.

关键词： human brain imaging white matter wm comprises gray matter gm diffusion MRI functional MRI blood oxygenation level dependent signals resting state functional MRI brain white matter

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