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Prescriptive Manufacturing in Society 5.0: Human Autonomous Organizations and “On-Demand” Smart Services

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IFAC-PapersOnLine 2024年第3期58卷 139-144页

作者： Xiao Wang Jun Huang Jing Yang Xiaoxing Wang Fei-Yue Wang Engineering Research Center of Autonomous Unmanned System Technology Ministry of Education Anhui University Hefei 230601 China Macau University of Science and Technology Macau 999078 China School of artificial Intelligence University of Chinese Academy of Sciences Beijing 100049 China Beijing SANBODY Technology Company Ltd Beijing 214000 China Qingdao Academy of Intelligent Industries Qingdao 266109 China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China

Society 5.0 addresses human behaviors and their consequences in social management, while emphasizing the integration of Cyber-Physical-Social Spaces to achieve a highly connected and circulated society. Prompted by the various social needs expressed on social media platforms, Prescriptive Manufacturing (PM) emerges as a proactive pre-manufacturing paradigm. This paper delves into PM within the context of Society 5.0. It highlights the development of Human Autonomous Organizations (HAOs) and the delivery of “on-demand” smart services, which are customized to meet both individual and broader societal demands. The social sensing framework of social needs across different social media platforms that enables the identification and anticipation of consumer demands in real-time is illustrated. This data-driven insight leverages artificial intelligence and robotics to facilitate customized design and on-demand production, aligning product creation with real user requirements. Furthermore, we discuss the implementation of human-centered smart services in PM, such as virtual try-on, virtual fitting, personalized retrofit, user-friendly scheduling, and proximity logistics, which not only enhance consumer experience but also optimize resource efficiency By examining the symbiosis between technological advancements and social need fulfillment, this study sheds light on how PM and HAOs can foster a more responsive, sustainable, and inclusive economy in society, ultimately contributing to a higher quality of life and sustainable development goals of Society 5.0.

关键词： Social Needs Sensing Human Autonomous Organization Customized Design personalized fine-tuning prescriptive manufacturing Human-Centric Smart Services

Exploring structure diversity in atomic resolution microscopy with graph neural networks

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arXiv 2024年

作者： Luo, Zheng Feng, Ming Gao, Zijian Yu, Jinyang Hu, Liang Wang, Tao Xue, Shenao Zhou, Shen Ouyang, Fangping Feng, Dawei Xu, Kele Wang, Shanshan Department of Materials Hunan Key Laboratory of Mechanism and Technology of Quantum Information College of Aerospace Science and Engineering National University of Defense Technology Changsha410000 China School of Computer State Key Laboratory of Complex & Critical Software Environment National University of Defense Technology Changsha410000 China College of Electronic and Information Engineering Tongji University Shanghai201804 China School of Material Science and Engineering Peking University Beijing100871 China School of Physics Central South University Changsha410083 China College of Science National University of Defense Technology Changsha410000 China School of Advanced Materials Peking University Shenzhen Graduate School Guangdong Shenzhen518055 China

The emergence of deep learning (DL) has provided great opportunities for the high-throughput analysis of atomic-resolution micrographs. However, the DL models trained by image patches in fixed size generally lack efficiency and flexibility when processing micrographs containing diversified atomic configurations. Herein, inspired by the similarity between the atomic structures and graphs, we describe a few-shot learning framework based on an equivariant graph neural network (EGNN) to analyze a library of atomic structures (e.g., vacancies, phases, grain boundaries, doping, etc.), showing significantly promoted robustness and three orders of magnitude reduced computing parameters compared to the image-driven DL models, which is especially evident for those aggregated vacancy lines with flexible lattice distortion. Besides, the intuitiveness of graphs enables quantitative and straightforward extraction of the atomic-scale structural features in batches, thus statistically unveiling the self-assembly dynamics of vacancy lines under electron beam irradiation. A versatile model toolkit is established by integrating EGNN sub-models for single structure recognition to process images involving varied configurations in the form of a task chain, leading to the discovery of novel doping configurations with superior electrocatalytic properties for hydrogen evolution reactions. This work provides a powerful tool to explore structure diversity in a fast, accurate, and intelligent manner. Copyright © 2024, The Authors. All rights reserved.

关键词： Grain boundaries

Modeling Multiple Views via Implicitly Preserving Global Consistency and Local Complementarity

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arXiv 2022年

作者： Li, Jiangmeng Qiang, Wenwen Zheng, Changwen Su, Bing Razzak, Farid Wen, Ji-Rong Xiong, Hui The University of Chinese Academy of Sciences Beijing China The Science & Technology on Integrated Information System Laboratory Institute of Software Chinese Academy of Sciences Beijing China The New York University Columbia University New York United States The Beijing Key Laboratory of Big Data Management and Analysis Methods Gaoling School of Artificial Intelligence Renmin University of China Beijing100872 China Guangzhou China Department of Computer Science & Engineering The Hong Kong University of Science and Technology Hong Kong

While self-supervised learning techniques are often used to mine hidden knowledge from unlabeled data via modeling multiple views, it is unclear how to perform effective representation learning in a complex and inconsistent context. To this end, we propose a new multi-view self-supervised learning method, namely consistency and complementarity network (CoCoNet), to comprehensively learn global inter-view consistent and local cross-view complementarity-preserving representations from multiple views. To capture crucial common knowledge which is implicitly shared among views, CoCoNet employs a global consistency module that aligns the probabilistic distribution of views by utilizing an efficient discrepancy metric based on the generalized sliced Wasserstein distance. To incorporate cross-view complementary information, CoCoNet proposes a heuristic complementarity-aware contrastive learning approach, which extracts a complementarity-factor jointing cross-view discriminative knowledge and uses it as the contrast to guide the learning of view-specific encoders. Theoretically, the superiority of CoCoNet is verified by our information-theoretical-based analyses. Empirically, our thorough experimental results show that CoCoNet outperforms the state-of-the-art self-supervised methods by a significant margin, for instance, CoCoNet beats the best benchmark method by an average margin of 1.1% on ImageNet. Copyright © 2022, The Authors. All rights reserved.

关键词： Supervised learning

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

Corrigendum to “Modified polyether gylcols supported ionic liquids for CO 2 adsorption and chemical fixation” [Mol. Catal. 492 (2020) 111008-111016]

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Molecular Catalysis 2020年 493卷

作者： Ying Liu Yan Song Jianhua Zhou Xiangping Zhang CAS Key Laboratory of Green Process and Engineering State Key Laboratory of Multiphase Complex System Beijing Key Laboratory of Ionic Liquids Clean Process Institute of Process Engineering Chinese Academy of Science Beijing 100190 China School of Chemistry and Pharmaceutical Engineering Qilu University of Technology Jinan 250353 China

Low-Light Enhancement Effect on Classification and Detection: An Empirical Study

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arXiv 2024年

作者： Wu, Xu Lai, Zhihui Jie, Zhou Gao, Can Hou, Xianxu Zhang, Ya-Nan Shen, Linlin The Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518060 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen518060 China School of AI and Advanced Computing Xi’an Jiaotong-Liverpool University China The National Engineering Laboratory for Big Data System Computing Technology Shenzhen University SZU Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society Guangdong Shenzhen518060 China

Low-light images are commonly encountered in real-world scenarios, and numerous low-light image enhancement (LLIE) methods have been proposed to improve the visibility of these images. The primary goal of LLIE is to generate clearer images that are more visually pleasing to humans. However, the impact of LLIE methods in high-level vision tasks, such as image classification and object detection, which rely on high-quality image datasets, is not well explored. To explore the impact, we comprehensively evaluate LLIE methods on these high-level vision tasks by utilizing an empirical investigation comprising image classification and object detection experiments. The evaluation reveals a dichotomy: While Low-Light Image Enhancement (LLIE) methods enhance human visual interpretation, their effect on computer vision tasks is inconsistent and can sometimes be harmful. Our findings suggest a disconnect between image enhancement for human visual perception and for machine analysis, indicating a need for LLIE methods tailored to support high-level vision tasks effectively. This insight is crucial for the development of LLIE techniques that align with the needs of both human and machine vision. Copyright © 2024, The Authors. All rights reserved.

关键词： Machine vision

Joint learning of discriminative low-dimensional image representations based on dictionary learning and two-layer orthogonal projections

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arXiv 2019年

作者： Wei, Xian Shen, Hao Li, Yuanxiang Tang, Xuan Jin, Bo Zhao, Lijun Murphey, Yi Lu Fujian Institute of Research on the Structure of Matter Chinese Academy of Sciences China Technical University of Munich Germany and fortiss GmbH Munich Germany Shanghai Key Lab for Trustworthy Computing School of Computer Science and Software Engineering East China Normal University China School of Aeronautics & Astronautics Shanghai Jiao Tong University Shanghai200240 China State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin150006 China Department of Electrical and Computer Engineering University of Michigan-Dearborn DearbornMI48128 United States

This work investigates the problem of efficiently learning discriminative low-dimensional representations of multi-class large-scale image objects. We propose a generic deep learning approach by taking advantages of Convolutional Neural Networks (CNN), sparse dictionary learning, and orthogonal projections. CNN is not only powerful on feature extraction, but also robust to spatial variance and changes. Sparse dictionary learning is well known for disentangling nonlinear underlying discriminative factors in data. The orthogonal projection is a notable efficient tool to project multi-class data onto low dimensional discriminative subspace. The proposed procedure can be summarized as follows. At first, a CNN is employed to extract high-dimensional (HD) preliminary convolutional features. Secondly, to avoid the high computational cost of direct sparse coding on HD CNN features, we present to learn sparse representation (SR) in an orthogonal projected space over a task-driven sparsifying dictionary. We then exploit the discriminative projection on SR. The whole learning process is treated as a joint optimization problem of trace quotient maximization, which involves the CNN parameters, the orthogonal projection on CNN features, the dictionary and the discriminative projection on sparse codes. The related cost function is well defined on product manifold of the Stiefel manifold, the Oblique manifold, and the Grassmann manifold. It is optimized via a geometrical stochastic gradient descent algorithm. Finally, the quality of dictionary and projections learned by the proposed approach is further investigated in terms of image classification. The experimental results show that the approach can achieve a strongly competitive performance with state-of-the-art image classification methods. Copyright © 2019, The Authors. All rights reserved.

关键词： Convolution

EEG Based Depression Recognition by Combining Functional Brain Network and Traditional Biomarkers

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EEG Based Depression Recognition by Combining Functional Bra...

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Shuting Sun Huayu Chen Xuexiao Shao Liangliang Liu Xiaowei Li Bin Hu Gansu Provincial Key Laboratory of Wearable Computing School of Information Science and Engineering Lanzhou University Lanzhou China CAS Center for Excellence in BrainScience and Intelligence Technology Shanghai Institutes for Biological Sciences Chinese Academy of Sciences Beijing China Chinese Academy of Sciences Joint Research Center for Cognitive Neurosensor Technology of Lanzhou University & Institute of Semiconductors Beijing China Ministry of Education Engineering Research Center of Open Source Software and Real-Time System (Lanzhou University) Lanzhou China

ISBN: (数字)9781728162157

ISBN: (纸本)9781728162164

This Electroencephalography (EEG)-based research is to explore the effective biomarkers for depression recognition. Resting-state EEG data were collected from 24 major depressive patients (MDD) and 29 normal controls using 128-electrode geodesic sensor net. To better identify depression, we extracted multi-type of EEG features including linear features (L), nonlinear features (NL), functional connectivity features phase lagging index (PLI) and network measures (NM) to comprehensively characterize the EEG signals in patients with MDD. And machine learning algorithms and statistical analysis were used to evaluate the EEG features. Combined multi-types features (All: L+ NL + PLI + NM) outperformed single-type features for classifying depression. Analyzing the optimal features set we found that compared to other type features, PLI occupied the largest proportion of which functional connections in intra-hemisphere were much more than that of in inter-hemisphere. In addition, when using PLI features and All features, high frequency bands (alpha, beta) could achieve obviously higher classification accuracy than low frequency bands (delta, theta). Parietal-occipital lobe in the high frequency bands had great effect in depression identification. In conclusion, combined multi-types EEG features along with a robust classifier can better distinguish depressive patients from normal controls. And intra-hemispheric functional connections might be an effective biomarker to detect depression. Hence, this paper may provide objective and potential electrophysiological characteristics in depression recognition.

关键词： Electroencephalography Depression Feature extraction Electrodes Biomarkers Support vector machines Entropy

Design and Application of a Portable Sleep Inertia Detection system Based on EEG Signals

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Design and Application of a Portable Sleep Inertia Detection...

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yunzhi Cui Fuze Tian Qinglin Zhao Bin Hu Gansu Provincial Key Laboratory of Wearable Computing School of Information Science and Engineering Lanzhou University Lanzhou China CAS Center for Excellence in Brain Science and Intelligence Technology Shanghai Institutes for Biological Sciences Chinese Academy of Sciences Chinese Academy of Sciences Joint Research Center for Cognitive Neurosensor Technology of Lanzhou University & Institute of Semiconductors Ministry of Education Engineering Research Center of Open Source Software and Real-Time System (Lanzhou University) Lanzhou China

ISBN: (纸本)9781665429825

Sleep inertia is a transitional state from sleep to wakefulness, accompanied by groggy feelings and cognitive impairment. Previous research on sleep inertia mainly used expensive and cumbersome equipment, and the analysis of physiological signals relied on computers. This work introduces a sleep inertia detection system that consists of a wearable low-power electroencephalogram (EEG) acquisition module based on STM32WB55 and ADS1299, and a data processing module based on the Xilinx ® Zynq ® -7000 XC7Z020. This work recorded the EEG signals of ten subjects in the alert and sleep inertia states to extract the delta power, alpha power, beta power, EEG vigilance, and sample entropy. A linear support vector machine (SVM) was then used to classify the two states based on all subjects’ EEG signals, with an accuracy of 72.5%, and the average accuracy based on a single participant was 8S.9%. Finally, the feature extraction algorithm and SVM parameters were entered into the Zynq ® system-on-chip (SoC) development board to realize onboard processing of the algorithm. The system is capable of evaluating the severity of human sleep inertia, which has reference significance for the practical application of sleep inertia detection.

关键词： Support vector machines Sleep Conferences Feature extraction Data processing Electroencephalography Physiology