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Digital Twins and Parallel Intelligence (DTPI), IEEE International Conference on

作者： Qiang Li Zhuoya Ju Lili Fan Xin Liu Wenhua Liu Feiyue Wang Scientific and Technological Innovation Center Beijing China The School of Automation Beijing Institute of Technology Beijing China School of Big Data & Cyber Security China University of Petroleum Qingdao China The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350349252

ISBN: (纸本)9798350349269

As social production and living spaces gradually expand from the physical and information domains to the cognitive and social domains, this article addresses the new changes brought about by these expansions. Starting from the new impact of era of intelligent connectivity on social cognitive management, it analyzes the urgent need to enhance social cognitive management capabilities centered on cognitive decision-making, identifying the core key as Adversarial Generated Intelligence (AGI). It delves into the conceptual characteristics, operating mechanisms, and winning strategies of AGI, identifies the new challenges it faces, and proposes a Parallel Intelligence empowering AGI (PiAGI) system architecture through the empowerment of parallel intelligence. This provides theoretical support for improving social cognitive management capabilities centered on cognitive decision-making.

关键词： Decision making systems architecture Production Digital twins Faces

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A Bioinspired retinal neural network for accurately extracting small-target motion information in cluttered backgrounds

arXiv

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arXiv 2021年

作者： Huang, Xiao Qiao, Hong Li, Hui Jiang, Zhihong School of Mechatronical Engineering Advanced Innovation Center for Intelligent Robots and Systems Key Laboratory of Biomimetic Robots and Systems of Chinese Ministry of Education Beijing Institute of Technology Beijing100081 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Beijing Key Laboratory of Research and Application for Robotic Intelligence of’Hand-Eye-Brain’ Interaction Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China

—Robust and accurate detection of small moving targets in cluttered moving backgrounds is a significant and challenging problem for robotic visual systems to perform search and tracking tasks. Inspired by the neural circuitry of elementary motion vision in the mammalian retina, this paper proposes a bioinspired retinal neural network based on a new neurodynamics-based temporal filtering and multiform 2-D spatial Gabor filtering. This model can estimate motion direction accurately via only two perpendicular spatiotemporal filtering signals, and respond to small targets of different sizes and velocities through adjusting the dendrite field size of spatial filter. Meanwhile, an algorithm of directionally selective inhibition is proposed to suppress the target-like features in the moving background, which can reduce the influence of background motion effectively. Extensive synthetic and real-data experiments show that the proposed model works stably for small targets of a wider size and velocity range, and has better detection performance than other bioinspired models. Additionally, it can also extract the information of motion direction and motion energy accurately and rapidly. Copyright © 2021, The Authors. All rights reserved.

关键词： Ophthalmology

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A Bioinspired approach-sensitive neural network for collision detection in cluttered and dynamic backgrounds

arXiv

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arXiv 2021年

—Rapid, accurate and robust detection of looming objects in cluttered moving backgrounds is a significant and challenging problem for robotic visual systems to perform collision detection and avoidance tasks. Inspired by the neural circuit of elementary motion vision in the mammalian retina, this paper proposes a bioinspired approach-sensitive neural network (ASNN) that contains three main contributions. Firstly, a direction-selective visual processing module is built based on the spatiotemporal energy framework, which can estimate motion direction accurately via only two mutually perpendicular spatiotemporal filtering channels. Secondly, a novel approach-sensitive neural network is modeled as a push-pull structure formed by ON and OFF pathways, which responds strongly to approaching motion while insensitivity to lateral motion. Finally, a method of directionally selective inhibition is introduced, which is able to suppress the translational backgrounds effectively. Extensive synthetic and real robotic experiments show that the proposed model is able to not only detect collision accurately and robustly in cluttered and dynamic backgrounds but also extract more collision information like position and direction, for guiding rapid decision making. © 2021, CC BY.

关键词： Mammals

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Capsule Network-Based Text Sentiment Classification

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IFAC-PapersOnLine 2020年第5期53卷 698-703页

作者： Bingyang Chen Zhidong Xu Xiao Wang Long Xu Weishan Zhang School of Computer Science and Technology China University of Petroleum Qingdao 266580 China School of security National Defence University PLA China Beijing 100091 China Beijing 100091 China Qingdao Academy of Intelligent Industries Shandong Qingdao 266109 China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China

To improve text sentiments classification issues, such as information loss and insensitivity to spatial information, this paper proposes a text sentiment classification model based on the capsule network (T-Caps), which uses the Transformer to extract low-level text features. The method iteratively updates capsule network parameters through optimized dynamic routing algorithms and global parameter sharing, and it obtains the relationship between local features of the text and the overall emotional polarity to save the information integrity of text features. By comparing with multiple models, we find that the Transformer has the strongest feature extraction capability. The experimental results show that our model is capable of extracting more discriminative semantic features and yields a significant performance gain compared to other baseline methods.

关键词： Sentiment classification Capsule network Transformer Deep learning Attention

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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

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Fault Tolerant control for Reconfigurable Manipulators Based on Adaptive Dynamic Programming with an Improved Performance Index Function

Fault Tolerant Control for Reconfigurable Manipulators Based...

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World Congress on Intelligent control and Automation

作者： Hongbing Xia Bo Zhao Fan Zhou Bo Dong Guangjun Liu Yuanchun Li Department of Control Engineering Changchun University of Technology Changchun Jilin Province China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Department of Aerospace Engineering Ryerson University 350 Victoria St. Toronto Ontario Canada M5B 2K3

ISBN: (纸本)9781467384155

This paper investigates a novel fault tolerant control (FTC) scheme for reconfigurable manipulators with actuator failures based on adaptive dynamic programming (ADP) with an improved performance index function. With the help of fault observer, the estimated actuator failure is utilized to construct the performance index function that reflects the failure, regulation and control simultaneously. The fault tolerant controller consists of the desired controller and the approximated optimal feedback one. The desired controller is obtained by the system model directly. By establishing a critic neural network, the policy iteration (PI) algorithm is utilized to solve the Hamilton-Jacobi-Bellman (HJB) equation, and then the approximated optimal feedback controller can be derived directly. It is proven that the closed-loop system is uniformly ultimately bounded (UUB) based on Lyapunov stability theorem. Finally, the simulation study is given to show the effectiveness of the proposed FTC scheme.

关键词： Actuators Manipulators Performance analysis Optimal control Biological neural networks Observers

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Benefit from public unlabeled data: A Frangi filtering-based pretraining network for 3D cerebrovascular segmentation

arXiv

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arXiv 2023年

作者： Shi, Gen Lu, Hao Hui, Hui Tian, Jie School of Engineering Medicine School of Biological Science and Medical Engineering Beihang University Beijing100191 China Ministry of Industry and Information Technology of China Beijing100191 China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academic of Science Beijing10086 China CAS Key Laboratory of Molecular Imaging Institute of Automation Chinese Academy of Sciences Beijing100190 China

The precise cerebrovascular segmentation in time-of-flight magnetic resonance angiography (TOF-MRA) data is crucial for clinically computer-aided diagnosis. However, the sparse distribution of cerebrovascular structures in TOF-MRA results in an exceedingly high cost for manual data labeling. The use of unlabeled TOF-MRA data holds the potential to enhance model performance significantly. In this study, we construct the largest preprocessed unlabeled TOF-MRA datasets (1510 subjects) to date. We also provide three additional labeled datasets totaling 113 subjects. Furthermore, we propose a simple yet effective pertraining strategy based on Frangi filtering, known for enhancing vessel-like structures, to fully leverage the unlabeled data for 3D cerebrovascular segmentation. Specifically, we develop a Frangi filtering-based preprocessing workflow to handle the large-scale unlabeled dataset, and a multi-task pretraining strategy is proposed to effectively utilize the preprocessed data. By employing this approach, we maximize the knowledge gained from the unlabeled data. The pretrained model is evaluated on four cerebrovascular segmentation datasets. The results have demonstrated the superior performance of our model, with an improvement of approximately 3% compared to state-of-the-art semi- and self-supervised methods. Furthermore, the ablation studies also demonstrate the generalizability and effectiveness of the pretraining method regarding the backbone structures. The code and data have been open source at: https://***/shigen-StoneRoot/FFPN. © 2023, CC BY.

关键词： Computer aided diagnosis

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Editorial-neural networks and learning systems come together

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IEEE Transactions on Neural Networks and Learning systems 2012年第1期23卷 1-6页

作者： Baesens, Bart Bouboulis, Pantelis Cruces, Sergio Domeniconi, Carlotta Ikeda, Shiro Li, Xuelong Melin, Patricia Rao, V. Sree Hari Schuller, Björn W. Shen, Yi Tang, Huajin Wang, Cong Yang, Jian Zhao, Dongbin Liu, Derong Catholic University of Leuven Belgium University of Athens Greece University of Seville Spain George Mason University United States Institute of Statistical Mathematics Japan University of London United Kingdom Tijuana Institute of Technology Mexico Jawaharlal Nehru Technological University India Technische Universität München Germany Huazhong University of Science and Technology China Institute for Infocomm Research Singapore Singapore South China University of Technology China Nanjing University of Science and Technology China Chinese Academy of Sciences China State Key Laboratory of Management Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China Department of Electrical Computer Engineering University of Illinois Chicago IL 60607 United States

This issue marks the beginning of the IEEE Transactions on Neural Networks and Learning systems (TNNLS). By adding «Learning systems» to the title, we now state explicitly the scope of the TRANSACTIONS to include neural networks as well as related learning systems. This issue marks a new era in the history of our TRANSACTIONS. The TRANSACTIONS is now ready to face the challenges of the next 10-20 years. With the evolution of the fields of neural networks in particular and computational intelligence in general, the IEEE Transactions on Neural Networks and Learning systems will continue to grow and to succeed in this ever-changing world. Also included are a few comments about the review process of TNN manuscripts and the introduction of 14 new TNNLS Associate Editors. © 2012 IEEE.

关键词： Learning systems

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Multi-Scale Scene Text Detection Based on Convolutional Neural Network

Multi-Scale Scene Text Detection Based on Convolutional Neur...

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Chinese Automation Congress (CAC)

作者： Yan-Feng Lu Ai-Xuan Zhang Yi Li Qian-Hui Yu Hong Qiao The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China China Academy of Aerospace Standardization and Product Assurance Beijing China School of Information Engineering Nanchang University Nanchang China College of Information and Communication Engineering Harbin Engineering University Harbin China

Faster R-CNN has advantages in object detection task. But in face of the variability of text and interference of the external factors, it cannot achieve perfect detection results in natural scene text detection. Moreover, the text detection algorithms based on deep learning need to use large data sets to train the network, while in some special scenarios where a mass of samples cannot be obtained, the performance of these algorithms is likely to be limited. How to accurately detect text in natural scene based on small data sets is a challenging issue. To address this issue, a multi-scale text feature extraction network with feature pyramid based on Faster R-CNN is proposed, which can accurately and comprehensively express complex and changeable text features in natural scenes even in the small data cases. Experiment results show that the proposed MSTD method is very competitive with existing related architectures.

关键词： Feature extraction Training Proposals Convolutional neural networks Object detection Task analysis Detection algorithms

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An Automatic Labeling Strategy for Locomotion Mode Recognition with Robotic Transtibial Prosthesis

An Automatic Labeling Strategy for Locomotion Mode Recogniti...

引用

IEEE International Conference on Cyber Technology in Automation, control, and Intelligent systems

作者： Enhao Zheng Qining Wang Hong Qiao The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences No. 95 of Zhongguancun East Road Beijing China The Beijing Innovation Center for Engineering Science and Advanced Technology (BIC-ESAT) Peking University Beijing China University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9781728107707

ISBN: (纸本)9781728107714

Doffing and Donning the prosthetic socket seriously influenced the performances of the locomotion mode recognition. To make the recognition algorithm adaptive to the disturbances, labeling the new coming data without human intervene was a key step. In this study, we proposed an automatic labeling method with on-prosthesis mechanical signals. The strategy was designed based on the dynamic time warping (DTW) to measure the similarities between the data of a whole completed gait cycle and the pre-defined templates. The automatic labeling algorithm was validated on 6 unilateral transtibial subjects wearing the robotic prosthesis. 2 experimental sessions with 5 locomotion mode and 8 locomotion transition tasks were investigated. Between the sessions, donning and doffing the prosthetic socket were done by the subjects without expertise manual configuration. We evaluated two template generation methods, i.e. the fixed template and the sliding template. The average accuracy of the automatic labeling after re-wearing the socket achieved 96.2% across the subjects, which was comparable to existing sEMG-based studies. The strategy provided a promising tool to accumulate training with inertial signals for locomotion mode recognition.

关键词： Prosthetics Robot sensing systems Labeling Sockets Heuristic algorithms

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