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The Future of management: DAO to Smart Organizations and Intelligent Operations

IEEE TRANSACTIONS ON systems MAN CYBERNETICS-systems 2023年第6期53卷 3389-3399页

作者： Li, Juanjuan Qin, Rui Wang, Fei-Yue Chinese Acad Sci Inst Automation State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Macau Univ Sci & Technol Macau Inst Syst Engn Taipa Macao Peoples R China

In the future, management in smart societies will revolve around knowledge workers and the works they produce. This article is committed to explore new management framework, model, paradigm, and solution for organizing, managing, and measuring knowledge works. First, the parallel management framework is presented that would allow for the virtual-real interactions of humans in social space, robots in physical space, and digital humans in cyberspace to realize descriptive, predictive, and prescriptive intelligence for management. Then, the management foundation models are proposed by fusing scenarios engineering with artificial intelligence foundation models and cyber-physical-social systems. Moreover, the new management paradigm driven by decentralized autonomous organizations and operations is formulated for the advancement of smart organizations and intelligent operations. On these basis, the management operating systems that highlight features of simple intelligence, provable security, flexible scalability, and ecological harmony are finally put forward as new management solution.

关键词： Decentralized autonomous organizations and operations (DAO) management foundation model management intelligence management operating system (MOS) metaverse parallel management

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Autonomous Dynamic Hitch-Hiking control of a Bionic Robotic Remora

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2024年第3期71卷 2893-2902页

作者： Zhang, Pengfei Wu, Zhengxing Chen, Di Tan, Min Yu, Junzhi Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Peking Univ Dept Adv Mfg & Robot Coll Engn State Key Lab Turbulence & Complex Syst Beijing 100871 Peoples R China Guangdong Ocean Univ Coll Elect & Informat Engn Zhanjiang 524088 Peoples R China

Remora suckerfish and its hitch-hiking behavior bring enormous inspiration into the engineering field. In this article, a bioinspired hitch-hiking behavior is accomplished automatically by a robotic remora, which creates possibilities for prolonging its endurance and multirobot cooperation. First, the definition of the hitch-hiking task and the mechatronic design of robotic remora are introduced. Then, aiming at the hitch-hiking task, the LED-marker-based underwater visual localization method and planar state synchronization controller are developed. The localization method includes a complete framework from LED detection to marker pose optimization, which considers the refraction correction to improve the localization precision in water. The synchronization controller is decomposed into the lateral and longitudinal subcontrollers to overcome the challenges caused by underactuated dynamic. Besides, a finite state machine is designed to model the state and action transition during the hitch-hiking task. Extensive experimental results demonstrate the effectiveness of the proposed method. The autonomous hitch-hiking task toward a moving host is successfully implemented by a robotic fish for the first time. Such results may offer valuable insights into the future autonomous operation of underwater robots.

关键词： Autonomous control bioinspired adhesion robotic fish underwater visual localization

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Separate control Strategy for a Biomimetic Gliding Robotic Fish

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IEEE-ASME TRANSACTIONS ON MECHATRONICS 2022年第5期27卷 2535-2544页

作者： Dong, Huijie Wu, Zhengxing Zhang, Pengfei Wang, Jian Tan, Min Yu, Junzhi Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Peking Univ Dept Adv Mfg & Robot Coll Engn State Key Lab Turbulence & Complex SystBIC ESAT Beijing 100871 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

In this article, we present a novel separate strategy to control the pitch attitude and the gliding direction for a biomimetic gliding robotic fish by the coordination of the external and internal control surfaces. First, we derive the gliding dynamics and hydrodynamics within the framework of the extended angle of attack (AOA), based on which the gliding model is decomposed into pitch and velocity terms. Next, the backstepping and model predictive controllers are designed to regulate pitch angle using a movable mass and an AOA using pectoral fins based on their control features, respectively. Further, extensive simulations encompassing separate control and path following with desired pitch attitude are conducted to verify the feasibility and superiority of the proposed control strategy. More importantly, to capture the real-time gliding states in practical environment, we develop a gliding measurement and control system. Through the system, the aquatic experiments are carried out to further verify the effectiveness of the proposed separate control strategy. The obtained results offer valuable insight into the development of complex motion control of the gliding robots, laying a solid foundation for diversified underwater missions besides visual perception and autonomous docking.

关键词： Biomimetic robot gliding robotic fish motion control pitch control underwater robotics

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Robust Multitask Learning With Sample Gradient Similarity

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IEEE TRANSACTIONS ON systems MAN CYBERNETICS-systems 2024年第1期54卷 497-506页

作者： Peng, Xinyu Chang, Cheng Wang, Fei-Yue Li, Li Tsinghua Univ Dept Automat Beijing 100084 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100080 Peoples R China Tsinghua Univ Dept Automat Tsinghua Natl Lab Informat Sci & Technol Beijing 100084 Peoples R China

Multitask learning has led to great success in many deep learning applications during the last decade. However, recent experiments have demonstrated that the performance of multitask learning depends on how to balance the relationship between different tasks. Therefore, many approaches have been proposed to adjust per-task gradient directions or design a more appropriate task reweighting scheme based on task-level statistics. In this article, we discuss how to boost the performance of multitask learning by using more fine-grained sample gradient information. To this end, we propose the concept of sample gradient similarity, which measures the agreement between the sample gradient for a task and the true gradient. Based on this concept, greater weight is assigned to more consistent tasks and more robust training samples to improve the training process of multitask learning. Extensive experimental results show that our proposed method outperforms the state-of-the-art algorithms on a series of challenging multitask datasets.

关键词： Deep learning Automation multitask learning sample gradient sample reweighting task reweighting

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"Be a Lighting Programmer": Supporting Children Collaborative Learning through Tangible Programming System

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INTERNATIONAL JOURNAL OF HUMAN-COMPUTER INTERACTION 2024年第10期40卷 2622-2640页

作者： Wang, Xinyuan Xing, Qian Jin, Qiao Wang, Danli Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing Peoples R China Univ Minnesota Dept Comp Sci & Engn Minneapolis MN USA

In order to cultivate children's computational thinking, researchers have developed many excellent programming systems. Among them, the tangible programming systems combined with graphic output have been widely accepted because of the intuitive input method and diversified visual feedback. However, few of them support collaborative learning or programming. Little is known about children's experiences, emotional states and behaviours on collaborative programming. Motivated by this gap, we present a novel tangible and collaborative enabled programming system named Lighters, which designed for children aged 7-10 and had two types of collaboration modes (block-based and role-based). We conducted an user experiment with 24 children, and collected physiological (EDA), questionnaire, video recording and interview data for analysis. Based on our experiment results, Lighters are effective in helping children learn to program collaboratively. In addition, Lighters can mobilize children's positive emotions and enthusiasm to learn programming. Compared with block-based collaboration, role-based collaboration is more likely to stimulate children's emotional states and has a better effect on learning programming.

关键词： Video recording

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Completed Part Transformer for Person Re-Identification

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IEEE TRANSACTIONS ON MULTIMEDIA 2024年 26卷 2303-2313页

作者： Zhang, Zhong He, Di Liu, Shuang Xiao, Baihua Durrani, Tariq S. Tianjin Normal Univ Tianjin Key Lab Wireless Mobile Commun & Power Tra Tianjin 300387 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Strathclyde Dept Elect & Elect Engn Glasgow G1 1XW Scotland

Recently, part information of pedestrian images has been demonstrated to be effective for person re-identification (ReID), but the part interaction is ignored when using Transformer to learn long-range dependencies. In this article, we propose a novel transformer network named Completed Part Transformer (CPT) for person ReID, where we design the part transformer layer to learn the completed part interaction. The part transformer layer includes the intra-part layer and the part-global layer, where they consider long-range dependencies from the aspects of the intra-part interaction and the part-global interaction, simultaneously. Furthermore, in order to overcome the limitation of fixed number of the patch tokens in the transformer layer, we propose the Adaptive Refined Tokens (ART) module to focus on learning the interaction between the informative patch tokens in the pedestrian image, which improves the discrimination of the pedestrian representation. Extensive experimental results on four person ReID datasets, i.e., MSMT17, Market1501, DukeMTMC-reID, and CUHK03, demonstrate that the proposed method achieves a new state-of-the-art performance, e.g., it achieves 68.0% mAP and 84.6% Rank-1 accuracy on MSMT17.

关键词： Person ReID transformer adaptive refined tokens

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Development and control of Underwater Gliding Robots:A Review

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IEEE/CAA Journal of Automatica Sinica 2022年第9期9卷 1543-1560页

作者： Jian Wang Zhengxing Wu Huijie Dong Min Tan Junzhi Yu the State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190 the School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China the State Key Laboratory for Turbulence and Complex System Department of Mechanics and Engineering ScienceBIC-ESATCollege of EngineeringPeking UniversityBeijing 100871China

As one of the most effective vehicles for ocean development and exploration,underwater gliding robots(UGRs)have the unique characteristics of low energy consumption and strong ***,by borrowing the motion principles of current underwater robots,a variety of novel UGRs have emerged with improving their maneuverability,concealment,and environmental friendliness,which significantly broadens the ocean *** this paper,we provide a comprehensive review of underwater gliding robots,including prototype design and their key *** the perspective of motion characteristics,we categorize the underwater gliding robots in terms of traditional underwater gliders(UGs),hybrid-driven UGs,bio-inspired UGs,thermal UGs,and ***,their buoyancy driven system,dynamic and energy model,and motion control are concluded with detailed ***,we have discussed the current critical issues and future *** review offers valuable insight into the development of next-generation underwater robots well-suited for various oceanic applications,and aims to gain more attention of researchers and engineers to this growing field.

关键词： Buoyancy driven motion control oceanic applications system development underwater gliding robots

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Cross-modality person re-identification using hybrid mutual learning

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IET COMPUTER VISION 2023年第1期17卷 1-12页

作者： Zhang, Zhong Dong, Qing Wang, Sen Liu, Shuang Xiao, Baihua Durrani, Tariq S. Tianjin Normal Univ Tianjin Key Lab Wireless Mobile Commun & Power Tr Tianjin Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing Peoples R China Univ Strathclyde Elect & Elect Engn Glasgow Lanark Scotland

Cross-modality person re-identification (Re-ID) aims to retrieve a query identity from red, green, blue (RGB) images or infrared (IR) images. Many approaches have been proposed to reduce the distribution gap between RGB modality and IR modality. However, they ignore the valuable collaborative relationship between RGB modality and IR modality. Hybrid Mutual Learning (HML) for cross-modality person Re-ID is proposed, which builds the collaborative relationship by using mutual learning from the aspects of local features and triplet relation. Specifically, HML contains local-mean mutual learning and triplet mutual learning where they focus on transferring local representational knowledge and structural geometry knowledge so as to reduce the gap between RGB modality and IR modality. Furthermore, Hierarchical Attention Aggregation is proposed to fuse local feature maps and local feature vectors to enrich the information of the classifier input. Extensive experiments on two commonly used data sets, that is, SYSU-MM01 and RegDB verify the effectiveness of the proposed method.

关键词： feature extraction local representational knowledge local features Sensor fusion image classification image representation query identity learning (artificial intelligence) RGB modality Algebra Machine learning (artificial intelligence) classifier input cross-modality person re-identification hierarchical attention aggregation image fusion Computer vision and image processing techniques local feature vectors RegDB red green blue images infrared images Image recognition cross-modality person Re-ID distribution gap SYSU-MM01 hybrid mutual learning triplet relation local-mean mutual learning triplet mutual learning infrared imaging image colour analysis structural geometry knowledge local feature maps Image sensors IR modality vectors

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Artificial Immune System of Secure Face Recognition Against Adversarial Attacks

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INTERNATIONAL JOURNAL OF COMPUTER VISION 2024年第12期132卷 5718-5740页

作者： Ren, Min Wang, Yunlong Zhu, Yuhao Huang, Yongzhen Sun, Zhenan Li, Qi Tan, Tieniu Beijing Normal Univ Sch Artificial Intelligence Beijing Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst MAIS Beijing Peoples R China China Acad Railway Sci Postgrad Dept Beijing Peoples R China

Deep learning-based face recognition models are vulnerable to adversarial attacks. In contrast to general noises, the presence of imperceptible adversarial noises can lead to catastrophic errors in deep face recognition models. The primary difference between adversarial noise and general noise lies in its specificity. Adversarial attack methods give rise to noises tailored to the characteristics of the individual image and recognition model at hand. Diverse samples and recognition models can engender specific adversarial noise patterns, which pose significant challenges for adversarial defense. Addressing this challenge in the realm of face recognition presents a more formidable endeavor due to the inherent nature of face recognition as an open set task. In order to tackle this challenge, it is imperative to employ customized processing for each individual input sample. Drawing inspiration from the biological immune system, which can identify and respond to various threats, this paper aims to create an artificial immune system to provide adversarial defense for face recognition. The proposed defense model incorporates the principles of antibody cloning, mutation, selection, and memory mechanisms to generate a distinct "antibody" for each input sample, wherein the term "antibody" refers to a specialized noise removal manner. Furthermore, we introduce a self-supervised adversarial training mechanism that serves as a simulated rehearsal of immune system invasions. Extensive experimental results demonstrate the efficacy of the proposed method, surpassing state-of-the-art adversarial defense methods. The source code is available here, or you can visit this website: https://***/RenMin1991/SIDE

关键词： Adversarial defense Face recognition Artificial immune system Self-supervised adversarial learning

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A Novel Attention-based Global and Local Information Fusion Neural Network for Group Recommendation

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Machine Intelligence Research 2022年第4期19卷 331-346页

作者： Song Zhang Nan Zheng Dan-Li Wang State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China

Due to the popularity of group activities in social media,group recommendation becomes increasingly *** aims to pursue a list of preferred items for a target *** deep learning-based methods on group recommendation have focused on learning group representations from single interaction between groups and ***,these methods may suffer from data sparsity *** for the interaction between groups and users,there also exist other interactions that may enrich group representation,such as the interaction between groups and *** interactions,which take place in the range of a group,form a local view of a certain *** addition to local information,groups with common interests may also show similar tastes on ***,group representation can be conducted according to the similarity among groups,which forms a global view of a certain *** this paper,we propose a novel global and local information fusion neural network(GLIF)model for group *** GLIF,an attentive neural network(ANN)activates rich interactions among groups,users and items with respect to forming a group′s local ***,our model also leverages ANN to obtain a group′s global representation based on the similarity among different ***,it fuses global and local representations based on attention mechanism to form a group′s comprehensive ***,group recommendation is conducted under neural collaborative filtering(NCF)*** experiments on three public datasets demonstrate its superiority over the state-of-the-art methods for group recommendation.

关键词： Group recommendation attentive neural network(ANN) global information local information recommender system

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