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A New Architecture and Mechanism for Decentralized Science MetaMarkets

IEEE TRANSACTIONS ON systemS MAN CYBERNETICS-systemS 2023年第9期53卷 5321-5330页

作者： Ding, Wenwen Li, Juanjuan Qin, Rui Kozma, Robert Wang, Fei-Yue Macau Univ Sci & Technol Macao Inst Syst Engn Macau Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Multimodal Artificial Intelligence S Beijing 100190 Peoples R China Univ Memphis Dept Math Memphis TN 38152 USA Obuda Univ H-1034 Budapest Hungary Chinese Acad Sci State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

The new generation of digital intelligence technology enables knowledge creation, dissemination, and application to undergoing parallel changes. Scientific systems face an increasingly uncertain, diverse, and complex environment, making adopting multidisciplinary, interdisciplinary, and transdisciplinary approaches to research issues inevitable. Existing scientific systems follow linear value streams, leading to problems, such as inefficiency, unfairness, and knowledge monopoly. Decentralized science (DeSci) is a new scientific development paradigm based on Web3, Metaverses, and decentralized autonomous organizations and operations (DAOs) technologies, that can solve organizational and management problems in scientific systems through organizing, coordinating, and executing techniques. However, new economic theories and methods are still needed to effectively solve the problem of linear value flow in scientific systems. Metaeconomics based on the parallel intelligence theory, also known as decentralized economics (DeEco), provides a new approach and idea for redesigning the economic system of scientific markets. Thus, this article proposes a research framework and core mechanisms of DeSci MetaMarkets based on parallel economic theory to provide effective and practical methodologies for scientific system governance.

关键词： Economics Production Knowledge engineering Organizations complex systems Personnel Monopoly Decentralized autonomous organizations and operations (DAOs) decentralized autonomous operations and organizations decentralized science (DeSci) decentralized economics (DeEco) decentralized management (DeMana) DeSci metaeconomics MetaMarkets metaverses parallel intelligence Web3

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EEG-Based Focus of Attention Tracking and Regulation During Dual-Task Training for Neural Rehabilitation of Stroke Patients

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IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING 2023年第3期70卷 920-930页

作者： Wang, Jiaxing Wang, Weiqun Hou, Zeng-Guang Chinese Acad Sci Inst Automation State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence State Key Lab Management & Control Complex Syst Beijing 100049 Peoples R China CAS Ctr Excellence Brain Sci & Intelligence Techno Beijing 100190 Peoples R China Macau Univ Sci & Technol Inst Syst Engn CASIA MUST Joint Lab Intelligence Sci & Technol Macau 999078 Peoples R China

Dual-task training under variable-priority instructions (DT-VP), during which subjects are required to vary their focus of attention (FOA) between two concurrent tasks, has shown a more significant improvement in neural rehabilitation than that under fixed-priority instructions. Failed FOA switching not only diminishes the recovery benefits, but also causes anxieties, which is detrimental to rehabilitation. Developing a strategy for tracking and regulating patients' FOA to achieve a better performance in task priority-following during DT-VP is thus imperative. In this study, fifteen stroke patients participated in DT-VP that comprised two tasks: a mathematical problem-solving task and a cycling task, during which their electroencephalograms were recorded simultaneously. The significantly differentiated power spectra of four brain regions extracted from single-task training were fed into a support vector machine to build a FOA tracking algorithm for patients' attention assessment during the DT-VP. Moreover, dual-task difficulty adaptation method was designed to regulate patients' FOA when their FOA and the high-priority task were not coincident. The comparison experimental results showed that the proposed method significantly improved patients' FOA distributed on the high-priority task (analysis of variance, p < 0.05). Meanwhile, the absolute power spectral densities of the motor cortex and the frontal region could also be improved during DT-VP under high motor and cognitive task priority instructions, respectively. These phenomena demonstrated the feasibility of the proposed method in helping stroke patients better implement FOA switching and maintenance.

关键词： Task analysis Training Stroke (medical condition) Electroencephalography Regulation Mathematical models Switches Neural rehabilitation brain-computer interface attention regulation difficulty adaptation task priority-following

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learning anomalous human actions using frames of interest and decoderless deep embedded clustering

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INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 2023年第10期14卷 3575-3589页

作者： Javed, Muhammad Hafeez Yu, Zeng Li, Tianrui Anwar, Noreen Rajeh, Taha M. M. Southwest Jiaotong Univ Sch Comp & Artificial Intelligence Chengdu 611756 Peoples R China Chinese Acad Sci Control Complex Syst Inst Automation State Key Lab Management Beijing Peoples R China

Inconsistent data and unclear labels make it difficult to learn anomalous behavior from video. Therefore, methods based on deep clustering are now trending in this area. A deep clustering strategy usually relies on encoding and reconstruction to facilitate information discovery. However, it seems pointless to reconstruct the input after the model's learning process is already concluded. On the other hand, multiple input types carry various features which may help identify the problem more accurately. Hence to mitigate the requirement of utilizing assorted features with clustering, we propose Skeletal Based Autoencoder (SKELBA), which allows us to process the different types of inputs parallelly. The model consists of a spatial graph convolution operator, which helps us convolve the skeletal data more precisely. A decoder-less deep clustering architecture is introduced to enhance the stability of clustering. The relation between reconstruction error and minimizing the lower bound of mutual information (MI) helps us look into decoder-free systems. The joint venture of local-global feature collection and decoder-free encoders techniques shows improved results. Extensive experiments performed on the various benchmark datasets highlight the proposed model's superiority among recently proposed approaches in the same field.

关键词： Deep learning

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Visual Place Recognition via a Multitask Learning Method With Attentive Feature Aggregation

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IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL systemS 2023年第3期15卷 1263-1278页

作者： Guan, Peiyu Cao, Zhiqiang Yu, Junzhi Tan, Min Wang, Shuo 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 Coll Engn Dept Adv Mfg & Robot State Key Lab Turbulence & Complex Syst Beijing 100871 Peoples R China

Visual place recognition has gained popularity in recent years. Mainstream convolutional neural network-based methods formulate it as a ranking task and optimize it in the paradigm of deep metric learning, however, the ranking-motivated losses concern only the ranking relationship for each query image and the compactness of intraplace feature distribution is seldom considered. It is still challenging due to varying viewpoints, illuminations, and even dynamic objects. In this article, a novel multitask learning framework is proposed, which combines the existing triplet ranking task and our designed binary classification task to jointly optimize the network for better generalization capability. Specifically, a binary classification network with the corresponding binary cross-entropy loss is designed in the classification task. In this way, the intraplace feature compactness and interplace feature separability are reinforced. At the testing stage, this classification network is discarded without increasing the computation cost. Furthermore, an attention module is presented to promote the network to concentrate on the salient regions by assigning different importance to each spatial position. Our method achieves the top-10 recalls of 97.27%, 94.6%, and 96.93% on Pitts250k-test, Tokyo 24/7, and TokyoTM-val data sets, respectively. Extensive experiments prove that the proposed network can learn discriminative global features with better robustness to viewpoints and environmental variations.

关键词： Attentive feature aggregation multitask learning visual place recognition

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Scenarios Engineering: Enabling Trustworthy and Effective AI for Autonomous Vehicles

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES

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IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2023年第5期8卷 3205-3210页

作者： Li, Xuan Wang, Fei-Yue Peng Cheng Lab Dept Math & Theories Shenzhen 518055 Peoples R China Chinese Acad Sci Inst Automation State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

The first journal impact factor (IF) of IEEE TIV is 5.009 with a CiteScore = 10.9 in 2022, signifying a promising start for our periodical. By April 2023, our real-time IF stands at 7.03 according to Web of Science, and CiteScore from Elsevier is 11.6. These numbers indicate that IEEE TIV is among the top-tier publications in the related fields.

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Autonomous Services: The Evolution of Services Through Intelligent Vehicles

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES

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IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2023年第11期8卷 4468-4473页

作者： Jing, Zheng Li, Lefei Lyu, Yudan Wang, Ridong Wang, Yutong Wang, Ding Wang, Fei-Yue Tsinghua Univ Dept Ind Engn Beijing 100084 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Shanghai Artificial Intelligence Lab Urban Comp Lab Shanghai 200235 Peoples R China Chinese Acad Sci State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Macau Univ Sci & Technol Macao Inst Syst Engn Taipa 999078 Macao Peoples R China

The emergence of intelligent vehicles brings unique opportunities for the service industry. This study introduces the concept of autonomous services, a new service paradigm, and autonomous services systems, in which intelligent vehicles are vital enabling technology. Specifically, autonomous services aim to minimize unnecessary human participation and effort in the service process by leveraging intelligence for service delivery, rather than relying on simple stimulus-response or rule-based program behavior. This letter reports on the first Decentralized and Hybrid Workshop (DHW) on autonomous services, aiming to reduce the cost of human labor and improve the quality and efficiency of service while tackling the challenges posed by a shrinking workforce. The introduction of intelligent vehicles enhances the capability of the service systems, while also significantly increasing complexity. To address the challenges associated with complexity, the systems Engineering (SE) approach is indispensable. The Requirements-Functional-Logical-Physical (RFLP) framework can implement Model-Based system Engineering (MBSE) and help researchers and managers to understand the autonomous services system more comprehensively, so as to better operate and manage it. We substitute "Implementation" for "Physical" in this research to define and elaborate on the autonomous services system. Finally, we outline the potential research opportunities within the autonomous services system.

关键词： Intelligent vehicles autonomous services cyber-physical-social systems(CPSSs) parallel intelligence requirement-functional-logical-physical(RFLP) framework

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Mobility 5.0: Smart Logistics and Transportation Services in Cyber-Physical-Social systems

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES

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IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2023年第6期8卷 3527-3532页

作者： Lin, Yilun Na, Xiaoxiang Wang, Ding Dai, Xingyuan Wang, Fei-Yue Shanghai Artificial Intelligence Lab Urban Comp Lab Shanghai 200235 Peoples R China Univ Cambridge Dept Engn Cambridge CB2 1PZ England Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100090 Peoples R China Chinese Acad Sci State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Macau Univ Sci & Technol Macao Inst Syst Engn Taipa 999078 Macao Peoples R China

The logistics and transportation industry has made significant progress in improving sustainability, efficiency, and accessibility, primarily due to the continuous development of technologies, including clean energy, advanced algorithms, unmanned vehicles, and privacy-enhancing techniques. Along with these advancements, the Mobility 5.0 paradigm has been proposed in the ITSS Intelligent Vehicle 5.0 project meeting. This letter discusses the changes in the supply and demand of mobility resources and looks at the basic components on which the implementation of the Mobility 5.0 relies on. By integrating emerging technologies into Cyber-Physical-Social systems (CPSS), we believe that Mobility 5.0 has tremendous potential in shaping a new era of intelligent logistics and transportation services and will bring significant benefits to individuals, businesses, the government, and the entire society.

关键词： Mobility logistics transportation cyber-physical-social systems

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Robot-Guided Crowd Evacuation in a Railway Hub Station in Case of Emergencies

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JOURNAL OF INTELLIGENT & ROBOTIC systemS 2022年第4期104卷 1-14页

作者： Zhou, Min Dong, Hairong Ge, Shichao Wang, Xiao Wang, Fei-Yue Beijing Jiaotong Univ State Key Lab Rail Traff Control & Safety Beijing 100044 Peoples R China Chinese Acad Sci CASIA Inst Automat State Key Lab Management & Control Complex Syst S Beijing 100190 Peoples R China

The rapid and orderly evacuation of passengers at the railway hub station in case of emergencies is an important issue for railway safety and efficiency. In this paper, a robot-guided passenger evacuation method is proposed to help passengers search evacuation paths and avoid potential risks. The number and initial positions of robots are determined by using a k-means clustering approach. The exit assignment and evacuation paths of robots are calculated by using a hybrid bi-level optimization approach taking into account the cooperative mechanism between robots. Then, a robot-guided crowd evacuation dynamical model is built based on a modified social force model, in which a navigation force is introduced to influence the speed and direction of evacuees. A case study of a typical railway hub station is used to demonstrate the effectiveness of the proposed approach. The scenarios of the mall and platform are designed to verify the evacuation efficiency under different robot distribution schemes. The experimental results prove that setting up robots can effectively reduce evacuation time, and the utilization of exits is more balanced. The proposed optimal scheme shows the best performance in evacuation efficiency, including evacuation time and exit utilization rate, compared to the uniform distribution scheme and no robot scheme.

关键词： Robot guided crowd evacuation Modified social force model Railway hub station Robot distribution Cooperative mechanism Emergencies

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Finite-time filtering of T-S fuzzy semi-Markov jump systems with asynchronous mode-dependent delays

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JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS 2023年第16期360卷 12707-12728页

作者： Ma, Chao Fu, Hang Wu, Wei Univ Sci & Technol Beijing Sch Mech Engn Beijing 100083 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

This paper studies the finite-time filter design of semi-Markov jump system by Takagi-Sugeno (T-S) fuzzy rules. Asynchronous mode-dependent delays are concerned to describe more practical random features, which implies that the semi-Markov process of system state is different from another semiMarkov process of mode-dependent time-varying delays. With the help of model transformation as well as mode-dependent Lyapunov-Krasovskii function method, sufficient criteria are first established for maintaining finite-time passivity performance of filtering errors. Then, the matrix technique is employed to synthesize the filter gain design. In the end, the applicableness and effectiveness of our developed results are confirmed by an illustrative example. (c) 2021 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.

关键词： Markov processes

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Online Model Predictive control of Robot Manipulator With Structured Deep Koopman Model

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IEEE ROBOTICS AND AUTOMATION LETTERS 2023年第5期8卷 3102-3109页

作者： Zhang, Jinxin Wang, Hongze Chinese Acad Sci Inst Automation State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China

The model predictive control (MPC) can provide the benefit of optimality (sub-optimality, exactly speaking) and explicitly treat hard constraints in both states and inputs, which makes it an attractive approach in the fields of robotics. However, the performance of this approach heavily depends on the system model and it is computationally intensive, which hinders its application in the real-time control of robotic systems with fast dynamics, such as the robotic manipulators. Data-driven modeling approaches based on the Koopman operator have the potential to remove the barriers to adopting the MPC in robotics, through learning a globally linear model. In this letter, we propose a novel Koopman model-the structured deep Koopman model, which can improve the accuracy of the learned linear model and reduce the number of states in the lifted space, through exploiting the deep Lipschitz neural network and making the lifted dynamics structured. We also prove the rationality of the presented method and provide a new perspective on Koopman operator-based models, which brings the local and global linearization methods under the same umbrella. The effectiveness of the presented method has been verified by simulations and a real-world robotic experiment.

关键词： Deep learning in robotics and automation model learning for control Koopman operator model predictive control Lipschitz neural network

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