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Multimodal Perception and Decision-Making systems for complex Roads Based on Foundation Models

IEEE TRANSACTIONS ON systems MAN CYBERNETICS-systems 2024年第11期54卷 6561-6569页

作者： Fan, Lili Wang, Yutong Zhang, Hui Zeng, Changxian Li, Yunjie Gou, Chao Yu, Hui Beijing Inst Technol Sch Automat Beijing 100081 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Beijing Jiaotong Univ Sch Comp & Informat Technol Beijing 100044 Peoples R China Chongqing Univ Coll Mech & Vehicle Engn Chongqing 400044 Peoples R China Beijing Inst Technol Sch Informat & Elect Beijing 100081 Peoples R China Sun Yat Sen Univ Sch Intelligent Syst Engn Shenzhen 518107 Peoples R China Univ Portsmouth Sch Creat Technol Portsmouth PO1 2DJ Hants England

Since the inception of Industry 5.0 in 2021, a growing number of researchers have begun to pay their attention to the revolutionary shift it brings. The principles of Industry 5.0, including human-centric, sustainability, and emphasis on ecological and social values, will become the new paradigm for future industrial development. In this transformative landscape, artificial intelligence (AI) plays a pivotal role, and foundation models based on ChatGPT are set to reshape the organizational structure of industries. In this article, we introduce a multimodal perception and decision-making system built upon a foundational model. This system integrates image and point cloud data to enhance perception accuracy and provide ample information for decision making. It is designed to achieve a deep integration of AI and human-centric autonomous driving within the context of Industry 5.0. We introduce a cross-domain learning approach in the system architecture, along with a model training method from foundation models to handle complex road conditions. The proposed method enables road drivable area segmentation on complex unstructured roads. To address the issue of increased variance caused by the residual structure employed in previous works, this article introduces a distribution correction module, which effectively mitigates this problem. Furthermore, to achieve high-performance perception systems in intricate road scenarios, we put forth a multimodal perception fusion method in this study. The experiments demonstrate the superiority of this approach over single-sensor perception. This work contributes to the ongoing discourse on the convergence of AI, human-centric values, and advanced driving systems within the framework of Industry 5.0.

关键词： Autonomous driving camera and four-dimensional (4-D) millimeter wave radar ChatGPT Industry 5.0 multimodal perception and decision making

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Event-Triggered-Based Consensus Neural Network Tracking control for Nonlinear Pure-Feedback Multiagent systems With Delayed Full-state Constraints

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2024年第4期21卷 7390-7400页

作者： Wang, Xiao-An Zhang, Guang-Ju Niu, Ben Wang, Ding Wang, Xiao-Mei Southeast Univ Sch Math Nanjing 211102 Jiangsu Peoples R China Shandong Normal Univ Sch Informat Sci & Engn Jinan 250014 Shandong Peoples R China Sichuan Univ Sch Elect Engn Chengdu 610065 Peoples R China Shandong Normal Univ Sch Chem Engn & Mat Sci Jinan 250014 Shandong Peoples R China Shandong Normal Univ Sch Informat Sci & Engn Jinan 250014 Shandong Peoples R China Beijing Univ Technol Fac Informat Technol Beijing 100124 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

This paper investigates the consensusability for a class of nonlinear pure-feedback multiagent systems (MASs) with delayed full-state constraints. First, by using a novel state-shifting transformation (SST), the constrained states are encapsulated into some barrier functions such that the new converted state variables and their dynamic model are generated. Then, the essential relationship between the post-transformation tracking errors and the pre-transformation tracking errors are further explained. By employing the radial basis function neural networks (RBF NNs) to deal with the unknown nonlinearities for each agent and introducing a relative threshold method to overcome the waste problem of system transmission resources, an event-triggered control protocol is developed for the considered system. The proposed protocol has its own advantages: 1) This is the first work to consider the pure-feedback MASs with delayed full-state constraints. By transforming the constrained states into unconstrained variables, the states of the MASs can be converged into ideal ranges within a predefined time $T$;2) Under this control protocol, only one adaptive law is necessary to be updated online in each follower and the use efficiency of system resources is improved by using reasonable event triggering mechanism. Finally, the effectiveness of the suggested consensus control protocol is demonstrated by the simulation results. Note to Practitioners-Delayed constraints often encountered in practical applications, which represent a type of constraints that may be violated initially but required to be satisfied sometime after system operation, so it is very necessary and important to converge states into ideal ranges within a predefined time when delayed constraints required. What's more, considering the requirement of high control accuracy in practical application and the current Barrier Lyapunov Function (BLF) methods need to restrict the virtual control signals within some given regio

关键词： Nonlinear multiagent systems delayed full state constraints event-triggered design asymptotic tracking control adaptive control

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Parallel Theaters in CPSS: From Shadows of ISDOS to Intelligence of Decision Theaters

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IEEE/CAA Journal of Automatica Sinica 2025年第6期12卷 1059-1062页

作者： Qinghua Ni Buday Viktória Fei Lin Jun Huang Levente Kovács Nan Zheng Fei-Yue Wang Department of Engineering Science Faculty of Innovation Engineering Macau University of Science and Technology Macau China Innovation Management Doctoral School Obuda University Budapest Hungary John von Neumann Faculty of Informatics Obuda University Budapest Hungary State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China DeSci Center of Parallel Intelligence and the University Research and Innovation Center at Obuda University State Key Laboratory for Management and Control of Complex Systems Chinese Academy of Sciences Beijing China

As Artificial Intelligence (AI) is moving fast from Large Language Models (LLMs) to AI Agents and Agentic Intelligence, the need to incorporate new AI into Decision Intelligence (DI) is becoming more and more urgent for both practical and theoretic reasons: both decision and process complexities would be significantly increased due to the use of advanced AI tools and agents, and both traditional and recent thinking must be rethought and reconstructed accordingly. Our perspective would like to address this important issue based on some historical milestone developments in Computer-Aided Software Engineering (CASE) and recent efforts in digital theatrical technology [1].

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A Simplified Variant for Graph Convolutional Network Based Knowledge Graph Completion Model 8

A Simplified Variant for Graph Convolutional Network Based K...

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8th International Conference on Data Science in Cyberspace, DSC 2023

作者： Wang, Yashen Li, Qiang Zhang, Yi Zhang, Xuecheng Ju, Zhuoya Sun, Jian Beijing China Scientific and Technological Innovation Center of Ari Institute of Automation Chinese Academy of Sciences The State Key Laboratory for Management and Control of Complex Systems Beijing China Cetc Academy of Electronics and Information Technology Group Co. Ltd. Beijing China Khoury College of Computer Science Northeastern University United States Scientific and Technological Innovation Center of Ari Beijing China Qi An Xin Technology Group Inc. Beijing China

ISBN: (纸本)9798350331035

Knowledge graphs (KGs) serve as useful resources for various applications including machine learning, data mining, and artificial intelligence. Knowledge Graph Completion (KGC) aims at reasoning over known (observed) facts and infer the missing (unobserved) relationships, to improve KG's coverage. Many recent works have laid stress on leveraging Graph Convolutional Networks (GCNs) for KGC task, which obtain good representation learning ability for graph-structured data, and have achieved satisfactory results. However, GCN-based researches continue to improve these lines of models by introducing an increasingly complex feature space. Especially, the hyper-parameter space of conventional GCN-based KGC model is too huge, resulting in low computational efficiency and excessive storage pressure. To overcome this problem, this paper investigates a simplified and generic variant for GCN-based KGC model, and shows that simple changes in the architecture of the underlying architecture (with a Subtraction Operator and an Addition Operator), can compte with state-of-the-art GCN-based models. Therefore, this paper proposes a easy-to-accomplish plug-in unit, which could be loaded into any current GCN-based KGC architectures, and help them maximize the maintenance of native model's original performance while significantly reducing parameter scale. © 2023 IEEE.

关键词： Graph Convolutional Network Knowledge Graph Knowledge Graph Completion Representation Learning

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Learning Representation with Q-irrelevance Abstraction for Reinforcement Learning 11

Learning Representation with Q-irrelevance Abstraction for R...

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11th International Conference on Intelligent control and Information Processing, ICICIP 2021

作者： Hao, Shuai Li, Luntong Liu, Minsong Zhu, Yuanheng Zhao, Dongbin Institute of Automation Chinese Academy of Sciences State Key Laboratory of Management and Control for Complex Systems Beijing China School of Software Beihang University Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781665425155

In order to improve the performance of deep reinforcement learning (DRL) algorithm in high-dimensional observation environments, we propose a new auxiliary task to learn representations to aggregate task-relevant information of observations. Inspired by Q-irrelevance abstraction, our auxiliary task trains a deep Q-network (DQN) to predict the true Q value distribution over all discrete actions. Then we use the output of DQN to train the encoder to discriminate states with different Q values. The encoder is used as the representation of proximal policy optimization (PPO). The resulting algorithm is called as Q-irrelevance Abstraction for Reinforcement Learning (QIARL). After training, the encoder can aggregate states with similar Q value distributions together for any policy and any action. Thus the encoder can encode the important information that is relevant to reinforcement learning task. We test QIARL in four Procgen environments compare with PPO, A2C and Rainbow. The experimental results show QIARL outperforms the other three algorithms. © 2021 IEEE.

关键词： Reinforcement learning

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Selective feature connection mechanism: Concatenating multi-layer CNN features with a feature selector

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PATTERN RECOGNITION LETTERS 2020年 129卷 108-114页

作者： Du, Chen Wang, Chunheng Wang, Yanna Shi, Cunzhao Xiao, Baihua Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing Peoples R China

Different layers of deep convolutional neural networks(CNNs) can encode different-level information. High-layer features always contain more semantic information, and low-layer features contain more detail information. However, low-layer features suffer from the background clutter and semantic ambiguity. During visual recognition, the feature combination of the low-layer and high-level features plays an important role in context modulation. If directly combining the high-layer and low-layer features, the background clutter and semantic ambiguity may be caused due to the introduction of detailed information. In this paper, we propose a general network architecture to concatenate CNN features of different layers in a simple and effective way, called Selective Feature Connection Mechanism (SFCM). Low-level features are selectively linked to high-level features with a feature selector which is generated by high-level features. The proposed connection mechanism can effectively overcome the above-mentioned drawbacks. We demonstrate the effectiveness, superiority, and universal applicability of this method on multiple challenging computer vision tasks, including image classification, scene text detection, and image-to-image translation. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Feature combination Network architecture Selective feature connection mechanism Convolutional neural network

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ACP-Net: Asymmetric Center Positioning Network for Real-Time Text Detection

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KNOWLEDGE-BASED systems 2024年 305卷

作者： Zhu, Boyuan Liu, Fagui Chen, Xi Tang, Quan Chen, C. L. Philip South China Univ Technol 342 Outer Ring East Rd Guangzhou 510006 Peoples R China Peng Cheng Lab 2 Xingke 1st St Shenzhen 518055 Nanshan Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst 95 Zhongguancun East Rd Beijing 100190 Peoples R China

Scene text detection is crucial across numerous application fields. However, despite the emphasis on real-time performance in scene text detection, most existing detection models utilize the Feature Pyramid Network (FPN) for feature extraction, often disregarding its inherent limitations. Integrating high-resolution multi-channel features into FPN requires substantial computational resources. While FPN treats local and global features equally and is stable in various applications, its suitability for text-specific features is questionable. To this end, we propose the Asymmetric Center Positioning Network (ACP-Net) to replace FPN, achieving accuracy and real-time text detection in complex scenarios. ACP-Net features an asymmetric feature structure with independent branches for global and local information, along with an adaptive weighted fusion module to capture long-range dependencies effectively. In addition, a text center positioning module enhances text feature understanding by learning feature centers. Comprehensive evaluations across various terminals confirmed ACP-Net's superior accuracy and speed.

关键词： Text Detection Real-Time Asymmetric Center Positioning Network

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PiAGI: The Concept, Mechanism and Framework

PiAGI: The Concept, Mechanism and Framework

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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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Finite-Time Observer-Based Variable Impedance control of Cable-Driven Continuum Manipulators

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IEEE TRANSACTIONS ON HUMAN-MACHINE systems 2022年第1期52卷 26-40页

作者： Liang, Xu He, Guangping Su, Tingting Wang, Weiqun Huang, Can Zhao, Quanliang Hou, Zeng-Guang North China Univ Technol Dept Mech & Elect Engn Beijing 100144 Peoples R China 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 CAS Ctr Excellence Brain Sci & Intelligence Techn Beijing 100190 Peoples R China Macau Univ Sci & Technol Inst Syst Engn Joint Lab Intelligence Sci & Technol Macau Peoples R China

Human-robot interaction has been widely studied, where compliant and safe human-robot interaction of continuum manipulators in the constrained environment is one of the key issues that have not been well addressed. In this study, finite-time observer-based variable impedance control of cable-driven continuum manipulators (CDCM) is proposed to overcome the limitation of existing methods. First, the pseudo-rigid modeling method is utilized to establish the kinematics and dynamics of the CDCM. Then, a variable impedance controller with selected controller parameters for the CDCM is designed to realize compliant and safe human-robot interaction operations with force-position coupling constraint, which is rarely studied in the literature. In order to realize the closed-loop variable impedance controller, a finite-time observer is designed to estimate acceleration feedbacks, which can avoid the difficulty of directly sensing the interaction forces and shows excellent robust stability in noisy environments. On this basis, by combining the advantages of the variable impedance controller and finite-time observer, the finite-time observer-based variable impedance controller is proposed, and the stabilities of the proposed method are analyzed. Finally, the feasibility of the proposed control scheme for the CDCM is demonstrated by some numerical simulations.

关键词： Manipulators Impedance Kinematics Observers Manipulator dynamics Robots Task analysis Compliant robots continuum manipulators control finite-time stabilization variable impedance

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The pathological risk score: A new deep learning-based signature for predicting survival in cervical cancer

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CANCER MEDICINE 2023年第2期12卷 1051-1063页

作者： Chen, Chi Cao, Yuye Li, Weili Liu, Zhenyu Liu, Ping Tian, Xin Sun, Caixia Wang, Wuliang Gao, Han Kang, Shan Wang, Shaoguang Jiang, Jingying Chen, Chunlin Tian, Jie Beihang Univ Sch Med & Engn Beijing Adv Innovat Ctr Big Data Based Precis Med Beijing Peoples R China Chinese Acad Sci Beijing Key Lab Mol Imaging State Key Lab Management & Control Complex Syst CAS Key Lab Mol ImagingInst Automat Beijing Peoples R China Southern Med Univ Nanfang Hosp Dept Obstet & Gynecol 1838 Guangzhou Ave North Guangzhou 510515 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China Henan Med Univ Affiliated Hosp 2 Dept Obstet & Gynecol Zhengzhou Peoples R China Hebei Med Univ Hosp 4 Dept Gynecol Shijiazhuang Hebei Peoples R China Yantai Yuhuangding Hosp Dept Gynecol Yantai Peoples R China Beihang Univ Minist Ind & Informat Technol Key Lab Big Data Based Precis Med Beijing Peoples R China

Purpose To develop and validate a deep learning-based pathological risk score (RS) with an aim of predicting patients' prognosis to investigate the potential association between the information within the whole slide image (WSI) and cervical cancer prognosis. Methods A total of 251 patients with the International Federation of Gynecology and Obstetrics (FIGO) Stage IA1-IIA2 cervical cancer who underwent surgery without any preoperative treatment were enrolled in this study. Both the clinical characteristics and WSI of each patient were collected. To construct a prognosis-associate RS, high-dimensional pathological features were extracted using a convolutional neural network with an autoencoder. With the score threshold selected by X-tile, Kaplan-Meier survival analysis was applied to verify the prediction performance of RS in overall survival (OS) and disease-free survival (DFS) in both the training and testing datasets, as well as different clinical subgroups. Results For the OS and DFS prediction in the testing cohort, RS showed a Harrell's concordance index of higher than 0.700, while the areas under the curve (AUC) achieved up to 0.800 in the same cohort. Furthermore, Kaplan-Meier survival analysis demonstrated that RS was a potential prognostic factor, even in different datasets or subgroups. It could further distinguish the survival differences after clinicopathological risk stratification. Conclusion In the present study, we developed an effective signature in cervical cancer for prognosis prediction and patients' stratification in OS and DFS.

关键词： cervical cancer deep learning disease-free survival overall survival whole slide image

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