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2022 Chinese Automation Congress, CAC 2022

作者： Jing, Sifeng Liu, Xiwei Gong, Xiaoyan Zhao, Hongxia Institute of Automation Chinese Academy of Sciences The State Key Laboratory for Management and Control of Complex Systems Beijing100190 China Qingdao Academy of Intelligent Industries Institute of Smart Education Systems Qingdao266044 China

ISBN: (纸本)9781665465335

With rapid progress of artificial intelligence (AI), it is urgent to promote educational reform to improve comprehensive competence level of primary and secondary school students (CCLPSSS). However, educational reform is a system engineering involving multiple and intertwined factors. In this research, a system dynamics-based model of education reform is developed. Then, based on this model, a series of simulation experiments are carried out to find main factors affecting improvement of CCLPSSS. Results show that: 1) this model could simulate trends of CCLPSSS under different educational reform measures;2) Measures including teaching mode reform, development and application of AI-based new teaching method, implementation of Double Reduction in compulsory education, implementation of deepening education evaluation reform policy, and development and application of AI-based education evaluation technology could play significant roles in promoting CCLPSSS and further strengthening cultivation of AI compound talents. © 2022 IEEE.

关键词： Students

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PiAGI: The Concept, Mechanism and Framework 4

PiAGI: The Concept, Mechanism and Framework

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4th IEEE International Conference on Digital Twins and Parallel Intelligence, DTPI 2024

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

ISBN: (纸本)9798350349252

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. © 2024 IEEE.

关键词： Adversarial machine learning

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An Immersive Learning System with Multimodal Cognitive Processes Inference

An Immersive Learning System with Multimodal Cognitive Proce...

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2022 Chinese Automation Congress, CAC 2022

作者： Bian, Siyu Liu, Xiwei Zhao, Hongxia Gong, Xiaoyan Jing, Sifeng The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Institute of Smart Education Systems Qingdao Academy of Intelligent Industries Qingdao266044 China

ISBN: (纸本)9781665465335

Using multimodal technology to infer learners' cognitive processes is an important means to support personalized learning. Previous cognitive process analysis is more about subjective reasoning, and less about combining physiological signals. With the rapid development of virtual reality technology, new requirements and more immersive schemes for learner's cognitive process inferring have been proposed. Based on this, this paper proposes an immersive learning system with multimodal cognitive processes inference, which is composed of macro, mesoscopic and micro environments. The system analyzes learner's actions, emotions and attention to infer learner's cognitive process through more comprehensive and objective multimodal data. The system also combines eye movement, brainwaves and other data, and proposes a multimodal data acquisition and analysis scheme using HMD-based VR. This paper also provides an immersive learning cognitive processes inference scheme for individual online learning, offline classroom learning scenarios. © 2022 IEEE.

关键词： Learning systems

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Parallel Factories for Smart Industrial Operations:From Big AI Models to Field Foundational Models and Scenarios Engineering

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IEEE/CAA Journal of Automatica Sinica 2022年第12期9卷 2079-2086页

作者： Jingwei Lu Xingxia Wang Xiang Cheng Jing Yang Oliver Kwan Xiao Wang Qingdao Academy of Intelligent Industries Qingdao 266114 The State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190China School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100190 Motion G Inc.Singapore 068898Singapore IEEE School of Artificial Intelligence Anhui UniversityHefei 230031

The rapid advancement of fundamental theories and computing capacity has brought artificial intelligence,internet of things, extended reality, and many other new intelligent technologies into our daily lives. Due to the lack of interpretability and reliability guarantees, it is extremely challenging to apply these technologies directly to real-world industrial systems. Here we present a new paradigm for establishing parallel factories in metaverses to accelerate the deployment of intelligent technologies in real-world industrial systems: QAII-1.0. Based on cyber-physical-social systems,QAII-1.0 incorporates complex social and human factors into the design and analysis of industrial operations and is capable of handling industrial operations involving complex social and human behaviors. In QAII-1.0, a field foundational model called Eu Artisan combined with scenarios engineering is developed to improve the intelligence of industrial systems while ensuring industrial interpretability and reliability. Finally, parallel oil fields in metaverses are established to demonstrate the operating procedure of QAII-1.0.

关键词： Cyber-physical-social system(CPSS) Industry5.0 Metaverses Parallel factories Parallel intelligence

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Unsupervised Domain Adaptation Based Automatic COVID-19 CT Segmentation 7

Unsupervised Domain Adaptation Based Automatic COVID-19 CT S...

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7th International Conference on Image, Vision and Computing, ICIVC 2022

作者： Zhang, Zhen Guo, Dalei University of Chinese Academy of Sciences School of Artificial Intelligence China Chinese Academy of Sciences State Key Lab. of Management and Control for Complex Systems Institute of Automation Beijing China

ISBN: (数字)9781665467346

ISBN: (纸本)9781665467346

The outbreak of COVID-19 has brought huge challenges to health systems around the world. Computed Tomography (CT), as an effective method to screen COVID-19, can provide explainable visual information which can be used for the diagnosis and quantification of COVID-19. It will make diagnosis easier and more effective by providing segmentation results of infections from given CT scans. Deep learning based methods can perform well in tasks that have abundant annotated data, while they may suffer from great performance drops when there is little annotated data available. To collect and annotate CT scans of COVID-19 cases is time-consuming and labor-intensive, which will demand highly-proficient radiology expertise. To tackle this bottleneck, this paper provides a novel unsupervised domain adaptation method that bridges the gap of distributions between the available annotated CT scans and private CT scans, thus reducing dependence on private annotated CT scans. Quantitative analysis demonstrates the effectiveness of our proposed method, which achieves 20 increments in Dice score compared to models without adaptation. © 2022 IEEE.

关键词： COVID-19

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Sustainable Mining in the Era of Artificial Intelligence

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IEEE/CAA Journal of Automatica Sinica 2024年第1期11卷 1-4页

作者： Long Chen Yuting Xie Yutong Wang Shirong Ge Fei-Yue Wang IEEE the State Key Laboratory for Management and Control of Complex Systems at the Institute of Automation Chinese Academy of SciencesBeijing 100190China the National Laboratory of Pattern Recognition at the Institute of Automation and Waytous Ltd.China the School of Computer Science and Engineering Sun Yat-Sen UniversityGuangzhou 510275GuangdongChina the School of Mechanical Electronic and Information Engineering China University of Mining and Technology BeijingBejing 100083China the School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100190China

The mining sector historically drove the global economy but at the expense of severe environmental and health repercussions,posing sustainability challenges[1]-[3].Recent advancements on artificial intelligence(AI)are revolutionizing mining through robotic and data-driven innovations[4]-[7].While AI offers mining industry advantages,it is crucial to acknowledge the potential risks associated with its widespread ***-reliance on AI may lead to a loss of human control over mining operations in the future,resulting in unpredictable consequences.

关键词： Sustainable mining consequences

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TASF: Terrestrial-Aerial Synergistic Framework for a New Generation of Intelligent Transportation 27

TASF: Terrestrial-Aerial Synergistic Framework for a New Gen...

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27th IEEE International Conference on Intelligent Transportation systems, ITSC 2024

作者： Wang, Cong Nie, Dujun Zhang, Ruijun Teng, Siyu Chen, Long Institute of Automation Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China Hong Kong Baptist University Kowloon Hong Kong Waytous Inc. Beijing China Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems The State Key Laboratory of Management and Control for Complex Systems Beijing100190 China Shenzhen518107 China Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Xi'an710049 China

ISBN: (纸本)9798331505929

Terrestrial-Aerial Synergistic Framework (TASF) represents a novel approach to intelligent transportation, integrating terrestrial and aerial vehicles to enhance urban transportation efficiency and capacity. In this paper, we offer an in-depth analysis of TASF, emphasizing its innovative applications in urban transportation, parcel delivery, and emergency response services. TASF incorporates a five-layer architecture comprising perception, communication, planning, control, and interaction. With the five-layer architecture, TASF can achieve effective collaboration among terrestrial and aerial vehicles, encompassing two principal features: terrestrial-aerial synergistic parallel perception and multi-agent parallel management. Parallel perception results from fusing Bird's Eye View (BEV) representations from Unmanned Aerial Vehicles (UAVs) and Unmanned Ground Vehicles (UGVs), whereas decentralized multi-agent management employs foundational models to enhance decision-making capabilities. Additionally, we consider challenges and potential solutions related to TASF, including safety, reliability, energy efficiency, management and integration. TASF marks a paradigm-shifting leap in the realm of intelligent transportation systems, providing a scalable and efficacious solution tailored to the dynamic requirements of urban mobility and emergency services. © 2024 IEEE.

关键词： Decision making

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Generalized Zero-Shot Learning via Implicit Attribute Composition

Generalized Zero-Shot Learning via Implicit Attribute Compos...

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2023 IEEE International Conference on systems, Man, and Cybernetics, SMC 2023

作者： Zhou, Lei Liu, Yang Li, Qiang School of Computer Science and Artificial Intelligence Wuhan University of Technology Wuhan China College of Computer Science and Technology Zhejiang University Hangzhou China Institute of Automation Chinese Academy of Sciences State Key Laboratory for Management and Control of Complex Systems Beijing China

ISBN: (纸本)9798350337020

Zero-shot learning (ZSL) is an important but challenging task in computer vision that aims to identify unseen classes without matching training samples. Current cutting-edge ZSL methods based on locality focus on acquiring the explicit locality of distinguishing characteristics, which could face a lack of adequate supervision at the class attribute level. This paper introduces a novel approach called IAC, which aims to learn Implicit Attribute Composition for ZSL. This method is more comprehensive compared to attribute localization that solely focuses on class-level attribute supervision. IAC utilizes subspace representations that efficiently capture the inherent structure of high-dimensional image features. Then, we learn implicit attribute composition through subspace representation learning. The superiority of the proposed IAC compared to the state-of-the-art is demonstrated through sufficient experiments conducted on three commonly used ZSL datasets, CUB, SUN, and AwA2. © 2023 IEEE.

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Continuous-Time Linear Parallel Output Regulation

Continuous-Time Linear Parallel Output Regulation

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2021 China Automation Congress, CAC 2021

作者： Li, Hongyang Wei, Qinglai Wang, Fei-Yue The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781665426473

A novel parallel output regulation method is presented in this paper. The framework of the parallel output regulation problem is first introduced. Then, the existence of parallel controller is analyzed detailly in the situation that only the measurable information is obtained. Finally, the simulation example is given to show the performance of the presented method. © 2021 IEEE

关键词： Continuous time systems

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Exploring painting synthesis with diffusion models 1

Exploring painting synthesis with diffusion models

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1st IEEE International Conference on Digital Twins and Parallel Intelligence, DTPI 2021

作者： Yi, Da Guo, Chao Bai, Tianxiang Institute of Automation Chinese Academy of Sciences The State Key Laboratory for Management and Control of Complex Systems Beijing China

ISBN: (纸本)9781665433372

As a significant composition of art, fine art painting is becoming a research hotspot in machine learning community. With unique aesthetic value, paintings have quite different representations from natural images, making them irreplaceable. Meanwhile, the lack of training data is common in painting-related machine learning tasks. Therefore, the synthesis of fine art painting is meaningful and challenging work. There are two main types of generative models for image synthesis: generative adversarial networks (GANs) and likelihood-based models. GAN-based models can obtain high-quality samples but usually sacrifice diversity and training stability. Diffusion models are a class of likelihood-based models and have recently been shown to achieve state-of-the-art quality on the image synthesis tasks. In this paper, we explore generating fine art paintings by using diffusion models. We carried out the experiments on the partial impression paintings from the Wikiart dataset. The results demonstrate that the diffusion model can generate high-quality samples, and it is easy to train to cover more target distribution than the GAN-based methods. © 2021 IEEE.

关键词： Generative adversarial networks

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