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Toward parallel intelligence: An interdisciplinary solution for complex systems

The Innovation 2023年第6期4卷 152-164页

作者： Yong Zhao Zhengqiu Zhu Bin Chen Sihang Qiu Jincai Huang Xin Lu Weiyi Yang Chuan Ai Kuihua Huang Cheng He Yucheng Jin Zhong Liu Fei-Yue Wang College of Systems Engineering National University of Defense TechnologyChangsha 410073China Hunan Institute of Advanced Technology Changsha 410073China Shanghai Institute of Infectious Disease and Biosecurity Shanghai 200032China Institute of Epidemiology Helmholtz Zentrum Minchen-German Research Center for Environmental Health(GmbH)NeuherbergGermany Department of Computer Science Hong Kong Baptist UniversityHong KongChina State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China

The growing complexity of real-world systems necessitates interdisciplinary solutions to confront myriad challenges in modeling,analysis,management,and *** meet these demands,the parallel systems method rooted in the artificial systems,computational experiments,and parallel execution(ACP)approach has been *** method cultivates a cycle termed parallel intelligence,which iteratively creates data,acquires knowledge,and refines the actual *** the past two decades,the parallel systems method has continuously woven advanced knowledge and technologies from various disciplines,offering vers atile interdisciplinary solutions for complex systems across diverse *** review explores the origins and fundamental concepts of the parallel systems method,showcasing its accomplishments as a diverse array of parallel technologies and applica-tions while also prognosticating potential *** posit that this method will considerably augment sustainable development while enhancing interdisciplinary communication and cooperation.

关键词： execution artificial offering

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A BERT-based Heterogeneous Graph Convolution Approach for Mining Organization-Related Topics

A BERT-based Heterogeneous Graph Convolution Approach for Mi...

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IEEE International Conference on Fuzzy systems (FUZZ-IEEE) / IEEE World Congress on Computational Intelligence (IEEE WCCI) / International Joint Conference on Neural Networks (IJCNN) / IEEE Congress on Evolutionary Computation (IEEE CEC)

作者： Qian, Haoda Yuan, Minjie Li, Qiudan Zeng, Daniel 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

ISBN: (数字)9781728186719

ISBN: (纸本)9781728186719

Mining organization-related topics is helpful to analyze the information dissemination situation. Existing methods based on graph neural networks mainly consider the association between words and documents, they ignore the semantic interactions between documents, and do not consider the heterogeneity of edges which are difficult to solve the challenge of blurred topic boundaries in real scenarios, resulting in performance loss. This paper proposes a BERT-based Heterogeneous Graph Convolution Network (BERT-HGCN) approach for semi-supervised topic mining that comprehensively considers multi-semantic relations between words and documents. It deeply combines the advantages of transductive learning with pre-training models. We model documents as graph-structured data and capture multiple semantic dependencies among wordword, word-doc, and doc-doc via information propagation mechanism. During the model learning process, a two-stream encoding mechanism is used to learn the structural and semantic representations, which combines a hierarchical graph convolution network (HGCN) and a BERT-based auto-encoder. It considers both edges heterogeneity and semantics of original documents. Finally, a dual-supervision loss is used to train the classifier based on graph nodes and semantic representations for topic mining. We empirically evaluate the performance of the proposed model on a real-world organization-related dataset, and the experimental results demonstrate the efficacy of the model.

关键词： topic mining multi-level semantic heterogeneous graph neural network pre-training model

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Sora for foundation robots with parallel intelligence:three world models,three robotic systems

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Frontiers of Information Technology & Electronic Engineering 2024年第7期25卷 917-923页

作者： Lili FAN Chao GUO Yonglin TIAN Hui ZHANG Jun ZHANG Fei-Yue WANG School of Information and Electronics Beijing Institute of TechnologyBeijing 100081China State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China School of Computer and Information Technology Beijing Jiaotong UniversityBeijing 100044China School of Electrical Engineering Wuhan UniversityWuhan 430072China

This paper outlines the initial steps and basic framework for developing foundation/infrastructure robots/robotics based on foundation models and parallel intelligence,as well as the potential applications of new art... 详细信息

关键词： artificial foundation intelligence

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Adversarial Binary Mutual Learning for Semi-Supervised Deep Hashing

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING systems 2022年第8期33卷 4110-4124页

作者： Wang, Guanan Hu, Qinghao Yang, Yang Cheng, Jian Hou, Zeng-Guang 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 Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit Beijing 100190 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 Joint Lab Intelligence Sci & Technol Inst Syst Engn Macau 999078 Peoples R China

Hashing is a popular search algorithm for its compact binary representation and efficient Hamming distance calculation. Benefited from the advance of deep learning, deep hashing methods have achieved promising performance. However, those methods usually learn with expensive labeled data but fail to utilize unlabeled data. Furthermore, the traditional pairwise loss used by those methods cannot explicitly force similar/dissimilar pairs to small/large distances. Both weaknesses limit existing methods' performance. To solve the first problem, we propose a novel semi-supervised deep hashing model named adversarial binary mutual learning (ABML). Specifically, our ABML consists of a generative model GH and a discriminative model DH, where DH learns labeled data in a supervised way and GH learns unlabeled data by synthesizing real images. We adopt an adversarial learning (AL) strategy to transfer the knowledge of unlabeled data to DH by making GH and DH mutually learn from each other. To solve the second problem, we propose a novel Weibull cross-entropy loss (WCE) by using the Weibull distribution, which can distinguish tiny differences of distances and explicitly force similar/dissimilar distances as small/large as possible. Thus, the learned features are more discriminative. Finally, by incorporating ABML with WCE loss, our model can acquire more semantic and discriminative features. Extensive experiments on four common data sets (CIFAR-10, large database of handwritten digits (MNIST), ImageNet-10, and NUS-WIDE) and a large-scale data set ImageNet demonstrate that our approach successfully overcomes the two difficulties above and significantly outperforms state-of-the-art hashing methods.

关键词： Data models Semantics Force Computational modeling Hash functions Binary codes Training data Adversarial learning (AL) deep learning hashing

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Optimization of Sucker Rod Pump Operations Using Parallel systems

IEEE JOURNAL OF RADIO FREQUENCY IDENTIFICATION

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IEEE JOURNAL OF RADIO FREQUENCY IDENTIFICATION 2022年 6卷 977-981页

作者： Cheng, Xiang Wang, Xingxia Liu, Shuliang Lu, Jingwei Zhang, Zhixue Liu, Zhaohai Wang, Fei-Yue Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Macau Univ Sci & Technol Macau Inst Syst Engn Macau Peoples R China Beijing Mot G Intelligent Technol Co Ltd Prod Management Dept Beijing Peoples R China Tangshan Jidong Petr Machinery Co Ltd Tangshan 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 101408 Peoples R China

With the exploitation of oil, oil pumping units are facing with the challenges such as low efficiency, increased mining difficulties and old infrastructures. The requirement of digital construction on oilfields is emerging, especially to these sucker sod pump (SRP) units built in last century. In this article, to facilitate the intelligent development of SRP systems, the concept of the parallel digital sucker rod pump systems (PDSRPS) was presented first, and their optimization framework was designed to efficiently improve the design and balance by data interactions. The real oil pump systems were integrated with their customized digital twins to establish the cyber-physical systems. The adaptive optimization on pumping processes was continuously developing with the interaction between physical pump units and their digital versions. The feasibility of the proposed framework has been demonstrated through the computational experiments on conventional beam-pumping units.

关键词： Parallel systems digital twins sucker rod pump operation optimization intelligent oilfields

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An IMU Error Correction Method Based on Multithreshold ZV Detection and SRCKF

An IMU Error Correction Method Based on Multithreshold ZV De...

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2023 International Conference on Cyber-Physical Social Intelligence, ICCSI 2023

作者： Luo, Jialiang Chen, Shichao Lv, Yisheng Sun, Wenqiao Yang, Fan Xu, Ruijie Institute of Automation Chinese Academy of Sciences School of Information Engineering China University of Geosciences Beijing The State Key Laboratory for Management and Control of Complex System State Key Laboratory of Multimodal Artificial Intelligence Systems China Institute of Automation Chinese Academy of Sciences The Center of National Railway Intelligent Transportation System Engineering and Technology China Academy of Railway Sciences Corporation Limited The State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing China Institute of Automation Chinese Academy of Sciences The State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing China Transportation and Economics Research Institute Center of National Railway Intelligent Transportation System Engineering and Technology China Academy of Railway Sciences Corporation Limited China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences College of Rail Transit Shan Dong JiaoTong University The State Key Laboratory for Management and Control of Complex System Jinan China The Hong Kong Polytechnic University Department of Aeronautical and Aviation Engineering Hong Kong Hong Kong

ISBN: (纸本)9798350312492

With the rapid development and widespread application of Inertial Measurement Units (IMUs), IMU-based human localization has become critically important in environments lacking Global Navigation Satellite systems (GNSS) signals. Methods such as UWB/WIFI and other anchor-based localization have been employed to enhance the positioning accuracy. However, these methods are susceptible to the environmental interference and increased costs. In contrast, SINS-based localization methods eliminate reliance on the external data. However, this method can suffer from drift errors due to cumulative integration errors. Inertial navigation error correction techniques like Zero Detection Update Technology (ZUPT) provide potential solutions to address drift errors. This paper proposes an error correction method based on the multithreshold zero-velocity detection and Square Root Cubature Kalman Filter (SRCKF) updates. Compared to the single fixed threshold detection, the multi-threshold zero-velocity detection reduces the likelihood of missed zero velocity detection and false alarms, providing accurate time intervals for error correction. Experimental results demonstrate that the SRCKF-based algorithm reduces position deviation to approximately 0.3146 meters, corresponding to an error ratio of1.14%. The proposed method enables cost-effective and accurate pedestrian localization, driving advancements in the field. © 2023 IEEE.

关键词： Cost effectiveness

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Fuzzy C-Means Clustering Based Deep Patch Learning With Improved Interpretability for Classification Problems

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IEEE ACCESS 2022年 10卷 49873-49891页

作者： Huang, Yunhu Chen, Dewang Zhao, Wendi Lv, Yisheng Fuzhou Univ Coll Comp & Data Sci Fuzhou 350108 Peoples R China Fujian Univ Technol Sch Transportat Fuzhou 350118 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Fuzhou Univ Key Lab Intelligent Metro Univ Fujian Prov Fuzhou 350118 Peoples R China

Grid partitioning for input space results in the exponential rise in the number of rules in adaptive network-based fuzzy inference system (ANFIS) and patch learning (PL) as the number of features increases, thus resulting in the huge computational load and deteriorating its interpretability. An improved PL (iPL) is put forward for the training of each sub-fuzzy system to overcome the rule-explosion problem. In the iPL, input partitioning is done using fuzzy c-means (FCM) clustering to avoid the heavy computational complexity arising due to the large number of rules generated from high dimensionality. In this paper, two novel classifiers, called FCM clustering based deep patch learning with improved high-level interpretability for classification problems, are presented, named as HI-FCMDPL-CP1 and HI-FCMDPL-CP2. The proposed classifiers have two characteristics: One is a stacked deep structure of component iPL fuzzy classifiers for high accuracy, and the other is the use of maximal information coefficient (MIC) and the maximum misclassification threshold (MMT) to optimize the deep structures. High interpretability is achieved at each layer by using the FCM clustering, concise structure and large input dimensionality. The MMT, random input (RI) and parameter sharing (PS) are integrated to improve their classification accuracy without losing their interpretability. Experiments on several real-word datasets demonstrated that MIC, RI and PS in HI-FCMDPL-CP1 and HI-FCMDPL-CP2 are effective individually, and integrating them all three can further improve the classification performance. A more concise deep fuzzy system is obtained with the number of features and fuzzy rules reduced simultaneously. Furthermore, MIC, RI and PS are used to determine the advantages and disadvantages of using serial versus parallel structures to avoid subjective selection of these two categories.

关键词： Computational modeling Training Microwave integrated circuits Deep learning Data models Artificial neural networks Training data Fuzzy c-means (FCM) clustering maximal information coefficient (MIC) random input (RI) deep patch learning classifier interpretability

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SIM: A Scenario IMagination Based Deep Reinforcement Learning Method for Outdoor Transportation Environment Exploration 11

SIM: A Scenario IMagination Based Deep Reinforcement Learnin...

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11th IEEE Data Driven control and Learning systems Conference, DDCLS 2022

作者： Li, Haoran Zhang, Qichao Chen, Yaran Zhao, Dongbin State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100190 China Peng Cheng Laboratory Shenzhen518000 China

ISBN: (数字)9781665496759

ISBN: (纸本)9781665496759

Autonomous exploration is very important for robotics, especially for mapping, navigation, and planning in an unknown environment. In recent years, automatic exploration methods in the indoor environment have been extensively studied, but there is little research on exploration in the outdoor transportation environment. Due to the limitations of outdoor traffic rules and the scale of the environment, the methods for the indoor environment are difficult to apply to the transportation environment. Aiming at exploration in the transportation environment, this paper proposes a deep reinforcement learning algorithm based on Scenario IMagination(SIM), which has two important components: 1) a mid-level action space, which combines the classical robot control algorithm, addressing the inefficient learning and unstable navigation of deep reinforcement learning algorithms in automatic exploration. With this action space, the deep reinforcement learning algorithm achieves excellent exploration performance in both normal scale environments and large-scale branchless environments;2) a scenarios buffer, which relieves hard exploration problems of deep reinforcement learning due to serious imbalances of samples in large-scale multibranch scenarios. Compared to the map-less navigation approaches, SIM achieves excellent exploration performance in large-scale multi-branch environments. © 2022 IEEE.

关键词： Navigation

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When Does Sora Show:The Beginning of TAO to Imaginative Intelligence and Scenarios Engineering

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IEEE/CAA Journal of Automatica Sinica 2024年第4期11卷 809-815页

作者： Fei-Yue Wang Qinghai Miao Lingxi Li Qinghua Ni Xuan Li Juanjuan Li Lili Fan Yonglin Tian Qing-Long Han School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190China Faculty of Innovation Engineering Macao University of Science and TechnologyMacao 999078China Department of Electrical and Computer Engineering Indiana University-Purdue University IndianapolisIndianapolisIN 46202USA Faculty of Innovation Engineering Macao University of Science and TechnologyMacao 999078China Virtual Reality Fundamental Research Laboratory Department of Mathematics and TheoriesPeng Cheng LaboratoryShenzhen 518000China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China School of Information and Electronics Beijing Institute of TechnologyBeijing 100081China School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourneVIC 3122Australia

DURING our discussion at workshops for writing“What Does ChatGPT Say:The DAO from Algorithmic Intelligence to Linguistic Intelligence”[1],we had expected the next milestone for Artificial Intelligence(AI)would be in the direction of Imaginative Intelligence(II),i.e.,something similar to automatic wordsto-videos generation or intelligent digital movies/theater technology that could be used for conducting new“Artificiofactual Experiments”[2]to replace conventional“Counterfactual Experiments”in scientific research and technical development for both natural and social studies[2]-[6].Now we have OpenAI’s Sora,so soon,but this is not the final,actually far away,and it is just the beginning.

关键词： something Intelligence replace

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Motor-Cortex-Like Recurrent Neural Network and Multitask Learning for the control of Musculoskeletal systems

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IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL systems 2022年第2期14卷 424-436页

作者： Chen, Jiahao Qiao, Hong 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 Beijing Key Lab Res & Applicat Robot Intelligence Beijing 100190 Peoples R China CAS Ctr Excellence Brain Sci & Intelligence Techn Shanghai 200031 Peoples R China

The musculoskeletal robot is a promising direction of the next-generation robots. However, current control methods of musculoskeletal robots lack multitask learning ability, great generalization, and biological plausibility. In this article, a motor-cortex-like recurrent neural network (RNN) and a reward-modulated multitask learning method are proposed. First, inspired by the dynamic system hypothesis of motor cortex, the RNN is introduced to transform movement targets into muscle excitations. The condition that makes an RNN generate motor-cortex-like consistent population response is investigated. Second, a reward-modulated multitask learning method of such an RNN is proposed. In the experiments, the control of a musculoskeletal system is realized with multitask learning ability, great generalization, and robustness for noises. Furthermore, the RNN and muscle excitations demonstrate motor-cortex-like consistent population response and human-like muscle synergies, respectively. Therefore, the proposed method has better performance and biological plausibility, and verifies the neural mechanisms in the robotic research.

关键词： Muscles Musculoskeletal system Robots Statistics Sociology Recurrent neural networks Neurons Biologically inspired motor cortex muscle synergy musculoskeletal system neuromuscular control recurrent neural network (RNN)

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