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    The new era of artificial intelligence
    Nanning ZHENG
    Chinese Journal of Intelligent Science and Technology    2019, 1 (1): 1-3.   DOI: 10.11959/j.issn.2096-6652.201914
    Abstract9817)   HTML12571)    PDF(pc) (506KB)(9180)       Save

    The goal of artificial intelligence (AI) is to make machines learn,think and understand like human beings,with the focus of using computers to imitate human intelligence.It integrates knowledge of multi-disciplines including intuitive reasoning (with all kinds of physical and social common senses),computer vision,natural language perception and interaction,and machine learning.Hybrid-augmented intelligence is the typical feature of the next generation of AI,and cognitive computing of visual and auditory information is the core research content.Therefore,comprehending the human visual and auditory cognition mechanism and building its computable model is of vital significance for AI’s research and development.

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    Artificial intelligence is entering the post deep-learning era
    Bo ZHANG
    Chinese Journal of Intelligent Science and Technology    2019, 1 (1): 4-6.   DOI: 10.11959/j.issn.2096-6652.201913
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    Points of views on the concept of artificial intelligence (AI),the progress AI has made,and the trend of AI’s future development were presented in this paper.

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    An overview on algorithms and applications of deep reinforcement learning
    Zhaoyang LIU, Chaoxu MU, Changyin SUN
    Chinese Journal of Intelligent Science and Technology    2020, 2 (4): 314-326.   DOI: 10.11959/j.issn.2096-6652.202034
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    Deep reinforcement learning (DRL) is mainly applied to solve the perception-decision problem, and has become an important research branch in the field of artificial intelligence.Two kinds of DRL algorithms based on value function and policy gradient were summarized, including deep Q network, policy gradient as well as related developed algorithms.In addition, the applications of DRL in video games, navigation, multi-agent cooperation and recommendation field were intensively reviewed.Finally, a prospect for the future research of DRL was made, and some research suggestions were given.

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    A survey of UAV-based edge intelligent computing
    Chao DONG,Yun SHEN,Yuben QU
    Chinese Journal of Intelligent Science and Technology    2020, 2 (3): 227-239.   DOI: 10.11959/j.issn.2096-6652.202025
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    Edge intelligent computing refers to the offloading of computationally intensive tasks generated by user nodes to edge servers with stronger computing capabilities for processing.Unmanned aerial vehicle (UAV)-based edge intelligent computing combines intelligent drone platforms on this basis and utilizes them with the advantages of strong mobility and easy deployment,it can provide edge computing services for ground user equipment more quickly and flexibly.At the same time,drones can also be used as user nodes to off load their computationally intensive tasks to the ground edge server for execution.Aiming at two different scenarios of UAV as a user node or an edge server,the current research on edge intelligent computing based on UAV is classified and summarized according to different optimization goals such as minimizing energy consumption,minimizing delay and maximizing utility,and the next research direction is considered and prospected.

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    Overview of intelligent game:enlightenment of game AI to combat deduction
    Yuxiang SUN, Yihui PENG, Bin LI, Jiawei ZHOU, Xinlei ZHANG, Xianzhong ZHOU
    Chinese Journal of Intelligent Science and Technology    2022, 4 (2): 157-173.   DOI: 10.11959/j.issn.2096-6652.202209
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    The field of intelligent game has gradually become one of the hotspots of AI research.A series of research breakthroughs have been made in the field of game AI and intelligent wargame in recent years.However, how to develop game AI and apply it to the actual intelligent combat deduction is still facing great difficulties.The overall progress of research in the field of intelligent games in domestic and overseas were explored, the main attribute requirements of intelligent combat deduction was tracked, and it was summarized with the latest advancements in reinforcement learning.The feasibility of developing game AI into intelligent combat deduction were comprehensively analyzed from three dimensions: mainstream research technology in the field of intelligent game, relevant intelligent decision technology and technical difficulties of combat deduction, and finally, some suggestions for the development of future intelligent combat deductiongives were given.This paper can introduce a clear development status and provide valuable research ideas for researchers in the field of intelligent game.

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    Decentralized autonomous organizations:the state of the art,analysis framework and future trends
    Wenwen DING,Shuai WANG,Juanjuan LI,Yong YUAN,Liwei OUYANG,Fei-Yue WANG
    Chinese Journal of Intelligent Science and Technology    2019, 1 (2): 202-213.   DOI: 10.11959/j.issn.2096-6652.201917
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    The decentralized autonomy is not a new concept,the self-organization phenomenon in natural ecosystems,the cyber movement organizations on the Internet,and the distributed artificial intelligence,etc.,can all be regarded as its early manifestations.In recent years,the rapid development of block chain technology has spawned the emergence of the so-called decentralized autonomous organization (DAO),which is a new organization form that the management and operational rules are typically encoded on block chain in the form of smart contracts,and can autonomously operate without centralized control or third-party intervention.Therefore,DAO is expected to become a new and effective organization to deal with uncertain,diverse and complex environments.Defining the conception and features is the first priority.Furthermore,the five layer structural model of DAO analyzing frame to analyze typical Aragon model was proposed and the current issues as well as the research direction in future were discussed,hoping to provide significant guidance and reference for following researchers.

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    A survey on vehicle re-identification
    Kai LIU, Yidong LI, Weipeng LIN
    Chinese Journal of Intelligent Science and Technology    2020, 2 (1): 10-25.   DOI: 10.11959/j.issn.2096-6652.202002
    Abstract2476)   HTML1969)    PDF(pc) (2568KB)(2393)       Save

    Given a vehicle image,vehicle re-identification aims to find the same vehicle caught by other cameras,it can be regarded as a sub-problem of image retrieval.In the real traffic surveillance system,vehicle re-identification can play a role in locating,supervising and criminal investigation of target vehicles.With the rise of deep neural networks and the release of large-scale dataset,improving the accuracy and efficiency of vehicle re-identification has become a research focus in the field of computer vision and multimedia in recent years.The vehicle re-identification methods from different perspectives were classified,and the overview,comparison and analysis in terms of feature extraction,design and performance were given in detail,and the challenges and future trends of vehicle re-identification were predicted.

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    A survey on federated learning in crowd intelligence
    Qiang YANG, Yongxin TONG, Yansheng WANG, Lixin FAN, Wei WANG, Lei CHEN, Wei WANG, Yan KANG
    Chinese Journal of Intelligent Science and Technology    2022, 4 (1): 29-44.   DOI: 10.11959/j.issn.2096-6652.202218
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    Crowd intelligence is emerging as a new artificial intelligence paradigm owing to the rapid development of the Internet.However, the data isolation and data privacy preservation problems make it difficult to share data among the crowd and to build crowd intelligent applications.Federated learning is a novel solution that aims to collaboratively build models by breaking the data barriers in crowd.Firstly, the basic ideas of federated learning and a comparison with crowd intelligence were introduced.Secondly, federated learning algorithms were divided into three categories according to the crowd organization, and further optimization techniques on privacy, accuracy and efficiency were discussed.Thirdly, federated learning operators based on linear models, tree models and neural network models were presented respectively.Finally, mainstream federated learningopensource platforms and typical applications were introduced, followed by the conclusion.

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    Research on architecture and system deployment of intelligent power plant based on digital twin
    Haidong FAN
    Chinese Journal of Intelligent Science and Technology    2019, 1 (3): 241-248.   DOI: 10.11959/j.issn.2096-6652.201930
    Abstract496)   HTML46)    PDF(pc) (4584KB)(1884)       Save

    Under the call of structural reform of energy supply and clean,efficient and intelligent coal-fired power production in China,based on the analysis of the research and practice status of intelligent power plant,combined with digital twin theory and methods,production and operation activities and elements of power plants were virtualized and digitized with the industrial big data platform and management cloud platform as the core,the intelligent power plant architecture were constructed including decision-making,intelligent supervision,intelligent control and intelligent equipment,which covered all aspects of production and operation of power plant equipment,so that the power plant had the characteristics of comprehensive perception,collaborative optimization,prediction and early warning and scientific decision-making.

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    NFT: blockchain-based non-fungible token and applications
    Rui QIN, Juanjuan LI, Xiao WANG, Jing ZHU, Yong YUAN, Fei-Yue WANG
    Chinese Journal of Intelligent Science and Technology    2021, 3 (2): 234-242.   DOI: 10.11959/j.issn.2096-6652.202125
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    Non-fungible token (NFT) based on blockchain is a kind of digital asset ownership recorded on blockchain, which is unique, irreplaceable and indivisible.It has been widely used in collectibles, encrypted artworks and games.Firstly, the concepts, characteristics and development processes of NFT were mainly introduced, and its core elements and typical application fields were analyzes.The problems and risks faced by NFT in the aspects of property right, value, technology and supervision were also pointed out.After that, the related literatures on NFT were reviewed, and the research issues of NFT in value evaluation, transaction mode and pricing were proposed.Finally, the trend of NFT driven digital capitalization was prospected.

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    Parallel agriculture:intelligent technology toward smart agriculture
    Mengzhen KANG,Xiujuan WANG,Jing HUA,Haoyu WANG,Fei-Yue WANG
    Chinese Journal of Intelligent Science and Technology    2019, 1 (2): 107-117.   DOI: 10.11959/j.issn.2096-6652.201904
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    Agricultural production is featured by strong uncertainty,diversity and complexity.Production benefit is related to natural conditions,national policies and market environment.Internet brings new challenges and chances to the management in agriculture.Generally speaking,smart agriculture refers to smart control on full production chain including management and service,using ICT techniques,in order to achieve high quality,high efficiency,security and controllability.Based on a brief introduction on the techniques related to these three aspects,the parallel agriculture as the methodology for decision support in smart agriculture was proposed,with artificial system for description intelligence,computational experiments for prediction intelligence,and parallel execution for prescription intelligence.Moreover,the link to agricultural enterprise resource planning (ERP) system,manufacturing execution system (MES) and process control system (PCS) was discussed.As China was developing large-scale agricultural production,methodology for managing scaled agriculture production was provided.

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    An overview of optimal consensus for data driven multi-agent system based on reinforcement learning
    Jinna LI, Weiran CHENG
    Chinese Journal of Intelligent Science and Technology    2020, 2 (4): 327-340.   DOI: 10.11959/j.issn.2096-6652.202035
    Abstract893)   HTML89)    PDF(pc) (815KB)(1658)       Save

    Multi-agent system has attracted extensive attention in the past two decades because of its potential applications in engineering, social science and natural science, etc.To achieving the consensus of multi-agent system, it is usually necessary to solve the correlation matrix equation to design the control protocol offline, which requires system model to be known accurately.However, the actual multi-agent system has the characteristics of large-scale, nonlinear coupling, and dynamic change of environment, which makes it very difficult to accurately model the system.This brings challenges to the design of model dependent multi-agent consensus protocol.Reinforcement learning is widely used to solve the optimal control and decision-making problems of complex systems because it can learn the optimal solution of control problems in real time by using the measurement data along the trajectory of the system.The existing theories and methods of online solving the optimal consensus of multi-agent system inreal-time by using reinforcement learning technology were summarized.The application of data-driven reinforcement learning technology in multi-agent system optimal consensus was introduced from the aspects of continuous and discrete, homogenous and heterogeneous, anti-interference robustness and so on.Finally, the future research direction of the optimal consensus problem of multi-agent system based on data-driven technology was discussed.

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    Development prospect of fuzzy system oriented to interpretable artificial intelligence and big data
    Dewang CHEN,Jijie CAI,Yunhu HUANG
    Chinese Journal of Intelligent Science and Technology    2019, 1 (4): 327-334.   DOI: 10.11959/j.issn.2096-6652.201937
    Abstract756)   HTML78)    PDF(pc) (907KB)(1623)       Save

    As a universal approximator with strong interpretability,fuzzy system has been widely used in various fields.Although the current theoretical research on fuzzy system is not mature enough,there are still many problems such as too many rules,optimization difficulties,dimension curse,which make it difficult to deal with high-dimensional large data.Although deep neural network has made remarkable progress and can process large data such as image and voice very well,its interpretability is not good and it is difficult to be used in important security-related occasions.Therefore,it is necessary to study an interpretable artificial intelligence algorithm based on fuzzy system.Combining the advantages of deep neural network and fuzzy system,it is possible to solve the problem of high dimensional and large data by studying the deep fuzzy system and its algorithm.The development history and research progress of fuzzy system separately was mainly reviews,and its future development direction according to its existing problems was pointed out,and the summary of this article and the prospect for further research about the problems were given.

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    The application of deep learning in data-driven modeling of process industries
    Xiaofeng YUAN,Yalin WANG,Chunhua YANG,Weihua GUI
    Chinese Journal of Intelligent Science and Technology    2020, 2 (2): 107-115.   DOI: 10.11959/j.issn.2096-6652.202012
    Abstract879)   HTML143)    PDF(pc) (1147KB)(1553)       Save

    Deep learning is an artificial intelligence technique developed in recent years.Compared with traditional shallow models,deep learning has a strong ability of feature representation and function fitting.It can extract hierarchical features from massive data,which has great potential for data-driven modeling in process industries.Firstly,the history of deep learning was introduced.Then,four widely used deep networks and their applications were introduced in data-driven modeling of process industries.At last,the conclusions about deep learning and its applications in process in dustries were given.

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    Technical analysis of typical intelligent game system and development prospect of intelligent command and control system
    Xiangang LI,Qiang LI
    Chinese Journal of Intelligent Science and Technology    2020, 2 (1): 36-42.   DOI: 10.11959/j.issn.2096-6652.202004
    Abstract588)   HTML70)    PDF(pc) (549KB)(1534)       Save

    In the future era of intelligence,the massive and heterogeneous battlefield data and the widespread use of unmanned platforms will accelerate the expansion of the combat space from the physical and information domains to the cognitive domains,which will put forward higher requirements for the accuracy,timeliness and effectiveness of command and control,and the intelligence of command and control will become the general trend.Based on the analysis of typical foreign intelligent command and control system,the machine game technology development course was clarified,the challenge of applying machine game technology to intelligent command and control system was analyzed,the future intelligent command and control in combat training,operational innovation and prospective of system architecture and implementation was put forward,so as to provide reference and enlightenment for the future development of command and control system.

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    A survey of affective brain-computer interface
    Bao-Liang LU, Yaqian ZHANG, Wei-Long ZHENG
    Chinese Journal of Intelligent Science and Technology    2021, 3 (1): 36-48.   DOI: 10.11959/j.issn.2096-6652.202104
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    An important research goal in emotion artificial intelligence is to make machines understand and recognize human emotions in real-time and facilitate human-computer interaction in a more natural and friendly way.Affective brain-computer interface (aBCI) is a type of BCI that can recognize and/or modulate human emotion.Thus, aBCI plays a critical role in promoting emotion artificial intelligence.The basic concepts and recent research development of aBCI were summarized, and the applications of aBCI in a wide range of domains were outlined.The roles that the aBCI can play in the development of artificial general intelligence and the challenges faced by the aBCI research community were discussed.

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    Design of transnational and intercontinental electricity market with blockchain and token economics
    Siyuan CHEN, Yuyang BAI, Jun ZHANG, Fei-yu WANG
    Chinese Journal of Intelligent Science and Technology    2019, 1 (1): 96-105.   DOI: 10.11959/j.issn.2096-6652.201910
    Abstract1827)   HTML76)    PDF(pc) (2573KB)(1489)       Save

    Transnational and intercontinental electricity trading (TIET) is a fundamental enabling component and efficient method to the development of Global Energy Internet (GEI),which aims to build an energy model centered on electricity consumption.The problems faced by TIET was analyzed,and a transnational and intercontinental electricity market (TIEM) based on blockchain and token economics was proposed.The TIEM was divided into five components,and each component was described and modeled respectively.A scale-up power grid based IEEE 39-nodes system was used to analyze the proposed market.The simulation results show that TIEM based on blockchain and token economy can ensure the effective implementation of cross-regional and multi-agent electricity transactions,and also encourage the generation companies (GENCOs) to participate in electricity market with clean energy.

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    Reinforcement learning:toward action-knowledge merged intelligent mechanisms and algorithms
    Fei-Yue WANG,Dongpu CAO,Qinglai WEI
    Chinese Journal of Intelligent Science and Technology    2020, 2 (2): 101-106.   DOI: 10.11959/j.issn.2096-6652.202011
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    This article discusses briefly the history,the state of the art and the future of reinforcement learning,and outlines a roadmap of evolution from learning by doing,doing with planning to parallel intelligence that combining learning virtually in artificial systems and acting accordingly in actual systems.

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    Parallel nuclear power:intelligent technology for smart nuclear power
    Jiachen HOU,Xisong DONG,Gang XIONG,Jun ZHANG,Ke TAN
    Chinese Journal of Intelligent Science and Technology    2019, 1 (2): 192-201.   DOI: 10.11959/j.issn.2096-6652.201912
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    A framework to incorporate nuclear plants using the ACP based parallel system theory was proposed.This research framework has established a convenient and safe platform for nuclear power plants to experiment,learn and work in complex environments,namely parallel nuclear power system.Proceeding from the basic concept of parallel nuclear power,the basic framework of parallel nuclear power system was proposed,and the elementary functions and implementation methods of description,prediction and guidance module were also introduced.The key technologies were also further discussed.At last,this paper looks forward to the nuclear power system application in emergency,and the future development direction of parallel nuclear power system was proposed.

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    Artificial intelligence technologies and applications in the metaverse
    Qiang WU, Xueting JI, Linyuan LYU
    Chinese Journal of Intelligent Science and Technology    2022, 4 (3): 324-334.   DOI: 10.11959/j.issn.2096-6652.202241
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    Metaverse integrates and applies a variety of digital technologies, resulting in an Internet social form that integrates virtual and reality, digital and application.Artificial intelligence (AI) are systems and machine that imitate human intelligence to perform tasks and iteratively improve themselves based on the information gathered.In the process of constructing the metaverse, AI technologies not only vigorously promote the development of crucial metaverse technologies (human-computer interaction, communication, robotics, etc.) but also enables direct content creation in the metaverse, organically connecting the real and virtual worlds.By sorting out the concepts and representative technologies of the metaverse and AI, the optimization progress of AI technologies for constructing key technologies in the metaverse was introduced and the application process of AI in the metaverse was detailed.Finally, the AI technology’s development and application trends in the metaverse prospected.

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    A novel blockchain-based surveillance and early-warning technology for infectious diseases
    Liwei OUYANG,Yong YUAN,Xinhu ZHENG,Jun ZHANG,Fei-Yue WANG
    Chinese Journal of Intelligent Science and Technology    2020, 2 (2): 135-143.   DOI: 10.11959/j.issn.2096-6652.202015
    Abstract572)   HTML64)    PDF(pc) (1285KB)(1337)       Save

    The key of early prevention and control of infectious diseases is to use the early warning technology and system to monitor the abnormal occurrence and trend of infectious diseases.There are still some problems in the existing infectious diseases automated-alert and response systems,such as lack of intelligence,poor efficiency in exchange of key information,as well as difficulties in distributed decision-making.Based on the distributed blockchain architecture,a novel blockchain-based surveillance and early-warning technology for infectious diseases was proposed,leveraging emerging information technologies including artificial intelligence,big data and smart contract.The technology could aggregate monitoring forces from multiple parties efficiently,integrate various early warning technologies flexibly,and establish a distributed and collaborative monitoring environment for knowledge integration and intelligent interconnection with guaranteed security and privacy protection.In this framework,smart contract was served as a “software-defined agent” to fuse decisions,monitor the outbreak,and issue warnings in an automatic and real-time fashion,so as to meet the key requirements of accuracy and timeliness,and avoid the decision bias of single evidence.

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    A review of prediction methods for moving target trajectories
    Wen LIU, Kunlin HU, Yan LI, Zhao LIU
    Chinese Journal of Intelligent Science and Technology    2021, 3 (2): 149-160.   DOI: 10.11959/j.issn.2096-6652.202115
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    With the rapid emergence of mobile terminal equipment in intelligent transportation system, the deep understanding and accurate prediction of moving target trajectories are capable of reducing the traffic accident probability, and promoting the location service-based intelligent transportation applications.The trajectory prediction methods prediction methods for moving target trajectories were reviewed from the data-driven prediction methods and the behavior-driven trajectories prediction methods.Firstly, the data-driven prediction methods were reviewed, including probabilistic statistics, neural networks, deep learning, and hybrid modeling.Then, the basic conceptions of target behavior-driven trajectories prediction methods were analyzed.The corresponding dynamical modeling and intention recognition methods were reviewed.The trajectory prediction applications were briefly analyzed and reviewed, such as target trajectory reconstruction, target abnormal behavior identification, and navigation route planning.Finally, the main problems and development directions related to prediction of moving target trajectories were discussed.

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    Review of pedestrian trajectory prediction methods
    Linhui LI, Bin ZHOU, Weiwei REN, Jing LIAN
    Chinese Journal of Intelligent Science and Technology    2021, 3 (4): 399-411.   DOI: 10.11959/j.issn.2096-6652.202140
    Abstract1588)   HTML344)    PDF(pc) (2826KB)(1318)       Save

    With the breakthrough of deep learning technology and the proposal of large data sets, the accuracy of pedestrian trajectory prediction has become one of the research hotspots in the field of artificial intelligence.The technical classification and research status of pedestrian trajectory prediction were mainly reviewed.According to the different modeling methods, the existing methods were divided into shallow learning and deep learning based trajectory prediction algorithms, the advantages and disadvantages of representative algorithms in each type of method were analyzed and introduced.Then, the current mainstream public data sets were summarized, and the performance of mainstream trajectory prediction methods based on the data sets was compared.Finally, the challenges faced by the trajectory prediction technology and the development direction of future work were prospected.

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    Formation control of mobile robots with UWB localization technology
    Yajun ZHENG,Lei XUE,Lu DONG,Qingling WANG
    Chinese Journal of Intelligent Science and Technology    2019, 1 (1): 83-87.   DOI: 10.11959/j.issn.2096-6652.201907
    Abstract1753)   HTML64)    PDF(pc) (1463KB)(1268)       Save

    In this paper,a novel multi-mobile robots formation control algorithm based on ultra-wideband (UWB) location technology was proposed.UWB location technology is relatively mature but is less applied to formation control problem.The framework of systems and hardware design of mobile multi-robot systems were first presented.Then,the systems model was established and the corresponding distributed control algorithm was proposed.Finally,the stability of multi-robot systems was proved and one simulation example was given to show the effectiveness of the proposed formation control algorithm.

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    AI in game intelligence—from multi-role game to parallel game
    Yu SHEN,Jinpeng HAN,Lingxi LI,Fei-Yue WANG
    Chinese Journal of Intelligent Science and Technology    2020, 2 (3): 205-213.   DOI: 10.11959/j.issn.2096-6652.202023
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    The domestic and overseas research progress of artificial intelligence technology in the field of games was summarized and the significance of the research progress in the field of games for real life was analyzed.In view of the gap between simulation and reality in model based methods and the lack of generality of the model-based approach in reinforcement learning,the idea and method of parallel game were put forward,and the advance of parallel game in solving the existing problems of single-role game and multi-role game was introduced.The parallel game method will be the cornerstone of the general artificial intelligence.

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    Emotion recognition based on brain and machine collaborative intelligence
    Dongjun LIU, Yuhan WANG, Wenfen LING, Yong PENG, Wanzeng KONG
    Chinese Journal of Intelligent Science and Technology    2021, 3 (1): 65-75.   DOI: 10.11959/j.issn.2096-6652.202107
    Abstract608)   HTML56)    PDF(pc) (2762KB)(1223)       Save

    Emotion recognition is a direct and effective mode of emotion recognition.Machine learning relies on the formal representation of image expressions, lacks the cognitive representation ability of the brain, and has poor recognition performance on small sample data sets or complex expression (camouflage) data sets.To this end, the formal representation of machine artificial intelligence was combined with the emotional cognitive ability of human brain general intelligence, and a brain-machine collaborative intelligence emotion recognition method was proposed.Firstly, electroencephalogram (EEG) emotional features were extracted from EEG to obtain the brain’s cognitive representation of emotions.Secondly, the visual features of the image were extracted from the emotional image to obtain the machine’s formal representation of the emotion.In order to enhance the generalization ability of the machine model, the transfer adaptation between samples was introduced in the feature learning.After obtaining image visual features and EEG emotional features, the random forest regression model was trained to obtain the brain-machine mapping relationship between image visual features and EEG emotional features.The visual features of the test image were generated through the brain-machine mapping relationship to generate virtual EEG emotional features, and then the virtual EEG emotional features and image visual features were fused for emotion recognition.This method has been verified on the Chinese facial affective picture system (CFAPS) and found that the average recognition accuracy of the seven emotions is 88.51%, which is 3%~5% higher than the image-based method.

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    Cloud native robot system based on edge computing
    Dawei WANG,Zhuo WANG,Peng WANG,Zhigang WANG,Haitao WANG
    Chinese Journal of Intelligent Science and Technology    2020, 2 (3): 275-283.   DOI: 10.11959/j.issn.2096-6652.202030
    Abstract645)   HTML78)    PDF(pc) (2651KB)(1193)       Save

    With the rapid development of artificial intelligence,the global robot market continues to grow quickly,and the capabilities of robots have evolved from performing fixed operations to the ability to autonomously sense,understand and make decisions.However,to achieve large-scale application of robots,robots need to have powerful computing capabilities and low deployment costs under the constraints of limited power consumption.Using edge computing to provide more cost-effective services,enhance the computing power of the robot body,and achieve large-scale deployment is the key to achieving this goal.The challenges faced by robot systems with the edge enhancement were analyzed,the concept of cloud-native robot systems based on edge computing was proposed,and four feasible technical solutions for implementing the system were discusses.The cloud-native robot system is the inevitable direction for the development of robot systems from intelligent systems based on robot ontology to cloud-edge-end fusion multi-robot collaborative intelligent systems and the key technology for promoting the large-scale application of robots.

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    General game AI with statistical forward planning algorithms
    LUCAS Simon, Tianyu SHEN, Xiao WANG, Jie ZHANG
    Chinese Journal of Intelligent Science and Technology    2019, 1 (3): 219-227.   DOI: 10.11959/j.issn.2096-6652.201935
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    Statistical forward planning (SFP) algorithms use a simulation model (also called forward model) to adaptively search for effective sequences of actions.They offer a simple and general way to provide rapidly adaptive AI controllers for a variety of games.The two powerful SFP example algorithms:Monte Carlo tree search and rolling horizon evolution were introduced in this paper and key insights into their working principles were provided.It is demonstrated that the algorithms are able to play a variety of video games surprisingly well without the need for any prior training.

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    The DAOs to AI for Science by DeSci: the state of the art and perspective
    Fei-Yue WANG, Qinghai MIAO, Junping ZHANG, Wenbo ZHENG, Wenwen DING
    Chinese Journal of Intelligent Science and Technology    2023, 5 (1): 1-6.   DOI: 10.11959/j.issn.2096-6652.202310
    Abstract827)   HTML253)    PDF(pc) (788KB)(1167)       Save

    The new wave of artificial intelligence technology represented by ChatGPT is promoting the comprehensive transformation of human society, the transformation of scientific research paradigm is accelerating, and an artificial intelligence-driven scientific research (AI for Science, AI4S) revolution is coming.The basic concepts and characteristics of AI4S were analyzed, and the development status of AI4S were briefly summarized from the perspectives of mathematics, physics, biology, and materials.Vigorously developing AI4S is of great significance to improving national competitiveness, developing social economy, and strengthening technical reserves.In order to promote the development of AI4S better, the following two points are essential: one is to change the contemporary teaching and education, and advocate AI for Education (AI4E) and Education for AI (E4AI); the other is to establish and adapt to the "new scientific research paradigm" with "new organization mode" in a "new research ecology", based on DAOs and DeSci, for open, fair and just sustainable support for AI4S.

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    Parallel control and digital twins:control theory revisited and reshaped
    Fei-Yue WANG
    Chinese Journal of Intelligent Science and Technology    2020, 2 (3): 293-300.   DOI: 10.11959/j.issn.2096-6652.202032
    Abstract849)   HTML91)    PDF(pc) (1524KB)(1129)       Save

    After a brief discussion of the origin and development of classic control,intelligent control,and parallel control,the mathematical equations for a class of parallel control systems was presented for problems with high precision digital twin models.The key idea was modeling the time derivative of system control instead of system control itself,which leads to differential equation based control rather than algebraic relationship based control,made the system and its control symmetrical in both form and content mathematically,thus provided the foundation for implementing human-like control and intelligent control.Same idea was also applied to system output equations,and proposed a new way for active sensing and design of parallel sensors.Preliminary results along those lines are illustrated and discussed.

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    Parallel security:generative adversarial systems for intelligent security in CPSS
    Yidong LI,Jun ZHANG,Yaodong TAO,Wei WANG,Yuanxiang GU,Fei-Yue WANG
    Chinese Journal of Intelligent Science and Technology    2020, 2 (2): 194-202.   DOI: 10.11959/j.issn.2096-6652.202022
    Abstract420)   HTML46)    PDF(pc) (2439KB)(1101)       Save

    Developing technologies such as AI and big data are changing the landscape of cybercrime for both attackers and defenders.Traditional defense technology mainly use the passive detect-then-defend model,which is hard to meet the security protection requirements brought by new attack characteristics.A novel security framework,named parallel security was proposed,which aimed at providing a comprehensive solution with descriptive security,predictive security and prescriptive security.The main idea was based on the ACP theory of parallel intelligent,and integrated new methods such as generative adversarial leaning model,parallel blockchain.Human elements,sociology and psychology were taken as an important component in the framework,in order to enhance the immunity of the system.In addition,a parallel security based platform was design and developed for several typical scenarios in the field of industrial internet as a proof of concept.

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    Artificial intelligence and deep learning methods for solving differential equations: the state of the art and prospects
    Jingwei LU, Xiang CHENG, Fei-Yue WANG
    Chinese Journal of Intelligent Science and Technology    2022, 4 (4): 461-476.   DOI: 10.11959/j.issn.2096-6652.202255
    Abstract885)   HTML119)    PDF(pc) (6809KB)(1031)       Save

    With the rapid advancement of fundamental theories and computing capacity, deep learning techniques have made impressive achievements in many fields.Differential equations, as an important tool for describing the physical world, have long been a focus of interest for researchers in various fields.Combining the two methods has gained popularity as a study issue in recent years.Since deep learning can efficiently extract features from large amounts of data and differential equations can reflect objective physical laws, the combination of the two can effectively improve the generalization ability of deep learning and enhance the interpretability of deep learning.Firstly, the problem of solving differential equations by deep learning was briefly introduced.Then, two types of deep learning methods for solving differential equations were introduced: data-driven and physical-informed methods.Furthermore, the applications of relevant deep learning-based solving methods were discussed.Meanwhile, DeDAO (differential equations DAO), a foundation model for artificial intelligence for science, was proposed to address existing challenges.Finally, conclusions of deep learning methods for solving differential equations were presented.

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    Dynamical modeling and intelligent control of space soft manipulator
    Xiang ZHANG,Hongwei LIU,Zhuoqun LIU,Zhenguo YAN,Xiaoqian CHEN,Yiyong HUANG
    Chinese Journal of Intelligent Science and Technology    2019, 1 (1): 52-61.   DOI: 10.11959/j.issn.2096-6652.201905
    Abstract1311)   HTML44)    PDF(pc) (5770KB)(997)       Save

    Space intelligent soft manipulator shows a broad prospect in the application of on-orbit service,due to its lightweight,dexterity and foldability.In this paper,a design of a modular soft manipulator with omnidirectional drivers was proposed.The dynamical model of the proposed soft manipulator was developed based on the rigid-flexible coupling system dynamics.The detection and recognition algorithm for space non-cooperative objects was designed based on deep regression convolution neural network.Both the dynamical model and the algorithm are the foundation for the intelligent control of the soft manipulator.Moreover,an experiment system of the soft manipulator was designed and developed,and the autonomous motion control and the recognition algorithm were validated.

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    Research review and prospect of intelligent dynamic wireless charging system for electric vehicles
    Hongye SU, Ze ZHOU, Zhitao LIU, Liyan Zhang
    Chinese Journal of Intelligent Science and Technology    2020, 2 (1): 1-9.   DOI: 10.11959/j.issn.2096-6652.202001
    Abstract747)   HTML137)    PDF(pc) (4720KB)(990)       Save

    In order to solve the problems of large volume of battery and mileage anxiety,and accelerate application of electric vehicles,dynamic wireless charging system (DWCS) was presented and studied.In this paper,the basic framework of intelligent DWCS for electric vehicles was introduced,and a detailed review of the research results for DWCS by the researchers in the world was described,including the circuit structure and system modeling,electric vehicle positioning,control strategy of DWCS and the interaction with smart grid,etc.Then the intelligent DWCS of electric vehicle was prospected,mainly including system structure and optimization,the integration with the driverless technology,the integration of technologies and the collaborative optimization strategy with power grid and traffic network.The research direction and development trend in the future were also analyzed in this paper.

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    Parallel transportation:virtual-real interaction for intelligent traffic management and control
    Yisheng LV,Yuanyuan CHEN,Junchen JIN,Zhenjiang LI,Peijun YE,Fenghua ZHU
    Chinese Journal of Intelligent Science and Technology    2019, 1 (1): 21-33.   DOI: 10.11959/j.issn.2096-6652.201908
    Abstract1813)   HTML137)    PDF(pc) (6986KB)(969)       Save

    The concept,architecture,methods and applications of parallel transportation systems were introduced.The main idea is to build software defined artificial transportation systems for specific traffic application tasks and scenarios;use computational experimental methods for experiments,analysis,evaluation,prediction,learning,and optimization;and parallel execution is conducted for management and control of urban transportation systems,leading to virtual-real interactive parallel intelligence.Parallel traffic management and control is an extension and application of ACP approach,which emphasizes using virtual-real interactive ways to generate parallel intelligence for urban transportation systems,towards improving the intelligence of urban traffic management and control.

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    A survey of 3D object detection algorithms
    Zhe HUANG, Yongcai WANG, Deying LI
    Chinese Journal of Intelligent Science and Technology    2023, 5 (1): 7-31.   DOI: 10.11959/j.issn.2096-6652.202312
    Abstract1062)   HTML163)    PDF(pc) (6364KB)(959)       Save

    3D object detection is a fundamental problem in autonomous driving,virtual reality,robotics,and other applications.Its goal is to extract the most accurate 3D box characterizing interested targets from the disordered point clouds,such as the closest 3D box surrounding the pedestrians or vehicles.The target 3D box's location,size,and orientation are also output.Currently,there are two primary approaches for 3D object detection: (1) pure point cloud based 3D object detection,in which the point clouds are created by binocular vision,RGB-D camera,and lidar; (2) fusion-based 3D object detection based on the fusion of image and point cloud.The various representations of 3D point clouds were introduced.Then representative methods were introduced from three aspects: traditional machine learning techniques; non-fusion deep learning based algorithms; and multimodal fusion-based deep learning algorithms in progressive relation.The algorithms within and across each category were examined and compared,and the differences and connections between the various methods were analyzed thoroughly.Finally,remaining challenges of 3D object detection were discussed and explored.And the primary datasets and metrics used in 3D object detection studies were summarized.

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    The architecture and scheme of the hybrid-augmented intelligence open innovation platform based on the virtual and real systems
    Jun ZHANG,Lingxi LI,Yilun LIN,Tianyun ZHANG,Ke ZHANG,Peidong XU,Keyu RUAN,Dan SHEN
    Chinese Journal of Intelligent Science and Technology    2019, 1 (4): 379-391.   DOI: 10.11959/j.issn.2096-6652.201942
    Abstract324)   HTML10)    PDF(pc) (1374KB)(943)       Save

    The overall goal and problems of the construction of the hybrid-augmented intelligent open innovation platform were first put forward,and a virtual and real systems driven platform architecture scheme was proposed.Then the basic form of the platform was described,and the mechanism and function of modules at all levels in the platform were elaborated,the key technologies were also involved,including human-computer interaction technology,data processing technology and digital virtual industry technology.Finally,the guarantee and incentive mechanism of open innovation was designed,and the application of block chain technology in data security of open innovation platform was discussed.

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    Multi-modal physiological signal emotion recognition based on 3D hierarchical convolution fusion
    Wenfen LING, Sihan CHEN, Yong PENG, Wanzeng KONG
    Chinese Journal of Intelligent Science and Technology    2021, 3 (1): 76-84.   DOI: 10.11959/j.issn.2096-6652.202108
    Abstract596)   HTML58)    PDF(pc) (2854KB)(918)       Save

    In recent years, physiological signals such as electroencephalograhpy (EEG) have gradually become popular objects of emotion recognition research because they can objectively reflect true emotions.However, the single-modal EEG signal has the problem of incomplete emotional information representation, and the multi-modal physiological signal has the problem of insufficient emotional information interaction.Therefore, a 3D hierarchical convolutional fusion model was proposed, which aimed to fully explore multi-modal interaction relationships and more accurately describe emotional information.The method first extracted the primary emotional representation information of EEG , electro-oculogram (EOG) and electromyography (EMG) by depthwise separable convolution network, and then performed 3D convolution fusion operation on the obtained multi-modal primary emotional representation information to realize the pairwise mode local interactions between states and global interactions among all modalities, so as to obtain multi-modal fusion representations containing emotional characteristics of different physiological signals.The results show that the accuracy in the valence and arousal of the two-class and four-class tasks on DEAP dataset are both 98% by the proposed model.

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    Three-dimensional reconstruction cloud studio based on semi-supervised generative adversarial networks
    Chong YU
    Chinese Journal of Intelligent Science and Technology    2019, 1 (1): 70-82.   DOI: 10.11959/j.issn.2096-6652.201909
    Abstract1475)   HTML43)    PDF(pc) (12802KB)(914)       Save

    Because of the intrinsic complexity in computation,three-dimensional (3D) reconstruction is an essential and challenging topic in computer vision research and applications.The existing methods for 3D reconstruction often produce holes,distortions and obscure parts in the reconstructed 3D models.While the 3D reconstruction algorithms based on machine learning can only reconstruct voxelized 3D models for simple isolated objects,they are not adequate for real usage.From 2014,the generative adversarial network (GAN) is widely used in generating unreal dataset and semi-supervised learning.So the focus of this paper is to achieve high quality 3D reconstruction performance by adopting GAN principle.A novel semi-supervised 3D reconstruction framework,namely SS-GAN-3D was proposed,which can iteratively improve any raw 3D reconstruction models by training the GAN models to converge.This new model only takes 2D observation images as the weak supervision,and doesn’t rely on prior knowledge of shape models or any referenced observations.Finally,through qualitative and quantitative experiments and analysis,this new method shows compelling advantages over the current state-of-the-art methods on Tanks & Temples and ETH3D reconstruction benchmark datasets.Based on SS-GAN-3D,the 3D reconstruction studio solution was proposed.

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    Research and application on combustion optimization of coal-fired boiler in thermal power plant based on artificial intelligence technology
    Peifeng NIU,Yunpeng MA,Xinxin ZHANG,Xiaobin HU
    Chinese Journal of Intelligent Science and Technology    2019, 1 (2): 163-170.   DOI: 10.11959/j.issn.2096-6652.201925
    Abstract1197)   HTML34)    PDF(pc) (1286KB)(906)       Save

    In order to reduce the NOxemission concentration and coal consumption of coal-fired boilers in thermal power plants,the sample increment quantum neural network and an improved quantum bee colony algorithm were proposed.The quantum sample incremental feed-forward neural network can dynamically establish a comprehensive model of Nitrogen and Oxygen emission concentration and boiler coal consumption of coal-fired boiler,and can realize rolling optimization of the model.Based on the established comprehensive model,the optimization of the primary and secondary air volume and coal and the opening degree of each secondary air valve were realized by using the improved quantum bee colony algorithm.Based on the above two methods,a set of intelligent combustion optimization software for coal-fired boiler was developed and applied to the 330 MW boiler of a thermal power plant.The test results show that the Nitrogen and Oxygen emission concentration and the coal consumption of the boiler have been reduced in varying degrees.It is shown that the modeling method and the optimization algorithm are effective.

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    Developing trend and key technical analysis of intelligent acupuncture robot
    Tiancheng XU,Xuejun WANG,Dongdong LU,Mengye LU,Qi LIN,Xiaoqiang ZHANG,Yi CHENG
    Chinese Journal of Intelligent Science and Technology    2019, 1 (3): 305-310.   DOI: 10.11959/j.issn.2096-6652.201918
    Abstract1224)   HTML186)    PDF(pc) (1138KB)(884)       Save

    Acupuncture therapy has been adopted in more than 183 countries and regions around the world.With the rapid development of robot technology,research and development of intelligent acupuncture robot become possible,based on its core technology related research,that the point of automatic positioning and intelligent distribution point is the key technology and difficulty in research and development of intelligent acupuncture robot was pointed out in this paper,and thus,the concept of communion robot potential application value to the development of medical robot was also discussed.The latest research results of intelligent acupoint matching were also provided.

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    A cooperative multi-agent reinforcement learning algorithm based on dynamic self-selection parameters sharing
    Han WANG, Yang YU, Yuan JIANG
    Chinese Journal of Intelligent Science and Technology    2022, 4 (1): 75-83.   DOI: 10.11959/j.issn.2096-6652.202214
    Abstract521)   HTML55)    PDF(pc) (3737KB)(875)       Save

    In multi-agent reinforcement learning, parameter sharing can effectively alleviate the inefficiency of learning caused by non-stationarity.However, maintaining the same policy forall agents during learning may have detrimental effects.To solve this problem, a new approach was introduced to give agents the ability to automatically identify agents that may benefit from parameter sharing and dynamically share parameters them during learning.Specifically, agents needed to encode empirical trajectories as implicit information that can represent their potential intentions, and selected peers to share parameters by comparing their intentions.Experiments show that the proposed method not only can improve the efficiency of parameter sharing, but also ensure the quality of policy learning in multi-agent system.

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    Brain abnormalities in depression based on multimodal imaging
    Shan LI,Yongchao LI,Ying ZOU,Lin YANG,Yin WANG,Zhijun YAO,Ubin H
    Chinese Journal of Intelligent Science and Technology    2020, 2 (2): 116-125.   DOI: 10.11959/j.issn.2096-6652.202013
    Abstract597)   HTML67)    PDF(pc) (4051KB)(869)       Save

    In recent years,structural magnetic resonance imaging (sMRI) and functional magnetic resonance imaging (fMRI) are widely used in depression research.From the perspectives of morphology,structural network and functional network,the brain abnormalities of depression were explored to understand the pathogenesis,and to assist doctors in clinical diagnosis,treatment and prognosis.A large number of researches have found that the hippocampus and amygdala of depression showed different degrees of atrophy,and the connection strength of brain network and graph theory attributes showed significant abnormal.Moreover,the abnormal brain areas were related to emotional regulation,attention and cognitive control,and the degree of abnormalities were highly correlated with the severity of depression.The research actuality of depression from different perspectives was reviewed,and some suggestions for future research were put forward.

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    Method of functional brain network modeling with group similarity constraint for mild cognitive impairment classification
    Weikai LI,Xin GAO,Tongjian JI,Zhengxia WANG
    Chinese Journal of Intelligent Science and Technology    2019, 1 (2): 145-153.   DOI: 10.11959/j.issn.2096-6652.201922
    Abstract686)   HTML29)    PDF(pc) (1712KB)(866)       Save

    Functional brain network provides an effective biomarker for understanding brain activation patterns,the development of neurodegenerative diseases,and the structure of brain signaling.How to use priori information to build accurate brain network is particularly important in subsequent applications.A functional brain network construction model based on group similarity constraints was proposed.By introducing a tensor low rank constraint and using the nuclear norm of the tensor,a brain network group with a low rank prior in the group was solved.The group similarity constraints to shrink the solution space of the brain network was used,thus constructing a better functional brain network effectively.For evaluating the performance of the proposed method,the constructed brain network was adopted for the discriminative task of mild cognitive impairment.The experimental results show that the proposed functional brain network modeling method based on group similarity constraints can construct more discriminative brain network.In addition,based on the brain network constructed by the proposed method,the significant connections consistent with previous studies are obtained,and the effectiveness of the proposed method is further verified.

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    A survey on evolutionary ensemble learning algorithm
    Yi HU, Boyang QU, Jing LIANG, Jie WANG, Yanli WANG
    Chinese Journal of Intelligent Science and Technology    2021, 3 (1): 18-35.   DOI: 10.11959/j.issn.2096-6652.202103
    Abstract529)   HTML37)    PDF(pc) (478KB)(860)       Save

    Evolutionary ensemble learning integrates advantages of ensemble learning and evolutionary algorithm and is widely used in machine learning, data mining, and pattern recognition.Firstly, the theoretical basis, formation, and taxonomy are introduced.Secondly, according to the optimization task of evolutionary algorithm in ensemble learning, some representative researches on evolutionary ensemble learning field were analysed from the aspects of instance selection, feature selection, parameter optimization, structure optimization, and fusion strategy optimization, and the characteristics of different evolutionary ensemble learning methods were summarized.Finally, the pros and cons of the current researches on evolutionary ensemble learning were analysed, and research directions in the future work were given.

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    Emotional state decoding using EEG-based microstates of functional connectivity
    Xinke SHEN, Yichao LI, Jin LIU, Sen SONG, Dan ZHANG
    Chinese Journal of Intelligent Science and Technology    2021, 3 (1): 49-58.   DOI: 10.11959/j.issn.2096-6652.202105
    Abstract563)   HTML53)    PDF(pc) (1798KB)(833)       Save

    Emotional state decoding based on electroencephalography (EEG) usually regards individual emotion as a relatively static state and uses spectral power or inter-channel correlations of EEG as features.Based on recent advancement of dynamic functional connectivity analysis in the area of network neuroscience, a method called microstates of functional connectivity was designed and implemented, which clustered the inter-regional functional connectivity patterns of the brain under different emotional states to obtain representative microstates, and the temporal statistics, such as coverage and transition probability were extracted as features for emotional state decoding.Based on a widely used publicly available EEG dataset DEAP, new features in microstates of dynamic functional connectivity analysis achieved regression mean squared errors of 3.87±0.28 and 3.25±0.30 on valence and arousal respectively, which were better than those using traditional spectral power features, 4.07±0.30 (p=0.005) and 3.41±0.31 (p=0.064).The results demonstrate the feasibility of emotional state decoding based on microstates of functional connectivity and provide deeper insight into understanding the neural mechanisms of emotion.

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    Parallel art:artistic creation under human-machine collaboration
    Chao GUO,Yue LU,Yilun LIN,Fan ZHUO,Fei-Yue WANG
    Chinese Journal of Intelligent Science and Technology    2019, 1 (4): 335-341.   DOI: 10.11959/j.issn.2096-6652.201938
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    The artistic creation of machine has drawn considerable attention and achieved significant development in recent years.There are more and more artworks processed partially by specific algorithms,or even created entirely by machines.Despite their popularity,it is hard for these artworks to be accepted by humans because of their underwhelming sensory impacts and lack of empathy.However,their impact is highly concerned by the art community.A theoretical framework called the parallel art system was proposed to solve the technical and human-machine-relationship challenges in artistic creation.The system aims to build a partnership between humans and machines,enabling them to collaborate in a parallel manner,and even provide a new way to integrate human emotion and machine logic.

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    Intelligent innovative regulatory tools on financial technology:concept,platform framework,and prospects
    Hongfeng CHAI,Shuai WANG,Xiaojun TU,Quan SUN,Xiaofeng MA,Jie WU,Hua CAI,Xiaolong ZHENG,Fei-Yue WANG
    Chinese Journal of Intelligent Science and Technology    2020, 2 (3): 214-226.   DOI: 10.11959/j.issn.2096-6652.202024
    Abstract459)   HTML50)    PDF(pc) (2729KB)(826)       Save

    In view of the complex and severe challenges faced by financial technology regulation and the lag behind situation of the existing regulatory sandbox which is highly dependent on manual operation,the important feature and development trend of the Chinese “innovative regulatory tools on financial technology” lie in “intelligence” were pointed out.A systematic platform framework of “intelligent innovative regulatory tools on financial technology” was proposed,that is,use modern means of science and technology to realize the full life cycle supervision of financial technology innovative applications.The elements of innovative regulatory tools were elaborated,including the underlying blockchain,public opinion monitoring platform,explainable artificial intelligence based on knowledge graphs,and presented the key technologies involved.The new iterative methodology used in the development of the innovative regulatory tools platform was introduced,i.e.,the spiral development model and iterative design driven by the hierarchy of needs.Towards the end,some thoughts and prospects on the proposed innovative regulatory tools were put forward,and the regulatory tools are expected to create a set of regulatory technology infrastructure to serve regulatory authorities and financial institutions were pointed out.

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    Parallel loading and unloading:smart technology toward intelligent logistics
    Dayong SHEN,Xiao WANG,Sheng LIU
    Chinese Journal of Intelligent Science and Technology    2019, 1 (1): 34-39.   DOI: 10.11959/j.issn.2096-6652.201911
    Abstract1397)   HTML79)    PDF(pc) (2472KB)(817)       Save

    The ACP theory plays an important role in modeling and controlling complex,uncertain systems.Parallel loading and unloading is the integration of this theory in intelligent logistics.The basic framework of the parallel loading and unloading system was proposed in this paper.In this framework,the artificial system was built and updated continuously to describe real loading and unloading process,then computing experiments perform all kinds experiments for predicting all possible development and evaluate the best scheme.Finally,a bridge between the real system and the artificial system was built by the parallel execution and a long-term guidance for the real system could be realized,while artificial system was updated based on information from the real system.

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    Research and application of cloud service system for cold chain
    Shichao CHEN,Bin TIAN,Yisheng LV,Bin HU,Shuangshuang HAN
    Chinese Journal of Intelligent Science and Technology    2019, 1 (2): 125-132.   DOI: 10.11959/j.issn.2096-6652.201919
    Abstract736)   HTML13)    PDF(pc) (4838KB)(784)       Save

    For the current challenges of lacking cold chain logistics information,traceability and other issues,the end-to-end cold chain cloud service system was designed in this paper,based on Internet of Things (IoT) technology and micro-service system architecture.Full cycle for cold chain logistics and data monitoring in different business scenarios could be realized by this system.It can solve the problem of information isolated island and data sharing in the existing cold chain monitoring system,and meet the actual demand of the constant chain of pharmaceutical cold chain monitoring.The designed cold chain cloud service system includes intelligent monitoring terminal,cloud service management platform and cold chain manager App.This system can be applied to logistics of medicine,fresh,fruit,flowers,etc.In this paper,the application of the system in the pharmaceutical industry was described in detail,especially in the three business scenarios of storage,transportation and sales of pharmaceutical cold chain.The proposed system is stable and has been applied in commercial operation.Besides,its advantages have been approved by many users in the pharmaceutical industry.

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