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    Deep learning-based multimodal trajectory prediction methods for autonomous driving: state of the art and perspectives
    Jun HUANG, Yonglin TIAN, Xingyuan DAI, Xiao WANG, Zhixing PING
    Chinese Journal of Intelligent Science and Technology    2023, 5 (2): 180-199.   DOI: 10.11959/j.issn.2096-6652.202317
    Abstract847)   HTML102)    PDF(pc) (6643KB)(608)       Save

    Although deep learning methods have achieved better results than traditional trajectory prediction algorithms, there are still problems such as information loss, interaction and uncertainty difficulties in modelling, and lack of interpretability of predictions when implementing multimodal high-precision prediction for autonomous vehicles in heterogeneous, highly dynamic and complex changing environments.The newly developed Transformer's long-range modelling capability and parallel computing ability make it a great success not only in the field of natural language processing, but also in solving the above problems when extended to the task of multimodal trajectory prediction for autonomous driving.Based on this, the aim of this paper is to provide a comprehensive summary and review of past deep neural network-based approaches, in particular the Transformer-based approach.The advantages of Transformer over traditional sequential network, graphical neural network and generative model were also analyzed and classified in relation to existing challenges, simultaneously.Transformer models can be better applied to multimodal trajectory prediction tasks, and that such models have better generalisation and interpretability.Finally, the future directions of multimodal trajectory prediction were presented.

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    Survey on multi-agent reinforcement learning methods from the perspective of population
    Fengtao XIANG, Junren LUO, Xueqiang GU, Jiongming SU, Wanpeng ZHANG
    Chinese Journal of Intelligent Science and Technology    2023, 5 (3): 313-329.   DOI: 10.11959/j.issn.2096-6652.202326
    Abstract519)   HTML66)    PDF(pc) (3737KB)(588)       Save

    Multi-agent systems are a cutting-edge research concept in the field of distributed artificial intelligence. Traditional multi-agent reinforcement learning methods mainly focus on topics such as group behavior emergence, multi-agent cooperation and coordination, communication and communication between agents, opponent modeling and prediction. However, they still face challenges such as observable environment, non-stationary opponent strategies, high dimensionality of decision space, and difficulty in understanding credit allocation. How to design multi-agent reinforcement learning methods that meet the large number and scale of intelligent agents and adapt to multiple different application scenarios is a cutting-edge topic in this field. This article first outlined the relevant research progress of multi-agent reinforcement learning. Secondly, a comprehensive overview and induction of multi-agent learning methods with multiple types and paradigms were conducted from the perspectives of scalability and population adaptation. Four major categories of scalable learning methods were systematically sorted out, including set permutation invariance, attention, graph and network theory, and mean field theory. There were four major categories of population adaptive reinforcement learning methods: transfer learning, course learning, meta learning, and meta game, and typical application scenarios were provided. Finally, the frontier research directions were prospected from five aspects: benchmark platform development, two-layer optimization architecture, adversarial strategy learning, human-machine collaborative value alignment and adaptive game decision-making loop, providing reference for the research on relevant frontier key issues of multi-agent reinforcement learning in multimodal environments.

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    AI-driven digital image art creation: methods and case analysis
    Changsheng WANG
    Chinese Journal of Intelligent Science and Technology    2023, 5 (3): 406-414.   DOI: 10.11959/j.issn.2096-6652.202333
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    In recent years, the rapid advancement of artificial intelligence technology has introduced novel tools for the realm of digital image creation.The use of AI tools for digital image creation has gradually become a common practice.In this context, this paper analyzed the methods and cases of AI-driven digital image art creation through interviews, surveys, and case analyses.The survey revealed that the majority of respondents believed that AI tools had a significant impact on their creative process, especially the widespread use of direct output from artificial intelligence methods in digital image art creation.By analyzing the cases in detail, this paper revealed the typical paradigms of AI-based digital image art creation and summarized them as the “AI art creation workflow”, which included AI direct output, AI assisted drawing, and ControlNet precision drawing.These methods can effectively replace repetitive steps in digital image creation and improve image output efficiency, assist in digital image drawing and break through the conventional drawing ideas, and achieve precise and controllable expression of digital image art content.

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    Diffusion Model and Artificial Intelligence Generated Content
    Chinese Journal of Intelligent Science and Technology    2023, 5 (3): 378-379.   DOI: 10.11959/j.issn.2096-6652.202334-1
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    In celebration of McCulloch-Pitts ANN model’s 80th anniversary: its origin, principle, and influence
    Qinghai MIAO, Yutong WANG, Yisheng LV, Xiaoxiang NA, Fei-Yue WANG
    Chinese Journal of Intelligent Science and Technology    2023, 5 (2): 133-142.   DOI: 10.11959/j.issn.2096-6652.202314
    Abstract531)   HTML137)    PDF(pc) (8731KB)(495)       Save

    In 1943, Warren McCulloch and Walter Pitts published a paper titled “A logical calculus of the ideas immanent in nervous activity”, which demonstrated that the functioning of neural networks could be described using logical calculus.This expanded the field of computational neuroscience and laid the foundation for the development of artificial neural networks.On the occasion of the 80th anniversary of the publication of the M-P paper, the intellectual origins of the M-P theory was explored, taking into account the historical context and the authors’ career trajectories.With the help of examples from the original paper, the basic principles of the M-P model were outlined, and its advantages and limitations were summarized.Furthermore, the impact of the M-P theory on the development of information science was discussed, focusing on the authors’ contributions to cybernetics, including their work on circular causality and feedback mechanisms, which provided a foundation for the development of modern artificial intelligence technologies such as parallel intelligence and large-scale models.

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    Potential risks and governance strategies of artificial intelligence generated content technology
    Yaling LI, Yuanqi QIN, Que WEI
    Chinese Journal of Intelligent Science and Technology    2023, 5 (3): 415-423.   DOI: 10.11959/j.issn.2096-6652.202332
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    Artificial intelligence generated content technology is reshaping the production and consumption patterns of digital content.The research was based on the basic concepts and technological development process of artificial intelligence generated content technology, focusing on exploring ethical risks and governance challenges such as privacy leakage, algorithm hegemony, digital divide, intellectual property protection and employment impact caused by artificial intelligence generated content.After reviewing the typical governance paths and experiences of developed countries abroad, this paper summarized and proposed measures and development suggestions for China to address the potential risks of AI generated content.One was to prioritize development and coordinate layout; Second was to strengthen the full factor investment in the research and development of artificial intelligence technology; Third was to improve the governance rule system of artificial intelligence technology; Fourth was to accelerate the research and development of artificial intelligence governance technology.

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    Abnormal cell segmentation for lung pathological image based on denseblock and attention mechanism
    Wencheng CUI, Keli WANG, Hong SHAO
    Chinese Journal of Intelligent Science and Technology    2023, 5 (4): 525-534.   DOI: 10.11959/j.issn.2096-6652.202210
    Abstract242)   HTML3)    PDF(pc) (3799KB)(487)       Save

    Aiming at the problems of unbalanced brightness of lung cell images and achieving accurate segmentation of abnormal cell contour difficultly, an abnormal cell segmentation model based on U-Net was proposed, which combined the dense connection mechanism and attention mechanism. Firstly, U-Net with encoder-decoder structure was used to segment abnormal cells. Secondly, the dense block was introduced into U-Net to improve the propagation ability between features and extract more characteristic information of abnormal cells. Finally, the attention mechanism was used to increase the weight of abnormal cell regions and reduce the interference of the imbalance of brightness to the model. The experimental results show that the IoU value and Dice similarity coefficient achieved by this method are 0.6928 and 0.8060, respectively. Compared with other models, this proposed method is able to segment low-contrast regions and abnormal cells with diverse shapes.

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    A survey on application of block chain in next generation intelligent manufacturing
    Zhiwei LIN, Songchuan ZHANG, Chengji WANG, Yiwei ZHOU
    Chinese Journal of Intelligent Science and Technology    2023, 5 (2): 200-211.   DOI: 10.11959/j.issn.2096-6652.202248
    Abstract454)   HTML61)    PDF(pc) (13396KB)(467)       Save

    In the process of promoting digital transformation of Chinese manufacturing enterprises, block chain technology has attracted extensive attention of researchers.The overall research progress domestically and abroad in the field of block chain technology was concluded.The recent researches on block chain technology in manufacturing process of food and military products, cloud manufacturing and other fields were summarized.The future path of combining block chain technology with intelligent manufacturing was predicted from 4 aspects: storage problem, scalability problem, computational power waste problem, security and privacy problem, aiming to provide valuable research ideas for further researchers.

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    Key problems and progress of pedestrian trajectory prediction methods: the state of the art and prospects
    Quancheng DU, Xiao WANG, Lingxi LI, Huansheng NING
    Chinese Journal of Intelligent Science and Technology    2023, 5 (2): 143-162.   DOI: 10.11959/j.issn.2096-6652.202315
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    Pedestrian trajectory prediction aims to use observed human historical trajectories and surrounding environmental information to predict the future position of the target pedestrian, which has important application value in reducing collision risks for autonomous vehicles in social interactions.However, traditional model-driven pedestrian trajectory prediction methods are difficult to predict pedestrian trajectories in complex and highly dynamic scenes.In contrast, datadriven pedestrian trajectory prediction methods rely on large-scale datasets and can better capture and model more complex pedestrian interaction relationships, thereby achieving more accurate pedestrian trajectory prediction results, and have become a research hotspot in fields such as autonomous driving, robot navigation and video surveillance.In order to macroscopically grasp the research status and key issues of pedestrian trajectory prediction methods, We started with the classification of pedestrian trajectory prediction technology and methods.First, the research progress of existing pedestrian trajectory prediction methods were elaborated and the current key issues and challenges were summarized.Second, according to the modeling differences of pedestrian trajectory prediction models, existing methods were divided into model-driven and data-driven pedestrian trajectory prediction methods, and the advantages, disadvantages and applicable scenarios of different methods were summarized.Then, the mainstream datasets used in pedestrian trajectory prediction tasks were summarized and the performance indicators of different algoriths were compared.Finally, the future development direction of pedestrian trajectory prediction was prospected.

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    Parallel scientific research institutes: from digital transformation to intelligent revolution
    Rui QIN, Xiaolong LIANG, Juanjuan LI, Wenwen DING, Jiachen HOU, Yutong WANG, Yonglin TIAN, Ding WEN
    Chinese Journal of Intelligent Science and Technology    2023, 5 (2): 212-221.   DOI: 10.11959/j.issn.2096-6652.202318
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    Aiming to address the dual complexities that current scientific research institutes face in both management work and research tasks, parallel scientific research institutes were proposed.Parallel scientific research institutes were constructed based on the virtual-real interactive parallel intelligence theory, which leveraged digital construction technologies based on digital twins and metaverse, distributed governance technologies based on blockchain and decentralized autonomous organizations and operations (DAOs), intelligent decision-making technologies based on big data and foundation models, as well as scientific innovation paradigms based on decentralized science (DeSci) and artificial intelligence for science (AI4S).Their core goal was to form a prescriptive scheme for scientific research institute transformation driven by complex science, thereby constructing a trustworthy, reliable, usable, efficient and effective intelligent research organizational and operational ecosystem.The systematic design and key technologies of parallel scientific research institutes were introduced, their main characteristics and advantages were described, and their typical application scenarios were explored.Parallel scientific research institutes go beyond the simple digital transformation of scientific research institutes, and emphasize a higher level of intelligent transformation, promoting the sustainable and healthy development of scientific research institutes.

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    A survey of deep learning-based MRI stroke lesion segmentation methods
    Weiyi YU, Tao CHEN, Junping ZHANG, Hongming SHAN
    Chinese Journal of Intelligent Science and Technology    2023, 5 (3): 293-312.   DOI: 10.11959/j.issn.2096-6652.202328
    Abstract430)   HTML97)    PDF(pc) (23370KB)(362)       Save

    Automatic stroke lesion segmentation has become a research hotspot in recent years.In order to comprehensively review current progress of deep learning-based MRI stroke lesion segmentation methods, start with the clinical problems of stroke treatment, we further elaborate the research background and challenges of deep learning-based lesion segmentation, and introduce common public datasets (ISLES and ATLAS) for stroke lesion segmentation.Then, we focus on the innovation and progress of deep learning-based stroke lesion segmentation methods, and summarize the research progress from three perspectives: network structure, training strategy, and loss function, and compare the advantages and disadvantages of various methods.Finally, we discusse the difficulties and challenges in this research and its future development trend.

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    Fine-grained urban flow inference based on diffusion models with incomplete data
    Yuhao ZHENG, Senzhang WANG
    Chinese Journal of Intelligent Science and Technology    2023, 5 (3): 389-396.   DOI: 10.11959/j.issn.2096-6652.202330
    Abstract182)   HTML12)    PDF(pc) (9260KB)(350)       Save

    To obtain detailed traffic flow data for each road segment of the city, it is necessary to deploy a large number of sensing devices and dense observation stations, which increases the costs of daily operations and equipment maintenance.At the same time, traditional traffic flow survey techniques are noisy and inaccurate, and the reliability of the detected data results is not guaranteed.Therefore, inferring fine-grained urban traffic flow based on coarse-grained and noiseinclusive sensor observations has become an important research topic.To address the above problems, we proposed a denoising diffusion model based on spatio-temporal attention, with the intention of providing fine-grained urban traffic base data in different scenarios of traffic demand, and laying the foundation for traffic planning and intelligent transportation system construction.

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    Research on path planning of material transmission platform based on A* and dynamic window method
    Wei TANG, Xiao TAN, Yu SUN, Jiapeng YAN, Guangrui YAN
    Chinese Journal of Intelligent Science and Technology    2023, 5 (4): 515-524.   DOI: 10.11959/j.issn.2096-6652.202304
    Abstract153)   HTML6)    PDF(pc) (4961KB)(349)       Save

    Due to the problems of traditional material transfer equipment such as single working mode and inflexible adjustment of transfer path, intelligent material transfer system has gradually become a research hotspot in the field of logistics transmission. A path planning algorithm based on A* and dynamic window method was proposed for the modular material transmission platform, in order to improve its obstacle avoidance ability during the transfer process by flexibly adjusting the material transfer path. In the global path planning, the smoothness and static obstacle avoidance of the transmission path were realized by improving the A* weight function and integrating the Bezier curve and matrix interference theory. And by introducing the dynamic window method and extracting the global path key points as transition points for local path guidance of the transmission target, dynamic obstacle avoidance was realized when the path falling into local optimization was avoided. The research results showed that the path planning algorithm based on A* and dynamic window method could reduce the total global path length by 4.6% and the total path turning angle by 42.3%, while the dynamic obstacles could be effectively avoided in the local planning, which verified the rationality of the path planning algorithm.

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    Research on the overseas communication effect of Chinese realistic theme TV series: take YouTube platform as an example
    Xue MENG, Ruonan YANG, Ruiqi LI
    Chinese Journal of Intelligent Science and Technology    2023, 5 (3): 343-351.   DOI: 10.11959/j.issn.2096-6652.202329
    Abstract214)   HTML29)    PDF(pc) (5290KB)(314)       Save

    YouTube is the international video website with the largest number of active users in the world, and it is also one of the main channels for Chinese film and television content to go global.Natural language processing technology was used to analyze the communication effect of Chinese realistic theme TV series on the YouTube platform.The factors affecting the communication effect of Chinese realistic theme TV series on YouTube could be found from different dimensions such as content, transmitter, and recipient.Then more works can be guided to accurately disseminate.It was found that the acceptance of overseas audiences to the work was affected by the type, content, team and translation of realistic TV series.The communication efficiency of Chinese realistic theme TV series on overseas platforms could be improved only by grasping user preferences, strengthening brand awareness, understanding channel characteristics and attaching importance to subtitle translation.The accurate and efficient dissemination of “Chinese-style modernization” stories on international video platforms could be realized.

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    Parallel Sharhili: a new approach to sustainable development and intelligent management of ecological resources
    Peiding PI, Qinghua NI, Jing YANG, Mengzhen KANG, Xuanhao LI, Yingkun DU, Fei-Yue WANG
    Chinese Journal of Intelligent Science and Technology    2023, 5 (3): 283-292.   DOI: 10.11959/j.issn.2096-6652.202325
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    Sharhili Nature Reserve is a natural treasure which enjoys both rare biological resources and incalculable ecological value, and it is known as “China’s last pure land” and “rare natural gene pool”.To ensure the sustainable development of this natural treasure, this study proposes the concept of “Parallel Sharhili”, which comprehensively assists in the precise management of ecological resources through intelligent means.The core idea involves utilizing “artificial systems” for description to build high-precision models, “computational experiments” for prediction to analyze ecosystem changes, and “parallel execution” for prescription or control and management to scientifically formulate conservation strategies.This study explores an intelligent management model for nature reserves, aiming at promoting the sustainable development of the reserve and its precious biological resources.

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    Analysis and prediction of GitHub company influence based on machine learning
    Mingyu WANG, Qingyuan GONG, Jingjing QU, Xin WANG
    Chinese Journal of Intelligent Science and Technology    2023, 5 (3): 330-342.   DOI: 10.11959/j.issn.2096-6652.202327
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    The influence of a company is not only related to its industry competitiveness, but also affects its public reputation and future development.However, there has been no unified standard for evaluating the influence of a company.GitHub is a representative open-source platform for software development code repositories.Existing research typically used the total number of stars a company receives for projects posted on GitHub to measure its influence, but this approach is difficult to measure the potential of small, micro, and nascent companies.The paper predicted the future influence level of a company by introducing the scientist's influence measure h-index, using GitHub as the information source, and modeling the company network.Features was extracted features based on this network to build the classifier, which predicted the future influence level of the company.The SHAP model explanation technique was further applied on this basis to identify the important features that determined the influence of a company.The experimental results showed that the XGBoost model achieved an accuracy of 0.92 and an average AUC of 0.93 on the real-world GitHub dataset.In summary, the proposed method could accurately and reliably predict the influence of companies.

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    Data augmentation method based on diffusion model for domain generalization
    Yujun TONG, Heqing WANG, Yueheng LUO, Wenxin NING, Mandan GUAN, Wenqing YU, Keyan HUANG, Jiaxun ZHANG, Zhanyu MA
    Chinese Journal of Intelligent Science and Technology    2023, 5 (3): 380-388.   DOI: 10.11959/j.issn.2096-6652.202334
    Abstract407)   HTML37)    PDF(pc) (7426KB)(275)       Save

    Domain generalization is an important and challenging problem in computer vision, arising from the distribution shift of real-world data.In practical applications, it is common to encounter training and testing data from different domains, and the difference in data distribution can lead to performance degradation during testing.In this paper, we propose a domain generalization method based on latent space data augmentation.Unlike traditional image-level data augmentation approaches, the method introduces a diffusion model in the latent space to achieve fine control and diversity generation of features, thereby achieving feature level data augmentation and enhancing the model's generalization ability in the target domain.Specifically, the classifier-based implicit diffusion model, trained within the latent space, can conditionally generate accurate and rich source domain features.It leverages efficient sampling techniques to expedite the generation of augmented features.Experimental results show that the method has achieved significant performance improvement in various domain generalization tasks, and has good effectiveness and robustness in real scenarios.The key innovation of this paper lies in shifting data augmentation to the latent space level and introducing the diffusion model for augmentation, providing a novel approach to address the domain generalization problem.

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    A survey on network tomography technology
    Xiaojia XU, Yongcai WANG, Deying LI
    Chinese Journal of Intelligent Science and Technology    2023, 5 (2): 163-179.   DOI: 10.11959/j.issn.2096-6652.202316
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    Network tomography is an efficient and convenient tool to infer the internal state of a network through end-toend path measurement.It has been widely used in link measurement and fault location of wired and wireless networks.This paper summarized and analyzed the model foundation of four basic models of network tomography, boolean network tomography technology, additive network tomography technology, bandwidth network tomography technology and the stochastic network tomography technology.This paper systematically combed the key problems in network tomography technology, including monitor placement, beacon and service placement, path construction and data analysis, the problem of identifiability, and attack in network tomography when identification was not guaranteed.At the same time, the new research and new problems in the field of network tomography such as network tomography with network coding, neural network tomography and node fault location method in NFV were sorted out.Finally, based on the development status of network tomography technology, the future development trend of network tomography was analyzed and discussed.

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    An empirical study on the impact of enterprise digital transformation on employment scale and structure
    Jiting HUANG, Kexin GUO, Jiayin QI
    Chinese Journal of Intelligent Science and Technology    2023, 5 (3): 352-365.   DOI: 10.11959/j.issn.2096-6652.202336
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    With the advent of the digital age, people are paying more and more attention to the employment scale and employment structure of enterprises.Used the enterprise panel data of listed companies from 2011 to 2019, the impact of enterprise digital transformation on employment scale and employment structure was studied deeply by the fixed-effect model and mediation effect model.The results show that, firstly, the digital transformation of enterprises significantly expand the employment scale of enterprises.At the same time, the impact of digitalization on the job structure has a strong"knowledge bias", showing a bias towards high skills and knowledge, and an increase in the demand for talents who master knowledge related to cutting-edge industries.The results of the intermediary effect test show that the digital transformation of enterprises indirectly promotes the employment scale of enterprises through market scale effect and product innovation.The results of heterogeneity analysis show that the high degree of digitalization of the environment and the degree of digitalization of non-state-owned enterprises have a more significant impact on the employment structure.

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    Parallel fuzzy control: a self-learning control method with virtual-real interaction and mutual enhancement
    Dewang CHEN, Jixiang OU
    Chinese Journal of Intelligent Science and Technology    2023, 5 (2): 267-273.   DOI: 10.11959/j.issn.2096-6652.202322
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    Fuzzy control has advantages such as interpretability and ease of implementation.However, it is limited by its weak self-learning capability, which makes it difficult to effectively utilize the large amount of data accumulated in the control process.Parallel control is a new intelligent control method that enables intelligent control with virtual-real interaction and mutual enhancement, effectively using the Internet and big data to achieve intelligent control.Fuzzy control and parallel control were combined as a new method, and the definition and framework of parallel fuzzy control were proposed and its possible applications were discussed.Parallel fuzzy control has the potential to extend the development direction of fuzzy control and become a new thinking for parallel control.It can effectively utilize big data and some machine learning algorithms based on data-driven to achieve self-learning control, while ensuring interpretability and credibility.

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    Digital teachers and parallel education: A paradigm shift in teaching and learning after ChatGPT
    Fei-Yue WANG
    Chinese Journal of Intelligent Science and Technology    2023, 5 (4): 454-463.   DOI: 10.11959/j.issn.2096-6652.202348
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    From its historical origin to present function, education is an inspiration and integral part of our quest for artificial intelligence(AI). We must ensure that teaching and education should be positioned as the first and most important field for research and development of intelligent science and technology. Otherwise increasingly complicated AI technology would not only remain unexplainable but also become ungovernable, and be manipulated easily by some minority of our human beings to harm the majority of us. Considering the current impact to all aspects of our society at the global level by ChatGPT and likewise artificial intelligence-generated content(AIGC)/artificial general intelligence(AGI) technologies, hereby we investigate a new approach to effectively deploy intelligent technology, especially digital teachers and parallel schools, for intelligent teaching and smart education. We hope this would lead to a paradigm shift in teaching and education for the benefits of humanity, and safe, healthy, sustainable development of our shared human community.

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    Game patrol strategy for hazardous gas leakage in chemical parks
    Yin CHEN, Lize ZHANG, Guohua SHUAI, Lili CHEN, Zhen WANG
    Chinese Journal of Intelligent Science and Technology    2023, 5 (3): 366-377.   DOI: 10.11959/j.issn.2096-6652.202338
    Abstract107)   HTML9)    PDF(pc) (33637KB)(218)       Save

    In the context of promoting the integration of smart cities, the pace of construction of smart chemical parks is gradually accelerating.Since accidents in China’s chemical industry have occurred frequently in recent years, causing great damage and loss to public safety, improving the safety management and emergency response capability of chemical parks is an urgent need to be solved.For this kind of safety problems, this paper proposed a game patrol strategy for hazardous gas leakage in chemical parks.Firstly, the convective diffusion model was used to describe the process of hazardous gas leakage.Secondly, game theory was introduced to model the confrontation process between the attacking and defending parties in the patrol problem, and the response time of the defenders to a safety incident was correlated with its benefit.Then, a multilinear programming-based GGC algorithm was proposed to solve this game model.Finally, the gains of the game model in this paper were compared with the other two basic methods in the scenarios of three real chemical park case with different sizes.The results show that the model can effectively improve the gains of the defender and reduce the gains of the attacker.

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    Parallel surgical robots
    Tianxiang BAI, Shuangyi WANG, Yating LIU, Hanzhong LI, Yi WEN
    Chinese Journal of Intelligent Science and Technology    2023, 5 (2): 222-233.   DOI: 10.11959/j.issn.2096-6652.202319
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    Medical robots improve medical efficiency and reduce patients’ suffering that owns social significance and scientific research value.Focusing on the difficulties of medical robots, we propose the concept of parallel medical robotics based on the ACP (artificial societies, computational experiments, parallel execution)-based parallel system method.parallel medical robot is mainly composed of a physical medical robot and its virtual counterpart, through which we can realize the management and control for the overall system, conduct experiments and evaluation for the treatment process, and learning and training for doctors and patients.Parallel medical robotics is supported by robot simulation, biomechanism, 3D printing and knowledge automation techniques, and holds three core functions that are descrition, prediction and prescription when combined with parallel learning.We demonstrate the framework and its potential usages with a robotic trans-esophageal ultrasound system.

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    Future of AI painting from a legal perspective: the balance between freedom and control
    Yinyuan MA
    Chinese Journal of Intelligent Science and Technology    2023, 5 (3): 424-430.   DOI: 10.11959/j.issn.2096-6652.202331
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    AI painting, as a representative of the new Internet technology, has a free environment consistent with the development of the early Internet.However, the free sharing of public resources is not eternal.The conflict between old system and new will inevitably brings many problems in the application of the existing laws.China’s existing laws and regulations for network service providers responsibility than the outside to be more perfect, again forced to increase the service provider or to serve the interests of the users responsibility will lead to imbalance, and there is no necessity for reference.Under the existing legal framework, the use of special legislation and industry autonomy to reinforce the power limits of the dominant player is enough, and the future of AI painting needs to always pay attention to the value of balance.

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    Parallel and digital police for new norm of public safety: from parallel security to peaceful China
    Fei-Yue WANG
    Chinese Journal of Intelligent Science and Technology    2023, 5 (4): 431-435.   DOI: 10.11959/j.issn.2096-6652.202347
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    Due to the impact of artificial intelligence generated content technologies such as AlphaGo and ChatGPT, the governance of intelligent science and technology becomes a significant concern all over the world. This review addressed related issues by integrating traditional public safety management with new artificial intelligence (AI) technologies and provided an alternative philosophy and approach for safety, security and sustainability in the future society of AI.

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    Parallel workshop scheduling model and system design
    Shaoming PENG, Gang XIONG, Zhen SHEN, Xisong DONG, Zhiping QU, Long FU, Zhikun TAO, Yunjun HAN
    Chinese Journal of Intelligent Science and Technology    2023, 5 (2): 254-266.   DOI: 10.11959/j.issn.2096-6652.202321
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    The workshop environment is a highly dynamic and tightly coupled complex system.A scheduling algorithm cannot be trained once and used for life.It needs to perform computational experiments and progressive learning according to the workshop environment and processing state.In this paper, a parallel workshop scheduling system model was proposed to realize scheduling optimization and system evolution.For this model, we described the modeling of artificial workshop scheduling system based on multi-agent system, the computational experiment for workshop scheduling tasks, and the parallel scheduling method for the virtual-real system, realizing the closed-loop control and iterative optimization.Finally, under the guidance of the parallel workshop scheduling model, we designed the architecture and basic functions of the parallel workshop scheduling system.

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    Lipsynthesis incorporating audio-visual synchronisation
    Cong JIN, Jie WANG, Zichun GUO, Jing WANG
    Chinese Journal of Intelligent Science and Technology    2023, 5 (3): 397-405.   DOI: 10.11959/j.issn.2096-6652.202335
    Abstract277)   HTML31)    PDF(pc) (13372KB)(173)       Save

    With the flourishing development of video-based information dissemination, audio and video synchronization is gradually becoming an important standard for measuring video quality.Deep synthesis technology has been entering the public's view in the international communication field, and lip-sync technology integrating audio and video synchronization has attracted more and more attention.The existing lip-synthesis models are mainly based on lip-synthesis of static images, which are not effective for synthesis of dynamic videos, and most of them use English datasets for training which results in poor synthesis of Chinese Mandarin.To address these problems, this paper conducted optimization experiments on the Wav2Lip lip synthesis model in Chinese context based on its research foundation, and tested the effect of different routes of training models through multiple sets of experiments, which provided important reference values for the subsequent Wav2Lip series research.This study realized lip synthesis from speech-driven to text-driven, discussed the application of lip synthesis in multiple fields such as virtual digital human, and laid the foundation for the broader application and development of lip synthesis technology.

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    Parallel anesthesia: from anesthesia automation to intelligent full-cycle anesthesia platform
    Yifei ZHAO, Le SHEN, Peijun YE, Jing WANG, Fei-Yue WANG
    Chinese Journal of Intelligent Science and Technology    2023, 5 (2): 234-246.   DOI: 10.11959/j.issn.2096-6652.202324
    Abstract140)   HTML12)    PDF(pc) (7432KB)(166)       Save

    Based on the parallel medical theory, this paper proposes the theory and method of parallel anesthesia, which aims to build an organic and unified intelligent full-cycle anesthesia platform composed of biological humans (medical personnel), robots (mechanical automation equipment) and digital humans (digital medical care) through artificial system modeling, computational experimental analysis and parallel execution of human-computer interaction.Starting from the current status quo and bottleneck of the development of anesthesiology, this paper first conceives multiple intelligent scene modes jointly constructed by biological humans, digital humans and robots in parallel anesthesia systems from the aspects of clinical anesthesia management, crisis management, scientific research, education and teaching, and operation management, and analyzes the role of biological humans in each scene and the complementary relationship between the three.At the same time, the improvement and improvement of parallel anesthesia on patients’ medical safety and medical work efficiency through computational experiments and the implementation correction mode of virtual and real combination are introduced.Finally, by analyzing the ethical issues in the construction of parallel anesthesia system, analyzing the boundary and regulations of automation and intelligent equipment participation in anesthesia, and taking “respect for life, fairness and transparency, efficient operation and labor saving” as the system criteria, an intelligent full-cycle anesthesia platform is established to reduce the repetitive work of medical mechanization, enhance the ability of intelligent decisionmaking assistance, and improve the level of refined treatment and management.

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    ParaDefender: the parallel supervision for cybersecurity based on ACP
    Xiaoguang CHEN, Jinpeng HAN, Manzhi YANG, Xiao WANG, Xin LIU, Zhen WANG, Fei-Yue WANG
    Chinese Journal of Intelligent Science and Technology    2023, 5 (2): 247-253.   DOI: 10.11959/j.issn.2096-6652.202320
    Abstract141)   HTML10)    PDF(pc) (9044KB)(145)       Save

    Cybersecurity originated from the theoretical testing of computer networks.With the development of information and communication technology (ICT), cybersecurity threats present the "4V" characteristics of high source volume, many varieties, process not visibility, and high-value losses.This is accompanied by a double change in the formation and complexity of cyberspace.With the recent rising of metaverse, the human-centered cyberspace has the ability to map the real world and exposes the real world to cybersecurity threats.However, cybersecurity supervision does not keep up with the changes in cyberspace and still seeks solutions in cyberspace.A parallel supervision system for cybersecurity, ParaDefender was introduced, which was constructed based on artificial systems, computational experiments and parallel execution of ACP method to improve the current dilemma of cybersecurity supervision from the perspective of cyber-physicalsocial systems (CPSS).We demonstrated provided the application solutions of ParaDefender in telecom anti-fraud and industrial IoT scenarios and inspiration and reference for cybersecurity supervision in different fields.

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    Parallel theaters: human-machine collaborative creation and intelligent management for theatrical art
    Qinghua NI, Chao GUO, Fei-Yue WANG
    Chinese Journal of Intelligent Science and Technology    2023, 5 (4): 436-445.   DOI: 10.11959/j.issn.2096-6652.202344
    Abstract130)   HTML57)    PDF(pc) (2259KB)(138)       Save

    Given the limitations of traditional theater production processes, which heavily rely on expert experience and manual labor, the theater industry faces a pressing demand for a transition from a paradigm primarily rooted in individual expertise and traditional competencies to one that harnesses intelligent technology and data-driven approaches. This transition aims to enhance production and operational efficiency while elevating artistic expression to address the diverse preferences of audiences more effectively. Within this context, this paper proposes the concept of "parallel theaters", specifically designed to facilitate collaborative human-machine creativity and intelligent management across the entire theater workflow. By establishing a comprehensive theater model, creating a computational experimentation platform for theatrical art and enabling a virtual-to-real feedback loop, the "parallel theaters" contributes to the intelligent and automated design and execution of critical theater production and management aspects, such as script writing, stage design, performance coordination, audience engagement and marketing. In addition, the paper explores the potential cross-disciplinary applications of "parallel theaters" in education and psychotherapy, analyzing its ability to foster the integration of the theater industry with other fields, thereby expanding the influence of the performing arts. As an avant-garde model for theater creation and management, "parallel theaters" opens new avenues for the industry in the intelligence era, promising enhanced efficiency, enriched artistic expression and a more diverse audience engagement.

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    Parallel financial budgeting: deep integration and intelligent services for complex business and finance
    Huaning CUI, Fei-Yue WANG, Juanjuan LI, Rui QIN, Ge WANG, Xiaolong LIANG, Jiachen HOU, Sangtian GUAN
    Chinese Journal of Intelligent Science and Technology    2023, 5 (4): 446-453.   DOI: 10.11959/j.issn.2096-6652.202346
    Abstract109)   HTML17)    PDF(pc) (2000KB)(127)       Save

    Financial budget management is an integral part of corporate governance, critically important for managing, controlling, coordinating and assessing internal resources. However, many enterprises still face challenges in financial budgeting, including unscientific target setting, slow execution control and ineffective budget outcome prediction. Although the advancement of digital and intelligent technologies offers strong support for mitigating these issues, it fail to address them fundamentally. Parallel intelligence theory, by creating artificial systems parallel to the real one and leveraging their virtual-real feedback and parallel execution, can realize description, prediction and prescription of financial budgeting, thereby enhancing the scientific accuracy of budget targets, the controllability of the budget process and the reliability of budget outcomes. This paper combined parallel intelligence theory with financial budgeting to propose a new paradigm of parallel financial budgeting. It provided a viable technological framework and theoretical guidance for the deep integration of business and finance as well as intelligent services supported by it in complex environments, fostering intelligent transformation and innovation in corporate financial budget management. The paper delineated the concept of parallel financial budgeting, presented its fundamental framework, and discussed its key characteristics and supporting technologies.

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    Research on 6G network scenario cognition based on knowledge graph
    Zhuoqiao ZHAO, Nan CHENG, Jie CHEN, Fangjiong CHEN, Changle LI
    Chinese Journal of Intelligent Science and Technology    2023, 5 (4): 494-504.   DOI: 10.11959/j.issn.2096-6652.202339
    Abstract187)   HTML10)    PDF(pc) (3502KB)(126)       Save

    The 6G network covers the entire space, air, ground, and sea. For diversified and personalized scenarios, the 6G network needs to provide customized services, that is, on-demand services. In order to realize on-demand services in all domains and scenarios, accurate, real-time, and intelligent cognition of the characteristics of the scenarios is an important prerequisite. How to enable the network to autonomously and intelligently recognize different scenarios and services, convert them into scenario-specific network key performance indicator (KPI), and further efficiently schedule network resources is a key problem that urgently needs to be solved. This paper applies the knowledge graph to the cognitive recognition of network scenarios, forms a standardized description of 6G network scenarios, and builds a knowledge graph based on the 6G scenario ontology. At the same time, a scene cognition reasoning method based on knowledge graph embedding is proposed, which realizes the embedding learning of graph nodes and relationships and reasons about scene feature nodes, achieving high accuracy. The method proposed in this paper helps to realize the autonomous control of the service life cycle of scene awareness, cognition, and on-demand services in the 6G full-scenario network, and has important innovation and guiding significance for improving the autonomy and intelligence of the next-generation network.

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    Online multi-stage Colonel Blotto game solving method for resource allocation under contested condition
    Shaofei CHEN, Mingwo ZOU, Xiaolong SU, Junren LUO, Junqiao FENG
    Chinese Journal of Intelligent Science and Technology    2023, 5 (4): 464-476.   DOI: 10.11959/j.issn.2096-6652.202341
    Abstract160)   HTML9)    PDF(pc) (1475KB)(118)       Save

    The allocation of combat resources on the future battlefield is a multi-stage confrontation problem with total resource budget constraints, which is characterized by high complexity of environment, dynamic uncertainty, and strong game confrontation. Based on the Blotto game model, the research firstly modelled the resource allocation problem in the multi-stage confrontation scenario as a two-level online Blotto game, then transformed the original problem into an online shortest path problem on a directed acyclic graph to realize the intuitive formulation of the resource allocation problem. The resource allocation problem was analyzed and solved by referring to the Lagrange game. In addition, the LagrangeBwK-Exp3-G algorithm was proposed to minimize the high probability regret of the resource allocation problem under the condition of multi-stage antagonism, and the high-probability regret bound of the algorithm on the time range T was obtained by mathematical derivation. Finally, a multi-channel power allocation experiment of satellite communication under the condition of multi-stage confrontation was designed to verify the good performance of LagrangeBwK-Exp3-G algorithm.

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    Parallel design: non-standard mechanical scheme design procedure in parallel manufacturing
    Shimeng LI, Fei-Yue WANG
    Chinese Journal of Intelligent Science and Technology    2023, 5 (2): 274-282.   DOI: 10.11959/j.issn.2096-6652.202323
    Abstract117)   HTML12)    PDF(pc) (2822KB)(118)       Save

    Parallel manufacturing is a new manufacturing form in industry, deeply integrating informalization, automation, and AI. In this paper we illustrated the procedure of non-standard mechanical design in parallel manufacturing, claiming it a nested parallel system. We proposed the ACP method based on the standard procedure and the emulated procedure, and defined the social-value-vector and trended-social-value-matrix so as to take the advantage of data in CPSS. We also presented Xinglun robot speed reducer as a case study of non-standard mechanical design in parallel design.

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    Attitude tracking control for quadrotor UAVs based on extremum seeking
    Dali GUO, Zhongyuan ZHAO, Zijuan LUO
    Chinese Journal of Intelligent Science and Technology    2023, 5 (4): 486-493.   DOI: 10.11959/j.issn.2096-6652.202337
    Abstract144)   HTML7)    PDF(pc) (1672KB)(100)       Save

    A robust control method based on extremum seeking is proposed for the attitude tracking problem of quadrotor UAV with uncertainty. Firstly, a nonlinear attitude model of a four rotor UAV was established, and considering the uncertainty of model parameters, a robust controller was designed to ensure the dynamic boundedness of tracking errors. Secondly, a learning-based controller was designed by combining a robust controller with a model-free learning algorithm, which automatically and iteratively adjusted the feedback gain of the robust controller and optimized the expected performance cost function online. Finally, numerical simulations were performed using MATLAB, which demonstrated a reduction of 0.246 in the steady-state tracking error of the proposed control method compared to the classical robust control method. This result confirms the robustness and superiority of the proposed method.

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    Point cloud registration method based on principal component analysis and feature map matching
    Weibin ZHENG, Guofu LIAN, Xueming ZHANG, Fang GUO
    Chinese Journal of Intelligent Science and Technology    2023, 5 (4): 543-552.   DOI: 10.11959/j.issn.2096-6652.202340
    Abstract143)   HTML7)    PDF(pc) (5997KB)(91)       Save

    Due to varying degrees of overlap in point cloud models, point cloud registration is prone to problems, such as feature matching errors and high difficulty in registration. Therefore, a point cloud registration method based on principal component analysis and feature map matching is proposed. Before registration, the principal component analysis method with spindle correction was used to adjust the initial pose, then the K-dimensional tree was established to search the overlapping area. Secondly, the fast point feature histograms features of the sampling points were calculated according to the overlapping area of the two-point cloud, and the point cloud feature graph matching and trimmed iterative closest point (TrICP) fine registration were performed. Registration experiments were carried out according to the existing datasets and the actual scanning model. The experimental results show that the method has good stability and higher accuracy, and the accuracy can be improved by more than 25% compared with other algorithms.

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    Visual SLAM based on semantic information and geometric constraints in dynamic environment
    Jiaming LI, Mingyang XIE, Min ZHANG, Congqing WANG
    Chinese Journal of Intelligent Science and Technology    2023, 5 (4): 477-485.   DOI: 10.11959/j.issn.2096-6652.202342
    Abstract97)   HTML9)    PDF(pc) (3289KB)(73)       Save

    Most existing visual SLAM systems assume that the external environment is static, ignoring the influence of dynamic objects on the SLAM system. This assumption largely affects the accuracy and robustness of autonomous navigation. To address this issue, a dynamic SLAM system was proposed, which combined semantic information based on object detection and geometric information from multi-view geometry constraints by defining and discriminating the dynamic feature points in the system based on the moving probability. Experiment results on the public TUM dataset and our robot in real environment showed that, when comparing with ORB-SLAM2, the absolute trajectory error could be reduced larger than 94%, and the average relative position and attitude errors were reduced at least 41% and 40%, respectively, in high dynamic environments. It means that the proposed SLAM system effectively removes dynamic feature points, thus improving the localization accuracy and robustness of the visual SLAM system within high dynamic environments.

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    Vehicle detection and recognition algorithm based on function improvement of YOLOv3
    Huajie SONG, Lei ZHOU
    Chinese Journal of Intelligent Science and Technology    2023, 5 (4): 535-542.   DOI: 10.11959/j.issn.2096-6652.202301
    Abstract101)   HTML14)    PDF(pc) (3831KB)(67)       Save

    YOLOv3 algorithm is characterized by high detection accuracy and high speed in the aspect of target detection and recognition. It is outstanding among similar algorithms, but there are still obvious problems. The loss operation of the wide and high part of the loss function of this algorithm is not obvious to the distinction between the big target and the small target, which leads to the problem that the loss calculation is not accurate enough and the prediction box size of the small target is not accurate. In view of this defect, the loss function of YOLOv3 algorithm was improved, the wide-height coordinate error was modified into the form of proportion, the non-maximum suppression method of the original model was improved and the overlap threshold was changed from the fixed value to the form of attenuation function. Finally, the model was applied to vehicle detection. The experimental results showed that the improved model improved the accuracy rate, recall rate and F1 value to different degrees while basically not affecting the vehicle detection speed, making the overall performance of the model better than the original YOLOv3 model.

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    Diagnostic of breast tumors based on improved EfficientNet
    Zhenqi FANG, Xue LI, Hong MO
    Chinese Journal of Intelligent Science and Technology    2023, 5 (4): 505-514.   DOI: 10.11959/j.issn.2096-6652.202343
    Abstract95)   HTML6)    PDF(pc) (5348KB)(56)       Save

    Breast tumors adversely affect the holistic well-being of women. Histopathological images are a critical substantiation for doctors to diagnose breast tumor types. The structure of various types of tumor cells exhibits significant correlations, thereby posing challenges to the diagnosis using conventional methods. In this work, the enhanced EfficientNet was employed for the diagnosis of breast tumors, which enabled the network model to learn the features of the disease automatically and improve the accuracy of the diagnosis of breast tumor types. Firstly, the convolutional block attention module was used to extract effective features. Secondly, the group convolution and channel shuffle operations were introduced to improve the feature representation ability of the model. Thirdly, the Hard-Swish activation function was applied to improve the convergence speed of the model. Finally, Experiments showed that the improved EfficientNet network achieved 98.4% accuracy in eight classifications on the BreakHis dataset, which was expected to act a decision aid tool in breast tumor diagnostic research.

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