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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
    Abstract1322)   HTML109)    PDF(pc) (869KB)(2556)       Save

    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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    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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    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
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    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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    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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    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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    Research development on automated robotic peg-in-hole assembly
    De XU, Fangbo QIN
    Chinese Journal of Intelligent Science and Technology    2022, 4 (2): 200-211.   DOI: 10.11959/j.issn.2096-6652.202223
    Abstract472)   HTML45)    PDF(pc) (1312KB)(611)       Save

    Peg-in-hole assembly is a typical operation task in manufactory.The research of peg-in-hole assembly based on industrial robots is valuable for the application of robots in the automated assembly area.For the peg-in-hole components with high precision or complex shapes, the efficient and reliable assembly is still very challenging.The development of automated robotic peg-in-hole assembly of peg-in-hole was reviewed from the view of control.First, the process of robotic peg-in-hole assembly was introduced.Secondly, the assembly control methods based on the traditional models were described.The newly emerged intelligent assembly methods based on learning mechanism were discussed, especially the applications of imitation learning and reinforcement learning in the automated robotic assembly.The combination of the traditional methods and the artificial intelligent methods will provide new energy for the automated robotic assembly, which will be one of the important developing tendencies in future.

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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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    Overview of metro train driving technology development:from manual driving to intelligent unmanned driving
    Wenzhu LAI, Dewang CHEN, Zhenfeng HE, Xinguo DENG, CARLO Marano GIUSEPPE
    Chinese Journal of Intelligent Science and Technology    2022, 4 (3): 335-343.   DOI: 10.11959/j.issn.2096-6652.202204
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    Based on the current development status of subway train driving technology in China and abroad, the four stages of subway train driving technology development were proposed and explained as manual driving, automatic driving, unmanned driving, and intelligent unmanned driving.After summarizing the construction situation of unmanned subway trains in China, the disadvantages of the current train control methods based on neural network-based machine learning methods were addressed.Then, the basic block diagram of metro intelligent unmanned driving based on man-machine hybrid intelligence was put forward and the deep fuzzy system was introduced.A promising solution for the combination of expert experience in dealing with emergency and interpretable AI algorithms for unmanned driving system to evolve into intelligent unmanned driving was provided.

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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
    Abstract784)   HTML209)    PDF(pc) (48558KB)(585)       Save

    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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    Research on metro train driverless system based on man-machine hybrid intelligence
    Benzun HUANG, Dewang CHEN, Zhenfeng HE, Xinguo DENG, Marano GIUSEPPECARLO
    Chinese Journal of Intelligent Science and Technology    2022, 4 (4): 584-591.   DOI: 10.11959/j.issn.2096-6652.202205
    Abstract213)   HTML37)    PDF(pc) (3698KB)(574)       Save

    Based on the development status of subway train driving technology at home and abroad, the necessity of subway train intelligent driving development and research was expounded.In view of the poor interpretability of machine learning algorithms used in current unmanned driving, with an introduction of fuzzy system, a metro train unmanned driving system based on man-machine hybrid intelligence was proposed, which realized man-machine hybrid intelligence in two ways.The subway train driverless system combined with cognitive system was explored, which a future-oriented solution for the realization of strong artificial intelligence subway train driverless system in the real sense was provided.

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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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    A survey on applications of ontology knowledge representation in robotics
    Yueguang GE, Shaolin ZHANG, Yinghao CAI, Tao LU, Dayong WEN, Haitao WANG, Shuo WANG
    Chinese Journal of Intelligent Science and Technology    2022, 4 (2): 212-222.   DOI: 10.11959/j.issn.2096-6652.202224
    Abstract276)   HTML44)    PDF(pc) (1371KB)(547)       Save

    The technology about knowledge representation plays an increasingly important role in the autonomous operation of robots facing the complex and unstructured working environment.Knowledge representation focuses on the model of knowledge symbols and how to realize knowledge processing through reasoning procedures automatically.The robot knowledge representation framework and the latest application progress based on ontology representation and reasoning were reviewed.The technical background, realization methods of knowledge representation and reasoning, and recent research progress in the robotics field were summarized from deterministic knowledge and uncertain knowledge.And the future research direction of knowledge-enabled robots was predicted.

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    Machine Learning Methods in AI 3.0
    Chinese Journal of Intelligent Science and Technology    2022, 4 (4): 542-543.   DOI: 10.11959/j.issn.2096-6652.202240-1
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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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    Research on the manipulator intelligent trajectory planning method based on the improved TD3 algorithm
    Qiang ZHANG, Wen WEN, Xiaodong ZHOU, Weihui LIU, Xiaoyu CHU
    Chinese Journal of Intelligent Science and Technology    2022, 4 (2): 223-232.   DOI: 10.11959/j.issn.2096-6652.202225
    Abstract339)   HTML33)    PDF(pc) (3862KB)(482)       Save

    An intelligent trajectory planning and obstacle avoidance method based on the improved twin delayed deep deterministic policy gradient algorithm (TD3) was proposed to solve the trajectory planning problem for a 4-DOF manipulator mounted on a satellite.The training strategy had 2 periods.In the pre-training stage, the target position was always guided combining with the output of the strategy network to optimize the trajectory.After the pre-training, the algorithm can autonomously output the velocity trajectory while the initial position and the target were specified randomly in the joint space of the manipulator.This target-guided mechanism decreased the unnecessary explorations and improved the learning efficiency in high dimensional action space.In the second training stage, a collision-free safety reference trajectory was firstly obtained by demonstration, and then this trajectory was constantly learned during the training process until the final output trajectory has the ability to avoid obstacles.

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    A review of continual learning for robotics
    Chao ZHAO, Jie XU, Xingyu CHEN, Kuizhi MEI, Xuguang LAN
    Chinese Journal of Intelligent Science and Technology    2022, 4 (3): 308-323.   DOI: 10.11959/j.issn.2096-6652.202235
    Abstract568)   HTML116)    PDF(pc) (1356KB)(477)       Save

    One of the limitations of robotics is that it is difficult for robots to adapt to fickle tasks.A robot will inevitably forget the knowledge from old environments or tasks when facing new environments or tasks.In order to summarize research in continual learning for robotics, firstly, the framework and evaluation protocols in continual learning were introduced.And then necessity and challenge of continual learning in the robotics were expounded.The research for continual learning was also summarized.Finally, the prospect of continual learning was predicted and some valuable research directions were put forward.

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    Curriculum design for artificial intelligence and quantitative trading
    Junhuan ZHANG, Zhengyi ZHU, Kewei CAI
    Chinese Journal of Intelligent Science and Technology    2023, 5 (1): 104-112.   DOI: 10.11959/j.issn.2096-6652.202311
    Abstract392)   HTML56)    PDF(pc) (840KB)(476)       Save

    With the development of computer technology, especially the development of artificial intelligence, big data technology and blockchain technology, the transaction mode of traditional economic society and financial market has been changed.Quantitative trading is an important emerging trading mode in the contemporary financial market.To meet the social demand for quantitative trading talents, it is particularly important to explore a reasonable course system of AI and quantitative trading.Firstly, the recent development of quantitative trading and the application of AI in quantitative trading were analyzed.Then, the current situations and problems of the quantitative trading course were summarized.Finally, according to the problems, the suggestions on the course design for AI and quantitative trading were put forward in four aspects, which included teaching content system, teaching practice simulation platform, teacher training, and multi-channel practice platform.

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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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    Short-term traffic state reasoning and precise prediction in urban networks
    Yuanqi QIN, Qingyuan JI, Jun GE, Xingyuan DAI, Yuanyuan CHEN, Xiao WANG
    Chinese Journal of Intelligent Science and Technology    2022, 4 (3): 380-395.   DOI: 10.11959/j.issn.2096-6652.202233
    Abstract322)   HTML40)    PDF(pc) (1718KB)(449)       Save

    The structure of urban traffic network has a significant impact on the formation and spatio-temporal pattern propagation of traffic congestions.However, in studies based on traditional traffic models or deep learning models, the generation of traffic mode can only be described indirectly by traffic indicators, without considering the traffic network feature.This makes it very difficult to accurately describe the propagation dynamics both in temporal and spatial dimensions and lacks specificity.To tackle the above-mentioned problems, a novel traffic state prediction approach based on traffic pattern reasoning (TP2) framework was proposed.The framework modeled congestion propagation as a dynamically evolving temporal knowledge graph (TKG), and applied an inferencing framework (TPP-TKG) that was based on a novel aggregator called RGraAN.TPP-TKG captured the spatial-temporal propagation pattern of traffic congestion, and combined related road links to a given link, and constructed correlated sub region of the traffic network.Then a traffic state predicting based on graph neural network was employed to predict short-term speed evolution of road links in this sub region.Comparing to the state-of-the-art benchmark models, TP2 achieves 1% ~ 2% higher accuracy.

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    Application of intelligent optimization algorithms in supply chain network
    Xin ZHANG, Zhihui ZHAN
    Chinese Journal of Intelligent Science and Technology    2022, 4 (2): 174-183.   DOI: 10.11959/j.issn.2096-6652.202202
    Abstract364)   HTML60)    PDF(pc) (1042KB)(449)       Save

    Supply chain network connects members by the relationship of demand and supply and facilitates the coordination and cooperation among these members, which is particularly important in the global competition environment.The optimization and improvement of supply chain network can reduce the operating cost of enterprises, increase the income of enterprises and the customer satisfaction, and then improve the competitiveness of enterprises.Firstly, the optimization problems of supply chain network were analyzed, and these problems from several different aspects were classified, such as modeling characteristics, decision variable types, and scene features, so as to introduce the optimization problems in the existing research of supply chain network more clearly.Then, three frequently used intelligent optimization algorithms and their applications in supply chain network optimization were introduced and analyzed, such as genetic algorithm, ant colony optimization algorithm and particle swarm optimization algorithm.Finally, the future research of the optimization of supply chain network was prospected.

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    Parallel reasoning: a virtual-real interactive knowledge collaboration framework based on ACP approach
    Xiao WANG, Linyao YANG, Bin HU, Jiachen HOU
    Chinese Journal of Intelligent Science and Technology    2023, 5 (1): 69-82.   DOI: 10.11959/j.issn.2096-6652.202254
    Abstract200)   HTML16)    PDF(pc) (1141KB)(444)       Save

    Knowledge graph represents empirical knowledge based on structured triples, which can effectively describe the semantic relationships between real-world entities.Knowledge graph has become a critical standard technology of the new generation of artificial intelligence.The development, typical applications of multi-source knowledge graphs, and problems of knowledge collaboration were summarized.A multi-source knowledge graph collaboration framework based on ACP approach, i.e., parallel reasoning, was proposed, which realized the extraction, fusion, completion, and unbiased application of multi-source heterogeneous knowledge based on artificial systems, computational experiments, and parallel execution.In the end, simulation experiments were conducted on power grid dispatching to evaluate the effectiveness of parallel learning for solving the management and control problems of complex systems.

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    A survey of visuotactile sensing technologies for robotic manipulation
    Shaowei CUI, Shuo WANG, Jingyi HU, Chaofan ZHANG
    Chinese Journal of Intelligent Science and Technology    2022, 4 (2): 186-199.   DOI: 10.11959/j.issn.2096-6652.202222
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    Thanks to the high spatial resolution and multi-mode tactile sensing, visuotactile sensing technology has been widely applied to various robotic manipulation tasks, such as robotic active perception, pose estimation, and in-hand manipulation.Firstly, the current mainstream visuotactile sensing technologies based on sensing principles were summarized, which could be mainly divided into three categories: GelSight-type visuotactile sensors, binocular (multi-view) visuotactile sensors, and other types.Meanwhile, the sensing methods of different tactile sensing modes were further summarized, including contact surface 3D geometry, force/torque, and sliding.Furthermore, focusing on the field of robot operation, the specific application scenarios of visuotactile sensors were discussed.Finally, future work of the visuotactile sensing technology and how they can be further applied to robotic dexterous manipulation tasks were given.

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    Automatic path planning program generation system based on swarm intelligence results
    Yuqian WANG, Rong DING
    Chinese Journal of Intelligent Science and Technology    2022, 4 (2): 255-263.   DOI: 10.11959/j.issn.2096-6652.202228
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    Path planning algorithms are widely used in various motion planning tasks, such as robot motion and autonomous driving.So far, many excellent path planning algorithms have been proposed for applications in different fields.For a specific task environment, choosing the appropriate path planning algorithm can plan a better path that satisfies the constraints more efficiently.Based on the results of swarm intelligence, the adaptability and path planning efficiency of rapidly-exploring random tree (RRT) path planning algorithm and its variants RRT-Star path planning algorithm and RRT-Star-Smart path planning algorithm under different task environments were studied.Using genetic programming algorithm as a framework to design a system, which could automatically analyze the map features of the current environment and combine the characteristics of RRT path planning algorithm and its variants to generate new path planning algorithms that were more suitable for the current environment.The generated path planning algorithm can efficiently plan a feasible path from the starting point to the target point.

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    Autonomous Agent Learning for Dexterous and Accurate Manipulations
    Chinese Journal of Intelligent Science and Technology    2022, 4 (2): 184-185.   DOI: 10.11959/j.issn.2096-6652.202232-1
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    A hybrid physics-data-knowledge driven approach for human-machine hybrid-augmented intelligence-based system management and control
    Jun ZHANG, Peidong XU, Siyuan CHEN, Tianlu GAO, Yuxin DAI, Ke ZHANG, Hang ZHAO, Jiemai GAO, Yuyang BAI, Jinxing LI, Haoran ZHANG, Xiang LI, Jiuxiang CHEN
    Chinese Journal of Intelligent Science and Technology    2022, 4 (4): 571-583.   DOI: 10.11959/j.issn.2096-6652.202237
    Abstract322)   HTML34)    PDF(pc) (10594KB)(421)       Save

    The core theories, methods and technologies of contemporary system cognition, management, and control have been transferred to big data and artificial intelligence technology, resulting in a gap between the limitations of current artificial intelligence technology and the needs of complex system cognition, management, and control.As a result, a real need has spawned a new form of artificial intelligence: human-machine hybrid-augmented intelligence form, that is, the cooperation of human intelligence and machine intelligence runs through the process of system cognition, management, control, and so on.Human cognition and machine intelligence cognition are mixed together to form enhanced intelligence form.This form is a feasible and important growth mode of artificial intelligence or machine intelligence.A hybrid physics-data-knowledge (PDK) driven approach for human-machine hybrid-augmented intelligence-based system management and control was proposed.The proposed approach was illustrated by the following: trustworthy distributed data, computing, and algorithm, physics-informed deep learning, hybrid deep reinforce learning incorporating system operation rules, causal analysis, and interpretable AI and virtual digital human.In the context of power system dispatch and control, three examples were used for explaining the applications and technical pathways of the proposed PDK approach.

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    Autonomous Underwater Vehicle
    Chinese Journal of Intelligent Science and Technology    2022, 4 (4): 491-492.   DOI: 10.11959/j.issn.2096-6652.2022049-1
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    Knowledge-driven order allocation method for raw material supply chain in metallurgical enterprises
    Yishun LIU, Chunhua YANG, Keke HUANG
    Chinese Journal of Intelligent Science and Technology    2022, 4 (3): 355-370.   DOI: 10.11959/j.issn.2096-6652.202238
    Abstract228)   HTML20)    PDF(pc) (765KB)(390)       Save

    The raw material supply chain is the premise of safe and stable production with an important position.As the front link of the supply chain, order allocation is the focus of enterprises.Due to the complex composition of raw materials, large qualitative differences in suppliers, and information coupling in the metallurgical industry, the current expert decision-making model is labor-intensive and difficult to deal with such complex order allocation problems, resulting in low decision-making efficiency, high procurement costs, and difficult to guarantee the quality of raw materials.Aiming at this problem, a knowledge-driven order allocation method for the raw material supply chain in metallurgical enterprises was proposed.First, on the basis of a multi-level supplier evaluation system, the entropy weight method and the fuzzy analytic hierarchy process were adopted to make full use of data knowledge and experience knowledge, and compatibility degree and different degree were introduced to reasonably allocate the importance of each evaluation index.Then, a multi-attribute decision-making evaluation model was built based on the technique for order preference by similarity to an ideal solution (TOPSIS) to automatically obtain the comprehensive performance and ranking of suppliers, so as to realize the efficient evaluation and management of suppliers.Finally, a multi-objective order quantity allocation model was established by comprehensively considering the supplier characteristics knowledge, ore blending mechanism knowledge, business status knowledge, etc., and the optimal order quantity of the each supplier under complex resource constraints was automatically obtained.Taking a domestic zinc smelting enterprise as an example, the validity and applicability of the proposed method are verified by the relevant data of the raw material supply chain.The results show that the proposed method can automatically complete knowledge-based work such as supplier evaluation and order quantity allocation, which will greatly liberate manual labor, improve decision-making efficiency, effectively reduce procurement cost and improve the quality of raw materials.

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    Parallel transportation systems in era of metaverse
    Qinghai MIAO, Yisheng LYU
    Chinese Journal of Intelligent Science and Technology    2023, 5 (1): 32-40.   DOI: 10.11959/j.issn.2096-6652.202302
    Abstract234)   HTML36)    PDF(pc) (1253KB)(385)       Save

    As the metaverse receives more and more attention, how the metaverse will affect the development of the transportation system has also become a hot topic.The origin and current status of the metaverse were introduced, the incubation period, development period and maturity period of the future development of the metaverse were prospected, and the potential problems brought about by the metaverse were also discussed.Combined with the need theory of Maslow, it was analyzed and pointed out that the metaverse would gradually affect urban travel demands, and would bring about profound changes in traffic after entering the mature period.The parallel intelligence methods would play an essential role in guiding the healthy development of the metaverse and effectively serving social travel.The implementation of the parallel transportation system would play a key role in traffic planning, precise control, fine service, emergency management, etc.

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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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    Metaverses and parallel systems: the state of the art, comparisons and prospects
    Yonglin TIAN, Yuanwen CHEN, Jing YANG, Yutong WANG, Xiao WANG, Qinghai MIAO, Ziran WANG, Fei-Yue WANG
    Chinese Journal of Intelligent Science and Technology    2023, 5 (1): 121-132.   DOI: 10.11959/j.issn.2096-6652.202313
    Abstract265)   HTML41)    PDF(pc) (2038KB)(364)       Save

    With the development of technologies such as artificial intelligence and virtual reality, digital technology is continuously changing and enriching human experiences and production methods, and has become a powerful tool for controlling and managing complex systems.Metaverses and parallel systems provide feasible ways for the construction of digital systems and have gained much attention in scientific research and industrial applications.The development status of the metaverses and parallel systems were reviewed, the differences and connections between them were analyzed, and their future development was prospected, which was expected to provide reference and inspiration for the development of intelligent industries, intelligent economies, and intelligent societies.

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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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    Architecture and key techniques of parallel creation through the fusion of human-cyber-physical intelligence in CPSS
    Chao GUO, Yue LU, Xiao WANG, Da YI, Xiao WANG, Fei-Yue WANG
    Chinese Journal of Intelligent Science and Technology    2022, 4 (3): 344-354.   DOI: 10.11959/j.issn.2096-6652.202246
    Abstract267)   HTML36)    PDF(pc) (3555KB)(357)       Save

    With the expansion of the fields covered by AI, artistic creation will become the next hot spot for AI research and applications.Building a metaverse with diverse styles, realistic contents, flexible strokes and accurate descriptions based on parallel system theory and ACP approach will provide a feasible way to improve AI creation capability.The intelligence of human, AI, and robots were fused to develop a parallel creation architecture through the creation by AI, the evaluation by humans, and the execution by robots.The parallel creation with the key methods of style transfer, content combination, stroke generation and image captioning in computational experiments were explained.The parallel creation system was validated through the painting experiments.The parallel creation system will improve the creation capability of artificial intelligence in cyberspace and physical space, and promote the human-cyber-physical collaborative creation through the fusion of them.

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    Systems agriculture: modeling and control based on social and economic attributes of agriculture
    Mengzhen KANG, Hequan SUN, Xiujuan WANG, Fei-Yue WANG
    Chinese Journal of Intelligent Science and Technology    2023, 5 (1): 41-50.   DOI: 10.11959/j.issn.2096-6652.202306
    Abstract195)   HTML33)    PDF(pc) (2535KB)(355)       Save

    With the arrival and development of the fifth industrial revolution, biotechnology, information technology and artificial intelligence are deeply integrated.This provides strong support for the informatization and intelligentization of agriculture.Due to the social and economic characteristics of agriculture, it has become a consensus to build an agricultural physical and social information system toward both the planting system with biophysical properties and the management system with socio-economic properties.Inspired by systems biology, systems agriculture was proposed, which combined two other dimensions of villages and farmers information using the parallel agriculture framework to build and study the agricultural system.Supported by biotechnology, information technology and artificial intelligence technology, systems agriculture had involved monitoring and integrating multi-scale, multi-dimensional and multimodal information, and carried out systematic research covering agriculture, villages and farmers using systems theory, in order to serve the development of future villages.The intelligent technology and the specific cases involved in systems agriculture were summarized and analyzed, and the perspectives of systems agriculture were presented.

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    Path planning for unmanned surface vehicle in complex dynamic environment based on improved RRT*-Smart
    Lu DONG, Ailing XIONG
    Chinese Journal of Intelligent Science and Technology    2022, 4 (2): 264-276.   DOI: 10.11959/j.issn.2096-6652.202229
    Abstract523)   HTML48)    PDF(pc) (2790KB)(351)       Save

    Aiming at the path planning problem of unmanned surface vehicle (USV) in complex dynamic environment with moving multi-obstacle ships, a path planning method based on improved RRT*-Smart (RTSNew) was designed.Firstly, the sampling mode of nodes was optimized, nodes were sampled in the polar coordinate system with USV as the origin, an elliptical sampling range constraint was adopted to avoid invalid sampling, and a historical path buffer pool was used to make full use of the historical path.The optimization greatly reduced the amount of calculation and improved the speed of path planning.Secondly, the expansion mode of nodes was improved.In order to avoid treating dynamic obstacles as static obstacles, time information was added to each node to realize dynamic collision detection and the full use of dynamic obstacles motion information greatly improves the feasibility of the planned path.At the same time, considering the maneuverability of USV, the angle constraint was added in expansion of nodes to ensure smooth path.Finally, virtual obstacles were designed to mobile obstacle ships to make the planned path comply with International Regulations for Preventing Collisions at Sea (COLREGS).Based on VREP platform, the USV navigation simulation experiments and comparative experiments were carried out.The results show that RTSNew can make USV reach the destination efficiently and safely, and it performs better in planning efficiency, path optimization and path security than traditional methods in complex dynamic environment with multi-obstacle ships.RTSNew ensures that the motion path complies with COLREGS, and solves the problems of traditional methods: treating the dynamic obstacles as static obstacles, ignoring COLREGS, large amount of calculation and low efficiency, not suitable for the complex dynamic environment with moving multi-obstacle ships.

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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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    Parallel Yuan-Ming Yuan Imperial Garden: from digital twin garden to metaverse smart heritage park
    Mengzhen KANG, Wenzhong QIU, Zifu CHEN, Meng WANG, Shasha XU, Xiujuan WANG, Aidong NI, Yujie JIANG, Shichao CHEN, Philippe DEREFFYE, Fei-Yue WANG
    Chinese Journal of Intelligent Science and Technology    2022, 4 (3): 301-307.   DOI: 10.11959/j.issn.2096-6652.202247
    Abstract587)   HTML62)    PDF(pc) (8115KB)(327)       Save

    Yuan-Ming Yuan Imperial Garden is a historically royal garden; it not only occupies an important position in the history of Chinese garden, but also enjoys a high reputation in the world.The historical and cultural values contained in Yuan-Ming Yuan Imperial Garden needs to be widely understood by the Chinese people and be remembered by the world through a new way.Focusing on the work policy for cultural relics of “protection first, strict management, mining value, rational utilization and good transmission”, a new solution of parallel Yuan-Ming Yuan Imperial Garden was proposed, which provided technical reference for the construction of the smart Yuan-Ming Yuan Imperial Garden.Parallel Yuan-Ming Yuan Imperial Garden is the application of ACP theory in the operation and management.Descriptive intelligence will be used to construct a virtual Yuan-Ming Yuan Imperial Garden, predictive intelligence will be used to conduct large-scale computational experiments in the virtual Yuan-Ming Yuan Imperial Garden, and prescriptive intelligence and parallel execution will be used to outbreak the geographical limit and lead to the smart management of Yuan-Ming Yuan Imperial Garden.It is expected that the development and enrichment of the Yuan-Ming Yuan Imperial Garden in the virtual world, and the increasingly parallel interaction and integration with the real world, will bring about a new operating mode.

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    Crypto management: a novel organizational management model based on blockchain
    Juanjuan LI, Ge WANG, Xiao WANG, Junqing LI, Yong YUAN, Fei-Yue WANG
    Chinese Journal of Intelligent Science and Technology    2022, 4 (2): 145-156.   DOI: 10.11959/j.issn.2096-6652.202232
    Abstract465)   HTML65)    PDF(pc) (1141KB)(326)       Save

    Aim to deal with the problem of data, trust and timeliness asymmetry faced by modern organizational management from the root, a novel organizational management model towards Web 3.0 namely crypto management was proposed.It was enabled by blockchain technology and smart contracts based on it, supported by the federated data, organized in the form of DAO (decentralized autonomous organization), and driven by the incentive mechanism with NFT (non-fungible token) as the core.The primary goal of crypto management was to realize trustable, reliable and usable real-time management decision-making under the premise of data security and privacy protection.The framework of crypto management was formulated, its core components and implementation mode were discussed, and its operation process using the example of personnel performance management was also introduced.Towards the end, the potential future works in this emerging new area were discussed.

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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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    Underwater image enhancement network based on visual Transformer with multiple loss functions fusion
    Xiaofeng CONG, Jie GUI, Jun ZHANG
    Chinese Journal of Intelligent Science and Technology    2022, 4 (4): 522-532.   DOI: 10.11959/j.issn.2096-6652.202252
    Abstract287)   HTML35)    PDF(pc) (13748KB)(310)       Save

    Due to the absorption and scattering of light in water, the images captured by underwater robots suffer from color distortion and reduced contrast.Aiming at alleviating the quality degradation phenomenon of underwater images, an underwater image enhancement network based on vision Transformer that be trained with multiple losses fusion strategy was proposed.The image enhancement network adopted an encoder-decoder architecture, and could be trained in an end-to-end manner.In order to effectively update the parameters of the network for enhancing underwater images, a linear combination of various losses was adopted as the overall optimization objective, including pixel loss, structure loss, edge loss and feature loss.Quantitative experiments were carried out on two large underwater datasets, and the proposed underwater image enhancement network was compared with 7 underwater image enhancement algorithms.The full reference evaluation metrics peak signal-to-noise ratio and structural similarity were calculated in experiment, and the non-referenced metric underwater image quality measure was also computed.The experimental results showed that the proposed underwater image enhancement network could effectively deal with color distortion and contrast reduction.

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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
    Abstract211)   HTML48)    PDF(pc) (40201KB)(307)       Save

    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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    HVAC model-free optimal control method based on double-pools DQN
    Shuai MA, Qiming FU, Jianping CHEN, Fan FENG, You LU, Zhengwei LI, Shunian QIU
    Chinese Journal of Intelligent Science and Technology    2022, 4 (3): 426-444.   DOI: 10.11959/j.issn.2096-6652.202208
    Abstract251)   HTML44)    PDF(pc) (2690KB)(297)       Save

    In the field of HVAC (heating, ventilation and air conditioning) control, the model-based optimal control method has been extensively studied and verified by scholars, but this method highly depends on the accuracy of the model, the collection of a large amount of historical data, and the deployment of sensors.In response to the above problems,combined with EnergyPlus, actual system parameters and historical data, the HVAC optimized control model was constructed, and an improved double pools-based DQN (DPs-DQN) algorithm was proposed.Finally, it was applied to the load distribution of different types of chillers, the combined optimal control of cooling tower fan frequency and cooling water pump frequency in HVAC system.Based on the constructed problem model, aiming at the problem of sample imbalance in the decision-making optimization process, the algorithm established two independent experience pools on the basis of DQN to store load distribution and non load distribution samples respectively.During the training process, followed a certain ratio to sample from the experience pool to speed up the algorithm convergence.The proposed method was compared with the model-based control method and the baseline method.The experimental results show that compared with the baseline method, the model-based HVAC controller can save 11.5% (optimal energy-saving efficiency), while the DPs-DQN can save energy by 7.5% in the first year.At the same time, as the system runs, the controller can obtain results close to the optimal energy saving efficiency in the eighth year.In addition, compared with the model-based HVAC controller, the controller does not depend on the system model, and requires less prior knowledge and sensors in the online control process, which is more valuable in actual engineering applications.

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    Rapider-YOLOX: lightweight object detection network with high precision
    Zhouyu GU, Yuecheng YU, Tiantian Zhe
    Chinese Journal of Intelligent Science and Technology    2023, 5 (1): 92-103.   DOI: 10.11959/j.issn.2096-6652.202303
    Abstract250)   HTML17)    PDF(pc) (3374KB)(296)       Save

    As a lightweight network structure, YOLOX-Nano has the advantage of fast running speed.However, the model still has the defects of weak feature extraction ability and insufficient detection accuracy in practical application.Therefore, an efficient object detection network Rapider-YOLOX which comprehensively balanced the detection speed and detection accuracy was proposed.Firstly, the highly efficient bottleneck module was designed to improve the feature extraction capability of depthwise convolutional blocks in the original YOLOX-Nano model.Secondly, the soft-SPP module was designed to avoid the loss of some important information in the original SPP module and improve the ability of multi-scale information fusion and information exchange between channels further.Finally, CIoU was introduced to improve the position accuracy of the prediction box by using the center distance and aspect ratio between the prediction box and the real box.The experimental results on PASCAL VOC2007 dataset showed that the mAP of Rapider-YOLOX model reached 77.92%, which was 3.79% higher than the original YOLOX-Nano.In addition, on GT1030 with only 384 CUDA cores, the FPS of the proposed method could reach 45.40.The FPS could also reach 23.94 on the CPU, which further improved detection accuracy and generalization performance of the network while ensuring the lightweight characteristics of the network.

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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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    Category-level object pose estimation from depth point cloud
    Renwu LI, Lingxiao ZHANG, Lin GAO, Chunpeng LI, Hao JIANG
    Chinese Journal of Intelligent Science and Technology    2022, 4 (2): 246-254.   DOI: 10.11959/j.issn.2096-6652.202227
    Abstract350)   HTML40)    PDF(pc) (2469KB)(281)       Save

    Aiming at the problem of category-level object pose estimation, a method was proposed to accurately estimate the pose of the target object by only taking the point cloud scanned by the depth camera as the input, with knowing the category of input point cloud only.The method did not reply on a huge amount of labeled dataset, but used virtual data produced by simulation instead, which achieved better accuracy on real-world dataset.This method first filtered the background noise of the input point cloud.Then standardized the point cloud through the well-designed center prediction module.After that, the normalized object coordinate space would be estimated through a shape template deformation module.Finally, the pose would be obtained from least squares.Experiments on real-world dataset demonstrates that the method achieve higher accuracy and better generalization ability.

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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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