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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 Abstract （1224）   HTML （320）    PDF（pc） （788KB）（1738）       Knowledge map    Save The new wave of artificial intelligence technology represented by ChatGPT is promoting the comprehensive transformation of human society, the transformation of scientific research paradigm is accelerating, and an artificial intelligence-driven scientific research (AI for Science, AI4S) revolution is coming.The basic concepts and characteristics of AI4S were analyzed, and the development status of AI4S were briefly summarized from the perspectives of mathematics, physics, biology, and materials.Vigorously developing AI4S is of great significance to improving national competitiveness, developing social economy, and strengthening technical reserves.In order to promote the development of AI4S better, the following two points are essential: one is to change the contemporary teaching and education, and advocate AI for Education (AI4E) and Education for AI (E4AI); the other is to establish and adapt to the ＂new scientific research paradigm＂ with ＂new organization mode＂ in a ＂new research ecology＂, based on DAOs and DeSci, for open, fair and just sustainable support for AI4S. Reference | Related Articles | Metrics

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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 Abstract （1113）   HTML （152）    PDF（pc） （6809KB）（1303）       Knowledge map    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. Table and Figures | Reference | Related Articles | Metrics

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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 Abstract （803）   HTML （90）    PDF（pc） （3737KB）（1263）       Knowledge map    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. Table and Figures | Reference | Related Articles | Metrics

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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 Abstract （1425）   HTML （155）    PDF（pc） （6643KB）（1240）       Knowledge map    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. Table and Figures | Reference | Related Articles | Metrics

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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 Abstract （1494）   HTML （237）    PDF（pc） （6364KB）（1195）       Knowledge map    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. Table and Figures | Reference | Related Articles | Metrics

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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 Abstract （187）   HTML （47）    PDF（pc） （2154KB）（913）       Knowledge map    Save Reference | Related Articles | Metrics

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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 Abstract （276）   HTML （37）    PDF（pc） （3698KB）（848）       Knowledge map    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. Table and Figures | Reference | Related Articles | Metrics

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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 Abstract （1104）   HTML （287）    PDF（pc） （48558KB）（781）       Knowledge map    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. Table and Figures | Reference | Related Articles | Metrics

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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 Abstract （652）   HTML （83）    PDF（pc） （2926KB）（723）       Knowledge map    Save 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. Table and Figures | Reference | Related Articles | Metrics

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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 Abstract （836）   HTML （123）    PDF（pc） （5939KB）（722）       Knowledge map    Save 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. Table and Figures | Reference | Related Articles | Metrics

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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 Abstract （914）   HTML （195）    PDF（pc） （8731KB）（697）       Knowledge map    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. Table and Figures | Reference | Related Articles | Metrics

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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 Abstract （497）   HTML （67）    PDF（pc） （840KB）（687）       Knowledge map    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. Table and Figures | Reference | Related Articles | Metrics

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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 Abstract （660）   HTML （104）    PDF（pc） （13396KB）（676）       Knowledge map    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. Table and Figures | Reference | Related Articles | Metrics

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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 Abstract （290）   HTML （8）    PDF（pc） （3799KB）（672）       Knowledge map    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. Table and Figures | Reference | Related Articles | Metrics

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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 Abstract （328）   HTML （61）    PDF（pc） （8131KB）（639）       Knowledge map    Save 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. Table and Figures | Reference | Related Articles | Metrics

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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 Abstract （307）   HTML （87）    PDF（pc） （745KB）（586）       Knowledge map    Save Reference | Related Articles | Metrics

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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 Abstract （244）   HTML （42）    PDF（pc） （2535KB）（573）       Knowledge map    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. Table and Figures | Reference | Related Articles | Metrics

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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 Abstract （260）   HTML （21）    PDF（pc） （1141KB）（560）       Knowledge map    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. Table and Figures | Reference | Related Articles | Metrics

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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 Abstract （291）   HTML （40）    PDF（pc） （1253KB）（557）       Knowledge map    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. Table and Figures | Reference | Related Articles | Metrics

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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 Abstract （367）   HTML （50）    PDF（pc） （2038KB）（550）       Knowledge map    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. Table and Figures | Reference | Related Articles | Metrics

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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 Abstract （423）   HTML （42）    PDF（pc） （10594KB）（550）       Knowledge map    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. Table and Figures | Reference | Related Articles | Metrics

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Embodied intelligent driving: concept, methods, the state of the art and beyond Tianyu SHEN, Zhiwei LI, Lili FAN, Tingzhen ZHANG, Dandan TANG, Meihua ZHOU, Huaping LIU, Kunfeng WANG Chinese Journal of Intelligent Science and Technology    2024, 6 (1): 17-32.   DOI: 10.11959/j.issn.2096-6652.202404 Abstract （916）   HTML （134）    PDF（pc） （5059KB）（542）       Knowledge map    Save Embodied intelligence transcends the boundaries of traditional artificial intelligence by emphasizing the importance of interaction between machines and the physical world, facilitating the development of intelligent entities that combine hardware and software to learn from and adapt to their environments, thereby solving real-world problems. Inspired by this philosophy, the concept and framework of embodied intelligent driving are introduced, aiming at integrating the idea of embodied intelligence into the development and application of autonomous vehicles. Through the continuous interaction between physical agents, virtual agents, and real traffic scenes, intelligent driving systems can achieve precise perception, efficient execution, and autonomous evolution in complex scenes, enhancing the long-term adaptability of autonomous vehicles in open traffic environments. Based on the embodied intelligent driving framework, the relevant technologies are summarize and the development status and existing problems of such technologies are analyzed. Furthermore, thoughts and prospects in this field are demonstrated by exploring the important roles and application potential of virtual-real interactive data intelligence, foundation models and foundation intelligence, continuous learning and parallel intelligence. This paper is expected to promote innovative research and the application on embodied intelligent driving in a wider range of scenarios, and provide new ideas and solutions for the development of mobile robot systems such as intelligent vehicles. Table and Figures | Reference | Related Articles | Metrics

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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 Abstract （189）   HTML （7）    PDF（pc） （4961KB）（538）       Knowledge map    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. Table and Figures | Reference | Related Articles | Metrics

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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 Abstract （215）   HTML （57）    PDF（pc） （498KB）（537）       Knowledge map    Save Reference | Related Articles | Metrics

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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 Abstract （703）   HTML （157）    PDF（pc） （23370KB）（515）       Knowledge map    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. Table and Figures | Reference | Related Articles | Metrics

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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 Abstract （242）   HTML （16）    PDF（pc） （9260KB）（496）       Knowledge map    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. Table and Figures | Reference | Related Articles | Metrics

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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 Abstract （300）   HTML （35）    PDF（pc） （5290KB）（482）       Knowledge map    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. Table and Figures | Reference | Related Articles | Metrics

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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 Abstract （275）   HTML （42）    PDF（pc） （19716KB）（468）       Knowledge map    Save 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. Table and Figures | Reference | Related Articles | Metrics

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Understanding of AI large model technology empowering the field of ships Zhaojie WANG, Lei YU, Jinhui XIONG, Huaiyu LI, Yunjun HAN, Zhen SHEN, Rui GUO, Yong ZHANG Chinese Journal of Intelligent Science and Technology    2024, 6 (1): 33-40.   DOI: 10.11959/j.issn.2096-6652.202408 Abstract （592）   HTML （77）    PDF（pc） （2187KB）（465）       Knowledge map    Save This paper summarized the focus, the development trend and the technical nature of AI large model research, analyzed the development strategy of AI at the national level, the urgent needs in the field of national defense, and the basis of applications in the field of ships. Then, from the aspects of the development of intelligent green ships, the innovation of defense equipment systems, the construction of management and control system and the transformation of knowledge-intensive industries, the broad prospect of applying AI large model technologies to the field of ships was discussed. The paper pointed out that the combination of AI large model technologies and concepts such as parallel systems, knowledge factories and digital employees can catalyze new designs, research and development and verification methods such as "AI design" + "digital factory" + "parallel verification". In addition, AI large model technology can inject intelligent and green elements into the shipping industry from aspects such as the hull design, ship construction, shipping management, energy conservation and emission reduction, can optimize ship functions, and improve efficiency, economy and environmental protection. Combined with new materials, new energy power and new information electronics and other technologies, AI large model technologies can shape the future marine defense equipment system based on new concepts and new patterns. At the same time, AI large model technologies can enable the construction of ship management and control systems, optimize planning, help scientific and technological innovation, improve management efficiency and improve the quality and efficiency of the corporations. In particular, with the establishment of knowledge factories in the field of ships, the training of digital employees, the promotion of industrial robots and the expansion of far-reaching sea fields, artificial intelligence large model technologies will be able to promote the organic combination and close collaboration of "natural persons", "robots" and "digital people" in the field of ships, and accelerate the upgrading of the ship industry to be knowledge intensive and intelligent intensive. This can transform the industrial ecology and value creation mode to be high-end, intelligent, and green, and realize a development mode that pays more attention to quality and efficiency for shipbuilding corporations. Table and Figures | Reference | Related Articles | Metrics

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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 Abstract （356）   HTML （52）    PDF（pc） （4075KB）（417）       Knowledge map    Save 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＆quot;knowledge bias＆quot;, 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. Table and Figures | Reference | Related Articles | Metrics

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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 Abstract （369）   HTML （46）    PDF（pc） （13748KB）（412）       Knowledge map    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. Table and Figures | Reference | Related Articles | Metrics

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Parallel drug systems: framework and methods based on large language models and three types of humans Fei LIN, Fei-Yue WANG, Yonglin TIAN, Xianting DING, Qinghua NI, Jing WANG, Le SHEN Chinese Journal of Intelligent Science and Technology    2024, 6 (1): 88-99.   DOI: 10.11959/j.issn.2096-6652.202409 Abstract （188）   HTML （45）    PDF（pc） （4853KB）（400）       Knowledge map    Save With the rapid development of the next generation of artificial intelligence technologies, such as the internet of things, large language models, and multimodal interactions, the traditional processes of drug research and production processing had been facing the challenges of an intelligent transition in recent years. In this context, this paper used the theory of parallel intelligence as the research philosophy and proposed a virtual-real interactive parallel drug systems, utilizing the ACP approach and large language models. It incorporated the concept of three types of beings—digital humans, robots, and natural persons—into the systems, providing a detailed discussion on the theoretical underpinnings, construction techniques, and potential application scenarios of the systems. The parallel drug systems covered the entire process of the pharmaceutical industry. For the drug development phase, it considered processes such as drug discovery, laboratory research, and clinical trials. In the production processing phase, it encompassed pharmaceutical manufacturing operations and system analysis predictions. The medical healthcare subsystem included personalized medication consultation, augmented reality drug guidance, and privacy security. The whole systems open up a digitized "drug space", aiming to establish a new paradigm for the drug systems and propel the revolution of intelligent medication. Table and Figures | Reference | Related Articles | Metrics

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RAG-PHI: RAG-driven parallel human and parallel intelligence Yonglin TIAN, Xingxia WANG, Yutong WANG, Jiangong WANG, Chao GUO, Lili FAN, Tianyu SHEN, Wansen WU, Hongmei ZHANG, Zhengqiu ZHU, Fei-Yue WANG Chinese Journal of Intelligent Science and Technology    2024, 6 (1): 41-51.   DOI: 10.11959/j.issn.2096-6652.2024015 Abstract （360）   HTML （30）    PDF（pc） （5743KB）（398）       Knowledge map    Save The advancement of large models offers new perspectives and foundation intelligence for building parallel human ecosystems comprised of biological humans, digital humans, and robotic humans. However, challenges such as time-limited updates to knowledge, inadequate specialized capabilities, and risks of information privacy leakage persist in the management and control of complex systems. To tackle these issues, a retrieval-augmented generation-driven parallel human and parallel intelligence framework (RAG-PHI) is introduced. It proposes to establish an open data platform that facilitates the integration of real-time, industry-specific, and private knowledge into the parallel human system. It develops dynamic routing and retrieval for context capture and the reconfiguration of parallel human capabilities, along with introducing context-aware prompt learning to enhance cognitive and behavioral skills. Furthermore, towards the organization and management, training and evaluation, operation and production of parallel human, the structures of parallel human community, parallel human school, and parallel human factory are proposed by the RAG-PHI architecture. These are designed to foster a parallel human ecosystem powered by RAG and large foundation models, thereby enhancing productivity in the age of intelligent industries. Table and Figures | Reference | Related Articles | Metrics

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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 Abstract （267）   HTML （52）    PDF（pc） （40201KB）（380）       Knowledge map    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. Table and Figures | Reference | Related Articles | Metrics

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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 Abstract （214）   HTML （19）    PDF（pc） （9044KB）（378）       Knowledge map    Save Cybersecurity originated from the theoretical testing of computer networks.With the development of information and communication technology (ICT), cybersecurity threats present the ＆quot;4V＆quot; 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. Table and Figures | Reference | Related Articles | Metrics

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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 Abstract （330）   HTML （19）    PDF（pc） （3374KB）（377）       Knowledge map    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. Table and Figures | Reference | Related Articles | Metrics

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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 Abstract （237）   HTML （22）    PDF（pc） （1670KB）（375）       Knowledge map    Save 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. Table and Figures | Reference | Related Articles | Metrics

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Sidelink resource allocation algorithm of C-V2X based on deep Q learning Hui XU Chinese Journal of Intelligent Science and Technology    2023, 5 (1): 83-91.   DOI: 10.11959/j.issn.2096-6652.202309 Abstract （190）   HTML （23）    PDF（pc） （895KB）（374）       Knowledge map    Save For the sidelink resource autonomous selection scheme of different priority services in celluar-vehicle to everything (C-V2X) system, the procedure of autonomous selection algorithm based on reference signal energy was analyzed, and the energy threshold equation was designed.For the energy equation parameter estimation problem, energy-based autonomous selection algorithm was combined with deep Q learning algorithm, and the optimal parameter value of the energy threshold equation was obtained by iteration of the finite-degree algorithm.Simulation results showed that the side resource allocation algorithm based on deep Q learning could ensure the side resource requirements of V2X services with different priorities, and improve the packet reception ratio performance of the system. Table and Figures | Reference | Related Articles | Metrics

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A survey of image-based few-shot 3D reconstruction Hang YU, Yanwei FU, Boyan JIANG, Xiangyang XUE Chinese Journal of Intelligent Science and Technology    2022, 4 (4): 544-559.   DOI: 10.11959/j.issn.2096-6652.202240 Abstract （422）   HTML （51）    PDF（pc） （12821KB）（370）       Knowledge map    Save Few-shot 3D reconstruction is considered one of the classic applications of the third generation of artificial intelligence.In the area of computer graphics and computer vision, few-shot 3D reconstruction has attracted the attention of many researchers during the past several decades because of its wide application scenarios and high research value.The area has grown significantly in recent years after the introduction of deep learning methods.The state-of-the-art methods in image-based few-shot 3D reconstruction were reviewed comprehensively and the series of works of our research group were introduced.The various 3D data types were introduced, and their applicability and general processing procedures in 3D reconstruction were discussed.Furthermore, the most widely used datasets were categorized.Finally, some representative experimental results of common 3D reconstructions were presented, and potential future research directions were proposed. Table and Figures | Reference | Related Articles | Metrics

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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 Abstract （587）   HTML （58）    PDF（pc） （7426KB）（370）       Knowledge map    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. Table and Figures | Reference | Related Articles | Metrics

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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 Abstract （386）   HTML （46）    PDF（pc） （13372KB）（358）       Knowledge map    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. Table and Figures | Reference | Related Articles | Metrics

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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 Abstract （353）   HTML （114）    PDF（pc） （2449KB）（355）       Knowledge map    Save 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. Table and Figures | Reference | Related Articles | Metrics

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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 Abstract （190）   HTML （28）    PDF（pc） （4933KB）（336）       Knowledge map    Save 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. Table and Figures | Reference | Related Articles | Metrics

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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 Abstract （280）   HTML （21）    PDF（pc） （6948KB）（324）       Knowledge map    Save 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. Table and Figures | Reference | Related Articles | Metrics

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Study on NeuroSymbolic learning and its applications Yinghao CAI, Hua YANG, Xuan AN, Wenshuo WANG, Yidong DU, Jiatao ZHANG, Zhigang WANG Chinese Journal of Intelligent Science and Technology    2022, 4 (4): 560-570.   DOI: 10.11959/j.issn.2096-6652.202234 Abstract （384）   HTML （34）    PDF（pc） （9108KB）（322）       Knowledge map    Save The continuous breakthrough of deep learning in perception has promoted the application of AI in various fields.It is found that we can not meet the requirements without improving the intelligence from perception level to higher cognition level.NeuroSymbolic learning can seamlessly integrate neural network methods, that are good at perception tasks, and logical symbolic methods, that are good at reasoning tasks.Therefore, it is one of the best candidates to achieve high-level cognitive intelligence.A practical framework for NeuroSymbolic learning:NSFOL was proposed.Moreover, three typical applications based on NSFOL: robot motion planning, robot task planning and video evaluation for educational experiment were presented.Experiments show that NSFOL can support these three specific applications successfully.Moreover, these implementations have advantages in learn ability, reasonability, interpretability and generalizability.Hope to stimulate more thinking and research to jointly promote research in NeuroSymbolic learning by sharing our preliminary studies in this direction. Table and Figures | Reference | Related Articles | Metrics

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Depression recognition based on emotional information fused with attentional mechanism Yan CHEN, Xueqin LUO, Wei LIANG, Yongfang XIE Chinese Journal of Intelligent Science and Technology    2022, 4 (4): 600-609.   DOI: 10.11959/j.issn.2096-6652.202221 Abstract （343）   HTML （75）    PDF（pc） （6410KB）（319）       Knowledge map    Save Aiming at the current research on using social media data to predict depression ignoring the characteristics of language style and emotional changes over time and lacking of research on the characteristics of emotional state and post metadata, a depression recognition model based on emotional information fused with attentional mechanism was proposed.Firstly, on the basis of the existing research on depression recognition, the text classification convolutional neural network was used to extract the post information and emotional information for each time period of the user in chronological order, and the attention mechanism was introduced to assign different attention weights to the obtained feature matrix to obtain user post information features and user emotional information features.Next, regular matching was used to extract the emotional tendency information, and the weighted emotional feature matrix output by the attention mechanism was spliced to enhance emotional learning expression.Then, metadata features describing social network posts were added, indicators that characterized user preference features were designed, and user language preference features through statistical characterization indicators were extracted.Finally, the three characteristics of user language information, user emotional information, and user language preference information were combined to establish a prediction model for the user’s depression state based on a multi-layer perceptron.The experimental results showed that the accuracy of the model in this paper had increased by 0.051, the recall rate had increased by 0.065, and F1 value had increased by 0.058. Table and Figures | Reference | Related Articles | Metrics

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Multi-AUV cooperative localization in adaptive sampling for marine environmental monitoring Jiaxin ZHANG, Senlin ZHANG, Meiqin LIU, Shanling DONG, Ronghao ZHENG Chinese Journal of Intelligent Science and Technology    2022, 4 (4): 503-512.   DOI: 10.11959/j.issn.2096-6652.202250 Abstract （241）   HTML （26）    PDF（pc） （7579KB）（310）       Knowledge map    Save Efficient and accurate water quality monitoring is of great significance to the development of marine resources, and Special Topic: Autonomous Underwater Vehicle (AUV) has broad application prospects in marine environmental monitoring.There are problems such as low efficiency, poor reliability, insufficient coverage and poor positioning accuracy when a single AUV performs water quality sampling tasks for ocean scalar field estimation.The multi-AUV-based cooperative localization and adaptive sampling system was proposed.Each AUV in the system broadcasted the collected sampling data to its teammates, and based on the data received, it corrected the location of itself based on the extended Kalman filter.With the collected sampling data, the AUV modeled the environmental scalar field with a Gaussian process and used a differential evolution path planner to plan its subsequent sampling path online.Simulation results showed that the proposed method effectively reduced the positioning error of AUVs, and improved the estimation accuracy of the environmental scalar field. Table and Figures | Reference | Related Articles | Metrics

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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 Abstract （204）   HTML （69）    PDF（pc） （1519KB）（304）       Knowledge map    Save 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. Table and Figures | Reference | Related Articles | Metrics

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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 Abstract （190）   HTML （15）    PDF（pc） （7432KB）（301）       Knowledge map    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. Table and Figures | Reference | Related Articles | Metrics

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Application of Fit CutMix data augmentation algorithm based on saliency information in medical images Xinhuan LUO, Yixuan WANG, Wei LI, Xi CHEN Chinese Journal of Intelligent Science and Technology    2023, 5 (1): 58-68.   DOI: 10.11959/j.issn.2096-6652.202307 Abstract （264）   HTML （31）    PDF（pc） （1857KB）（284）       Knowledge map    Save Deep convolutional neural network is one of the mainstream algorithms in the field of image classification, but its training requires a large number of labeled data, which leads to over fitting on small datasets such as Alzheimer's medical images.Data augmentation can increase the amount of training data, and CutMix data augmentation algorithm has been widely used recently.However, the augmented images generated by the CutMix series methods often ignore the significant area of the original image, and the design of the label of the augmented image takes only single factor into consideration.In order to solve these problems, the Fit CutMix data augmentation algorithm was proposed.Firstly, the region replacement strategy based on the transfer of saliency extreme value was used to generate augmented samples, so as to concentrate the regions with high saliency value in the source samples and target samples.Secondly, the area and saliency information of the source samples and the target samples were combined to assign the augmented sample label, which provided effective supervision information for the convolutional neural network.The experimental results showed that when Fit CutMix was used in ResNet50 to diagnose Alzheimer's disease, the accuracy was 96.6%, which was about 7% higher than that of directly using ResNet50, and at least 3% higher than that of applying existing methods.Therefore, the Fit CutMix data augmentation algorithm can effectively improve the recognition accuracy of deep convolutional neural network for medical images. Table and Figures | Reference | Related Articles | Metrics