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15 June 2021, Volume 3 Issue 2

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Review Intelligence

Parallel museums: intelligent management and control of museum operations in the new era

Chunfa WANG, Fei-Yue WANG, Yue LU, Huabiao LI, Chao GUO

2021, 3(2):  125-136.  doi:10.11959/j.issn.2096-6652.202113

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The development of society and the growth of public cultural needs bring new challenges to the operations and management of museums in the new era.Considering the current state of the museum management and its challenges in exhibition, education, and safety, the parallel museums framework as a new solution to the museum management was proposed, and technical reference for constructing intelligent museum was proposed.The parallel museums framework was the application of ACP theory in museum operations and management, descriptive intelligence was used to construct a virtual museum, predictive intelligence was used to do large-scale computational experiments in the virtual museum, and prescriptive intelligence and parallel execution were used to control the real museum.

Special Topic: Intelligent Transportation Systems and Applications

Information security of the industrial control system for rail：analysis and prospect

Yidong LI, Zikai ZHANG, Hairong DONG, Honglei ZHANG, Haoyu CHEN, Yushan HAN

2021, 3(2):  139-148.  doi:10.11959/j.issn.2096-6652.202114

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As one of the important national infrastructures, more and more attentions are attracted by the industrial control system for rail (ICS-R), with the rapid development of information technology and the increasingly severe network situation.The system composition of ICS-R was analyzed, the types of security threats faced by the ICS-R were summarized and analyzed, the practical cases of threat propagation were given, and the information security threat trend of ICS-R was analyzed considering the development trend of technology.Finally, several development suggestions were prospected as promising techniques.

A review of prediction methods for moving target trajectories

Wen LIU, Kunlin HU, Yan LI, Zhao LIU

2021, 3(2):  149-160.  doi:10.11959/j.issn.2096-6652.202115

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With the rapid emergence of mobile terminal equipment in intelligent transportation system, the deep understanding and accurate prediction of moving target trajectories are capable of reducing the traffic accident probability, and promoting the location service-based intelligent transportation applications.The trajectory prediction methods prediction methods for moving target trajectories were reviewed from the data-driven prediction methods and the behavior-driven trajectories prediction methods.Firstly, the data-driven prediction methods were reviewed, including probabilistic statistics, neural networks, deep learning, and hybrid modeling.Then, the basic conceptions of target behavior-driven trajectories prediction methods were analyzed.The corresponding dynamical modeling and intention recognition methods were reviewed.The trajectory prediction applications were briefly analyzed and reviewed, such as target trajectory reconstruction, target abnormal behavior identification, and navigation route planning.Finally, the main problems and development directions related to prediction of moving target trajectories were discussed.

A survey of research on demand responsive transit and its route optimization

Shuai FENG, Xiaoming LIU

2021, 3(2):  161-171.  doi:10.11959/j.issn.2096-6652.202116

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With the widespread application of new infrastructure and other technologies, demand responsive transit (DRT) is becoming the trend of urban traffic development in the future.In order to further clarify the research status of DRT at home and abroad, firstly, the classification of DRT and the operational production factors were analyzed.Secondly, the production process and operation practice of DRT mode were described.Thirdly, the DRT optimization problem was summarized and analyzed from the three dimensions including optimization objectives, constraint selection and solving algorithm, especially the meta heuristic algorithm.Finally, the prospect of DRT scenarios, model construction, algorithm solution, and the collaborative optimization were proposed.Several hotspots and possible directions of future research were suggested.

A deep learning short-term traffic flow prediction method considering spatial-temporal association

Yang ZHANG, Yue HU, Dongrong XIN

2021, 3(2):  172-178.  doi:10.11959/j.issn.2096-6652.202117

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The short-term traffic flow prediction is too dependent on the time correlation characteristics, which due to the problems that the correlation factors of the spatial correlation characteristics are too complicated and difficult to quantify.In response to this defect, a deep learning short-term traffic flow prediction method considering spatial-temporal association was proposed.Firstly, by constructing a spatial association measurement function that simultaneously considers distance, flow similarity, and speed similarity, the spatial correlation between the target road segment and the surrounding associated road segments was quantified and predicted.Then, a convolutional neural network model with long short-term memory neurons embedded was constructed.The long short-term memory neurons were used to extract the temporal correlation characteristics between the data, and the spatial correlation metric and the convolution transmission of traffic data were used to extract the spatial correlation characteristics between the data, so as to realize the traffic flow prediction considering the spatial-temporal association.The experimental results show that the proposed method can adapt to short-term forecasting under different traffic flow characteristics such as weekdays and weekends, and the prediction accuracy is better than that of the classical methods.In weekdays and weekends, the forecast bias are 10.45% and 12.35% respectively.

Algorithm for automatically generating a large number of speed curves of subway trains based on AlphaZero

Yuqi LU, Dewang CHEN, Zhaolin ZHAO

2021, 3(2):  179-184.  doi:10.11959/j.issn.2096-6652.202118

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In the previous research on subway trains automatic driving, subway driving data is usually obtained through simulation to generate a single operating curve and manual driving data sampling.Not only is the implementation method more complicated, but also the efficiency is low and the versatility is not strong.Inspired by AlphaZero system, the idea of artificial generation of virtual metro operation data was put forward.Firstly, according to a running method of five section subway trains speed curve, the calculation of virtual data was realized.Then, combined with the experience of human experts, the actual parameters such as the classification of traction braking section, the classification of actual running speed, the classification of station spacing and variable speed distance were set to narrow the range of curve data and rationalize it.Finally, a large amount of data was obtained by Python programming, saved as a data set, and the frequency distribution map of subway trains operation time was drawn.It can be observed that the virtual data covers all kinds of operation time, which is more conducive to the research of subway trains intelligent driving algorithm than traditional data.

Special Topic: Industrial Internet of Minds

Key technologies and applications of intelligent guiding robots for epidemic prevention

Teng WANG, Jing PAN, Lu DONG, Changyin SUN

2021, 3(2):  187-194.  doi:10.11959/j.issn.2096-6652.202119

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During the period of epidemic prevention, intelligent robots play a significant role in the fields of medical treatment, inspection and distribution, making more people realize the importance of robots in solving public health and safety problems.Focusing on the intelligent guiding robots, the key technologies and research status of the intelligent guiding robots were discussed, including robot disinfection, face recognition, speech interaction, autonomous localization, path planning.Then, taking TMiRob intelligent guiding robot as an example to discuss how these key technologies were implemented in reality.Finally, the challenges and development trends of intelligent guiding robots were presented.

Fault discovery based on text information extraction for on-board equipment of CTCS

Xi CHEN, Runmei LI, Jian WANG, Shuyun DONG

2021, 3(2):  195-201.  doi:10.11959/j.issn.2096-6652.202120

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The on-board safety computer of Chinese Train Control System generates a large number of Log files during operation every day, which together with the quality analysis record sheet, record the running status of all equipment and provide data support for the fault discovery and processing of the equipment of train control system.The use of these two types of text data is still limited to manual record, query and analysis, which has the problems of low efficiency, strong subjectivity and easy omission and error.The on-board safety computer Log files and the quality analysis record sheet were taken as the research object, and designs an information extraction method and the fault discovery automation scheme to replace the existing manual working mode.The information of Log files and manual recording by the staffs were analyzed, the word segmentation algorithm and information extraction algorithm were selected to find useful information automatically from two kinds of record to avoiding the cumbersome manual search.Then a failure analysis dictionary was built.The automatic fault discovery application was established by using regular expression algorithm.The automatic fault discovery and analysis functions of the application were validated by experiment.

Power policy quantification based on PMC index model and its application in load forecasting

Tianbin LIU, Hang ZHAO, Chen WANG, Hongxia YUAN, Yinya ZHANG, Chenxi HU, Jinxing LI, Tianlu GAO, Jun ZHANG

2021, 3(2):  202-210.  doi:10.11959/j.issn.2096-6652.202121

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Policy has a direct impact on power system load.In order to fully explore the relationship between policy factors and load, and improve the accuracy of load forecasting, a quantitative method of power policy based on policy modeling consistency (PMC) index was proposed and it was applied to load forecasting.Firstly, the PMC evaluation system of electric power field was established, and then the PMC index of power policy text was obtained by text mining technology.Finally, the load forecasting model based on long shrot term memory was constructed.The quantitative index of power policy, weather, date and other influencing factors were input into the model, and compared with the model without considering policy factors.The experiment shows that the load forecasting model with policy factors achieves good results.After adding policy quantitative data, the error mean absolute percentage error of load forecasting model is reduced from 1.67 to 0.98, and mean absolute error is reduced from 28.97 to 19.68, which indicates that PMC model has a certain ability of policy quantification.

Planning and practice of the Yangtze River smart city platform

Jun WU, Jun ZHANG

2021, 3(2):  211-217.  doi:10.11959/j.issn.2096-6652.202122

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Firstly, the background, demand, status quo and challenges of the “Yangtze River smart city” at Wuhan were analyzed.Then, the planning design of the overall smart city framework was discussed in details on infrastructure, urban operating system and application ecology.Finally, the latest progress of the smart city construction was also summarized at the end with a perspective on the future of the Yangtze River smart city.

Parallel port: new formation and system architecture of port industry Internet of minds in smart and green era

Yuzhen WU, Jun ZHANG, Tianlu GAO, Yujian SUN, Jinxu LIU

2021, 3(2):  218-227.  doi:10.11959/j.issn.2096-6652.202123

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The background, demand, new formation, system architecture, and platform technology of port industry in smart and green era were analyzed.Firstly, the background and demand of port industry in smart and green era were introduced.Then the new formation and system architecture of parallel port industry were demonstrated.Next, the novel platform technology of port industry in smart and green era was discussed, including the Internet of minds, knowledge automation, hybrid-augmented intelligence and so on.Finally, an application case study of parallel systems in smart and green port was presented, i.e., port ship management system.

Papers and Reports

Research on key technologies of social computing for urban complex system

Xiaofeng JIA, Song GAO, Xi JIANG, Hongwei QI, Xiao WANG, Jun ZHANG, Rui QIN, Liwei OUYANG

2021, 3(2):  228-233.  doi:10.11959/j.issn.2096-6652.202124

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Urban complex system has the characteristics of multiple scattered elements, complex multi-dimensional relationships, dynamic structural changes and high degree of social function coupling.Real-time global data perception, hierarchical decentralized collaborative scheduling and complex dynamic relationship construction are the key issues of accurate modeling and fine management of urban system.The social computing technologies based on ACP parallel intelligent method provides theoretical basis for the problems.Based on this, the large-scale, multi-modal, high-dimensional and total factor modeling of urban complex system was carried out.Based on the chain code mechanism of virtual-real dual parallel regulation, the key technologies of social computing were proposed and its system architecture was designed to realize the global chain communication and polymorphic application of urban complex system.The architecture can provide an important paradigm for the oriented distributed, intelligent and active response of complex urban management in the future.

NFT: blockchain-based non-fungible token and applications

Rui QIN, Juanjuan LI, Xiao WANG, Jing ZHU, Yong YUAN, Fei-Yue WANG

2021, 3(2):  234-242.  doi:10.11959/j.issn.2096-6652.202125

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Non-fungible token (NFT) based on blockchain is a kind of digital asset ownership recorded on blockchain, which is unique, irreplaceable and indivisible.It has been widely used in collectibles, encrypted artworks and games.Firstly, the concepts, characteristics and development processes of NFT were mainly introduced, and its core elements and typical application fields were analyzes.The problems and risks faced by NFT in the aspects of property right, value, technology and supervision were also pointed out.After that, the related literatures on NFT were reviewed, and the research issues of NFT in value evaluation, transaction mode and pricing were proposed.Finally, the trend of NFT driven digital capitalization was prospected.