Pub Date : 2020-06-01DOI: 10.1109/ICAICA50127.2020.9182465
F. Gao, Da Huang
Bayesian network is an important model for reasoning in an uncertain environment. A reliable node rank is required by K2 algorithm to learn Bayesian network structure better. To provide a high-quality node rank tailored for K2 algorithm, we propose a node priority-based sorting algorithm. Given observable data only, our algorithm can be employed to learn a node rank without expert knowledge. Specifically, MCMC algorithm is first utilized to yield some Bayesian network structures that can sufficiently fit the observed data. We then define the priority of each node in these network structures. Node rank is finally obtained through weighted scoring based on the priority. The empirical results show that our sorting algorithm performs significantly better than commonly used methods, e.g., randomly sorting and MCMC algorithm, on an Asia network-learning dataset.
{"title":"A Node Sorting Method for K2 Algorithm in Bayesian Network Structure Learning","authors":"F. Gao, Da Huang","doi":"10.1109/ICAICA50127.2020.9182465","DOIUrl":"https://doi.org/10.1109/ICAICA50127.2020.9182465","url":null,"abstract":"Bayesian network is an important model for reasoning in an uncertain environment. A reliable node rank is required by K2 algorithm to learn Bayesian network structure better. To provide a high-quality node rank tailored for K2 algorithm, we propose a node priority-based sorting algorithm. Given observable data only, our algorithm can be employed to learn a node rank without expert knowledge. Specifically, MCMC algorithm is first utilized to yield some Bayesian network structures that can sufficiently fit the observed data. We then define the priority of each node in these network structures. Node rank is finally obtained through weighted scoring based on the priority. The empirical results show that our sorting algorithm performs significantly better than commonly used methods, e.g., randomly sorting and MCMC algorithm, on an Asia network-learning dataset.","PeriodicalId":113564,"journal":{"name":"2020 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132989179","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-06-01DOI: 10.1109/ICAICA50127.2020.9182498
Xu Li, Liqiang Wen, Jinjun Wang, Ming Zeng
Although video action recognition has achieved great progress in recent years, it is still a challenging task due to the huge computational complexity. Designing a lightweight network is a feasible solution, but it may reduce the spatio-temporal information modeling capability. In this paper, we propose a novel novel spatio-temporal collaborative convolution (denote as “STC-Conv”), which can efficiently encode spatio-temporal information. STC-Conv collaboratively learn spatial and temporal feature in one convolution filter kernel. In short, temporal convolution and spatial convolution are integrated in the one STC convolution kernel, which can effectively reduce the model complexity and improve the computational efficiency. STC-Conv is a universal convolution, which can be applied to the existing 2D CNNs, such as ResNet, DenseNet. The experimental results on the temporal-related dataset Something Something V1 prove the superiority of our method. Noticeably, STC-Conv enjoys more excellent performance than 3D CNNs at even lower computation cost than standard 2D CNNs.
{"title":"Spatio-temporal Collaborative Convolution for Video Action Recognition","authors":"Xu Li, Liqiang Wen, Jinjun Wang, Ming Zeng","doi":"10.1109/ICAICA50127.2020.9182498","DOIUrl":"https://doi.org/10.1109/ICAICA50127.2020.9182498","url":null,"abstract":"Although video action recognition has achieved great progress in recent years, it is still a challenging task due to the huge computational complexity. Designing a lightweight network is a feasible solution, but it may reduce the spatio-temporal information modeling capability. In this paper, we propose a novel novel spatio-temporal collaborative convolution (denote as “STC-Conv”), which can efficiently encode spatio-temporal information. STC-Conv collaboratively learn spatial and temporal feature in one convolution filter kernel. In short, temporal convolution and spatial convolution are integrated in the one STC convolution kernel, which can effectively reduce the model complexity and improve the computational efficiency. STC-Conv is a universal convolution, which can be applied to the existing 2D CNNs, such as ResNet, DenseNet. The experimental results on the temporal-related dataset Something Something V1 prove the superiority of our method. Noticeably, STC-Conv enjoys more excellent performance than 3D CNNs at even lower computation cost than standard 2D CNNs.","PeriodicalId":113564,"journal":{"name":"2020 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA)","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133739394","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-06-01DOI: 10.1109/ICAICA50127.2020.9181856
Yuming Tang, Hong Liang, Shi Chen, Hongyu Song
Cyanobacteria are a large class of single-cell prokaryotes capable of oxygen-producing photosynthesis. When cyanobacteria are stimulated by nitrogen, phosphorus and other elements, it will cause eutrophication of the water body and cause the phenomenon of “bloom” in the lake, which seriously endangers the safety of humans, livestock, fish and shrimp. The monitoring and management of cyanobacteria blooms have been plagued by lake management units. At present, the product functions related to the prevention and control of cyanobacteria blooms are very single. The product functions are roughly divided into two categories, some of which focus only on the monitoring link and the other only focus on the salvage and processing link. There is no one product that can combine the two links well. In view of this situation, this article has designed and developed a set of cyanobacteria and algae prevention and control disposal management system, which effectively combines the monitoring and salvage links. The system includes four subsystems: operation report subsystem, monitoring data management system, algae mud environmental protection whole process management system and real-time cyanobacteria monitoring system. The three-dimensional interaction between the WEB terminal and the mobile terminal makes the system more efficient and convenient. The system has the following two innovations: Innovation one: Effectively integrate the control and salvage links of cyanobacteria blooms and jointly build them into a system. Innovation point 2: Apply the K-means algorithm in machine learning to image classification, and replace artificial artificial unattended with AI to improve the recognition rate and reduce the error rate.
{"title":"Cyanobacteria and Bloom Control Management System","authors":"Yuming Tang, Hong Liang, Shi Chen, Hongyu Song","doi":"10.1109/ICAICA50127.2020.9181856","DOIUrl":"https://doi.org/10.1109/ICAICA50127.2020.9181856","url":null,"abstract":"Cyanobacteria are a large class of single-cell prokaryotes capable of oxygen-producing photosynthesis. When cyanobacteria are stimulated by nitrogen, phosphorus and other elements, it will cause eutrophication of the water body and cause the phenomenon of “bloom” in the lake, which seriously endangers the safety of humans, livestock, fish and shrimp. The monitoring and management of cyanobacteria blooms have been plagued by lake management units. At present, the product functions related to the prevention and control of cyanobacteria blooms are very single. The product functions are roughly divided into two categories, some of which focus only on the monitoring link and the other only focus on the salvage and processing link. There is no one product that can combine the two links well. In view of this situation, this article has designed and developed a set of cyanobacteria and algae prevention and control disposal management system, which effectively combines the monitoring and salvage links. The system includes four subsystems: operation report subsystem, monitoring data management system, algae mud environmental protection whole process management system and real-time cyanobacteria monitoring system. The three-dimensional interaction between the WEB terminal and the mobile terminal makes the system more efficient and convenient. The system has the following two innovations: Innovation one: Effectively integrate the control and salvage links of cyanobacteria blooms and jointly build them into a system. Innovation point 2: Apply the K-means algorithm in machine learning to image classification, and replace artificial artificial unattended with AI to improve the recognition rate and reduce the error rate.","PeriodicalId":113564,"journal":{"name":"2020 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132500132","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-06-01DOI: 10.1109/ICAICA50127.2020.9182505
Xiangdong Jiang, Nai-Yuan Pa, Wen-Chang Wang, Tian-Tian Yang, Wen-Tsao Pan
This article comprehensively considers timeliness of emergency rescue and cost constraints. Based on the transportation costs from the rescue center to the disaster site and the cost of setting up the rescue center, golden rescue timeis taken into account. The penalty cost caused by losing the golden rescue time is considered, thereby quantifying timeliness as another dimension of cost. The problem is solved using K-means clustering algorithm and fruit fly algorithm (FOA). With the purpose of minimizing the weighted sum of construction costs, transportation costs and penalty costs of emergency rescue centers, suitable location is selected for establishment of emergency rescue center. Finally, modified two algorithms (RWFOA and MFOA) are compared in optimization performance. The K-means clustering analysis and FOA are used to simplify and solve the original model, which can solve complex problems. In comparison between RWFOA and MFOA, the optimal value of MFOA is lower.
{"title":"Site Selection and Layout of Earthquake Rescue Center Based on K-Means Clustering and Fruit Fly Optimization Algorithm","authors":"Xiangdong Jiang, Nai-Yuan Pa, Wen-Chang Wang, Tian-Tian Yang, Wen-Tsao Pan","doi":"10.1109/ICAICA50127.2020.9182505","DOIUrl":"https://doi.org/10.1109/ICAICA50127.2020.9182505","url":null,"abstract":"This article comprehensively considers timeliness of emergency rescue and cost constraints. Based on the transportation costs from the rescue center to the disaster site and the cost of setting up the rescue center, golden rescue timeis taken into account. The penalty cost caused by losing the golden rescue time is considered, thereby quantifying timeliness as another dimension of cost. The problem is solved using K-means clustering algorithm and fruit fly algorithm (FOA). With the purpose of minimizing the weighted sum of construction costs, transportation costs and penalty costs of emergency rescue centers, suitable location is selected for establishment of emergency rescue center. Finally, modified two algorithms (RWFOA and MFOA) are compared in optimization performance. The K-means clustering analysis and FOA are used to simplify and solve the original model, which can solve complex problems. In comparison between RWFOA and MFOA, the optimal value of MFOA is lower.","PeriodicalId":113564,"journal":{"name":"2020 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132515537","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-06-01DOI: 10.1109/ICAICA50127.2020.9182549
Xiaofei Zhang, Zhen Liu, Bing Han
As a software representation of assets and processes, digital twins is valuable for the design of future marine monitoring and control systems. Such a system usually requires the integration of technologies from cloud and edge, WebAccess/SCADA and SaaS Composer from Advantech Technology were adopted in our proposed framework for ships running in coastal waters and a digital twins based marine monitoring application was given in the paper. The framework is compatible with various network access technologies, including near shore oriented cellular communication and off shore oriented satellite communication. The focus of this paper is on how to realize the virtual modeling and innovated application with marine monitoring data and its universality can derive to more promising applications.
{"title":"Toward Digital Twins Based Marine SCADA System","authors":"Xiaofei Zhang, Zhen Liu, Bing Han","doi":"10.1109/ICAICA50127.2020.9182549","DOIUrl":"https://doi.org/10.1109/ICAICA50127.2020.9182549","url":null,"abstract":"As a software representation of assets and processes, digital twins is valuable for the design of future marine monitoring and control systems. Such a system usually requires the integration of technologies from cloud and edge, WebAccess/SCADA and SaaS Composer from Advantech Technology were adopted in our proposed framework for ships running in coastal waters and a digital twins based marine monitoring application was given in the paper. The framework is compatible with various network access technologies, including near shore oriented cellular communication and off shore oriented satellite communication. The focus of this paper is on how to realize the virtual modeling and innovated application with marine monitoring data and its universality can derive to more promising applications.","PeriodicalId":113564,"journal":{"name":"2020 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130090650","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-06-01DOI: 10.1109/ICAICA50127.2020.9182680
Xiaoyu Li, Wangjie Li
In the early centuries of 21st, self-driving becomes increasingly popular. Because of that, some technologies related to that are being studied and applied. Smart car control technology is one of these technologies including embedded technology, automatic control technology and so on. As for smart car field, there are two important methods to identify road, which are using electromagnetic sensors and using camera. In each method, there are different hardware structures to collect data and different code for microprocessor to process data. So in this essay, it will introduce the background technologies of smart car firstly. After that, basic principle of using electromagnetic sensors and camera will be given. Moreover, it will explain the code about using camera to achieve direction control and speed control. In this part, it will also give some applications of using camera to identify road elements. Finally, it will give a new method to control smart car by according to advantages and disadvantages of using camera and using electromagnetic sensors.
{"title":"Road identifying by using electromagnetic sensor and camera in smart car field","authors":"Xiaoyu Li, Wangjie Li","doi":"10.1109/ICAICA50127.2020.9182680","DOIUrl":"https://doi.org/10.1109/ICAICA50127.2020.9182680","url":null,"abstract":"In the early centuries of 21st, self-driving becomes increasingly popular. Because of that, some technologies related to that are being studied and applied. Smart car control technology is one of these technologies including embedded technology, automatic control technology and so on. As for smart car field, there are two important methods to identify road, which are using electromagnetic sensors and using camera. In each method, there are different hardware structures to collect data and different code for microprocessor to process data. So in this essay, it will introduce the background technologies of smart car firstly. After that, basic principle of using electromagnetic sensors and camera will be given. Moreover, it will explain the code about using camera to achieve direction control and speed control. In this part, it will also give some applications of using camera to identify road elements. Finally, it will give a new method to control smart car by according to advantages and disadvantages of using camera and using electromagnetic sensors.","PeriodicalId":113564,"journal":{"name":"2020 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114905375","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-06-01DOI: 10.1109/ICAICA50127.2020.9182488
Guan Li, Xinxin Huang
This article introduces a sports platform APP developed based on Android system. The APP contains multiple sections, users can perform autonomous extracurricular exercises according to the goals specified by professional teachers; users can query the fitness test results through the APP; professional teachers can use the APP to collect users'exercise data and physical fitness data, and develop course goals of different exercise intensity and exercise load, guide users to choose a suitable course and through the data analysis system in the APP, provide hierarchical guidance to user groups. In the Android Studio environment, the team uses Java to write the client APP; In the Net beans IDE environment, the client APP is written by Java to respond to the request; the C / S architecture is implemented, and it cooperates with other tools like the MySql database, Tomcat server, and Python program. Then the basic functions of the software can work well.
{"title":"Research and Technology Development of University Campus Sports Data Platform","authors":"Guan Li, Xinxin Huang","doi":"10.1109/ICAICA50127.2020.9182488","DOIUrl":"https://doi.org/10.1109/ICAICA50127.2020.9182488","url":null,"abstract":"This article introduces a sports platform APP developed based on Android system. The APP contains multiple sections, users can perform autonomous extracurricular exercises according to the goals specified by professional teachers; users can query the fitness test results through the APP; professional teachers can use the APP to collect users'exercise data and physical fitness data, and develop course goals of different exercise intensity and exercise load, guide users to choose a suitable course and through the data analysis system in the APP, provide hierarchical guidance to user groups. In the Android Studio environment, the team uses Java to write the client APP; In the Net beans IDE environment, the client APP is written by Java to respond to the request; the C / S architecture is implemented, and it cooperates with other tools like the MySql database, Tomcat server, and Python program. Then the basic functions of the software can work well.","PeriodicalId":113564,"journal":{"name":"2020 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116224476","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-06-01DOI: 10.1109/ICAICA50127.2020.9182707
Yunhan Gu, Ning Liu
In this thesis, an modified algorithm for grey wolf optimization in swarm intelligence optimization algorithm is proposed, which is called an adaptive grey wolf algorithm (AdGWO) based on population system and bacterial foraging optimization algorithm (BFO). In view of the disadvantages of premature convergence and local optimization in solving complex optimization problems, the AdGWO algorithm uses a three-stage nonlinear change function to simulate the decreasing change of the convergence factor, and at the same time integrates the half elimination mechanism of the BFO. These improvements are more in line with the actual situation of natural wolves. The algorithm is based on 23 famous test functions and compared with GWO. Experimental results demonstrate that this algorithm is able to avoid sinking into the local optimum, has good accuracy and stability, is a more competitive algorithm.
{"title":"An Adaptive Grey Wolf Algorithm Based on Population System and Bacterial Foraging Algorithm","authors":"Yunhan Gu, Ning Liu","doi":"10.1109/ICAICA50127.2020.9182707","DOIUrl":"https://doi.org/10.1109/ICAICA50127.2020.9182707","url":null,"abstract":"In this thesis, an modified algorithm for grey wolf optimization in swarm intelligence optimization algorithm is proposed, which is called an adaptive grey wolf algorithm (AdGWO) based on population system and bacterial foraging optimization algorithm (BFO). In view of the disadvantages of premature convergence and local optimization in solving complex optimization problems, the AdGWO algorithm uses a three-stage nonlinear change function to simulate the decreasing change of the convergence factor, and at the same time integrates the half elimination mechanism of the BFO. These improvements are more in line with the actual situation of natural wolves. The algorithm is based on 23 famous test functions and compared with GWO. Experimental results demonstrate that this algorithm is able to avoid sinking into the local optimum, has good accuracy and stability, is a more competitive algorithm.","PeriodicalId":113564,"journal":{"name":"2020 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116468499","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Real-time capacity of a battery is normally indicated by the state of charge (SOC). In this paper, the SOC prediction methods of vanadium redox-flow battery (VRB) are introduced and the advantages and disadvantages of those are compared. Based on the nonlinear characteristic of SOC, the method of using BP neural network to predict SOC of VRB is proposed. The BP neural network is optimized with Levenberg-Marquardt optimization algorithm and Bayesian regulation algorithm, respectively. The neural network improved with Bayesian regulation can predict SOC in real time during the VRB testing process. The experimental results show that the neural network improved by Bayesian regulation algorithm can improve the real-time prediction accuracy of SOC and has a good application prospect.
{"title":"State of Charge Prediction Study of Vanadium Redox-Flow Battery with BP Neural Network","authors":"Hongtao Niu, Jianqiong Huang, Chenguang Wang, Xiaoyan Zhao, Zhifeng Zhang, Wei Wang","doi":"10.1109/ICAICA50127.2020.9182403","DOIUrl":"https://doi.org/10.1109/ICAICA50127.2020.9182403","url":null,"abstract":"Real-time capacity of a battery is normally indicated by the state of charge (SOC). In this paper, the SOC prediction methods of vanadium redox-flow battery (VRB) are introduced and the advantages and disadvantages of those are compared. Based on the nonlinear characteristic of SOC, the method of using BP neural network to predict SOC of VRB is proposed. The BP neural network is optimized with Levenberg-Marquardt optimization algorithm and Bayesian regulation algorithm, respectively. The neural network improved with Bayesian regulation can predict SOC in real time during the VRB testing process. The experimental results show that the neural network improved by Bayesian regulation algorithm can improve the real-time prediction accuracy of SOC and has a good application prospect.","PeriodicalId":113564,"journal":{"name":"2020 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114641278","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-06-01DOI: 10.1109/ICAICA50127.2020.9182364
Fengda Zhang, Jingchang Pan, Gaoyu Jiang
Information object refers to anything that can be perceived or conceived in the form of information, including concrete and abstract concepts, such as people, events, architecture, engineering, trees, houses, prices, public opinion, etc. The evolution, change and relevance of these information objects depend on the two key characteristics of historical events and geographic information. The main content of this research is to propose a spatiotemporal information relevance model based on information object-state. Using the spatiotemporal information relevance model, through the reasonable design of a large amount of historical and geographic information storage database, it can show the spatiotemporal evolution of historical information objects, and tap the relevance between historical information objects, so that historical and geographical information can be scientifically and objectively displayed. This research topic will use big data, database, image processing and other technologies to clearly show the development context and relevance of information objects, improve the intuitiveness of historical and geographic information data display, and help to provide historical geographic information researchers with a way to obtain and display data.
{"title":"Design of Multidimensional Spatiotemporal Information Relevance Model","authors":"Fengda Zhang, Jingchang Pan, Gaoyu Jiang","doi":"10.1109/ICAICA50127.2020.9182364","DOIUrl":"https://doi.org/10.1109/ICAICA50127.2020.9182364","url":null,"abstract":"Information object refers to anything that can be perceived or conceived in the form of information, including concrete and abstract concepts, such as people, events, architecture, engineering, trees, houses, prices, public opinion, etc. The evolution, change and relevance of these information objects depend on the two key characteristics of historical events and geographic information. The main content of this research is to propose a spatiotemporal information relevance model based on information object-state. Using the spatiotemporal information relevance model, through the reasonable design of a large amount of historical and geographic information storage database, it can show the spatiotemporal evolution of historical information objects, and tap the relevance between historical information objects, so that historical and geographical information can be scientifically and objectively displayed. This research topic will use big data, database, image processing and other technologies to clearly show the development context and relevance of information objects, improve the intuitiveness of historical and geographic information data display, and help to provide historical geographic information researchers with a way to obtain and display data.","PeriodicalId":113564,"journal":{"name":"2020 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA)","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132201228","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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