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2020 IEEE International Conference on Industrial Application of Artificial Intelligence (IAAI)最新文献

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Evaluation Method of Combat Effectiveness of Electronic Warfare Equipment Based on Improved Fruit Fly Algorithm 基于改进果蝇算法的电子战装备战斗力评估方法
2020 IEEE International Conference on Industrial Application of Artificial Intelligence (IAAI) Pub Date : 2020-12-25 DOI: 10.1109/IAAI51705.2020.9332809
Liu Jing, Li Jiangwei, Xie Kai
{"title":"Evaluation Method of Combat Effectiveness of Electronic Warfare Equipment Based on Improved Fruit Fly Algorithm","authors":"Liu Jing, Li Jiangwei, Xie Kai","doi":"10.1109/IAAI51705.2020.9332809","DOIUrl":"https://doi.org/10.1109/IAAI51705.2020.9332809","url":null,"abstract":"When using the original method to evaluate the operational effectiveness of electronic warfare equipment, due to the influence of too many states involved, it is impossible to build the combat simulation model of electronic warfare equipment, and the calculation results of unit damage probability in the range of 1000–3000 meters in the configuration area are inaccurate. Therefore, an operational effectiveness evaluation of electronic warfare equipment based on improved fruit fly algorithm is proposed method. The operational simulation model of EW equipment is established, which mainly includes battlefield environment model, entity model and combat action model. Using the method of decomposing and evaluating targets, the index set is initially drawn up, and the initial index set is compared, selected, and de-correlated. Through the screening index, the operational effectiveness evaluation model of electronic warfare equipment is constructed to realize the operational effectiveness evaluation of electronic warfare equipment. The original method is compared with this method, and the experimental results prove that the accuracy of the calculation result of the damage probability of the soldier group of the method is higher than that of the original method, and the performance is improved.","PeriodicalId":232194,"journal":{"name":"2020 IEEE International Conference on Industrial Application of Artificial Intelligence (IAAI)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134142246","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}
引用次数: 0
Research on Automatic Dance Arrangement Method Based on Augmented Reality Technology 基于增强现实技术的舞蹈自动编排方法研究
2020 IEEE International Conference on Industrial Application of Artificial Intelligence (IAAI) Pub Date : 2020-12-25 DOI: 10.1109/IAAI51705.2020.9332864
M. Lou
{"title":"Research on Automatic Dance Arrangement Method Based on Augmented Reality Technology","authors":"M. Lou","doi":"10.1109/IAAI51705.2020.9332864","DOIUrl":"https://doi.org/10.1109/IAAI51705.2020.9332864","url":null,"abstract":"In order to improve the accuracy of augmented reality for automatic choreography, it is necessary to perform automatic choreography and augmented reality processing. This paper proposes an automatic choreography motion feature detection technology based on augmented reality image feature detection, which combines multi-resolution image recognition and detection methods to perform automatic choreography augmented reality computer vision image feature analysis and dance key motion feature point detection processing, and track tracking method based on morphology. According to the detailed feature collection results of dance automatic choreography augmented reality computer vision images, the region segmentation of dance automatic choreography augmented reality is carried out by adopting three-dimensional recombination method of key motion feature points data of computer vision dance, the feature analysis of dance automatic choreography augmented reality is carried out by combining fuzzy image dance key motion feature points detection technology, the fringe pattern analysis of computer vision is carried out by adopting edge contour feature detection method, the error correction and compensation processing of dance motion feature detection are carried out according to light resolution characteristics, and the motion feature detection of dance automatic choreography is realized by combining shape feature distribution. The simulation results show that this method has better resolution of atlas and higher accuracy of parameter estimation of key dance action feature points, which effectively realizes the action correction of automatic dance arrangement and improves the accuracy of automatic dance arrangement.","PeriodicalId":232194,"journal":{"name":"2020 IEEE International Conference on Industrial Application of Artificial Intelligence (IAAI)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129262616","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}
引用次数: 0
Incremental Mining Method of Spatial Co-Location Pattern based on Genetic Algorithm 基于遗传算法的空间共位模式增量挖掘方法
2020 IEEE International Conference on Industrial Application of Artificial Intelligence (IAAI) Pub Date : 2020-12-25 DOI: 10.1109/IAAI51705.2020.9332874
Liu Zhao, Ran Jun-Yi
{"title":"Incremental Mining Method of Spatial Co-Location Pattern based on Genetic Algorithm","authors":"Liu Zhao, Ran Jun-Yi","doi":"10.1109/IAAI51705.2020.9332874","DOIUrl":"https://doi.org/10.1109/IAAI51705.2020.9332874","url":null,"abstract":"In the traditional incremental mining under the spatial co-location mode, parallel computing is often used for obstacle clustering, which causes the running time of the mining method to be greatly affected by the conditional parameters. Therefore, an incremental mining method of spatial co-location pattern based on genetic algorithm is proposed. Obstacles are divided by Euclidean distance and thresholds are set, genetic algorithms are used to cluster the obstacles, the fitness function of the obstacles is established, and the frequent clustering increment method is used to increase the clustering set. Based on the IULEP clustering set incremental mining algorithm, the calculation is performed to complete the incremental mining. In order to verify the effectiveness of the designed method, a simulation experiment is designed, and the experimental results prove that the running time of the designed incremental mining method is less affected by the conditional parameters, and the mining time is shorter, which is feasible.","PeriodicalId":232194,"journal":{"name":"2020 IEEE International Conference on Industrial Application of Artificial Intelligence (IAAI)","volume":"250 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124243662","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}
引用次数: 0
Quality Evaluation Model of Intelligent Building Project Cost Audit based on Integrated Learning 基于集成学习的智能建筑工程造价审计质量评价模型
2020 IEEE International Conference on Industrial Application of Artificial Intelligence (IAAI) Pub Date : 2020-12-25 DOI: 10.1109/IAAI51705.2020.9332831
Du Ling-ling
{"title":"Quality Evaluation Model of Intelligent Building Project Cost Audit based on Integrated Learning","authors":"Du Ling-ling","doi":"10.1109/IAAI51705.2020.9332831","DOIUrl":"https://doi.org/10.1109/IAAI51705.2020.9332831","url":null,"abstract":"A high-quality audit can ensure that the construction project cost is more reasonable and accurate, and make the use of funds for the entire construction project more orderly. Therefore, this paper constructs a quality evaluation model of intelligent building engineering cost audit based on integrated learning. The construction of evaluation system includes the design principle of evaluation index system, the primary selection of evaluation index, the selection of evaluation index and the construction of evaluation index system; the weight of index is calculated by AHP; the evaluation model is constructed by using fuzzy comprehensive evaluation method according to the weight value to realize the quality evaluation of project cost audit. The results show that: under the application of the model, the overall evaluation result of the project cost audit quality is “general”, which proves the effectiveness of the model.","PeriodicalId":232194,"journal":{"name":"2020 IEEE International Conference on Industrial Application of Artificial Intelligence (IAAI)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124088951","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}
引用次数: 0
Research on Adaptive Grouping Method of TSP Path Planning Based on Genetic Algorithm 基于遗传算法的TSP路径规划自适应分组方法研究
2020 IEEE International Conference on Industrial Application of Artificial Intelligence (IAAI) Pub Date : 2020-12-25 DOI: 10.1109/IAAI51705.2020.9332852
Xi Zhao, Ying Li
{"title":"Research on Adaptive Grouping Method of TSP Path Planning Based on Genetic Algorithm","authors":"Xi Zhao, Ying Li","doi":"10.1109/IAAI51705.2020.9332852","DOIUrl":"https://doi.org/10.1109/IAAI51705.2020.9332852","url":null,"abstract":"The traditional adaptive grouping of robot TSP path planning, the calculation of the adaptive function of the algorithm is insufficient, which makes the workload of the adaptive grouping larger, resulting in the data processing time of the grouping too long. Therefore, an adaptive grouping method for TSP path planning based on genetic algorithm is proposed. Firstly establish the TSP grid structure, confirm the path planning environment and working space, and use genetic algorithm to solve the grid structure path. After obtaining the data results, use the fitness function to determine the weight data in the genetic algorithm, and finally establish a mathematical model for self-adaptation Grouping. The feasibility of the above method is verified. The experimental results show that the adaptive grouping method of TSP path planning based on genetic algorithm has shorter processing time for data adaptive grouping, which proves that the method is more effective.","PeriodicalId":232194,"journal":{"name":"2020 IEEE International Conference on Industrial Application of Artificial Intelligence (IAAI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122105650","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}
引用次数: 0
Research on Integrated Data Acquisition Method of wind Power Generation based on Deep Learning 基于深度学习的风力发电综合数据采集方法研究
2020 IEEE International Conference on Industrial Application of Artificial Intelligence (IAAI) Pub Date : 2020-12-25 DOI: 10.1109/IAAI51705.2020.9332835
Ming-fei Qu, Dong-bao Ma
{"title":"Research on Integrated Data Acquisition Method of wind Power Generation based on Deep Learning","authors":"Ming-fei Qu, Dong-bao Ma","doi":"10.1109/IAAI51705.2020.9332835","DOIUrl":"https://doi.org/10.1109/IAAI51705.2020.9332835","url":null,"abstract":"The traditional integrated collection method of power generation data uses analog separation components, which are subject to many interference factors of charging and discharging, resulting in the problems of redundancy and data loss in the integrated collection of data. In order to solve the above problems, this paper studies the integrated data acquisition method of wind power generation based on deep learning. Use current sensor, voltage sensor, wind speed and direction meter and other sensing devices to collect data in the process of wind and solar power generation, and use SQL Server 2008 to establish a database to store the collected wind and solar power data. On this basis, deep learning is used to fill the missing data in the database, and then DTS tool is used to complete the data integration after filling. Through comparison experiments with traditional methods, it can be seen that the integrated acquisition method based on deep learning can effectively reduce data redundancy and lack, and has better reliability.","PeriodicalId":232194,"journal":{"name":"2020 IEEE International Conference on Industrial Application of Artificial Intelligence (IAAI)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121848110","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}
引用次数: 0
Research on Image Texture Feature Recognition Method based on Rotation Invariant Histogram 基于旋转不变直方图的图像纹理特征识别方法研究
2020 IEEE International Conference on Industrial Application of Artificial Intelligence (IAAI) Pub Date : 2020-12-25 DOI: 10.1109/IAAI51705.2020.9332841
Chunrong Zhou, Zhenghong Jiang
{"title":"Research on Image Texture Feature Recognition Method based on Rotation Invariant Histogram","authors":"Chunrong Zhou, Zhenghong Jiang","doi":"10.1109/IAAI51705.2020.9332841","DOIUrl":"https://doi.org/10.1109/IAAI51705.2020.9332841","url":null,"abstract":"The traditional image texture feature recognition method is deeply affected by the complexity of the image background and the amount of noise, resulting in a low recognition rate. To solve the above problems, an image texture feature recognition method based on rotation invariant histogram is proposed. After denoising the image to be recognized, the image texture feature is extracted and the image feature texture set is established. After the rotation invariant region of the image histogram is selected, the classifier is used to recognize the image texture features. Design a comparative experiment with the current recognition method. The experimental results show that the research method can improve the recognition accuracy by about 12.9%, and the recognition time is short and reliable.","PeriodicalId":232194,"journal":{"name":"2020 IEEE International Conference on Industrial Application of Artificial Intelligence (IAAI)","volume":"51 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126941702","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}
引用次数: 0
Design of Open Source Personalized Information Recommendation System for Web Pages in Big Data Environment 大数据环境下网页的开源个性化信息推荐系统设计
2020 IEEE International Conference on Industrial Application of Artificial Intelligence (IAAI) Pub Date : 2020-12-25 DOI: 10.1109/iaai51705.2020.9332829
Liao Xiao-qing, Yang Xiao-mei, Chen Gui-rong
{"title":"Design of Open Source Personalized Information Recommendation System for Web Pages in Big Data Environment","authors":"Liao Xiao-qing, Yang Xiao-mei, Chen Gui-rong","doi":"10.1109/iaai51705.2020.9332829","DOIUrl":"https://doi.org/10.1109/iaai51705.2020.9332829","url":null,"abstract":"The current web information recommendation system can not meet the personalized needs of users. To solve this problem, an open source personalized information recommendation system for web pages is proposed in the large data environment. The hardware of the system is PC-side web pages, and its development environment consists of the underlying database, software environment, development technology and user interface. Based on the hardware design of the system, the software is designed and the corresponding functions are realized, so that the software can run steadily on the platform. By collecting user data, user feature collaborative filtering, personalized correlation matching, recommendation ranking weighting and recommendation evaluation criteria, the software is designed and implemented by code. The experimental results show that the system can recommend suitable personalized information for users, and the system has advantages and reliability.","PeriodicalId":232194,"journal":{"name":"2020 IEEE International Conference on Industrial Application of Artificial Intelligence (IAAI)","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113970338","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}
引用次数: 1
Surface texture defect recognition method of machined parts based on machine vision 基于机器视觉的机加工零件表面纹理缺陷识别方法
2020 IEEE International Conference on Industrial Application of Artificial Intelligence (IAAI) Pub Date : 2020-12-25 DOI: 10.1109/IAAI51705.2020.9332868
Xiong Ru-yi
{"title":"Surface texture defect recognition method of machined parts based on machine vision","authors":"Xiong Ru-yi","doi":"10.1109/IAAI51705.2020.9332868","DOIUrl":"https://doi.org/10.1109/IAAI51705.2020.9332868","url":null,"abstract":"In order to improve the recognition rate of surface texture defects of machined parts, a machine vision-based method for identifying surface texture defects of machined parts is proposed. By analyzing the characteristics of the detection content of small parts, the image is filtered, the improved edge detection algorithm is used to accurately extract the edge position of the surface, the image is divided into different regions by the edge, the candidate defects are screened, and the BP network is finally used Algorithms classify and mark defects. The experimental results show that the method can stably recognize and detect the surface defects of small parts, the defect recognition rate is up to 97.05%, and the detection speed is also fast, reaching the expected detection target.","PeriodicalId":232194,"journal":{"name":"2020 IEEE International Conference on Industrial Application of Artificial Intelligence (IAAI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126200298","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}
引用次数: 0
Design of Eco-Tourism Service Quality Evaluation Model based on Artificial Neural Network 基于人工神经网络的生态旅游服务质量评价模型设计
2020 IEEE International Conference on Industrial Application of Artificial Intelligence (IAAI) Pub Date : 2020-12-25 DOI: 10.1109/iaai51705.2020.9332881
Wei Yi, Huo Hui-min
{"title":"Design of Eco-Tourism Service Quality Evaluation Model based on Artificial Neural Network","authors":"Wei Yi, Huo Hui-min","doi":"10.1109/iaai51705.2020.9332881","DOIUrl":"https://doi.org/10.1109/iaai51705.2020.9332881","url":null,"abstract":"In order to better improve the quality of eco-tourism services, this paper designs an eco-tourism service quality evaluation model based on artificial neural networks. This paper discusses the environment, housing, diet, characteristics, tour guide and other aspects of the tourism system, lists a number of index systems to analyze the tourism evaluation model, and then uses the artificial neural network to design the evaluation model of ecotourism service quality by combining qualitative and quantitative methods, and constructs the evaluation scale of ecotourism service quality combined with the actual characteristics In order to achieve the research goal of the evaluation model of ecotourism service quality, the importance of tourists to the five dimensions of service is sorted.","PeriodicalId":232194,"journal":{"name":"2020 IEEE International Conference on Industrial Application of Artificial Intelligence (IAAI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125357088","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}
引用次数: 1
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