International Symposium on Computer Science and Intelligent Control最新文献

{"title":"Human Body Prediction of Size and Shape A Hormonal Framework","authors":"J. V. Vugt","doi":"10.1145/3386164.3387262","DOIUrl":"https://doi.org/10.1145/3386164.3387262","url":null,"abstract":"To achieve high prediction accuracy with minimal inputs from online retail respondents, a method was developed and tested to predict the size and shape of the human body in 3D using a hormonal framework. The prediction method is based on hormonal literature, geometric morphometrics, image analysis, and kernel partial least squares regression. The inputs required are answers to three closed-ended questions and a passport photo. Prediction accuracy was tested with the 3D body scan dataset of the Civilian American and European Surface Anthropometry Resource project. Results from the test dataset showed that approximately 82% of the error expectations of landmarks followed a log-normal distribution with an expectation of 8.816 mm and standard deviation of 1.180 mm. The remaining 18% of the error expectations of landmarks followed a log-normal distribution with an expectation of 18.454 mm and standard deviation of 8.844 mm, which may herald future research. Benchmarked with two other methods, the proposed method features much less input. In addition to high accuracy, the method in this paper allows for visualisation of results as real-size meshes in millimeters.","PeriodicalId":222760,"journal":{"name":"International Symposium on Computer Science and Intelligent Control","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114918084","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}

{"title":"FAQ question Answering method based on semantic similarity matching","authors":"Tenghao Ji","doi":"10.1109/ISCSIC57216.2022.00030","DOIUrl":"https://doi.org/10.1109/ISCSIC57216.2022.00030","url":null,"abstract":"Frequently asked questions (FAQ) is a typical limited domain-oriented retrieval question answering system, and semantic similarity matching is the key technology of FAQ question answering. Aiming at the FAQ problems of insufficient deep semantic representation and extraction and synonyms and polysemy, this paper proposes a cascade matching model combining coarse-grained recall and fine-grained sorting. Firstly, an improved Okapi BM25 algorithm based on inverse word frequency is proposed. This paper proposes a new solution to FAQ answer based on the traditional text matching and retrieval pipeline, and gives a new semantic similarity matching model. Additionally, we further analyze the shortcomings of semantic matching and the advantages of data-driven semantic similarity matching model. Finally, the proposed hybrid matching model is evaluated. Experimental results show that the proposed method not only ensures the retrieval speed of question and answer, but improves its performance","PeriodicalId":222760,"journal":{"name":"International Symposium on Computer Science and Intelligent Control","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114953496","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}

{"title":"The Application of Data Mining Technology Based on Factor Analysis in the Evaluation of Students' Learning Quality","authors":"Fangming Guo, Hua Song","doi":"10.1109/ISCSIC54682.2021.00048","DOIUrl":"https://doi.org/10.1109/ISCSIC54682.2021.00048","url":null,"abstract":"This paper analyzes the results of three graduates of software engineering majors, and establishes a comprehensive performance evaluation model, in order to more comprehensively and accurately reflect students' learning quality. Empirical research based on SPSS software, the results show that: using factor analysis to evaluate student performance can find out which aspects of the professional training program actually cultivate students' abilities, and the proportion of these abilities that affect the student's total score is to improve the total score Which courses students should focus on improving their grades. The evaluation of student performance based on factor analysis has important practical significance for discovering and improving students' learning quality.","PeriodicalId":222760,"journal":{"name":"International Symposium on Computer Science and Intelligent Control","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126124154","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}

{"title":"Real-time Estimation of AOS based on Constant Wind Field Assumption","authors":"Zhubin Fan, Ru Yuan","doi":"10.1109/ISCSIC57216.2022.00044","DOIUrl":"https://doi.org/10.1109/ISCSIC57216.2022.00044","url":null,"abstract":"Generally, the angle of sideslip (AOS) of the aircraft is measured by the AOS sensor during the flight test. A real-time AOS estimation method which uses the parameters from the GPS, inertial system, the angle of attack vane and air data system is presented in this paper. The method mainly uses the coordinate transformation to get the true airspeed vector in the body axis system with a constant wind field assumption, and then to calculate the AOS according to the true airspeed from the air data system. In order to get the wind field information, a zero AOS segment should be added to the flight procedure. The method can be applied in on-board or telemetry flight test monitoring system to replace the AOS sensor, and provide real-time AOS for the flight crew and ground analyst.","PeriodicalId":222760,"journal":{"name":"International Symposium on Computer Science and Intelligent Control","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116320082","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}

{"title":"Research on Navigation Attitude of Air Cushion Vehicle Based on BP Neural Network","authors":"Kejie Chen, Liuyan Wang, Shengjie Xu","doi":"10.1109/ISCSIC54682.2021.00083","DOIUrl":"https://doi.org/10.1109/ISCSIC54682.2021.00083","url":null,"abstract":"Air cushion vehicle (ACV) is a kind of highperformance ship type, which uses skirt lifting system to form air cushion between the bottom and the water surface, the air cushion pressure supports the ship to sail on the water surface, and is propelled by air propeller. ACV should keep a proper attitude when sailing, especially in the stage of high-speed operation. This not only affects the overall performance of its rapidity, manoeuvrability and stability, but also is important for its navigation safety. Therefore, it is necessary to study the navigation attitude of ACV. Compared with the displacement ships, the ACV is mainly supported by air cushion pressure, and its navigation attitude is more easily disturbed by various factors. The operation environment of water-air coupling is special, which forms a complex system with many factors and nonlinearity. In view of the limited similarity condition of model test and the low accuracy of theoretical calculation or numerical simulation, it is very difficult to accurately predict the navigation attitude of an ACV in various situations. In this paper, based on the navigation data of an ACV, the factors related to the navigation attitude are selected as the input vectors, and the navigation trimming angle and heeling angle are taken as the output vectors. The number of hidden layer nodes is reasonably selected, and the BP neural network model which can be used to predict the navigation attitude of the ACV is trained and established. The prediction accuracy of the neural network is further verified by the test data of start acceleration, high-speed straight navigation, braking, rotation, sideslip and other special states. The neural network model is used to analyse the change of ACV's navigation attitude caused by the change of single factor, and the influence rules of the factors such as speed, drift angle, wind direction, rudder angle and pitch difference on the navigation attitude of ACV are summarized. Compared with the displacement ships, the ACV is mainly supported by air cushion pressure, and its navigation attitude is more easily disturbed by various factors. The operation environment of water-air coupling is special, which forms a complex system with many factors and nonlinearity. In view of the limited similarity condition of model test and the low accuracy of theoretical calculation or numerical simulation, it is very difficult to accurately predict the navigation attitude of an ACV in various situations. In this paper, based on the navigation data of an ACV, the factors related to the navigation attitude are selected as the input vectors, and the navigation trimming angle and heeling angle are taken as the output vectors. The number of hidden layer nodes is reasonably selected, and the BP neural network model which can be used to predict the navigation attitude of the ACV is trained and established. The prediction accuracy of the neural network is further verified by the test data of start acce","PeriodicalId":222760,"journal":{"name":"International Symposium on Computer Science and Intelligent Control","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115833580","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}