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

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Design of Long Sentence Split Translation Model based on Support Vector Machine 基于支持向量机的长句分译模型设计
2020 IEEE International Conference on Industrial Application of Artificial Intelligence (IAAI) Pub Date : 2020-12-25 DOI: 10.1109/iaai51705.2020.9332815
Yin Cui-jiao, Gong Hai-Xiao
{"title":"Design of Long Sentence Split Translation Model based on Support Vector Machine","authors":"Yin Cui-jiao, Gong Hai-Xiao","doi":"10.1109/iaai51705.2020.9332815","DOIUrl":"https://doi.org/10.1109/iaai51705.2020.9332815","url":null,"abstract":"Aiming at the problem of high translation misalignment rate due to the ambiguity of semantics in statistical translation methods, this paper proposes a long-sentence translation model based on support vector machines. Firstly, the translation words to be disassembled in long sentences are processed, and the semantic registration process based on support vector machine is set up to calculate the utilization rate of different semantic elements in the language evaluation set, which is used as the information label to construct the language evaluation set model. On this basis, the support vector machine analysis factor is calculated, and the support vector machine analysis factor is used as the constraint parameter to extract the feature information of the long sentence decomposing type to construct the translation model. The experimental results show that the model has the lowest inaccuracy rate of 0.01, which proves that the model has accurate translation effect.","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":"133737786","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 Monitoring System for Uneven Settlement of Soft Soil Foundation based on Web GIS 基于Web GIS的软土地基不均匀沉降监测系统设计
2020 IEEE International Conference on Industrial Application of Artificial Intelligence (IAAI) Pub Date : 2020-12-25 DOI: 10.1109/iaai51705.2020.9332814
Honghong Wang
{"title":"Design of Monitoring System for Uneven Settlement of Soft Soil Foundation based on Web GIS","authors":"Honghong Wang","doi":"10.1109/iaai51705.2020.9332814","DOIUrl":"https://doi.org/10.1109/iaai51705.2020.9332814","url":null,"abstract":"Due to the limitation of the monitoring range of the existing soft soil foundation uneven settlement monitoring system, there is a defect of poor real-time monitoring and early warning, so a Web-GIS-based soft soil foundation uneven settlement monitoring system is designed. System hardware unit includes data acquisition unit, serial communication unit, A/D conversion unit and LCD liquid crystal display unit; software module includes data acquisition module, coordinator middleware module, soft soil foundation uneven immersion prediction module, dynamic early warning module and database Module. Through the design of hardware unit and software module, the monitoring system of uneven settlement of soft soil foundation based on Web GIS is realized. The experimental results show that: compared with the average value of the existing system, the designed system has better real-time monitoring performance and early warning real-time performance, which fully indicates that the designed system has better monitoring performance and is suitable for vigorously promoting and using.","PeriodicalId":232194,"journal":{"name":"2020 IEEE International Conference on Industrial Application of Artificial Intelligence (IAAI)","volume":"164 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":"132896520","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
Research on Lidar Data Error Correction Method Based on Bayesian Network 基于贝叶斯网络的激光雷达数据误差校正方法研究
2020 IEEE International Conference on Industrial Application of Artificial Intelligence (IAAI) Pub Date : 2020-12-25 DOI: 10.1109/IAAI51705.2020.9332846
Chen Fei
{"title":"Research on Lidar Data Error Correction Method Based on Bayesian Network","authors":"Chen Fei","doi":"10.1109/IAAI51705.2020.9332846","DOIUrl":"https://doi.org/10.1109/IAAI51705.2020.9332846","url":null,"abstract":"Data quality analysis is the first and key step of remote sensing mechanism/application research (especially quantitative remote sensing), which directly affects the accuracy of remote sensing inversion and the effect of remote sensing applications. Lidar (light detection and ranging, referred to as lidar) is a new active remote sensing technology. The research on hardware system development and data post-processing algorithm needs to be further strengthened, especially for the quality analysis of lidar data. After initializing the Bayesian network and checking the error, a correction mathematical model is established. Experiments have proved that after correction, the angle error of the radar is significantly improved, which verifies the feasibility and reliability of the precision orbit star calibration method.","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":"130021598","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
Grey Prediction Model of Bearing Capacity of Bridge Construction Support based on Genetic Algorithm 基于遗传算法的桥梁构筑物支座承载力灰色预测模型
2020 IEEE International Conference on Industrial Application of Artificial Intelligence (IAAI) Pub Date : 2020-12-25 DOI: 10.1109/IAAI51705.2020.9332838
Wang Hong-hong
{"title":"Grey Prediction Model of Bearing Capacity of Bridge Construction Support based on Genetic Algorithm","authors":"Wang Hong-hong","doi":"10.1109/IAAI51705.2020.9332838","DOIUrl":"https://doi.org/10.1109/IAAI51705.2020.9332838","url":null,"abstract":"In order to obtain more diversified research results, the genetic algorithm is applied to the research of the gray prediction model of the bearing capacity of the bridge construction support, and a gray prediction model of the bearing capacity of the bridge construction support based on the genetic algorithm is proposed. The finite element software MIDAS/Civil was used to establish a finite element model according to the structure of the bridge construction support structure, and the node rotation stiffness and boundary conditions were simulated. Based on genetic algorithm for the prediction code of the bearing capacity of the bridge construction support, the grey prediction model of the bearing capacity of the bridge construction support is constructed through the bearing capacity prediction software. Through the verification of the support case and the project case, it is proved that the gray prediction model of the bearing capacity of the bridge construction support based on the genetic algorithm can accurately predict the bearing capacity of the bridge construction support.","PeriodicalId":232194,"journal":{"name":"2020 IEEE International Conference on Industrial Application of Artificial Intelligence (IAAI)","volume":"22 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":"123951003","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
Automatic Detection of English Pronunciation Errors based on Statistical Pattern Recognition 基于统计模式识别的英语发音错误自动检测
2020 IEEE International Conference on Industrial Application of Artificial Intelligence (IAAI) Pub Date : 2020-12-25 DOI: 10.1109/IAAI51705.2020.9332905
Cai Xiao-Ming
{"title":"Automatic Detection of English Pronunciation Errors based on Statistical Pattern Recognition","authors":"Cai Xiao-Ming","doi":"10.1109/IAAI51705.2020.9332905","DOIUrl":"https://doi.org/10.1109/IAAI51705.2020.9332905","url":null,"abstract":"In the automatic detection of English pronunciation errors, it is easy to be affected by multiple stress problems, resulting in large errors. Therefore, an automatic detection method of English pronunciation errors based on statistical pattern recognition is proposed. In order to solve the problem of noise in the original speech information, the speech signal is preprocessed by adding windows and framing. Statistical pattern recognition technology is used to recognize the acoustic features of speech signals, and the acoustic model is established. At the same time, the phoneme mispronunciation network is constructed based on the wrong pronunciation features. The acoustic model is used to detect English pronunciation errors automatically under the network of phoneme errors. The experimental results show that the Sentence Rhythm detected by the proposed method is basically consistent with the actual pronunciation, and the actual detection level of the method is improved.","PeriodicalId":232194,"journal":{"name":"2020 IEEE International Conference on Industrial Application of Artificial Intelligence (IAAI)","volume":"63 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":"124214567","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
Visual Mining Method of College Students' Daily Emotion Index Based on Physiological Big Data 基于生理大数据的大学生日常情绪指数可视化挖掘方法
2020 IEEE International Conference on Industrial Application of Artificial Intelligence (IAAI) Pub Date : 2020-12-25 DOI: 10.1109/iaai51705.2020.9332887
Gao Hui, Peng Jie
{"title":"Visual Mining Method of College Students' Daily Emotion Index Based on Physiological Big Data","authors":"Gao Hui, Peng Jie","doi":"10.1109/iaai51705.2020.9332887","DOIUrl":"https://doi.org/10.1109/iaai51705.2020.9332887","url":null,"abstract":"College Students' daily emotions are affected by many factors, and there will be many differences. Therefore, traditional methods rely on data mining algorithm to mine emotion index. However, due to the lack of unified parameter adjustment mechanism in traditional mining, the speed of obtaining information is slow, which reduces the work efficiency. Therefore, this paper studies the visualization mining of College Students' daily emotion index based on physiological big data Excavation method. By collecting the big data of College Students' physiology, this method obtains the change rules of College Students' daily emotions, and uses clustering algorithm to cross analyze the intrinsic nature of emotions, and constructs a comprehensive visual mining algorithm to visually mine the daily emotion index of college students. Experimental results: the mining speed of this research method is 271s and 346.5s faster than the two traditional methods. It can be seen that the method of this study improves the work efficiency under the premise of reliable mining.","PeriodicalId":232194,"journal":{"name":"2020 IEEE International Conference on Industrial Application of Artificial Intelligence (IAAI)","volume":"49 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":"129943055","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
A Two-Person Lift Image Detail Extraction Method based on Global Texture Feature Optimization 基于全局纹理特征优化的二人升降机图像细节提取方法
2020 IEEE International Conference on Industrial Application of Artificial Intelligence (IAAI) Pub Date : 2020-12-25 DOI: 10.1109/iaai51705.2020.9332878
Z. Haiyan
{"title":"A Two-Person Lift Image Detail Extraction Method based on Global Texture Feature Optimization","authors":"Z. Haiyan","doi":"10.1109/iaai51705.2020.9332878","DOIUrl":"https://doi.org/10.1109/iaai51705.2020.9332878","url":null,"abstract":"When traditional information extraction methods are used to extract image feature points, image denoising degree is not sufficient, and there are still small scale noise points in detail images, which leads to the spatial dimension error of information extraction. To solve this problem, a new method based on global optimization texture feature is proposed to extract the details of double lift image. Using laser scanning measurement technology, the data of double lift image is collected, the point cloud data of image is preprocessed, and the detail feature points are extracted. The texture sequence of feature points is obtained by using the global optimization texture feature technology, and the detail color and texture are mapped into the grid to obtain the detail information of the lifting image. The experimental results show that the spatial dimension error of this method is smaller than that of the two traditional methods, which makes the extracted information meet the requirements of high precision and increase the reliability of information extraction.","PeriodicalId":232194,"journal":{"name":"2020 IEEE International Conference on Industrial Application of Artificial Intelligence (IAAI)","volume":"24 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":"130039436","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
Real Time Prediction of Rebound Trajectory of Rotating Table Tennis based on Binocular Vision 基于双目视觉的旋转乒乓球回弹轨迹实时预测
2020 IEEE International Conference on Industrial Application of Artificial Intelligence (IAAI) Pub Date : 2020-12-25 DOI: 10.1109/iaai51705.2020.9332830
Tang Wei
{"title":"Real Time Prediction of Rebound Trajectory of Rotating Table Tennis based on Binocular Vision","authors":"Tang Wei","doi":"10.1109/iaai51705.2020.9332830","DOIUrl":"https://doi.org/10.1109/iaai51705.2020.9332830","url":null,"abstract":"With the deepening of the research on table tennis, intelligent table tennis trajectory prediction and positioning has become a hot issue in the research. The traditional rotating table tennis trajectory prediction method has poor ability to divide the background of table tennis, resulting in poor accuracy of rebound trajectory prediction. Therefore, a real-time prediction method based on binocular vision is designed. The binocular vision model is constructed to obtain the three-dimensional model of table tennis. According to the color space theory, the motion model and background subtraction method are combined to obtain the moving area of the rotating table tennis target. The rebound trajectory of the rotating table tennis ball is predicted in the target active area. So far, the design of real-time prediction of the rebound trajectory of rotating table tennis ball based on binocular vision is completed. The experimental results show that the prediction accuracy of the proposed method is high, which provides a theoretical reference for future research.","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":"130453573","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 Fatigue Crack Detection Method of Asphalt Concrete Pavement Based on Machine Learning 基于机器学习的沥青混凝土路面疲劳裂纹检测方法研究
2020 IEEE International Conference on Industrial Application of Artificial Intelligence (IAAI) Pub Date : 2020-12-25 DOI: 10.1109/IAAI51705.2020.9332897
Xulan Gui, Weiwei Zhan, Xinghang Long
{"title":"Research on Fatigue Crack Detection Method of Asphalt Concrete Pavement Based on Machine Learning","authors":"Xulan Gui, Weiwei Zhan, Xinghang Long","doi":"10.1109/IAAI51705.2020.9332897","DOIUrl":"https://doi.org/10.1109/IAAI51705.2020.9332897","url":null,"abstract":"Based on the original research results, this paper proposes a method for fatigue crack detection of asphalt concrete pavement based on machine learning. Obtain the asphalt concrete pavement fatigue crack data set composed of public data sets and manually collected picture data, and divide the crack pictures into three types: horizontal crack pictures, longitudinal crack pictures and reticular crack pictures. The crack image is pre-processed including grayscale processing, histogram equalization operation processing, noise preprocessing, and Gamma correction processing. After the preprocessing operation, the subsequent target segmentation, extraction and positioning of the crack image are performed. Since there are fractures in the extracted fracture image, it is necessary to splice multiple fracture connected region targets into a complete fracture connected region. On this basis, the fatigue crack detection network model of asphalt concrete pavement is built based on machine learning. The collected data are input into the model to train the model, and the fatigue crack detection of asphalt concrete pavement is realized. The experimental results show that the detection accuracy of this method is high for all kinds of fracture types, and the detection effect is ideal when the model training iterations to 10000 times.","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":"127608003","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
Service Life Prediction Model of Reinforced Concrete Structure based on Evolutionary Neural Network 基于进化神经网络的钢筋混凝土结构寿命预测模型
2020 IEEE International Conference on Industrial Application of Artificial Intelligence (IAAI) Pub Date : 2020-12-25 DOI: 10.1109/IAAI51705.2020.9332873
Keying Han
{"title":"Service Life Prediction Model of Reinforced Concrete Structure based on Evolutionary Neural Network","authors":"Keying Han","doi":"10.1109/IAAI51705.2020.9332873","DOIUrl":"https://doi.org/10.1109/IAAI51705.2020.9332873","url":null,"abstract":"When the traditional model predicts the service life of a concrete structure, the number of iterations of its stress and strain parameter points is less, which leads to errors in the calculation value of the concrete structure's bearing capacity. To this end, a prediction model for the service life of reinforced concrete structures based on evolutionary neural networks is constructed. Use different material properties of reinforced concrete structure to numerically simulate multiple mechanical properties, use evolutionary neural network, evolve mechanical parameter points, use concrete buckling strength and buckling bearing capacity as the main parameters of the model, obtain the parameter limit load value, and use the full probability Distribution method, the decay time of the concrete structure is obtained, which is the remaining service life. Comparing this model with two traditional models, the results show that this model reduces the calculation error of the structural bearing capacity, the model input parameters are more accurate, and the reliability of the life prediction value of the reinforced concrete structure is improved.","PeriodicalId":232194,"journal":{"name":"2020 IEEE International Conference on Industrial Application of Artificial Intelligence (IAAI)","volume":"290 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":"114185821","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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