Pub Date : 1900-01-01DOI: 10.23977/jaip.2023.060102
{"title":"Research and Application of Health Code Recognition Based on Paddle OCR under the Background of Epidemic Prevention and Control","authors":"","doi":"10.23977/jaip.2023.060102","DOIUrl":"https://doi.org/10.23977/jaip.2023.060102","url":null,"abstract":"","PeriodicalId":293823,"journal":{"name":"Journal of Artificial Intelligence Practice","volume":"66 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":"115984555","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 : 1900-01-01DOI: 10.23977/jaip.2023.060104
{"title":"Research on semantic segmentation of unmanned aerial vehicle visual image based on deep learning—take the outdoor environment of Anhui University of Finance & Economics as an example","authors":"","doi":"10.23977/jaip.2023.060104","DOIUrl":"https://doi.org/10.23977/jaip.2023.060104","url":null,"abstract":"","PeriodicalId":293823,"journal":{"name":"Journal of Artificial Intelligence Practice","volume":"10 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":"124784853","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 : 1900-01-01DOI: 10.23977/jaip.2022.050305
Boyuan Wang, Hai Lin, Shenglin Xia
: Population Spatio-temporal big data mining and analysis techniques have been applied to risk assessment of disease transmission, which can describe disease transmission pathways and high-risk areas in fine detail. Based on spatial statistical analysis and artificial intelligence technology, this study seeks to break through the previous risk warning model of a single data source from medical institutions in the era of small data and designs an AI health risk assessment system for the dietary hygiene of key populations. The system is designed to collect multi-source Spatio-temporal big data consisting of urban population positioning, a sanitary inspection of restaurant premises, foodborne disease cases in medical institutions, and environmental monitoring. Spatial location attributes are assigned to the monitoring data, and food and multi-source data are fused across borders. Through the Internet of Things (IoT) technology, the system is designed with an IoT system consisting of sensors for automatic monitoring and wearable devices for real-time warning. Based on the spatial and artificial intelligence models, the system designs personalized and real-time early warning information for critical populations to prevent dietary health risks and provide scientific basis and support for public health departments to prevent foodborne diseases.
{"title":"Design of an AI Health Risk Assessment System for Dietary Hygiene of Key Groups Based on IoT Wearable Devices","authors":"Boyuan Wang, Hai Lin, Shenglin Xia","doi":"10.23977/jaip.2022.050305","DOIUrl":"https://doi.org/10.23977/jaip.2022.050305","url":null,"abstract":": Population Spatio-temporal big data mining and analysis techniques have been applied to risk assessment of disease transmission, which can describe disease transmission pathways and high-risk areas in fine detail. Based on spatial statistical analysis and artificial intelligence technology, this study seeks to break through the previous risk warning model of a single data source from medical institutions in the era of small data and designs an AI health risk assessment system for the dietary hygiene of key populations. The system is designed to collect multi-source Spatio-temporal big data consisting of urban population positioning, a sanitary inspection of restaurant premises, foodborne disease cases in medical institutions, and environmental monitoring. Spatial location attributes are assigned to the monitoring data, and food and multi-source data are fused across borders. Through the Internet of Things (IoT) technology, the system is designed with an IoT system consisting of sensors for automatic monitoring and wearable devices for real-time warning. Based on the spatial and artificial intelligence models, the system designs personalized and real-time early warning information for critical populations to prevent dietary health risks and provide scientific basis and support for public health departments to prevent foodborne diseases.","PeriodicalId":293823,"journal":{"name":"Journal of Artificial Intelligence Practice","volume":"59 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":"116319958","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 : 1900-01-01DOI: 10.23977/jaip.2023.060103
{"title":"UAV planar passive pure orientation positioning under different conditions","authors":"","doi":"10.23977/jaip.2023.060103","DOIUrl":"https://doi.org/10.23977/jaip.2023.060103","url":null,"abstract":"","PeriodicalId":293823,"journal":{"name":"Journal of Artificial Intelligence Practice","volume":"48 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":"123611487","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}
: As technology rapidly advances, semiconductor devices play a crucial role in various fields. However, these devices experience aging over time, leading to performance degradation, failure, or system crashes. Real-time aging detection of semiconductor devices is essential. This paper presents a real-time aging detection technique for semiconductor devices, combining deep learning and evolutionary algorithms, effectively assessing and predicting device aging states using Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN). These features are then input into evolutionary algorithm frameworks, such as Genetic Algorithms (GA) and Genetic Algorithms (PSO), to identify and predict aging trends. The adaptation of evolutionary algorithms ensures good generalization for various semiconductor devices. Through extensive experimental data analysis, the proposed technique demonstrates excellent accuracy and real-time performance compared to traditional aging detection methods. In addition, it also monitors their operation in real-time, providing valuable support for maintenance and management personnel. The findings contribute to improving semiconductor device reliability and stability, providing a robust foundation for intelligent and automated maintenance.
{"title":"Applications and challenges of hybrid artificial intelligence in chip age testing: a comprehensive review","authors":"Cong Xu, Wensheng Chen, Mingkuan Lin, Jianli Lu, Yunghsiao Chung, Jiahui Zou, Ciliang Yang","doi":"10.23977/jaip.2023.060309","DOIUrl":"https://doi.org/10.23977/jaip.2023.060309","url":null,"abstract":": As technology rapidly advances, semiconductor devices play a crucial role in various fields. However, these devices experience aging over time, leading to performance degradation, failure, or system crashes. Real-time aging detection of semiconductor devices is essential. This paper presents a real-time aging detection technique for semiconductor devices, combining deep learning and evolutionary algorithms, effectively assessing and predicting device aging states using Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN). These features are then input into evolutionary algorithm frameworks, such as Genetic Algorithms (GA) and Genetic Algorithms (PSO), to identify and predict aging trends. The adaptation of evolutionary algorithms ensures good generalization for various semiconductor devices. Through extensive experimental data analysis, the proposed technique demonstrates excellent accuracy and real-time performance compared to traditional aging detection methods. In addition, it also monitors their operation in real-time, providing valuable support for maintenance and management personnel. The findings contribute to improving semiconductor device reliability and stability, providing a robust foundation for intelligent and automated maintenance.","PeriodicalId":293823,"journal":{"name":"Journal of Artificial Intelligence Practice","volume":"164 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":"127421699","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 : 1900-01-01DOI: 10.23977/jaip.2023.060203
{"title":"Research on the application of artificial intelligence in computer recognition technology","authors":"","doi":"10.23977/jaip.2023.060203","DOIUrl":"https://doi.org/10.23977/jaip.2023.060203","url":null,"abstract":"","PeriodicalId":293823,"journal":{"name":"Journal of Artificial Intelligence Practice","volume":"57 8","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132511125","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 : 1900-01-01DOI: 10.23977/jaip.2023.060506
Wenfang Zhang, Xiaodong Wang
: English is the most widely used language in the world, and the pronunciation of its spoken language is equally important. The traditional methods are not high in complexity, accuracy and fluency (CAF) for spoken English recognition. Therefore, it is very important to use AI and corpus to optimize and evaluate spoken English CAF. This paper aims to study the optimization and evaluation of spoken English CAF using AI and corpus, and proposes to use the Hidden Markov (HMM) model and convolutional neural network (CNN) model in the field of AI to optimize and evaluate spoken English CAF. By selecting a variety of English voices from the BNC corpus for model training and testing, and selecting the complexity, accuracy, fluency and harmonic average of the CNN model recognition as evaluation indicators, the HMM model's recognition spectrogram is added up and analyzed. In the experimental test, it was found that when the number of frames is 210, the indicators of the CNN model have been greatly improved, so the number of frames selected for the test in this paper is 210. The results show that the A value obtained by the HMM model test is about 85%, the CNN model is 67%, and the traditional SVM model is only 35%. The HMM model is tested with a C value of about 60%, the CNN model is 65%, and the traditional model is only 45%. The F-value obtained from the test of the HMM model is about 83%, the CNN model is 67%, and the traditional model is 46%. In contrast, the HMM model has higher recognition accuracy for spoken English, and the recognition results are more fluent. However, the CNN model can recognize spoken English with higher complexity, and both the CNN model and the HMM model can improve the CAF optimization effect of spoken English.
{"title":"Optimization and Evaluation of Spoken English CAF Based on Artificial Intelligence and Corpus","authors":"Wenfang Zhang, Xiaodong Wang","doi":"10.23977/jaip.2023.060506","DOIUrl":"https://doi.org/10.23977/jaip.2023.060506","url":null,"abstract":": English is the most widely used language in the world, and the pronunciation of its spoken language is equally important. The traditional methods are not high in complexity, accuracy and fluency (CAF) for spoken English recognition. Therefore, it is very important to use AI and corpus to optimize and evaluate spoken English CAF. This paper aims to study the optimization and evaluation of spoken English CAF using AI and corpus, and proposes to use the Hidden Markov (HMM) model and convolutional neural network (CNN) model in the field of AI to optimize and evaluate spoken English CAF. By selecting a variety of English voices from the BNC corpus for model training and testing, and selecting the complexity, accuracy, fluency and harmonic average of the CNN model recognition as evaluation indicators, the HMM model's recognition spectrogram is added up and analyzed. In the experimental test, it was found that when the number of frames is 210, the indicators of the CNN model have been greatly improved, so the number of frames selected for the test in this paper is 210. The results show that the A value obtained by the HMM model test is about 85%, the CNN model is 67%, and the traditional SVM model is only 35%. The HMM model is tested with a C value of about 60%, the CNN model is 65%, and the traditional model is only 45%. The F-value obtained from the test of the HMM model is about 83%, the CNN model is 67%, and the traditional model is 46%. In contrast, the HMM model has higher recognition accuracy for spoken English, and the recognition results are more fluent. However, the CNN model can recognize spoken English with higher complexity, and both the CNN model and the HMM model can improve the CAF optimization effect of spoken English.","PeriodicalId":293823,"journal":{"name":"Journal of Artificial Intelligence Practice","volume":"1 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":"131345850","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 : 1900-01-01DOI: 10.23977/jaip.2023.060403
Yuan Chen
: Under the background of the continuous improvement of human science and technology, the artificial intelligence industry has developed rapidly. At present, computer artificial intelligence recognition technology has been widely used in social production and life, from the perspective of social production, this technology can not only improve the automatic control level of enterprises, but also greatly improve the quality and efficiency of production, so as to create greater social and economic benefits; From the perspective of social life, it helps to improve people's convenience in life. Starting from the connotation and categories of computer artificial intelligence recognition technology, this paper analyzes the specific application of this technology, and discusses its future development direction, hoping to provide reference for relevant colleagues.
{"title":"Research based on computer artificial intelligence recognition technology and its application","authors":"Yuan Chen","doi":"10.23977/jaip.2023.060403","DOIUrl":"https://doi.org/10.23977/jaip.2023.060403","url":null,"abstract":": Under the background of the continuous improvement of human science and technology, the artificial intelligence industry has developed rapidly. At present, computer artificial intelligence recognition technology has been widely used in social production and life, from the perspective of social production, this technology can not only improve the automatic control level of enterprises, but also greatly improve the quality and efficiency of production, so as to create greater social and economic benefits; From the perspective of social life, it helps to improve people's convenience in life. Starting from the connotation and categories of computer artificial intelligence recognition technology, this paper analyzes the specific application of this technology, and discusses its future development direction, hoping to provide reference for relevant colleagues.","PeriodicalId":293823,"journal":{"name":"Journal of Artificial Intelligence Practice","volume":"3 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":"126709147","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 : 1900-01-01DOI: 10.23977/jaip.2023.060209
Jianzhong Qiu, C. Liu, Jun Wu, B. Zhao
: In the process of monitoring the behavior of the elderly, wearable devices and visual devices are easily limited by the site and environment, resulting in poor monitoring results. This paper proposes a posture behavior detection method and system based on pressure data. The convolutional neural network algorithm is used to identify the pressure data to detect the posture, calculate the posture holding time and posture change frequency, judge the posture change action process according to the trajectory of the pressure center point, and finally record and analyze the user's behavior. The correct rate of pose classification of the model used in this paper has reached 98.69%, and the correct rate of pose retention time has reached 98.06%. Finally completed the research and development of the relevant monitoring system, which can be used in the field of medical treatment and daily care.
{"title":"Neural network and system for attitude and behavior detection based on pressure data","authors":"Jianzhong Qiu, C. Liu, Jun Wu, B. Zhao","doi":"10.23977/jaip.2023.060209","DOIUrl":"https://doi.org/10.23977/jaip.2023.060209","url":null,"abstract":": In the process of monitoring the behavior of the elderly, wearable devices and visual devices are easily limited by the site and environment, resulting in poor monitoring results. This paper proposes a posture behavior detection method and system based on pressure data. The convolutional neural network algorithm is used to identify the pressure data to detect the posture, calculate the posture holding time and posture change frequency, judge the posture change action process according to the trajectory of the pressure center point, and finally record and analyze the user's behavior. The correct rate of pose classification of the model used in this paper has reached 98.69%, and the correct rate of pose retention time has reached 98.06%. Finally completed the research and development of the relevant monitoring system, which can be used in the field of medical treatment and daily care.","PeriodicalId":293823,"journal":{"name":"Journal of Artificial Intelligence Practice","volume":"31 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":"127695609","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 : 1900-01-01DOI: 10.23977/jaip.2022.050402
Yuting Zhang, Xingxiang Liu
: The vascular robot is used to treat diseases related to blood vessels. The vascular robot can carry drugs into blood vessels to treat diseases related to blood vessels. At the same time, the operator needs a week of maintenance before he can continue to work. If the robot is not scheduled to work, it also needs maintenance, which will incur corresponding costs. This paper studies how to determine the number of vessels and manipulators to be purchased in vascular robots under different constraints. Firstly, this paper establishes a multi-step decision-making model and analyzes the best time to purchase the container boat and the operator. Then using the least squares curve fitting to analyze the data, through multivariate linear programming, multi-step decision, integer programming and other methods to solve, finally determine the optimal number of ordering vascular robots.
{"title":"Ordering Problem of Vascular Robot Based on Time Series Prediction","authors":"Yuting Zhang, Xingxiang Liu","doi":"10.23977/jaip.2022.050402","DOIUrl":"https://doi.org/10.23977/jaip.2022.050402","url":null,"abstract":": The vascular robot is used to treat diseases related to blood vessels. The vascular robot can carry drugs into blood vessels to treat diseases related to blood vessels. At the same time, the operator needs a week of maintenance before he can continue to work. If the robot is not scheduled to work, it also needs maintenance, which will incur corresponding costs. This paper studies how to determine the number of vessels and manipulators to be purchased in vascular robots under different constraints. Firstly, this paper establishes a multi-step decision-making model and analyzes the best time to purchase the container boat and the operator. Then using the least squares curve fitting to analyze the data, through multivariate linear programming, multi-step decision, integer programming and other methods to solve, finally determine the optimal number of ordering vascular robots.","PeriodicalId":293823,"journal":{"name":"Journal of Artificial Intelligence Practice","volume":"204 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":"121094940","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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