Journal of Artificial Intelligence Practice最新文献

Research and Application of Health Code Recognition Based on Paddle OCR under the Background of Epidemic Prevention and Control 疫情防控背景下基于桨形OCR的健康码识别研究与应用

Journal of Artificial Intelligence Practice Pub Date : 1900-01-01 DOI: 10.23977/jaip.2023.060102

引用次数: 0

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 基于深度学习的无人机视觉图像语义分割研究——以安徽财经大学户外环境为例

Journal of Artificial Intelligence Practice Pub Date : 1900-01-01 DOI: 10.23977/jaip.2023.060104

引用次数: 0

Design of an AI Health Risk Assessment System for Dietary Hygiene of Key Groups Based on IoT Wearable Devices 基于物联网可穿戴设备的重点人群饮食卫生AI健康风险评估系统设计

Journal of Artificial Intelligence Practice Pub Date : 1900-01-01 DOI: 10.23977/jaip.2022.050305

Boyuan Wang, Hai Lin, Shenglin Xia

{"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}

引用次数: 0

UAV planar passive pure orientation positioning under different conditions 不同条件下无人机平面无源纯方位定位

Journal of Artificial Intelligence Practice Pub Date : 1900-01-01 DOI: 10.23977/jaip.2023.060103

引用次数: 0

Applications and challenges of hybrid artificial intelligence in chip age testing: a comprehensive review 混合人工智能在芯片年龄测试中的应用与挑战综述

Journal of Artificial Intelligence Practice Pub Date : 1900-01-01 DOI: 10.23977/jaip.2023.060309

Cong Xu, Wensheng Chen, Mingkuan Lin, Jianli Lu, Yunghsiao Chung, Jiahui Zou, Ciliang Yang

{"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}

引用次数: 0

Research on the application of artificial intelligence in computer recognition technology 人工智能在计算机识别技术中的应用研究

Journal of Artificial Intelligence Practice Pub Date : 1900-01-01 DOI: 10.23977/jaip.2023.060203

引用次数: 0

Optimization and Evaluation of Spoken English CAF Based on Artificial Intelligence and Corpus 基于人工智能和语料库的英语口语CAF优化与评价

Journal of Artificial Intelligence Practice Pub Date : 1900-01-01 DOI: 10.23977/jaip.2023.060506

Wenfang Zhang, Xiaodong Wang

{"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}

引用次数: 0

Research based on computer artificial intelligence recognition technology and its application 研究基于计算机的人工智能识别技术及其应用

Journal of Artificial Intelligence Practice Pub Date : 1900-01-01 DOI: 10.23977/jaip.2023.060403

Yuan Chen

引用次数: 0

Neural network and system for attitude and behavior detection based on pressure data 基于压力数据的姿态和行为检测神经网络与系统

Journal of Artificial Intelligence Practice Pub Date : 1900-01-01 DOI: 10.23977/jaip.2023.060209

Jianzhong Qiu, C. Liu, Jun Wu, B. Zhao

引用次数: 0

Ordering Problem of Vascular Robot Based on Time Series Prediction 基于时间序列预测的血管机器人排序问题

Journal of Artificial Intelligence Practice Pub Date : 1900-01-01 DOI: 10.23977/jaip.2022.050402

Yuting Zhang, Xingxiang Liu

引用次数: 0