The Internet of Things (IoT) and Machine Learning (ML) are two very hot technologies these days. IoT requires a lot of data processing, and ML is a useful means of processing data. Therefore, the combination of IoT and ML has become a very promising research direction. This paper is a investigation of the combination of IoT and ML. It first introduces the development history of IoT and ML, then introduces some achievements that have emerged in the field of ML and IoT combination. After that, the paper refers some ML technologies which will play important roles in IoT. In this process, this paper also proposes a scheme to improve the accuracy of YOLO algorithm by identifying picture groups. Finally, the paper discusses the existing problems and future development directions of the combination of IoT and ML and provides some references and suggestions for scholars who study the combination of ML and IoT technology.
{"title":"Machine learning techniques in Internet of Things","authors":"Siqi Bai, Xinyue Cui","doi":"10.1117/12.2671334","DOIUrl":"https://doi.org/10.1117/12.2671334","url":null,"abstract":"The Internet of Things (IoT) and Machine Learning (ML) are two very hot technologies these days. IoT requires a lot of data processing, and ML is a useful means of processing data. Therefore, the combination of IoT and ML has become a very promising research direction. This paper is a investigation of the combination of IoT and ML. It first introduces the development history of IoT and ML, then introduces some achievements that have emerged in the field of ML and IoT combination. After that, the paper refers some ML technologies which will play important roles in IoT. In this process, this paper also proposes a scheme to improve the accuracy of YOLO algorithm by identifying picture groups. Finally, the paper discusses the existing problems and future development directions of the combination of IoT and ML and provides some references and suggestions for scholars who study the combination of ML and IoT technology.","PeriodicalId":120866,"journal":{"name":"Artificial Intelligence and Big Data Forum","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124474488","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}
In order to accurately obtain the short-time OD passenger flow distribution of the subway line network, so as to efficiently coordinate the transportation capacity and passenger demand, a multi-time granularity subway line network short-time OD passenger flow prediction model based on LightGBM was constructed by combining the idea of ensemble learning. The model uses the subway automatic ticket sales and inspection data to analyze the temporal and spatial distribution characteristics of OD passenger flow on the line network, introduces a variety of temporal and spatial influencing factors to train and predict the data of the whole network, and studies the relationship between the prediction accuracy of the subway line network OD passenger flow and the time granularity. relationship between. Taking the Suzhou subway as an example, the results show that: compared with other models, the model can not only effectively reduce the prediction error, but also can effectively fit the peak passenger flow, and improve the accuracy of short-time OD passenger flow prediction of the subway network.
{"title":"Multi-time granularity subway line network short-time OD passenger flow forecasting based on LightGBM model","authors":"Heng Zhang, Wei Xiao, MIngjiao Zhang","doi":"10.1117/12.2672715","DOIUrl":"https://doi.org/10.1117/12.2672715","url":null,"abstract":"In order to accurately obtain the short-time OD passenger flow distribution of the subway line network, so as to efficiently coordinate the transportation capacity and passenger demand, a multi-time granularity subway line network short-time OD passenger flow prediction model based on LightGBM was constructed by combining the idea of ensemble learning. The model uses the subway automatic ticket sales and inspection data to analyze the temporal and spatial distribution characteristics of OD passenger flow on the line network, introduces a variety of temporal and spatial influencing factors to train and predict the data of the whole network, and studies the relationship between the prediction accuracy of the subway line network OD passenger flow and the time granularity. relationship between. Taking the Suzhou subway as an example, the results show that: compared with other models, the model can not only effectively reduce the prediction error, but also can effectively fit the peak passenger flow, and improve the accuracy of short-time OD passenger flow prediction of the subway network.","PeriodicalId":120866,"journal":{"name":"Artificial Intelligence and Big Data Forum","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124634173","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}
The influencing factors of aluminum electrolysis production process are complex, and current efficiency is an important evaluation index. In order to study the influence of various parameters on the current efficiency in the aluminum electrolysis production process, a LightGBM-TPE current efficiency optimization model was established in this paper. First, the production data is preprocessed, and the industrial parameters are fitted using the LightGBM prediction model. Then, to further increase the model's prediction accuracy, the TPE optimization method is used to optimize the LightGBM hyperparameters. Finally, the optimization of current efficiency is realized through Optuna combined with TPE Bayesian optimization algorithm. The experimental results demonstrate that the model is capable of accurately identifying the realization conditions and process parameters of high current efficiency in the production process, as well as providing a parameter control foundation for the effective operation of the actual electrolytic aluminum production, ultimately achieving the goal of power consumption reduction.
{"title":"Optimization method of aluminum electrolysis current efficiency based on LightGBM-TPE","authors":"Ying-lan Fang, Chenyang Liu, Zhenliang Li","doi":"10.1117/12.2671649","DOIUrl":"https://doi.org/10.1117/12.2671649","url":null,"abstract":"The influencing factors of aluminum electrolysis production process are complex, and current efficiency is an important evaluation index. In order to study the influence of various parameters on the current efficiency in the aluminum electrolysis production process, a LightGBM-TPE current efficiency optimization model was established in this paper. First, the production data is preprocessed, and the industrial parameters are fitted using the LightGBM prediction model. Then, to further increase the model's prediction accuracy, the TPE optimization method is used to optimize the LightGBM hyperparameters. Finally, the optimization of current efficiency is realized through Optuna combined with TPE Bayesian optimization algorithm. The experimental results demonstrate that the model is capable of accurately identifying the realization conditions and process parameters of high current efficiency in the production process, as well as providing a parameter control foundation for the effective operation of the actual electrolytic aluminum production, ultimately achieving the goal of power consumption reduction.","PeriodicalId":120866,"journal":{"name":"Artificial Intelligence and Big Data Forum","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122793299","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}
The video surveillance command and management system based on GIS technology is studied, which enables users to interact with real scenes, and can effectively solve the spatial difference in multi-point surveillance. The system consists of two parts: hardware and software design. The hardware design includes intelligent monitoring front-end , transmission equipment and background monitoring center; the software design consists of GIS visualization display, scene fusion simulation and stereoscopic display. Through the test of the system, the demand for three-dimensional display of command and management information of the 3D GIS intelligent video surveillance system integrated with multiple scenes has been realized.
{"title":"Information three-dimensional display design of video surveillance command management system based on GIS technology","authors":"Du Sen, Ren Zheng, Liang Haiqu","doi":"10.1117/12.2672131","DOIUrl":"https://doi.org/10.1117/12.2672131","url":null,"abstract":"The video surveillance command and management system based on GIS technology is studied, which enables users to interact with real scenes, and can effectively solve the spatial difference in multi-point surveillance. The system consists of two parts: hardware and software design. The hardware design includes intelligent monitoring front-end , transmission equipment and background monitoring center; the software design consists of GIS visualization display, scene fusion simulation and stereoscopic display. Through the test of the system, the demand for three-dimensional display of command and management information of the 3D GIS intelligent video surveillance system integrated with multiple scenes has been realized.","PeriodicalId":120866,"journal":{"name":"Artificial Intelligence and Big Data Forum","volume":"160 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123023201","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}
Aiming at the complex and changeable driving scenarios of intelligent vehicles and the need to quickly and accurately identify obstacles, an improved YOLOV4 algorithm is proposed. To limit the number of neural network parameters, the CSP-darknet53 backbone of the original YOLOV4 was replaced with the Ghostnet backbone. In addition, to improve the neural network's accuracy, a lightweight attention mechanism ECA is added to the three effective feature layers generated by the backbone using residual block connections. Experiments have shown that the improved YOLOV4 has a 2.8% increase in mAP compared to the original YOLOV4. Without changing the accuracy, The network model's memory size is lowered by 39%, as well as a 50% improvement in detecting speed. Therefore, the improved YOLOV4 accuracy and real-time performance are better than the original network detection, providing a strong guarantee for intelligent vehicle obstacle avoidance.
{"title":"Lightweight intelligent vehicle target detection algorithm based on Yolov4","authors":"Youhua Peng, Peng Zhang, Zheng Fang, D. Xing, Zhijun Guo, Shuaijie Zheng","doi":"10.1117/12.2671289","DOIUrl":"https://doi.org/10.1117/12.2671289","url":null,"abstract":"Aiming at the complex and changeable driving scenarios of intelligent vehicles and the need to quickly and accurately identify obstacles, an improved YOLOV4 algorithm is proposed. To limit the number of neural network parameters, the CSP-darknet53 backbone of the original YOLOV4 was replaced with the Ghostnet backbone. In addition, to improve the neural network's accuracy, a lightweight attention mechanism ECA is added to the three effective feature layers generated by the backbone using residual block connections. Experiments have shown that the improved YOLOV4 has a 2.8% increase in mAP compared to the original YOLOV4. Without changing the accuracy, The network model's memory size is lowered by 39%, as well as a 50% improvement in detecting speed. Therefore, the improved YOLOV4 accuracy and real-time performance are better than the original network detection, providing a strong guarantee for intelligent vehicle obstacle avoidance.","PeriodicalId":120866,"journal":{"name":"Artificial Intelligence and Big Data Forum","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127928953","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 the key basic supporting platform of electric power enterprises, the data center often has internal attribute revocation, which seriously affects the efficiency of information attribute encryption. This paper proposes a method of information attribute encryption for data centers based on a hash algorithm. Then, update that data platform information, ensuring that the encryption method has low overhead and high efficiency. Extract the attribute of the data platform information based on a hash algorithm and encrypt the attribute of the information. The simulation results show that the proposed method occupies only 24% of the task process. The encryption time is relatively short, which verifies that the method has low overhead and high efficiency in the process of information attribute encryption and has a certain contribution value to ensure the information security of the data center.
{"title":"Data center information Atte encrypributtion method based on hash algorithm","authors":"JieJuan Guo, Zhongying Zhao","doi":"10.1117/12.2672772","DOIUrl":"https://doi.org/10.1117/12.2672772","url":null,"abstract":"As the key basic supporting platform of electric power enterprises, the data center often has internal attribute revocation, which seriously affects the efficiency of information attribute encryption. This paper proposes a method of information attribute encryption for data centers based on a hash algorithm. Then, update that data platform information, ensuring that the encryption method has low overhead and high efficiency. Extract the attribute of the data platform information based on a hash algorithm and encrypt the attribute of the information. The simulation results show that the proposed method occupies only 24% of the task process. The encryption time is relatively short, which verifies that the method has low overhead and high efficiency in the process of information attribute encryption and has a certain contribution value to ensure the information security of the data center.","PeriodicalId":120866,"journal":{"name":"Artificial Intelligence and Big Data Forum","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126958831","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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