Lutao Zhang, Songsong Chen, Ying Zhou, Haijing Zhang, Y. Li
Along with the continuous development and deepening reform of the Chinese power market, the main body of the power market trading is increasing day by day, the marketization of the transaction electricity has increased substantially, and the types and cycles of the transactions also become more diversified, which puts forward higher requirements for the settlement of transactions in the Chinese power market. This paper is based on the research of the current development status of Chinese power market transactions settlement, explores the effective operation mode of the power market transactions settlement mechanism, which can promote the establishment of the power market transactions settlement mechanism better and faster.
{"title":"Research on the trading settlement mechanism of electricity market","authors":"Lutao Zhang, Songsong Chen, Ying Zhou, Haijing Zhang, Y. Li","doi":"10.1117/12.2679274","DOIUrl":"https://doi.org/10.1117/12.2679274","url":null,"abstract":"Along with the continuous development and deepening reform of the Chinese power market, the main body of the power market trading is increasing day by day, the marketization of the transaction electricity has increased substantially, and the types and cycles of the transactions also become more diversified, which puts forward higher requirements for the settlement of transactions in the Chinese power market. This paper is based on the research of the current development status of Chinese power market transactions settlement, explores the effective operation mode of the power market transactions settlement mechanism, which can promote the establishment of the power market transactions settlement mechanism better and faster.","PeriodicalId":342847,"journal":{"name":"International Conference on Algorithms, Microchips and Network Applications","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125448817","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 prediction of traffic accident duration is the basis of highway emergency management. Timely and accurate prediction of traffic accident duration can provide a reliable basis for road guidance and rescue organization. This paper discusses the traffic accident duration prediction method of N-BEATS model in detail. Through the change of sliding window size and the continuous adjustment of the number of iterations, the appropriate parameters are found to produce a good prediction effect. The dataset used in this paper is US Accidents, a nation-wide dataset of traffic accidents covering 49 states in the US. The experimental results show that compared with the classical time series prediction models such as Bi-LSTM, SVM, RNN-GRU and AttnAR, prediction of traffic accident duration model based on N-BEATS proposed in this paper is optimal in the three evaluation indicators of RMSE, MAE and SD, which shows that the model has the highest prediction accuracy and good performance.
{"title":"Prediction of traffic accident duration based on N-BEATS","authors":"Y. He, Senchang Zhang, Peiyao Zhong, Zhenliang Li","doi":"10.1117/12.2679093","DOIUrl":"https://doi.org/10.1117/12.2679093","url":null,"abstract":"The prediction of traffic accident duration is the basis of highway emergency management. Timely and accurate prediction of traffic accident duration can provide a reliable basis for road guidance and rescue organization. This paper discusses the traffic accident duration prediction method of N-BEATS model in detail. Through the change of sliding window size and the continuous adjustment of the number of iterations, the appropriate parameters are found to produce a good prediction effect. The dataset used in this paper is US Accidents, a nation-wide dataset of traffic accidents covering 49 states in the US. The experimental results show that compared with the classical time series prediction models such as Bi-LSTM, SVM, RNN-GRU and AttnAR, prediction of traffic accident duration model based on N-BEATS proposed in this paper is optimal in the three evaluation indicators of RMSE, MAE and SD, which shows that the model has the highest prediction accuracy and good performance.","PeriodicalId":342847,"journal":{"name":"International Conference on Algorithms, Microchips and Network Applications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122977339","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}
Yu Xing, Xianguang Dong, Yanling Sun, Min Li, Ling Zhang
As a metering device, the electric energy meter is used to detect the electric quantity at each detection point. This paper studies a monitoring method and system, storage medium, and terminal for the verification data of electric energy meters, involving the field of electric power. The main purpose is to improve the existing problem that the operating data in the verification process cannot be grasped in real time, and there is a risk of damaging the pipeline hardware, thus reducing the monitoring efficiency. The process includes analyzing the target verification items of the electric energy meter in the verification work order and determine the monitoring components matching the target verification items. After starting the verification operation of the electric energy meter, send the data acquisition command to the hardware monitoring subunit, and send the data recording command to the software monitoring subunit. If the problems found in the detection cannot be grasped at the first time, the pipeline hardware may be burnt, and other risks may be caused. Obtain the data collected by the hardware monitoring subunit and the operation status data recorded by the software monitoring subunit. Reduced monitoring is a problem of work efficiency. In reality, work efficiency is very important, especially in terms of risks and hidden dangers. Extract the hardware monitoring features of image data and/or infrared signals, and the software monitoring features of operation status data, and monitor the hardware monitoring features and software monitoring features based on the data status monitoring model to determine the monitoring results of verification data.
{"title":"Monitoring method for verification data of electric energy meters","authors":"Yu Xing, Xianguang Dong, Yanling Sun, Min Li, Ling Zhang","doi":"10.1117/12.2679289","DOIUrl":"https://doi.org/10.1117/12.2679289","url":null,"abstract":"As a metering device, the electric energy meter is used to detect the electric quantity at each detection point. This paper studies a monitoring method and system, storage medium, and terminal for the verification data of electric energy meters, involving the field of electric power. The main purpose is to improve the existing problem that the operating data in the verification process cannot be grasped in real time, and there is a risk of damaging the pipeline hardware, thus reducing the monitoring efficiency. The process includes analyzing the target verification items of the electric energy meter in the verification work order and determine the monitoring components matching the target verification items. After starting the verification operation of the electric energy meter, send the data acquisition command to the hardware monitoring subunit, and send the data recording command to the software monitoring subunit. If the problems found in the detection cannot be grasped at the first time, the pipeline hardware may be burnt, and other risks may be caused. Obtain the data collected by the hardware monitoring subunit and the operation status data recorded by the software monitoring subunit. Reduced monitoring is a problem of work efficiency. In reality, work efficiency is very important, especially in terms of risks and hidden dangers. Extract the hardware monitoring features of image data and/or infrared signals, and the software monitoring features of operation status data, and monitor the hardware monitoring features and software monitoring features based on the data status monitoring model to determine the monitoring results of verification data.","PeriodicalId":342847,"journal":{"name":"International Conference on Algorithms, Microchips and Network Applications","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123013571","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 problem that the traditional algorithm is easy to overfitting, which leads to low prediction accuracy of the model. This paper designs a traffic accident impact range prediction model based on CatBoost ensemble algorithm. The model uses linear fitting for range prediction and uses the ordered boosting method to introduce the prior term and weight coefficient. It can automatically adjust dynamically in each calculation, so as to effectively avoid the condition offset and gradient deviation and reduce the overfitting. Under small-scale training, the algorithm can achieve high accuracy prediction and has strong generalization ability.
{"title":"Prediction of traffic accident impact range based on CatBoost ensemble algorithm","authors":"Songwei Zhang, Haibo Liu, Yundi Yang, Senchang Zhang, Zhongshan Zhang, Chunyu Wang, Mengnan Wang","doi":"10.1117/12.2679147","DOIUrl":"https://doi.org/10.1117/12.2679147","url":null,"abstract":"Aiming at the problem that the traditional algorithm is easy to overfitting, which leads to low prediction accuracy of the model. This paper designs a traffic accident impact range prediction model based on CatBoost ensemble algorithm. The model uses linear fitting for range prediction and uses the ordered boosting method to introduce the prior term and weight coefficient. It can automatically adjust dynamically in each calculation, so as to effectively avoid the condition offset and gradient deviation and reduce the overfitting. Under small-scale training, the algorithm can achieve high accuracy prediction and has strong generalization ability.","PeriodicalId":342847,"journal":{"name":"International Conference on Algorithms, Microchips and Network Applications","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125575324","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}
Yalin Huang, Yu Tian, Kunbo Zhang, Kaiwen Zhang, Zhenan Sun
Face spoofing detection techniques have performed well in the digital and physical domains separately. However, existing methods do not work well when both types of spoofing attacks need to be resisted at the same time. We propose a new polarization-based unified spoofing detection method for cross-domain face spoofing attacks. With our cross-domain unified spoofing detection framework, our methods can automatically detect and identify face spoofing attacks in both digital and physical domains. In addition, we build a new face anti-spoofing dataset containing polarized modality. We first provide a method for generating polarimetric face images from visible images, which are used to provide a digital domain spoofing attack. Then, we fake faces through physical methods such as photo and mask. In our new dataset, extensive experiments show that our method has better performance and robustness in face cross-domain attack detection and can still defend against cross-domain face attacks with a very small training data size.
{"title":"Unified polarimetric method for cross-domain face attacks detection","authors":"Yalin Huang, Yu Tian, Kunbo Zhang, Kaiwen Zhang, Zhenan Sun","doi":"10.1117/12.2678926","DOIUrl":"https://doi.org/10.1117/12.2678926","url":null,"abstract":"Face spoofing detection techniques have performed well in the digital and physical domains separately. However, existing methods do not work well when both types of spoofing attacks need to be resisted at the same time. We propose a new polarization-based unified spoofing detection method for cross-domain face spoofing attacks. With our cross-domain unified spoofing detection framework, our methods can automatically detect and identify face spoofing attacks in both digital and physical domains. In addition, we build a new face anti-spoofing dataset containing polarized modality. We first provide a method for generating polarimetric face images from visible images, which are used to provide a digital domain spoofing attack. Then, we fake faces through physical methods such as photo and mask. In our new dataset, extensive experiments show that our method has better performance and robustness in face cross-domain attack detection and can still defend against cross-domain face attacks with a very small training data size.","PeriodicalId":342847,"journal":{"name":"International Conference on Algorithms, Microchips and Network Applications","volume":"12635 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129746406","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}
At present, curve and surface fitting is widely used in three-dimensional measurement, industrial design, archaeology, medicine and other fields, and curve and surface fitting has also become a hot spot and a difficulty at present. The surface point cloud data scanned by high-precision 3D laser scanning instruments on site are often complex, and the data are relatively dense for curves. If the approximation fitting is used, complex information may not be reflected enough, and the interpolation fitting may produce over-fitting phenomenon. This paper proposes a feature point selection algorithm, which is more targeted for dense point cloud data than the general cubic B-spline interpolation algorithm. The feature point selection algorithm can retain feature points and remove non-feature points and minimize the number of fitting segments on the premise of meeting the accuracy requirements of the final fitting curve.
{"title":"An improved feature point selection algorithm for point cloud data","authors":"Xuedong Jing, Xueqi Shan, Yuwei Zhang","doi":"10.1117/12.2679106","DOIUrl":"https://doi.org/10.1117/12.2679106","url":null,"abstract":"At present, curve and surface fitting is widely used in three-dimensional measurement, industrial design, archaeology, medicine and other fields, and curve and surface fitting has also become a hot spot and a difficulty at present. The surface point cloud data scanned by high-precision 3D laser scanning instruments on site are often complex, and the data are relatively dense for curves. If the approximation fitting is used, complex information may not be reflected enough, and the interpolation fitting may produce over-fitting phenomenon. This paper proposes a feature point selection algorithm, which is more targeted for dense point cloud data than the general cubic B-spline interpolation algorithm. The feature point selection algorithm can retain feature points and remove non-feature points and minimize the number of fitting segments on the premise of meeting the accuracy requirements of the final fitting curve.","PeriodicalId":342847,"journal":{"name":"International Conference on Algorithms, Microchips and Network Applications","volume":"12635 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130168658","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}
Due to the high cost, complex construction, and immature algorithm of existing force feedback equipment, the majority of it is utilized in precise industries such as telemedicine and aerospace. Nevertheless, specific industrial production tasks require force feedback devices immediately. In order to make force feedback equipment extensively employed in the field of industrial robots, a spring-based variable force machine manpower feedback control system is developed. The force state of the robot in the working environment is communicated back to the manipulator via spring deformation, allowing the manipulator to intuitively sense the force environment in the working environment, hence facilitating robot control. The experiment demonstrates that the force feedback control system based on this method has a stable feedback effect and a simple structure, as well as the ability to realize the feedback of varied forces based on diverse application circumstances, which has a high promotional and practical value.
{"title":"Interactive control system of variable force feedback robot based on spring model","authors":"Liang Wang, Long Xue, Jiqiang Huang","doi":"10.1117/12.2678892","DOIUrl":"https://doi.org/10.1117/12.2678892","url":null,"abstract":"Due to the high cost, complex construction, and immature algorithm of existing force feedback equipment, the majority of it is utilized in precise industries such as telemedicine and aerospace. Nevertheless, specific industrial production tasks require force feedback devices immediately. In order to make force feedback equipment extensively employed in the field of industrial robots, a spring-based variable force machine manpower feedback control system is developed. The force state of the robot in the working environment is communicated back to the manipulator via spring deformation, allowing the manipulator to intuitively sense the force environment in the working environment, hence facilitating robot control. The experiment demonstrates that the force feedback control system based on this method has a stable feedback effect and a simple structure, as well as the ability to realize the feedback of varied forces based on diverse application circumstances, which has a high promotional and practical value.","PeriodicalId":342847,"journal":{"name":"International Conference on Algorithms, Microchips and Network Applications","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133547154","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 provide a set of quantitative evaluation of the comfort of hand-held sports products, this study preliminarily constructed the comfort evaluation index system of hand-held sports products from the perspectives of performance, tactile characteristics, quality and appearance, physical stimulation, discomfort, morphological characteristics, user demographic characteristics and hand characteristics. Secondly, 165 groups of data were collected through the experiment, and factor analysis was used to reduce the dimension of the original index. Then neural network was used to establish the mapping relationship between the evaluation index and the comfort evaluation, and the evaluation model of the comfort of hand-held sports products was obtained. The results show that the relative error between the predicted value and the actual value of the network model is basically less than 5% in the practical application of the evaluation method taking the badminton racket as an example, which reflects that the comfort evaluation method can effectively reduce the correlation between the original indexes, achieve the accurate evaluation of the comfort of products, and verify the effectiveness and feasibility of the evaluation method. The combination of neural network and factor analysis can quantitatively analyze and evaluate the comfort of hand-held sports products and provide reliable suggestions for manufacturers to design and produce more comfortable products in the form of data.
{"title":"Neural network-based comfort evaluation method for handheld sports products","authors":"Xiaoyu Tang, Ruiqiu Zhang","doi":"10.1117/12.2678894","DOIUrl":"https://doi.org/10.1117/12.2678894","url":null,"abstract":"In order to provide a set of quantitative evaluation of the comfort of hand-held sports products, this study preliminarily constructed the comfort evaluation index system of hand-held sports products from the perspectives of performance, tactile characteristics, quality and appearance, physical stimulation, discomfort, morphological characteristics, user demographic characteristics and hand characteristics. Secondly, 165 groups of data were collected through the experiment, and factor analysis was used to reduce the dimension of the original index. Then neural network was used to establish the mapping relationship between the evaluation index and the comfort evaluation, and the evaluation model of the comfort of hand-held sports products was obtained. The results show that the relative error between the predicted value and the actual value of the network model is basically less than 5% in the practical application of the evaluation method taking the badminton racket as an example, which reflects that the comfort evaluation method can effectively reduce the correlation between the original indexes, achieve the accurate evaluation of the comfort of products, and verify the effectiveness and feasibility of the evaluation method. The combination of neural network and factor analysis can quantitatively analyze and evaluate the comfort of hand-held sports products and provide reliable suggestions for manufacturers to design and produce more comfortable products in the form of data.","PeriodicalId":342847,"journal":{"name":"International Conference on Algorithms, Microchips and Network Applications","volume":"107 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134469237","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 problem that the information difference between modalities in the current multimodal sentiment analysis model and the insufficient fusion between modalities lead to the low accuracy of network prediction, this paper designs a multimodal sentiment analysis model based on BERT-ResNet50. The model uses BERT and ResNet50 to extract text and image features respectively, fuses multi-modal information through the encoder layer of Transformer, and finally uses the Softmax layer to classify multi-modal information. The dataset used in this paper is the Twitter sarcasm public dataset. Through experiments, the BERT-ResNet50 model proposed in this paper is higher than the comparison models in accuracy, recall rate and F1 value, and the accuracy reaches 74.05%. Ablation experiments show that the accuracy of the model in multi-modal sentiment analysis is higher than that in single-modal sentiment analysis.
{"title":"Multimodal sentiment analysis with BERT-ResNet50","authors":"Senchang Zhang, Yue He, Lei Li, Yaowen Dou","doi":"10.1117/12.2679113","DOIUrl":"https://doi.org/10.1117/12.2679113","url":null,"abstract":"Aiming at the problem that the information difference between modalities in the current multimodal sentiment analysis model and the insufficient fusion between modalities lead to the low accuracy of network prediction, this paper designs a multimodal sentiment analysis model based on BERT-ResNet50. The model uses BERT and ResNet50 to extract text and image features respectively, fuses multi-modal information through the encoder layer of Transformer, and finally uses the Softmax layer to classify multi-modal information. The dataset used in this paper is the Twitter sarcasm public dataset. Through experiments, the BERT-ResNet50 model proposed in this paper is higher than the comparison models in accuracy, recall rate and F1 value, and the accuracy reaches 74.05%. Ablation experiments show that the accuracy of the model in multi-modal sentiment analysis is higher than that in single-modal sentiment analysis.","PeriodicalId":342847,"journal":{"name":"International Conference on Algorithms, Microchips and Network Applications","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122685433","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}
Backfill scheduling is a common scheduling strategy in high-performance computing systems that allows priority execution of low-priority jobs to make better use of available resources. Job running time is an important parameter that affects the performance of backfill scheduling algorithm. However, in order to avoid job killing due to lack of time, the running time requested by users is often several times higher than the actual running time, resulting in a certain degree of resource waste. In order to improve resource utilization, a new job running time prediction algorithm is proposed by combining classification and ensemble learning methods. The algorithm first classifies the historical job set according to the application type, then uses Jaccard coefficient to calculate the similarity between the jobs, and further classifies the jobs. At the same time, different integration models are constructed for the jobs of different application types. New jobs are categorized, and the class's integration model is used to predict the running time of the new job. The algorithm was tested on the historical job data of the National Supercomputing Center Kunshan, Hefei Advanced Computing Center and "Wuzhen Light" supercomputing Center and compared with GA-sim algorithm and IRPA algorithm. The experimental results show that compared with the IRPA algorithm, the average absolute error of the algorithm is improved by 60% on the three data sets on average. Compared with the GA-sim algorithm, the average prediction accuracy of the algorithm is improved by 20% on the three data sets on average. Through the in-depth analysis of the experimental results, the amplification method for the low estimation of long and short jobs is given.
{"title":"Job running time prediction algorithm based on classification and ensemble learning","authors":"Xubo Kong, Dandan Zhang, Yu Zheng, Qing Ji","doi":"10.1117/12.2679217","DOIUrl":"https://doi.org/10.1117/12.2679217","url":null,"abstract":"Backfill scheduling is a common scheduling strategy in high-performance computing systems that allows priority execution of low-priority jobs to make better use of available resources. Job running time is an important parameter that affects the performance of backfill scheduling algorithm. However, in order to avoid job killing due to lack of time, the running time requested by users is often several times higher than the actual running time, resulting in a certain degree of resource waste. In order to improve resource utilization, a new job running time prediction algorithm is proposed by combining classification and ensemble learning methods. The algorithm first classifies the historical job set according to the application type, then uses Jaccard coefficient to calculate the similarity between the jobs, and further classifies the jobs. At the same time, different integration models are constructed for the jobs of different application types. New jobs are categorized, and the class's integration model is used to predict the running time of the new job. The algorithm was tested on the historical job data of the National Supercomputing Center Kunshan, Hefei Advanced Computing Center and \"Wuzhen Light\" supercomputing Center and compared with GA-sim algorithm and IRPA algorithm. The experimental results show that compared with the IRPA algorithm, the average absolute error of the algorithm is improved by 60% on the three data sets on average. Compared with the GA-sim algorithm, the average prediction accuracy of the algorithm is improved by 20% on the three data sets on average. Through the in-depth analysis of the experimental results, the amplification method for the low estimation of long and short jobs is given.","PeriodicalId":342847,"journal":{"name":"International Conference on Algorithms, Microchips and Network Applications","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121598256","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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