Microbial fermentation is a typical microbial fermentation process. Microbial bacteria ingest the nutrients of raw materials in the fermentation tank. Under appropriate conditions, enzymes in the body catalyze complex biochemical reactions to produce microorganisms. In order to guarantee the quality of modeling data and meet the accuracy, integrity, and consistency of data quality requirements, it needs to preprocess the input and output data. In this paper, the parameter model is solved by the particle swarm algorithm. Updating the parameter value of the next moment in real time constitutes a feedback correction to the prediction model. Theil inequality approach is adopted to test the tracking performance of the above model’s adaptive correction method. The Monte Carlo method is applied to generate multiple groups of different kinetic model values, which are substituted into the fermentation kinetic model as the real model parameter values. After the experimental analysis, the measured value of the model established by the method in this paper is closer to the predicted value, which has the effect of feedback correction and optimal control. The external conditions in the fermentation process are optimally controlled to achieve the effects of shortening the production period. It improves the yield of fermentation terminal target products and reduces the consumption of raw materials.
{"title":"Microbial fermentation optimal control method based on improved particle swarm optimization","authors":"Yilin Liang","doi":"10.1117/12.2671313","DOIUrl":"https://doi.org/10.1117/12.2671313","url":null,"abstract":"Microbial fermentation is a typical microbial fermentation process. Microbial bacteria ingest the nutrients of raw materials in the fermentation tank. Under appropriate conditions, enzymes in the body catalyze complex biochemical reactions to produce microorganisms. In order to guarantee the quality of modeling data and meet the accuracy, integrity, and consistency of data quality requirements, it needs to preprocess the input and output data. In this paper, the parameter model is solved by the particle swarm algorithm. Updating the parameter value of the next moment in real time constitutes a feedback correction to the prediction model. Theil inequality approach is adopted to test the tracking performance of the above model’s adaptive correction method. The Monte Carlo method is applied to generate multiple groups of different kinetic model values, which are substituted into the fermentation kinetic model as the real model parameter values. After the experimental analysis, the measured value of the model established by the method in this paper is closer to the predicted value, which has the effect of feedback correction and optimal control. The external conditions in the fermentation process are optimally controlled to achieve the effects of shortening the production period. It improves the yield of fermentation terminal target products and reduces the consumption of raw materials.","PeriodicalId":120866,"journal":{"name":"Artificial Intelligence and Big Data Forum","volume":"10 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":"129200428","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}
To solve the quantitative problem of enterprise innovation capability, a data driven quantitative method of enterprise innovation capability is proposed. Firstly, it analyzes and summarizes seven factors which affect the innovation ability of enterprises; Secondly, the enterprise is adaptively divided into different data clusters by deep clustering method; Thirdly, a Gaussian mixture model is constructed to quantify the innovation capability of the evaluated enterprise. The proposed method adopts data mining technology and can provide reference for enterprise development.
{"title":"Evaluation of enterprise's innovation capability with data driven approach","authors":"Lin-Lang Tang, Ji He, Xiaochen Zhang","doi":"10.1117/12.2671671","DOIUrl":"https://doi.org/10.1117/12.2671671","url":null,"abstract":"To solve the quantitative problem of enterprise innovation capability, a data driven quantitative method of enterprise innovation capability is proposed. Firstly, it analyzes and summarizes seven factors which affect the innovation ability of enterprises; Secondly, the enterprise is adaptively divided into different data clusters by deep clustering method; Thirdly, a Gaussian mixture model is constructed to quantify the innovation capability of the evaluated enterprise. The proposed method adopts data mining technology and can provide reference for enterprise development.","PeriodicalId":120866,"journal":{"name":"Artificial Intelligence and Big Data Forum","volume":"8 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":"117001991","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}
Dong Zhou, Fei Liu, Xiangfei Dou, Jie Chen, Zhexin Wen
At present, the detection of drainage pipe defects adopts manual frame-by-frame naked eye discrimination, which has low detection efficiency and high cost, so a two-path multi-receptive convolutional neural network is designed, which also takes into account a certain small volume on the basis of obtaining the highest classification index. The experimental results show that the volume accuracy of the designed model is 92.3%, the recall rate is 91.1%, the F1 score is 91.7%, the model volume is 30.7M, the parameter quantity is 8.97M, and the calculation amount is 2.25G. Compared with other networks, this model is more suitable for automatic identification of drainage pipes.
{"title":"Drainage pipe defect identification based on convolutional neural network","authors":"Dong Zhou, Fei Liu, Xiangfei Dou, Jie Chen, Zhexin Wen","doi":"10.1117/12.2671480","DOIUrl":"https://doi.org/10.1117/12.2671480","url":null,"abstract":"At present, the detection of drainage pipe defects adopts manual frame-by-frame naked eye discrimination, which has low detection efficiency and high cost, so a two-path multi-receptive convolutional neural network is designed, which also takes into account a certain small volume on the basis of obtaining the highest classification index. The experimental results show that the volume accuracy of the designed model is 92.3%, the recall rate is 91.1%, the F1 score is 91.7%, the model volume is 30.7M, the parameter quantity is 8.97M, and the calculation amount is 2.25G. Compared with other networks, this model is more suitable for automatic identification of drainage pipes.","PeriodicalId":120866,"journal":{"name":"Artificial Intelligence and Big Data Forum","volume":"4 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":"114263048","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 positioning problems existing in rail transit system, this paper proposes an intelligent image recognition and positioning algorithm, which adopts deep learning technology to identify vehicles. And, through field test, the experimental results show the effectiveness of the algorithm. Its implementation cost is significantly lower than the existing equipment and can meet the requirements of the existing engineering practice.
{"title":"Research on rail intelligent security system based on YOLO","authors":"Tianyuanye Wang, Kaijiang Zhao","doi":"10.1117/12.2671278","DOIUrl":"https://doi.org/10.1117/12.2671278","url":null,"abstract":"Aiming at the positioning problems existing in rail transit system, this paper proposes an intelligent image recognition and positioning algorithm, which adopts deep learning technology to identify vehicles. And, through field test, the experimental results show the effectiveness of the algorithm. Its implementation cost is significantly lower than the existing equipment and can meet the requirements of the existing engineering practice.","PeriodicalId":120866,"journal":{"name":"Artificial Intelligence and Big Data Forum","volume":"129 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":"131996817","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 the multi-parameter sequence in the industrial electrolyzer, in order to solve the problem that the traditional method is difficult to predict the nonlinear features and obtain the hidden feature information in the sequence, this paper uses the VARMA model to fit the multi-parameter features and combines the Time2Vec vector to embed the time form as the neural network. Augmented data sources for automated feature engineering and generalization of deep learning techniques; multivariate parameters were dimensionally reduced and KS tests were used to capture correlations in order to explore relationships between electrolyzers. The experimental results show that the model is superior to other comparative models in terms of computational efficiency, accuracy, and network structure, which verifies the effectiveness of its prediction in the multi-parameter field.
{"title":"Prediction and clustering models based on multivariate parameters","authors":"Ying-lan Fang, Qilin Sun, Pengfei Zhang","doi":"10.1117/12.2671657","DOIUrl":"https://doi.org/10.1117/12.2671657","url":null,"abstract":"In the multi-parameter sequence in the industrial electrolyzer, in order to solve the problem that the traditional method is difficult to predict the nonlinear features and obtain the hidden feature information in the sequence, this paper uses the VARMA model to fit the multi-parameter features and combines the Time2Vec vector to embed the time form as the neural network. Augmented data sources for automated feature engineering and generalization of deep learning techniques; multivariate parameters were dimensionally reduced and KS tests were used to capture correlations in order to explore relationships between electrolyzers. The experimental results show that the model is superior to other comparative models in terms of computational efficiency, accuracy, and network structure, which verifies the effectiveness of its prediction in the multi-parameter field.","PeriodicalId":120866,"journal":{"name":"Artificial Intelligence and Big Data Forum","volume":"9 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":"126758030","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}
Wearing a safety helmet can effectively reduce or prevent injury to the worker's head caused by hazardous materials in the construction site. However, due to poor supervision, safety accidents often occur when workers don't wear safety helmets. In this paper, we propose a safety helmet detection algorithm based on face detection and ridge regression. Firstly, we get the location information of the face box and the five key points of the face through face detection algorithm, and then get the helmet detection box corresponding to face through ridge regression model. We collected 4000 images of people wearing helmets for training and testing of ridge regression models. Compared with some of the most advanced methods, we have achieved very good results in the test set. The results show that mIoU reaches 70.118% and the detection rate is improved.
{"title":"Safety helmet detection based on face detection and regression","authors":"Yating Huang, Lingrui Zhu","doi":"10.1117/12.2671555","DOIUrl":"https://doi.org/10.1117/12.2671555","url":null,"abstract":"Wearing a safety helmet can effectively reduce or prevent injury to the worker's head caused by hazardous materials in the construction site. However, due to poor supervision, safety accidents often occur when workers don't wear safety helmets. In this paper, we propose a safety helmet detection algorithm based on face detection and ridge regression. Firstly, we get the location information of the face box and the five key points of the face through face detection algorithm, and then get the helmet detection box corresponding to face through ridge regression model. We collected 4000 images of people wearing helmets for training and testing of ridge regression models. Compared with some of the most advanced methods, we have achieved very good results in the test set. The results show that mIoU reaches 70.118% and the detection rate is improved.","PeriodicalId":120866,"journal":{"name":"Artificial Intelligence and Big Data Forum","volume":"117 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":"123045428","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}
This paper constructs a 5G message-based smart energy efficiency billing system to address the data interaction and diversity problems in the digital transformation process in China's electric energy efficiency field. The publish/subscribe algorithm based on a hierarchical mechanism ensures high efficiency and stability in the process of data transmission. Through this smart energy efficiency billing system, the business service quality of State Grid Corporation of China can be improved to a great extent, and the efficiency of capital flow recovery of State Grid Corporation of China can be improved. The system provides users with convenient and comfortable services.
{"title":"Research on the application of 5G messaging in the field of energy efficiency billing","authors":"Hui Zhu, Ning Xi, Lu Zhang","doi":"10.1117/12.2671573","DOIUrl":"https://doi.org/10.1117/12.2671573","url":null,"abstract":"This paper constructs a 5G message-based smart energy efficiency billing system to address the data interaction and diversity problems in the digital transformation process in China's electric energy efficiency field. The publish/subscribe algorithm based on a hierarchical mechanism ensures high efficiency and stability in the process of data transmission. Through this smart energy efficiency billing system, the business service quality of State Grid Corporation of China can be improved to a great extent, and the efficiency of capital flow recovery of State Grid Corporation of China can be improved. The system provides users with convenient and comfortable services.","PeriodicalId":120866,"journal":{"name":"Artificial Intelligence and Big Data Forum","volume":"44 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":"128687637","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 ability of the separator to capture the context-detailed features of speech signals and the number of parameters directly affect the accuracy and efficiency of speech separation in time-domain speech separation network (TasNet). This paper combines lightweight external attention with convolution and extends external attention to channel dimension; while satisfying the fine-grained extraction and modeling of spatial-channel correlation, it maintains small parameters and computation. Convolutional position coding is also used to integrate the contextual relationship and relative position information of speech features better. The above module then applies as a separator in the encoder-decoder structure based on TasNet, and a new convolution-augment external attention model for time-domain speech separation is proposed: ExConNet. The comparative experimental results show that ExConNet achieves considerable accuracy of speech separation, while its model parameters and calculation amount are significantly reduced, which can better meet the need for efficiency of speech separation.
{"title":"Convolution-augmented external attention model for time domain speech separation","authors":"Yuning Zhang, He Yan, Linshan Du, Mengxue Li","doi":"10.1117/12.2671718","DOIUrl":"https://doi.org/10.1117/12.2671718","url":null,"abstract":"The ability of the separator to capture the context-detailed features of speech signals and the number of parameters directly affect the accuracy and efficiency of speech separation in time-domain speech separation network (TasNet). This paper combines lightweight external attention with convolution and extends external attention to channel dimension; while satisfying the fine-grained extraction and modeling of spatial-channel correlation, it maintains small parameters and computation. Convolutional position coding is also used to integrate the contextual relationship and relative position information of speech features better. The above module then applies as a separator in the encoder-decoder structure based on TasNet, and a new convolution-augment external attention model for time-domain speech separation is proposed: ExConNet. The comparative experimental results show that ExConNet achieves considerable accuracy of speech separation, while its model parameters and calculation amount are significantly reduced, which can better meet the need for efficiency of speech separation.","PeriodicalId":120866,"journal":{"name":"Artificial Intelligence and Big Data Forum","volume":"59 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":"128570122","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 power network's operation security contributes to the power grid's smooth operation. Aiming at the problem that each network node in the conventional power communication network attack penetration system is fragile, and the global network attack graph cannot be generated, which leads to the failure of the network attack penetration vulnerability test, this study introduces the knowledge map into it and designs a power communication network attack penetration test system. In hardware, the FPGA chips and RAM are designed. In terms of software, the software architecture of the test system is established to control the network attack penetration globally, and then the knowledge map is used to construct the communication network attack graph model and generate the network global attack graph, so as to realize the effective test of the electric power communication network attack penetration vulnerability. By using the method of system testing, it is verified that the number of vulnerabilities tested by the system is consistent with the actual situation, and it can be applied to real life.
{"title":"Power communication network attack penetration testing system based on knowledge map","authors":"Wei Wang, Xuqiu Chen, Xin He, Kunhua Chen, Zhuojun Ying, Keyao Chun","doi":"10.1117/12.2672765","DOIUrl":"https://doi.org/10.1117/12.2672765","url":null,"abstract":"The power network's operation security contributes to the power grid's smooth operation. Aiming at the problem that each network node in the conventional power communication network attack penetration system is fragile, and the global network attack graph cannot be generated, which leads to the failure of the network attack penetration vulnerability test, this study introduces the knowledge map into it and designs a power communication network attack penetration test system. In hardware, the FPGA chips and RAM are designed. In terms of software, the software architecture of the test system is established to control the network attack penetration globally, and then the knowledge map is used to construct the communication network attack graph model and generate the network global attack graph, so as to realize the effective test of the electric power communication network attack penetration vulnerability. By using the method of system testing, it is verified that the number of vulnerabilities tested by the system is consistent with the actual situation, and it can be applied to real life.","PeriodicalId":120866,"journal":{"name":"Artificial Intelligence and Big Data Forum","volume":"117 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":"124187994","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 era of big data and cloud computing has driven the rapid expansion of the number and scale of data centers worldwide, and the ensuing huge power consumption has put pressure on resources and the environment. Accurate prediction of data center power consumption can provide an important basis for current power management techniques, while effectively improving the efficiency of intelligent operation and maintenance of modern data centers. To address this problem, a server power consumption prediction model based on a combination of principal component analysis (PCA) and DeepAR is proposed in the paper. The model uses the time series of server power consumption and performance index data from the Zhengzhou Inspur data center to predict future moment power consumption, performs principal component analysis on the performance index, and inputs the effective principal components and historical power consumption data into the DeepAR network for prediction. The model is experimentally validated on all three server datasets, and the results show that the model outperform the DeepAR network model as well as other comparison models in terms of prediction. When compared with the DeepAR network, the MAPE of this model is reduced by 0.23%, 0.12%, and 0.05% on the data1, data2, and data3 datasets, respectively.
{"title":"Data center power consumption prediction based on principal component analysis and DeepAR","authors":"Wenyue Zhang, Leijun Hu, Fengyu Guo, Xiaotong Wang, Yihai Duan","doi":"10.1117/12.2671479","DOIUrl":"https://doi.org/10.1117/12.2671479","url":null,"abstract":"The era of big data and cloud computing has driven the rapid expansion of the number and scale of data centers worldwide, and the ensuing huge power consumption has put pressure on resources and the environment. Accurate prediction of data center power consumption can provide an important basis for current power management techniques, while effectively improving the efficiency of intelligent operation and maintenance of modern data centers. To address this problem, a server power consumption prediction model based on a combination of principal component analysis (PCA) and DeepAR is proposed in the paper. The model uses the time series of server power consumption and performance index data from the Zhengzhou Inspur data center to predict future moment power consumption, performs principal component analysis on the performance index, and inputs the effective principal components and historical power consumption data into the DeepAR network for prediction. The model is experimentally validated on all three server datasets, and the results show that the model outperform the DeepAR network model as well as other comparison models in terms of prediction. When compared with the DeepAR network, the MAPE of this model is reduced by 0.23%, 0.12%, and 0.05% on the data1, data2, and data3 datasets, respectively.","PeriodicalId":120866,"journal":{"name":"Artificial Intelligence and Big Data Forum","volume":"15 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":"133185825","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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