A brain-controlled wheelchair system based on TGAM module is proposed, which can improve people quality of life which suffering from severe movement disorders. The TGAM is used as the EEG signals acquisition and processing module. The EEG data is transmitted to the micro-controller through the Bluetooth module. The data is validated and the concentration parameter is parsed, the concentration value is converted into the speed parameter of the wheelchair, and the key state is converted into the wheelchair movement direction parameter, to control the wheelchair movement according to the user's real-time concentration. The test results show that the TGAM module can accurately collect EEG signals, and the micro-controller can analyze the concentration data, and control the wheelchair's forward, backward and turn through the motor. The intelligent wheelchair is simple, easy to operate, and stable in function. It can be operated only through the user's concentration, providing a new convenient wheelchair control mode for people with walking difficulties.
{"title":"A smart brain controlled wheelchair based on TGAM","authors":"Xinying Yu, Shaoda Xie","doi":"10.1117/12.2689436","DOIUrl":"https://doi.org/10.1117/12.2689436","url":null,"abstract":"A brain-controlled wheelchair system based on TGAM module is proposed, which can improve people quality of life which suffering from severe movement disorders. The TGAM is used as the EEG signals acquisition and processing module. The EEG data is transmitted to the micro-controller through the Bluetooth module. The data is validated and the concentration parameter is parsed, the concentration value is converted into the speed parameter of the wheelchair, and the key state is converted into the wheelchair movement direction parameter, to control the wheelchair movement according to the user's real-time concentration. The test results show that the TGAM module can accurately collect EEG signals, and the micro-controller can analyze the concentration data, and control the wheelchair's forward, backward and turn through the motor. The intelligent wheelchair is simple, easy to operate, and stable in function. It can be operated only through the user's concentration, providing a new convenient wheelchair control mode for people with walking difficulties.","PeriodicalId":118234,"journal":{"name":"4th International Conference on Information Science, Electrical and Automation Engineering","volume":"71 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113962405","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 verifies the MPC control strategy by building a controller hardware-in-the-loop experimental platform. This paper firstly introduces the principle of FCS-MPC and discretizes the current equation by the working principle of the inverter to get the predicted current equation; then the value function is taken as the sum of squares. The model is transformed by Vivado and the experiments are conducted by combining FPGA with MT3200. The waveform output graph indicates that this experimental platform can effectively verify the control strategy
{"title":"Validation of FCS-MPC control strategy based on CHIL","authors":"Xi Wang, Li Lu","doi":"10.1117/12.2690110","DOIUrl":"https://doi.org/10.1117/12.2690110","url":null,"abstract":"This paper verifies the MPC control strategy by building a controller hardware-in-the-loop experimental platform. This paper firstly introduces the principle of FCS-MPC and discretizes the current equation by the working principle of the inverter to get the predicted current equation; then the value function is taken as the sum of squares. The model is transformed by Vivado and the experiments are conducted by combining FPGA with MT3200. The waveform output graph indicates that this experimental platform can effectively verify the control strategy","PeriodicalId":118234,"journal":{"name":"4th International Conference on Information Science, Electrical and Automation Engineering","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116330186","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 improve the accuracy of PM2.5 concentration prediction, a CNN-GRU deep learning model based on fusion of Luong Attention is proposed. Firstly, the correlation between various air pollutants and meteorological factors and PM2.5 concentration is comprehensively analyzed, and the high correlation data is formed into a feature set. Secondly, the feature set is input into CNN for feature dimensioning, and then the output results of each time step are extracted through GRU. Finally, by introducing the Luong attention mechanism, the attention scores of the hidden states at each position of the output sequence are calculated, and the context vector is weighted to highlight the input step information that plays a key role in the prediction of PM2.5 concentration. The results show that using the CNN-GRU model with attention mechanism to predict the PM2.5 concentration in the next 24 hours, compared with the machine model and other deep learning models, RMSE and MAE have a certain decline, and have a higher generalization ability.
{"title":"PM2.5 concentration prediction based on CNN-GRU model fused with Luong attention","authors":"Zhen Wang, Lizhi Liu","doi":"10.1117/12.2689345","DOIUrl":"https://doi.org/10.1117/12.2689345","url":null,"abstract":"In order to improve the accuracy of PM2.5 concentration prediction, a CNN-GRU deep learning model based on fusion of Luong Attention is proposed. Firstly, the correlation between various air pollutants and meteorological factors and PM2.5 concentration is comprehensively analyzed, and the high correlation data is formed into a feature set. Secondly, the feature set is input into CNN for feature dimensioning, and then the output results of each time step are extracted through GRU. Finally, by introducing the Luong attention mechanism, the attention scores of the hidden states at each position of the output sequence are calculated, and the context vector is weighted to highlight the input step information that plays a key role in the prediction of PM2.5 concentration. The results show that using the CNN-GRU model with attention mechanism to predict the PM2.5 concentration in the next 24 hours, compared with the machine model and other deep learning models, RMSE and MAE have a certain decline, and have a higher generalization ability.","PeriodicalId":118234,"journal":{"name":"4th International Conference on Information Science, Electrical and Automation Engineering","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116526344","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 many problems caused by the large-scale construction of roof distributed photovoltaic power stations in the future, and realize the group control of photovoltaic power stations, an intelligent Internet of Thing (IoT) communication system, which based on high-reliability power line carrier communication and 5G wireless communication technology, is proposed in this paper. Meanwhile, for solving the problem of resource collision and critical data loss caused by a large number of intelligent fusion terminals accessing the wireless network, a priority-based random access congestion control algorithm is proposed. By performing priority grouping, the algorithm allocates the backoff window dynamically. Through experimental simulation, it can be seen that compared with the classic binary backoff algorithm, this algorithm is able to significantly improve the stability of data transmission, reduce the packet loss rate, and play a role in alleviating network congestion and optimizing network performance.
{"title":"Research on intelligent monitoring of roof distributed photovoltaics based on high-reliable power line and wireless communication","authors":"Qin Mei, Lingyin Jiang, Shuhao Yuan, Jiawei Ma, Biyao Huang","doi":"10.1117/12.2689420","DOIUrl":"https://doi.org/10.1117/12.2689420","url":null,"abstract":"To solve many problems caused by the large-scale construction of roof distributed photovoltaic power stations in the future, and realize the group control of photovoltaic power stations, an intelligent Internet of Thing (IoT) communication system, which based on high-reliability power line carrier communication and 5G wireless communication technology, is proposed in this paper. Meanwhile, for solving the problem of resource collision and critical data loss caused by a large number of intelligent fusion terminals accessing the wireless network, a priority-based random access congestion control algorithm is proposed. By performing priority grouping, the algorithm allocates the backoff window dynamically. Through experimental simulation, it can be seen that compared with the classic binary backoff algorithm, this algorithm is able to significantly improve the stability of data transmission, reduce the packet loss rate, and play a role in alleviating network congestion and optimizing network performance.","PeriodicalId":118234,"journal":{"name":"4th International Conference on Information Science, Electrical and Automation Engineering","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114463962","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}
Yunfei Zhu, Xiao Zhang, Rongcai Zhao, Can Ding, Qinglei Zhou
A low-power RISC-V-based convolutional neural network acceleration processor is proposed to cope with the problem that the increasing resource requirements of convolutional neural networks in the direction of hardware convolutional acceleration are difficult to be met on embedded devices. The processor is designed with three instructions that can configure the parameters of each CNN layer to accommodate different input data, multiplex computational resources to reduce power consumption, and execute operations that repeat a large number of executions in parallel to speed up operation efficiency. Through comparison experiments, it can be found that this processor acceleration instruction set is 20.93 times, 7.67 times, and 8.97 times faster than the base RISC-V instruction set after verified with the same data on three operations, including convolution, activation, and pooling, respectively. The experimental results show that the total power consumption of the processor with this custom instruction set is only 0.221 W at 16 MHZ operating frequency, which is advantageous in terms of performance-to-power ratio compared to other RISC-V accelerated processors with less resource consumption and lower power consumption.
{"title":"Design of low-power acceleration processor for convolutional neural networks based on RISC-V","authors":"Yunfei Zhu, Xiao Zhang, Rongcai Zhao, Can Ding, Qinglei Zhou","doi":"10.1117/12.2689314","DOIUrl":"https://doi.org/10.1117/12.2689314","url":null,"abstract":"A low-power RISC-V-based convolutional neural network acceleration processor is proposed to cope with the problem that the increasing resource requirements of convolutional neural networks in the direction of hardware convolutional acceleration are difficult to be met on embedded devices. The processor is designed with three instructions that can configure the parameters of each CNN layer to accommodate different input data, multiplex computational resources to reduce power consumption, and execute operations that repeat a large number of executions in parallel to speed up operation efficiency. Through comparison experiments, it can be found that this processor acceleration instruction set is 20.93 times, 7.67 times, and 8.97 times faster than the base RISC-V instruction set after verified with the same data on three operations, including convolution, activation, and pooling, respectively. The experimental results show that the total power consumption of the processor with this custom instruction set is only 0.221 W at 16 MHZ operating frequency, which is advantageous in terms of performance-to-power ratio compared to other RISC-V accelerated processors with less resource consumption and lower power consumption.","PeriodicalId":118234,"journal":{"name":"4th International Conference on Information Science, Electrical and Automation Engineering","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114682716","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 further improve the separation and detection accuracy of bearing fault characteristics, A new method for early fault diagnosis of rolling bearings based on Maximum Correlated Kurtosis Deconvolution and autocorrelation kurtograph was proposed. Firstly, the vibration signal of bearing fault is denoised by Maximum Correlated Kurtosis Deconvolution; Then, the improved autocorrelation spectral kurtograph is used to select the optimal frequency center and bandwidth of fault features. According to the optimal frequency center and bandwidth, the band pass filtering is carried out to remove noise and random pulse irrelevant components in the band signal. Finally, the sub-signal after bandpass filtering is analyzed by envelope spectrum, identify fault frequency and realize early fault diagnosis of rolling bearing. In the experiment, different types of bearing fault data verify the effectiveness of the proposed method.
{"title":"Rolling bearing fault feature extraction based on maximum correlated kurtosis deconvolution and improved autocorrelation spectral kurtograph","authors":"Chencheng He, Wenbo Wang","doi":"10.1117/12.2689626","DOIUrl":"https://doi.org/10.1117/12.2689626","url":null,"abstract":"In order to further improve the separation and detection accuracy of bearing fault characteristics, A new method for early fault diagnosis of rolling bearings based on Maximum Correlated Kurtosis Deconvolution and autocorrelation kurtograph was proposed. Firstly, the vibration signal of bearing fault is denoised by Maximum Correlated Kurtosis Deconvolution; Then, the improved autocorrelation spectral kurtograph is used to select the optimal frequency center and bandwidth of fault features. According to the optimal frequency center and bandwidth, the band pass filtering is carried out to remove noise and random pulse irrelevant components in the band signal. Finally, the sub-signal after bandpass filtering is analyzed by envelope spectrum, identify fault frequency and realize early fault diagnosis of rolling bearing. In the experiment, different types of bearing fault data verify the effectiveness of the proposed method.","PeriodicalId":118234,"journal":{"name":"4th International Conference on Information Science, Electrical and Automation Engineering","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134283815","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 future, with the large-scale integration of distributed generation (DG) and electric vehicle (EV), due to the dual uncertainty of time and space, it is bound to pose new challenges to the economic and safe operation of urban distribution network. As one of the important means of power grid optimization, distribution network reconfiguration can dynamically adjust the power grid structure according to the spatial and temporal changes of EV charging load. Therefore, in order to improve the economy and safety of urban distribution network operation, this paper proposes a dynamic reconfiguration model of active distribution network considering EV charging demand under the guidance of real-time electricity price. At the same time, the reconfiguration period is divided based on the peak-valley membership degree. The ratio of active network loss at each moment of the system and the operating loss cost after the introduction of time-of-use electricity price is used as the operation index, and the reconstruction period is reasonably divided by the change rate of membership degree. The demand response (DR) mechanism is introduced before the reconfiguration, and the active distribution network reconfiguration model with the minimum operating loss cost is established. The model is solved by the improved binary particle swarm optimization algorithm. Finally, a case study of a city's traffic network and an improved IEEE33 node coupling system is carried out to verify that the time-sharing reconstruction method in this paper can effectively deal with the influence of DG output, EV charging and other factors on the urban distribution network, and improve the economy and safety of the overall distribution network operation.
{"title":"Dynamic reconfiguration of active distribution network considering electric vehicle charging demand under real-time electricity price","authors":"Yingliang Li, Boxu Bai","doi":"10.1117/12.2689324","DOIUrl":"https://doi.org/10.1117/12.2689324","url":null,"abstract":"In the future, with the large-scale integration of distributed generation (DG) and electric vehicle (EV), due to the dual uncertainty of time and space, it is bound to pose new challenges to the economic and safe operation of urban distribution network. As one of the important means of power grid optimization, distribution network reconfiguration can dynamically adjust the power grid structure according to the spatial and temporal changes of EV charging load. Therefore, in order to improve the economy and safety of urban distribution network operation, this paper proposes a dynamic reconfiguration model of active distribution network considering EV charging demand under the guidance of real-time electricity price. At the same time, the reconfiguration period is divided based on the peak-valley membership degree. The ratio of active network loss at each moment of the system and the operating loss cost after the introduction of time-of-use electricity price is used as the operation index, and the reconstruction period is reasonably divided by the change rate of membership degree. The demand response (DR) mechanism is introduced before the reconfiguration, and the active distribution network reconfiguration model with the minimum operating loss cost is established. The model is solved by the improved binary particle swarm optimization algorithm. Finally, a case study of a city's traffic network and an improved IEEE33 node coupling system is carried out to verify that the time-sharing reconstruction method in this paper can effectively deal with the influence of DG output, EV charging and other factors on the urban distribution network, and improve the economy and safety of the overall distribution network operation.","PeriodicalId":118234,"journal":{"name":"4th International Conference on Information Science, Electrical and Automation Engineering","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122165831","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}
Yixuan Zhao, Baolei Hu, Feiyang Liu, Tanbao Yan, Han Gao
Convolutional neural networks (CNNs) have been widely used in the field of image recognition. To meet the massive computational requirements of CNNs, GPUs or other intelligent computing hardware are typically used for data processing. FPGA supports parallel computing and is characterized by programmability, high performance, low energy consumption, and strong stability. In this paper, we improved and optimized the YOLOv2-Tiny algorithm by combining it with the hardware implementation based on FPGA's hardware structure. We divided the neural network tasks and preprocessed data using the 16-bit fixed-point method to reduce hardware resource consumption. By using the PYNQ-z2 development platform to accelerate the YOLOv2-Tiny CNN, we achieved target object detection and recognition. Compared with CPU (i7-10710U), the processing capacity was 2.94 times that of CPU, and the power consumption was 3.1% of CPU.
{"title":"Design of YOLOv2-tiny accelerator based on PYNQ-Z2 platform","authors":"Yixuan Zhao, Baolei Hu, Feiyang Liu, Tanbao Yan, Han Gao","doi":"10.1117/12.2689581","DOIUrl":"https://doi.org/10.1117/12.2689581","url":null,"abstract":"Convolutional neural networks (CNNs) have been widely used in the field of image recognition. To meet the massive computational requirements of CNNs, GPUs or other intelligent computing hardware are typically used for data processing. FPGA supports parallel computing and is characterized by programmability, high performance, low energy consumption, and strong stability. In this paper, we improved and optimized the YOLOv2-Tiny algorithm by combining it with the hardware implementation based on FPGA's hardware structure. We divided the neural network tasks and preprocessed data using the 16-bit fixed-point method to reduce hardware resource consumption. By using the PYNQ-z2 development platform to accelerate the YOLOv2-Tiny CNN, we achieved target object detection and recognition. Compared with CPU (i7-10710U), the processing capacity was 2.94 times that of CPU, and the power consumption was 3.1% of CPU.","PeriodicalId":118234,"journal":{"name":"4th International Conference on Information Science, Electrical and Automation Engineering","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125848270","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}
With the increase of the coverage area of the domestic distribution network, the probability of failure of the distribution network system is also greatly increased. In this paper, a grounding fault line selection system is designed for the small current grounding system of the distribution network. Firstly, taking the fault recorder as the data source, a single-phase grounding fault line selection system is built to monitor the single-phase grounding fault of the small current grounding system in the distribution network of each substation and select the fault line accurately and quickly. Finally, a 380 V physical simulation platform is built to simulate the fault line selection test. Taking Changchun area as an example, a single-phase ground fault occurs in the 66 kV system of Jingyang substation. Through the analysis of the system, the feasibility and accuracy of the line selection system are verified
{"title":"Design of fault line selection for small current grounding system in distribution network","authors":"Xin Liu, Tianjiao Yu, Zeyi Wang, Mingxi Jiao, Daliang Wang, Wenyang Pei, Weiying Deng","doi":"10.1117/12.2689466","DOIUrl":"https://doi.org/10.1117/12.2689466","url":null,"abstract":"With the increase of the coverage area of the domestic distribution network, the probability of failure of the distribution network system is also greatly increased. In this paper, a grounding fault line selection system is designed for the small current grounding system of the distribution network. Firstly, taking the fault recorder as the data source, a single-phase grounding fault line selection system is built to monitor the single-phase grounding fault of the small current grounding system in the distribution network of each substation and select the fault line accurately and quickly. Finally, a 380 V physical simulation platform is built to simulate the fault line selection test. Taking Changchun area as an example, a single-phase ground fault occurs in the 66 kV system of Jingyang substation. Through the analysis of the system, the feasibility and accuracy of the line selection system are verified","PeriodicalId":118234,"journal":{"name":"4th International Conference on Information Science, Electrical and Automation Engineering","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123498071","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 traditional research of power text information mostly uses manual input into the computer, and then uses machine learning or deep learning methods to further study the text. It can be seen that it needs to spend a lot of human resources to input the corresponding text information. In order to solve the above problems, the research of power text information system based on image detection and recognition under the MVC framework is proposed. First, the power text information is recognized using image detection technology, Convert to the form of digital matrix, and then extract the contextual semantic information in the digital matrix using the cyclic neural network. In addition, in order to further improve the effect of information extraction, the attention mechanism is introduced in semantic information extraction, which focuses on the words that have a great impact on the final result, so as to improve the effect, and then the MVC architecture is used to design and implement the final information recognition system, The experimental results show that the proposed power text information system based on image detection and recognition under the MVC framework can effectively improve the effect of text information research.
{"title":"Research on power text information system based on image detection and recognition under MVC framework","authors":"Guanzhong Xu, Zhiwei Huang","doi":"10.1117/12.2690087","DOIUrl":"https://doi.org/10.1117/12.2690087","url":null,"abstract":"The traditional research of power text information mostly uses manual input into the computer, and then uses machine learning or deep learning methods to further study the text. It can be seen that it needs to spend a lot of human resources to input the corresponding text information. In order to solve the above problems, the research of power text information system based on image detection and recognition under the MVC framework is proposed. First, the power text information is recognized using image detection technology, Convert to the form of digital matrix, and then extract the contextual semantic information in the digital matrix using the cyclic neural network. In addition, in order to further improve the effect of information extraction, the attention mechanism is introduced in semantic information extraction, which focuses on the words that have a great impact on the final result, so as to improve the effect, and then the MVC architecture is used to design and implement the final information recognition system, The experimental results show that the proposed power text information system based on image detection and recognition under the MVC framework can effectively improve the effect of text information research.","PeriodicalId":118234,"journal":{"name":"4th International Conference on Information Science, Electrical and Automation Engineering","volume":"177 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124841544","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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