For smart grid sensor system-on-chips, traditional bandgap reference circuits have high power consumption, so low-power bandgap reference circuits must be designed that can meet their complex operating environments. This work designs a low-power bandgap reference voltage source for smart grid sensor system chips. Compared to traditional bandgap reference voltage sources, this design combines the high stability of the traditional BJT bandgap reference and the low power consumption characteristics of the sub-threshold bandgap reference, and they are controlled by a digital signal. In normal mode, only conventional BJT bandgap reference (main bandgap) works, and only the sub-threshold low-power bandgap reference (auxiliary bandgap) works in sleep mode. Realized in UMC 55nm ULP CMOS process, the normal mode power consumption is 65 µ W, the temperature coefficient (TC) is 7.7ppm/°C, and the PSRR is - 78dB; After switching to the sleep mode, the power consumption is 2.38 µ W with a TC 1.45ppm/°C. It provides a stable 1.2V reference voltage when the power supply voltage is 1.6V to meet the demand for stable power supply of the on-chip smart grid sensor system under complex working conditions.
{"title":"A Low-power Bandgap Reference Voltage Source for Smart Grid Sensor System on Chip","authors":"Changbao Xu, Mingyong Xin, Yulei Wang, Junfei Tang, Dehong Liu, Xiaowen Jiang","doi":"10.1109/ICCECE58074.2023.10135246","DOIUrl":"https://doi.org/10.1109/ICCECE58074.2023.10135246","url":null,"abstract":"For smart grid sensor system-on-chips, traditional bandgap reference circuits have high power consumption, so low-power bandgap reference circuits must be designed that can meet their complex operating environments. This work designs a low-power bandgap reference voltage source for smart grid sensor system chips. Compared to traditional bandgap reference voltage sources, this design combines the high stability of the traditional BJT bandgap reference and the low power consumption characteristics of the sub-threshold bandgap reference, and they are controlled by a digital signal. In normal mode, only conventional BJT bandgap reference (main bandgap) works, and only the sub-threshold low-power bandgap reference (auxiliary bandgap) works in sleep mode. Realized in UMC 55nm ULP CMOS process, the normal mode power consumption is 65 µ W, the temperature coefficient (TC) is 7.7ppm/°C, and the PSRR is - 78dB; After switching to the sleep mode, the power consumption is 2.38 µ W with a TC 1.45ppm/°C. It provides a stable 1.2V reference voltage when the power supply voltage is 1.6V to meet the demand for stable power supply of the on-chip smart grid sensor system under complex working conditions.","PeriodicalId":120030,"journal":{"name":"2023 3rd International Conference on Consumer Electronics and Computer Engineering (ICCECE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124350816","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}
Pub Date : 2023-01-06DOI: 10.1109/ICCECE58074.2023.10135230
Jianhua Chen, Songsen Yu, Junle Liang
Fine-grained image classification refers to the more fine-grained sub-categories division based on the basic categories that have been divided. It has become a very challenging research task, due to the characteristics of data with large inter-class differences and small intra-class differences. This paper proposes a cross-layer self-attention (CS) network for learning refined discriminative image features across layers. The network consists of a backbone and a cross-layer self-attention module including three submodules, i.e., cross-layer channel attention, cross-layer space attention and feature fusion submodules. Cross-layer channel attention module can bring a channel self-attention by interacting information between low-layer and high-layer in convolutional networks and then load the channel self-attention into low-level to obtain finer low-level features. Cross-layer spatial attention module has similar effect and can obtain finer low level features in the spatial dimension. The feature fusion module fuses low-level features with high-level features where low-level features can be obtained through combining channel and spatial features. The experiments on three benchmark datasets show that the network based on backbone ResNet101 outperform the most mainstream models on the classification accuracy.
{"title":"A Cross-layer Self-attention Learning Network for Fine-grained Classification","authors":"Jianhua Chen, Songsen Yu, Junle Liang","doi":"10.1109/ICCECE58074.2023.10135230","DOIUrl":"https://doi.org/10.1109/ICCECE58074.2023.10135230","url":null,"abstract":"Fine-grained image classification refers to the more fine-grained sub-categories division based on the basic categories that have been divided. It has become a very challenging research task, due to the characteristics of data with large inter-class differences and small intra-class differences. This paper proposes a cross-layer self-attention (CS) network for learning refined discriminative image features across layers. The network consists of a backbone and a cross-layer self-attention module including three submodules, i.e., cross-layer channel attention, cross-layer space attention and feature fusion submodules. Cross-layer channel attention module can bring a channel self-attention by interacting information between low-layer and high-layer in convolutional networks and then load the channel self-attention into low-level to obtain finer low-level features. Cross-layer spatial attention module has similar effect and can obtain finer low level features in the spatial dimension. The feature fusion module fuses low-level features with high-level features where low-level features can be obtained through combining channel and spatial features. The experiments on three benchmark datasets show that the network based on backbone ResNet101 outperform the most mainstream models on the classification accuracy.","PeriodicalId":120030,"journal":{"name":"2023 3rd International Conference on Consumer Electronics and Computer Engineering (ICCECE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129820048","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}
Giving full play to the advantages of the military Internet of Things and equipment cloud platform, using historical accumulated data and fusing multiple base classifiers and meta-classifiers, a Stacking model for generic equipment status monitoring is established, realizing real-time monitoring of equipment technical status, providing a data basis for accurate planning of security tasks and arranging maintenance activities at all levels, and providing technical support for equipment management and maintenance decisions.
{"title":"Real-time Monitoring of the Technical Status of General Equipment Based on Integrated Learning","authors":"Wei Zhang, Xiaowei Zhang, Wen Dong, Wenshi Wang, Yucai Dong","doi":"10.1109/ICCECE58074.2023.10135358","DOIUrl":"https://doi.org/10.1109/ICCECE58074.2023.10135358","url":null,"abstract":"Giving full play to the advantages of the military Internet of Things and equipment cloud platform, using historical accumulated data and fusing multiple base classifiers and meta-classifiers, a Stacking model for generic equipment status monitoring is established, realizing real-time monitoring of equipment technical status, providing a data basis for accurate planning of security tasks and arranging maintenance activities at all levels, and providing technical support for equipment management and maintenance decisions.","PeriodicalId":120030,"journal":{"name":"2023 3rd International Conference on Consumer Electronics and Computer Engineering (ICCECE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127876958","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}
Pub Date : 2023-01-06DOI: 10.1109/ICCECE58074.2023.10135373
Qi Wang, Wen-hong Lv, Ge Gao, Guimin Gong
In order to reduce the travel of taxis and private cars and meet the personalized travel needs of passengers, a site selection method is proposed to avoid the conventional bus service area and compete for taxi customers. Flexible bus site selection includes fixed site selection and dynamic site selection. Firstly, 1000 points in the morning and evening peak periods of Shenzhen taxi track data were selected, and the theoretical fixed stations were obtained by using the clustering algorithm combined with DBSCAN and K-means. The service areas of conventional bus stations were marked to avoid the conventional bus passenger sources, and the location optimization from theoretical stations to actual stations was realized. Secondly, a dynamic site selection model aiming at minimizing the total cost of the system was constructed and solved by genetic algorithm. Finally, it is verified by an example. The results show that this method has good usability in avoiding the regular bus passenger source and competing with the taxi passenger source by taking the taxi data as the demand point and avoiding the service area of the regular station.
{"title":"Research on Dynamic Site Selection of Flexible Transit Considering Passenger Source Competition","authors":"Qi Wang, Wen-hong Lv, Ge Gao, Guimin Gong","doi":"10.1109/ICCECE58074.2023.10135373","DOIUrl":"https://doi.org/10.1109/ICCECE58074.2023.10135373","url":null,"abstract":"In order to reduce the travel of taxis and private cars and meet the personalized travel needs of passengers, a site selection method is proposed to avoid the conventional bus service area and compete for taxi customers. Flexible bus site selection includes fixed site selection and dynamic site selection. Firstly, 1000 points in the morning and evening peak periods of Shenzhen taxi track data were selected, and the theoretical fixed stations were obtained by using the clustering algorithm combined with DBSCAN and K-means. The service areas of conventional bus stations were marked to avoid the conventional bus passenger sources, and the location optimization from theoretical stations to actual stations was realized. Secondly, a dynamic site selection model aiming at minimizing the total cost of the system was constructed and solved by genetic algorithm. Finally, it is verified by an example. The results show that this method has good usability in avoiding the regular bus passenger source and competing with the taxi passenger source by taking the taxi data as the demand point and avoiding the service area of the regular station.","PeriodicalId":120030,"journal":{"name":"2023 3rd International Conference on Consumer Electronics and Computer Engineering (ICCECE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121391208","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}
Pub Date : 2023-01-06DOI: 10.1109/ICCECE58074.2023.10135351
Zhuangzhuang Feng, Wei Wu
Fine-grained image recognition is a challenging task. Due to the small differences between the categories of fine-grained images and the large differences within the categories, traditional networks based on CNN or Transformer have their own shortcomings in feature extraction. This paper gives full consideration to the characteristics of CNN and Transformer, and proposes a fine-grained recognition algorithm combining WS-DAN (Weakly Supervised Data Augmentation Network) and ViT (Vision Transformer). Firstly, the image patch is obtained by WS-DAN to eliminate the incomplete semantic information of image patch caused by traditional ViT. Then, the image patch is encoded based on Transformer framework and global token is introduced for topological relationship constraints among components, which overcomes the locality of features extracted from traditional CNN network. Finally, the training based on the combination of cross entropy and contrast loss function further improves the recognition ability of the network. The proposed algorithm has achieved satisfactory results on the CUB-200-2011, FGVC-Aircraft and Stanford Cars datasets.
{"title":"Fine-grained Recognition Algorithm For Transformer Based On Part Features","authors":"Zhuangzhuang Feng, Wei Wu","doi":"10.1109/ICCECE58074.2023.10135351","DOIUrl":"https://doi.org/10.1109/ICCECE58074.2023.10135351","url":null,"abstract":"Fine-grained image recognition is a challenging task. Due to the small differences between the categories of fine-grained images and the large differences within the categories, traditional networks based on CNN or Transformer have their own shortcomings in feature extraction. This paper gives full consideration to the characteristics of CNN and Transformer, and proposes a fine-grained recognition algorithm combining WS-DAN (Weakly Supervised Data Augmentation Network) and ViT (Vision Transformer). Firstly, the image patch is obtained by WS-DAN to eliminate the incomplete semantic information of image patch caused by traditional ViT. Then, the image patch is encoded based on Transformer framework and global token is introduced for topological relationship constraints among components, which overcomes the locality of features extracted from traditional CNN network. Finally, the training based on the combination of cross entropy and contrast loss function further improves the recognition ability of the network. The proposed algorithm has achieved satisfactory results on the CUB-200-2011, FGVC-Aircraft and Stanford Cars datasets.","PeriodicalId":120030,"journal":{"name":"2023 3rd International Conference on Consumer Electronics and Computer Engineering (ICCECE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126266021","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}
Pub Date : 2023-01-06DOI: 10.1109/ICCECE58074.2023.10135496
Kun Lang, Xiaoli Chai
With the rapid development of sensors and positioning technology, a huge amount of GPS data generates every day and night. Taking cabs as an example, behind the GPS track information of cabs, there is a large amount of information to be mined, which is crucial for urban governance and consumer behavior analysis. In this paper, we will analyze point data of cab with clustering algorithm, optimize K-means by utilizing the Canopy algorithm for pre-clustering, and parallelize the implementation of the algorithm based on the spark framework. Experiments show that the improved clustering algorithm works well, and the computational efficiency and speedup also improve effectively.
{"title":"Research on clustering algorithm based on spark","authors":"Kun Lang, Xiaoli Chai","doi":"10.1109/ICCECE58074.2023.10135496","DOIUrl":"https://doi.org/10.1109/ICCECE58074.2023.10135496","url":null,"abstract":"With the rapid development of sensors and positioning technology, a huge amount of GPS data generates every day and night. Taking cabs as an example, behind the GPS track information of cabs, there is a large amount of information to be mined, which is crucial for urban governance and consumer behavior analysis. In this paper, we will analyze point data of cab with clustering algorithm, optimize K-means by utilizing the Canopy algorithm for pre-clustering, and parallelize the implementation of the algorithm based on the spark framework. Experiments show that the improved clustering algorithm works well, and the computational efficiency and speedup also improve effectively.","PeriodicalId":120030,"journal":{"name":"2023 3rd International Conference on Consumer Electronics and Computer Engineering (ICCECE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126750440","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}
Pub Date : 2023-01-06DOI: 10.1109/ICCECE58074.2023.10135208
Haomin Lin, Tianyou Yu
Network pruning has been demonstrated as a feasible approach in reducing model complexity and accelerating the process of inference, which make it possible to deploy deep neural network in resource-limited devices. Many previous works on network pruning consider the magnitude of parameters or other intrinsic properties in point-estimates based network as the criterion of module selection, which are incapable of estimating uncertainty of parameters. In this paper, we propose a novel Bayesian filter pruning method, which leverages the advantage of Bayesian Deep Learning (BDL), by exploring the properties of distribution in weight. The proposed method removes redundant filters from a Bayesian network by a criterion of the proposed Signal to Noise Ratio (SNR) that combines properties of importance with uncertainty of filters. Experimental results on two benchmark datasets show the efficiency of our method in maintaining balance between compression and acceleration.
{"title":"Bayesian Filter Pruning for Deep Convolutional Neural Network Compression","authors":"Haomin Lin, Tianyou Yu","doi":"10.1109/ICCECE58074.2023.10135208","DOIUrl":"https://doi.org/10.1109/ICCECE58074.2023.10135208","url":null,"abstract":"Network pruning has been demonstrated as a feasible approach in reducing model complexity and accelerating the process of inference, which make it possible to deploy deep neural network in resource-limited devices. Many previous works on network pruning consider the magnitude of parameters or other intrinsic properties in point-estimates based network as the criterion of module selection, which are incapable of estimating uncertainty of parameters. In this paper, we propose a novel Bayesian filter pruning method, which leverages the advantage of Bayesian Deep Learning (BDL), by exploring the properties of distribution in weight. The proposed method removes redundant filters from a Bayesian network by a criterion of the proposed Signal to Noise Ratio (SNR) that combines properties of importance with uncertainty of filters. Experimental results on two benchmark datasets show the efficiency of our method in maintaining balance between compression and acceleration.","PeriodicalId":120030,"journal":{"name":"2023 3rd International Conference on Consumer Electronics and Computer Engineering (ICCECE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120881367","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}
Pub Date : 2023-01-06DOI: 10.1109/ICCECE58074.2023.10135277
J. Ren, Anxi Cao, Yongxing Jin, Ye Jiang
This paper takes 3000TEU container as the research object, vibration and response were studied. The 3000TEU container ship has been transformed from a diesel engine driven ship to a LNG powered ship, so it is necessary to improve the effectiveness and accuracy of vibration prediction before the transformation. Considering the excitation effect of the main vibration sources such as engine and propeller, the natural frequency of the ship is calculated by finite element method using Femap with NX Nastran software, and then the vibration prediction of the whole ship is carried out to obtain the response results of each region. According to the analysis of the ship's natural frequency and vibration response results, the ship meets the ISO 6954:E2000 vibration standard.
{"title":"Vibration prediction analysis of 3000TEU container ship","authors":"J. Ren, Anxi Cao, Yongxing Jin, Ye Jiang","doi":"10.1109/ICCECE58074.2023.10135277","DOIUrl":"https://doi.org/10.1109/ICCECE58074.2023.10135277","url":null,"abstract":"This paper takes 3000TEU container as the research object, vibration and response were studied. The 3000TEU container ship has been transformed from a diesel engine driven ship to a LNG powered ship, so it is necessary to improve the effectiveness and accuracy of vibration prediction before the transformation. Considering the excitation effect of the main vibration sources such as engine and propeller, the natural frequency of the ship is calculated by finite element method using Femap with NX Nastran software, and then the vibration prediction of the whole ship is carried out to obtain the response results of each region. According to the analysis of the ship's natural frequency and vibration response results, the ship meets the ISO 6954:E2000 vibration standard.","PeriodicalId":120030,"journal":{"name":"2023 3rd International Conference on Consumer Electronics and Computer Engineering (ICCECE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127270547","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}
Pub Date : 2023-01-06DOI: 10.1109/ICCECE58074.2023.10135360
Xiang Luo, Jianfeng Cui, Shuai Ma
With the development of machine learning, fair machine learning has started to receive gradual attention. How to mitigate or eliminate the possible unfair decision results of machine learning has become a popular research topic in this field. At present, the research on fair machine learning is still in its initial stage. In this paper, we analyzed the research and articles related to fair machine learning (January 2011 to December 2022) using CiteSpace visualization software, explored research collaboration networks (authors, institutions, and countries), keyword co-occurrence and clustering networks, and literature co-citation and clustering networks, and analyzed and constructed knowledge graphs. To understand the research foundation, related research progress, the latest research directions, and the research methods receiving attention in the field of fair machine learning through the analysis of the knowledge graph. Relevant key articles are discussed, and future research directions are envisioned.
{"title":"Fair Machine Learning-An Analytical Study Based on CiteSpace","authors":"Xiang Luo, Jianfeng Cui, Shuai Ma","doi":"10.1109/ICCECE58074.2023.10135360","DOIUrl":"https://doi.org/10.1109/ICCECE58074.2023.10135360","url":null,"abstract":"With the development of machine learning, fair machine learning has started to receive gradual attention. How to mitigate or eliminate the possible unfair decision results of machine learning has become a popular research topic in this field. At present, the research on fair machine learning is still in its initial stage. In this paper, we analyzed the research and articles related to fair machine learning (January 2011 to December 2022) using CiteSpace visualization software, explored research collaboration networks (authors, institutions, and countries), keyword co-occurrence and clustering networks, and literature co-citation and clustering networks, and analyzed and constructed knowledge graphs. To understand the research foundation, related research progress, the latest research directions, and the research methods receiving attention in the field of fair machine learning through the analysis of the knowledge graph. Relevant key articles are discussed, and future research directions are envisioned.","PeriodicalId":120030,"journal":{"name":"2023 3rd International Conference on Consumer Electronics and Computer Engineering (ICCECE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129741974","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}
Pub Date : 2023-01-06DOI: 10.1109/ICCECE58074.2023.10135453
Zhengyi Zha
CNN and Transformer have been used widely through computer vision problems, including object detection and instance segmentation. But usually, CNN and Transformer are utilized independently. Recently, a new method called CMT has combined the advantages of both. It applies convolution to mitigate the computation overhead. In this work, we combine the advantages of CMT and swin transformer to enrich feature extraction. And build a framework that used the new backbone to achieve instance segmentation. Finally, we have done experiments in driving scenes and achieved good results.
{"title":"Instance Segmentation Combined CMT and Swin Transformer in Driving Scenes","authors":"Zhengyi Zha","doi":"10.1109/ICCECE58074.2023.10135453","DOIUrl":"https://doi.org/10.1109/ICCECE58074.2023.10135453","url":null,"abstract":"CNN and Transformer have been used widely through computer vision problems, including object detection and instance segmentation. But usually, CNN and Transformer are utilized independently. Recently, a new method called CMT has combined the advantages of both. It applies convolution to mitigate the computation overhead. In this work, we combine the advantages of CMT and swin transformer to enrich feature extraction. And build a framework that used the new backbone to achieve instance segmentation. Finally, we have done experiments in driving scenes and achieved good results.","PeriodicalId":120030,"journal":{"name":"2023 3rd International Conference on Consumer Electronics and Computer Engineering (ICCECE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124228765","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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