Jinke Li, Qiang Zhang, ZhiJun Zhang, Fan Wang, Xijie Li
With the development of China's economic globalization, the stock market has gradually demonstrated its important position in the development of China's market economy. First, this paper selects the average market-to-sales ratio as the valuation level, uses an evaluation model to calculate the valuation level of the Chinese A-share market and the US NASDAQ market in 2018, and calculates the valuation premium or discount level of these two markets. Secondly, we establish a multiple linear regression model to quantitatively analyze the relationship between the valuation indicators and fundamental indicators and liquidity indicators of China A-shares and the US NASDAQ market. Then, a grey forecast model is established to predict and analyze the fundamental indicators and liquidity indicators of the Chinese A-share market and the US NASDAQ market in 2019. According to the forecast results, the valuation indicators of these two markets in 2019 are calculated. The results found that the valuation level of my country's first batch of sci-tech innovation board companies fluctuates around 5 times, which is smaller than that of the United States, indicating that China's stock market has greater potential.
{"title":"Valuation Analysis of Chinese and American Listed Companies Based on Multiple Linear Regression and Grey Forecasting Model","authors":"Jinke Li, Qiang Zhang, ZhiJun Zhang, Fan Wang, Xijie Li","doi":"10.1145/3484274.3484308","DOIUrl":"https://doi.org/10.1145/3484274.3484308","url":null,"abstract":"With the development of China's economic globalization, the stock market has gradually demonstrated its important position in the development of China's market economy. First, this paper selects the average market-to-sales ratio as the valuation level, uses an evaluation model to calculate the valuation level of the Chinese A-share market and the US NASDAQ market in 2018, and calculates the valuation premium or discount level of these two markets. Secondly, we establish a multiple linear regression model to quantitatively analyze the relationship between the valuation indicators and fundamental indicators and liquidity indicators of China A-shares and the US NASDAQ market. Then, a grey forecast model is established to predict and analyze the fundamental indicators and liquidity indicators of the Chinese A-share market and the US NASDAQ market in 2019. According to the forecast results, the valuation indicators of these two markets in 2019 are calculated. The results found that the valuation level of my country's first batch of sci-tech innovation board companies fluctuates around 5 times, which is smaller than that of the United States, indicating that China's stock market has greater potential.","PeriodicalId":143540,"journal":{"name":"Proceedings of the 4th International Conference on Control and Computer Vision","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124571031","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 rehabilitation center is a department set up by the hospital for sports medicine. When patients undergo rehabilitation training, they need to be trained and rested in strict accordance with the doctor's requirements. However, sometimes there are too many patients and the number of doctors and nurses is limited, so each patient cannot be fully taken care of. Therefore, the training of patients who do not have the concept of time or the rest time is too long or too short, which will cause unsatisfactory medical effects. The components of the patient rehabilitation training and rest time monitoring device are STM32F103C8T6 microcontroller core board, buttons, travel switch and buzzer. The status of the travel switch distinguishes whether the patient is resting (lying down or sitting down) or training (standing). The function of the button is to set the patient's training and rest time. When the patient's training time does not reach the specified value, if the patient is resting, the buzzer will alarm and monitor the rest time. If the patient is training at this time, the buzzer will not act and monitor the training time; when the patient's rest time reaches the specified value, the beeper The buzzer will alarm and monitor the rest time. If the patient is training at this time, the buzzer will not act and monitor the training time.
{"title":"Design of Patient Rehabilitation Training and Rest Monitoring Device","authors":"Meili Liu","doi":"10.1145/3484274.3484305","DOIUrl":"https://doi.org/10.1145/3484274.3484305","url":null,"abstract":"The rehabilitation center is a department set up by the hospital for sports medicine. When patients undergo rehabilitation training, they need to be trained and rested in strict accordance with the doctor's requirements. However, sometimes there are too many patients and the number of doctors and nurses is limited, so each patient cannot be fully taken care of. Therefore, the training of patients who do not have the concept of time or the rest time is too long or too short, which will cause unsatisfactory medical effects. The components of the patient rehabilitation training and rest time monitoring device are STM32F103C8T6 microcontroller core board, buttons, travel switch and buzzer. The status of the travel switch distinguishes whether the patient is resting (lying down or sitting down) or training (standing). The function of the button is to set the patient's training and rest time. When the patient's training time does not reach the specified value, if the patient is resting, the buzzer will alarm and monitor the rest time. If the patient is training at this time, the buzzer will not act and monitor the training time; when the patient's rest time reaches the specified value, the beeper The buzzer will alarm and monitor the rest time. If the patient is training at this time, the buzzer will not act and monitor the training time.","PeriodicalId":143540,"journal":{"name":"Proceedings of the 4th International Conference on Control and Computer Vision","volume":"99 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133145754","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}
Acne is an incredibly common skin condition caused by oil from clog hair follicles and dead skin. It is usually found in adolescence to young adulthood and may affects people of all ages. The diagnosis of acne usually requires manual examination by a well-trained dermatologist, which can take a lot of effort. Therefore, it is necessary to design an automatic classification algorithm for acne lesion. However, due to the lack of proper imaging method, acne-specific facial image analysis is still a difficult task. To address this issue we propose a novel approach using high-definite VISIA image. By incorporating better image and better models, an overall accuracy above 80% is achieved on a large-scale dataset consists of more than one thousand people. The results imply that automatic acne classification is a promising direction.
{"title":"Automatic Acne Classification using VISIA","authors":"Yuxuan Wang, Annan Li, Chengxu Li, Yong Cui","doi":"10.1145/3484274.3484291","DOIUrl":"https://doi.org/10.1145/3484274.3484291","url":null,"abstract":"Acne is an incredibly common skin condition caused by oil from clog hair follicles and dead skin. It is usually found in adolescence to young adulthood and may affects people of all ages. The diagnosis of acne usually requires manual examination by a well-trained dermatologist, which can take a lot of effort. Therefore, it is necessary to design an automatic classification algorithm for acne lesion. However, due to the lack of proper imaging method, acne-specific facial image analysis is still a difficult task. To address this issue we propose a novel approach using high-definite VISIA image. By incorporating better image and better models, an overall accuracy above 80% is achieved on a large-scale dataset consists of more than one thousand people. The results imply that automatic acne classification is a promising direction.","PeriodicalId":143540,"journal":{"name":"Proceedings of the 4th International Conference on Control and Computer Vision","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128916784","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}
Graph convolutions networks (GCN) have drawn attention for skeleton-based action recognition because a skeleton with joints and bones can be naturally regarded as a graph structure. However, the existing methods are limited in temporal sequence modeling of human actions. To consider temporal factors in action modeling, we present a novel Temporal-Aware Graph Convolution Network (TA-GCN). First, we design a causal temporal convolution (CTCN) layer to ensure no impractical future information leakage to the past. Second, we present a novel cross-spatial-temporal graph convolution (3D-GCN) layer that extends an adaptive graph from the spatial to the temporal domain to capture local cross-spatial-temporal dependencies among joints. Involving the two temporal factors, TA-GCN can model the sequential nature of human actions. Experimental results on two large-scale datasets, NTU-RGB+D and Kinetics-Skeleton, indicate that our network achieves accuracy improvement (about 1% on the two datasets) over previous methods.
{"title":"Temporal-Aware Graph Convolution Network for Skeleton-based Action Recognition","authors":"Yulai Xie, Yang Zhang, Fang Ren","doi":"10.1145/3484274.3484288","DOIUrl":"https://doi.org/10.1145/3484274.3484288","url":null,"abstract":"Graph convolutions networks (GCN) have drawn attention for skeleton-based action recognition because a skeleton with joints and bones can be naturally regarded as a graph structure. However, the existing methods are limited in temporal sequence modeling of human actions. To consider temporal factors in action modeling, we present a novel Temporal-Aware Graph Convolution Network (TA-GCN). First, we design a causal temporal convolution (CTCN) layer to ensure no impractical future information leakage to the past. Second, we present a novel cross-spatial-temporal graph convolution (3D-GCN) layer that extends an adaptive graph from the spatial to the temporal domain to capture local cross-spatial-temporal dependencies among joints. Involving the two temporal factors, TA-GCN can model the sequential nature of human actions. Experimental results on two large-scale datasets, NTU-RGB+D and Kinetics-Skeleton, indicate that our network achieves accuracy improvement (about 1% on the two datasets) over previous methods.","PeriodicalId":143540,"journal":{"name":"Proceedings of the 4th International Conference on Control and Computer Vision","volume":"25 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125672498","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 work presents an approach for highlight separation of real high resolution portrait image. In order to obtain reliable ground truth of real images, a controllable portrait image collection system with 156 groups of light sources has been built. It has 4 cameras to collect the portrait images of 36 subjects from different angles, and then we use 4 data processing strategies on these images to obtain 4 training datasets. Based on these datasets, 4 U-Net networks are trained by using a single image as input. To test and evaluate, we input the 2560*2560 resolution images into 4 models, and finally determine the best data processing strategy and trained network. Our method creates precise and believable highlight separation results for 2560*2560 high resolution images, including when the subject is not looking straight at the camera.
{"title":"Learning Highlight Separation of Real High Resolution Portrait Image","authors":"Ruikang Ju, Dongdong Weng, Bin Liang","doi":"10.1145/3484274.3484278","DOIUrl":"https://doi.org/10.1145/3484274.3484278","url":null,"abstract":"∗This work presents an approach for highlight separation of real high resolution portrait image. In order to obtain reliable ground truth of real images, a controllable portrait image collection system with 156 groups of light sources has been built. It has 4 cameras to collect the portrait images of 36 subjects from different angles, and then we use 4 data processing strategies on these images to obtain 4 training datasets. Based on these datasets, 4 U-Net networks are trained by using a single image as input. To test and evaluate, we input the 2560*2560 resolution images into 4 models, and finally determine the best data processing strategy and trained network. Our method creates precise and believable highlight separation results for 2560*2560 high resolution images, including when the subject is not looking straight at the camera.","PeriodicalId":143540,"journal":{"name":"Proceedings of the 4th International Conference on Control and Computer Vision","volume":"140 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133005985","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}
Text is the media to convey and transmit information. Braille is extremely important for vision impaired people to exchange information through reading and writing. A braille translator is crucial tool for aiding people to understand braille messages. In this paper, we implement character-based braille translator using ResNet, there are three versions of ResNet we implement for braille classifiers, including ResNet-18, ResNet-34, and ResNet-50. We also implement a word-based braille detector using a novel solution called Adaptive Bezier-Curve Network (ABCNet), which is a Scene Text Recognition (STR) method for detecting word-based text in natural scenes. A comparison is present to evaluate the performance of ABCNet.
{"title":"Braille Recognition Using Deep Learning","authors":"Changjian Li, Weiqi Yan","doi":"10.1145/3484274.3484280","DOIUrl":"https://doi.org/10.1145/3484274.3484280","url":null,"abstract":"Text is the media to convey and transmit information. Braille is extremely important for vision impaired people to exchange information through reading and writing. A braille translator is crucial tool for aiding people to understand braille messages. In this paper, we implement character-based braille translator using ResNet, there are three versions of ResNet we implement for braille classifiers, including ResNet-18, ResNet-34, and ResNet-50. We also implement a word-based braille detector using a novel solution called Adaptive Bezier-Curve Network (ABCNet), which is a Scene Text Recognition (STR) method for detecting word-based text in natural scenes. A comparison is present to evaluate the performance of ABCNet.","PeriodicalId":143540,"journal":{"name":"Proceedings of the 4th International Conference on Control and Computer Vision","volume":"37 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120992788","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 reduce the energy waste of the road sweeper and simplify the operation process of the driver, a set of intelligent cleaning device for road sweeper is designed, which can automatically identify road garbage and adjust the cleaning mechanism to the required power. This device is composed by a monocular camera, an industrial computer, a vehicle DC power, and a cleaning mechanism. In terms of algorithms, two Mask R-CNN neural network models are used to detect road garbage. First, the road surface information is obtained by the first model to obtain the workable area of the road sweeper, which can reduce the influence of factors such as vehicles and pedestrians. Secondly, the road surface information in the workable area is divided into two types, road-specific information and garbage, the garbage detection and marking are completed after the second model is tested. Finally, the garbage coverage rate is used as a feature to adjust the power of the cleaning device. The result of testing and analysis of this algorithms shows that the real-time performance and recognition accuracy can achieve the expected results.
{"title":"Research on Recognition of Working Area and Road Garbage for Road Sweeper Based on Mask R-CNN Neural Network","authors":"Teng Liu, Xuexun Guo, Xiaofei Pei","doi":"10.1145/3484274.3484287","DOIUrl":"https://doi.org/10.1145/3484274.3484287","url":null,"abstract":"In order to reduce the energy waste of the road sweeper and simplify the operation process of the driver, a set of intelligent cleaning device for road sweeper is designed, which can automatically identify road garbage and adjust the cleaning mechanism to the required power. This device is composed by a monocular camera, an industrial computer, a vehicle DC power, and a cleaning mechanism. In terms of algorithms, two Mask R-CNN neural network models are used to detect road garbage. First, the road surface information is obtained by the first model to obtain the workable area of the road sweeper, which can reduce the influence of factors such as vehicles and pedestrians. Secondly, the road surface information in the workable area is divided into two types, road-specific information and garbage, the garbage detection and marking are completed after the second model is tested. Finally, the garbage coverage rate is used as a feature to adjust the power of the cleaning device. The result of testing and analysis of this algorithms shows that the real-time performance and recognition accuracy can achieve the expected results.","PeriodicalId":143540,"journal":{"name":"Proceedings of the 4th International Conference on Control and Computer Vision","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126992223","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 growth of the information technology field, machine learning, artificial intelligence and other fields have begun to require higher computing performance. Networked heterogeneous computing is one of the critical solutions for them. The hardware composition, software design and use methods in the Networked Heterogeneous Computing Platform are quite different from those in the traditional computing platform. There is no mature test method or testing system at present. Therefore, this paper studies the Networked Heterogeneous Computing Platform, from the composition of the platform to the construction of the testing system. The testing system of Networked Heterogeneous Computing Platform is proposed, and each test indicator is discussed in detail. Finally, the test method is provided.
{"title":"Research on Testing System of Networked Heterogeneous Computing Resource Platform","authors":"Yanan Yang, Yan Gao, Zhenhao Xu","doi":"10.1145/3484274.3484303","DOIUrl":"https://doi.org/10.1145/3484274.3484303","url":null,"abstract":"With the growth of the information technology field, machine learning, artificial intelligence and other fields have begun to require higher computing performance. Networked heterogeneous computing is one of the critical solutions for them. The hardware composition, software design and use methods in the Networked Heterogeneous Computing Platform are quite different from those in the traditional computing platform. There is no mature test method or testing system at present. Therefore, this paper studies the Networked Heterogeneous Computing Platform, from the composition of the platform to the construction of the testing system. The testing system of Networked Heterogeneous Computing Platform is proposed, and each test indicator is discussed in detail. Finally, the test method is provided.","PeriodicalId":143540,"journal":{"name":"Proceedings of the 4th International Conference on Control and Computer Vision","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127825064","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 recent years, the problem of lake eutrophication has become increasingly severe. The monitoring and control of cyanobacteria in lakes are of great significance. The information obtained by existing monitoring methods is relatively lagging, and it is impossible to monitor the sudden outbreak of cyanobacteria in time. Getting cyanobacteria information directly through camera images is a breakthrough. In this paper, after analyzing the characteristics of time series cyanobacteria images, we propose a block prediction scheme based on the CNN model. Experiments show that this method can quickly calculate the coverage of cyanobacteria in the monitoring image in a short time. It can also effectively distinguish cyanobacteria-rich water areas, which significantly facilitates water quality monitoring and cyanobacteria management. We can draw a chart of the changes in the coverage of cyanobacteria by analyzing multi-day time-series images. The chart helps us conduct a short-term water quality analysis to better deal with the outbreak of cyanobacteria.
{"title":"Prediction of the Cyanobacteria Coverage in Time-series Images based on Convolutional Neural Network","authors":"Xiangyu Ye, Zhiquan Lai, Dongsheng Li","doi":"10.1145/3484274.3484298","DOIUrl":"https://doi.org/10.1145/3484274.3484298","url":null,"abstract":"In recent years, the problem of lake eutrophication has become increasingly severe. The monitoring and control of cyanobacteria in lakes are of great significance. The information obtained by existing monitoring methods is relatively lagging, and it is impossible to monitor the sudden outbreak of cyanobacteria in time. Getting cyanobacteria information directly through camera images is a breakthrough. In this paper, after analyzing the characteristics of time series cyanobacteria images, we propose a block prediction scheme based on the CNN model. Experiments show that this method can quickly calculate the coverage of cyanobacteria in the monitoring image in a short time. It can also effectively distinguish cyanobacteria-rich water areas, which significantly facilitates water quality monitoring and cyanobacteria management. We can draw a chart of the changes in the coverage of cyanobacteria by analyzing multi-day time-series images. The chart helps us conduct a short-term water quality analysis to better deal with the outbreak of cyanobacteria.","PeriodicalId":143540,"journal":{"name":"Proceedings of the 4th International Conference on Control and Computer Vision","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127843307","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 recent years, the applications of 3D point cloud data representing terrain surfaces have been growing rapidly. Such data typically have a very fine spatial resolution, which can lead to computational and visualisation issues. To overcome these issues, it is a common practice to reduce the density of point cloud data during initial data processing. As such, various simplification methods had been developed and used in practice. The choice of those methods is crucial to preserve features and shapes of the terrain in the simplified point cloud data. Previous studies on this matter were focused mainly on the methods commonly used in geosciences, but did not consider those in computer graphics. In this study, a total of eight simplification methods that are used widely in both geosciences and computer graphics were compared and analyzed using four sets of terrain surface point cloud data. In addition, unlike previous studies where a global RMSE (root mean squared error) was used as the metric for comparing different methods, the standard deviation of local RMSEs (root mean squared errors) was also calculated in this study to check the uniformity of local RMSEs over the whole terrain areas considered. The results show that the adaptive sampling method yielded thinned point cloud data of higher overall accuracy and more consistent local RMSEs than those obtained using the other methods considered.
{"title":"Comparisons of Eight Simplification Methods for Data Reduction of Terrain Point Cloud","authors":"Yuan Fang, L. Fan","doi":"10.1145/3484274.3484307","DOIUrl":"https://doi.org/10.1145/3484274.3484307","url":null,"abstract":"In recent years, the applications of 3D point cloud data representing terrain surfaces have been growing rapidly. Such data typically have a very fine spatial resolution, which can lead to computational and visualisation issues. To overcome these issues, it is a common practice to reduce the density of point cloud data during initial data processing. As such, various simplification methods had been developed and used in practice. The choice of those methods is crucial to preserve features and shapes of the terrain in the simplified point cloud data. Previous studies on this matter were focused mainly on the methods commonly used in geosciences, but did not consider those in computer graphics. In this study, a total of eight simplification methods that are used widely in both geosciences and computer graphics were compared and analyzed using four sets of terrain surface point cloud data. In addition, unlike previous studies where a global RMSE (root mean squared error) was used as the metric for comparing different methods, the standard deviation of local RMSEs (root mean squared errors) was also calculated in this study to check the uniformity of local RMSEs over the whole terrain areas considered. The results show that the adaptive sampling method yielded thinned point cloud data of higher overall accuracy and more consistent local RMSEs than those obtained using the other methods considered.","PeriodicalId":143540,"journal":{"name":"Proceedings of the 4th International Conference on Control and Computer Vision","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133838994","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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