Pub Date : 2021-08-01DOI: 10.1109/ICCEAI52939.2021.00091
Zhou Chunli, C. Zhijun
2G, 3G, 4G and WLAN (Wireless Local Area Networks) form the four network integration. The communication transmission rate and the related spectrum efficiency are limited in traditional mobile communication. Depending on key technology of four network integration, TD-LTE (Time Division Long Term), upward peak rate could be up to 50Mbps unit and descending peak rate reaches up to 100Mbps unit. But the relation between 2G, 3G, 4G and WLAN is complicated. The incorrect station location may increase the cost of network system, and even bring great difficulties to the network operation and maintenance. Because determining base station location appropriately is essential, we max the research between the genetic algorithm and the greedy algorithm to solve this issue. In this paper, relay wireless backhaul technology is assumed, and two types of base stations, RuralStar station and butterfly antenna station, are considered. In order to find out the best base station allocation to achieve the minimum cost and the maximum coverage, we make use of subpopulation initialization, fracture hybridization and mutation of genetic algorithm based on greedy algorithm.
2G、3G、4G和WLAN (Wireless Local Area Networks)构成四网融合。在传统的移动通信中，通信传输速率和相关频谱效率受到限制。依托四网融合的关键技术TD-LTE (Time Division Long Term)，上行峰值速率可达50Mbps，下行峰值速率可达100Mbps。但是2G、3G、4G和WLAN之间的关系是复杂的。不正确的站点位置可能会增加网络系统的成本，甚至给网络运维带来很大的困难。由于基站位置的合理确定至关重要，因此我们将遗传算法与贪心算法相结合进行研究来解决这一问题。本文假设采用中继无线回程技术，并考虑了两种类型的基站:RuralStar站和蝴蝶天线站。为了找到成本最小、覆盖范围最大的最佳基站分配方案，采用了基于贪心算法的遗传算法中的亚种群初始化、断裂杂交和突变等方法。
{"title":"A Practical Base Station Location Optimization Based On Four Networks Integration","authors":"Zhou Chunli, C. Zhijun","doi":"10.1109/ICCEAI52939.2021.00091","DOIUrl":"https://doi.org/10.1109/ICCEAI52939.2021.00091","url":null,"abstract":"2G, 3G, 4G and WLAN (Wireless Local Area Networks) form the four network integration. The communication transmission rate and the related spectrum efficiency are limited in traditional mobile communication. Depending on key technology of four network integration, TD-LTE (Time Division Long Term), upward peak rate could be up to 50Mbps unit and descending peak rate reaches up to 100Mbps unit. But the relation between 2G, 3G, 4G and WLAN is complicated. The incorrect station location may increase the cost of network system, and even bring great difficulties to the network operation and maintenance. Because determining base station location appropriately is essential, we max the research between the genetic algorithm and the greedy algorithm to solve this issue. In this paper, relay wireless backhaul technology is assumed, and two types of base stations, RuralStar station and butterfly antenna station, are considered. In order to find out the best base station allocation to achieve the minimum cost and the maximum coverage, we make use of subpopulation initialization, fracture hybridization and mutation of genetic algorithm based on greedy algorithm.","PeriodicalId":331409,"journal":{"name":"2021 International Conference on Computer Engineering and Artificial Intelligence (ICCEAI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131653030","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 : 2021-08-01DOI: 10.1109/ICCEAI52939.2021.00081
Lei Gao, Weibin Zhang
With the advent of the era of intelligent transportation, the timeliness and accuracy of license plate(LP) recognition is very important for vehicle management. Traditional LP recognition algorithms rely on fixed scenes and complex image capture systems, no LP recognition algorithm can be widely used in a variety of scenarios; this paper proposes a LP recognition algorithm based on deep learning. First, you only look once(Yolo) v3-tiny with reducing the layers of Yolo v3 is used to roughly locate the LP in the video or image. Then with the landmark detection to precisely detect the LP, and finally recognition the LP with deep convolutional neural network(CNN) end to end. At the same time, in case of the difficulty of data collection, we propose an automatic LP generation algorithm, and we pre-trained base models first, then added real scene data fine-tuning the model for different scenarios to improve the portability and robustness of our models. Through experiments comparison proves that our method has significant advantages in real scenarios with timeliness and high accuracy.
{"title":"Research on License Plate Detection and Recognition Based on Deep Learning","authors":"Lei Gao, Weibin Zhang","doi":"10.1109/ICCEAI52939.2021.00081","DOIUrl":"https://doi.org/10.1109/ICCEAI52939.2021.00081","url":null,"abstract":"With the advent of the era of intelligent transportation, the timeliness and accuracy of license plate(LP) recognition is very important for vehicle management. Traditional LP recognition algorithms rely on fixed scenes and complex image capture systems, no LP recognition algorithm can be widely used in a variety of scenarios; this paper proposes a LP recognition algorithm based on deep learning. First, you only look once(Yolo) v3-tiny with reducing the layers of Yolo v3 is used to roughly locate the LP in the video or image. Then with the landmark detection to precisely detect the LP, and finally recognition the LP with deep convolutional neural network(CNN) end to end. At the same time, in case of the difficulty of data collection, we propose an automatic LP generation algorithm, and we pre-trained base models first, then added real scene data fine-tuning the model for different scenarios to improve the portability and robustness of our models. Through experiments comparison proves that our method has significant advantages in real scenarios with timeliness and high accuracy.","PeriodicalId":331409,"journal":{"name":"2021 International Conference on Computer Engineering and Artificial Intelligence (ICCEAI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132143855","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 : 2021-08-01DOI: 10.1109/ICCEAI52939.2021.00068
JianFeng Zhao, Yifan Liang, Qianchao Liang, Mengjie Li
Numerical modeling is an important supplementary means for the study on fuel cell power system. Therefore, the research on modeling method has been a hot topic in academia from Lumped parameter modeling to three-dimensional modeling. But above modeling approaches share the common feature that the accuracy of the model is highly dependent on the key parameters' data veracity in the cell (e.g., conductivity, exchange current density, electrode area, etc.). However, these parameters are often difficult to determine for research work on commercial fuel cell applications and require extensive experimentation or even disassembly of the fuel cell for internal measurements. This paper proposes an intelligent modeling method for proton exchange membrane fuel cell (PEMFC) based on Grey Theory, and filter the optimal model by analyzing and comparing the simulation accuracy of several sub-models. The results show that the proposed intelligent modeling method can build a fuel cell model using limited experimental data and ensure the simulation accuracy, which can simplify the modeling of proton exchange membrane fuel cell work.
{"title":"Intelligent Modeling Method of Proton Exchange Membrane Fuel Cell Based on Grey Theory","authors":"JianFeng Zhao, Yifan Liang, Qianchao Liang, Mengjie Li","doi":"10.1109/ICCEAI52939.2021.00068","DOIUrl":"https://doi.org/10.1109/ICCEAI52939.2021.00068","url":null,"abstract":"Numerical modeling is an important supplementary means for the study on fuel cell power system. Therefore, the research on modeling method has been a hot topic in academia from Lumped parameter modeling to three-dimensional modeling. But above modeling approaches share the common feature that the accuracy of the model is highly dependent on the key parameters' data veracity in the cell (e.g., conductivity, exchange current density, electrode area, etc.). However, these parameters are often difficult to determine for research work on commercial fuel cell applications and require extensive experimentation or even disassembly of the fuel cell for internal measurements. This paper proposes an intelligent modeling method for proton exchange membrane fuel cell (PEMFC) based on Grey Theory, and filter the optimal model by analyzing and comparing the simulation accuracy of several sub-models. The results show that the proposed intelligent modeling method can build a fuel cell model using limited experimental data and ensure the simulation accuracy, which can simplify the modeling of proton exchange membrane fuel cell work.","PeriodicalId":331409,"journal":{"name":"2021 International Conference on Computer Engineering and Artificial Intelligence (ICCEAI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132775437","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 : 2021-08-01DOI: 10.1109/ICCEAI52939.2021.00084
Ze Lv, Lecai Cai, Zhiming Wu, Kui Cheng, B. Chen, Keyuan Tang
The accuracy of target locating for vision-based robots would be affect by environment factors such as light. In order to solve the problem, we proposed a target locating method based on binocular vision locating technology, together with laser scanning positioning technology, considering that laser scanning can obtain three-dimensional environmental information with high definition, and is little influenced by light. In this method, the binocular vision pixel image and the laser scanning point cloud image are first jointly calibrated to map the image pixels with the point cloud data; secondly, the visual image and the laser two-dimensional depth map are detected separately using YOLOv3; then, a decision-level fusion method is utilized to fuse point cloud depth image and the camera image; finally, YOLOv3 is used to detect bound box and confidence of the fused map. The results show that the proposed method has the ability to locate object accurately.
{"title":"Target Locating of Robots Based on the Fusion of Binocular Vision and Laser Scanning","authors":"Ze Lv, Lecai Cai, Zhiming Wu, Kui Cheng, B. Chen, Keyuan Tang","doi":"10.1109/ICCEAI52939.2021.00084","DOIUrl":"https://doi.org/10.1109/ICCEAI52939.2021.00084","url":null,"abstract":"The accuracy of target locating for vision-based robots would be affect by environment factors such as light. In order to solve the problem, we proposed a target locating method based on binocular vision locating technology, together with laser scanning positioning technology, considering that laser scanning can obtain three-dimensional environmental information with high definition, and is little influenced by light. In this method, the binocular vision pixel image and the laser scanning point cloud image are first jointly calibrated to map the image pixels with the point cloud data; secondly, the visual image and the laser two-dimensional depth map are detected separately using YOLOv3; then, a decision-level fusion method is utilized to fuse point cloud depth image and the camera image; finally, YOLOv3 is used to detect bound box and confidence of the fused map. The results show that the proposed method has the ability to locate object accurately.","PeriodicalId":331409,"journal":{"name":"2021 International Conference on Computer Engineering and Artificial Intelligence (ICCEAI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133727585","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 : 2021-08-01DOI: 10.1109/ICCEAI52939.2021.00054
Junaid Abdul Wahid, S. Hussain, Hailing Wang, Zhaoyang Wu, Lei Shi, Yufei Gao
Social media has become one of the most important sources of information dissemination during crisis and pandemics. The unknown nature of these disasters makes it hard to analyze the comprehensive situational awareness through different aspects and sentiments to support authorities. Current aspect detection and sentiment analysis system largely relies on labelled data and also categorize the aspects manually. So, in this research, we proposed a hybrid text analytical framework to do aspect level public sentiments analysis. Our approach consists of three layers, first we extracted and clustered the aspects from the data by utilizing the widely used Latent dirichlet allocation (LDA) topic modelling, then we extracted the sentiments and label the dataset by using the linguistic inquiry and word count (LIWC) lexicon, then in third layer of our framework we mapped the aspects into sentiments and sentiments are then classified with well-known machine learning classifiers. Experiments with real dataset gives us promising results as compared to existing aspect oriented sentiment analysis approaches and our method with different variant of classifiers outperforms existing methods with highest F1 scores of 91 %.
{"title":"Aspect oriented Sentiment classification of COVID-19 twitter data; an enhanced LDA based text analytic approach","authors":"Junaid Abdul Wahid, S. Hussain, Hailing Wang, Zhaoyang Wu, Lei Shi, Yufei Gao","doi":"10.1109/ICCEAI52939.2021.00054","DOIUrl":"https://doi.org/10.1109/ICCEAI52939.2021.00054","url":null,"abstract":"Social media has become one of the most important sources of information dissemination during crisis and pandemics. The unknown nature of these disasters makes it hard to analyze the comprehensive situational awareness through different aspects and sentiments to support authorities. Current aspect detection and sentiment analysis system largely relies on labelled data and also categorize the aspects manually. So, in this research, we proposed a hybrid text analytical framework to do aspect level public sentiments analysis. Our approach consists of three layers, first we extracted and clustered the aspects from the data by utilizing the widely used Latent dirichlet allocation (LDA) topic modelling, then we extracted the sentiments and label the dataset by using the linguistic inquiry and word count (LIWC) lexicon, then in third layer of our framework we mapped the aspects into sentiments and sentiments are then classified with well-known machine learning classifiers. Experiments with real dataset gives us promising results as compared to existing aspect oriented sentiment analysis approaches and our method with different variant of classifiers outperforms existing methods with highest F1 scores of 91 %.","PeriodicalId":331409,"journal":{"name":"2021 International Conference on Computer Engineering and Artificial Intelligence (ICCEAI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114886460","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 : 2021-08-01DOI: 10.1109/ICCEAI52939.2021.00065
Shuyuan Dong, Dingzhou Fei
Rapidly rising national health care expenditure is a problem for both developed and developing countries. Based on the data of medical insurance of insurance companies, this study explores the influencing factors of medical insurance cost. Furthermore, the influencing factors are used as characteristic variables to establish decision tree regression model and linear regression model, and predict the medical insurance cost. The main conclusions are as follows: (1) The characteristics of “region” and “sex” do not affect the insurance cost.(2) Smoking has the greatest influence on insurance cost. Smoking is a characteristic of body mass index (BMI) and has a driving effect on insurance cost. (3) The regression correlation coefficient of decision tree is about 81%, and the linear regression correlation coefficient is 65%, that is, the prediction result of decision tree is more accurate.
{"title":"Improve the interpretability by decision tree regression: exampled by an insurance dataset","authors":"Shuyuan Dong, Dingzhou Fei","doi":"10.1109/ICCEAI52939.2021.00065","DOIUrl":"https://doi.org/10.1109/ICCEAI52939.2021.00065","url":null,"abstract":"Rapidly rising national health care expenditure is a problem for both developed and developing countries. Based on the data of medical insurance of insurance companies, this study explores the influencing factors of medical insurance cost. Furthermore, the influencing factors are used as characteristic variables to establish decision tree regression model and linear regression model, and predict the medical insurance cost. The main conclusions are as follows: (1) The characteristics of “region” and “sex” do not affect the insurance cost.(2) Smoking has the greatest influence on insurance cost. Smoking is a characteristic of body mass index (BMI) and has a driving effect on insurance cost. (3) The regression correlation coefficient of decision tree is about 81%, and the linear regression correlation coefficient is 65%, that is, the prediction result of decision tree is more accurate.","PeriodicalId":331409,"journal":{"name":"2021 International Conference on Computer Engineering and Artificial Intelligence (ICCEAI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129372328","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 : 2021-08-01DOI: 10.1109/ICCEAI52939.2021.00057
Xinna Ma, Lin Qi, Meng Zhao
A bearing fault diagnosis model based on the deep residual network is proposed for the situation that the model recognition rate is low and the classification effect is poor due to the large difference of fault sample distribution in the actual working condition. Firstly the collected bearing fault signals are constructed as fault samples, reconstructs the one-dimensional time series signals into grayscale maps, and initially obtains the input data which is suitable for the deep residual network. To solve the situation of insufficient effective samples, data augmentation by sliding sampling is used to expand the bearing vibration dataset; the samples are further divided into training and testing sets as the input of ResNet101, and data normalization is used to make the training and testing sets learn the same distribution to shorten the training time; then a hybrid attention mechanism is introduced at the appropriate parts to effectively suppress the redundant features and enhance the feature extraction capability of the model. And then a softmax classifier is used for fault classification to achieve intelligent fault diagnosis of rolling bearings. Finally, the Western Reserve University bearing dataset (CWRU) is used to verify the effectiveness of the model. The experimental results show that the proposed bearing fault diagnosis method based on hybrid attention mechanism and residual network can achieve more than 99 % diagnostic accuracy, and it achieves good generalization performance on the high-speed rail wheel pair dataset with an accuracy above 94 %.
{"title":"Bearing fault diagnosis based on attention mechanism and deep residual network","authors":"Xinna Ma, Lin Qi, Meng Zhao","doi":"10.1109/ICCEAI52939.2021.00057","DOIUrl":"https://doi.org/10.1109/ICCEAI52939.2021.00057","url":null,"abstract":"A bearing fault diagnosis model based on the deep residual network is proposed for the situation that the model recognition rate is low and the classification effect is poor due to the large difference of fault sample distribution in the actual working condition. Firstly the collected bearing fault signals are constructed as fault samples, reconstructs the one-dimensional time series signals into grayscale maps, and initially obtains the input data which is suitable for the deep residual network. To solve the situation of insufficient effective samples, data augmentation by sliding sampling is used to expand the bearing vibration dataset; the samples are further divided into training and testing sets as the input of ResNet101, and data normalization is used to make the training and testing sets learn the same distribution to shorten the training time; then a hybrid attention mechanism is introduced at the appropriate parts to effectively suppress the redundant features and enhance the feature extraction capability of the model. And then a softmax classifier is used for fault classification to achieve intelligent fault diagnosis of rolling bearings. Finally, the Western Reserve University bearing dataset (CWRU) is used to verify the effectiveness of the model. The experimental results show that the proposed bearing fault diagnosis method based on hybrid attention mechanism and residual network can achieve more than 99 % diagnostic accuracy, and it achieves good generalization performance on the high-speed rail wheel pair dataset with an accuracy above 94 %.","PeriodicalId":331409,"journal":{"name":"2021 International Conference on Computer Engineering and Artificial Intelligence (ICCEAI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123716171","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 : 2021-08-01DOI: 10.1109/ICCEAI52939.2021.00017
X. Shen, Chenghui Dong, Weiwei Liu, Yan Lan, M. Zhang
Expansive soil is a kind of soil with special properties, widely distributed, difficult treatment technology, and affecting the engineering safety of banks and dams. It is particularly necessary to summarize the development status and trend of the monitoring and warning system of banks and dams for expansive soil based on the characteristics of expansion soil. Based on extensive literatures, the progress of the hardware and software were systematically analyzed for the monitoring and warning system of banks and dams for expansive soil. Specially, the hardware mainly includes: sensors, data acquisition, data transmission, and postprocessing platform and so on; The software mainly includes: multi-sensor integration, data sharing, post-processing, human-computer interface, and route planning of unmanned aerial vehicle and so on. Accordingly, the research directions of the hardware and software of monitoring and early warning system are put forward, and the results will be used for references in the development of monitoring and warning system, engineering construction, engineering management, and emergency management and so on for banks and dams of expansive soil.
{"title":"Development Survey of a Monitoring and Early Warning System for Banks and Dams of Expansive Soil","authors":"X. Shen, Chenghui Dong, Weiwei Liu, Yan Lan, M. Zhang","doi":"10.1109/ICCEAI52939.2021.00017","DOIUrl":"https://doi.org/10.1109/ICCEAI52939.2021.00017","url":null,"abstract":"Expansive soil is a kind of soil with special properties, widely distributed, difficult treatment technology, and affecting the engineering safety of banks and dams. It is particularly necessary to summarize the development status and trend of the monitoring and warning system of banks and dams for expansive soil based on the characteristics of expansion soil. Based on extensive literatures, the progress of the hardware and software were systematically analyzed for the monitoring and warning system of banks and dams for expansive soil. Specially, the hardware mainly includes: sensors, data acquisition, data transmission, and postprocessing platform and so on; The software mainly includes: multi-sensor integration, data sharing, post-processing, human-computer interface, and route planning of unmanned aerial vehicle and so on. Accordingly, the research directions of the hardware and software of monitoring and early warning system are put forward, and the results will be used for references in the development of monitoring and warning system, engineering construction, engineering management, and emergency management and so on for banks and dams of expansive soil.","PeriodicalId":331409,"journal":{"name":"2021 International Conference on Computer Engineering and Artificial Intelligence (ICCEAI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125329213","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 : 2021-08-01DOI: 10.1109/ICCEAI52939.2021.00024
Jiahui Wang, Zhengyou Wang, W. Lu, Shanna Zhuang
At present, due to the different imaging characteristics of infrared and low light bands, they are complementary, and are widely used for multi-modal image registration and fusion. Image registration is a precondition for image fusion. For infrared and low light image registration, this paper first performs rough matching of image features based on the grid motion statistics method. Then, precision matching algorithm based on the combination of distance constraint and slope consistency is proposed, and the coarse matching feature points are initially screened for precision matching. Finally, the coarse matching after screening is selected by the random sampling consensus algorithm for the secondary screening of fine matching, and the final feature matching is obtained. The image registration strategy in this paper performs well in the evaluation indexes of accuracy and recall, which improve the accuracy of image registration.
{"title":"Infrared and Low Light Image Registration from Coarse-to-Fine Matching","authors":"Jiahui Wang, Zhengyou Wang, W. Lu, Shanna Zhuang","doi":"10.1109/ICCEAI52939.2021.00024","DOIUrl":"https://doi.org/10.1109/ICCEAI52939.2021.00024","url":null,"abstract":"At present, due to the different imaging characteristics of infrared and low light bands, they are complementary, and are widely used for multi-modal image registration and fusion. Image registration is a precondition for image fusion. For infrared and low light image registration, this paper first performs rough matching of image features based on the grid motion statistics method. Then, precision matching algorithm based on the combination of distance constraint and slope consistency is proposed, and the coarse matching feature points are initially screened for precision matching. Finally, the coarse matching after screening is selected by the random sampling consensus algorithm for the secondary screening of fine matching, and the final feature matching is obtained. The image registration strategy in this paper performs well in the evaluation indexes of accuracy and recall, which improve the accuracy of image registration.","PeriodicalId":331409,"journal":{"name":"2021 International Conference on Computer Engineering and Artificial Intelligence (ICCEAI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126233195","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 : 2021-08-01DOI: 10.1109/ICCEAI52939.2021.00093
Lijuan Gao, Lingling Wu
objective to investigate the application of fuming Tablet combined with Liraglutid in the treatment of diabetic retinopathy and the effect on the level of insulin-like growth factor-1 (IGF-1) and serum vascular endothelial growth factor (VEGF). Methods: 112 cases of diabetic retinopathy were randomly divided into observation group and control group, 56 cases in each group. The control group was treated with liraglutide, and the observation group was treated with fuming tablet and combined treatment with liraglutide. After 4weeks, 3 months and 6 months, the clinical efficacy, visual acuity, macular thickness, VEGF, IGF-1 and the incidence of adverse reactions in 2 groups were statistically compared. Results: The total effective rate of observation group was higher than that of control group (P<0.05). After treatment, the visual acuity, macular thickness, VEGF and IGF-1 levels in the observation group were lower than those in the control group (P <0.05). Conclusion: Fuming tablet combined with liraglutide can improve visual acuity in patients with diabetic retinopathy.
{"title":"Application of Fuming Tablet Combined with Liraglutide in the Treatment of Diabetic Retinopathy","authors":"Lijuan Gao, Lingling Wu","doi":"10.1109/ICCEAI52939.2021.00093","DOIUrl":"https://doi.org/10.1109/ICCEAI52939.2021.00093","url":null,"abstract":"objective to investigate the application of fuming Tablet combined with Liraglutid in the treatment of diabetic retinopathy and the effect on the level of insulin-like growth factor-1 (IGF-1) and serum vascular endothelial growth factor (VEGF). Methods: 112 cases of diabetic retinopathy were randomly divided into observation group and control group, 56 cases in each group. The control group was treated with liraglutide, and the observation group was treated with fuming tablet and combined treatment with liraglutide. After 4weeks, 3 months and 6 months, the clinical efficacy, visual acuity, macular thickness, VEGF, IGF-1 and the incidence of adverse reactions in 2 groups were statistically compared. Results: The total effective rate of observation group was higher than that of control group (P<0.05). After treatment, the visual acuity, macular thickness, VEGF and IGF-1 levels in the observation group were lower than those in the control group (P <0.05). Conclusion: Fuming tablet combined with liraglutide can improve visual acuity in patients with diabetic retinopathy.","PeriodicalId":331409,"journal":{"name":"2021 International Conference on Computer Engineering and Artificial Intelligence (ICCEAI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126078793","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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