With the rapid development of aviation electromechanical technology, new aircraft are equipped with a large number of sensors and electronic control devices. In order to meet the data transmission requirements of avionics systems, Ethernet Data Distribution (DDS) protocol has gradually become the standard solution for published/subscribed data in distributed real-time systems. Facing the new requirements of bus data processing using DDS protocol, starting from the characteristics and format of Ethernet DDS data recording, this paper gives the overall architecture and core processing method of the processing software. This paper analyzes the key problems of Ethernet DDS data processing and proposes solutions for DDS bus Interface Control Document (ICD) plane structure, multi-level parallel data processing algorithm, data detection and filtering. The method is validated and can correctly process Ethernet DDS data.
{"title":"Ethernet data distribution service data processing method","authors":"Yu Wu, Xiaoya Li, Huiyang Hu","doi":"10.1117/12.2679124","DOIUrl":"https://doi.org/10.1117/12.2679124","url":null,"abstract":"With the rapid development of aviation electromechanical technology, new aircraft are equipped with a large number of sensors and electronic control devices. In order to meet the data transmission requirements of avionics systems, Ethernet Data Distribution (DDS) protocol has gradually become the standard solution for published/subscribed data in distributed real-time systems. Facing the new requirements of bus data processing using DDS protocol, starting from the characteristics and format of Ethernet DDS data recording, this paper gives the overall architecture and core processing method of the processing software. This paper analyzes the key problems of Ethernet DDS data processing and proposes solutions for DDS bus Interface Control Document (ICD) plane structure, multi-level parallel data processing algorithm, data detection and filtering. The method is validated and can correctly process Ethernet DDS data.","PeriodicalId":342847,"journal":{"name":"International Conference on Algorithms, Microchips and Network Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123465310","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 this paper, to predict the nuclear weapons, we first introduce evaluation indicators that affect the possession of nuclear weapons, economic indicators, scientific and technological indicators, and establish a TOPSIS evaluation model improved by the optimal assignment method to predict countries with evaluation values less than 20, as countries that will possess nuclear weapons in the next 100 years. Then, in view of the fact that the number of nuclear weapons is calculated in years and changes over time, and considering the global consensus to limit the number of nuclear weapons from 2022 when the Treaty on the Prohibition of Nuclear Weapons and other policies come into force, it is decided to build a Verhulst prediction model with saturation based on the LS-SVM algorithm, and finally to improve the accuracy and reasonableness of the model by using the metabolic data processing method of equal-dimensional neutrosophic recurrence prediction. By predicting the number of nuclear weapons, countries can make reasonable plans for future nuclear weapons production and hope to reach a global consensus, which will help to solve the nuclear crisis.
{"title":"Nuclear weapon prediction based on the verhulst method of comprehensive weighting and LS-SVM equidimensional information supplement","authors":"Hongyi Duan, Jianan Zhang","doi":"10.1117/12.2678905","DOIUrl":"https://doi.org/10.1117/12.2678905","url":null,"abstract":"In this paper, to predict the nuclear weapons, we first introduce evaluation indicators that affect the possession of nuclear weapons, economic indicators, scientific and technological indicators, and establish a TOPSIS evaluation model improved by the optimal assignment method to predict countries with evaluation values less than 20, as countries that will possess nuclear weapons in the next 100 years. Then, in view of the fact that the number of nuclear weapons is calculated in years and changes over time, and considering the global consensus to limit the number of nuclear weapons from 2022 when the Treaty on the Prohibition of Nuclear Weapons and other policies come into force, it is decided to build a Verhulst prediction model with saturation based on the LS-SVM algorithm, and finally to improve the accuracy and reasonableness of the model by using the metabolic data processing method of equal-dimensional neutrosophic recurrence prediction. By predicting the number of nuclear weapons, countries can make reasonable plans for future nuclear weapons production and hope to reach a global consensus, which will help to solve the nuclear crisis.","PeriodicalId":342847,"journal":{"name":"International Conference on Algorithms, Microchips and Network Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123183940","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 continuous development of intelligent connected vehicle industry, cameras and other vehicle-mounted devices are widely used, so the amount of data collection is increasing. There is a large amount of sensitive information hidden in the image data generated by connected vehicles. Once the data leakage event occurs, it may cause very serious consequences. In order to improve the security of connected vehicle data and reduce the threat of sensitive information leakage in image data, this paper provides a desensitization process of connected vehicle image data, and desensitizes sensitive information based on instance segmentation technology. In this paper, a real road image dataset is collected, and realizes desensitization of the modified dataset based on proposed framework.
{"title":"Desensitization method of image data in the Internet of Vehicles based on instance segmentation","authors":"Shuang Li, Yue Zhou, Xin Zhang, Meng Zhang","doi":"10.1117/12.2679260","DOIUrl":"https://doi.org/10.1117/12.2679260","url":null,"abstract":"With the continuous development of intelligent connected vehicle industry, cameras and other vehicle-mounted devices are widely used, so the amount of data collection is increasing. There is a large amount of sensitive information hidden in the image data generated by connected vehicles. Once the data leakage event occurs, it may cause very serious consequences. In order to improve the security of connected vehicle data and reduce the threat of sensitive information leakage in image data, this paper provides a desensitization process of connected vehicle image data, and desensitizes sensitive information based on instance segmentation technology. In this paper, a real road image dataset is collected, and realizes desensitization of the modified dataset based on proposed framework.","PeriodicalId":342847,"journal":{"name":"International Conference on Algorithms, Microchips and Network Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114130015","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 proposes a fall detection technology based on the YOLOv5s algorithm to solve the problem of hit injury. The method is designed based on the embedded ARM development board of Orange Pi Zero 2. The camera is used to collect human data in real-time, and algorithms train the collected data and are finally verified. The experimental results show that: (1) this method has a reasonable success rate of recognition for standing, walking, and falling, but the success rate of recognition for squatting needs to be improved; (2) Compared with the OpenPose algorithm, the YOLOv5 algorithm has better accuracy, precision, and average accuracy means, but the performance in recall rate is not very good.
本文提出了一种基于YOLOv5s算法的跌倒检测技术，以解决碰撞损伤问题。该方法是基于嵌入式ARM开发板Orange Pi Zero 2设计的。该摄像机用于实时采集人体数据，算法对采集到的数据进行训练并最终验证。实验结果表明:(1)该方法对站立、行走和跌倒的识别成功率都比较合理，但对蹲下的识别成功率还有待提高;(2)与OpenPose算法相比，YOLOv5算法具有更好的正确率、精密度和平均正确率均值，但在召回率方面表现不佳。
{"title":"Human fall detection scheme based on YOLO visual recognition and embedded ARM architecture","authors":"Zhuoya Jia, Hanbo Zhang, Yang Jia, Yunjing Zheng, Dong Li, Shaobo Jia","doi":"10.1117/12.2678904","DOIUrl":"https://doi.org/10.1117/12.2678904","url":null,"abstract":"This paper proposes a fall detection technology based on the YOLOv5s algorithm to solve the problem of hit injury. The method is designed based on the embedded ARM development board of Orange Pi Zero 2. The camera is used to collect human data in real-time, and algorithms train the collected data and are finally verified. The experimental results show that: (1) this method has a reasonable success rate of recognition for standing, walking, and falling, but the success rate of recognition for squatting needs to be improved; (2) Compared with the OpenPose algorithm, the YOLOv5 algorithm has better accuracy, precision, and average accuracy means, but the performance in recall rate is not very good.","PeriodicalId":342847,"journal":{"name":"International Conference on Algorithms, Microchips and Network Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133193555","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 adoption of network technology for control and measurement makes Industrial Control Systems (ICSs) and Internet of Things (IoTs) more and more powerful, whose communication data plays an important role to realize data intercommunication and sharing. However, communication data have different characteristics, even if the same communication protocol is used in ICS or IT, the communication characteristics and modes are quite different. In order to understand the difference of communication data between internet and industrial ethernet, in this paper, we conduct a series of experiments that use information entropy algorithm to data changes based on different industrial protocol and http protocol, which always is used to measure the uncertainty and describe the uncertainty of data packets. The experimental result analysis shows that there exists a big difference in industrial ethernet data and internet data, the industrial ethernet data are more relatively balanced and more regular than those of internet data.
{"title":"Research on matrix entropy value between industrial ethernet data and internet data based on information entropy","authors":"Hao Jiang, Jiongxuan Jia, Jianlei Gao, Fuyan Wang, Fengjuan Xu","doi":"10.1117/12.2679243","DOIUrl":"https://doi.org/10.1117/12.2679243","url":null,"abstract":"The adoption of network technology for control and measurement makes Industrial Control Systems (ICSs) and Internet of Things (IoTs) more and more powerful, whose communication data plays an important role to realize data intercommunication and sharing. However, communication data have different characteristics, even if the same communication protocol is used in ICS or IT, the communication characteristics and modes are quite different. In order to understand the difference of communication data between internet and industrial ethernet, in this paper, we conduct a series of experiments that use information entropy algorithm to data changes based on different industrial protocol and http protocol, which always is used to measure the uncertainty and describe the uncertainty of data packets. The experimental result analysis shows that there exists a big difference in industrial ethernet data and internet data, the industrial ethernet data are more relatively balanced and more regular than those of internet data.","PeriodicalId":342847,"journal":{"name":"International Conference on Algorithms, Microchips and Network Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133525516","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 implement precision management on the campus, the decisions need data support, and the crowd density on campus is one of the important parts. Based on campus wireless network logs, which is widely used on the campus, this paper proposes an analysis algorithm to obtain online wireless network user numbers in real time and draws the conclusion that the numbers of online users can represent crowd density on campus. Experimental results show that this algorithm can effectively get the numbers of online users in each area of the campus, and the campus heat map made with these data can reflect the real-time distribution of campus crowd and crowd density. This method uses log analysis method which is a general solution for some problems and has practical value for in-depth analysis.
{"title":"An analysis algorithm for real-time monitoring of campus crowd density based on campus wireless network logs","authors":"Ying Xia, Shuping Wu, Hui-qun Yu","doi":"10.1117/12.2679233","DOIUrl":"https://doi.org/10.1117/12.2679233","url":null,"abstract":"In order to implement precision management on the campus, the decisions need data support, and the crowd density on campus is one of the important parts. Based on campus wireless network logs, which is widely used on the campus, this paper proposes an analysis algorithm to obtain online wireless network user numbers in real time and draws the conclusion that the numbers of online users can represent crowd density on campus. Experimental results show that this algorithm can effectively get the numbers of online users in each area of the campus, and the campus heat map made with these data can reflect the real-time distribution of campus crowd and crowd density. This method uses log analysis method which is a general solution for some problems and has practical value for in-depth analysis.","PeriodicalId":342847,"journal":{"name":"International Conference on Algorithms, Microchips and Network Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128804315","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Aiming at the calculation of the air threat range of the ship route, the paper described the requirements and judges whether the route segment was under threat. On this basis, an algorithm model was established for the specific threat range of the threatened route segment, and a calculation example was given for verification. The analysis and verification results showed that the calculation model proposed in the paper could quickly and accurately calculate the specific threat range of the ship route, it can provide effective auxiliary decision-making reference for commanders or operators when drawing routes.
{"title":"A calculation model of the air threat range of ship route","authors":"F. Long, Ziang He, Qi Su","doi":"10.1117/12.2678896","DOIUrl":"https://doi.org/10.1117/12.2678896","url":null,"abstract":"Aiming at the calculation of the air threat range of the ship route, the paper described the requirements and judges whether the route segment was under threat. On this basis, an algorithm model was established for the specific threat range of the threatened route segment, and a calculation example was given for verification. The analysis and verification results showed that the calculation model proposed in the paper could quickly and accurately calculate the specific threat range of the ship route, it can provide effective auxiliary decision-making reference for commanders or operators when drawing routes.","PeriodicalId":342847,"journal":{"name":"International Conference on Algorithms, Microchips and Network Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129906325","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}
Under the development of people's living environment, more and more people are willing to use their money to invest in financial projects such as stocks and insurance. Nowadays, science and technology are widely applied in people's life. Machine learning is one of them. Machine learning is particularly important to apply to stock forecasting to better meet the requirements of people who want to gain more benefits. The purpose of this work is to compare using GRU, LSTM, and bidirectional LSTM's MAE and RMSE on the closing price. The method of this experiment is to compare with root mean squared error (RMSE) and mean absolute error (MSE) after the input variables of the past 63 trading days passing through those three models. The results of the experiment indicate that MAE of GRU model is lowest. Still, only nine of fifteen experiments show that RMSE of GRU model is lowest, and five of fifteen experiments show that RMSE of LSTM is lowest. One of fifteen experiments expresses that RMSE of bidirectional LSTM has the lowest RMSE. Thus, GRU is considered to be the best model for stock price regression.
{"title":"Research on gated recurrent unit based stock price prediction model with multi-features under low time scale","authors":"Yinan Lyu, Yuanhao You","doi":"10.1117/12.2636639","DOIUrl":"https://doi.org/10.1117/12.2636639","url":null,"abstract":"Under the development of people's living environment, more and more people are willing to use their money to invest in financial projects such as stocks and insurance. Nowadays, science and technology are widely applied in people's life. Machine learning is one of them. Machine learning is particularly important to apply to stock forecasting to better meet the requirements of people who want to gain more benefits. The purpose of this work is to compare using GRU, LSTM, and bidirectional LSTM's MAE and RMSE on the closing price. The method of this experiment is to compare with root mean squared error (RMSE) and mean absolute error (MSE) after the input variables of the past 63 trading days passing through those three models. The results of the experiment indicate that MAE of GRU model is lowest. Still, only nine of fifteen experiments show that RMSE of GRU model is lowest, and five of fifteen experiments show that RMSE of LSTM is lowest. One of fifteen experiments expresses that RMSE of bidirectional LSTM has the lowest RMSE. Thus, GRU is considered to be the best model for stock price regression.","PeriodicalId":342847,"journal":{"name":"International Conference on Algorithms, Microchips and Network Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125105095","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 online toxic comments cause enormous harm to the society, where toxicity is defined as anything rude, disrespectful or otherwise likely to make someone leave a discussion. To have a safer, more collaborative internet, grateful contributions are made by a main area of focus on machine learning models to identify toxicity in English, whereas part of misinformation disseminates in other languages. Over the past year, pretraining multilingual language models give rise to impressive gains for cross lingual toxicity classification. This paper presents an approach to build toxicity models applying the Jigsaw Multilingual Toxic Comment Classification dataset provided by Kaggle. We set our ensemble model in three parts based on Besides, we implement subsample, Pseudo-labeling with open-subtitles, translating non-English languages to English language, and Post Processing to improve the classification accuracy indispensably. Our final model achieved an AUC of 0.9469 for the training set and 0.9485 for the validation set, demonstrating the effectiveness of performance under cross-lingual toxicity detectors.
{"title":"An ensemble multilingual model for toxic comment classification","authors":"Gaofei Xie","doi":"10.1117/12.2636419","DOIUrl":"https://doi.org/10.1117/12.2636419","url":null,"abstract":"The online toxic comments cause enormous harm to the society, where toxicity is defined as anything rude, disrespectful or otherwise likely to make someone leave a discussion. To have a safer, more collaborative internet, grateful contributions are made by a main area of focus on machine learning models to identify toxicity in English, whereas part of misinformation disseminates in other languages. Over the past year, pretraining multilingual language models give rise to impressive gains for cross lingual toxicity classification. This paper presents an approach to build toxicity models applying the Jigsaw Multilingual Toxic Comment Classification dataset provided by Kaggle. We set our ensemble model in three parts based on Besides, we implement subsample, Pseudo-labeling with open-subtitles, translating non-English languages to English language, and Post Processing to improve the classification accuracy indispensably. Our final model achieved an AUC of 0.9469 for the training set and 0.9485 for the validation set, demonstrating the effectiveness of performance under cross-lingual toxicity detectors.","PeriodicalId":342847,"journal":{"name":"International Conference on Algorithms, Microchips and Network Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126979081","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 proposes an improved Faster R-DRN (Dense Residual Network, DRN) algorithm, which is based on Faster R-CNN, using densely connected residual network DRNet to replace VGG network. This algorithm is suitable for special scenes of building recognition. It has a residual network and a deep convolution residual network structure, which can efficiently perform image detection, classification and recognition. This design optimizes the problem of algorithm overfitting due to the increase of network depth. In this paper, a comprehensive sample data set for various landmark buildings is established, and samples with different weather, different lighting, and different angles are taken to effectively improve the resistance of the training model. Combined with the optimization of the network structure and the training of targeted data sets, the final feature block diagram generated by DRNet not only does not lose the lowlevel edge texture information, but also reuses the low-level feature block diagrams in the deep convolutional network to make the fused feature block Richer feature information effectively improves the model's recognition rate for photos taken in complex environments. The experimental results show that the accuracy of this method for predicting landmark buildings can reach 82.0% of mAP, and the recognition performance of images taken in complex environments is excellent.
{"title":"A landmark building detection and recognition based on improved Faster R-RDN algorithm","authors":"Wu Jun, Kai Yan, ZiBo Huang, Haiyan Tan, Xiaofang Tu, Chengjun Zhu","doi":"10.1117/12.2636502","DOIUrl":"https://doi.org/10.1117/12.2636502","url":null,"abstract":"This paper proposes an improved Faster R-DRN (Dense Residual Network, DRN) algorithm, which is based on Faster R-CNN, using densely connected residual network DRNet to replace VGG network. This algorithm is suitable for special scenes of building recognition. It has a residual network and a deep convolution residual network structure, which can efficiently perform image detection, classification and recognition. This design optimizes the problem of algorithm overfitting due to the increase of network depth. In this paper, a comprehensive sample data set for various landmark buildings is established, and samples with different weather, different lighting, and different angles are taken to effectively improve the resistance of the training model. Combined with the optimization of the network structure and the training of targeted data sets, the final feature block diagram generated by DRNet not only does not lose the lowlevel edge texture information, but also reuses the low-level feature block diagrams in the deep convolutional network to make the fused feature block Richer feature information effectively improves the model's recognition rate for photos taken in complex environments. The experimental results show that the accuracy of this method for predicting landmark buildings can reach 82.0% of mAP, and the recognition performance of images taken in complex environments is excellent.","PeriodicalId":342847,"journal":{"name":"International Conference on Algorithms, Microchips and Network Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124331830","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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