Pub Date : 2020-07-01DOI: 10.1109/CISCE50729.2020.00098
Weizhen Lin, Lei Zhang
With the continuous development of neural network technology, it has been paid more and more attention in digital image processing. In this paper, the convolution neural network is designed on the programmable logic device (FPGA). Using the characteristics of the hardware circuit, the convolution kernel is implemented with the parallel data processing in the core and the parallel processing between the convolution cores. The double buffer is used to reduce the access to memory devices. At the same time, the characteristics of cyclic block and sparse matrix are used to optimize the network structure, improve the network speed and reduce the power consumption. ARM processor is used to preprocess the images and configure the corresponding registers to control the number of network layers of CNN network. The test results show that the recognition rate of handwritten numeral can reach 97% by using CNN accelerator based on FPGA. Meanwhile, the power consumption and speed are significantly improved, which meets the requirements of portable mobile devices.
{"title":"Design of convolutional neural network SoC system based on FPGA","authors":"Weizhen Lin, Lei Zhang","doi":"10.1109/CISCE50729.2020.00098","DOIUrl":"https://doi.org/10.1109/CISCE50729.2020.00098","url":null,"abstract":"With the continuous development of neural network technology, it has been paid more and more attention in digital image processing. In this paper, the convolution neural network is designed on the programmable logic device (FPGA). Using the characteristics of the hardware circuit, the convolution kernel is implemented with the parallel data processing in the core and the parallel processing between the convolution cores. The double buffer is used to reduce the access to memory devices. At the same time, the characteristics of cyclic block and sparse matrix are used to optimize the network structure, improve the network speed and reduce the power consumption. ARM processor is used to preprocess the images and configure the corresponding registers to control the number of network layers of CNN network. The test results show that the recognition rate of handwritten numeral can reach 97% by using CNN accelerator based on FPGA. Meanwhile, the power consumption and speed are significantly improved, which meets the requirements of portable mobile devices.","PeriodicalId":101777,"journal":{"name":"2020 International Conference on Communications, Information System and Computer Engineering (CISCE)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130337401","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 : 2020-07-01DOI: 10.1109/cisce50729.2020.00015
Chen Wang, Peng-yang Liu, Shuangchang Feng, Xiaochang Liu
With the rapid development of China’s economy, special equipment has penetrated into the most subtle aspects of society, and the safety production supervision is particularly important. At present, Shanghai has become the region with the largest number of special equipment per capita and the highest distribution density in China. The utility boiler is a typical special equipment. Once an accident occurs, the economic loss of the utility boiler is heavy and the social impact is bad. Due to the particularity of boiler inspection in power plant, many inspection organizations are required to conduct joint inspection, and various inspection reports are issued. With the growth of inspection business, it is a difficult problem to design an information system to meet the existing power plant boiler inspection for the special equipment practitioners. In this paper, through the integration and carding of the existing system, it uses J2EE technology to start from the top-level design. By considering the system design in an all-round way, it makes each system linear and modular, and establishes an easy way to expand power plant boiler inspection management information system framework, so that the system can better serve the society.
{"title":"Design and Development of Inspection Management Information System for Power Plant Boiler Based on J2EE","authors":"Chen Wang, Peng-yang Liu, Shuangchang Feng, Xiaochang Liu","doi":"10.1109/cisce50729.2020.00015","DOIUrl":"https://doi.org/10.1109/cisce50729.2020.00015","url":null,"abstract":"With the rapid development of China’s economy, special equipment has penetrated into the most subtle aspects of society, and the safety production supervision is particularly important. At present, Shanghai has become the region with the largest number of special equipment per capita and the highest distribution density in China. The utility boiler is a typical special equipment. Once an accident occurs, the economic loss of the utility boiler is heavy and the social impact is bad. Due to the particularity of boiler inspection in power plant, many inspection organizations are required to conduct joint inspection, and various inspection reports are issued. With the growth of inspection business, it is a difficult problem to design an information system to meet the existing power plant boiler inspection for the special equipment practitioners. In this paper, through the integration and carding of the existing system, it uses J2EE technology to start from the top-level design. By considering the system design in an all-round way, it makes each system linear and modular, and establishes an easy way to expand power plant boiler inspection management information system framework, so that the system can better serve the society.","PeriodicalId":101777,"journal":{"name":"2020 International Conference on Communications, Information System and Computer Engineering (CISCE)","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115679079","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 : 2020-07-01DOI: 10.1109/CISCE50729.2020.00070
Guanghui Feng, Chunfu Zhang, Yujuan Si, Liuqi Lang
This paper proposes a stream cipher algorithm. Its main principle is conducting the binary random dynamic hash with the help of key. At the same time of calculating the hash mapping address of plaintext, change the value of plaintext through bits scrambling, and then map it to the ciphertext space. This encryption method has strong randomness, and the design of hash functions and bits scrambling is flexible and diverse, which can constitute a set of encryption and decryption methods. After testing, the code evenness of the ciphertext obtained using this method is higher than that of the traditional method under some extreme conditions..
{"title":"An Encryption and Decryption Algorithm Based on Random Dynamic Hash and Bits Scrambling","authors":"Guanghui Feng, Chunfu Zhang, Yujuan Si, Liuqi Lang","doi":"10.1109/CISCE50729.2020.00070","DOIUrl":"https://doi.org/10.1109/CISCE50729.2020.00070","url":null,"abstract":"This paper proposes a stream cipher algorithm. Its main principle is conducting the binary random dynamic hash with the help of key. At the same time of calculating the hash mapping address of plaintext, change the value of plaintext through bits scrambling, and then map it to the ciphertext space. This encryption method has strong randomness, and the design of hash functions and bits scrambling is flexible and diverse, which can constitute a set of encryption and decryption methods. After testing, the code evenness of the ciphertext obtained using this method is higher than that of the traditional method under some extreme conditions..","PeriodicalId":101777,"journal":{"name":"2020 International Conference on Communications, Information System and Computer Engineering (CISCE)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127215194","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 : 2020-07-01DOI: 10.1109/CISCE50729.2020.00022
Lv Shouguo, L. Kai, Qiao Yaohua, L. Yunqi, Sun Yang, Liang Zhenyu
Defect detection methods based on machine learning extremely accelerate the transmission lines routine inspection process. In this paper, we propose an automatic defect detection method based on improved YOLOv3. Random feature pyramid (RFP) structure is introduced for the highly discriminative feature map construction. Focal loss function, which focus on differentiating between easy and hard examples, is employed to deal with the class imbalance problem. Experimental results demonstrate that the proposed approach obtains competitive performance compared with state-of-the-art deep learning object detection methods.
基于机器学习的缺陷检测方法极大地加快了输电线路的例行检查过程。本文提出了一种基于改进YOLOv3的缺陷自动检测方法。引入随机特征金字塔(RFP)结构，构建具有高度判别性的特征映射。采用焦点损失函数(Focal loss function)来处理类不平衡问题，重点是区分简单和困难的例子。实验结果表明，与目前最先进的深度学习目标检测方法相比，该方法具有较好的性能。
{"title":"Automatic Detection Method for Small Size Transmission Lines Defect Based on Improved YOLOv3","authors":"Lv Shouguo, L. Kai, Qiao Yaohua, L. Yunqi, Sun Yang, Liang Zhenyu","doi":"10.1109/CISCE50729.2020.00022","DOIUrl":"https://doi.org/10.1109/CISCE50729.2020.00022","url":null,"abstract":"Defect detection methods based on machine learning extremely accelerate the transmission lines routine inspection process. In this paper, we propose an automatic defect detection method based on improved YOLOv3. Random feature pyramid (RFP) structure is introduced for the highly discriminative feature map construction. Focal loss function, which focus on differentiating between easy and hard examples, is employed to deal with the class imbalance problem. Experimental results demonstrate that the proposed approach obtains competitive performance compared with state-of-the-art deep learning object detection methods.","PeriodicalId":101777,"journal":{"name":"2020 International Conference on Communications, Information System and Computer Engineering (CISCE)","volume":"122 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114945244","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 : 2020-07-01DOI: 10.1109/CISCE50729.2020.00048
Ya Zhou, Can Tao
Existing problem difficulty prediction models are based on professionals’ estimation of the difficulty of the problem, or mining relevant feature information from a large number of user records. The recently proposed BERT model is pre-trained on a large unsupervised corpus and has achieved impressive results in various natural language processing tasks. In order to reduce the required feature information and improve the accuracy of problem difficulty prediction, a problem difficulty prediction method based on multi-task BERT (MTBERT) is proposed. Experiments were carried out on the real data sets of LeetCode and ZOJ, and several neural network models were compared to verify the effectiveness of the method.
{"title":"Multi-task BERT for problem difficulty prediction","authors":"Ya Zhou, Can Tao","doi":"10.1109/CISCE50729.2020.00048","DOIUrl":"https://doi.org/10.1109/CISCE50729.2020.00048","url":null,"abstract":"Existing problem difficulty prediction models are based on professionals’ estimation of the difficulty of the problem, or mining relevant feature information from a large number of user records. The recently proposed BERT model is pre-trained on a large unsupervised corpus and has achieved impressive results in various natural language processing tasks. In order to reduce the required feature information and improve the accuracy of problem difficulty prediction, a problem difficulty prediction method based on multi-task BERT (MTBERT) is proposed. Experiments were carried out on the real data sets of LeetCode and ZOJ, and several neural network models were compared to verify the effectiveness of the method.","PeriodicalId":101777,"journal":{"name":"2020 International Conference on Communications, Information System and Computer Engineering (CISCE)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132657431","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 : 2020-07-01DOI: 10.1109/cisce50729.2020.00085
Zhang Shao-jie, Zhang Kuang-wei
With the popularity of expressway, it has become the main choice for people to travel. However, traffic accidents caused by collapse of highway slopes are common. This paper uses ZigBee, GPRS technology to design a real-time online transmission system for slope runoff and sediment content. The system can monitor important information such as runoff and sediment content of highway slopes, and monitor and store runoff and sediment content in real time according to the collection requirements of the monitoring personnel of the monitoring center without manual guarding. The real-time data is transmitted to the monitoring center through the network, so that the monitoring personnel of the monitoring center can query the slope runoff and sediment content in time.
{"title":"Real-time Online Transmission System for Slope Runoff and Sediment Content","authors":"Zhang Shao-jie, Zhang Kuang-wei","doi":"10.1109/cisce50729.2020.00085","DOIUrl":"https://doi.org/10.1109/cisce50729.2020.00085","url":null,"abstract":"With the popularity of expressway, it has become the main choice for people to travel. However, traffic accidents caused by collapse of highway slopes are common. This paper uses ZigBee, GPRS technology to design a real-time online transmission system for slope runoff and sediment content. The system can monitor important information such as runoff and sediment content of highway slopes, and monitor and store runoff and sediment content in real time according to the collection requirements of the monitoring personnel of the monitoring center without manual guarding. The real-time data is transmitted to the monitoring center through the network, so that the monitoring personnel of the monitoring center can query the slope runoff and sediment content in time.","PeriodicalId":101777,"journal":{"name":"2020 International Conference on Communications, Information System and Computer Engineering (CISCE)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129434905","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 : 2020-07-01DOI: 10.1109/CISCE50729.2020.00067
Sikun Guo
The graph structure is one of the critical data structures in the real world, and its applications focus on graphs, where scholars study entity features and interactions among various entities. Recently, developments in graph neural networks (GNNs) have heightened the need for learning graph representations effectively. Simultaneously, graphs can be large and complex as well as noisy, posing obstacles for graph-related tasks. However, by incorporating the attention mechanism in graph neural networks, it is possible for GNNs to focus on the most important entities and interactions in graphs, contributing to better decisions. Therefore, this paper conducts a comprehensive survey about literature on GAT-like graph neural networks. According to inputs and outputs, types of attention mechanisms, tasks, this paper proposes a taxonomy to group recent works followed by detailed examples, aiming to overlook GAT-like GNNs from different perspectives. At last, this paper discusses the existing problems and challenges in this area, hoping to provide insights for future research directions.
{"title":"A Survey on GAT-like Graph Neural Networks","authors":"Sikun Guo","doi":"10.1109/CISCE50729.2020.00067","DOIUrl":"https://doi.org/10.1109/CISCE50729.2020.00067","url":null,"abstract":"The graph structure is one of the critical data structures in the real world, and its applications focus on graphs, where scholars study entity features and interactions among various entities. Recently, developments in graph neural networks (GNNs) have heightened the need for learning graph representations effectively. Simultaneously, graphs can be large and complex as well as noisy, posing obstacles for graph-related tasks. However, by incorporating the attention mechanism in graph neural networks, it is possible for GNNs to focus on the most important entities and interactions in graphs, contributing to better decisions. Therefore, this paper conducts a comprehensive survey about literature on GAT-like graph neural networks. According to inputs and outputs, types of attention mechanisms, tasks, this paper proposes a taxonomy to group recent works followed by detailed examples, aiming to overlook GAT-like GNNs from different perspectives. At last, this paper discusses the existing problems and challenges in this area, hoping to provide insights for future research directions.","PeriodicalId":101777,"journal":{"name":"2020 International Conference on Communications, Information System and Computer Engineering (CISCE)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114198388","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 : 2020-07-01DOI: 10.1109/CISCE50729.2020.00025
Xinyu Zhou
With the rapid development of information technology and the increasing popularity of information and communication technology, the information age has come. Enterprises must adapt to changes in the times, introduce network and computer technologies in a timely manner, and establish more efficient and reasonable information systems and platforms. Large-scale information system construction is inseparable from related audit work, and network security risks have become an important part of information system audit concerns. This paper analyzes the objectives and contents of information system audits under the background of network information security through theoretical analysis, and on this basis, proposes how the IS audit work will be carried out.
{"title":"Improvement of information System Audit to Deal With Network Information Security","authors":"Xinyu Zhou","doi":"10.1109/CISCE50729.2020.00025","DOIUrl":"https://doi.org/10.1109/CISCE50729.2020.00025","url":null,"abstract":"With the rapid development of information technology and the increasing popularity of information and communication technology, the information age has come. Enterprises must adapt to changes in the times, introduce network and computer technologies in a timely manner, and establish more efficient and reasonable information systems and platforms. Large-scale information system construction is inseparable from related audit work, and network security risks have become an important part of information system audit concerns. This paper analyzes the objectives and contents of information system audits under the background of network information security through theoretical analysis, and on this basis, proposes how the IS audit work will be carried out.","PeriodicalId":101777,"journal":{"name":"2020 International Conference on Communications, Information System and Computer Engineering (CISCE)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114474253","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 : 2020-07-01DOI: 10.1109/CISCE50729.2020.00057
Ya Zhou, Meng Li
In order to evaluate the quality of online courses, this paper proposes a framework based on online course feature extraction and sentiment analysis, and applies this framework to the online courses of MOOC. Extract the word pair of the review data through the word frequency syntactic dependency, and merge the word pair into the sentiment classification of the BERT model to realize the fine-grained feature analysis of the online course review data, so as to obtain online courses in each Use this aspect to evaluate the quality of the course. Experiments conducted on MOOC online course reviews show that the BERT model incorporating binary features has improved accuracy, recall, and F1 values compared to traditional machine learning methods.
{"title":"Online Course Quality Evaluation Based on BERT","authors":"Ya Zhou, Meng Li","doi":"10.1109/CISCE50729.2020.00057","DOIUrl":"https://doi.org/10.1109/CISCE50729.2020.00057","url":null,"abstract":"In order to evaluate the quality of online courses, this paper proposes a framework based on online course feature extraction and sentiment analysis, and applies this framework to the online courses of MOOC. Extract the word pair of the review data through the word frequency syntactic dependency, and merge the word pair into the sentiment classification of the BERT model to realize the fine-grained feature analysis of the online course review data, so as to obtain online courses in each Use this aspect to evaluate the quality of the course. Experiments conducted on MOOC online course reviews show that the BERT model incorporating binary features has improved accuracy, recall, and F1 values compared to traditional machine learning methods.","PeriodicalId":101777,"journal":{"name":"2020 International Conference on Communications, Information System and Computer Engineering (CISCE)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133016749","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 : 2020-07-01DOI: 10.1109/CISCE50729.2020.00033
Yining Zhao, Yuelai Su
Nowadays, there are high incidences of epidemic diseases so it is very important to predict the incidence of them. There are many prediction methods for epidemic diseases at present. In various situations, different models have different applications. This article will select three prediction models, namely ARIMA model, grey model and BP neural network model. Taking the number of people infected by epidemics of Shandong from 2014 to 2019 as an example, based on the structure and performance of the model, it can be found that ARIMA model is suitable for the prediction of seasonal epidemics in schools and other densely populated places. The grey model needs less data and is suitable for the short-term prediction of some grass-roots prevention and control personnel. The BP neural network model has high prediction accuracy but complicated prediction process, and is suitable for the prediction of scientific research institutions.
{"title":"Comparison of Three Prediction Models for the Incidence of Epidemic Diseases","authors":"Yining Zhao, Yuelai Su","doi":"10.1109/CISCE50729.2020.00033","DOIUrl":"https://doi.org/10.1109/CISCE50729.2020.00033","url":null,"abstract":"Nowadays, there are high incidences of epidemic diseases so it is very important to predict the incidence of them. There are many prediction methods for epidemic diseases at present. In various situations, different models have different applications. This article will select three prediction models, namely ARIMA model, grey model and BP neural network model. Taking the number of people infected by epidemics of Shandong from 2014 to 2019 as an example, based on the structure and performance of the model, it can be found that ARIMA model is suitable for the prediction of seasonal epidemics in schools and other densely populated places. The grey model needs less data and is suitable for the short-term prediction of some grass-roots prevention and control personnel. The BP neural network model has high prediction accuracy but complicated prediction process, and is suitable for the prediction of scientific research institutions.","PeriodicalId":101777,"journal":{"name":"2020 International Conference on Communications, Information System and Computer Engineering (CISCE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130584757","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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