Pub Date : 2019-11-01DOI: 10.1109/IICSPI48186.2019.9095873
Jinyin Chen, Yuwei Li, Jingxin Zhao
With the rapid development of deep learning model in computer vision area, it has outperformed most of traditional machine learning algorithms. Since tire factories pay much attention to defects detection of tires based on x-ray image, lots of tire x-ray image based defects detection methods are brought up. However, there are still challenges in detection accuracy. This paper put forward a novel deep learning model and modified Faster R-CNN to conduct x-ray defects detection. Some proper processing is done on x-ray image before extracting the features and detecting the defects and then adjusting the feature extractor, proposal generator and box classifier of Faster R-CNN respectively. Comprehensive experiments are carried out to testify that our proposed model is capable of achieving higher detection accuracy compared with other methods.
{"title":"X-ray of Tire Defects Detection via Modified Faster R-CNN","authors":"Jinyin Chen, Yuwei Li, Jingxin Zhao","doi":"10.1109/IICSPI48186.2019.9095873","DOIUrl":"https://doi.org/10.1109/IICSPI48186.2019.9095873","url":null,"abstract":"With the rapid development of deep learning model in computer vision area, it has outperformed most of traditional machine learning algorithms. Since tire factories pay much attention to defects detection of tires based on x-ray image, lots of tire x-ray image based defects detection methods are brought up. However, there are still challenges in detection accuracy. This paper put forward a novel deep learning model and modified Faster R-CNN to conduct x-ray defects detection. Some proper processing is done on x-ray image before extracting the features and detecting the defects and then adjusting the feature extractor, proposal generator and box classifier of Faster R-CNN respectively. Comprehensive experiments are carried out to testify that our proposed model is capable of achieving higher detection accuracy compared with other methods.","PeriodicalId":318693,"journal":{"name":"2019 2nd International Conference on Safety Produce Informatization (IICSPI)","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115664789","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 : 2019-11-01DOI: 10.1109/IICSPI48186.2019.9095923
Jing-song Xiang, Jiajian Zhou, Sheng Huang
Word alignment is an important step in statistical machine translation. Chinese-English bilingual language has a large difference in language characteristics, which may lead to some inconsistent results in word alignment. In this paper, a word alignment method based on recurrent neural network (RNN) is proposed. Firstly, Chinese-English bilingual words are transformed into word embedding, which are input to RNN model and incorporate context information. RNN uses internal memory to process input sequences of arbitrary time series. The experimental results show that compared with DNN and IBM4 models, this method improves the accuracy of word alignment and the quality of machine translation.
{"title":"A Bilingual Word Alignment Method of Chinese-English based on Recurrent Neural Network","authors":"Jing-song Xiang, Jiajian Zhou, Sheng Huang","doi":"10.1109/IICSPI48186.2019.9095923","DOIUrl":"https://doi.org/10.1109/IICSPI48186.2019.9095923","url":null,"abstract":"Word alignment is an important step in statistical machine translation. Chinese-English bilingual language has a large difference in language characteristics, which may lead to some inconsistent results in word alignment. In this paper, a word alignment method based on recurrent neural network (RNN) is proposed. Firstly, Chinese-English bilingual words are transformed into word embedding, which are input to RNN model and incorporate context information. RNN uses internal memory to process input sequences of arbitrary time series. The experimental results show that compared with DNN and IBM4 models, this method improves the accuracy of word alignment and the quality of machine translation.","PeriodicalId":318693,"journal":{"name":"2019 2nd International Conference on Safety Produce Informatization (IICSPI)","volume":"206 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116192070","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 : 2019-11-01DOI: 10.1109/IICSPI48186.2019.9095939
Xueli Wang, Yufeng Zhang, Xing Zhang, Wenbo Wang
In this paper, a traffic model is proposed based on shrinkage estimation with link load traffic data generated from the wireless cellular system. Compared with the traditional method, the spatiotemporal properties of different base stations (BSes) are considered, and a shrinkage estimation method Random Lasso is used to make variables selection, and to estimate the parameters of selected variables. The results show that the characteristics of traffic for the entire wireless cellular system can be captured effectively, and the prediction accuracy improves significantly. Besides, our research could be extended to other fields of spatiotemporal analysis with multivariate time series.
{"title":"Traffic Prediction for Wireless Cellular System Based on Shrinkage Estimation","authors":"Xueli Wang, Yufeng Zhang, Xing Zhang, Wenbo Wang","doi":"10.1109/IICSPI48186.2019.9095939","DOIUrl":"https://doi.org/10.1109/IICSPI48186.2019.9095939","url":null,"abstract":"In this paper, a traffic model is proposed based on shrinkage estimation with link load traffic data generated from the wireless cellular system. Compared with the traditional method, the spatiotemporal properties of different base stations (BSes) are considered, and a shrinkage estimation method Random Lasso is used to make variables selection, and to estimate the parameters of selected variables. The results show that the characteristics of traffic for the entire wireless cellular system can be captured effectively, and the prediction accuracy improves significantly. Besides, our research could be extended to other fields of spatiotemporal analysis with multivariate time series.","PeriodicalId":318693,"journal":{"name":"2019 2nd International Conference on Safety Produce Informatization (IICSPI)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127563914","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}
Lithium iron phosphate batteries have been widely applied in large-scale energy storage systems due to their predominant performance. However, because of the sophisticated characteristics of lithium iron phosphate battery, the consistency problem is one of the major issues for lithium battery management system. This paper mainly discusses the structure and function of the lithium battery management system, analyzes the causes of consistency problems, and proposes a new management strategy for the lithium iron phosphate battery management system based on the consistency management technology.
{"title":"Research on a Strategy of Consistency Management System for Lithium Iron Phosphate Battery","authors":"Yan Li, Yifei Fan, Darui He, Pengyu Guo, Chengjie Cao, Guodao Tong, Xisong Chen, Qipeng Shen, Zhijie Zhong","doi":"10.1109/IICSPI48186.2019.9095973","DOIUrl":"https://doi.org/10.1109/IICSPI48186.2019.9095973","url":null,"abstract":"Lithium iron phosphate batteries have been widely applied in large-scale energy storage systems due to their predominant performance. However, because of the sophisticated characteristics of lithium iron phosphate battery, the consistency problem is one of the major issues for lithium battery management system. This paper mainly discusses the structure and function of the lithium battery management system, analyzes the causes of consistency problems, and proposes a new management strategy for the lithium iron phosphate battery management system based on the consistency management technology.","PeriodicalId":318693,"journal":{"name":"2019 2nd International Conference on Safety Produce Informatization (IICSPI)","volume":"133 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120976141","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 : 2019-11-01DOI: 10.1109/IICSPI48186.2019.9095878
Guorong Chen, Liu Yao, Hongli He, Li Jie, Gao Min, Ren Hong
Acoustic Signal Identification has become an important subject in the field of machine perception in recent years. It has achieved good results in application scenarios such as voice recognition, and it still has low precision in other Acoustic Signal recognition applications. Therefore, this paper proposes an acoustic signal recognition model based on convolutional neural network to improve the recognition accuracy. In this model, the first problem to be solved is the processing of acoustic source data. The model converts acoustic signals such as barking dogs, crying babies, waves and rain into one-dimensional spectral signals by using Fourier transform, and then inputs the data into one-dimensional CNN for training, and finally obtains the classification accuracy of ten categories of acoustic signals. The classification accuracy of this model CNN classifier is 69 %. In addition, this paper adds the pipeline micro-leakage data collected from actual engineering projects to the CNN model, and obtains better identification results. In general, this model outperform others.
{"title":"An Acoustic Signal Identification Method Based on Convolutional Neural Networks","authors":"Guorong Chen, Liu Yao, Hongli He, Li Jie, Gao Min, Ren Hong","doi":"10.1109/IICSPI48186.2019.9095878","DOIUrl":"https://doi.org/10.1109/IICSPI48186.2019.9095878","url":null,"abstract":"Acoustic Signal Identification has become an important subject in the field of machine perception in recent years. It has achieved good results in application scenarios such as voice recognition, and it still has low precision in other Acoustic Signal recognition applications. Therefore, this paper proposes an acoustic signal recognition model based on convolutional neural network to improve the recognition accuracy. In this model, the first problem to be solved is the processing of acoustic source data. The model converts acoustic signals such as barking dogs, crying babies, waves and rain into one-dimensional spectral signals by using Fourier transform, and then inputs the data into one-dimensional CNN for training, and finally obtains the classification accuracy of ten categories of acoustic signals. The classification accuracy of this model CNN classifier is 69 %. In addition, this paper adds the pipeline micro-leakage data collected from actual engineering projects to the CNN model, and obtains better identification results. In general, this model outperform others.","PeriodicalId":318693,"journal":{"name":"2019 2nd International Conference on Safety Produce Informatization (IICSPI)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120946601","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 : 2019-11-01DOI: 10.1109/IICSPI48186.2019.9095958
Meili Liu, Caizhong Zhang
Based on the scientific and technological plan of Shandong Province and the vocational education reform project of Shandong Province, this paper carefully designed a hierarchical monitoring system for crop growth environment based on Arduino Yún development platform after analyzing the current development of agricultural equipment in China. The system uses DHT11 temperature and humidity sensor, GY-30 light intensity sensor and MG811 carbon dioxide concentration sensor to collect environmental information, which is designed from three aspects: sensing layer, transmission layer and application layer to realize intelligent control of the environment inside the agricultural greenhouse. The cost of agricultural production and the labor intensity of employees will increase the quantity and quality of agricultural products and achieve the goal of automation and intelligence of agricultural production.
{"title":"Design of Hierarchical Monitoring System for Crop Growth Environment Based on Arduino Yún Development Platform","authors":"Meili Liu, Caizhong Zhang","doi":"10.1109/IICSPI48186.2019.9095958","DOIUrl":"https://doi.org/10.1109/IICSPI48186.2019.9095958","url":null,"abstract":"Based on the scientific and technological plan of Shandong Province and the vocational education reform project of Shandong Province, this paper carefully designed a hierarchical monitoring system for crop growth environment based on Arduino Yún development platform after analyzing the current development of agricultural equipment in China. The system uses DHT11 temperature and humidity sensor, GY-30 light intensity sensor and MG811 carbon dioxide concentration sensor to collect environmental information, which is designed from three aspects: sensing layer, transmission layer and application layer to realize intelligent control of the environment inside the agricultural greenhouse. The cost of agricultural production and the labor intensity of employees will increase the quantity and quality of agricultural products and achieve the goal of automation and intelligence of agricultural production.","PeriodicalId":318693,"journal":{"name":"2019 2nd International Conference on Safety Produce Informatization (IICSPI)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129650379","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 : 2019-11-01DOI: 10.1109/IICSPI48186.2019.9096044
Hongxia Yang
In this paper, a data acquisition system based on the Scrapy crawler framework was designed and implemented, which can not only obtain data according to the user’s own needs, but also manage its own collection tasks simply. The Django MTV mode is used for development, and the underlying data collection framework applies Scrapy, an asynchronous crawler application framework implemented by Python. The web page analysis uses the method in combination of XPath and regular expression. The jQuery tree plug-in zTree is utilized to realize tree management of tasks, the bootstrap to achieve the effect of task name with the keyword combination query and page.
{"title":"Design and Implementation of Data Acquisition System Based on Scrapy Technology","authors":"Hongxia Yang","doi":"10.1109/IICSPI48186.2019.9096044","DOIUrl":"https://doi.org/10.1109/IICSPI48186.2019.9096044","url":null,"abstract":"In this paper, a data acquisition system based on the Scrapy crawler framework was designed and implemented, which can not only obtain data according to the user’s own needs, but also manage its own collection tasks simply. The Django MTV mode is used for development, and the underlying data collection framework applies Scrapy, an asynchronous crawler application framework implemented by Python. The web page analysis uses the method in combination of XPath and regular expression. The jQuery tree plug-in zTree is utilized to realize tree management of tasks, the bootstrap to achieve the effect of task name with the keyword combination query and page.","PeriodicalId":318693,"journal":{"name":"2019 2nd International Conference on Safety Produce Informatization (IICSPI)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130869144","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 : 2019-11-01DOI: 10.1109/IICSPI48186.2019.9095916
Wang Ding, Gong Dan, Wu Di, F. Feng
Through the analysis of concepts related to equipment readiness evaluation, with state information access and selection criteria provided, equipment readiness evaluation system model was established, and the key units of the equipment were defined. According to the characteristics of status information, the data were processed with D-S evidence theory and fuzzy comprehensive evaluation. Equipment readiness evaluation was realized with a reference to index weight of evaluation system. Finally, an example was introduced to verify the practicability and validity of the evaluation model and calculation method in the readiness evaluation of an active nuclear, biochemical and protection equipment of certain type.
{"title":"Research on Equipment Readiness Evaluation Method Based on State Information","authors":"Wang Ding, Gong Dan, Wu Di, F. Feng","doi":"10.1109/IICSPI48186.2019.9095916","DOIUrl":"https://doi.org/10.1109/IICSPI48186.2019.9095916","url":null,"abstract":"Through the analysis of concepts related to equipment readiness evaluation, with state information access and selection criteria provided, equipment readiness evaluation system model was established, and the key units of the equipment were defined. According to the characteristics of status information, the data were processed with D-S evidence theory and fuzzy comprehensive evaluation. Equipment readiness evaluation was realized with a reference to index weight of evaluation system. Finally, an example was introduced to verify the practicability and validity of the evaluation model and calculation method in the readiness evaluation of an active nuclear, biochemical and protection equipment of certain type.","PeriodicalId":318693,"journal":{"name":"2019 2nd International Conference on Safety Produce Informatization (IICSPI)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133256385","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 : 2019-11-01DOI: 10.1109/IICSPI48186.2019.9095869
Li Cheng, X. Xia
The delay time (DT) and embedding dimension (EM)of the rolling bearing vibration time series are different because of different methods. The improved weighted first-order local prediction model (IWFLPM) based on fusion technology is established. The delay DT obtained by the mutual information method and the ED obtained by the Cao method are used to form the parameter pair, and then the parameter pair sequence is constructed. The IWFLPM is used for one-step prediction. Finally, the bootstrap maximum entropy method is used to fuse the prediction result and MATLAB is used to perform all mathematical operations. The experimental results show that the accuracy of the fusion prediction results is significantly better than the IWFLPM, and the optimal delay time and optimal embedding dimension are obtained.
{"title":"Fusion Chaotic Prediction Model for Bearing Performance by Computer Technique","authors":"Li Cheng, X. Xia","doi":"10.1109/IICSPI48186.2019.9095869","DOIUrl":"https://doi.org/10.1109/IICSPI48186.2019.9095869","url":null,"abstract":"The delay time (DT) and embedding dimension (EM)of the rolling bearing vibration time series are different because of different methods. The improved weighted first-order local prediction model (IWFLPM) based on fusion technology is established. The delay DT obtained by the mutual information method and the ED obtained by the Cao method are used to form the parameter pair, and then the parameter pair sequence is constructed. The IWFLPM is used for one-step prediction. Finally, the bootstrap maximum entropy method is used to fuse the prediction result and MATLAB is used to perform all mathematical operations. The experimental results show that the accuracy of the fusion prediction results is significantly better than the IWFLPM, and the optimal delay time and optimal embedding dimension are obtained.","PeriodicalId":318693,"journal":{"name":"2019 2nd International Conference on Safety Produce Informatization (IICSPI)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114498417","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 : 2019-11-01DOI: 10.1109/IICSPI48186.2019.9095966
Yong-lu Yan, Yali Li
As the basic industry, the modernization of agriculture has the advantage with low cost and instant effect, which is an important choice for the cities. However, the irrational cost control of supply chain makes a great contribution to the price rising of green agricultural products. On the basis of analysing the cost structure of supply chain, this study structures the target cost model and gives an optimizing solution to the problem based on Particle Swarm Optimization. Moreover, this study takes one of green agricultural product supply chains in China for empirical research to examine the above optimizing method, the results shows that the target cost of green agricultural product supply chain can be obtained by the method of improved Particle Swarm Optimization.
{"title":"Cost Optimization of Supply Chain of Green Agricultural Product Basing on Particle Swarm Optimization","authors":"Yong-lu Yan, Yali Li","doi":"10.1109/IICSPI48186.2019.9095966","DOIUrl":"https://doi.org/10.1109/IICSPI48186.2019.9095966","url":null,"abstract":"As the basic industry, the modernization of agriculture has the advantage with low cost and instant effect, which is an important choice for the cities. However, the irrational cost control of supply chain makes a great contribution to the price rising of green agricultural products. On the basis of analysing the cost structure of supply chain, this study structures the target cost model and gives an optimizing solution to the problem based on Particle Swarm Optimization. Moreover, this study takes one of green agricultural product supply chains in China for empirical research to examine the above optimizing method, the results shows that the target cost of green agricultural product supply chain can be obtained by the method of improved Particle Swarm Optimization.","PeriodicalId":318693,"journal":{"name":"2019 2nd International Conference on Safety Produce Informatization (IICSPI)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114761052","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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