Pub Date : 2020-03-01DOI: 10.1109/ICAIIS49377.2020.9194792
Yang Zhou
The recommendation system is a widely researched business tool that provides recommendations such as products or technologies that users are interested. The recommendation system learns user information by analyzing customer behavior and recommends products to meet customer needs. The existing mainstream recommendation systems still have room for improvement in terms of dynamically adding new users or new products to the system. The proposed method dynamically adds user and product information to the original recommendation system while recommending target content to new users or new products. While retaining the CIN and DNN structures, the networks associations are added to the before and after sequences. The input part of the add sequence is added to the previous sequence, the purpose is to dynamically update user and product information, adjust networks parameters based on sequence associations and learn high-order and low-order features of information. The results of comparative experiments show that our method could add new information dynamically with the low computing cost.
{"title":"A Dynamically Adding Information Recommendation System based on Deep Neural Networks","authors":"Yang Zhou","doi":"10.1109/ICAIIS49377.2020.9194792","DOIUrl":"https://doi.org/10.1109/ICAIIS49377.2020.9194792","url":null,"abstract":"The recommendation system is a widely researched business tool that provides recommendations such as products or technologies that users are interested. The recommendation system learns user information by analyzing customer behavior and recommends products to meet customer needs. The existing mainstream recommendation systems still have room for improvement in terms of dynamically adding new users or new products to the system. The proposed method dynamically adds user and product information to the original recommendation system while recommending target content to new users or new products. While retaining the CIN and DNN structures, the networks associations are added to the before and after sequences. The input part of the add sequence is added to the previous sequence, the purpose is to dynamically update user and product information, adjust networks parameters based on sequence associations and learn high-order and low-order features of information. The results of comparative experiments show that our method could add new information dynamically with the low computing cost.","PeriodicalId":416002,"journal":{"name":"2020 IEEE International Conference on Artificial Intelligence and Information Systems (ICAIIS)","volume":"96 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120971208","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 : 2020-03-01DOI: 10.1109/ICAIIS49377.2020.9194803
Yun Zhang, Zongze Jin, Fan Liu, Weilin Zhu, Weimin Mu, Weiping Wang
Although user-generated image data increases more and more quickly on the current Internet, many image methods have attracted widespread attention from industry and academia. Recently, some image classification approaches using deep learning have demonstrated that they can potentially enhance the accuracy of the classification based on the high quality datasets. However, the existing methods only consider the accuracy of the classification and ignore the quality of the datasets. To address these issues, we propose a new image data cleaning framework using deep neural networks, named ImageDC, to improve the quality of the datasets. ImageDC not only uses cleaning with the minority class to remove the images of the rarely classes, but also adopts cleaning with the low recognition rate to remove the noisy data to enhance the recognition rate of the datasets. Experimental results conducted on a variety of datasets demonstrate that our model significantly outperforms the whole approaches.
{"title":"ImageDC: Image Data Cleaning Framework Based on Deep Learning","authors":"Yun Zhang, Zongze Jin, Fan Liu, Weilin Zhu, Weimin Mu, Weiping Wang","doi":"10.1109/ICAIIS49377.2020.9194803","DOIUrl":"https://doi.org/10.1109/ICAIIS49377.2020.9194803","url":null,"abstract":"Although user-generated image data increases more and more quickly on the current Internet, many image methods have attracted widespread attention from industry and academia. Recently, some image classification approaches using deep learning have demonstrated that they can potentially enhance the accuracy of the classification based on the high quality datasets. However, the existing methods only consider the accuracy of the classification and ignore the quality of the datasets. To address these issues, we propose a new image data cleaning framework using deep neural networks, named ImageDC, to improve the quality of the datasets. ImageDC not only uses cleaning with the minority class to remove the images of the rarely classes, but also adopts cleaning with the low recognition rate to remove the noisy data to enhance the recognition rate of the datasets. Experimental results conducted on a variety of datasets demonstrate that our model significantly outperforms the whole approaches.","PeriodicalId":416002,"journal":{"name":"2020 IEEE International Conference on Artificial Intelligence and Information Systems (ICAIIS)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114988847","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 : 2020-03-01DOI: 10.1109/ICAIIS49377.2020.9194795
Xu Junying, Liu Yan, Liang Ziyu
This paper proposes to combine workflow technology and Web Services technology to design a dynamic workflow model. Combined with the Spring MVC framework and JBPM, a workflow management system based on Service-oriented architecture is implemented. This paper encapsulates the workflow into a Web service, enabling it to communicate with its original information systems, thereby integrating multiple independent internal applications into a unified framework.
{"title":"Research and Implementation on Communication Organization Workflow Management System Based on Service-oriented Architecture","authors":"Xu Junying, Liu Yan, Liang Ziyu","doi":"10.1109/ICAIIS49377.2020.9194795","DOIUrl":"https://doi.org/10.1109/ICAIIS49377.2020.9194795","url":null,"abstract":"This paper proposes to combine workflow technology and Web Services technology to design a dynamic workflow model. Combined with the Spring MVC framework and JBPM, a workflow management system based on Service-oriented architecture is implemented. This paper encapsulates the workflow into a Web service, enabling it to communicate with its original information systems, thereby integrating multiple independent internal applications into a unified framework.","PeriodicalId":416002,"journal":{"name":"2020 IEEE International Conference on Artificial Intelligence and Information Systems (ICAIIS)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132659799","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 : 2020-03-01DOI: 10.1109/ICAIIS49377.2020.9194813
Xiaoyong Sun, Zhen Zuo, Shaojing Su, Xiaopeng Tan
In this paper, we propose and demonstrate a blind chromatic dispersion (CD) estimation method based on fractional Fourier transformation (FrFT). When FrFT acts on linear frequency modulated (LFM) signal with energy convergence, it is compared to fiber chromatic dispersion signal, and the best order can be obtained by scanning or search method. Through numerical simulations, reliable CD estimation is demonstrated for 28 GBaud PM-QPSK and PM-16QAM signals with a range of (-70,45) ps/nm. The average estimation error of CD estimation is 0.15% and 0.5% when the transmission distance over 100km~2000km standard single mode fiber (SSMF).
{"title":"Blind Chromatic Dispersion Estimation Using Fractional Fourier Transformation in Coherent Optical Communications","authors":"Xiaoyong Sun, Zhen Zuo, Shaojing Su, Xiaopeng Tan","doi":"10.1109/ICAIIS49377.2020.9194813","DOIUrl":"https://doi.org/10.1109/ICAIIS49377.2020.9194813","url":null,"abstract":"In this paper, we propose and demonstrate a blind chromatic dispersion (CD) estimation method based on fractional Fourier transformation (FrFT). When FrFT acts on linear frequency modulated (LFM) signal with energy convergence, it is compared to fiber chromatic dispersion signal, and the best order can be obtained by scanning or search method. Through numerical simulations, reliable CD estimation is demonstrated for 28 GBaud PM-QPSK and PM-16QAM signals with a range of (-70,45) ps/nm. The average estimation error of CD estimation is 0.15% and 0.5% when the transmission distance over 100km~2000km standard single mode fiber (SSMF).","PeriodicalId":416002,"journal":{"name":"2020 IEEE International Conference on Artificial Intelligence and Information Systems (ICAIIS)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130781144","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 : 2020-03-01DOI: 10.1109/ICAIIS49377.2020.9194864
Yajuan Jia, Juanjuan Wang, Lisha Shang
The rapid development of urban rail transit in our country also puts forward further requirements for its braking system, so it is of great significance to carry out auxiliary design of its braking system and to carry out simulation calculation of its working characteristics. The running time between stations of metro vehicles is generally 2 ~ 4 min, which is in a state of frequent starting and braking. The traction load of metro vehicles is nonlinear, random and time-varying. Compared with other loads in the power grid, subway load has its particularity. ActiveX is used to analyze the traction and braking characteristics of subway trains. The traction load theory of subway trains is studied in detail, the parameters of load modeling are determined, the corresponding effective data are obtained, the corresponding data processing scheme is formulated, and the real-time simulation tool MATLAB is used to simulate the operation of subway trains. The braking energy distribution model can provide theoretical support for various urban rail transit braking energy recovery methods, and can also provide basis for selection of various braking energy recovery devices.
{"title":"Research on Electric Braking Simulation Program of Rail Transit Based on MATLAB Simulation Technology","authors":"Yajuan Jia, Juanjuan Wang, Lisha Shang","doi":"10.1109/ICAIIS49377.2020.9194864","DOIUrl":"https://doi.org/10.1109/ICAIIS49377.2020.9194864","url":null,"abstract":"The rapid development of urban rail transit in our country also puts forward further requirements for its braking system, so it is of great significance to carry out auxiliary design of its braking system and to carry out simulation calculation of its working characteristics. The running time between stations of metro vehicles is generally 2 ~ 4 min, which is in a state of frequent starting and braking. The traction load of metro vehicles is nonlinear, random and time-varying. Compared with other loads in the power grid, subway load has its particularity. ActiveX is used to analyze the traction and braking characteristics of subway trains. The traction load theory of subway trains is studied in detail, the parameters of load modeling are determined, the corresponding effective data are obtained, the corresponding data processing scheme is formulated, and the real-time simulation tool MATLAB is used to simulate the operation of subway trains. The braking energy distribution model can provide theoretical support for various urban rail transit braking energy recovery methods, and can also provide basis for selection of various braking energy recovery devices.","PeriodicalId":416002,"journal":{"name":"2020 IEEE International Conference on Artificial Intelligence and Information Systems (ICAIIS)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133346130","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 : 2020-03-01DOI: 10.1109/ICAIIS49377.2020.9194888
Shufang Zhang, Xianfei Pan, Hua Mu
Cooperative navigation which can improve the individual localization accuracy through utilizing the relative information among multiple individuals is a problem worthy of discussing. In the case of limited satellite signals, pedestrian navigation based on the micro inertial measurement unit can achieve autonomous and continuous navigation results, but during the inertial navigation calculation process, the navigation error of a single pedestrian is easy to diverge. Multi-pedestrian cooperative navigation is a navigation method that integrates and shares navigation information of multiple pedestrians. The relative distance between pedestrians can be measured by ultra-wide band technology and used to form a constraint relationship between multi-pedestrian navigation states. Then, the Kalman filter principle is used to establish a multi-pedestrian cooperative navigation system model in order to control the divergence of navigation errors, thereby improving overall navigation accuracy. The experimental results show that the overall navigation performance is effectively improved and the navigation accuracy of some pedestrians is significantly optimized when the multi-pedestrian cooperative navigation and positioning method is adopted.
{"title":"A multi-pedestrian cooperative navigation and positioning method based on UWB technology","authors":"Shufang Zhang, Xianfei Pan, Hua Mu","doi":"10.1109/ICAIIS49377.2020.9194888","DOIUrl":"https://doi.org/10.1109/ICAIIS49377.2020.9194888","url":null,"abstract":"Cooperative navigation which can improve the individual localization accuracy through utilizing the relative information among multiple individuals is a problem worthy of discussing. In the case of limited satellite signals, pedestrian navigation based on the micro inertial measurement unit can achieve autonomous and continuous navigation results, but during the inertial navigation calculation process, the navigation error of a single pedestrian is easy to diverge. Multi-pedestrian cooperative navigation is a navigation method that integrates and shares navigation information of multiple pedestrians. The relative distance between pedestrians can be measured by ultra-wide band technology and used to form a constraint relationship between multi-pedestrian navigation states. Then, the Kalman filter principle is used to establish a multi-pedestrian cooperative navigation system model in order to control the divergence of navigation errors, thereby improving overall navigation accuracy. The experimental results show that the overall navigation performance is effectively improved and the navigation accuracy of some pedestrians is significantly optimized when the multi-pedestrian cooperative navigation and positioning method is adopted.","PeriodicalId":416002,"journal":{"name":"2020 IEEE International Conference on Artificial Intelligence and Information Systems (ICAIIS)","volume":"174 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129474820","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 : 2020-03-01DOI: 10.1109/ICAIIS49377.2020.9194833
Y. Wenmin, Hou Xiuqun, Li Yuanjiao, Jiang Qinglei, Bao Binbin
Nuclear power plants have accumulated a large amount of process monitoring data, but most of the data are not marked with specified patterns, which cannot be directly applied to the data-driven intelligent early warning and fault diagnosis. On-site alarm threshold can only locate a small number of abnormal vibration data, ignoring a large number of data that doesn't exceed alarm threshold but is obviously abnormal fluctuation of vibration phenomenon. To solve this problem, a method is proposed to locate abnormal vibration data based on correlation coefficient in this paper. This method takes the correlation coefficient of vibration data and corresponding time as the fluctuation index of measuring vibration data, and calculates the fluctuation threshold through historical data statistics, so as to locate abnormal vibration data. The vibration monitoring data of nuclear pump show that the proposed method can effectively detect the abnormal fluctuation of the data and locate the starting point of abnormal vibration.
{"title":"Research on Anomaly Location Method for Nuclear Pump Vibration Monitoring Data Based on Correlation Coefficient","authors":"Y. Wenmin, Hou Xiuqun, Li Yuanjiao, Jiang Qinglei, Bao Binbin","doi":"10.1109/ICAIIS49377.2020.9194833","DOIUrl":"https://doi.org/10.1109/ICAIIS49377.2020.9194833","url":null,"abstract":"Nuclear power plants have accumulated a large amount of process monitoring data, but most of the data are not marked with specified patterns, which cannot be directly applied to the data-driven intelligent early warning and fault diagnosis. On-site alarm threshold can only locate a small number of abnormal vibration data, ignoring a large number of data that doesn't exceed alarm threshold but is obviously abnormal fluctuation of vibration phenomenon. To solve this problem, a method is proposed to locate abnormal vibration data based on correlation coefficient in this paper. This method takes the correlation coefficient of vibration data and corresponding time as the fluctuation index of measuring vibration data, and calculates the fluctuation threshold through historical data statistics, so as to locate abnormal vibration data. The vibration monitoring data of nuclear pump show that the proposed method can effectively detect the abnormal fluctuation of the data and locate the starting point of abnormal vibration.","PeriodicalId":416002,"journal":{"name":"2020 IEEE International Conference on Artificial Intelligence and Information Systems (ICAIIS)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132576940","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 : 2020-03-01DOI: 10.1109/ICAIIS49377.2020.9194841
Peng Liu, W. Ren, Ruoqi Huang
Based on the principle of composite left and right hand transmission lines (CRLH- TL), a new type of multi-band hybrid multi-mode mobile phone antenna is designed. It is a combination of a monopole antenna and two Zero-order Resonator (ZOR) antennas. The antenna is printed on the PCB board of a typical smartphone with the size of 70 mm × 160 mm × 3.5 mm. Since the resonant frequency of ZOR antenna does not depend on the size characteristics, it will be helpful in the miniaturization of the antenna. Simulation results show that the center resonant frequencies locate at 1.04 GHz, 1.70 GHz, 2.52 GHz, 3.65 GHz, 4.26 GHz, and 4.9 GHz, covering the 4G LTE2300 (2305–2400 MHz), LTE2500 (2575–2635 MHz), DCS1800 (1710–1880 MHz), WLAN (2.4-2.485 GHz), and WiMAX (2.5-2.69 GHz, 3.3 -3.7 GHz) with good omnidirectional radiation characteristics and acceptable gain.
{"title":"Design of a 5G Multi-band Mobile Phone Antenna Based on CRLH-TL","authors":"Peng Liu, W. Ren, Ruoqi Huang","doi":"10.1109/ICAIIS49377.2020.9194841","DOIUrl":"https://doi.org/10.1109/ICAIIS49377.2020.9194841","url":null,"abstract":"Based on the principle of composite left and right hand transmission lines (CRLH- TL), a new type of multi-band hybrid multi-mode mobile phone antenna is designed. It is a combination of a monopole antenna and two Zero-order Resonator (ZOR) antennas. The antenna is printed on the PCB board of a typical smartphone with the size of 70 mm × 160 mm × 3.5 mm. Since the resonant frequency of ZOR antenna does not depend on the size characteristics, it will be helpful in the miniaturization of the antenna. Simulation results show that the center resonant frequencies locate at 1.04 GHz, 1.70 GHz, 2.52 GHz, 3.65 GHz, 4.26 GHz, and 4.9 GHz, covering the 4G LTE2300 (2305–2400 MHz), LTE2500 (2575–2635 MHz), DCS1800 (1710–1880 MHz), WLAN (2.4-2.485 GHz), and WiMAX (2.5-2.69 GHz, 3.3 -3.7 GHz) with good omnidirectional radiation characteristics and acceptable gain.","PeriodicalId":416002,"journal":{"name":"2020 IEEE International Conference on Artificial Intelligence and Information Systems (ICAIIS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114067221","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 : 2020-03-01DOI: 10.1109/ICAIIS49377.2020.9194863
Gang Sha, Junsheng Wu, Bin Yu
Because of the problem that the complexity of spine CT images, the irregular shape of vertebral boundary, low contrast, noise and unevenness in images, meanwhile there are artificial deviations and low efficiencies in clinic, which needs doctors' prior knowledge and clinical experience to determine lesions location in CT images, so it can not meet the clinical realtime needs. In this paper, we use deep learning to process the CT images of spine, and to detect and locate lesion of (cervical fracture, cfracture), (thoracic fracture, tfracture), (lumbar fracture, lfracture) by the improved Faster-RCNN[1]. Through improving the RPN network in Faster-RCNN and changing the number of anchor, we choose appropriate length-width ratio to improve detection efficiency and accuracy. The experiment shows the results are more accurate, and mAP (mean average precision) of detection algorithm is 73.3%, detection rate is 0.03810 seconds per detection, which can basically meet the clinical real-time needs.
由于脊柱CT图像的复杂性、椎体边界形状不规则、图像对比度低、噪声和不均匀等问题，同时在临床中存在人为偏差和低效率，需要医生的先验知识和临床经验来确定CT图像中的病变位置，因此不能满足临床实时性的需要。在本文中，我们利用深度学习对脊柱的CT图像进行处理，通过改进的Faster-RCNN对(颈椎骨折，c骨折)、(胸椎骨折，t骨折)、(腰椎骨折，l骨折)病变进行检测和定位[1]。通过对Faster-RCNN中的RPN网络进行改进，改变锚点个数，选择合适的长宽比，提高检测效率和准确率。实验结果表明，检测算法的mAP (mean average precision)为73.3%，每次检测的检出率为0.03810秒，基本可以满足临床实时性的需求。
{"title":"Detection of Spinal Fracture Lesions Based on Improved Faster-RCNN","authors":"Gang Sha, Junsheng Wu, Bin Yu","doi":"10.1109/ICAIIS49377.2020.9194863","DOIUrl":"https://doi.org/10.1109/ICAIIS49377.2020.9194863","url":null,"abstract":"Because of the problem that the complexity of spine CT images, the irregular shape of vertebral boundary, low contrast, noise and unevenness in images, meanwhile there are artificial deviations and low efficiencies in clinic, which needs doctors' prior knowledge and clinical experience to determine lesions location in CT images, so it can not meet the clinical realtime needs. In this paper, we use deep learning to process the CT images of spine, and to detect and locate lesion of (cervical fracture, cfracture), (thoracic fracture, tfracture), (lumbar fracture, lfracture) by the improved Faster-RCNN[1]. Through improving the RPN network in Faster-RCNN and changing the number of anchor, we choose appropriate length-width ratio to improve detection efficiency and accuracy. The experiment shows the results are more accurate, and mAP (mean average precision) of detection algorithm is 73.3%, detection rate is 0.03810 seconds per detection, which can basically meet the clinical real-time needs.","PeriodicalId":416002,"journal":{"name":"2020 IEEE International Conference on Artificial Intelligence and Information Systems (ICAIIS)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134197891","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 : 2020-03-01DOI: 10.1109/ICAIIS49377.2020.9194948
Wenhua Wu, Wei Cao, Ting Yan, Zhiguo Wang, Shu Wang
Most of the existing evidence dynamic reliability assessments are based on most principles, this method is not suitable for the case where there is only two evidences, and it is impossible to use this method to evaluate the reliability of two adjacent evidences in the time-domain evidence sequence. The concepts related to intuitionistic fuzzy sets and evidence theory is introduced and the relationship between intuitionistic fuzzy sets and BPAF are described. The research focuses on the dynamic reliability evaluation of evidence sequences. Combining the relationship between BPAF and intuitionistic fuzzy sets in evidence theory. Finally, a new method for evaluating the reliability of evidence is proposed, which realizes the evaluation of the reliability of each evidence source in the absence of prior knowledge.
{"title":"Evaluation Method of Evidence Reliability Based on Intuitionistic Fuzzy Sets","authors":"Wenhua Wu, Wei Cao, Ting Yan, Zhiguo Wang, Shu Wang","doi":"10.1109/ICAIIS49377.2020.9194948","DOIUrl":"https://doi.org/10.1109/ICAIIS49377.2020.9194948","url":null,"abstract":"Most of the existing evidence dynamic reliability assessments are based on most principles, this method is not suitable for the case where there is only two evidences, and it is impossible to use this method to evaluate the reliability of two adjacent evidences in the time-domain evidence sequence. The concepts related to intuitionistic fuzzy sets and evidence theory is introduced and the relationship between intuitionistic fuzzy sets and BPAF are described. The research focuses on the dynamic reliability evaluation of evidence sequences. Combining the relationship between BPAF and intuitionistic fuzzy sets in evidence theory. Finally, a new method for evaluating the reliability of evidence is proposed, which realizes the evaluation of the reliability of each evidence source in the absence of prior knowledge.","PeriodicalId":416002,"journal":{"name":"2020 IEEE International Conference on Artificial Intelligence and Information Systems (ICAIIS)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121863941","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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