2020 5th International Conference on Computational Intelligence and Applications (ICCIA)最新文献_第5页

High-quality Panorama Stitching based on Asymmetric Bidirectional Optical Flow 基于非对称双向光流的高质量全景拼接

2020 5th International Conference on Computational Intelligence and Applications (ICCIA) Pub Date : 2020-06-01 DOI: 10.1109/ICCIA49625.2020.00030

Ming Meng, Shaojun Liu

引用次数: 5

A Low-Complexity Adaptive Extended Min-Sum Algorithm for Non-Binary LDPC Codes 非二进制LDPC码的低复杂度自适应扩展最小和算法

2020 5th International Conference on Computational Intelligence and Applications (ICCIA) Pub Date : 2020-06-01 DOI: 10.1109/ICCIA49625.2020.00048

Min-Ho Kim, Kyeong-Bin Park, Ki-Seok Chung

引用次数: 0

A novel fault identification method for HVDC transmission line based on Stransform multi-scale area 基于strtransform多尺度区域的高压直流输电线路故障识别新方法

2020 5th International Conference on Computational Intelligence and Applications (ICCIA) Pub Date : 2020-06-01 DOI: 10.1109/ICCIA49625.2020.00045

Chen Ying, Fan Songhai, Wang Qiaomei, Wu Tianbao, Luo Lei, Mai Xiaomin, Gong Yiyu

{"title":"A novel fault identification method for HVDC transmission line based on Stransform multi-scale area","authors":"Chen Ying, Fan Songhai, Wang Qiaomei, Wu Tianbao, Luo Lei, Mai Xiaomin, Gong Yiyu","doi":"10.1109/ICCIA49625.2020.00045","DOIUrl":"https://doi.org/10.1109/ICCIA49625.2020.00045","url":null,"abstract":"Aiming at the problem that traditional traveling wave protection is difficult to take into account both quick-action and selectivity, an intelligent fault identification method for HVDC transmission lines based on S-transform multi-scale area is proposed. This method combines Radial Basis Function Network (RBFN) can accurately distinguish between internal and external faults, and at the same time achieve fault pole selection. First, the discrete S-transform is performed on the transient current signal, and multiple frequency scale signals are selected to calculate the area of the frequency signal after the fault. The S-transform multi-scale area is used to characterize the internal and external fault features and fault pole characteristics. The S-transform multi-scale area is used to form a feature vector, and the feature vector set is divided into a training set and a test set. The training set is trained to obtain an RBFN model, and the test set is used for testing. The prediction result obtained is the classification of different fault types. A large number of simulation results show that the method can effectively realize the internal and external fault identification and fault pole selection under different fault distances and different transition resistances, and has a strong ability to withstand transition resistances.","PeriodicalId":237536,"journal":{"name":"2020 5th International Conference on Computational Intelligence and Applications (ICCIA)","volume":"517 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123098456","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}

引用次数: 1

A negative selection algorithm based on adaptive immunoregulation 一种基于适应性免疫调节的负选择算法

2020 5th International Conference on Computational Intelligence and Applications (ICCIA) Pub Date : 2020-06-01 DOI: 10.1109/ICCIA49625.2020.00041

H. Deng, Tao Yang

引用次数: 1

Location-based Hybrid Deep Learning Model for Purchase Prediction 基于位置的混合深度学习购买预测模型

2020 5th International Conference on Computational Intelligence and Applications (ICCIA) Pub Date : 2020-06-01 DOI: 10.1109/ICCIA49625.2020.00038

B. Zhu, Weiqiang Tang, Xiai Mao, Wenchuan Yang

引用次数: 2

A Short-Term Hybrid Forecasting Approach for Regional Electricity Consumption Based on Grey Theory and Random Forest 基于灰色理论和随机森林的区域用电量短期混合预测方法

2020 5th International Conference on Computational Intelligence and Applications (ICCIA) Pub Date : 2020-06-01 DOI: 10.1109/ICCIA49625.2020.00044

Kai Li, Yidan Yedda Xing, Haijia Zhu, Wei Nai

{"title":"A Short-Term Hybrid Forecasting Approach for Regional Electricity Consumption Based on Grey Theory and Random Forest","authors":"Kai Li, Yidan Yedda Xing, Haijia Zhu, Wei Nai","doi":"10.1109/ICCIA49625.2020.00044","DOIUrl":"https://doi.org/10.1109/ICCIA49625.2020.00044","url":null,"abstract":"Electricity consumption reflects the development level of a certain region to a great extent, and it is always in a changing process with fluctuation. Entities or agencies who provide the electricity power supply services are always eager to know the data of regional electricity consumption, and hope to obtain the accurate forecast of future power consumption from these data, so that more appropriate and reasonable power supply service arrangement can be implemented. Till now, many scholars have reported their research on doing forecasting work by employing algorithms for regression such as Grey Theory or Random Forest, however, there are some drawbacks in both algorithms in using available data for prediction. In this paper, a short-term hybrid forecasting approach has been proposed based on both algorithms, it can not only realize the prediction from relatively less available data, but ensure high accuracy in prediction as well. By an empirical study on the electricity power consumption of a certain region in central western China, the effectiveness of the proposed method is verified.","PeriodicalId":237536,"journal":{"name":"2020 5th International Conference on Computational Intelligence and Applications (ICCIA)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114831281","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}

引用次数: 1

Lossless Image Compression Algorithm Based on Long Short-term Memory Neural Network 基于长短期记忆神经网络的无损图像压缩算法

2020 5th International Conference on Computational Intelligence and Applications (ICCIA) Pub Date : 2020-06-01 DOI: 10.1109/ICCIA49625.2020.00023

Caixin Zhu, Huaiyao Zhang, Yun Tang

{"title":"Lossless Image Compression Algorithm Based on Long Short-term Memory Neural Network","authors":"Caixin Zhu, Huaiyao Zhang, Yun Tang","doi":"10.1109/ICCIA49625.2020.00023","DOIUrl":"https://doi.org/10.1109/ICCIA49625.2020.00023","url":null,"abstract":"People have relatively higher requirements for image storage in some specific fields, such as high-resolution cultural relic data image, medical image, infrared remote sensing image, high-precision astronomical observation image. There cannot be any pixel loss in the storage process, so the image can only be compressed by lossless compression. In this paper, a lossless image compression algorithm based on the neural network of long short-term memory (LSTM) is proposed: a LSTM model predictor based on attention mechanism is built by utilizing the memory characteristic of cyclic neural network. The previous pixel value of the image was taken as the input of the model, then the predicted pixel was obtained through the cyclic neural network, and finally the calculated difference between these values was encoded by the mixed run-length encoding and Golomb-Rice encoding. Compared with the traditional predictive lossless image compression algorithm, this algorithm proposed here comprehensively considers the correlation between more pixels and encoded pixels. The experimental results show that compared with the lossless image compression algorithms JPEG-LS and CALIC, the proposed algorithm improves the compression rate by 25% and 12% respectively.","PeriodicalId":237536,"journal":{"name":"2020 5th International Conference on Computational Intelligence and Applications (ICCIA)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124478400","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}

引用次数: 3

Particle swarm optimization with adaptive elite opposition-based learning for large-scale problems 大规模问题的自适应精英对立学习粒子群优化

2020 5th International Conference on Computational Intelligence and Applications (ICCIA) Pub Date : 2020-06-01 DOI: 10.1109/ICCIA49625.2020.00016

Hua-Hui Xu, Ruoli Tang

{"title":"Particle swarm optimization with adaptive elite opposition-based learning for large-scale problems","authors":"Hua-Hui Xu, Ruoli Tang","doi":"10.1109/ICCIA49625.2020.00016","DOIUrl":"https://doi.org/10.1109/ICCIA49625.2020.00016","url":null,"abstract":"A novel particle swarm optimization with elite opposition-based learning algorithm is proposed in an attempt to improve the performance on solving large-scale optimization problems (LSOP) in maximum power point tracking (MPPT) of photovoltaic system. The standard particle swarm optimization (PSO) algorithm shows its weakness on LSOP, such as easily falling into local optimum, slow convergence and low accuracy at later evolution process. Therefore, this paper develops a modified PSO algorithm based on elite opposition-based learning mechanism and adaptive multi-context cooperatively coevolving (AM-CC) framework. In every iteration, the current high-priority individuals execute dynamic generalized opposition-based learning to generate their opposite solutions which enhance the ability of local exploration and help the particles escape from local optimum. The simulation experiments are conducted on a comprehensive set of benchmarks (up to 2000 real-valued variables), as well as on a large-scale MPPT application. Compared with some state-of-the-art variants of PSO and differential evolution (DE), the results show that the improved algorithm has higher convergence speed and accuracy. Besides, it can avoid premature phenomenon effectively and is suitable to solve the large-scale optimization problem.","PeriodicalId":237536,"journal":{"name":"2020 5th International Conference on Computational Intelligence and Applications (ICCIA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130570101","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}

引用次数: 3

Automatic Fingerprint Extraction of Mobile APP Users in Network Traffic 基于网络流量的手机APP用户指纹自动提取

2020 5th International Conference on Computational Intelligence and Applications (ICCIA) Pub Date : 2020-06-01 DOI: 10.1109/ICCIA49625.2020.00036

Faqiang Sun, Li Zhao, Bo Zhou, Yong Wang

{"title":"Automatic Fingerprint Extraction of Mobile APP Users in Network Traffic","authors":"Faqiang Sun, Li Zhao, Bo Zhou, Yong Wang","doi":"10.1109/ICCIA49625.2020.00036","DOIUrl":"https://doi.org/10.1109/ICCIA49625.2020.00036","url":null,"abstract":"Network operators often need a clear visibility of the mobile APPs and their user scales running in the network traffic. This is critical for network management and network security. Analysis of the network traffic using the extracted APP features and user fingerprints is helpful for effective network operations, management, and security monitoring of LANs, MANs, and WANs. In China, the number of mobile APP users continues to increase, and the proportion of Internet users using mobile APPs to access the Internet far exceeds that of computers, making this task significant and difficult. The traditional methods are mainly APP identifications or identifications of specific APP users, which cannot satisfy the requirements of globally monitoring of APPs and their user scales at the same time. Especially when many users share the same network IPs (4G, home broadband, NAT), this work becomes challenging and time-consuming. This paper proposes an automatic fingerprint extraction approach of mobile APP users in network traffic. By analyzing the plaintext of the HTTP requests initiated by APPs in training datasets, we extract the APPs’ features and the users’ fingerprints simultaneously. Both of them are the combinations of strings which are distinguishable of APPs and their users in the network traffic. The proposed method is evaluated with the top 49 popular APPs in Huawei App Store. The experimental results show that the recalls of the extractions of APPs’ features and users’ fingerprints are respectively 77.5% and 55.1% in total.","PeriodicalId":237536,"journal":{"name":"2020 5th International Conference on Computational Intelligence and Applications (ICCIA)","volume":"78 15","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120821103","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}

引用次数: 1