2022 IEEE 2nd International Conference on Computer Communication and Artificial Intelligence (CCAI)最新文献

Analysis of the Impact of UCA Element’s Radiation Pattern on the OAM Communication Performance UCA单元辐射方向图对OAM通信性能的影响分析

2022 IEEE 2nd International Conference on Computer Communication and Artificial Intelligence (CCAI) Pub Date : 2022-05-06 DOI: 10.1109/CCAI55564.2022.9807776

Chengxiang Liu, Wei Yu, Bin Zhou, Yu Zhao

{"title":"Analysis of the Impact of UCA Element’s Radiation Pattern on the OAM Communication Performance","authors":"Chengxiang Liu, Wei Yu, Bin Zhou, Yu Zhao","doi":"10.1109/CCAI55564.2022.9807776","DOIUrl":"https://doi.org/10.1109/CCAI55564.2022.9807776","url":null,"abstract":"Orbital Angular Momentum (OAM) based wireless communications can greatly increase the channel capacity due to its mode degree of freedom (DOF) besides time and frequency. Currently, OAM generation based on Uniform Circular Arrays (UCAs) draws much attention because it can flexibly perform OAM mode multiplexing. Channel models of UCA based OAM communications in the previous researches were built without consideration of radiation patterns of antenna elements in the UCA. In order to further explore the impact of UCA element’s radiation pattern on the OAM communication performance, in this paper, we consider the radiation pattern of antenna elements in both the transmit and receive UCAs and reconstruct the channel model of UCA based OAM communications in the line of sight (LOS) and coaxial scenarios. For the antenna element’s radiation pattern function $G(theta,phi)$, we theoretically prove that only when $G(theta,phi)$ is symmetric in the dimension of $phi$ can the orthogonality among each OAM mode be maintained. The simulations indicate that the mode isolation, the channel capacity and the bit error rate (BER) performance all decline sharply when $G(theta,phi)$ is not symmetric in the dimension of $phi$. Our research provides useful insights for the designing of antenna elements for CA based OAM communications.","PeriodicalId":340195,"journal":{"name":"2022 IEEE 2nd International Conference on Computer Communication and Artificial Intelligence (CCAI)","volume":"600 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132056500","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: 0

A New Prediction Approach Using FCM and Deep Cascade Elastic Broad Learning for Photovoltaic Power Generation in Short Term 基于FCM和深度级联弹性广义学习的光伏发电短期预测新方法

2022 IEEE 2nd International Conference on Computer Communication and Artificial Intelligence (CCAI) Pub Date : 2022-05-06 DOI: 10.1109/CCAI55564.2022.9807821

Zhaoyang Wei, Wu Deng, Huimin Zhao, Jiameng Xue

{"title":"A New Prediction Approach Using FCM and Deep Cascade Elastic Broad Learning for Photovoltaic Power Generation in Short Term","authors":"Zhaoyang Wei, Wu Deng, Huimin Zhao, Jiameng Xue","doi":"10.1109/CCAI55564.2022.9807821","DOIUrl":"https://doi.org/10.1109/CCAI55564.2022.9807821","url":null,"abstract":"In recent years, photovoltaic power generation prediction has mainly faced some difficulties, such as low prediction accuracy, time-consuming model training, and so on. In order to improve the prediction accuracy of photovoltaic power generation in short term, it is greatly beneficial to optimize power allocating plans and improve economic benefits. Therefore, a new prediction method of Photovoltaic (PV) power generation based on Fuzzy C-Means (FCM) and deep cascade elastic broad learning is proposed in this paper. Firstly, the fuzzy c-means clustering algorithm is used to classify the input factors. Then, the feature mapping nodes of the broad learning model are constructed by the deep cascade method to extract the features of the input factors fully. The elastic net regularization method is employed to constrain the network output weight, measure the influence of the nodes on the weight, extract the more influential nodes, and sparse network structure. A deep cascade elastic broad learning network is established for short-term photovoltaic power prediction. Finally, the effectiveness of the method is verified through the original data. The experimental results verify the model can better predict the actual photovoltaic power generation and has a good application value.","PeriodicalId":340195,"journal":{"name":"2022 IEEE 2nd International Conference on Computer Communication and Artificial Intelligence (CCAI)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134286293","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: 0

Image Retrieval-Based Localization Under Seasonal Changes 季节变化下基于图像检索的定位

2022 IEEE 2nd International Conference on Computer Communication and Artificial Intelligence (CCAI) Pub Date : 2022-05-06 DOI: 10.1109/CCAI55564.2022.9807825

Hao Zhu

引用次数: 0

Entropy Based Generative Adversarial Network for PolSAR Image Classification 基于熵的生成对抗网络PolSAR图像分类

2022 IEEE 2nd International Conference on Computer Communication and Artificial Intelligence (CCAI) Pub Date : 2022-05-06 DOI: 10.1109/CCAI55564.2022.9807703

Meng Tian, Shuyin Zhang, Yitao Cai, Chao Xu

{"title":"Entropy Based Generative Adversarial Network for PolSAR Image Classification","authors":"Meng Tian, Shuyin Zhang, Yitao Cai, Chao Xu","doi":"10.1109/CCAI55564.2022.9807703","DOIUrl":"https://doi.org/10.1109/CCAI55564.2022.9807703","url":null,"abstract":"In order to solve the problem that the effect of generator feature learning is not paid enough attention in generative adversarial network(GAN) for Polarimetric synthetic aperture radar(PolSAR) data, a new GAN called Entropy-based Auxiliary Classifier Generative Adversarial Networks (E-ACGAN) was proposed in this paper. The decomposition discrepancy was obtained by calculating the entropy decomposition of the real data and the generated data. Then the decomposition discrepancy is used to measure the similarity between the generated data and the real data. This discrepancy will be introduced into the model as an additional optimization goal of the generator. Therefore, the generator can learn more characteristics of PolSAR data to generate more realistic data. In the step of adversarial learning, the discrimination and classification capabilities of the discriminator are also improved with the generator. The experimental results on the Flevoland2 data set show that the classification accuracy of E-ACGAN is 2.36% higher than that of the original ACGAN and it is also improved to different degrees than other traditional classification methods.","PeriodicalId":340195,"journal":{"name":"2022 IEEE 2nd International Conference on Computer Communication and Artificial Intelligence (CCAI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115348900","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: 0

A High Performance Blockchain PGPBFT Consensus Algorithm in Integrated Circuit Protection Applications 集成电路保护应用中的高性能区块链PGPBFT一致性算法

2022 IEEE 2nd International Conference on Computer Communication and Artificial Intelligence (CCAI) Pub Date : 2022-05-06 DOI: 10.1109/CCAI55564.2022.9807793

Di Zhang, Yue Wu, Hua Chen, Faxin Yu, Jiongjiong Mo, Min Zhou

{"title":"A High Performance Blockchain PGPBFT Consensus Algorithm in Integrated Circuit Protection Applications","authors":"Di Zhang, Yue Wu, Hua Chen, Faxin Yu, Jiongjiong Mo, Min Zhou","doi":"10.1109/CCAI55564.2022.9807793","DOIUrl":"https://doi.org/10.1109/CCAI55564.2022.9807793","url":null,"abstract":"In the fierce global semiconductor Intellectual Property (IP) competition, there is an increasing risk of IP tampering, which hinders the efficiency of IP reuse. Therefore, the effective protection of IP is of great importance. Circuit IP protection chain, which is anonymous, traceable, and immutability, not only solve the security issues of traditional centralized IP transaction data centers but also improves the regulatory system for data misuse. Although the rapid development of IP protection chains is accompanied by higher design efficiency and shorter design cycles, it also imposes higher concurrency of applications and node load capacity requirements on the blockchain. Since blockchain performance can be limited by the lowest performing node, long waiting time or unresponsiveness will occur under high concurrency with node performance differences. Therefore, this paper proposes an improved practical byzantine fault tolerant consensus algorithm based on node performance grouping (PGPBFT). The algorithm utilizes the grouping strategy of the Mamdani fuzzy model to preferentially select high performance nodes to participate in consensus. The experimental results show that the algorithm has 12.23% higher throughput than the traditional algorithm in scenarios with node performance varies and high concurrency requirements, maximizing the utilization of node performance.","PeriodicalId":340195,"journal":{"name":"2022 IEEE 2nd International Conference on Computer Communication and Artificial Intelligence (CCAI)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116495657","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: 0

Chinese-Korean Weibo Sentiment Classification Based on Pre-trained Language Model and Transfer Learning 基于预训练语言模型和迁移学习的中韩微博情感分类

2022 IEEE 2nd International Conference on Computer Communication and Artificial Intelligence (CCAI) Pub Date : 2022-05-06 DOI: 10.1109/CCAI55564.2022.9807755

Hengxuan Wang, Zhenguo Zhang, Xu Cui, Rong-yi Cui

{"title":"Chinese-Korean Weibo Sentiment Classification Based on Pre-trained Language Model and Transfer Learning","authors":"Hengxuan Wang, Zhenguo Zhang, Xu Cui, Rong-yi Cui","doi":"10.1109/CCAI55564.2022.9807755","DOIUrl":"https://doi.org/10.1109/CCAI55564.2022.9807755","url":null,"abstract":"Korean is the native and official language spoken by Chinese-Korean people, and Weibo is a social media software with a huge number of users in China. Currently, there is few studies related to sentiment analysis of Korean-language Weibo texts posted by Chinese-Korean users. In this paper, we propose a sentiment classification method for Chinese-Korean Weibo based on pre-trained language model and transfer learning. Firstly, we crawled the Chinese-Korean Weibo data from Sina Weibo and label them with sentiment to get the Chinese-Korean Weibo sentiment analysis (CKWSA) dataset. Secondly, to solve the problem of few training samples of the Chinese-Korean Weibo sentiment analysis dataset, we fine-tune the classifier based on the pre-trained Korean language model on the Korean Twitter sentiment analysis dataset to obtain the Korean Twitter sentiment classification model; and further fine-tune the model on CKWSA dataset to get Chinese-Korean Weibo sentiment classification model. The experiments show that the proposed classification method based on pre-trained language model and transfer learning has great performance, and there is an improvement compared other baselines on the Chinese-Korean Weibo sentiment analysis dataset.","PeriodicalId":340195,"journal":{"name":"2022 IEEE 2nd International Conference on Computer Communication and Artificial Intelligence (CCAI)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121659323","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

Defect Detection of Integrated Circuit Based on YOLOv5 基于YOLOv5的集成电路缺陷检测

2022 IEEE 2nd International Conference on Computer Communication and Artificial Intelligence (CCAI) Pub Date : 2022-05-06 DOI: 10.1109/CCAI55564.2022.9807758

Yucheng Lu, Chen Sun, Xiangning Li, Liye Cheng

{"title":"Defect Detection of Integrated Circuit Based on YOLOv5","authors":"Yucheng Lu, Chen Sun, Xiangning Li, Liye Cheng","doi":"10.1109/CCAI55564.2022.9807758","DOIUrl":"https://doi.org/10.1109/CCAI55564.2022.9807758","url":null,"abstract":"In Integrated Circuit (IC) manufacturing, defect detection is a necessary task. The small size and high density of IC, and the complex characteristics of various defects, which burdens the defect detection system. It is difficult for the existing detection methods to accurately detect various types of defects while ensuring the detection speed. We implement a deep convolutional neural network in IC defect detection, based on YOLOv5, we add a prediction head to detect objects at different scales. In addition, we also integrate the Squeeze-and-Excitation layer (SELayer) to help the detection network find attention region on scenarios with dense objects, extract key features, enhance the network’s ability to detect difficult-to-detect samples, and generally improve the accuracy of the detection network. Extensive experiments on the IC defect dataset made by us show that the AP result of YOLOv5 with integrated attention module in IC defect detection is 95.4%, which is 0.5% better than the network without SELayer. Compared with other detection networks, it has obvious accuracy advantage, which is encouraging and promising competitive.","PeriodicalId":340195,"journal":{"name":"2022 IEEE 2nd International Conference on Computer Communication and Artificial Intelligence (CCAI)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125132227","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: 2

Research on Post Earthquake Public Opinion Analysis Based on XLNet-BiGRU-A Algorithm 基于XLNet-BiGRU-A算法的震后舆情分析研究

2022 IEEE 2nd International Conference on Computer Communication and Artificial Intelligence (CCAI) Pub Date : 2022-05-06 DOI: 10.1109/CCAI55564.2022.9807700

L. Chenxi, F. Jilin, Huan Meng, Wang Zhonghao

引用次数: 0

PANGO: Prediction Model Based on Clustering of Time Series for Traffic Flow to Venues PANGO:基于时间序列聚类的场馆交通流预测模型

2022 IEEE 2nd International Conference on Computer Communication and Artificial Intelligence (CCAI) Pub Date : 2022-05-06 DOI: 10.1109/CCAI55564.2022.9807722

Huayi Zhou, Peng Xu

{"title":"PANGO: Prediction Model Based on Clustering of Time Series for Traffic Flow to Venues","authors":"Huayi Zhou, Peng Xu","doi":"10.1109/CCAI55564.2022.9807722","DOIUrl":"https://doi.org/10.1109/CCAI55564.2022.9807722","url":null,"abstract":"With the increasing number of venues in the city, it provides better services for people. However, the ever-changing flow of people has also brought challenges to the management of venues, especially under the current regular prevention and control measures for COVID-19. Therefore, it is extremely necessary to propose a suitable prediction model for pedestrian volume to venues. The timing characteristics of venue’s traffic flow determine that the accuracy of the prediction results by applying classical prediction models is unsatisfactory. In order to resolve the problem, in this paper, a Long Short Term Memory network (LSTM) combined with clustering of time series named PANGO is proposed. In PANGO, the temporal clustering is proposed to solve the short-term dependence of traffic flow data, while the long-term cycle prediction model is applied to obtain the longterm cycle characteristics, so as to improve the accuracy of prediction. Finally, the results of multi-dimensional experiments show that the prediction accuracy of PANGO model is improved by 11.8% compared with the traditional LSTM model.","PeriodicalId":340195,"journal":{"name":"2022 IEEE 2nd International Conference on Computer Communication and Artificial Intelligence (CCAI)","volume":"122 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134619878","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: 0

Research on Intelligent Recognition Algorithm of College Students’ Classroom Behavior Based on Improved SSD 基于改进SSD的大学生课堂行为智能识别算法研究

2022 IEEE 2nd International Conference on Computer Communication and Artificial Intelligence (CCAI) Pub Date : 2022-05-06 DOI: 10.1109/CCAI55564.2022.9807756

Lv Wenchao, Huan Meng, Zhang Yuping, Liu Shuai

{"title":"Research on Intelligent Recognition Algorithm of College Students’ Classroom Behavior Based on Improved SSD","authors":"Lv Wenchao, Huan Meng, Zhang Yuping, Liu Shuai","doi":"10.1109/CCAI55564.2022.9807756","DOIUrl":"https://doi.org/10.1109/CCAI55564.2022.9807756","url":null,"abstract":"This paper takes the classroom images of more than 50 classrooms in a university for nearly 4 months in a semester as the research object. The LabelImg manual annotation method is used to construct a detection data set including four behavioral states: listening to class, taking notes, playing with mobile phones, and sleeping. In order to effectively improve the detection accuracy of the data annotation model, we used data enhancement techniques such as cropping, rotation, and shading transformation to expand the number of dataset. Based on this dataset, an improved SSD model based on deep learning target detection technology is adopted. ResNet module is used to solve the problem that VGG module has poor detection results of students’ behavior state in picture analysis, FPN module is added to build RF-SSD detection model to improve the efficiency of image recognition to solve the problem of the low efficiency of small target recognition in the back of class. The experimental results show that RF-SSD has a great improvement in feature extraction ability and small target recognition accuracy compared with native SSD in self-constructed dataset, and can provide technical support and new ideas and methods for teaching management in universities.","PeriodicalId":340195,"journal":{"name":"2022 IEEE 2nd International Conference on Computer Communication and Artificial Intelligence (CCAI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116127885","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