发布求助
文献互助 智能选刊 最新文献

2021 7th International Conference on Systems and Informatics (ICSAI)最新文献

筛选
英文 中文
Quantitative Analysis of Facial Symmetry Among Different Expressions 不同表情中面部对称性的定量分析
2021 7th International Conference on Systems and Informatics (ICSAI) Pub Date : 2021-11-13 DOI: 10.1109/icsai53574.2021.9664110
Ke Wu, Weiyang Chen
{"title":"Quantitative Analysis of Facial Symmetry Among Different Expressions","authors":"Ke Wu, Weiyang Chen","doi":"10.1109/icsai53574.2021.9664110","DOIUrl":"https://doi.org/10.1109/icsai53574.2021.9664110","url":null,"abstract":"Facial expression recognition technology has gradually become a research hotspot. Facial symmetry is an important facial feature. This paper quantitatively analyzes the differences of various facial symmetry indexes among different facial expressions. Firstly, image preprocessing standardizes the images and finds out the landmarks. Then five asymmetric features of the angle of key landmarks, regional asymmetry, centroid, singular value, and structural dissimilarity are extracted from the facial images. The experimental results show the following conclusions. The asymmetric features of happy are more aggregated than other expressions. The mild asymmetry accounted for the largest proportion of happy. The moderate asymmetry accounted for the largest proportion of most expressions. The severe asymmetry of all expressions accounted for the least.","PeriodicalId":131284,"journal":{"name":"2021 7th International Conference on Systems and Informatics (ICSAI)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131199404","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
Prediction of Hospital Outpatient Volume Based on Combined Neural Network 基于联合神经网络的医院门诊量预测
2021 7th International Conference on Systems and Informatics (ICSAI) Pub Date : 2021-11-13 DOI: 10.1109/ICSAI53574.2021.9664145
Zhen Chen, Wei Li
{"title":"Prediction of Hospital Outpatient Volume Based on Combined Neural Network","authors":"Zhen Chen, Wei Li","doi":"10.1109/ICSAI53574.2021.9664145","DOIUrl":"https://doi.org/10.1109/ICSAI53574.2021.9664145","url":null,"abstract":"The hospital outpatient volume has temporal property, and the importance of features is different. In this paper, a new neural network model was proposed in order to the regression prediction of outpatient volume, which adds attention layers to the original network model. In our paper, we use the powerful feature extraction ability of convolution neural network(CNN) to extract important features of data, and the attention mechanism is added in CNN, it helps the model to solve the blindness problem of CNN feature extraction, so that our feature extraction model can gives more sources on the important features and weaken the influence of other features. We connect the output of CNN to the input of long short-term memory network(LSTM), and add an attention layer to the LSTM hidden layers in order to better learn the time characteristics, the output of the last time step of LSTM is connected to a deep neural network to predict the results. The comparison with common algorithms shows that the error of the algorithm proposed in this paper is smaller.","PeriodicalId":131284,"journal":{"name":"2021 7th International Conference on Systems and Informatics (ICSAI)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121656424","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
RVFL-NLNB Rainfall Forecasting Model Based on Feature Extraction of MGF and PCA 基于MGF和PCA特征提取的RVFL-NLNB降雨预报模型
2021 7th International Conference on Systems and Informatics (ICSAI) Pub Date : 2021-11-13 DOI: 10.1109/ICSAI53574.2021.9664186
Xing Zhang, Yeqiong Shi, Hui Zhou
{"title":"RVFL-NLNB Rainfall Forecasting Model Based on Feature Extraction of MGF and PCA","authors":"Xing Zhang, Yeqiong Shi, Hui Zhou","doi":"10.1109/ICSAI53574.2021.9664186","DOIUrl":"https://doi.org/10.1109/ICSAI53574.2021.9664186","url":null,"abstract":"In view of the difficulty in determining the modeling factors and the poor stability of the prediction model in precipitation prediction modeling. In this paper, firstly, the extension factor matrix is generated by using the mean generation function, and the dimension of the extension matrix is reduced by making use of the principal component analysis technology. The effective data features are extracted as independent variables and the original precipitation series as dependent variables. Secondly, by comparing the architecture of random vector functional link model and random vector functional link that has no input-to-output links and no bias in output neurons, then the random vector functional link with no input-to-output links and no bias in output neurons prediction model of monthly precipitation in Liuzhou city is established. Finally, the prediction model is established for Liuzhou April precipitation data. This method makes full use of the reconstructed data of mean generation function and principal component analysis to produce precipitation factors, and employs random vector functional link with no input-to-output links and no bias in output neurons network model, the monthly precipitation prediction model of Liuzhou is established. The experimental results express that the model has reliability, which provides a reliable method for precipitation prediction.","PeriodicalId":131284,"journal":{"name":"2021 7th International Conference on Systems and Informatics (ICSAI)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122503714","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
An Improved Cuckoo Search Algorithm 一种改进的杜鹃搜索算法
2021 7th International Conference on Systems and Informatics (ICSAI) Pub Date : 2021-11-13 DOI: 10.1109/ICSAI53574.2021.9664073
Runyu Zheng
{"title":"An Improved Cuckoo Search Algorithm","authors":"Runyu Zheng","doi":"10.1109/ICSAI53574.2021.9664073","DOIUrl":"https://doi.org/10.1109/ICSAI53574.2021.9664073","url":null,"abstract":"Swarm intelligence optimization algorithm is a new type of optimization algorithm, which connects individuals into groups through team cooperation, generates swarm intelligence, and to solve practical problems. Cuckoo search algorithm is one of the typical swarm intelligence algorithms. It has the characteristics of high efficiency and simple implementation, but it has the problems of slow convergence speed and low accuracy. To overcome these shortcomings, this paper proposes a cuckoo search algorithm based on multi nest update, which uses three better nests to update other nests, so as to improve the optimal value accuracy and convergence speed of the algorithm. Experimental results show that the performance of the improved cuckoo search algorithm is significantly improved compared with the original algorithm.","PeriodicalId":131284,"journal":{"name":"2021 7th International Conference on Systems and Informatics (ICSAI)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125123494","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
Comparison of Deep Learning Models on Detection and Classification of Diabetic Retinopathy 深度学习模型在糖尿病视网膜病变检测与分类中的比较
2021 7th International Conference on Systems and Informatics (ICSAI) Pub Date : 2021-11-13 DOI: 10.1109/icsai53574.2021.9664179
Juan Cao, Jiaran Chen
{"title":"Comparison of Deep Learning Models on Detection and Classification of Diabetic Retinopathy","authors":"Juan Cao, Jiaran Chen","doi":"10.1109/icsai53574.2021.9664179","DOIUrl":"https://doi.org/10.1109/icsai53574.2021.9664179","url":null,"abstract":"Diabetic retinopathy is a common complication to diabetes, also is the main cause of the current diabetic blindness. Traditional methods of manually classifying retinal pathological images have difficulty in feature extraction, and differences in the level of medical personnel also result in low classification efficiency. In this paper, four deep learning network models based on LeNet, AlexNet, GoogLeNet and Res-Net 50 are used to compare and study the automatic classification of diabetic retinopathy images. The experimental data set comes from the data modeling and data analysis competition platform (Kaggle). The experimental results show that RES-NET 50 can accurately classify the degree of retinopathy with an accuracy of 89.71%, but the convergence rate is slow, while AlexNet can quickly converge with a low accuracy of 63.21%. This research can provide a good research foundation for the diagnosis and treatment of retinal diseases and the classification of disease severity in the future.","PeriodicalId":131284,"journal":{"name":"2021 7th International Conference on Systems and Informatics (ICSAI)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125589987","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
Parallel Sparse LU Factorization With Machine-Learning Method on Multi-core Processors 基于机器学习的多核处理器并行稀疏LU分解
2021 7th International Conference on Systems and Informatics (ICSAI) Pub Date : 2021-11-13 DOI: 10.1109/icsai53574.2021.9664163
Junsheng Zhou, Wangdong Yang, Minlu Dai, Qinyun Cai, Hao Wang, KenLi Li
{"title":"Parallel Sparse LU Factorization With Machine-Learning Method on Multi-core Processors","authors":"Junsheng Zhou, Wangdong Yang, Minlu Dai, Qinyun Cai, Hao Wang, KenLi Li","doi":"10.1109/icsai53574.2021.9664163","DOIUrl":"https://doi.org/10.1109/icsai53574.2021.9664163","url":null,"abstract":"Since the emergence of multi-core systems, many efforts must be taken to make existing software take advantage of these new architectures. We exploit dense matrix kernels and node parallelism in the sparse LU factorization, at the same time, also relying on third-party optimized multithreaded BLAS libraries. We introduce multi-threaded unified management mode and Task parallel optimization, targeting multi-core architectures. Our approach avoids a deep redesign and fully benefits from the numerical kernels and features of the original code. In this context, we propose simple approaches to take advantage of NUMA architectures. The performance gains are analyzed in detail on test problems, compared with MKL libraries.","PeriodicalId":131284,"journal":{"name":"2021 7th International Conference on Systems and Informatics (ICSAI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129720435","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
Analysis of ECG De-Noising Using Non-Local Means with Approximate Coefficients and Particle Swarm Optimization 基于近似系数非局部均值和粒子群优化的心电信号去噪分析
2021 7th International Conference on Systems and Informatics (ICSAI) Pub Date : 2021-11-13 DOI: 10.1109/icsai53574.2021.9664075
Jianjian Cao, Wenjie Cai, Shuaicong Hu, Jingying Yang, Yufeng Ji, Jadera Acen
{"title":"Analysis of ECG De-Noising Using Non-Local Means with Approximate Coefficients and Particle Swarm Optimization","authors":"Jianjian Cao, Wenjie Cai, Shuaicong Hu, Jingying Yang, Yufeng Ji, Jadera Acen","doi":"10.1109/icsai53574.2021.9664075","DOIUrl":"https://doi.org/10.1109/icsai53574.2021.9664075","url":null,"abstract":"Electrocardiogram (ECG) is a widely employed tool for clinical diagnosis. Removing ECG noise to obtain a high-quality ECG signal is the premise of subsequent analysis. In this study, we proposed a robust hybrid method for removing Additive White Gaussian Noise (AWGN) from ECG signals. Firstly, AWGN was artificially added to clean test signals from MIT-BIH database for single-scale Discrete Wavelet Transform (DWT) decomposition to obtain approximate coefficients, which are then reconstructed for Non-Local Means (NLM) estimate. A Linear Decay Weighted-particle swarm optimization (LD-PSO) technique is used to search the key parameters in the NLM method. Finally, the denoising process is realized by re-modeling according to the searched coordinates. The proposed method was tested in the MIT-BIH arrhythmia database and evaluated by mean square error (MSE), SNR improvement (SNRimp), and distortion percentage (PRD). The effectiveness of the proposed method is validated by comparing it with evaluation metrics of the existing similar methods. The proposed algorithm effectively removes the AWGN from ECG signals with less manual intervention. This work might help to obtain better quality ECG waveforms for diagnosis.","PeriodicalId":131284,"journal":{"name":"2021 7th International Conference on Systems and Informatics (ICSAI)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127891133","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
Automatic Text Summarization Technology of Keyword Replacement Based on Seq2Seq 基于Seq2Seq的关键词替换文本自动摘要技术
2021 7th International Conference on Systems and Informatics (ICSAI) Pub Date : 2021-11-13 DOI: 10.1109/ICSAI53574.2021.9664167
Xinglong Wang, Wei Lu
{"title":"Automatic Text Summarization Technology of Keyword Replacement Based on Seq2Seq","authors":"Xinglong Wang, Wei Lu","doi":"10.1109/ICSAI53574.2021.9664167","DOIUrl":"https://doi.org/10.1109/ICSAI53574.2021.9664167","url":null,"abstract":"With the improvement of the data technology, the development of Internet technology has brought great convenience to people, and at the same time brought the problem of information overload. In order to effectively extract important information from documents to improve people's work efficiency, this paper proposes a method of using deep learning combined with keyword substitution to automatically generate Chinese short text summaries. First, the original input is encoded into a context vector by constructing a Seq2seq framework based on the Attention mechanism, and then the context vector is decoded and output by the improved Beamsearch (cluster search), and finally the generated abstract is replaced by the keywords of the original text. The final summary result. The comparison of the quality of the generated abstracts through three indicators of ROUGE-1, ROUGE-2 and ROUGE-L verifies the feasibility of the method and improves the accuracy of the abstract and the fluency of the language.","PeriodicalId":131284,"journal":{"name":"2021 7th International Conference on Systems and Informatics (ICSAI)","volume":"569 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124692224","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 Multi-Agent Models for Collective Behavior 集体行为的多智能体模型研究
2021 7th International Conference on Systems and Informatics (ICSAI) Pub Date : 2021-11-13 DOI: 10.1109/icsai53574.2021.9664108
J. Duan, Hua Li, Qiubai Sun, Xue-bo Chen
{"title":"Research on Multi-Agent Models for Collective Behavior","authors":"J. Duan, Hua Li, Qiubai Sun, Xue-bo Chen","doi":"10.1109/icsai53574.2021.9664108","DOIUrl":"https://doi.org/10.1109/icsai53574.2021.9664108","url":null,"abstract":"Recently, the rapid development of multi-agent systems for collective behavior has become a hotspot in the field of complex networks. The paper reviews precious studies in the literatures on multi-agent models for collective behavior and classifies the models due to the different properties. For an arbitrary multi-agent model, we clear application of tasks and analyze their distribution protocols, convergence rates, and conditions for implementation. Through the simple combing of this paper, collective behavior simulation methods commonly used by scholars are introduced to the field. It is found that the commonly used behavior models can only describe most simple and orderly collective behaviors. However, the control ability for complex systems cannot be guaranteed. The optimized models can improve the simulation performance. In addition, future research directions are proposed with the aim of developing new models that focus on more robust, adaptive and efficient multiagent systems.","PeriodicalId":131284,"journal":{"name":"2021 7th International Conference on Systems and Informatics (ICSAI)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128328460","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
Sparse Array Synthesis Based on Multi-beam Joint Convex Optimization Algorithm 基于多波束联合凸优化算法的稀疏阵列综合
2021 7th International Conference on Systems and Informatics (ICSAI) Pub Date : 2021-11-13 DOI: 10.1109/icsai53574.2021.9664221
Tiezhen Jiang, Q. An, Jianhua Wang, Xiaobo Wang
{"title":"Sparse Array Synthesis Based on Multi-beam Joint Convex Optimization Algorithm","authors":"Tiezhen Jiang, Q. An, Jianhua Wang, Xiaobo Wang","doi":"10.1109/icsai53574.2021.9664221","DOIUrl":"https://doi.org/10.1109/icsai53574.2021.9664221","url":null,"abstract":"A multi-beam joint convex optimization algorithm based on the compressed sensing theory is presented for simultaneous-multi-beam synthesis. It is based on the directional pattern envelope and beam scanning characteristics of a uniform array. In this algorithm the element number, position and excitation of uniform array are optimized for the desired directional pattern envelope of certain-angle-range-different beams. The number and position of the sparse array elements under each beam pointing are then fixed within a beam scanning range for multiple envelopes of different expected patterns. This algorithm optimizes the array element excitation with the convex optimization method. Numerical results show the high precision and efficiency of this method to achieve multiple desired radiation patterns with a sparse non-uniform linear array.","PeriodicalId":131284,"journal":{"name":"2021 7th International Conference on Systems and Informatics (ICSAI)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132470188","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
首页 上一页 下一页 尾页
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整，为了账户安全，请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
请完成安全验证×
相关产品
×
本文献相关产品
联系我们：info@booksci.cn Book学术提供免费学术资源搜索服务，方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号：604180095
Book学术官方微信