2022 8th International Conference on Systems and Informatics (ICSAI)最新文献_第2页

An Auto-testing System for S-Band Multi-channel T/R Module s波段多通道收发模块自动测试系统

2022 8th International Conference on Systems and Informatics (ICSAI) Pub Date : 2022-12-10 DOI: 10.1109/ICSAI57119.2022.10005402

Yin Liwei, Z. Heng, Wang Zhonghua

引用次数: 0

Two-Point Boundary Value Problems in the Quotient Space of Fuzzy Number 模糊数商空间中的两点边值问题

2022 8th International Conference on Systems and Informatics (ICSAI) Pub Date : 2022-12-10 DOI: 10.1109/ICSAI57119.2022.10005423

Zhiyong Xiao, Kun Liu, Xinxin Wang

引用次数: 0

Research on Algorithms of Positive and Negative Co-occurrence in Spatio-temporal Datasets 时空数据集正负共现算法研究

2022 8th International Conference on Systems and Informatics (ICSAI) Pub Date : 2022-12-10 DOI: 10.1109/ICSAI57119.2022.10005536

Jing Du, Zhanquan Wang, Mengfei Ye

引用次数: 0

Perturbation Analysis Based Simulation Approach for Electricity Market Research and Investigation 基于摄动分析的电力市场研究与调查仿真方法

2022 8th International Conference on Systems and Informatics (ICSAI) Pub Date : 2022-12-10 DOI: 10.1109/ICSAI57119.2022.10005350

Xiaohu Wu, Xinyong Wang, W. Lin, R. Wang, X. Fan

引用次数: 0

Facial Expression Emotion Recognition Based on Transfer Learning and Generative Model 基于迁移学习和生成模型的面部表情情感识别

2022 8th International Conference on Systems and Informatics (ICSAI) Pub Date : 2022-12-10 DOI: 10.1109/ICSAI57119.2022.10005478

Tomoki Kusunose, Xin Kang, Keita Kiuchi, Ryota Nishimura, M. Sasayama, Kazuyuki Matsumoto

{"title":"Facial Expression Emotion Recognition Based on Transfer Learning and Generative Model","authors":"Tomoki Kusunose, Xin Kang, Keita Kiuchi, Ryota Nishimura, M. Sasayama, Kazuyuki Matsumoto","doi":"10.1109/ICSAI57119.2022.10005478","DOIUrl":"https://doi.org/10.1109/ICSAI57119.2022.10005478","url":null,"abstract":"Facial expression emotion recognition has been a popular research topic, which played an important role in assisting the natural human-machine conversation. The conventional method for emotion estimation from facial expressions is to learn a CNN-based image classification model from scratch, However, learning such model requires a large number of labeled facial expression images, which is still a limited resource until now. To solve this problem, we propose a data augmentation method based on StyleGAN2 to generate artificial expression images with respect to seven emotions and use them as the additional training data. We further train an expression emotion recognition model based on a VGG16 network through transfer learning. In this research, we proposed a method using transfer learning and augmented images of facial expressions using trained VGG16 and StyleGAN2 and conducted experiments to achieve higher recognition accuracy for racial expression emotion recognition. Our experiment based on the CFEE dataset suggested that an emotion recognition accuracy of 75.10% could be obtained through transfer learning and the accuracy could further improved to 82.04% with the augmented expression images.","PeriodicalId":339547,"journal":{"name":"2022 8th International Conference on Systems and Informatics (ICSAI)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126663280","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 Trust Prediction Method Based on Heterogeneous Information Networks 基于异构信息网络的信任预测方法

2022 8th International Conference on Systems and Informatics (ICSAI) Pub Date : 2022-12-10 DOI: 10.1109/ICSAI57119.2022.10005405

Ruili Xiao, Xiangrong Tong, Yinggang Li

{"title":"A Trust Prediction Method Based on Heterogeneous Information Networks","authors":"Ruili Xiao, Xiangrong Tong, Yinggang Li","doi":"10.1109/ICSAI57119.2022.10005405","DOIUrl":"https://doi.org/10.1109/ICSAI57119.2022.10005405","url":null,"abstract":"It is essential to predict the level of trust among users before they interact to reduce the risk of interaction. Due to the sparsity of trust relationships, it is inefficient to simply use explicit trust relationships to predict the trust among users, and even the trust path may be lost. On the other hand, there are implicit trust relationships among users such as the joint items that several users all rated. Once the trust relationship is extracted, it will greatly expand the number of trusted users. To this end, a trust prediction method incorporating rating information is proposed to address this problem. It first constructs a heterogeneous information network consisting of social and rating information. Secondly, in the trust prediction period, if the user has no trusted users to choose from, the joint item is used as a bridge to find implicit trusted users from users who have jointly rated the item. Finally, the Dueling DQN algorithm is used to calculate the strength of the trust path, and the predicted trust value is derived by aggregating multiple trust paths based on an aggregation function. The experimental results on two datasets indicate the presented approach outperforms most existing trust prediction methods.","PeriodicalId":339547,"journal":{"name":"2022 8th International Conference on Systems and Informatics (ICSAI)","volume":"205 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122443289","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

Automated Chicken Counting Using YOLO-v5x Algorithm 使用YOLO-v5x算法的自动鸡计数

2022 8th International Conference on Systems and Informatics (ICSAI) Pub Date : 2022-12-10 DOI: 10.1109/ICSAI57119.2022.10005522

Xiangyuan Zhu, Chuhui Wu, Yefeng Yang, Yuelin Yao, Yanshan Wu

引用次数: 2

Population Prediction in China Based on Lasso-FGM Model 基于Lasso-FGM模型的中国人口预测

2022 8th International Conference on Systems and Informatics (ICSAI) Pub Date : 2022-12-10 DOI: 10.1109/ICSAI57119.2022.10005515

Yanan Li, Yunyan Wang, Yanfang Wang

{"title":"Population Prediction in China Based on Lasso-FGM Model","authors":"Yanan Li, Yunyan Wang, Yanfang Wang","doi":"10.1109/ICSAI57119.2022.10005515","DOIUrl":"https://doi.org/10.1109/ICSAI57119.2022.10005515","url":null,"abstract":"According to the relevant data queried by China Statistical Yearbook, we can see that China’s population has been declining in recent years. In order to better grasp the trend of population development, this paper comprehensively considers the factors affecting the number of China’s population, uses Lars and Glmnet to screen variables based on Lasso model, and determines the main factors affecting the number of China’s population screened by Lars Lasso model by comparing the results and searching relevant literature. Further, this paper introduces multivariate fractional order grey model to predict the population of China, 2005-2017 under different order forecast error is determine the differential order number, 2018-2020 data in model verification, improve the model accuracy, in order to predict the future ten years, the population of predicted results found that by 2030, The total population of China will fall to 1,348,3740 million, which is a certain gap from the number predicted by the national population planning policy. In order to achieve the expected size of the national population planning policy, the future population development should focus on how to effectively increase the fertility rate and improve the birth policy, so as to increase the number of China’s population.","PeriodicalId":339547,"journal":{"name":"2022 8th International Conference on Systems and Informatics (ICSAI)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134407274","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

Missing Small Bolt Detection on High-speed Train Using Improved Yolov5 基于改进Yolov5的高速列车小螺栓缺失检测

2022 8th International Conference on Systems and Informatics (ICSAI) Pub Date : 2022-12-10 DOI: 10.1109/ICSAI57119.2022.10005485

Yiming Wei, Xiaobo Lu

引用次数: 0

Learning a Parallel Network for Emotion Recognition Based on Small Training Data 基于小训练数据的情感识别并行网络学习

2022 8th International Conference on Systems and Informatics (ICSAI) Pub Date : 2022-12-10 DOI: 10.1109/ICSAI57119.2022.10005394

Arata Ochi, Xin Kang

引用次数: 0