2021 International Symposium on Computer Technology and Information Science (ISCTIS)最新文献_第8页

Research on the MAX038 Simulation Model for Electromagnetic Inspection MAX038电磁检测仿真模型的研究

2021 International Symposium on Computer Technology and Information Science (ISCTIS) Pub Date : 2021-06-01 DOI: 10.1109/ISCTIS51085.2021.00086

Guo Qing, Hongbo Zhang, Hongzhi Hu, Qin Chang

引用次数: 0

A Shadow Detection Method Based on SLICO Superpixel Segmentation 基于SLICO超像素分割的阴影检测方法

2021 International Symposium on Computer Technology and Information Science (ISCTIS) Pub Date : 2021-06-01 DOI: 10.1109/ISCTIS51085.2021.00067

Kun-Peng Lei, Xin-xi Feng, Yu Wang

引用次数: 0

A BP neural network model for the demand forecasting of road freight transportation system 基于BP神经网络的道路货物运输系统需求预测模型

2021 International Symposium on Computer Technology and Information Science (ISCTIS) Pub Date : 2021-06-01 DOI: 10.1109/ISCTIS51085.2021.00061

Yun Wu, Shuai Wang, Yingying Zhang, Jiangzhou Zhang

引用次数: 0

Missing data filling based on the spectral analysis and the Long Short- Term Memory network 基于谱分析和长短期记忆网络的缺失数据填充

2021 International Symposium on Computer Technology and Information Science (ISCTIS) Pub Date : 2021-06-01 DOI: 10.1109/ISCTIS51085.2021.00049

Jie Wu, N. Li, Yan Zhao

引用次数: 2

General Model-Agnostic Transfer Learning for Natural Degradation Image Enhancement 自然退化图像增强的通用模型不可知迁移学习

2021 International Symposium on Computer Technology and Information Science (ISCTIS) Pub Date : 2021-06-01 DOI: 10.1109/ISCTIS51085.2021.00059

Xiangyu Yin, Jun Ma

引用次数: 1

Super-resolution algorithm combining SAE dictionary learning and anchored neighborhood regression 结合SAE字典学习和锚定邻域回归的超分辨率算法

2021 International Symposium on Computer Technology and Information Science (ISCTIS) Pub Date : 2021-06-01 DOI: 10.1109/ISCTIS51085.2021.00026

Huang Weiqin, Guo Yijing, Chen Junren

{"title":"Super-resolution algorithm combining SAE dictionary learning and anchored neighborhood regression","authors":"Huang Weiqin, Guo Yijing, Chen Junren","doi":"10.1109/ISCTIS51085.2021.00026","DOIUrl":"https://doi.org/10.1109/ISCTIS51085.2021.00026","url":null,"abstract":"To improve the efficiency of the super resolution algorithm based on dictionary learning, a super-resolution algorithm combining sparse autoencoder dictionary learning and anchored neighborhood regression is proposed. The sparse autoencoder with outstanding learning ability is used to learn a dictionary witch has better feature expression ability in the stage of dictionary learning. For the improvement of autoencoder, the mean absolute error principle is taken as the reconstruction error term to improve the accuracy of model error measurement. In the stage of data preprocessing, the whitening technology is used to construct the low redundancy input data to improve the generalization ability of sparse autoencoder dictionary learning model. In the stage of image reconstruction, the dictionary obtained is applied to the super-resolution algorithm based on anchored neighborhood regression to achieve fast real-time reconstruction by reducing the computation of sparse coding. In this study, the proposed super-resolution algorithm combines the advantages of sparse autoencoder model and anchored neighborhood regression, which can not only improve the quality of image reconstruction, but also guarantee the reconstruction speed at the same time. So it has high reconstruction efficiency.","PeriodicalId":403102,"journal":{"name":"2021 International Symposium on Computer Technology and Information Science (ISCTIS)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130490715","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

Named entity recognition of legal documents based on cascade model 基于级联模型的法律文件命名实体识别

2021 International Symposium on Computer Technology and Information Science (ISCTIS) Pub Date : 2021-06-01 DOI: 10.1109/ISCTIS51085.2021.00073

Xiaolin Li, Zhuohao Chen, Gang Xu, Bowen Huang

引用次数: 0

3D Real-Scene Model of Oblique Photography Based on Retinex 基于Retinex的倾斜摄影三维实景模型

2021 International Symposium on Computer Technology and Information Science (ISCTIS) Pub Date : 2021-06-01 DOI: 10.1109/ISCTIS51085.2021.00045

Cheng Zhu, Liwen Chen, Wei Lu, Q. Xiao

{"title":"3D Real-Scene Model of Oblique Photography Based on Retinex","authors":"Cheng Zhu, Liwen Chen, Wei Lu, Q. Xiao","doi":"10.1109/ISCTIS51085.2021.00045","DOIUrl":"https://doi.org/10.1109/ISCTIS51085.2021.00045","url":null,"abstract":"The traditional 3D modeling adopts the method of manual field measurement data, which meets the accuracy required by production and construction, but the efficiency of data acquisition is too low. In order to improve the overall work efficiency, the UAV oblique photography is used in the 3D modeling. It not only liberates technicians from the hard field work, greatly reduces the field work workload, but also can quickly build 3D real-scene model, shorten the drawing period, reduce the production cost, which has a positive role in promoting the development of geographic information industry. However, in the process of UAV tilt photography, the image effect is often not very ideal due to the influence of bad weather such as uncertain rain or shine and wind and fog, and uneven illumination phenomena such as underexposure, overexposure and shadow appear, which directly affect the quality of the 3D real-scene model in the later stage. In order to eliminate this effect, Retinex method in image enhancement technology is applied to preprocess the oblique photography image to ensure the image quality, which lays a foundation for the quality of the subsequent 3D real-scene model.","PeriodicalId":403102,"journal":{"name":"2021 International Symposium on Computer Technology and Information Science (ISCTIS)","volume":"68 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120893535","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

MCSRec: Modeling Cognitive Similarity in Sequential Recommendation with Social Networks MCSRec:基于社会网络的顺序推荐认知相似性建模

2021 International Symposium on Computer Technology and Information Science (ISCTIS) Pub Date : 2021-06-01 DOI: 10.1109/ISCTIS51085.2021.00054

Zhongwang Zhang, Yilei Wang, Xueqin Chen, Jifeng Ye, Yijin Cai, Longjiang Chen

{"title":"MCSRec: Modeling Cognitive Similarity in Sequential Recommendation with Social Networks","authors":"Zhongwang Zhang, Yilei Wang, Xueqin Chen, Jifeng Ye, Yijin Cai, Longjiang Chen","doi":"10.1109/ISCTIS51085.2021.00054","DOIUrl":"https://doi.org/10.1109/ISCTIS51085.2021.00054","url":null,"abstract":"Combining user social relationships in sequence recommendation helps model users' potential preferences and improves the performance of the recommendation system. However, recommendation with social networks faces two challenging problems, and has not been well-studied in most existing works. The first is cognitive differences. Even users with similar preferences face the same recommended objects, they will make different choices due to cognitive differences. Therefore, modeling the cognitive similarity of users is crucial. The second is the influence strength of her friends might be different. To solve the above problems, this paper proposes a new deep learning model called MCSRec. Specifically, based on users' long short- term personal preferences, design a memory cognitive module to model the cognitive similarity between users and their friends. Then, after obtaining friends' preferences which are similar to users' cognition, model social influence with a graph attention network. The experimental results on three public data sets prove the effectiveness of our proposed MCSRec model on several competitive baselines, including state-of-the-art models.","PeriodicalId":403102,"journal":{"name":"2021 International Symposium on Computer Technology and Information Science (ISCTIS)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123848035","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

Anti-confrontational Domain Data Generation Based on Improved WGAN 基于改进WGAN的抗对抗域数据生成

2021 International Symposium on Computer Technology and Information Science (ISCTIS) Pub Date : 2021-06-01 DOI: 10.1109/ISCTIS51085.2021.00050

Haibo Luo, Xingchi Chen, Jianhu Dong

引用次数: 0