2018 15th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)最新文献

Direct-Drive Wind Turbine Fault Diagnosis Based on Logistic Regression 基于Logistic回归的直驱风电机组故障诊断

2018 15th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP) Pub Date : 2018-12-01 DOI: 10.1109/ICCWAMTIP.2018.8632605

Wenshuo Zhao, Chun-Yi Qu, Hui-Ben Zhang

引用次数: 3

Deeplearning Model Used in Text Classification 用于文本分类的深度学习模型

2018 15th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP) Pub Date : 2018-12-01 DOI: 10.1109/ICCWAMTIP.2018.8632592

Jingjing Cai, Jianping Li, Wei Li, Ji Wang

引用次数: 43

Research on Control of 6-Dof Redundant Parallel Mechanisms 六自由度冗余并联机构控制研究

2018 15th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP) Pub Date : 2018-12-01 DOI: 10.1109/ICCWAMTIP.2018.8632574

T. Gao, Cong Zhou, Xiang Zheng, Xiaochai Li

引用次数: 0

Electricy Cost Optimization of Data Center Interactive Services with UPS 数据中心与UPS交互业务的电费优化

2018 15th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP) Pub Date : 2018-12-01 DOI: 10.1109/ICCWAMTIP.2018.8632610

Yan Huang, G. Li, Peng Wang, Fengjia Chang, Jian-Ping Li

引用次数: 3

Key Pre-Distribution Scheme for WSN Based on Hash Function 基于哈希函数的WSN密钥预分配方案

2018 15th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP) Pub Date : 2018-12-01 DOI: 10.1109/ICCWAMTIP.2018.8632569

Chao Xiong, Shuquan Li, Lei Liu, Rui-Qiong Li

引用次数: 3

Ontology-Based Academic Article Recommendation 基于本体的学术文章推荐

2018 15th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP) Pub Date : 2018-12-01 DOI: 10.1109/ICCWAMTIP.2018.8632601

Gohar Rehman Chughtai, Jia Lee, Asif Kabir, Rashid Abbasi, Muhammad Arshad Shehzad Hassan

引用次数: 3

Hyper-Spectral Remote Sensing Image De-Noising with Three Dimensional Wavelet Transform Utilizing Smooth Nonlinear Soft Thresholding Function 基于光滑非线性软阈值函数的三维小波变换高光谱遥感图像去噪

2018 15th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP) Pub Date : 2018-12-01 DOI: 10.1109/ICCWAMTIP.2018.8632597

Noorbakhsh Amiri Golilarz, Hui Gao, Waqar Ali, Mohammad Shahid

{"title":"Hyper-Spectral Remote Sensing Image De-Noising with Three Dimensional Wavelet Transform Utilizing Smooth Nonlinear Soft Thresholding Function","authors":"Noorbakhsh Amiri Golilarz, Hui Gao, Waqar Ali, Mohammad Shahid","doi":"10.1109/ICCWAMTIP.2018.8632597","DOIUrl":"https://doi.org/10.1109/ICCWAMTIP.2018.8632597","url":null,"abstract":"A hyper-spectral image can be subject to additive noise during the acquisition process. The main objective in noise removal is to enhance the visual quality of the corrupted image using de-noising techniques. Most of the techniques try to discard the noise in the pre-processing stage prior to further analysis. The main focus in this paper is removing the noise from hyper-spectral remote sensing images. A new method is proposed for image de-noising by applying a smooth nonlinear soft threshold on high frequency sub-bands of the images after applying 3D un-decimated wavelet transform (3D-UWT). This proposed threshold function is referred to as the improved soft (smooth nonlinear)thresholding function. The proposed method is compared with de-noising based on 3D-UWT using standard hard and soft thresholding techniques. Results show the superiority of the proposed method over the standard and alternative methods in the literature by means of visual quality and peak signal to noise ratio (PSNR).","PeriodicalId":117919,"journal":{"name":"2018 15th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)","volume":"59 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124317277","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: 18

The Evaluation and Comparative Analysis of Role Based Access Control and Attribute Based Access Control Model 基于角色的访问控制与基于属性的访问控制模型的评价与比较分析

2018 15th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP) Pub Date : 2018-12-01 DOI: 10.1109/ICCWAMTIP.2018.8632578

Muhammad Umar Aftab, Zhiguang Qin, Zakria, S. Ali, Pirah, Jalaluddin Khan

引用次数: 18

Outlier Detection Using Diverse Neighborhood Graphs 基于不同邻域图的离群点检测

2018 15th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP) Pub Date : 2018-12-01 DOI: 10.1109/ICCWAMTIP.2018.8632604

Chao Wang, Hui Gao, Zhen Liu, Yan Fu

引用次数: 5

Sequence Labeling of Chinese Text Based on Bidirectional Gru-Cnn-Crf Model 基于Gru-Cnn-Crf双向模型的中文文本序列标注

2018 15th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP) Pub Date : 2018-12-01 DOI: 10.1109/ICCWAMTIP.2018.8632570

Dil Iu, Xinyi Zou

{"title":"Sequence Labeling of Chinese Text Based on Bidirectional Gru-Cnn-Crf Model","authors":"Dil Iu, Xinyi Zou","doi":"10.1109/ICCWAMTIP.2018.8632570","DOIUrl":"https://doi.org/10.1109/ICCWAMTIP.2018.8632570","url":null,"abstract":"Sequence labeling is the basis for many tasks in natural language processing (NLP). It plays an important role in tasks such as word segmentation, named entity recognition (NER), and part-of-speech (POS)tagging. The current mainstream method for sequence labeling is to combine neural network with conditional random field (CRF). The common model is usually a bidirectional RNN-CRF model, which can solve the problem that the labeling task with traditional method cannot be combined well with the context. This paper proposes a Chinese sequence labeling model based on bidirectional GRU-CNN-CRF, which can pay more attention to local features and context relationships, and has better performance in word segmentation and NER. This paper takes the corpus provided by Chinese Wikipedia as the training data set and preprocesses the text by word embedding. The data are then processed through a three-tier architecture of bidirectional Gated Recurrent Unit (GRU), Convolution Neural Network (CNN)and CRF, and finally complete the task of sequence annotation. Compared with the traditional Chinese word segmentation system, this method is more accurate. And it performs better than bidirectional GRU-CRF model on NER issues.","PeriodicalId":117919,"journal":{"name":"2018 15th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115092771","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