2020 3rd International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE)最新文献_第9页

The Health Status Prediction of the Wind Turbine Based on the Anomaly Analysis and the LSTM Prediction 基于异常分析和LSTM预测的风力机健康状态预测

2020 3rd International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE) Pub Date : 2020-04-01 DOI: 10.1109/AEMCSE50948.2020.00146

Yiqing Zhou, Jian Wang, Hanfeng Zheng

{"title":"The Health Status Prediction of the Wind Turbine Based on the Anomaly Analysis and the LSTM Prediction","authors":"Yiqing Zhou, Jian Wang, Hanfeng Zheng","doi":"10.1109/AEMCSE50948.2020.00146","DOIUrl":"https://doi.org/10.1109/AEMCSE50948.2020.00146","url":null,"abstract":"The traditional data driven faulty prediction model aims to build a nonlinear mapping between the reference signal input and the target degradation index. This kind of faulty detection model has achieved a certain degree of detection accuracy, However, most of these prediction models execute prediction based on the current input data [1]. With the development of the modern industrial process, the modern industrial manufacturing equipment is becoming high complexity and large scale. The health situation of the manufacturing equipment is usually associated with not only the current input data, but also the historical data. In this article, the hybrid approach combined with the anomaly clustering and the LSTM is proposed for the faulty classification of the wind turbine. The anomaly analysis is first used to choose the input sensing signal which can well represent the situation of the health situation of the wind turbine, afterwards, the chosen signal input is used as the input of the LSTM data. The result shows that the proposed model performs quite well in the faulty prediction of the wind turbine.","PeriodicalId":246841,"journal":{"name":"2020 3rd International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121612723","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 Multi-Neural Network Fusion Based Method for Financial Event Subject Extraction 基于多神经网络融合的财务事件主题提取方法

2020 3rd International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE) Pub Date : 2020-04-01 DOI: 10.1109/AEMCSE50948.2020.00084

Zhunqin Wang, Zhiming Liu, Lingyun Luo, Xianglong Chen

引用次数: 1

Automatic 3D Modelling for Heel Based on the Images of 2D Sketches 基于2D草图图像的鞋跟自动三维建模

2020 3rd International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE) Pub Date : 2020-04-01 DOI: 10.1109/AEMCSE50948.2020.00038

Chao Wu, Jiping Li, Wanrong Gu, Kai Zhu

引用次数: 0

Deep Learning Development Review 深度学习发展综述

2020 3rd International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE) Pub Date : 2020-04-01 DOI: 10.1109/AEMCSE50948.2020.00043

Wen-hao Lv, Ju-yang Lei

引用次数: 2

Analysis of the Stress Intensity Factor Along the Thickness 沿厚度方向的应力强度因子分析

2020 3rd International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE) Pub Date : 2020-04-01 DOI: 10.1109/AEMCSE50948.2020.00207

Ying Zhou, Hao Chen

引用次数: 0

Chinese Explanatory Opinion Relationship Recognition Based on Improved Target Attention Mechanism 基于改进目标注意机制的汉语解释性意见关系识别

2020 3rd International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE) Pub Date : 2020-04-01 DOI: 10.1109/AEMCSE50948.2020.00126

X. Cao, Chenghao Zhu, Chengguo Lv

{"title":"Chinese Explanatory Opinion Relationship Recognition Based on Improved Target Attention Mechanism","authors":"X. Cao, Chenghao Zhu, Chengguo Lv","doi":"10.1109/AEMCSE50948.2020.00126","DOIUrl":"https://doi.org/10.1109/AEMCSE50948.2020.00126","url":null,"abstract":"Opinion relationship recognition is an important part of the opinion mining task. Its main purpose is to extract the opinion element tuple from the user comment data and identify the relationship between them, such as evaluation object, evaluation content, opinion explanation, opinion object. Because the comments of the network having are characterized by randomness, diversity of opinions and different formats, it will become more difficult for the opinion mining task. If we can extract the interrelationships between the various explanatory opinion elements, it not only makes subsequent tasks easier but also applies its extracted results to other related tasks. For example, applying the opinion seven-tuple from the opinion extraction task to the text summary generation task can greatly improve the effectiveness of the text summary generation task. In this paper, we have improved on the traditional LSTM-Attention model and proposed an opinion relationship recognition framework based on improved Target Attention Mechanism. Also, we conducted experiments in two different domains, and the experimental results show that the performance has been effectively improved in two domains. We also explored two different pre-training strategies, Word2vec and Elmo, to further analyze the impact of pre-training on this experiment.","PeriodicalId":246841,"journal":{"name":"2020 3rd International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE)","volume":"110 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122307067","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 Sizing Method of Tram Vehicle Hybrid Energy Storage System 有轨电车混合储能系统选型方法研究

2020 3rd International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE) Pub Date : 2020-04-01 DOI: 10.1109/AEMCSE50948.2020.00178

Ming Li, Haonan Mo, Xiaochun Fang, F. Lin, Zhongping Yang

引用次数: 0

A Classification Approach to Text Normalization 文本规范化的分类方法

2020 3rd International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE) Pub Date : 2020-04-01 DOI: 10.1109/AEMCSE50948.2020.00125

Guozhang Zhao, Chenkai Ma, Wenxian Feng, Rui Zhang

引用次数: 1

Occluded Face Restoration Based on Generative Adversarial Networks 基于生成对抗网络的遮挡人脸恢复

2020 3rd International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE) Pub Date : 2020-04-01 DOI: 10.1109/AEMCSE50948.2020.00074

Mingming Zhang, Liang Huang, Maojing Zhu

{"title":"Occluded Face Restoration Based on Generative Adversarial Networks","authors":"Mingming Zhang, Liang Huang, Maojing Zhu","doi":"10.1109/AEMCSE50948.2020.00074","DOIUrl":"https://doi.org/10.1109/AEMCSE50948.2020.00074","url":null,"abstract":"In recent years, the combination of Convolutional Neural Networks and Generative Adversarial Networks has played a huge potential in the field of face restoration. In order to effectively repair the large area of random occlusion face, this paper constructs an improved Generative Adversarial Networks model based on the Context Encoder, and proposes a self-localization occlusion face image restoration algorithm. Firstly, the occluded part of the face is marked by occlusion locator, and then the marked face image is sent to the generator of Generative Adversarial Networks for restoration. The model generator uses the Convolutional Neural Networks of the Variational Autoencoder structure, and adds the Batch Normalization layer in the model to enhance the information prediction ability of the generator. At the same time, the discriminator is constructed by combining with VGG19, and the discriminator is trained against the generator. Through the experiment on CelebA face data set, this algorithm is significantly better than other methods in the aspect of random large area occlusion face image restoration.","PeriodicalId":246841,"journal":{"name":"2020 3rd International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131148566","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

Hot Deformation Behavior and Zener-Holomon Parameter Constitutive Model of Ti-6Al-7Nb Alloy Ti-6Al-7Nb合金热变形行为及Zener-Holomon参数本构模型

2020 3rd International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE) Pub Date : 2020-04-01 DOI: 10.1109/AEMCSE50948.2020.00135

Mingjun Zhong, Ke-lu Wang, D. Ouyang, Xin Li, Xuan Zhou, Rui Feng

引用次数: 0