2021 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE)最新文献_第9页

A 3D Space Violation Detection Method of Substations Based on the Deep Neural Network 基于深度神经网络的变电站三维空间违章检测方法

2021 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE) Pub Date : 2021-07-01 DOI: 10.1109/MLISE54096.2021.00011

Guoheng Ruan, Yerong Zhong, Jiaming Jiang

{"title":"A 3D Space Violation Detection Method of Substations Based on the Deep Neural Network","authors":"Guoheng Ruan, Yerong Zhong, Jiaming Jiang","doi":"10.1109/MLISE54096.2021.00011","DOIUrl":"https://doi.org/10.1109/MLISE54096.2021.00011","url":null,"abstract":"At present, smart grid construction has entered a new stage of the comprehensive and rapid development. In the field of sub stations, according to the requirements of the development plan formulated by the China Southern Power Grid Company Limited, the application of smart substations is imperative when the operation mode of traditional substations cannot meet the current development needs. Aiming at the problem of low efficiencies in the detection of violations and great harms to substations, this paper proposes a substation violation detection method based on a deep neural network from three-dimensional space. Based on the characteristic information of different violations, the characteristic expression of violations is established. Furthermore, an intelligent detection scheme for violations is established, in which the deep neural network is utilized to train samples. Effectivities and accuracies of the proposed intelligent detection scheme are verified by simulation experiments, and the results show that the algorithm can achieve good violation detection precisions.","PeriodicalId":430495,"journal":{"name":"2021 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE)","volume":"836 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116423367","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

Characteristic analysis and experimental research on vibrator of square-ring type liner standing wave motor 方环型直线驻波电机振动器特性分析与实验研究

2021 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE) Pub Date : 2021-07-01 DOI: 10.1109/MLISE54096.2021.00053

Xiaozhu Wang, Jian Zhang

引用次数: 1

Analysis and Optimization for Universal Rolling Process of Steel Rail Based on Main Composite 基于主复合材料的钢轨万能轧制工艺分析与优化

2021 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE) Pub Date : 2021-07-01 DOI: 10.1109/MLISE54096.2021.00095

Xingqiang Tan, Benwen Zhang

引用次数: 0

Deep learning for Aspect-based Sentiment Analysis 基于方面的情感分析的深度学习

2021 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE) Pub Date : 2021-07-01 DOI: 10.1109/MLISE54096.2021.00056

Jie Wang, Bingxin Xu, Yujie Zu

{"title":"Deep learning for Aspect-based Sentiment Analysis","authors":"Jie Wang, Bingxin Xu, Yujie Zu","doi":"10.1109/MLISE54096.2021.00056","DOIUrl":"https://doi.org/10.1109/MLISE54096.2021.00056","url":null,"abstract":"With the popularity of the Internet and the amount of information people can get from the Internet increases exponentially, sentiment analysis depending on text information has become a rather important tool in daily life, which helps people improve the quality of products and their lifestyle. According to the different granularity of sentiment text, sentiment analysis can be divided into three levels: word, sentence, and document level. While coarse-grained sentiment analysis only focuses on the polarity of emotions and neglects the finer-grained tendency of emotions. Driven by this demand, some researchers put forward the aspect-based sentiment analysis. ABSA can be categorized into two basic steps: aspect extraction and sentiment classification, on which researchers are mainly focusing to improve its performance. Deep learning has gradually become a research hotspot in recent years and has been widely used in CV and NLP fields. Recently, some efforts have begun to apply deep learning to ABSA tasks. In this paper, we further summarize and analyze the recent achievements of ABSA in the application of different deep learning models in terms of their completeness, accuracy, and detail. We also summarize ABSA's current challenges and difficulties and look forward to the future development and improvement of ABSA based on the existing experience.","PeriodicalId":430495,"journal":{"name":"2021 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131759086","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: 24

Tracking control Scheme for photoelectric tracking platform with predictor-structure ESO 预测结构ESO光电跟踪平台跟踪控制方案

2021 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE) Pub Date : 2021-07-01 DOI: 10.1109/MLISE54096.2021.00028

Fan Wang, Ranjun Wang, Mei-lin Xie, Peng Liu, Feng Jing, Bo Liu

引用次数: 1

Film CTR Prediction Based on Deep Learning 基于深度学习的电影点击率预测

2021 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE) Pub Date : 2021-07-01 DOI: 10.1109/MLISE54096.2021.00013

Pian Shi, Mingze Shi

引用次数: 0

System Identification Based on Szego Kernels 基于Szego内核的系统识别

2021 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE) Pub Date : 2021-07-01 DOI: 10.1109/MLISE54096.2021.00078

Shuang Li

引用次数: 0

Rectal Carcinoma Segmentation Based On Image Pre-processing and Improved U-Net with Residual Block 基于图像预处理和残差块改进U-Net的直肠癌分割

2021 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE) Pub Date : 2021-07-01 DOI: 10.1109/MLISE54096.2021.00034

Liu Runchen, Wang Hao, He Gang, Khwaja Mutahir Ahmad, Yu Wenxin

{"title":"Rectal Carcinoma Segmentation Based On Image Pre-processing and Improved U-Net with Residual Block","authors":"Liu Runchen, Wang Hao, He Gang, Khwaja Mutahir Ahmad, Yu Wenxin","doi":"10.1109/MLISE54096.2021.00034","DOIUrl":"https://doi.org/10.1109/MLISE54096.2021.00034","url":null,"abstract":"Accurate staging of rectal carcinoma is crucial for surgery plan and the accuracy is mostly depending on the segmentation algorithm for CT images. There have been a lot of promising recent works utilizing deep network architectures, such as Full Convolutional Networks (FCNs) and If-Nets, for developing automated medical image segmentation, and there are few proposed for rectal carcinoma segmentation. A new effective image pre-processing method combined with CT window technique and image cropping is proposed in this paper and the proposed model established is an encoder-decoder convolutional network. The residual block instead of the convolutional block was used for U-Nets. Besides, the image augmentation technique was also utilized in this paper and the results demonstrated that the performance of segmentation can be greatly improved. The Dice Similarity Coefficient (DSC) reaches at 0.974 in validation set which exceeds most of the latest work on rectal carcinoma segmentation and it can be concluded that the proposed preprocessing technique on U-Nets with residual block can greatly improve the overall performance of rectal carcinoma segmentation.","PeriodicalId":430495,"journal":{"name":"2021 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE)","volume":"38 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120992175","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

Research on Energy Management of Hybrid Electric Vehicle 混合动力汽车能量管理研究

2021 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE) Pub Date : 2021-07-01 DOI: 10.1109/MLISE54096.2021.00097

Man-lei Huang, Yi Guo

引用次数: 0

Multi-feature keyword extraction method based on TF-IDF and Chinese grammar analysis 基于TF-IDF和汉语语法分析的多特征关键词提取方法

2021 International Conference on Machine Learning and Intelligent Systems Engineering (MLISE) Pub Date : 2021-07-01 DOI: 10.1109/MLISE54096.2021.00075

Yongrui Li, Hongyun Ning

引用次数: 1