2021 IEEE International Conference on Power Electronics, Computer Applications (ICPECA)最新文献_第4页

Weighted network overlapping community partition based on node membership 基于节点隶属度的加权网络重叠社区划分

2021 IEEE International Conference on Power Electronics, Computer Applications (ICPECA) Pub Date : 2021-01-22 DOI: 10.1109/ICPECA51329.2021.9362636

Lidong Fu, Ruoyu Chen, Wei Hao

引用次数: 1

Research on Large Aircraft Operation Support System in Plateau and Complex Terrain Based on System Engineering 基于系统工程的高原复杂地形大型飞机作战保障系统研究

2021 IEEE International Conference on Power Electronics, Computer Applications (ICPECA) Pub Date : 2021-01-22 DOI: 10.1109/ICPECA51329.2021.9362536

Wu Qi

引用次数: 0

Quantifying hepatic functional reserve in rats via dynamic PET imaging using [68Ga]-NOTA-LSA [68Ga]-NOTA-LSA动态PET成像定量大鼠肝功能储备

2021 IEEE International Conference on Power Electronics, Computer Applications (ICPECA) Pub Date : 2021-01-22 DOI: 10.1109/ICPECA51329.2021.9362592

Peihan Li, Wenxiang Ding, Qian Xia, Qiu Huang

{"title":"Quantifying hepatic functional reserve in rats via dynamic PET imaging using [68Ga]-NOTA-LSA","authors":"Peihan Li, Wenxiang Ding, Qian Xia, Qiu Huang","doi":"10.1109/ICPECA51329.2021.9362592","DOIUrl":"https://doi.org/10.1109/ICPECA51329.2021.9362592","url":null,"abstract":"Preoperative evaluation of hepatic functional reserve determines the safe extent in liver resection. Existing methods assess only the global reserve rather than the local distribution. This work aims to quantify the regional hepatic functional reserve in vivo. We used [68Ga]-NOTA-LSA PET/CT to image nine rats, two with cirrhosis. Each rat underwent a 20-min dynamic PET scan. Data were modelled by a two-compartment model with a hepatic dual-input function. Based on the model, we calculated the distribution volume (DV) of [68Ga]-NOTA-LSA in the liver via two approaches (denoted DV1 and DV2). For comparison, we also calculated the static PET metric standard uptake value (SUV). Using DV and SUV maps, we calculated functional liver ratio (FLR). The results of DV maps show local heterogeneity of the liver. Significant difference between normal and liver-impaired rats can be seen both in DV1 and DV2, but not in SUV. FLR, when obtained through the DV map, is negatively correlated with three test indices of the liver function, i.e., alanine transferase (ALT), aspartate transaminase (AST) and Lactate dehydrogenase (LDH) levels. Specifically, FLR obtained using DV2 shows stronger negative correlation (rALT =-0.932;rAST =-0.914;rLDI1=-0.907, all with p<0.01) than that obtained using DV1 (rALT =-0.924;rAST =-0.905;rLDH=-0.901, all with p<0.01). No correlation is found in FLR if it is calculated using SUV. In conclusions, Dynamic PET with [68Ga]-NOTA-LSA evaluates the regional hepatic functional reserve, and enables differentiation between normal and impaired livers. However, SUV, the static [68Ga]-NOTA-LSA uptake, alone does not.","PeriodicalId":119798,"journal":{"name":"2021 IEEE International Conference on Power Electronics, Computer Applications (ICPECA)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129871119","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 Spiking Neural Network for Tooth Chromaticity Detection 基于脉冲神经网络的牙齿色度检测

2021 IEEE International Conference on Power Electronics, Computer Applications (ICPECA) Pub Date : 2021-01-22 DOI: 10.1109/ICPECA51329.2021.9362600

Junyu Yao, Jianxing Wu, F. Liang, Guohe Zhang

引用次数: 0

Tibetan Comment Text Sentiment Recognition Algorithm Based on Syllables 基于音节的藏文评论文本情感识别算法

2021 IEEE International Conference on Power Electronics, Computer Applications (ICPECA) Pub Date : 2021-01-22 DOI: 10.1109/ICPECA51329.2021.9362579

Xianghe Meng, Hongzhi Yu, Tao Xu, Jieben Dao

{"title":"Tibetan Comment Text Sentiment Recognition Algorithm Based on Syllables","authors":"Xianghe Meng, Hongzhi Yu, Tao Xu, Jieben Dao","doi":"10.1109/ICPECA51329.2021.9362579","DOIUrl":"https://doi.org/10.1109/ICPECA51329.2021.9362579","url":null,"abstract":"Text sentiment recognition is to analyze and process the text with sentimental color, and then divide the text into different sentiment categories. This paper uses Tibetan syllables as the basic features of the text. The first is to obtain the contextual global sequence features of Tibetan comment texts through the bidirectional recurrent neural network model. Then through the Self-Attention mechanism, further obtain the relationship features between the Tibetan syllable feature units in the sequence. Finally, convolutional neural network models with different convolution kernels are used to obtain fine grained partial features of the text. After completing the text representation, input it to the fully connected layer for sentiment recognition. This paper improves the performance of text sentiment recognition by obtaining richer and comprehensive semantic features in Tibetan comment texts. On the Tibetan-Chinese bilingual comment corpus, a variety of deep learning models are compared. The model proposed in this paper effectively improves the accuracy of text sentiment recognition.","PeriodicalId":119798,"journal":{"name":"2021 IEEE International Conference on Power Electronics, Computer Applications (ICPECA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129565130","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

RX infrared small target detection algorithm based on multi-scale local strength and gradient fusion 基于多尺度局部强度和梯度融合的RX红外小目标检测算法

2021 IEEE International Conference on Power Electronics, Computer Applications (ICPECA) Pub Date : 2021-01-22 DOI: 10.1109/ICPECA51329.2021.9362567

Jing Tian, D. Song, Yingguang Hao, Weiwei Yin, Rui Li, Ke Han

引用次数: 0

An improvement and application of a model conducive to productivity optimization 一种有利于生产率优化的模型的改进与应用

2021 IEEE International Conference on Power Electronics, Computer Applications (ICPECA) Pub Date : 2021-01-22 DOI: 10.1109/ICPECA51329.2021.9362627

Xupeng Kou, Ying He, Ye Qian

{"title":"An improvement and application of a model conducive to productivity optimization","authors":"Xupeng Kou, Ying He, Ye Qian","doi":"10.1109/ICPECA51329.2021.9362627","DOIUrl":"https://doi.org/10.1109/ICPECA51329.2021.9362627","url":null,"abstract":"The metal defect detection and identification technology have been successfully applied in actual industrial production. But there are still some problems in the steel strip defect detection task, such as the difficulty in data collection and the poor identification effect caused by the small defect target. Aiming at these problems, this paper presents an algorithm based on convolutional neural network for the detection of steel strip small target defects. First, the original image is clipped by the way that moving the slider. At the same time, the feature pyramid structure is increased to conduct feature fusion for the output of the feature extraction part and Group Normalization is used to accelerate the convergence of the model, so that the model can better adapt to the target task, effectively solve the problem of small defect object information loss on the surface of the target steel strip, and enhance the generalization of the model. In order to evaluate and test the validity of the model, a steel strip defect dataset (BS5-DET) is constructed. The experimental results show that the mAP of the model reaches 54.50% on the defect data set of the BS5-DET steel belt, which has a certain application value.","PeriodicalId":119798,"journal":{"name":"2021 IEEE International Conference on Power Electronics, Computer Applications (ICPECA)","volume":"241 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129111507","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

Semi-automatic construction method of power safety ontology based on AR-K-means 基于ar - k均值的电力安全本体半自动构建方法

2021 IEEE International Conference on Power Electronics, Computer Applications (ICPECA) Pub Date : 2021-01-22 DOI: 10.1109/ICPECA51329.2021.9362555

Dezhi Zhao, Jian Sun, Xiaoyu Chen, Xiaoyong Bo, Mingli Yi, Lin Xia

引用次数: 0

Online Abnormal Component Locating of Aircraft Fuel System Using Bayesian Method 基于贝叶斯方法的飞机燃油系统异常部件在线定位

2021 IEEE International Conference on Power Electronics, Computer Applications (ICPECA) Pub Date : 2021-01-22 DOI: 10.1109/ICPECA51329.2021.9362717

Yufei Lin, Jiong Zhang

{"title":"Online Abnormal Component Locating of Aircraft Fuel System Using Bayesian Method","authors":"Yufei Lin, Jiong Zhang","doi":"10.1109/ICPECA51329.2021.9362717","DOIUrl":"https://doi.org/10.1109/ICPECA51329.2021.9362717","url":null,"abstract":"Aircraft systems now become more and more complex as the new technology developed. Since abnormal component (LRU) can trigger a cascade of dangerous accidents, it is very important to develop methods for locating the abnormal component online. However, current fault isolation methods for multiple component systems usually do not consider the practical constraint that only a small part of components can be monitored in real world, which is entirely possible to happen in an aircraft system. This paper develops a probabilistic framework for abnormal component locating of multiple component systems with the consideration of the practical constraint that only a small part of components can be monitored. First of all, a Gaussian Mixture Model (GMM) is introduced to describe the sensor data collected from multiple components. Secondly, based on an estimator of minimum volume set, the criteria to determine whether the monitored samples are abnormal or not is obtained. Lastly, a Bayesian method is used to calculate the posterior probabilities belonging to each possible abnormal component. The proposed method is applied in aircraft fuel system example, which illustrates the efficiency of the proposed method.","PeriodicalId":119798,"journal":{"name":"2021 IEEE International Conference on Power Electronics, Computer Applications (ICPECA)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123108403","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

Feasibility study of power demand response for 5G base station 5G基站电力需求响应可行性研究

2021 IEEE International Conference on Power Electronics, Computer Applications (ICPECA) Pub Date : 2021-01-22 DOI: 10.1109/icpeca51329.2021.9362670

Junhui Liu, Shiqian Wang, Qinchen Yang, Hujun Li, F. Deng, Wenjie Zhao

引用次数: 5