2021 IEEE Asia Conference on Information Engineering (ACIE)最新文献

Corrosion Prediction Model of Circulating Water in Refinery Unit Based on PCA-PSO-BP 基于PCA-PSO-BP的炼油装置循环水腐蚀预测模型

2021 IEEE Asia Conference on Information Engineering (ACIE) Pub Date : 2021-01-29 DOI: 10.1109/ACIE51979.2021.9381095

Guanyu Suo, Jing Lei, Liang-Chao Chen, Jianfeng Yang, Zhan Dou

{"title":"Corrosion Prediction Model of Circulating Water in Refinery Unit Based on PCA-PSO-BP","authors":"Guanyu Suo, Jing Lei, Liang-Chao Chen, Jianfeng Yang, Zhan Dou","doi":"10.1109/ACIE51979.2021.9381095","DOIUrl":"https://doi.org/10.1109/ACIE51979.2021.9381095","url":null,"abstract":"The corrosion of circulating water in oil refinery units is prominent due to water quality problems. The establishment of corrosion prediction model based on long-term monitoring data of circulating water quality is of great significance to control the quality of circulating water and identify its corrosion state. In this paper, a prediction model of circulating water corrosion based on optimized back propagation (BP) neural network is established by using 10 kinds of circulating water quality detection indexes and coupon corrosion rate data of a circulating water field in two years. Firstly, the data collection frequency of each index is unified by downsampling, and the data normalization pretreatment is carried out. Then, principal component analysis (PCA) is used to analyze the original water quality data, and 6 new principal components are obtained as the input data of the prediction model; at the same time, in order to improve the prediction accuracy of the model, the parameters of the neural network are optimized by particle swarm optimization algorithm (PSO). Finally, the PCA-PSO-BP prediction model is established and its prediction mean absolute percentage error is 8.32%, which has a better prediction effect and generalization ability than other models.","PeriodicalId":264788,"journal":{"name":"2021 IEEE Asia Conference on Information Engineering (ACIE)","volume":"16 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120921186","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

Wavelet-Supervision Convolutional Neural Network for Restoration of JPEG-LS Near Lossless Compression Image 小波监督卷积神经网络在JPEG-LS近无损压缩图像恢复中的应用

2021 IEEE Asia Conference on Information Engineering (ACIE) Pub Date : 2021-01-29 DOI: 10.1109/ACIE51979.2021.9381071

Zhengwen Cao, Tao Zhang, Maomei Liu, Hangzai Luo

{"title":"Wavelet-Supervision Convolutional Neural Network for Restoration of JPEG-LS Near Lossless Compression Image","authors":"Zhengwen Cao, Tao Zhang, Maomei Liu, Hangzai Luo","doi":"10.1109/ACIE51979.2021.9381071","DOIUrl":"https://doi.org/10.1109/ACIE51979.2021.9381071","url":null,"abstract":"JPEG-LS near lossless compression algorithm is widely used in remote sensing image compression. However, the run-length coding of the algorithm results in horizontal stripe distortion of the decompressed image, which will greatly affect the quality of remote sensing image. In order to solve such distortion and restore the image, a wavelet-supervision convolutional neural network (WSCNN) with large receptive field is proposed. WSCNN can make full use of the information both in spatial and frequency domains. With translation invariance, convolutional neural network (CNN) is very adept at extracting features in pixel space. To further explore spatial information, we enlarge the receptive field of our WSCNN. Alternatively, wavelet coefficients show promising of frequency information digging, we adopt them to supervise our WSCNN. With this wavelet-supervision, WSCNN can focus on the horizontal stripe distortion in frequency domain. Besides, we have collected a dataset, it consists of original remote sensing images at a resolution of $4096times 4096$ and corresponding JPEG-LS near-lossless compression images as data pairs. Subjective and objective experiments have verified the effectiveness of our WSCNN for the restoration of LPEG-LS near-lossless compression images.","PeriodicalId":264788,"journal":{"name":"2021 IEEE Asia Conference on Information Engineering (ACIE)","volume":"179 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116161916","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 Time Series Prediction Model for the Trend of Corrosion Rate 腐蚀速率趋势的时间序列预测模型研究

2021 IEEE Asia Conference on Information Engineering (ACIE) Pub Date : 2021-01-29 DOI: 10.1109/ACIE51979.2021.9381080

Liangchao Chen, Jianfeng Yang, Xin-yuan Lu

引用次数: 1

A Fast Algorithm for CU Depth Decision Based on the Minimum Risk Bayesian Criterion 基于最小风险贝叶斯准则的CU深度快速决策算法

2021 IEEE Asia Conference on Information Engineering (ACIE) Pub Date : 2021-01-29 DOI: 10.1109/ACIE51979.2021.9381078

Jianlong Guo, Jiang Xue, Manhua Wen

引用次数: 0

An Efficient CRC-Aided Parity-Check Concatenated Polar Coding 一种有效的crc辅助奇偶校验连接极性编码

2021 IEEE Asia Conference on Information Engineering (ACIE) Pub Date : 2021-01-29 DOI: 10.1109/ACIE51979.2021.9381073

Wanzhen Li, Zhongqiu He

引用次数: 2

Signal Integrity Analysis Based on SVR Improved Algorithm 基于SVR改进算法的信号完整性分析

2021 IEEE Asia Conference on Information Engineering (ACIE) Pub Date : 2021-01-29 DOI: 10.1109/ACIE51979.2021.9381085

Kaixing Cheng, Zhongqiang Luo, Xingzhong Xiong, L. Cheng, Xiaohan Wei, Leilei Chen, Wei Zhang

引用次数: 2

Thigh Skin Strain Model for Human Gait Movement 人体步态运动大腿皮肤应变模型

2021 IEEE Asia Conference on Information Engineering (ACIE) Pub Date : 2021-01-29 DOI: 10.1109/ACIE51979.2021.9381089

Hongcheng Liu, Xiaodong Zhang, Ke Zhu, Hang Niu

引用次数: 1

Research and Design of a New Type of High Current Bidirectional Hall Current Sensor 新型大电流双向霍尔电流传感器的研究与设计

2021 IEEE Asia Conference on Information Engineering (ACIE) Pub Date : 2021-01-29 DOI: 10.1109/ACIE51979.2021.9381083

Zhu Jun, Wang Liqun, Chen Xuefei, Jiang Yi

引用次数: 1

On Time-frequency Feature Selection Method for Neural Imaging Analysis With Small Sample Size 小样本神经成像分析的时频特征选择方法

2021 IEEE Asia Conference on Information Engineering (ACIE) Pub Date : 2021-01-29 DOI: 10.1109/ACIE51979.2021.9381093

Xiangnan He, Tian Tian, Wenlian Lu

{"title":"On Time-frequency Feature Selection Method for Neural Imaging Analysis With Small Sample Size","authors":"Xiangnan He, Tian Tian, Wenlian Lu","doi":"10.1109/ACIE51979.2021.9381093","DOIUrl":"https://doi.org/10.1109/ACIE51979.2021.9381093","url":null,"abstract":"In most functional studies in neuroimages, such as electro-encephalography (EEG) and functional magnetic resonance imaging (fMRI), only time-average characteristics were extracted from the time series of signals in region-of-interest (ROI) or links between ROIs, which implies that temporal sequential information in the images may be lost. Therefore, provided with a small sample size, this sort of methods are incapable for significant statistic detection for a large load of family-wise error rate (FWER) control. In this paper, we propose a novel approach for difference detection of data of time series between groups. By taking the time-frequency features into considerations and employing the Fisher's pooling method, our approach demonstrates a significant enhancement of statistical power, particularly for a small size of data but strict FWER control. The simulation model shows that it can greatly reduce the false positive rate with a minor loss of false negative rate. We employ our approach to two sets of experimental data: EEG of schizophrenia subjects and resting-state fMRI for anxiety subjects. It is shown that our approach performs better to identify statistically significant spatial characteristic, such as ROI and link of pairs of ROIs, between patient and healthy control groups. Moreover, this approach enables to identify the significant frequency-band feature in the group comparison.","PeriodicalId":264788,"journal":{"name":"2021 IEEE Asia Conference on Information Engineering (ACIE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130682131","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 Asset Information Integration Meta-Model of Power Project 电力项目资产信息集成元模型研究

2021 IEEE Asia Conference on Information Engineering (ACIE) Pub Date : 2021-01-29 DOI: 10.1109/ACIE51979.2021.9381094

Dinglin Li, Zengtao Zhao, Jun She, Xiaojun Wang, Lei Zhao

引用次数: 1