2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)最新文献_第2页

Research on Defect Detection of Castings Based on Deep Residual Network 基于深度残余网络的铸件缺陷检测研究

2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) Pub Date : 2018-10-01 DOI: 10.1109/CISP-BMEI.2018.8633254

X. Jiang, Xiaofeng Wang, Dongfang Chen

{"title":"Research on Defect Detection of Castings Based on Deep Residual Network","authors":"X. Jiang, Xiaofeng Wang, Dongfang Chen","doi":"10.1109/CISP-BMEI.2018.8633254","DOIUrl":"https://doi.org/10.1109/CISP-BMEI.2018.8633254","url":null,"abstract":"In this study, we proposed a method for detecting the appearance defect of castings based on deep residual network, which is used to solve the problems of low accuracy, difficult application conditions and insufficient robustness of traditional defect detection methods. This method divides the casting into multiple regions, preprocesses the image of each region, and then inputs the processed image into the convolutional neural network to extract the features, and finally determines whether the sample has defects. The deep residual network ResNet-34 was chosen as the network model, and its activation function was improved. The ASoftReL U function was proposed to alleviate the neuron-death problem and improve the accuracy and fitting speed of the network. Finally, the improved defect detection system was tested on the data set of castings. Through the comparison and analysis of the experimental results, the network model with the highest accuracy and the most generalization ability was obtained. Experimental results show that the accuracy of this method is much higher than the traditional method.","PeriodicalId":117227,"journal":{"name":"2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128994878","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: 3

Dynamic Thermal Prediction Model for the Electronic Equipment Cabin Based on RVFL Network 基于RVFL网络的电子设备舱室动态热预测模型

2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) Pub Date : 2018-10-01 DOI: 10.1109/CISP-BMEI.2018.8633235

Zhiyong Sheng, Zhiqiang Zeng, Qing Tian, Yanping Wang

{"title":"Dynamic Thermal Prediction Model for the Electronic Equipment Cabin Based on RVFL Network","authors":"Zhiyong Sheng, Zhiqiang Zeng, Qing Tian, Yanping Wang","doi":"10.1109/CISP-BMEI.2018.8633235","DOIUrl":"https://doi.org/10.1109/CISP-BMEI.2018.8633235","url":null,"abstract":"Accurate modeling of heat transfer devices is important for airborne electronic equipment cabin thermal prediction and thermal management. The current thermal models are mostly lumped model based on Thermal Network Model (TNM). The least square method is often used to compute and identify its parameters. Because of the principle of lumped parameter, thermal network model cannot correctly characterize the nonlinear temperature change process in the cabin, and the prediction accuracy is poor. Recently, neural network has gradually become a major research direction of heat transfer process modeling due to its powerful learning ability and data approximation performance. In order to achieve more accurate online thermal modeling of electronic equipment cabin, a sliding time window method based on Random Vector Function Link (RVFL) is proposed. By training the measured temperature in the electronic equipment cabin, the sliding window RVFLNN is built to predict the temperature of the equipment in the subsequent window. When the accuracy of this method cannot meet the requirement, the model is quickly updated according to the data acquired in real time. The real data experiments verify the effectiveness of this method as well as fast modeling speed.","PeriodicalId":117227,"journal":{"name":"2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129637549","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: 2

A New Method for Time Series Signal Decomposition 时间序列信号分解的一种新方法

2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) Pub Date : 2018-10-01 DOI: 10.1109/CISP-BMEI.2018.8633021

Kaiguo Fan, Bojian Xu, Ming Zhang, Mingxing Nan, Jianguo Huang

引用次数: 0

Hyperspectral Anomaly Dectection on Multicore DSPs 多核dsp的高光谱异常检测

2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) Pub Date : 2018-10-01 DOI: 10.1109/CISP-BMEI.2018.8633118

Yuan Li, Wei Li, Lu Li

{"title":"Hyperspectral Anomaly Dectection on Multicore DSPs","authors":"Yuan Li, Wei Li, Lu Li","doi":"10.1109/CISP-BMEI.2018.8633118","DOIUrl":"https://doi.org/10.1109/CISP-BMEI.2018.8633118","url":null,"abstract":"As one of the major technological breakthroughs made by human beings in earth observation since the 1980s, the good spectral diagnostic ability of hyperspectral images makes it very suitable for the discovery of artificial targets against the natural background and therefore receives more and more attention. Hyperspectral images are characterized by their high spectral resolution and large band. As they provide detailed observation information in more fields, they also bring about an increase in the amount of data redundancy, which brings about a great deal of difficulty corresponding transmission, storage, processing and application. In this paper, the multi-core DSP is applied to realize the hyperspectral images anomaly detection. Firstly, the hyperspectral image is split into several blocks. And then background spectral information in each blocks is extracted by Sherman-Morrison formula sequentially. Finally, the parallelization of multi-core DSP with high-speed computing performance can realize the realtime required in the application with RX detection algorithm. The real hyperspectral dataset is applied for hyperspectral image anomaly detection to verify the validity of the proposed method. Furthermore, comparing with MATLAB and CPU experimental results, DSP parallel detection system has better detection performance and high-efficient.","PeriodicalId":117227,"journal":{"name":"2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121326788","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

Guideline Design of an Active Gene Annotation Corpus for the Purpose of Drug Repurposing 以药物再利用为目的的活性基因注释语料库的指南设计

2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) Pub Date : 2018-10-01 DOI: 10.1109/CISP-BMEI.2018.8633253

Yuxing Wang, Xinzhi Yao, Kaiyin Zhou, Xuan Qin, Jin-Dong Kim, K. Cohen, Jingbo Xia

{"title":"Guideline Design of an Active Gene Annotation Corpus for the Purpose of Drug Repurposing","authors":"Yuxing Wang, Xinzhi Yao, Kaiyin Zhou, Xuan Qin, Jin-Dong Kim, K. Cohen, Jingbo Xia","doi":"10.1109/CISP-BMEI.2018.8633253","DOIUrl":"https://doi.org/10.1109/CISP-BMEI.2018.8633253","url":null,"abstract":"In order to develop a gold corpus for Biomedical Natural Language Processing community for the sake of knowledge discovery in drug repurposing, an active gene annotation corpus (AGAC) was developed in this research. Five semantic trigger labels and three root regulatory trigger labels were designed as molecular- and cell- level biological entity annotations, which focused on the information of function change in biological processes resulted from mutated genes. In addition, predicates ‘ThemeOf’ and ‘CauseOf’ were as well annotated manually for the semantic knowledge extraction. Eventually, roles of gene mutation including gain of function (GOF) and loss of function (LOF) were curated through the AGAC annotation guideline. The information from AGAC annotation effectively bridge the association between mutation, gene, drug and disease, and make it possible to predict new drug direction in a large scale. AGAC corpus availability: The corpus is available in PubAn-notation platform11http://pubannotation.org/projects/HZAU_Active_Gene_Corpus.","PeriodicalId":117227,"journal":{"name":"2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124445611","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: 7

Local Field Potential Functional Network Analysis of the Left and Right Hippocampus of Pigeons in Goal-Directed Task 目标导向任务中鸽子左右海马局部场电位功能网络分析

2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) Pub Date : 2018-10-01 DOI: 10.1109/CISP-BMEI.2018.8633068

Siyang Shen, Kun Zhao, Mengmeng Li, Hong Wan

{"title":"Local Field Potential Functional Network Analysis of the Left and Right Hippocampus of Pigeons in Goal-Directed Task","authors":"Siyang Shen, Kun Zhao, Mengmeng Li, Hong Wan","doi":"10.1109/CISP-BMEI.2018.8633068","DOIUrl":"https://doi.org/10.1109/CISP-BMEI.2018.8633068","url":null,"abstract":"Pigeons have excellent spatial cognition and navigation ability. The hippocampus $(text{Hp})$ is the key brain area for navigation behavior. However, the difference of the neural cluster electrical activity in the goal-directed task in the left and right hippocampus of the pigeon brain is still unclear. In this paper, the function network of the left hemisphere and the right hemisphere local field potential (LFP)signals is constructed using Pearson correlation method, the LFP functional networks and their topological characteristics of different Hp are analyzed in different task areas. The results show that the connection density of LFP functional networks in different areas is different, the clustering coefficient (CC)and global efficiency (E)of the pigeons in the straight area(SA)are higher than the waiting area(WA)and the turning area(TA)$(mathrm{P} < 0.05)$, at the same time, the topological characteristics of the LFP functional network in the left Hp were significantly higher than that in the right Hp $(mathrm{P} < 0.05)$. Further research also found it is not affected by the goal location $(mathrm{P} < 0.01)$. This study further analyzes the role of the hippocampus functional network in pigeon navigation behavior, and makes a useful attempt to analyze the brain neural mechanisms of bird spatial cognition and navigation.","PeriodicalId":117227,"journal":{"name":"2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124531487","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: 3

A Multi-Scale Parallel Convolutional Neural Network for Automatic Sleep Apnea Detection Using Single-Channel EEG Signals 基于多尺度并行卷积神经网络的单通道脑电信号睡眠呼吸暂停自动检测

2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) Pub Date : 2018-10-01 DOI: 10.1109/CISP-BMEI.2018.8633132

Dihong Jiang, Yu Ma, Yuanyuan Wang

{"title":"A Multi-Scale Parallel Convolutional Neural Network for Automatic Sleep Apnea Detection Using Single-Channel EEG Signals","authors":"Dihong Jiang, Yu Ma, Yuanyuan Wang","doi":"10.1109/CISP-BMEI.2018.8633132","DOIUrl":"https://doi.org/10.1109/CISP-BMEI.2018.8633132","url":null,"abstract":"Sleep apnea is a kind of widespread and serious sleep disorder that disrupts breathing during the sleep of apnea patients. Clinically, sleep apnea events can be monitored and manually scored from whole-night polysomnography by specialists. However, this task tends to be time-consuming and error-prone. In this paper, we propose an automatic sleep apnea detection scheme using single-channel electroencephalography (EEG)signals. The segmented EEG signals with a length of 30-second are firstly filtered by a band-pass filter to denoise. A short time Fourier transform is then used to generate the time-frequency images from corresponding EEG signals. A multi-scale parallel convolutional neural network with mixed depth of layers and mixed sizes of convolutional filters is designed to automatically learn the features from time-frequency representations and make the classification between sleep apnea periods and other periods. Experimental results show the superior performance of the proposed method in terms of sensitivity, specificity, and accuracy, compared to state-of-the-art works. This method provides the possibility to record and analyze the sleep apnea automatically in sleep monitoring.","PeriodicalId":117227,"journal":{"name":"2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126223706","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: 11

Integrating Binary Similarity Measures in the Link Prediction Task 二值相似度量在链路预测任务中的集成

2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) Pub Date : 2018-10-01 DOI: 10.1109/CISP-BMEI.2018.8633089

A. Milani, Valentina Franzoni, Giulio Biondi, Yuanxi Li

引用次数: 1

Optical Fiber Intrusion Signal Recognition Based on Improved Mel Frequency Cepstrum Coefficient 基于改进Mel倒谱系数的光纤入侵信号识别

2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) Pub Date : 2018-10-01 DOI: 10.1109/CISP-BMEI.2018.8633226

Yuan Zhang, Lu Zhao, Qing Tian, Jun Fan

{"title":"Optical Fiber Intrusion Signal Recognition Based on Improved Mel Frequency Cepstrum Coefficient","authors":"Yuan Zhang, Lu Zhao, Qing Tian, Jun Fan","doi":"10.1109/CISP-BMEI.2018.8633226","DOIUrl":"https://doi.org/10.1109/CISP-BMEI.2018.8633226","url":null,"abstract":"Oil and gas resources pipelines, boundary lines and other places need to monitor their safety status in real time. The fiber early warning system becomes a good choice for its high sensitivity, corrosion resistance and concealment. The system provides early warning of the detection of fiber vibration signals. In this paper, an improved Mel frequency cepstrum coefficient (MFCC) method is proposed for the cepstrum characteristics recognition of different typical optical fiber vibration signals. Firstly, we pre-process the intrusion signals and obtain its power spectral density (PSD) to quantify the difference of frequency spectrum in respective intrusions. Secondly, the adaptive filter bank is designed according to the distribution of signal power spectrum to improve the conventional MFCC method. Through the analysis of the characteristic parameters, the MFCC coefficients are obtained. Finally, the Mean-crossing rates (MCR) of MFCC are calculated and the appropriate thresholds are selected to classify the typical vibration signals. Compared with the traditional MFCC, this improved MFCC method realizes adaptive division of frequency band according to the distribution of signal power spectrum. Experiments show that the algorithm can identify the manual signal, the mechanical signal and the vehicle signal in the research of the vibration signal recognition of the optical fiber pre-warning system (OFPS).","PeriodicalId":117227,"journal":{"name":"2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125670492","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: 5

An E-Commerce Coupon Target Population Positioning Model Based on Random Forest and eXtreme Gradient Boosting 基于随机森林和极端梯度增强的电子商务优惠券目标人群定位模型

2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) Pub Date : 2018-10-01 DOI: 10.1109/CISP-BMEI.2018.8633247

Zhang-Fa Yan, Yu-Lin Shen, Wei-Jun Liu, Jie-Min Long, Qingyang Wei

引用次数: 2