2019 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS)最新文献_第7页

Image classification method based on improved deep residual networks 基于改进深度残差网络的图像分类方法

2019 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS) Pub Date : 2019-11-01 DOI: 10.1109/ICIIBMS46890.2019.8991495

Wenbo Li, R. Hua

引用次数: 0

A Design of FPGA Acceleration System for Myers bit-vector based on OpenCL 基于OpenCL的Myers位矢量FPGA加速系统设计

2019 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS) Pub Date : 2019-11-01 DOI: 10.1109/ICIIBMS46890.2019.8991468

Liangwei Cai, Qi Wu, Tongsheng Tang, Zhi Zhou, Yuan Xu

引用次数: 6

Detection of Adventitious Respiratory Sounds based on Convolutional Neural Network 基于卷积神经网络的非定音呼吸声检测

2019 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS) Pub Date : 2019-11-01 DOI: 10.1109/ICIIBMS46890.2019.8991459

Renyu Liu, Shengsheng Cai, Kexin Zhang, Nan Hu

{"title":"Detection of Adventitious Respiratory Sounds based on Convolutional Neural Network","authors":"Renyu Liu, Shengsheng Cai, Kexin Zhang, Nan Hu","doi":"10.1109/ICIIBMS46890.2019.8991459","DOIUrl":"https://doi.org/10.1109/ICIIBMS46890.2019.8991459","url":null,"abstract":"Nowadays, the respiratory disease has become one of the most dangerous diseases that threaten human health, especially in the developing countries. The early diagnosis of respiratory disease gives patients the opportunity to receive proper treatment in time, and hence artificial intelligent (AI) auscultation using electronic stethoscope may play a promising role here. The core idea of AI auscultation of respiratory disease is to detect or recognize two kinds of adventitious respiratory sounds related to respiratory diseases: wheeze and crackle. Constrained by the number of available data, in the traditional methods, subjectively defined features were extracted and used to detect these adventitious respiratory sounds. However, to make the detection results robust, the features had better to be learned automatically from the data, which can be realized by applying deep learning in a big data. In this paper, the convolutional neural network (CNN) is exploited to detect adventitious sounds. The data used in this study consists of two parts: the public database provided by the International Conference on Biomedical and Health Informatics (ICBHI) involving 126 subjects and our recorded pediatric auscultation data including 222 subjects. The detection performance of employed CNN is evaluated using ICBHI database, our pediatric auscultation database as well as the combination of them.","PeriodicalId":444797,"journal":{"name":"2019 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127006810","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: 23

Fault detection of gas sensor arrays based on KNN rules 基于KNN规则的气体传感器阵列故障检测

2019 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS) Pub Date : 2019-11-01 DOI: 10.1109/ICIIBMS46890.2019.8991492

Yang Rui-jun, Ding Dan-feng, Cheng Yan

{"title":"Fault detection of gas sensor arrays based on KNN rules","authors":"Yang Rui-jun, Ding Dan-feng, Cheng Yan","doi":"10.1109/ICIIBMS46890.2019.8991492","DOIUrl":"https://doi.org/10.1109/ICIIBMS46890.2019.8991492","url":null,"abstract":"Fault detection techniques are critical to improving the efficiency of airborne overall equipment. It has been recognized that fault detection based on the k nearest neighbor rules (kNN) can effectively handle some of the characteristics of the gas sensor process, such as multimode bulk trajectories and nonlinearities. However, the computational complexity and storage space involved in kNN's neighbor search prevents it from being monitored online, especially for high-dimensional situations. In order to solve this problem, Before the implementation of kNN rules to fault detection, we reduce dimensions by principal component analysis (PCA) and propose a K-fold cross-verification method in the training stage of the model to learn from multiple directions, which plays a good role in improving the accuracy of fault diagnosis. This method enables the verification set to traverse all the data sets during the division of the training set and the test set, thus avoiding the problem of local minima to a certain extent. In the specified range of K values, the smaller the K value is, the fewer the nearest neighbors are, and the more useful training samples can not be included, resulting in the increase of false recognition rate; the K value is too large and the noise significantly affects the error rate; the optimal K value is obtained by cross-verification K nearest neighbor algorithm, so that the error rate of pattern recognition is the lowest. The practical application shows that this method can realize the on-line diagnosis and location of array gas sensor faults, and can be applied to other similar systems.","PeriodicalId":444797,"journal":{"name":"2019 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126896310","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

Design of data sharing module based on medical block chain 基于医疗区块链的数据共享模块设计

2019 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS) Pub Date : 2019-11-01 DOI: 10.1109/ICIIBMS46890.2019.8991529

Yang Rui-jun, Huang Jin-bo, Cheng Yan

引用次数: 1

Model for non-contact blood pressure measurement based on the facial feature amount of amplitude and phase analysis 基于人脸特征量的幅值和相位分析的非接触式血压测量模型

2019 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS) Pub Date : 2019-11-01 DOI: 10.1109/ICIIBMS46890.2019.8991535

Yu Kato, K. Nagumo, K. Oiwa, A. Nozawa

引用次数: 1

Raman Spectroscopy Classification of Foodborne Pathogenic Bacteria Based on PCA-Stacking Model 基于pca叠加模型的食源性致病菌拉曼光谱分类

2019 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS) Pub Date : 2019-11-01 DOI: 10.1109/ICIIBMS46890.2019.8991526

Zeng Wan-dan, Shi Ru-jin, Wu Cheng-wei, Li Qian-xue, Xia Zhi-ping

引用次数: 1

Research on Anti-swing of Bridge Crane Based on Fuzzy Self-adaptive PID Controller 基于模糊自适应PID控制器的桥式起重机抗摆研究

2019 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS) Pub Date : 2019-11-01 DOI: 10.1109/ICIIBMS46890.2019.8991501

Dandan Liu, Wei-ming Lin

引用次数: 1

ICIIBMS 2019 Authors Index ICIIBMS 2019作者索引

2019 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS) Pub Date : 2019-11-01 DOI: 10.1109/iciibms46890.2019.8991473

引用次数: 0

Short-term Heart Rate Variability Analysis during Driving a Powered Wheelchair 驾驶电动轮椅时的短期心率变异性分析

2019 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS) Pub Date : 2019-11-01 DOI: 10.1109/ICIIBMS46890.2019.8991482

N. Bu, H. Ohtsuka

{"title":"Short-term Heart Rate Variability Analysis during Driving a Powered Wheelchair","authors":"N. Bu, H. Ohtsuka","doi":"10.1109/ICIIBMS46890.2019.8991482","DOIUrl":"https://doi.org/10.1109/ICIIBMS46890.2019.8991482","url":null,"abstract":"Heart rate variability (HRV) is an important biomarker for monitoring physical and mental conditions of human beings when they are executing a task. This study attempts to achieve HRV analysis to investigate variation of user’s stress level during driving a powered wheelchair. In such cases, situations and environment issues during driving are temporary and always change dynamically. This means most of the traditional HRV analysis methods are not appropriate, since there is a requirement of relatively long HRV data for each evaluation, for example five minutes. In order to deal with this problem, this paper applies four short-term HRV analysis indices to evaluate user’s stress level during driving and compares evaluation performance of these indices. Wheelchair driving experiments have been conducted with two test courses, namely, a straight course and a crank course. Since the time durations of driving through these courses were relatively short, the evaluation window length of HRV data was set as 15 and 20 s, respectively. With an overlap of 10 s, the evaluation has been achieved with time resolution as 5 and 10 s.","PeriodicalId":444797,"journal":{"name":"2019 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS)","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120817826","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