2019 Tenth International Conference on Intelligent Control and Information Processing (ICICIP)最新文献

Broad Learning System-Based Learning Controller for Course Control of Marine Vessels 基于广义学习系统的船舶航向控制学习控制器

2019 Tenth International Conference on Intelligent Control and Information Processing (ICICIP) Pub Date : 2019-12-01 DOI: 10.1109/ICICIP47338.2019.9012200

Liang'en Yuan, Tie-shan Li, C. L. P. Chen, Qihe Shan, Min Han

引用次数: 2

Intelligent Flame Detection Based on Principal Component Analysis and Support Vector Machine 基于主成分分析和支持向量机的智能火焰检测

2019 Tenth International Conference on Intelligent Control and Information Processing (ICICIP) Pub Date : 2019-12-01 DOI: 10.1109/ICICIP47338.2019.9012179

Fan Lin, Zhelong Wang, Debin Shen, Kaida Li, Hongyu Zhao, S. Qiu, Fang Xu

引用次数: 3

Steel Sheet Defect Detection Based on Deep Learning Method 基于深度学习方法的钢板缺陷检测

2019 Tenth International Conference on Intelligent Control and Information Processing (ICICIP) Pub Date : 2019-12-01 DOI: 10.1109/ICICIP47338.2019.9012199

Weizhen Zeng, Zhiyuan You, Mingyue Huang, Zelong Kong, Yikuan Yu, Xinyi Le

引用次数: 12

Sample Augmentation for Classification of Schizophrenia Patients and Healthy Controls Using ICA of fMRI Data and Convolutional Neural Networks 基于功能磁共振成像数据和卷积神经网络的样本增强在精神分裂症患者和健康对照分类中的应用

2019 Tenth International Conference on Intelligent Control and Information Processing (ICICIP) Pub Date : 2019-12-01 DOI: 10.1109/ICICIP47338.2019.9012169

Yan-Wei Niu, Qiuhua Lin, Yue Qiu, Li-Dan Kuang, V. Calhoun

{"title":"Sample Augmentation for Classification of Schizophrenia Patients and Healthy Controls Using ICA of fMRI Data and Convolutional Neural Networks","authors":"Yan-Wei Niu, Qiuhua Lin, Yue Qiu, Li-Dan Kuang, V. Calhoun","doi":"10.1109/ICICIP47338.2019.9012169","DOIUrl":"https://doi.org/10.1109/ICICIP47338.2019.9012169","url":null,"abstract":"Convolutional neural networks (CNN) have exhibited great success in image classification. The application of CNN to classification of patients with brain disorders and healthy controls is also promising using functional magnetic resonance imaging (fMRI) data. However, the shortage of the number of subjects is a challenge for training CNN. Spatial maps separated from the fMRI data by independent component analysis (ICA) can provide a solution to this problem within an ICA-CNN framework. As such, we propose three strategies for both prior to and post ICA sample augmentation in the ICA-CNN framework. More precisely, we propose to increase the number of samples by performing spatial smoothing and band-pass filtering on the observed fMRI data before ICA, and spatial smoothing on the spatial maps after ICA. We evaluate the proposed methods using 82 resting-state fMRI datasets including 42 Schizophrenia patients and 40 healthy controls. The spatial map of the default mode network is used for classification, and each data augmentation is constrained to have the same numbers of samples for a fair comparison. The results show a 2%~15% increase in an average accuracy compared to the existing multiple-model-order method when adopting each of the proposed sample augmentation strategies. The spatial smoothing on the spatial maps is the most accurate among the three proposed methods. When using a combination of the proposed spatial smoothing on the spatial maps with the multiple-model-order method, the average accuracy increases above 90%.","PeriodicalId":431872,"journal":{"name":"2019 Tenth International Conference on Intelligent Control and Information Processing (ICICIP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126596932","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: 14

Spectral Analysis Based Green Tide Identification in High-suspended Sediment Wasters in South Yellow Sea of China 基于光谱分析的南黄海高悬浮沉积物绿潮识别

2019 Tenth International Conference on Intelligent Control and Information Processing (ICICIP) Pub Date : 2019-12-01 DOI: 10.1109/ICICIP47338.2019.9012193

Xiang Wang, Xinxin Wang, Xiu Su, Jianchao Fan, Lin Wang, Qinghui Meng

引用次数: 0

Single Channel Sleep Staging Based on Unsupervised Feature Learning 基于无监督特征学习的单通道睡眠分期

2019 Tenth International Conference on Intelligent Control and Information Processing (ICICIP) Pub Date : 2019-12-01 DOI: 10.1109/ICICIP47338.2019.9012163

Yutong Wang, Yikun Wang, Li Yao, Xiao-jie Zhao

{"title":"Single Channel Sleep Staging Based on Unsupervised Feature Learning","authors":"Yutong Wang, Yikun Wang, Li Yao, Xiao-jie Zhao","doi":"10.1109/ICICIP47338.2019.9012163","DOIUrl":"https://doi.org/10.1109/ICICIP47338.2019.9012163","url":null,"abstract":"Sleep staging based on electroencephalogram (EEG) signal, as one of the vital bases of study on sleep diagnosis, has been under massive attention. With the spring up of deep learning these years, the idea of combining deep learning structure with automatic sleep staging has been an attractive topic. However, the labeling of sleep stages requires professional knowledge as well as plenty of time, which raise the barrier to evaluate this idea. In this study, the method of unsupervised feature learning based on a mass of unlabeled data and a small number of labeled data was proposed to accomplish sleep staging. The unsupervised feature learning structure was built based on a pair of symmetric convolutional neural networks, with the help of a shallow neural network classifier to classify sleep stages. The results showed that under the condition of the very few labeled data, sleep staging based on unsupervised feature learning can achieve similar accuracy to supervised feature learning, which provides a new direction for the application of deep learning method in dealing with data that is difficult to label or lack of prior knowledge.","PeriodicalId":431872,"journal":{"name":"2019 Tenth International Conference on Intelligent Control and Information Processing (ICICIP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125910710","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

Autonomous Exploration for Automated Valet Parking Based on Road Structure 基于道路结构的自动代客泊车自主探索

2019 Tenth International Conference on Intelligent Control and Information Processing (ICICIP) Pub Date : 2019-12-01 DOI: 10.1109/ICICIP47338.2019.9012204

Yao Hu, Ming Yang, B. Wang, Chunxiang Wang, Boya Xu

{"title":"Autonomous Exploration for Automated Valet Parking Based on Road Structure","authors":"Yao Hu, Ming Yang, B. Wang, Chunxiang Wang, Boya Xu","doi":"10.1109/ICICIP47338.2019.9012204","DOIUrl":"https://doi.org/10.1109/ICICIP47338.2019.9012204","url":null,"abstract":"Automated valet parking technology allows the vehicle to automatically drive into the parking lot and park itself without a human in the vehicle, relieving humans from parking completely. However, the existing automated valet parking system relies on infrastructural intelligence or pre-acquisition of parking lot maps. Given the disadvantages of these researches, this paper proposes a general method for automatic valet parking system based on autonomous exploration. This method depends on no prior knowledge of the parking lot. Our method extracts road structure from perception result using the Voronoi diagram. A multi-factor exploration strategy we proposed is used to generate exploration candidates for autonomous exploration from the road structure. Also, a motion planning method based on the lateral priority of the road guides the vehicle to the candidates while obeys the rules as far as possible. The autonomous exploration will continue until it finds free parking slots or all the spaces in the parking lot are explored. Related experiments have verified the effectiveness of the method presented in this paper.","PeriodicalId":431872,"journal":{"name":"2019 Tenth International Conference on Intelligent Control and Information Processing (ICICIP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132476145","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

Urban UAV Images Semantic Segmentation Based on Fully Convolutional Networks with Digital Surface Models 基于数字曲面模型的全卷积网络城市无人机图像语义分割

2019 Tenth International Conference on Intelligent Control and Information Processing (ICICIP) Pub Date : 2019-12-01 DOI: 10.1109/ICICIP47338.2019.9012207

Bowen Zhang, Y. Kong, H. Leung, Shiyu Xing

引用次数: 6

Finite-set Model Predictive Speed and Heading Control of Autonomous Surface Vehicles with Unmeasured States 状态不可测自动地面车辆的有限集模型预测速度与航向控制

2019 Tenth International Conference on Intelligent Control and Information Processing (ICICIP) Pub Date : 2019-12-01 DOI: 10.1109/ICICIP47338.2019.9012181

Zhouhua Peng, Baogang Zhang, Oiuvue Sun, Dan Wang, Min Han, Lu Liu, Haoliang Wang

引用次数: 1

Reversible Data Hiding Based on Improved Rhombus Prediction Method 基于改进菱形预测法的可逆数据隐藏

2019 Tenth International Conference on Intelligent Control and Information Processing (ICICIP) Pub Date : 2019-12-01 DOI: 10.1109/ICICIP47338.2019.9012191

Simi Lu, X. Liao, Nankun Mu, Jiahui Wu, Junqing Le

引用次数: 1