2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)最新文献_第9页

A Fuzzy Adaptive Strong Tracking Algorithm with Fading Factor 一种带衰落因子的模糊自适应强跟踪算法

2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) Pub Date : 2021-10-23 DOI: 10.1109/CISP-BMEI53629.2021.9624316

Shuai Fang, Chuchu Zhao, Jinping Sun

{"title":"A Fuzzy Adaptive Strong Tracking Algorithm with Fading Factor","authors":"Shuai Fang, Chuchu Zhao, Jinping Sun","doi":"10.1109/CISP-BMEI53629.2021.9624316","DOIUrl":"https://doi.org/10.1109/CISP-BMEI53629.2021.9624316","url":null,"abstract":"The maximum acceleration parameter determines the effect of the current statistical (CS) model. Thus, when tracking weak maneuvering targets or targets whose actual acceleration exceeds the given value, the tracking performance of the traditional algorithm that sets with a priori fixed value will drop sharply. To solve this problem, a fuzzy adaptive strong tracking algorithm with fading factor (IAFCS-IMM) is proposed. The algorithm adopts a two-level fuzzy logic system. Through the first-level fuzzy logic, a maneuvering factor representing the maneuverability of the target is obtained according to the estimated acceleration information of the model, and the maximum acceleration parameter is adaptively modified. The second-level fuzzy logic is adopted to adjust the model update probability of interacting multiple model (IMM) algorithm according to the maneuver factor. Besides, a fading factor is introduced in the filtering process, which can enhance the robustness of the filter to the sharp mutation of the target state. Simulation results demonstrate that IAFCS-IMM algorithm achieves good results in filtering accuracy and tracking stability of maneuvering targets.","PeriodicalId":131256,"journal":{"name":"2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","volume":"62 139 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131896114","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 Dynamic Impedance Matching System based on Phase Difference Detection for Ultrasonic Generator in Focused Ultrasound Surgery 聚焦超声手术中超声发生器相位差检测的动态阻抗匹配系统

2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) Pub Date : 2021-10-23 DOI: 10.1109/CISP-BMEI53629.2021.9624321

Zizhuang Chen, G. Shen, Wenjie Liu, Hao Wu, Huaxin Lu, Jian Tao, Zihao Liu

引用次数: 1

Safety Helmet Detection Based On YOLOV3N 基于YOLOV3N的安全帽检测

2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) Pub Date : 2021-10-23 DOI: 10.1109/CISP-BMEI53629.2021.9624363

Li Liu, R. Han, Xiaoying Huang, Xiongwei Jiang, Qiancheng Hong, S. Gao

引用次数: 3

AI-aided Data Mining in Gut Microbiome: The Road to Precision Medicine 肠道微生物组人工智能辅助数据挖掘:精准医学之路

2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) Pub Date : 2021-10-23 DOI: 10.1109/CISP-BMEI53629.2021.9624432

Xiaoqing Jiang, Congmin Xu, Qian Guo, Huaiqiu Zhu

{"title":"AI-aided Data Mining in Gut Microbiome: The Road to Precision Medicine","authors":"Xiaoqing Jiang, Congmin Xu, Qian Guo, Huaiqiu Zhu","doi":"10.1109/CISP-BMEI53629.2021.9624432","DOIUrl":"https://doi.org/10.1109/CISP-BMEI53629.2021.9624432","url":null,"abstract":"The gut microbiome, related to human health and various diseases, is becoming the new biomarker of pathogenesis, phenotype, prognosis, and therapeutic response. Thus, it is expected to play an integral role in the precision medicine field. Artificial intelligence (AI)-based data mining approaches have been applied to facilitate the microbiome analysis with large amounts of massive omics data. In this paper, we presented several works we have made in human gut microbiome data mining, using a variety of AI-aided approaches, such as machine learning, deep learning, etc. We have made progression in the quantitative analysis of the gut microbiome, and the results may help in the application of microbiome-based precision medicine treatments. We reported the alterations of the gut microbiome in aging progression, inflammatory bowel disease (IBD), and the traditional Chinese medicine treatments on acute ischemic stroke (AIS) and identified the subsets of the gut microbiota as potential biomarkers or therapeutic targets using the developed artificial intelligence methods. With the advanced data mining approaches, our computational tools could dig out the correlations between the gut microbiome and human health and diseases. Our efforts presented in this paper also demonstrated the vital role of AI-aided data mining approaches, at least in the direction of precision medicine.","PeriodicalId":131256,"journal":{"name":"2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","volume":"123 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123576910","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

Insights from computational modelling: Characterising Midget and Parasol Retinal Ganglion Cells using Electrical Stimulation 来自计算模型的见解:利用电刺激表征侏儒和阳伞视网膜神经节细胞

2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) Pub Date : 2021-10-23 DOI: 10.1109/CISP-BMEI53629.2021.9624239

Xiaoyu Song, Donglin Wu, Shirong Qiu, Zhengyang Liu, Feng Zhou, Saidong Ma, Liming Li, Tianruo Guo

引用次数: 1

Tropical Cyclone Intensity Estimation with a Soft Label Boosted Network 用软标签增强网络估计热带气旋强度

2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) Pub Date : 2021-10-23 DOI: 10.1109/CISP-BMEI53629.2021.9624336

Chuang Li, Zhao Chen

{"title":"Tropical Cyclone Intensity Estimation with a Soft Label Boosted Network","authors":"Chuang Li, Zhao Chen","doi":"10.1109/CISP-BMEI53629.2021.9624336","DOIUrl":"https://doi.org/10.1109/CISP-BMEI53629.2021.9624336","url":null,"abstract":"Tropical cyclone (TC) intensity refers to maximum sustained wind (MSW) speed near the center of a cyclone. TC intensity estimation can provide early warnings for coastal areas to avoid economic damage and life casualty. Recently, deep learning for remote sensing images has been applied to TC intensity estimation and enabled accurate MSW regression. In this paper, we first construct a new cyclone dataset, namely FY4A-TC, using the multispectral images (MSIs) of 81 cyclones captured by China's FY4A meteorological satellite from 2018–2021. Then we propose a Convolutional Neural Network boosted by Soft Labels (CNN-SL) to estimate TC intensity. The CNN is designed for MSW regression. Specifically, we superimpose a novel soft-label regularizer on the regression loss to increase estimation accuracy. The soft labels are generated from cyclone intensity categories following Gaussian distributions to provide additional information for supervision. To facilitate wind speed estimation, we also propose a series of preprocessing and postprocessing procedures, including MSW smoothing that utilizes temporal relevance of TCs to increase estimation accuracy. Experimental results on the FY4A-TC dataset show that CNN-SL outperforms several state-of-the-art methods for TC intensity estimation.","PeriodicalId":131256,"journal":{"name":"2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114845308","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 Convolution Network of Multi-Windows Spatial-Temporal Feature Analysis For Single-trial EEG Classification in RSVP Task 基于多窗口时空特征分析的卷积网络在RSVP任务中一次脑电分类中的应用

2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) Pub Date : 2021-10-23 DOI: 10.1109/CISP-BMEI53629.2021.9624450

Y. Tan, Boyu Zang, Yanfei Lin, Xiaorong Gao

{"title":"A Convolution Network of Multi-Windows Spatial-Temporal Feature Analysis For Single-trial EEG Classification in RSVP Task","authors":"Y. Tan, Boyu Zang, Yanfei Lin, Xiaorong Gao","doi":"10.1109/CISP-BMEI53629.2021.9624450","DOIUrl":"https://doi.org/10.1109/CISP-BMEI53629.2021.9624450","url":null,"abstract":"It is a challenge to reducing the calibration time of the brain-computer interfaces (BCI) system in the rapid serial visual presentation (RSVP) paradigm. However, the short calibration time can cause the problems, such as small training data, the extremely low signal-to-noise ratio of event-related potentials (ERPs), and inter-trial variability of ERPs, which will increase classification difficulty. In this work, a novel convolution network of multi-windows spatial-temporal features analysis was proposed to alleviate the temporal variability and improve the classification performance for single-trial EEG data. According to the phase-locked information of ERPs, the single-trial was split as the input of the network using the sliding window method. The network adopted three depthwise convolution layers to learn the spatiotemporal features in different windows. The separable convolution was utilized to extract the global features of all windows. Compared with several state-of-the-art algorithms using RSVP datasets of 12 subjects, the proposed network had better classification performance and the online application potential of RSVP-BCI.","PeriodicalId":131256,"journal":{"name":"2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121845793","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

The Application of Sensors in the Auxiliary Medical Devices 传感器在医疗辅助设备中的应用

2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) Pub Date : 2021-10-23 DOI: 10.1109/CISP-BMEI53629.2021.9624398

Li Cheng, Chenru Hao, Huaihao Li, Ziqiang Chi, Lisha Guo, Yanru Wu, Shifei Chen, Huifang Hu, Xu Liu, Xinyu Bai, Ruibin Zhao

引用次数: 0

The Model of Thrombosis Simulation Based on Adaptive Mesh Refinement 基于自适应网格细化的血栓形成仿真模型

2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) Pub Date : 2021-10-23 DOI: 10.1109/CISP-BMEI53629.2021.9624224

Shuo Bian, Chaoqing Ma

引用次数: 0

Improved GRASP-MHT for Possibly Unresolved Measurements 改进grip - mht可能无法解决的测量

2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) Pub Date : 2021-10-23 DOI: 10.1109/CISP-BMEI53629.2021.9624369

Qile Lin, Jinping Sun, Wei Li

引用次数: 0