International Conference on Signal Processing Systems最新文献_第9页

Fast star identification of super large infrared star catalog 超大红外星表的快速恒星识别

International Conference on Signal Processing Systems Pub Date : 2021-01-20 DOI: 10.1117/12.2588933

Chunxiao Zhang, S. Cao, Hongyan He

{"title":"Fast star identification of super large infrared star catalog","authors":"Chunxiao Zhang, S. Cao, Hongyan He","doi":"10.1117/12.2588933","DOIUrl":"https://doi.org/10.1117/12.2588933","url":null,"abstract":"For the high-sensitivity cameras of a super-large star catalog, the conventional effective star identification methods for star sensors will face huge storage and calculations that current computers cannot afford. This paper presents a two-stage full-sky star identification method. 3~4 prominent stars are firstly quickly identified from a simplified star catalog, to determine the view direction. Then, three different strategies are adopted to recognize other remaining stars in the field of view: one strategy is to automatically load the K-vector table of the corresponding sky zone; one strategy is to temporarily generate a K-vector table from the candidate star set, and then remaining stars are identified according to the angular distance from the prominent stars; the third strategy is to obtain the image coordinates of the candidate star set, the proximity position constraint is considered while constraining the angular distance from the prominent stars. Experiments show that the speed of the third strategy is increased by about 20% and maintains a higher recognition rate (F1 is about 0.92). This two-stage recognition method ingeniously resolves the huge amount of calculation caused by the super-large star catalog, and can identify enough stars (ten thousand stars) in a single frame, and provides sufficient control points for the subsequent intrinsic calibration.","PeriodicalId":415097,"journal":{"name":"International Conference on Signal Processing Systems","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132414958","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

Electronic medical record based machine learning methods for adverse pregnancy outcome prediction 基于电子病历的机器学习方法用于不良妊娠结局预测

International Conference on Signal Processing Systems Pub Date : 2021-01-20 DOI: 10.1117/12.2581720

Yuwei Hang, Yan Zhang, Yan Lv, Wenbin Yu, Yi Lin

引用次数: 1

Camera intrinsic calibration based on star points 基于星点的摄像机内禀标定

International Conference on Signal Processing Systems Pub Date : 2021-01-20 DOI: 10.1117/12.2588935

Chunxiao Zhang, N. Zhou

引用次数: 0

An adaptive weak light image enhancement method 一种自适应弱光图像增强方法

International Conference on Signal Processing Systems Pub Date : 2021-01-20 DOI: 10.1117/12.2581266

Wencheng Wang, Zhenxue Chen, Xiaohui Yuan, Fengnian Guan

引用次数: 5

Probability hypothesis density filter for adjacent multi-target tracking 相邻多目标跟踪的概率假设密度滤波

International Conference on Signal Processing Systems Pub Date : 2021-01-20 DOI: 10.1117/12.2588928

Mian Wu, Daikun Zheng, Junquan Yuan, A. Chen, Chang Zhou, Wenfeng Chen

引用次数: 0

The application of Lanczos interpolation in video scaling system based on FPGA 基于FPGA的Lanczos插值在视频缩放系统中的应用

International Conference on Signal Processing Systems Pub Date : 2021-01-20 DOI: 10.1117/12.2589518

Long He, Haixia Wu, Xing Zhao

引用次数: 3

Semi-supervised learning for fault identification in electricity distribution networks 配电网故障识别的半监督学习

International Conference on Signal Processing Systems Pub Date : 2021-01-20 DOI: 10.1117/12.2589229

Xinyang Li, Hong-fa Meng, Xiaoling Peng

{"title":"Semi-supervised learning for fault identification in electricity distribution networks","authors":"Xinyang Li, Hong-fa Meng, Xiaoling Peng","doi":"10.1117/12.2589229","DOIUrl":"https://doi.org/10.1117/12.2589229","url":null,"abstract":"The detection and identification of faults in electricity distribution networks is essential in improving the reliability of power supply. After observing many fault current signals we found that: (1) features of many recorded fault electrical signals were unknown or obscure; (2) the fault types of most sample signals had no clear definition, that is, the labeled sample were very limited. In this situation, the semi-supervised support vector machine (S3VM) and SVM active learning were firstly introduced to distinguish the short circuit and grounding in distribution networks. We used wavelet packet analysis to extract features based on energy spectrum as the physical features of electric signals, then some statistical characteristics were also computed and selected to form a mixed feature set. A case study was conducted on a real data set including 72 labeled and 7720 unlabeled electrical signals for fault diagnosis. By performing transductive support vector machine (TSVM) and SVM active learning with mixed features, our experimental results showed that both of the two models can effectively identify the fault types. Meanwhile, the accuracy of TSVM is higher than that of SVM active learning.","PeriodicalId":415097,"journal":{"name":"International Conference on Signal Processing Systems","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129409794","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

Video abnormal event detection based on CNN and multiple instance learning 基于CNN和多实例学习的视频异常事件检测

International Conference on Signal Processing Systems Pub Date : 2021-01-20 DOI: 10.1117/12.2589031

Guangli Wu, Zhenzhou Guo, Mianzhao Wang, Leiting Li, Chengxiang Wang

引用次数: 2

Speech recognition based on concatenated acoustic feature and lightGBM model 基于串联声学特征和lightGBM模型的语音识别

International Conference on Signal Processing Systems Pub Date : 2021-01-20 DOI: 10.1117/12.2581426

Jiali Yu, Yuanyuan Qu, Zhongkai Zhang, Qidong Lu, Zhiliang Qin, Xiaowei Liu

{"title":"Speech recognition based on concatenated acoustic feature and lightGBM model","authors":"Jiali Yu, Yuanyuan Qu, Zhongkai Zhang, Qidong Lu, Zhiliang Qin, Xiaowei Liu","doi":"10.1117/12.2581426","DOIUrl":"https://doi.org/10.1117/12.2581426","url":null,"abstract":"In this paper, we focus on the application of the LightGBM model for audio sound classification. Though convolutional neural networks (CNN) generally have superior performance, LightGBM model possess certain notable advantages, such as low computational costs, feasibility of parallel implementations, and comparable accuracies over many datasets. In order to improve the generalization ability of the model, data augmentation operations are performed on the audio clips including pitch shifting, time stretching, compressing the dynamic range and adding white noise. The accuracy of speech recognition heavily depends on the reliability of the representative features extracted from the audio signal. The audio signal is originally a one-dimensional time series signal, which is difficult to visualize the frequency change. Hence it is necessary to extract the discernible components in the audio signal. To improve the representative capacity of our proposed model, we use the Mel spectrum and MFCC (Mel-Frequency Cepstral Coefficients) to select features as twodimensional input to accurately characterize the internal information of the signal. The techniques mentioned in this paper are mainly trained on Google Speech Commands dataset. The experimental results show that the method, which is an optimized LightGBM model based on the Mel spectrum, can achieve high word classification accuracy.","PeriodicalId":415097,"journal":{"name":"International Conference on Signal Processing Systems","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131369321","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

Sparse least mean fourth adaptive algorithm for censored regression 截尾回归的稀疏最小平均四次自适应算法

International Conference on Signal Processing Systems Pub Date : 2021-01-20 DOI: 10.1117/12.2588927

Bing Chen, Haiquan Zhao, Yingying Zhu

引用次数: 0