scholarly journals Rapid detection method of radioactive source based on low-count gamma spectra

2021 ◽  
Author(s):  
Yonggang Yuan ◽  
Jinhui Qu ◽  
Jingtao He ◽  
Zhaoyi Tan ◽  
Yixin Liu
Keyword(s):  
Principal Component Analysis ◽  
Rapid Detection ◽  
Detection Method ◽  
Principal Component ◽  
Radioactive Source ◽  
Wide Area ◽  
Source Information ◽  
Key Technology ◽  
Source Signals ◽  
Gamma Spectra

In order to solve the problem of searching a radioactive source in a wide area, we developed a mobile CsI detector. This paper presented the performance of the detector during the spectra collection investigation. The 1 s spectrum collected by the detector was low-count spectrum and it is hard to distinguish whether it contains radioactive source signals. A rapid detection method of radioactive source based on low-count gamma spectra was proposed. Principal component analysis (PCA) was the key technology of the method. According to the PCA, the source information was efficiently extracted. With the method, the detect sensitivity and accuracy of the detector were optimized.

scholarly journals Rapid detection method of radioactive source based on low-count gamma spectra

2021 ◽  
Author(s):  
Yonggang Yuan ◽  
Jinhui Qu ◽  
Jingtao He ◽  
Zhaoyi Tan ◽  
Yixin Liu
Keyword(s):  
Principal Component Analysis ◽  
Rapid Detection ◽  
Detection Method ◽  
Principal Component ◽  
Radioactive Source ◽  
Wide Area ◽  
Source Information ◽  
Key Technology ◽  
Source Signals ◽  
Gamma Spectra

In order to solve the problem of searching a radioactive source in a wide area, we developed a mobile CsI detector. This paper presented the performance of the detector during the spectra collection investigation. The 1 s spectrum collected by the detector was low-count spectrum and it is hard to distinguish whether it contains radioactive source signals. A rapid detection method of radioactive source based on low-count gamma spectra was proposed. Principal component analysis (PCA) was the key technology of the method. According to the PCA, the source information was efficiently extracted. With the method, the detect sensitivity and accuracy of the detector were optimized.

scholarly journals Detecting Shilling Attacks with Automatic Features from Multiple Views

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Yaojun Hao ◽  
Fuzhi Zhang ◽  
Jian Wang ◽  
Qingshan Zhao ◽  
Jianfang Cao
Keyword(s):  
Principal Component Analysis ◽  
Recommender Systems ◽  
Detection Method ◽  
Principal Component ◽  
Component Analysis ◽  
Experimental Results ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Multiple Views ◽  
Fine Grained

Due to the openness of the recommender systems, the attackers are likely to inject a large number of fake profiles to bias the prediction of such systems. The traditional detection methods mainly rely on the artificial features, which are often extracted from one kind of user-generated information. In these methods, fine-grained interactions between users and items cannot be captured comprehensively, leading to the degradation of detection accuracy under various types of attacks. In this paper, we propose an ensemble detection method based on the automatic features extracted from multiple views. Firstly, to collaboratively discover the shilling profiles, the users’ behaviors are analyzed from multiple views including ratings, item popularity, and user-user graph. Secondly, based on the data preprocessed from multiple views, the stacked denoising autoencoders are used to automatically extract user features with different corruption rates. Moreover, the features extracted from multiple views are effectively combined based on principal component analysis. Finally, according to the features extracted with different corruption rates, the weak classifiers are generated and then integrated to detect attacks. The experimental results on the MovieLens, Netflix, and Amazon datasets indicate that the proposed method can effectively detect various attacks.

scholarly journals Crater Detection Method using Principal Component Analysis and its Evaluation

2016 ◽  
Vol 14 (ists30) ◽  
pp. Pt_7-Pt_14
Author(s):  
Tatsuya TAKINO ◽  
Izuru NOMURA ◽  
Misako MORIBE ◽  
Hiroyuki KAMATA ◽  
Keiki TAKADAMA ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Detection Method ◽  
Principal Component ◽  
Component Analysis ◽  
Crater Detection
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