scholarly journals Sequential emitter identification method based on D-S evidence theory

2018 ◽  
Vol 38 (2) ◽  
pp. 155-165
Author(s):  
Xiaotao Guo ◽  
Xing Wang ◽  
Dongqing Zhou ◽  
Yubing Wang
Keyword(s):  
Signal Processing ◽  
Simulation Analysis ◽  
Recognition Rate ◽  
Evidence Theory ◽  
Individual Identification ◽  
Rule Based ◽  
Identification Method ◽  
Utmost Care ◽  
Decision Vector ◽  
Self Adaptive

This paper proposes a novel sequential identification method for enhancing the anti-jamming performance and for accurate recognition rate of the emitters’ individual identification in the complicated environment. The proposed method integrates the D-S evidence theory and features extraction that can get the utmost out of features of information systems and decrease the influence of uncertain factors in the signal processing. Firstly, selected features are extracted from intercepted signals. Then, the proposed self-adaptive fusing rule based on the decision vector is utilized to fuse the evidences that are transformed by features and the previous fusing information. Finally, recognition results can be obtained by judgment rules. The simulation analysis demonstrates that self-adaptive fusing rule can achieve a great balance between computational efficiency and accurate identifying rate. While comparing with other identifying methods, the proposed sequential identifying method can provide more accurate and stable recognition results, which makes the utmost care and use of existing information.

Online Biological Literature MRI Image Classification Study

2013 ◽  
Vol 694-697 ◽  
pp. 2336-2340
Author(s):  
Yun Feng Yang ◽  
Feng Xian Tang
Keyword(s):  
Recognition Performance ◽  
Recognition Rate ◽  
Image Features ◽  
Feature Identification ◽  
Identification Method ◽  
Image Library ◽  
Text Information ◽  
Correct Recognition Rate ◽  
Biological Literature ◽  
Mri Image

In order to construct a certain standard structure MRI (Magnetic resonance imaging) image library by extracting and collating unstructured literature data information, an identification method of the image and text information fusion is proposed. The method makes use of PHOW (Pyramid Histogram Of Words) to represent image features, combines with the word frequency characteristics of the embedded icon note (text), and then uses posterior multiplication fusion method to complete the classification and identification of the online biological literature MRI image. The experimental results show that this method has better correct recognition rate and better recognition performance than feature identification method only based on PHOW or text. The study can offer use for reference to construct other structured professional database from online literature.

scholarly journals A Fiber Vibration Signal Identification Method Based on the Combination of EWT-FE and LSTM

2021 ◽  
Vol 2112 (1) ◽  
pp. 012020
Author(s):  
Xin Zhang ◽  
Qingmo Ja ◽  
SaiSai Ruan ◽  
Qin Hu
Keyword(s):  
Feature Extraction ◽  
Recognition Rate ◽  
Real Life ◽  
Vibration Signal ◽  
Machine Learning Algorithms ◽  
Signal Identification ◽  
Feature Vectors ◽  
Identification Method ◽  
Effective Manner ◽  
Empirical Wavelet Transform

Abstract As the optical fiber perimeter security system is widely used in real life, how to identify the types of intrusion events in a timely and effective manner is becoming a major research hotspot. At present, in this field, various signal feature extraction algorithms are usually used to extract intrusion signal features to form feature vectors, and then machine learning algorithms are used to classify the feature vectors to achieve the role of identifying the types of intrusion events. As a common signal feature extraction algorithm, the EMD algorithm has been widely used in the feature extraction of various vibration signals, but it will have the problem of modal aliasing and affect the feature extraction effect of the signal. Therefore, EWT, VMD and other algorithms have been successively used proposed to improve modal aliasing. On the basis of fully comparing the existing algorithms, this paper proposes a fiber vibration signal identification method that decomposes the signal through the empirical wavelet transform (EWT) algorithm and then extracts the fuzzy entropy (FE) of each component, and uses LSTM for classification. The final experiment shows that the method can identify four kinds of fiber intrusion signals in time and effectively, with an average recognition accuracy rate of 97.87%, especially for flap and knock recognition rate of 100%.

Experimental Robot Identification: Advantages of Combining Internal and External Measurements and of Using Periodic Excitation

2001 ◽  
Vol 123 (4) ◽  
pp. 630-636 ◽  
Author(s):  
Walter Verdonck ◽  
Jan Swevers ◽  
Jean-Claude Samin
Keyword(s):  
Signal Processing ◽  
Parameter Estimation ◽  
Effective Means ◽  
Base Plate ◽  
Experimental Results ◽  
Parameter Estimates ◽  
Periodic Excitation ◽  
Identification Method ◽  
Reaction Forces ◽  
Improved Model

This paper discusses the advantages of using periodic excitation and of combining internal and external measurements in experimental robot identification. This discussion is based on the robot identification method developed by Swevers et al., a method that is recognized by industry as an effective means of robot identification that is frequently used, Hirzinger, G., Fischer, M., Brunner, B., Koeppe, R., Otter, M., Grebenstein, M., and Schafer, I, 1999, “Advances is Robotics: The DLR Experiment,” The International Journal of Robotics Research, Vol. 18, No. 11, pp. 1064–1087 [3]. Experimental results on a KUKA IR 361 show that the periodicity of the robot excitation is a key element of this method. Nonperiodic robot excitation complicates the signal processing preceding the parameter estimation, often yielding less accurate parameter estimates. An extension of this identification method combines internal and external measurements, Chenut, X., Samin, J. C., Swevers, J., and Ganseman, C., 2000, “Combining Internal and External robot Models for improved Model Parameter Estimation,” Mechanical Systems and Signal Processing. Vol. 14, No. 5, pp. 691–704 [4], yielding robot models that allow to accurately predict the actuator torques and the reaction forces/torques of the robot on its base plate, which are both important for the path planning. This paper presents and critically discusses the first experimental results obtained with this method.

Face Recognition

2011 ◽  
pp. 163-254
Author(s):  
Daijin Kim ◽  
Jaewon Sung
Keyword(s):  
Face Recognition ◽  
Access Control ◽  
Individual Identification ◽  
Modern Life ◽  
Identification Methods ◽  
Personal Security ◽  
Identification Method ◽  
The Individual ◽  
Biometric Features

In the modern life, the need for personal security and access control is becoming an important issue. Biometrics is the technology which is expected to replace traditional authentication methods that are easily stolen, forgotten and duplicated. Fingerprints, face, iris, and voiceprints are commonly used biometric features. Among these features, face provides a more direct, friendly and convenient identification method and is more acceptable compared with the individual identification methods of other biometrics features. Thus, face recognition is one of the most important parts in biometrics.

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