scholarly journals Pattern Recognition and Information Fusion Using Belief Functions: Some Recent Developments

2007 ◽  
pp. 1-1 ◽  
Author(s):  
Thierry Denœux
Keyword(s):  
Pattern Recognition ◽  
Information Fusion ◽  
Belief Functions ◽  
Recent Developments

Software pattern recognition applied to nanodiffraction patterns from a STEM instrument

1986 ◽  
Vol 44 ◽  
pp. 694-695
Author(s):  
G.Y. Fan ◽  
J.M. Cowley
Keyword(s):  
Image Processing ◽  
Pattern Recognition ◽  
Computer Software ◽  
Processing System ◽  
Light Level ◽  
Host Computer ◽  
Low Light ◽  
Image Processing System ◽  
Recent Developments ◽  
On Line

In recent developments, the ASU HB5 has been modified so that the timing, positioning, and scanning of the finely focused electron probe can be entirely controlled by a host computer. This made the asynchronized handshake possible between the HB5 STEM and the image processing system which consists of host computer (PDP 11/34), DeAnza image processor (IP 5000) which is interfaced with a low-light level TV camera, array processor (AP 400) and various peripheral devices. This greatly facilitates the pattern recognition technique initiated by Monosmith and Cowley. Software called NANHB5 is under development which, instead of employing a set of photo-diodes to detect strong spots on a TV screen, uses various software techniques including on-line fast Fourier transform (FFT) to recognize patterns of greater complexity, taking advantage of the sophistication of our image processing system and the flexibility of computer software.

scholarly journals Features modeling with an α-stable distribution: Application to pattern recognition based on continuous belief functions

2013 ◽  
Vol 14 (4) ◽  
pp. 504-520 ◽  
Author(s):  
Anthony Fiche ◽  
Jean-Christophe Cexus ◽  
Arnaud Martin ◽  
Ali Khenchaf
Keyword(s):  
Pattern Recognition ◽  
Stable Distribution ◽  
Belief Functions

Using Z-number to measure the reliability of new information fusion method and its application in pattern recognition

2021 ◽  
pp. 107658
Author(s):  
Ye Tian ◽  
Xiangjun Mi ◽  
Huizi Cui ◽  
Pengdan Zhang ◽  
Bingyi Kang
Keyword(s):  
Pattern Recognition ◽  
Information Fusion ◽  
Fusion Method ◽  
New Information

Explainable Deep Learning for Efficient and Robust Pattern Recognition: A Survey of Recent Developments

2021 ◽  
pp. 108102
Author(s):  
Xiao Bai ◽  
Xiang Wang ◽  
Xianglong Liu ◽  
Qiang Liu ◽  
Jingkuan Song ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Deep Learning ◽  
Recent Developments

scholarly journals Evolution of the C-Type Lectin-Like Receptor Genes of the DECTIN-1 Cluster in the NK Gene Complex

2012 ◽  
Vol 2012 ◽  
pp. 1-11 ◽  
Author(s):  
Susanne Sattler ◽  
Hormas Ghadially ◽  
Erhard Hofer
Keyword(s):  
Pattern Recognition ◽  
Sequence Data ◽  
Gene Duplications ◽  
Gene Complex ◽  
Adaptive Immune ◽  
Recognition Function ◽  
Recent Developments ◽  
Evolutionary Emergence ◽  
Related Proteins ◽  
Nk Gene Complex

Pattern recognition receptors are crucial in initiating and shaping innate and adaptive immune responses and often belong to families of structurally and evolutionarily related proteins. The human C-type lectin-like receptors encoded in the DECTIN-1 cluster within the NK gene complex contain prominent receptors with pattern recognition function, such as DECTIN-1 and LOX-1. All members of this cluster share significant homology and are considered to have arisen from subsequent gene duplications. Recent developments in sequencing and the availability of comprehensive sequence data comprising many species showed that the receptors of the DECTIN-1 cluster are not only homologous to each other but also highly conserved between species. Even inCaenorhabditis elegans, genes displaying homology to the mammalian C-type lectin-like receptors have been detected. In this paper, we conduct a comprehensive phylogenetic survey and give an up-to-date overview of the currently available data on the evolutionary emergence of the DECTIN-1 cluster genes.

scholarly journals Pattern Recognition of Different Window Size Control Charts Based on Convolutional Neural Network and Information Fusion

Symmetry ◽  
2020 ◽  
Vol 12 (9) ◽  
pp. 1472
Author(s):  
Tao Zan ◽  
Zifeng Su ◽  
Zhihao Liu ◽  
Deyin Chen ◽  
Min Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Convolutional Neural Network ◽  
Control Chart ◽  
Information Fusion ◽  
Control Charts ◽  
Size Control ◽  
Window Size ◽  
Recognition Method ◽  
Simulation Experiments

Control charts are an important tool for statistical process control (SPC). SPC has the characteristics of fluctuation and asymmetry in the symmetrical coordinate system. It is a graph with control limits used to analyze and judge whether the process is in a stable state. Its fast and accurate identification is of great significance to the actual production. The existing control chart pattern recognition (CCPR) method can only recognize a control chart with fixed window size, but cannot adjust with different window sizes according to the actual production needs. In order to solve these problems and improve the quality management effect in the manufacturing process, a new CCPR method is proposed based on convolutional neural network (CNN) and information fusion. After undergoing feature learning, CNN is used to extract the best feature set from the control chart, while at the same time, expert features (including one shape features and four statistical features) are fused to complete the CCPR. In this paper, the control charts of 10 different window sizes are generated by the Monte Carlo simulation method, and various data patterns are drawn into images, then the CCPR model is set up. Finally, simulation experiments and a real example are addressed to validate its feasibility and effectiveness. The results of simulation experiments demonstrate that the recognition method based on CNN can be used for pattern recognition for different window size control charts, and its recognition accuracy is higher than the traditional ones. In addition, the recognition method based on information fusion performs much better. The result of a real example shows that the method has potential application in solving the pattern recognition problem of control charts with different window sizes.

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