Fractional-weighted entropy-based fuzzy G-2DLDA algorithm: a new facial feature extraction method

Ghosh, Manas; Dey, Aniruddha

doi:10.1007/s11042-022-13328-7

Cited by 4 publications

(1 citation statement)

References 48 publications

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“…With the continuous growth of industrial technology, energy consumption and environmental pollution are becoming more and more serious. When there is a growing shortage of non renewable resources such as oil and coal and people's demand for sustainable use of clean energy is increasing [1][2]. As a green, safe, reliable, economical and practical device, the new efficient, energy-saving and environmental protection equipment is widely used in power plants or other power equipment.…”

Section: Introductionmentioning

confidence: 99%

Experimental Heat Transfer Performance of Plate Fin Heat Exchanger Based on Fuzzy Algorithm

2023

KME

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With the continuous growth of industrial technology, the waste of energy is becoming more and more serious. As a kind of energy-saving, environmental protection and higher economic benefits, plate fin heat exchanger(PFHE) can effectively alleviate the resource tension. Therefore, the experimental study of plate fin regenerator is of great significance. This paper mainly introduces the mass transfer performance and working principle of the plate type heat recovery device at home and abroad. Combined with the actual situation, the test, analysis and calculation are carried out, and the design ideas and relevant equipment structure drawings after optimizing the process parameters and improving measures are proposed, so as to improve the heat exchange efficiency and reduce the production cost. Therefore, based on the fuzzy algorithm, this paper designs a PFHE model, and tests the model. The test results show that the pressure drop on both sides of the heat exchanger decreases slightly, and the number of channels on both sides increases slightly. The optimization results of this module show the applicability of fuzzy algorithm, as a modern optimization method, in the optimization design of PFHE, and the superiority of genetic algorithm in the optimization process and results compared with traditional design methods.

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Section: Introductionmentioning

confidence: 99%

Experimental Heat Transfer Performance of Plate Fin Heat Exchanger Based on Fuzzy Algorithm

2023

KME

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A weighted fuzzy belief factor-based D-S evidence theory of sensor data fusion method and its application to face recognition

Ghosh¹,

Dey

Kahali

2023

Multimed Tools Appl

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T2-fuzzy multi-fused facial image fusion (T2FMFImgF): An efficient face recognition

Dey¹,

Ghosh²,

Chowdhury³

et al. 2023

IFS

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This paper presents a novel decision-making method for face recognition where the features were extracted from the original image fused with its corresponding true and partial diagonal images. To extract features, we adopted the generalized two-dimensional FLD (G2DFLD) feature extraction technique. The feature vectors from a test image are given as input to neural network-based classifier. It is trained with the feature vectors of original image and diagonally fused images and thereby the merit weights with respect to different classes were generated. To address the factors that affect the face recognition accuracy and uncertainty related to raw biometric data, a fuzzy score for each of the classes is generated by treating a type-2 fuzzy set. This type-2 fuzzy set is formed by the feature vectors of both the diagonally fused training samples and the test image of the respective classes. A concluding score for each of the classes under consideration is computed by fusing complemented merit weight with the complemented fuzzy score. These class-wise concluding scores are considered in the face recognition process. In this study, the well-known face databases (AT&T, UMIST and CMU-PIE) are used to evaluate the performance of the proposed method. The experimental results illustrate the fact that the proposed method has exhibited superior classification precision as compared with other state-of-art methods. Our T2FMFImgF method achieves highest face recognition accuracies of 99.41%, 98.36% and 89.80% in case of AT&T, UMIST and CMU-PIE (with expression), respectively while for CMU-PIE (with Light) the highest recognition accuracy is 97.957% . In addition to it, the presented method is quite successful in fusing and classifying textural information from the original and partial diagonal images by integrating them with type-2 fuzzy set-based treatment.

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Fractional-weighted entropy-based fuzzy G-2DLDA algorithm: a new facial feature extraction method

Cited by 4 publications

References 48 publications

Experimental Heat Transfer Performance of Plate Fin Heat Exchanger Based on Fuzzy Algorithm

Experimental Heat Transfer Performance of Plate Fin Heat Exchanger Based on Fuzzy Algorithm

A weighted fuzzy belief factor-based D-S evidence theory of sensor data fusion method and its application to face recognition

T2-fuzzy multi-fused facial image fusion (T2FMFImgF): An efficient face recognition

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