Geometric optimisation of thermo-electric coolers using simulated annealing

Khanh, Doan V. K.; Vasant, Pandian; Elamvazuthi, Irraivan; Vo, Dieu Ngoc

doi:10.1504/ijcse.2017.084164

Cited by 7 publications

(5 citation statements)

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“…Limitations in the thermoelectric cooling instrument's design, such as a relatively low energy conversion efficiency and the capacity to disperse only a limited quantity of heat flux, have the potential to cause significant harm to the instrument's lifetime and performance. Khanh [24] suggested an innovative strategy for maximising the cooling rate by optimising the dimensions of the TEC through the use of simulated annealing. They came up with a more effective geometric design for a single-stage thermoelectric cooler, that maximized the cooling rate.…”

Section: International Journal Of Innovative Research In Computer Sci...mentioning

confidence: 99%

Simulated Annealing and Its Applications to Mechanical Engineering: A Review

Giri¹

2023

International Journal of Innovative Research in Computer Science &Amp; Technology (IJIRCST)

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This work focuses on the significant family of stochastic approaches for global optimization known as simulated annealing (SA), which is based on the key concept of annealing, which describes the cooling of a solid until it reaches the configuration of lowest energy. This paper presents a review of simulated annealing applications meant to solve many problems in the field of mechanical engineering.

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Section: International Journal Of Innovative Research In Computer Sci...mentioning

confidence: 99%

Simulated Annealing and Its Applications to Mechanical Engineering: A Review

Giri¹

2023

International Journal of Innovative Research in Computer Science &Amp; Technology (IJIRCST)

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“…This second subproblem, i.e., EE is very important because material mapping, abundance estimation and other subsequent applications depend on it. Many researchers have used the concept of the geometry of the data in the various applications (Khanh et al, 2017;Miglino and Walker, 2004). EE is also attempted using convex geometry by many researchers (Wu and Chang, 2013).…”

Section: Related Workmentioning

confidence: 99%

Hyperspectral endmember extraction using Pearson's correlation coefficient

Shah

Zaveri

2021

IJCSE

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Hyperspectral unmixing is a source separation problem. The spectral unmixing process is simply the composition of the three-step chain (subspace identification, endmember extraction and abundance estimation). A critical step in this chain is endmember extraction which finds endmembers from the image for the estimation of abundances. In this paper, a novel framework is proposed which uses the concept of Pearson's correlation coefficient and convex geometry. The novel framework extracts endmembers from the convex set of the two bands extracted using Pearson's correlation coefficient so it is named as Pearson's correlation coefficient-based convex geometry for endmember extraction (PCGE). This PCGE framework is different from other commonly used frameworks due to only two bands convex geometry. Due to only two bands, the computation time for the proposed framework is less. The proposed framework is applied to a synthetic dataset and four popular real hyperspectral datasets. In the simulation results, the proposed framework is compared with other popular frameworks based on standard evaluation parameters (spectral angle error, spectral information divergence and normalised cross-correlation). It has been observed from the simulation results that the proposed framework outperforms popular frameworks. It has been also observed that the proposed framework takes less time than others for extracting endmembers.

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“…Furthermore, the authors argued that compared to genetic algorithm and particle swarm optimization algorithm, the Powell method provides a fast-ultimate convergence rate. Khanh et al [121] performed geometry optimization of thermoelectric coolers (TECs) using simulated annealing. The dimension of TECs were optimized using simulated annealing to maximum the rate of refrigeration.…”

Section: Multi-objective Optimizationmentioning

confidence: 99%