The Genetic Algorithm: Using Biology to Compute Liquid Crystal Director Configurations

Yang, Shouzhuo; Collings, Peter J.

doi:10.3390/cryst10111041

Cited by 8 publications

(1 citation statement)

References 20 publications

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“…The utilization of AMIS shall be applied to ascertain the most favorable weighting scheme for amalgamating dissimilar solution types acquired from a variety of segmentation methods and architectures. This specific approach will be juxtaposed with alternative decision fusion strategies-notably, the unweighted average model (UWM), along with the adapted differential evolution algorithm (DE) proposed by Kabanikhin [68], and the modified Genetic algorithm (GA) proposed by S. Yang & Collings [69]. By employing this comparative analysis, we aim to discern the efficacy and superiority of AMIS in enhancing the fusion of diverse solutions, thereby contributing to advancements in the field of segmentation methodologies and architectural integration.…”

Section: Parallel-amis-ensemble Model (P-amis-e)mentioning

confidence: 99%

Automated Classification of Agricultural Species through Parallel Artificial Multiple Intelligence System–Ensemble Deep Learning

Sriprateep,

Khonjun,

Golinska-Dawson

et al. 2024

Mathematics

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The classification of certain agricultural species poses a formidable challenge due to their inherent resemblance and the absence of dependable visual discriminators. The accurate identification of these plants holds substantial importance in industries such as cosmetics, pharmaceuticals, and herbal medicine, where the optimization of essential compound yields and product quality is paramount. In response to this challenge, we have devised an automated classification system based on deep learning principles, designed to achieve precision and efficiency in species classification. Our approach leverages a diverse dataset encompassing various cultivars and employs the Parallel Artificial Multiple Intelligence System–Ensemble Deep Learning model (P-AMIS-E). This model integrates ensemble image segmentation techniques, including U-Net and Mask-R-CNN, alongside image augmentation and convolutional neural network (CNN) architectures such as SqueezeNet, ShuffleNetv2 1.0x, MobileNetV3, and InceptionV1. The culmination of these elements results in the P-AMIS-E model, enhanced by an Artificial Multiple Intelligence System (AMIS) for decision fusion, ultimately achieving an impressive accuracy rate of 98.41%. This accuracy notably surpasses the performance of existing methods, such as ResNet-101 and Xception, which attain 93.74% accuracy on the testing dataset. Moreover, when applied to an unseen dataset, the P-AMIS-E model demonstrates a substantial advantage, yielding accuracy rates ranging from 4.45% to 31.16% higher than those of the compared methods. It is worth highlighting that our heterogeneous ensemble approach consistently outperforms both single large models and homogeneous ensemble methods, achieving an average improvement of 13.45%. This paper provides a case study focused on the Centella Asiatica Urban (CAU) cultivar to exemplify the practical application of our approach. By integrating image segmentation, augmentation, and decision fusion, we have significantly enhanced accuracy and efficiency. This research holds theoretical implications for the advancement of deep learning techniques in image classification tasks while also offering practical benefits for industries reliant on precise species identification.

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Section: Parallel-amis-ensemble Model (P-amis-e)mentioning

confidence: 99%

Automated Classification of Agricultural Species through Parallel Artificial Multiple Intelligence System–Ensemble Deep Learning

Sriprateep,

Khonjun,

Golinska-Dawson

et al. 2024

Mathematics

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General application of the genetic algorithm to the estimation of the Liquid Crystal Director in PA-LC devices

Colomina,

Sirvent-Verdú,

Pérez-Bernabeu

et al. 2023

EPJ Web Conf.

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The study of liquid crystal (LC) director distribution is an important area of research in materials science and technology. Parallel-aligned liquid crystal (PA-LC) devices have been extensively studied due to their applications in liquid crystal displays, optical devices, and sensors. Estimating the LC director distribution is a critical step in designing and optimising PA-LC devices. This work shows the results derived from apply-ing novel optimisation techniques to estimate the liquid crystal (LC) director distribution in parallel-aligned liquid crystal (PA-LC) devices. Moreover, the genetic algorithm (GA) has been applied and compared with the minimisation of the Frank-Oseen free energy through the Euler-Lagrange equations. The GA is a stochastic optimisation technique that can effectively explore the search space and find the global optimum. Overall, this study’s results demonstrate the GA’s effectiveness in estimating the LC director distribution in PA-LC devices. This approach can improve the performance and design of liquid crystal displays, optical devices, and sensors. Furthermore, it can be extended to other fields where the optimisation of complex systems is required. Further research is needed to optimise the GA parameters and to explore its potential in other applications.

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Comparative analysis of the acoustical impedance of a set of traditional trombones versus P-Bone

Rico¹,

Guarinos

Gallego

et al. 2023

Eur. J. Phys.

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In this work, the impedance response of a set of trombones is analysed in order to find correlations between their sound and timbre and the main characteristics of the impedance curves. For this specific application, an impedance tube has been designed and manufactured to measure the different trombones' impedance curves. A plastic trombone (P-Bone) has been considered in the experiments to determine the importance of the material used to fabricate the instrument. In order to parametrise the trombones considered here, the instrument profile has been theoretically studied using a conical approach. More precisely, the tube and bell cone of the trombone has been discretised along its length in a set of small conical segments. Also, the influence of the mouthpiece is considered in the analytical formalism, and all these factors are optimised by employing a genetic algorithm (GA) to find the physical parameters that fit the experimental impedance curves. The experimental results are also compared with the opinions obtained from a survey about the subjective assessment of the same music passage recorded with the different instruments considered here. The results show that the impedance curve and some of the fitted parameters obtained correlate with the assessment of trombones in the blind survey. Moreover, the quality of the impedance tube here designed reveals that it is an excellent option to find leakages or dysfunction in this instrument.

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The Genetic Algorithm: Using Biology to Compute Liquid Crystal Director Configurations

Cited by 8 publications

References 20 publications

Automated Classification of Agricultural Species through Parallel Artificial Multiple Intelligence System–Ensemble Deep Learning

Automated Classification of Agricultural Species through Parallel Artificial Multiple Intelligence System–Ensemble Deep Learning

General application of the genetic algorithm to the estimation of the Liquid Crystal Director in PA-LC devices

Comparative analysis of the acoustical impedance of a set of traditional trombones versus P-Bone

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