Critical parameters in integration of thermoelectric generators and phase change materials by numerical and Taguchi methods

Rezania, Alireza; Atouei, Saeed Ahmadi; Rosendahl, Lasse

doi:10.1016/j.mtener.2019.100376

Cited by 18 publications

(4 citation statements)

References 23 publications

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“…Compared to Table 1, the results of false positive CP were eliminated using multi-factor Due to the multi-factor analysis being complex, signal-to-noise (S/N) ratios of these four GLCM texture features for WT and CP-resistant ovarian adenocarcinoma cells were calculated and shown in Table 2. The basic concept was according to Taguchi method [19]. In this study, S/N ratio was meaning the degree of the images influenced by the various factors.…”

Section: Resultsmentioning

confidence: 99%

Identification of Human Ovarian Adenocarcinoma Cells with Cisplatin-Resistance by Feature Extraction of Gray Level Co-Occurrence Matrix Using Optical Images

Huang

Lian

et al. 2020

Diagnostics

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Ovarian cancer is the most malignant of all gynecological cancers. A challenge that deteriorates with ovarian adenocarcinoma in neoplastic disease patients has been associated with the chemoresistance of cancer cells. Cisplatin (CP) belongs to the first-line chemotherapeutic agents and it would be beneficial to identify chemoresistance for ovarian adenocarcinoma cells, especially CP-resistance. Gray level co-occurrence matrix (GLCM) was characterized imaging from a numeric matrix and find its texture features. Serous type (OVCAR-4 and A2780), and clear cell type (IGROV1) ovarian carcinoma cell lines with CP-resistance were used to demonstrate GLCM texture feature extraction of images. Cells were cultured with cell density of 6 × 105 in a glass-bottom dish to form a uniform coverage of the glass slide to get the optical images by microscope and DVC camera. CP-resistant cells included OVCAR-4, A2780 and IGROV and had the higher contrast and entropy, lower energy, and homogeneity. Signal to noise ratio was used to evaluate the degree for chemoresistance of cell images based on GLCM texture feature extraction. The difference between wile type and CP-resistant cells was statistically significant in every case (p < 0.001). It is a promising model to achieve a rapid method with a more reliable diagnostic performance for identification of ovarian adenocarcinoma cells with CP-resistance by feature extraction of GLCM in vitro or ex vivo.

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Section: Resultsmentioning

confidence: 99%

Identification of Human Ovarian Adenocarcinoma Cells with Cisplatin-Resistance by Feature Extraction of Gray Level Co-Occurrence Matrix Using Optical Images

Huang

Lian

et al. 2020

Diagnostics

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“…The best experimental results obtained by the calculation of average S/N ratio for each factor and the optimal level of the process parameters were indicated by the level with the largest S/N ratio. We analyzed the S/N ratio using three different performance characteristics: larger-is-better, nominal-is-better, and smaller-is-better [17]. In this study, 'larger-is-better' performance characteristics were preferred.…”

Section: Taguchi Methodsmentioning

confidence: 99%

Using 2D CNN with Taguchi Parametric Optimization for Lung Cancer Recognition from CT Images

2020

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Lung cancer is one of the common causes of cancer deaths. Early detection and treatment of lung cancer is essential. However, the detection of lung cancer in patients produces many false positives. Therefore, increasing the accuracy of the classification of diagnosis or true detection by computed tomography (CT) is a difficult task. Solving this problem using intelligent and automated methods has become a hot research topic in recent years. Hence, we propose a 2D convolutional neural network (2D CNN) with Taguchi parametric optimization for automatically recognizing lung cancer from CT images. In the Taguchi method, 36 experiments and 8 control factors of mixed levels were selected to determine the optimum parameters of the 2D CNN architecture and improve the classification accuracy of lung cancer. The experimental results show that the average classification accuracy of the 2D CNN with Taguchi parameter optimization and the original 2D CNN in lung cancer recognition are 91.97% and 98.83% on the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) dataset, and 94.68% and 99.97% on the International Society for Optics and Photonics with the support of the American Association of Physicists in Medicine (SPIE-AAPM) dataset, respectively. The proposed method is 6.86% and 5.29% more accurate than the original 2D CNN on the two datasets, respectively, proving the superiority of proposed model.

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“…3 Energy storage technologies and energy conversion devices are crucial for addressing the energy crisis and alleviating environmental degradation. Thermoelectric generation (TEG) is an all-solid-state energy conversion technology, 4 which does not involve chemical reactions or fluid mediums. Based on the Seebeck effect, it was found that a TEG utilizes temperature differences to convert thermal energy directly into electrical energy.…”

Section: Introductionmentioning

confidence: 99%

A flexible phase change organohydrogel created using Pickering emulsion technology for thermoelectric conversion and temperature sensing

Yuan

Qian

et al. 2022

J. Mater. Chem. A

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Critical parameters in integration of thermoelectric generators and phase change materials by numerical and Taguchi methods

Cited by 18 publications

References 23 publications

Identification of Human Ovarian Adenocarcinoma Cells with Cisplatin-Resistance by Feature Extraction of Gray Level Co-Occurrence Matrix Using Optical Images

Identification of Human Ovarian Adenocarcinoma Cells with Cisplatin-Resistance by Feature Extraction of Gray Level Co-Occurrence Matrix Using Optical Images

Using 2D CNN with Taguchi Parametric Optimization for Lung Cancer Recognition from CT Images

A flexible phase change organohydrogel created using Pickering emulsion technology for thermoelectric conversion and temperature sensing

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