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  • Chong, Kil To  (4)
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  • 1
    Online Resource
    Online Resource
    A Study of Neural Network Framework for Power Generation Prediction of a Solar Power Plant
    MDPI AG ; 2022
    In:  Energies Vol. 15, No. 22 ( 2022-11-16), p. 8582-
    Details
    In: Energies, MDPI AG, Vol. 15, No. 22 ( 2022-11-16), p. 8582-
    Abstract: In the process of creating a prediction model using artificial intelligence by utilizing a deep neural network, it is of utmost significance to know the amount of insolation that has an absolute effect on the quantity of power generation of a solar cell. To predict the power generation quantity of a solar power plant, a deep neural network requires previously accumulated power generation data of a power plant. However, if there is no equipment to measure solar radiation in the internal facilities of the power plant and if there is no record of the existence of solar radiation in the past data, it is inevitable to obtain the solar radiation information of the nearest point in an effort to accurately predict the quantity of power generation. The site conditions of the power plant are affected by the geographical topography which acts as a stumbling block while anticipating favorable weather conditions. In this paper, we introduce a method to solve these problems and predict the quantity of power generation by modeling the power generation characteristics of a power plant using a neural network. he average of the error between the actual quantity and the predicted quantity for the same period was 1.99, that represents the predictive model is efficient to be used in real-time.
    Type of Medium: Online Resource
    ISSN: 1996-1073
    URL: Article
    DOI: 10.3390/en15228582
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2437446-5
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  • 2
    Online Resource
    Online Resource
    Analysis of Bending Degree of Basilar Artery Using Image Processing
    MDPI AG ; 2022
    In:  Diagnostics Vol. 12, No. 9 ( 2022-08-26), p. 2066-
    Details
    In: Diagnostics, MDPI AG, Vol. 12, No. 9 ( 2022-08-26), p. 2066-
    Abstract: The basilar artery, which is the core of the posterior circulation, supplies blood to the brainstem and cerebellum. When basilar artery blood circulation is impaired, several symptoms can occur. In addition, the bending of the basilar artery causes stroke and infarction. Therefore, an image processing method for analyzing the bending degree of the basilar artery is introduced herein. To analyze the bending degree, the coordinates of the center points of the basilar artery are extracted using image processing techniques such as Canny edge detection, the contour technique, and the distance conversion technique. An image reconstructed using the three-dimensional scatter plot function in MATLAB and vector plots is used to calculate the vectors for each central point of the basilar artery. Meanwhile, the angle of each central point is calculated by selecting the first central point where the basilar artery begins, the central point with the greatest bending degree, and the central point at which the branching ends. The greater the bending degree of the basilar artery is, the larger the magnitude of the vector in the bending direction. The obtained results are verified by experts in the field, and the proposed algorithm demonstrates good performance.
    Type of Medium: Online Resource
    ISSN: 2075-4418
    URL: Article
    DOI: 10.3390/diagnostics12092066
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2662336-5
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  • 3
    Online Resource
    Online Resource
    BrainSeg-Net: Brain Tumor MR Image Segmentation via Enhanced Encoder–Decoder Network
    MDPI AG ; 2021
    In:  Diagnostics Vol. 11, No. 2 ( 2021-01-25), p. 169-
    Details
    In: Diagnostics, MDPI AG, Vol. 11, No. 2 ( 2021-01-25), p. 169-
    Abstract: Efficient segmentation of Magnetic Resonance (MR) brain tumor images is of the utmost value for the diagnosis of tumor region. In recent years, advancement in the field of neural networks has been used to refine the segmentation performance of brain tumor sub-regions. The brain tumor segmentation has proven to be a complicated task even for neural networks, due to the small-scale tumor regions. These small-scale tumor regions are unable to be identified, the reason being their tiny size and the huge difference between area occupancy by different tumor classes. In previous state-of-the-art neural network models, the biggest problem was that the location information along with spatial details gets lost in deeper layers. To address these problems, we have proposed an encoder–decoder based model named BrainSeg-Net. The Feature Enhancer (FE) block is incorporated into the BrainSeg-Net architecture which extracts the middle-level features from low-level features from the shallow layers and shares them with the dense layers. This feature aggregation helps to achieve better performance of tumor identification. To address the problem associated with imbalance class, we have used a custom-designed loss function. For evaluation of BrainSeg-Net architecture, three benchmark datasets are utilized: BraTS2017, BraTS 2018, and BraTS 2019. Segmentation of Enhancing Core (EC), Whole Tumor (WT), and Tumor Core (TC) is carried out. The proposed architecture have exhibited good improvement when compared with existing baseline and state-of-the-art techniques. The MR brain tumor segmentation by BrainSeg-Net uses enhanced location and spatial features, which performs better than the existing plethora of brain MR image segmentation approaches.
    Type of Medium: Online Resource
    ISSN: 2075-4418
    URL: Article
    DOI: 10.3390/diagnostics11020169
    Language: English
    Publisher: MDPI AG
    Publication Date: 2021
    detail.hit.zdb_id: 2662336-5
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  • 4
    Online Resource
    Online Resource
    DNC4mC-Deep: Identification and Analysis of DNA N4-Methylcytosine Sites Based on Different Encoding Schemes By Using Deep Learning
    MDPI AG ; 2020
    In:  Cells Vol. 9, No. 8 ( 2020-07-22), p. 1756-
    Details
    In: Cells, MDPI AG, Vol. 9, No. 8 ( 2020-07-22), p. 1756-
    Abstract: N4-methylcytosine as one kind of modification of DNA has a critical role which alters genetic performance such as protein interactions, conformation, stability in DNA as well as the regulation of gene expression same cell developmental and genomic imprinting. Some different 4mC site identifiers have been proposed for various species. Herein, we proposed a computational model, DNC4mC-Deep, including six encoding techniques plus a deep learning model to predict 4mC sites in the genome of F. vesca, R. chinensis, and Cross-species dataset. It was demonstrated by the 10-fold cross-validation test to get superior performance. The DNC4mC-Deep obtained 0.829 and 0.929 of MCC on F. vesca and R. chinensis training dataset, respectively, and 0.814 on cross-species. This means the proposed method outperforms the state-of-the-art predictors at least 0.284 and 0.265 on F. vesca and R. chinensis training dataset in turn. Furthermore, the DNC4mC-Deep achieved 0.635 and 0.565 of MCC on F. vesca and R. chinensis independent dataset, respectively, and 0.562 on cross-species which shows it can achieve the best performance to predict 4mC sites as compared to the state-of-the-art predictor.
    Type of Medium: Online Resource
    ISSN: 2073-4409
    URL: Article
    DOI: 10.3390/cells9081756
    Language: English
    Publisher: MDPI AG
    Publication Date: 2020
    detail.hit.zdb_id: 2661518-6
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