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p-Index From 2021 - 2026
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All Journal International Journal of Advanced Science Computing and Engineering Journal of Emerging Science and Engineering International Journal of Renewable Energy Development
Nguyen, Thi Bich Ngoc
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Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Electrical & Electronics Engineering Energy Engineering Environmental Science

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The Potential of Coir Composite in The Production of Spare Parts and Equipment for Vehicles Transporting Goods by Road and Water Nguyen, Dinh Tuyen; Nguyen, Thi Bich Ngoc
International Journal of Advanced Science Computing and Engineering Vol. 2 No. 3 (2020)
Publisher : SOTVI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/ijasce.2.3.107

Abstract

Traditional materials used in the production of industrial equipment parts are now wood and metal. But these materials are gradually scarce, the need to find alternative materials is necessary. On that basis, there have been many studies on fiber reinforced composites in which glass fiber reinforcement is widely applied. However, fiberglass-reinforced composites are difficult to decompose and recycle, leading to serious environmental impacts. To solve this problem, scientists are working to apply bio-fibers to replace glass fibers. In recent years, there have been many studies on coir fiber and its application in the manufacture of coir fiber fortified composites. This article reviews the potential of coir fiber fortified materials applied in the production of industrial products.
Biomass resources and thermal conversion biomass to biofuel for cleaner energy: A review Nguyen, Thi Bich Ngoc; Le, Nguyen Viet Linh
Journal of Emerging Science and Engineering Vol. 1 No. 1 (2023)
Publisher : BIORE Scientia Academy

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61435/jese.2023.2

Abstract

Biofuel is considered as one of the solutions to future energy problems. Unlike fossil fuels, biofuel is a renewable fuel source produced from biomass. Biomass comes from a wide variety of plants and animals and even waste. Therefore, the production of biofuel from biomass is promising not only to solve energy problems but also to solve other social problems. This study will present some of the most potential biomass sources and thermal conversion processes of biomass for biofuel production.
Towards self-diagnostic solar farms: Leveraging EfficientNet and class activation mapping for predictive maintenance Nguyen, Du; Nguyen, Thi Bich Ngoc; Nguyen, Duc Chuan; Chau, Thanh Hieu; Duong, Minh Thai; Dang, Thanh Nam
International Journal of Renewable Energy Development Vol 15, No 2 (2026): March 2026
Publisher : Center of Biomass & Renewable Energy (CBIORE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61435/ijred.2026.62298

Abstract

The high rate of utility photovoltaic (PV) system development has increased the demand for stable, automated, and interpretable fault diagnostic systems that can be utilised in real-world environments. Solar farms with a large size are increasingly making conventional manual inspection methods impractical, and triggering the use of intelligent data-driven solutions. This paper presents a justifiable deep learning model for automated fault classification of solar panels based on the EfficientNet-B2 architecture combined with Gradient-weighted Class Activation Mapping (Grad-CAM). A six-class image dataset made of clean panels and five prevalent fault types is used. The two stages of transfer learning used to train the model include a warm-up phase and selective fine-tuning of upper network layers. Data augmentation is also performed extensively to make it more robust to changing illumination, viewing angles, and environmental noise. The experimental findings reveal consistent convergence and excellent generalization ability, and a high level of classification accuracy of all types of faults, as it achieved high classification accuracy, macro-averaged F1-scores exceeding 0.90 for most fault classes, and a macro-averaged ROC–AUC of approximately 0.981, highlighting the robustness and reliability of the proposed diagnostic model. The suggested structure will provide a scalable, interpretable, and realistic predictive maintenance of solar farms of the next generation with self-diagnostic capabilities.
Co-Authors Chau, Thanh Hieu Dang, Thanh Nam Duong, Minh Thai Le, Nguyen Viet Linh Nguyen, Dinh Tuyen Nguyen, Du Nguyen, Duc Chuan