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Geochemistry & Geophysics
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(2022)
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Summary: This paper proposes an automatic defect detection mechanism using texture feature analysis and supervised machine learning method for classifying failures in photovoltaic modules. The proposed technique combines infrared thermography, fuzzy-based edge detection, and gray level co-occurrence matrix to extract texture features and uses a support vector machine classifier for classification. The results demonstrate high accuracy and early failure detection advantages of the developed algorithm.
INTERNATIONAL JOURNAL OF SYSTEM ASSURANCE ENGINEERING AND MANAGEMENT
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Elizabeth Cook et al.
Summary: With the rapid increase in residential photovoltaic (PV) system installations, it is important for utilities to identify the locations of these components and manage the power flow in distribution grids. This study proposes a machine learning approach using labeled data to model the solar detection problem. The proposed methods based on autoencoders have been tested and validated on synthetic and real-world data, showing robust detection accuracy.
IEEE TRANSACTIONS ON POWER SYSTEMS
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Construction & Building Technology
Asghar Aryanfar et al.
Summary: This paper discusses the heterogeneous microstructure of fiber-reinforced concrete composed of aggregates, fibers, and a matrix, and the development of a computational measure for predicting fiber efficacy based on energy absorption within crack initiation areas. The coupling of stress localization at crack tips and random crack distribution leads to spatial strain fields. The use of percolation-based crack quantification in the early stages is particularly beneficial for effective fiber utilization during cracking progression, as verified through numerical simulations and experimental data.
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Summary: This paper describes three computationally proficient MPC algorithms for Wiener models based on LSSVM, and introduces a pruning algorithm that can effectively remove the majority of support vectors without compromising modeling accuracy. The proposed MPC algorithms demonstrate good control performance and computational efficiency, showcasing the advantages of the methods proposed in this study.
TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL
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Engineering, Electrical & Electronic
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Summary: The proposed method utilizes Wiener filtering and CFAR as the primary detector to address the issue of accurate depth estimation for unknown targets in the time gating method. By combining the information obtained from the primary detection, a time gating method is constructed, resulting in improved detection of shallowly buried objects.
IET SIGNAL PROCESSING
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Summary: In this study, a new image transmission method based on visible light communication (VLC) was proposed and tested against traditional methods in underground facilities. Photodiodes (PDs) were compared to solar panels (SPs) as image signal detectors, with PDs showing better image detection and recovery capabilities. Additionally, the influence of different RGB optical components on VLC-based image transmission was investigated, revealing that red optical components increased image intensity and energy, while blue components decreased the low-frequency part of the image.
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Materials Science, Multidisciplinary
Jin Zhao et al.
Summary: The Ba3Y(BO3)(3): Ce3+, Nd3+ phosphor with NIR emission can improve the efficiency of silicon solar cells by transferring sunlight to the optimal response range of the cells. Ce3+ acts as an efficient sensitizer with broad absorption cross-section, making it a promising application for solar spectral conversion in silicon-based solar cells.
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Green & Sustainable Science & Technology
Lawrence Pratt et al.
Summary: EL images allow for defect detection in PV modules that are invisible to the naked eye, and modern deep learning techniques can be used to extract useful information from these images. This can improve efficiency and reliability of PV modules by identifying process issues and reducing the number of faulty modules installed. The study focuses on developing a deep learning method for computer vision independent of equipment, module design, and image quality.
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Neurosciences
Minho Shin et al.
Summary: Recent advances in neuroimaging have led to varied findings on the human reasoning process, potentially due to the intricate nature of reasoning involving multiple cognitive processes and brain regions. The development of BMACS provides a fast and accurate way to infer spatial patterns of cognitive processes, revealing a common activation pattern in the multiple-demand system for inductive and deductive reasoning.
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Computer Science, Information Systems
Xiong Zhang et al.
Summary: The study proposed a multi-feature region proposal fusion network structure to detect surface defects on solar cell surfaces, reducing information redundancy and improving network accuracy through a multiscale region proposal selection strategy. The method outperforms others by achieving a higher detection accuracy.
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Computer Science, Information Systems
Nengquan Duan et al.
Summary: A new fault diagnosis method for wind power gearbox, named MSGloG, is proposed in this paper to extract the characteristics of composite faults, successfully detecting faults of bearing outer ring and rolling elements. The method overcomes the difficulties caused by noise and effectively improves the signal-to-noise ratio of vibration signals.
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MULTIMEDIA TOOLS AND APPLICATIONS
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Review
Green & Sustainable Science & Technology
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IET RENEWABLE POWER GENERATION
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INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY
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Guilherme F. Roberto et al.
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