Review Article
Application of 2-D Molybdenum Disulfide in the Field of Photoelectric Detection
Xiaochen Sun,
Jiaying Jian*,
Zengyun Jian
Issue:
Volume 9, Issue 4, August 2024
Pages:
53-62
Received:
22 January 2024
Accepted:
7 August 2024
Published:
27 August 2024
Abstract: The research of photodetectors is rooted in the principle of photoelectric effect, which has become indispensable in human society. Photodetectors convert light signals into electrical signals and represent a crucial subdivision within modern optoelectronic technology. They play significant roles in optical communications, remote sensing, biomedical applications, industrial automation, and more. Two-dimensional MoS2 has attracted considerable attention in optoelectronics due to its unique structure and performance characteristics. The research methods for photodetectors primarily include: Material Selection: Using semiconductor materials such as silicon, germanium, gallium arsenide, and indium arsenide. Silicon, in particular, is widely applied in optical communications, computer networks, medical diagnostics, and more. Technological Improvements: This involves high sensitivity detection techniques, automatic alignment technologies, and composite integration techniques to enhance the performance and application domains of photodetectors. Application Development: Exploring new applications of photodetectors in optical communications, medical imaging, security monitoring, etc., and improving their reliability and efficiency in practical applications.Research on photodetectors not only enhances their efficiency and performance in fields like communication, medicine, and security monitoring but also lays a solid foundation for future technological innovation and application expansion. With continuous advancements in technology, photodetectors are demonstrating vast application prospects and substantial market potential. Finally, the prospects and challenges associated with photodetectors in practical applications are also discussed.
Abstract: The research of photodetectors is rooted in the principle of photoelectric effect, which has become indispensable in human society. Photodetectors convert light signals into electrical signals and represent a crucial subdivision within modern optoelectronic technology. They play significant roles in optical communications, remote sensing, biomedica...
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Research Article
Development of a Device for Primary Purification of Mung Bean Grain from Pods and Optimization of Its Main Design Dimensions and Operating Modes
Achilov Elyor Temirovich*
Issue:
Volume 9, Issue 4, August 2024
Pages:
63-68
Received:
24 July 2024
Accepted:
13 August 2024
Published:
27 August 2024
Abstract: It is important to create a technically and technologically improved device that reduces the damage to the mung during the process of removing the pods from the currently cultivated mung grain. Therefore, it is necessary to develop the design of the device, to justify its parameters and operating modes, for the complete separation and preliminary cleaning of mung beans without damaging them. The article presents the results of optimization of the main design dimensions and operating modes of the device for separating mung bean from the pods. The results of experimental studies showed that the proposed device has a high degree of completeness in separating mung bean from its pods and a low level of damage, low grain damage and high grain purity, which can be ensured with a sag length of 170 mm, a shaft oscillation number of 300 min-1, the amplitude of the shaft is 11.5 mm, its angle of inclination is 12 degrees. The design of the device for preliminary cleaning of mung grain by separating it from pods has been developed. The scientific significance of the research results is based on the structural dimensions and operating modes, as well as the resulting analytical connections and mathematical models, to ensure the quality of initial cleaning, to separate mung beans from pods completely and without damage, using less energy and resources. can be used to justify the parameters of similar devices. The developed device reduces the consumption of energy and labor during the initial cleaning of mung grain from pods, as well as the loss of grain.
Abstract: It is important to create a technically and technologically improved device that reduces the damage to the mung during the process of removing the pods from the currently cultivated mung grain. Therefore, it is necessary to develop the design of the device, to justify its parameters and operating modes, for the complete separation and preliminary c...
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Research Article
Automatic Road Crack Detection Using Convolutional Neural Network Based on Semi-Supervised Learning
Issue:
Volume 9, Issue 4, August 2024
Pages:
69-82
Received:
31 July 2024
Accepted:
21 August 2024
Published:
30 August 2024
Abstract: Crack detection in pavements is a critical task for infrastructure maintenance, but it often requires extensive manual labeling of training samples, which is both time-consuming and labor-intensive. To address this challenge, this paper proposes a semi-supervised learning approach based on a DenseNet classification model to detect pavement cracks more efficiently. The primary objective is to leverage a small set of labeled samples to improve the model's performance by incorporating a large number of unlabeled samples through semi-supervised learning. This method enhances the DenseNet model's ability to generalize by iteratively learning from new unlabeled datasets. As a result, the proposed approach not only reduces the need for extensive manual labeling but also mitigates issues related to label inconsistency and errors in the original labels. The experimental results demonstrate that the semi-supervised DenseNet model achieves a prediction precision of 96.77% and a recall of 94.17%, with an F1 score of 95.45% and an Intersectidn over Union (IoU) of 91.30%. These metrics highlight the model's high accuracy and effectiveness in crack detection. The proposed method not only improves label quality and model performance but also offers practical value for engineering applications in the field of pavement maintenance, making it a valuable tool for infrastructure management.
Abstract: Crack detection in pavements is a critical task for infrastructure maintenance, but it often requires extensive manual labeling of training samples, which is both time-consuming and labor-intensive. To address this challenge, this paper proposes a semi-supervised learning approach based on a DenseNet classification model to detect pavement cracks m...
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