Integrating energy systems with information systems in smart grids offers a promising avenue for combating electricity theft by leveraging real-time data insights. Suspicious activity indicative of theft can be identi...
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Thunderstorm detection based on the Atmospheric Electric Field(AEF)has evolved from time-domain models to space-domain *** is especially important to evaluate and determine the particularly Weather Attribute(WA),which...
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Thunderstorm detection based on the Atmospheric Electric Field(AEF)has evolved from time-domain models to space-domain *** is especially important to evaluate and determine the particularly Weather Attribute(WA),which is directly related to the detection reliability and *** this paper,a strategy is proposed to integrate three currently competitive WA's evaluation ***,a conventional evaluation method based on AEF statistical indicators is *** evaluation approaches include competing AEF-based predicted value intervals,and AEF classification based on fuzzy *** AEF attributes contribute to a more accurate AEF classification to different *** resulting dynamic weighting applied to these attributes improves the classification *** evaluation method is applied to evaluate the WA of a particular AEF,to obtain the corresponding evaluation *** integration in the proposed strategy takes the form of a score *** cumulative score levels correspond to different final WA *** imaging is performed to visualize thunderstorm activities using those AEFs already evaluated to exhibit thunderstorm *** results confirm that the proposed strategy effectively and reliably images thunderstorms,with a 100%accuracy of WA *** is the first study to design an integrated thunderstorm detection strategy from a new perspective of WA evaluation,which provides promising solutions for a more reliable and flexible thunderstorm detection.
The escalating reliance on biometric systems for identity verification underscores the imperative for robust data protection mechanisms. Biometric authentication, leveraging unique biological and behavioral characteri...
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The escalating reliance on biometric systems for identity verification underscores the imperative for robust data protection mechanisms. Biometric authentication, leveraging unique biological and behavioral characteristics, offers unparalleled precision in individual identification. However, the integrity and confidentiality of biometric data remain paramount concerns, given its susceptibility to compromise. This research delineates the development and implementation of an innovative framework for cancellable biometrics, focusing on facial and fingerprint recognition. This study introduces a novel cancellable biometrics framework that integrates graph theory encryption with three-dimensional chaotic logistic mapping. The methodology encompasses a multifaceted approach: initially employing graph theory for the secure and efficient encryption of biometric data, subsequently enhanced by the complexity and unpredictability of three-dimensional chaotic logistic mapping. This dual-layered strategy ensures the robustness of the encryption, thereby significantly elevating the security of biometric data against unauthorized access and potential compromise. Thus, the resulting cancellable biometrics, characterized by the ability to transform biometric data into an adjustable representation, addresses critical challenges in biometric security. It allows for the revocation and reissuance of biometric credentials, thereby safeguarding the original biometric characteristics of individuals. This feature not only enhances user privacy and data security but also introduces a dynamic aspect to biometric authentication, facilitating adaptability across diverse systems and applications. Preliminary evaluations of the proposed framework demonstrate a marked improvement in the security of face and fingerprint recognition systems. Through the application of graph theory encryption, coupled with three-dimensional chaotic logistic mapping, our framework mitigates the risks associated with t
A general model is developed for forward-biased p-n junctions with arbitrary doping concentrations biased at any injection level. It goes beyond the commonly practiced depletion approximation, which is valid only unde...
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Voltage regulation of power transformers is critical to controlling and maintaining system voltage levels in the electrical transmission and distribution network system. It is vital to keep the transmission and distri...
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The research investigates the laboratory-scale setup and functional testing of distance protection techniques for the Transnet traction system. The Transnet network, which powers South Africa's railway system, req...
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Graph Neural Networks (GNNs) have shown significant promise in various domains, such as recommendation systems, bioinformatics, and network analysis. However, the irregularity of graph data poses unique challenges for...
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Communication plays a significant role in an electrical grid that includes Distributed Energy Resources (DERs). Modern control and automation systems used by these systems are predominantly distributed using domain st...
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The Internet of Things (IoT) has enabled the adoption of automated aeroponics equipment, which has transformed our understanding of horticulture. Aeroponics is a method of vegetative production that does not use soil....
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Detecting edges in image processing is an important process in image analysis or enhancement. Many methods detected edge information based on the differences in brightness values. Prewitt, the most widely used edge de...
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