High reliability and efficiency of microgrids are being threatened by cyber attacks. this paper investigates a Secondary Frequency Control Scheme (SFCS) for an islanded microgrid with Event-Triggering Mechanism (ETM) ...
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Edge computing has transformed technology by enabling seamless connections between IoT devices, but it also introduces significant security challenges. EC is crucial for providing minimal latency processing and reduci...
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ISBN:
(数字)9798331531935
ISBN:
(纸本)9798331531942
Edge computing has transformed technology by enabling seamless connections between IoT devices, but it also introduces significant security challenges. EC is crucial for providing minimal latency processing and reducing the load on centralized servers, ensuring timely and scalable intrusion detection for IoT environments. the findings underscore the potential of combining edge computing, IoT, and deep learning to address evolving security challenges in distributed systems. To mitigate these risks, efficient models for detecting malicious activities are crucial. this study investigates the application of edge computing, Internet of things (IoT), and deep learning to improve the performance of Intrusion Detection Systems (IDS). By leveraging the UNSW-NB15 dataset, the research assesses the capability of deep learning models to identify and mitigate cyber threats effectively. the results demonstrate better performance, showing their suitability for real-time security applications. these findings highlight the potential of intelligent intrusion detection systems powered by robust datasets and deep learning for addressing evolving security threats in edge computing environments.
the main purpose of this article is to design a stealthy false data injection (FDI) attack and corresponding scalable detection mechanism for DC Microgrids. Firstly, a DC microgrid model is established that includes d...
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the structure of the rocket-borne model is inherently complex, with processed images exhibiting high resolution and generating substantial amounts of data and calculations. Achieving robust real-time computing on an e...
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ISBN:
(数字)9798331531881
ISBN:
(纸本)9798331531898
the structure of the rocket-borne model is inherently complex, with processed images exhibiting high resolution and generating substantial amounts of data and calculations. Achieving robust real-time computing on an embedded platform poses significant challenges due to strictly limited resources, power consumption constraints, and size limitations. Our review of rocket-borne applications reveals considerable variability in the design resources of different devices, indicating a need for expanded design approaches. Upon evaluating existing methods, we identified two primary drawbacks. First, certain operators within the high-resolution target detection model are difficult to parallelize, resulting in significant inference delays that hinder the ability to meet task requirements. Although existing methods have been extended, there remains significant potential for performance enhancement in core scheduling for poor acceleration. this paper proposes an optimized architecture for the target detection algorithm accelerator designed for high-resolution images, along with a novel highly parallel data pre-processing and post-processing module implemented on FPGA to address these issues. Compared to the ARM implementation, this architecture demonstrates an improved performance of 24.64x. Furthermore, to ensure flexible application across various rocket launch scenarios, we introduce an optimization structure for convolution, pooling, and fusion operators and a multi-core expansion optimization method. this approach yields a 1.29x improvement in computing unit utilization compared to state-of-the-art multi-core scaling efforts. Finally, we assessed the accelerator architecture across multiple FPGA platforms, achieving a peak processing element utilization rate of 99.71% for a single core and layer. the overall computing efficiency, excluding the first layer, exceeded 90%. the peak computing power for the four cores reached 1638.4 GOPS, and the end-to-end computation time for
this paper presents a distributed structured controller design method in the case of finite number of subsystems with sensor failures. the constraint on the number of sensor failures is represented by a cardinality co...
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the rise of mental health issues as a public health concern necessitates efficient detection methods. Social media is now an integral part of daily life, where users frequently share stress-related experiences. this s...
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In this study, we propose a feature transformation approach to improve the performance of Ensemble Learning Systems. Our method operates on the predictions of base classifiers within an ensemble system, known as meta-...
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Although the idea of affective computing was proposed two decades ago, it is still not used to its full potential. this paper delves into its application in video games by introducing a gameplay persona...
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this paper addresses the distributed load frequency control (LFC) problem for multi-area interconnected power systems (MAIPSs) withthermal and wind power generations, considering both inter-area and intra-area transm...
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In edge computing, optimizing data mobility is crucial for minimizing latency and maximizing bandwidth efficiency by processing data closer to its source. this reduces the need to transfer large amounts of data to cen...
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