Manufacturers must be able to figure out the most suitable technique capable of generating rapid and accurate performance when developing a precise modelling approach for the development of an efficient machining proc...
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Optimising routing in Wireless Sensor Networks (WSNs) is crucial for enhancing their performance, with a focus on energy conservation, reliable data transmission, and network stability. This research presents a unique...
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ISBN:
(数字)9798331522100
ISBN:
(纸本)9798331522117
Optimising routing in Wireless Sensor Networks (WSNs) is crucial for enhancing their performance, with a focus on energy conservation, reliable data transmission, and network stability. This research presents a unique approach to WSN routing optimization using Chernobyl Disaster Optimization (CDO), a nature-inspired algorithm modeled on the rehabilitation and ecological restoration efforts following the Chernobyl incident. CDO simulates the process of resource redistribution and recovery after a catastrophic event to identify optimal routing paths in WSNs. The study assesses the CDO algorithm's effectiveness against the traditional Gravitational Search Algorithm (GSA) using key performance indicators such as energy usage, jitter, average end-to-end delay (EED), packet loss, and packet delivery fraction (PFD). Simulation results show that the CDO-based routing optimisation method consistently surpasses GSA, delivering improved end-to-end delay, decreased jitter, reduced packet loss, and enhanced energy efficiency. This investigation highlights CDO's potential as a viable alternative to conventional optimisation techniques, offering a more efficient and adaptable solution for WSN routing optimisation, particularly in dynamic, large-scale scenarios.
Deep convolutional neural networks (CNNs) have facilitated remarkable success in recognizing various food items and agricultural stress. A decent performance boost has been witnessed in solving the agro-food challenge...
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Pumpkin leaf diseases are significant threats to agricultural productivity, requiring a timely and precise diagnosis for effective management. Traditional identification methods are laborious and susceptible to human ...
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Deep learning(DL) systems exhibit multiple behavioral characteristics such as correctness, robustness, and fairness. Ensuring that these behavioral characteristics function properly is crucial for maintaining the accu...
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Generative Adversarial Network (GAN) inversion have demonstrated excellent performance in image inpainting that aims to restore lost or damaged image texture using its unmasked content. Previous GAN inversion-based me...
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Generative Adversarial Network (GAN) inversion have demonstrated excellent performance in image inpainting that aims to restore lost or damaged image texture using its unmasked content. Previous GAN inversion-based methods usually utilize well-trained GAN models as effective priors to generate the realistic regions for missing holes. Despite excellence, they ignore a hard constraint that the unmasked regions in the input and the output should be the same, resulting in a gap between GAN inversion and image inpainting and thus degrading the performance. Besides, existing GAN inversion approaches often consider a single modality of the input image, neglecting other auxiliary cues in images for improvements. Addressing these problems, we propose a novel GAN inversion approach, dubbed MMInvertFill, for image inpainting. MMInvertFill contains primarily a multimodal guided encoder with a pre-modulation and a GAN generator with F&W+ latent space. Specifically, the multimodal encoder aims to enhance the multi-scale structures with additional semantic segmentation edge texture modalities through a gated mask-aware attention module. Afterwards, a pre-modulation is presented to encode these structures into style vectors. To mitigate issues of conspicuous color discrepancy and semantic inconsistency, we introduce the F&W+ latent space to bridge the gap between GAN inversion and image inpainting. Furthermore, in order to reconstruct faithful and photorealistic images, we devise a simple yet effective Soft-update Mean Latent module to capture more diversified in-domain patterns for generating high-fidelity textures for massive corruptions. In our extensive experiments on six challenging datasets, including CelebA-HQ, Places2, OST, CityScapes, MetFaces and Scenery, we show that our MMInvertFill qualitatively and quantitatively outperforms other state-of-the-arts and it supports the completion of out-of-domain images effectively. Our project webpage including code and results will b
Remote sensing (RS) image interpretation includes multi-label picture classification, a key problem. The complicated and diverse character of many remote sensing scenes, which are produced by the spatial combination a...
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The rapid evolution in power electronics have brought significant attention to the optimization of power converters, which are essential for efficient energy conversion and management. Traditional techniques for optim...
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