Network data analytics function (NWDAF), introduced to provision data analytics and machine learning model training in the 5G core network, is expected to be an essential functional entity and play a significant role ...
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This paper presents a comprehensive approach to enhancing autonomous docking maneuvers through machine visual perception and sim-to-real transfer learning. By leveraging relative vectoring techniques, we aim to replic...
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The faults prospective are higher for any BLDC motor, driven under a constant and continuous operating period. The possibility of getting susceptible to winding short-circuit faults and rotor demagnetisation effects, ...
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Object Constraint Language(OCL)is one kind of lightweight formal specification,which is widely used for software verification and validation in NASA and Object Management Group *** OCL provides a simple expressive syn...
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Object Constraint Language(OCL)is one kind of lightweight formal specification,which is widely used for software verification and validation in NASA and Object Management Group *** OCL provides a simple expressive syntax,it is hard for the developers to write correctly due to lacking knowledge of the mathematical foundations of the first-order logic,which is approximately half accurate at the first stage of devel-opment.A deep neural network named DeepOCL is proposed,which takes the unre-stricted natural language as inputs and automatically outputs the best-scored OCL candidates without requiring a domain conceptual model that is compulsively required in existing rule-based generation *** demonstrate the validity of our proposed approach,ablation experiments were conducted on a new sentence-aligned dataset named *** experiments show that the proposed DeepOCL can achieve state of the art for OCL statement generation,scored 74.30 on BLEU,and greatly outperformed experienced developers by 35.19%.The proposed approach is the first deep learning approach to generate the OCL expression from the natural *** can be further developed as a CASE tool for the software industry.
Due to the short life cycles of electronic products, trial run lots of new products are crucial in IC packaging for production verification and engineering adjustments. The processing time of trial run lots may differ...
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3D integration promises to resolve many of the heat and die size limitations of 2D integrated circuits. A critical step in the design of 3D many-cores and MPSOCs is the layout of their 3D network-on-chip (NoC). In thi...
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Induction motors(IMs)typically fail due to the rate of stator *** of the similarity of the thermal images produced by various instances of short-circuit and the minor interclass distinctions between categories,non-des...
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Induction motors(IMs)typically fail due to the rate of stator *** of the similarity of the thermal images produced by various instances of short-circuit and the minor interclass distinctions between categories,non-destructive fault detection is universally perceived as a difficult *** paper adopts the deep learning model combined with feature fusion methods based on the image’s low-level features with higher resolution and more position and details and high-level features with more semantic information to develop a high-accuracy classification-detection approach for the fault diagnosis of *** on the publicly available thermal images(IRT)dataset related to condition monitoring of electrical equipment-IMs,the proposed approach outperforms the highest training accuracy,validation accuracy,and testing accuracy,i.e.,99%,100%,and 94%,respectively,compared with 8 benchmark approaches based on deep learning models and 3 existing approaches in the literature for 11-class IMs *** the training loss,validation loss,and testing loss of the eleven deployed deep learning models meet industry standards.
Monitoring the environment and managing water bodies are crucial for preserving ecosystems and ensuring sustainable resource utilization. This study aims to propose a robust approach for segmenting water bodies by com...
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The phenomenon of atmospheric haze arises due to the scattering of light by minute particles suspended in the atmosphere. This optical effect gives rise to visual degradation in images and videos. The degradation is p...
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The phenomenon of atmospheric haze arises due to the scattering of light by minute particles suspended in the atmosphere. This optical effect gives rise to visual degradation in images and videos. The degradation is primarily influenced by two key factors: atmospheric attenuation and scattered light. Scattered light causes an image to be veiled in a whitish veil, while attenuation diminishes the image inherent contrast. Efforts to enhance image and video quality necessitate the development of dehazing techniques capable of mitigating the adverse impact of haze. This scholarly endeavor presents a comprehensive survey of recent advancements in the domain of dehazing techniques, encompassing both conventional methodologies and those founded on machine learning principles. Traditional dehazing techniques leverage a haze model to deduce a dehazed rendition of an image or frame. In contrast, learning-based techniques employ sophisticated mechanisms such as Convolutional Neural Networks (CNNs) and different deep Generative Adversarial Networks (GANs) to create models that can discern dehazed representations by learning intricate parameters like transmission maps, atmospheric light conditions, or their combined effects. Furthermore, some learning-based approaches facilitate the direct generation of dehazed outputs from hazy inputs by assimilating the non-linear mapping between the two. This review study delves into a comprehensive examination of datasets utilized within learning-based dehazing methodologies, elucidating their characteristics and relevance. Furthermore, a systematic exposition of the merits and demerits inherent in distinct dehazing techniques is presented. The discourse culminates in the synthesis of the primary quandaries and challenges confronted by prevailing dehazing techniques. The assessment of dehazed image and frame quality is facilitated through the application of rigorous evaluation metrics, a discussion of which is incorporated. To provide empiri
A promising solution for operations in the moderate voltage and high-power ranges, the Alternate Arm Multilevel Converter (AAMC) distinguishes itself with a distinctive topology as an AC-DC Voltage Source Converter (V...
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