Amidst rising distributed generation and its potential role in grid management, this article presents a new realistic approach to determine the operational space and flexibility potential of an unbalanced active distr...
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Convolutional neural network (CNN) is widely used for analyzing time series data as it allows for the rapid learning of inherent characteristics in the series with a small number of parameters through filter operation...
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The time-sensitive Internet of Things (IoT) applications within 5G and edge computing environments presents unique challenges in network resource management. Current systems struggle with efficiently managing the high...
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The smart and autonomous learning and recognition of human activities will certainly lead to an incredible progression for several applications and services in public healthcare, education, entertainment, safety and s...
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Accurate and timely diagnosis of pulmonary diseases is critical in the field of medical imaging. While deep learning models have shown promise in this regard, the current methods for developing such models often requi...
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Accurate and timely diagnosis of pulmonary diseases is critical in the field of medical imaging. While deep learning models have shown promise in this regard, the current methods for developing such models often require extensive computing resources and complex procedures, rendering them impractical. This study focuses on the development of a lightweight deep-learning model for the detection of pulmonary diseases. Leveraging the benefits of knowledge distillation (KD) and the integration of the ConvMixer block, we propose a novel lightweight student model based on the MobileNet architecture. The methodology begins with training multiple teacher model candidates to identify the most suitable teacher model. Subsequently, KD is employed, utilizing the insights of this robust teacher model to enhance the performance of the student model. The objective is to reduce the student model's parameter size and computational complexity while preserving its diagnostic accuracy. We perform an in-depth analysis of our proposed model's performance compared to various well-established pre-trained student models, including MobileNetV2, ResNet50, InceptionV3, Xception, and NasNetMobile. Through extensive experimentation and evaluation across diverse datasets, including chest X-rays of different pulmonary diseases such as pneumonia, COVID-19, tuberculosis, and pneumothorax, we demonstrate the robustness and effectiveness of our proposed model in diagnosing various chest infections. Our model showcases superior performance, achieving an impressive classification accuracy of 97.92%. We emphasize the significant reduction in model complexity, with 0.63 million parameters, allowing for efficient inference and rapid prediction times, rendering it ideal for resource-constrained environments. Outperforming various pre-trained student models in terms of overall performance and computation cost, our findings underscore the effectiveness of the proposed KD strategy and the integration of the Conv
In contrast to traditional utility monitoring and operational tools, advanced distribution management systems (ADMS) provide the advanced operational features to monitor, secure and operate the distribution system in ...
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In contrast to traditional utility monitoring and operational tools, advanced distribution management systems (ADMS) provide the advanced operational features to monitor, secure and operate the distribution system in an integrated man- ner. Large-scale adoption of ADMS at utilities is in the early stage since advanced applications within ADMS environments are still evolving. The topology estimation module is one of the important and challenging ADMS application with enhanced automation. For accurate topology estimation, the topology estimator should capture both uncertainties due to load and PV injection in node measurement data. Load/PV estimation module can provide individual/disaggregated load and PV estimates and supports accurate network estimates. This paper provides a proof-of- the concept for the integration of advanced applications (Load/PV estimation and topology estimation) within an industrial ADMS environment using utility feeder data. IEEE
This paper presents a novel millimeter-wave (mmWave) antenna design for 5G applications, featuring a parasitic elliptical patch antenna with beam-switching capabilities and coaxial feeding. The antenna was initially d...
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This paper investigates the input-to-state stabilization of discrete-time Markov jump systems. A quantized control scheme that includes coding and decoding procedures is proposed. The relationship between the error in...
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Accurate estimation of the state of health (SoH) of lithium-ion battery (LIB) application systems is a critical concern in the domain of electric vehicles (EVs). Precise SoH holds significance due to its direct impact...
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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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