This article presents a time-division multiplexing (TDM) multiple-input-multiple-output (MIMO) radar system that differs from existing ones by incorporating a heterodyne self-injection-locking (HSIL) architecture for ...
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This work presents the design, analysis and experimental validation of a novel chip-based 3D-printed UHF RFID sensor designed for leaf moisture detection for smart farming applications. The presented sensor is designe...
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Accurate prediction of the remaining useful life(RUL)is crucial for the design and management of lithium-ion *** various machine learning models offer promising predictions,one critical but often overlooked challenge ...
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Accurate prediction of the remaining useful life(RUL)is crucial for the design and management of lithium-ion *** various machine learning models offer promising predictions,one critical but often overlooked challenge is their demand for considerable run-to-failure data for *** of such training data leads to prohibitive testing efforts as the run-to-failure tests can last for ***,we propose a semi-supervised representation learning method to enhance prediction accuracy by learning from data without RUL *** approach builds on a sophisticated deep neural network that comprises an encoder and three decoder heads to extract time-dependent representation features from short-term battery operating data regardless of the existence of RUL *** approach is validated using three datasets collected from 34 batteries operating under various conditions,encompassing over 19,900 charge and discharge *** method achieves a root mean squared error(RMSE)within 25 cycles,even when only 1/50 of the training dataset is labelled,representing a reduction of 48%compared to the conventional *** also demonstrate the method's robustness with varying numbers of labelled data and different weights assigned to the three decoder *** projection of extracted features in low space reveals that our method effectively learns degradation features from unlabelled *** approach highlights the promise of utilising semi-supervised learning to reduce the data demand for reliability monitoring of energy devices.
Controlled islanding is an important approach to prevent instability in power *** this paper,a novel approach is proposed for power system separation,which consists of two steps:1)Finding multiple islanding scenarios;...
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Controlled islanding is an important approach to prevent instability in power *** this paper,a novel approach is proposed for power system separation,which consists of two steps:1)Finding multiple islanding scenarios;2)Choosing the best option to obtain the most desirable *** the first step,different islanding solutions are determined by a proposed hierarchical clustering *** this algorithm,which is based on a minimum active power flow disruption objective function,the generator coherency constraints are considered in the clustering *** the second step,the best separation scenario is chosen based on an arbitrary objective ***,in this paper,the amount of load shedding and the voltage profile deviation after separation are considered as the final criteria to select the best solution among available *** so doing,the degree of load importance is also taken into *** proposed two-step method is applied on an IEEE 9-bus test system and it is also evaluated on an IEEE 39-bus *** simulation results on the IEEE 39-bus grid and the comparative analysis with a state-of-the-art method confirm that the final islanding solution is more optimized based on the secondary criteria,which have not been addressed in the existing ***,the proposed method is computationally efficient and can be employed in real-scale power grids.
The rapid advancement of electric vehicles (EVs) offers a significant reduction in carbon emissions by replacing fossil fuels with more sustainable energy sources. This article studies wireless charging technology for...
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Guided mode resonance (GMR) structures offer simplicity and have spectral resonance capabilities such as narrowband resonance, high Q-factor, whereas topological surface features offer spatial light control. In this p...
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Typically, optimal power flow (OPF) analysis in a power grid is a nonlinear and non-convex problem. While analyzing OPF in a centralized manner in modern large power systems, nonlinearity generates a computational bur...
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Image inpainting consists of filling holes or missing parts of an image. Inpainting face images with symmetric characteristics is more challenging than inpainting a natural scene. None of the powerful existing models ...
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This article presents an O(N log N) method for numerical solution of Maxwell's equations for dielectric scatterers using a 3D boundary integral equation (BIE) method. The underlying BIE method used is based on a h...
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Brain signal analysis from electroencephalogram(EEG)recordings is the gold standard for diagnosing various neural disorders especially epileptic *** signals are highly chaotic compared to normal brain signals and thus...
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Brain signal analysis from electroencephalogram(EEG)recordings is the gold standard for diagnosing various neural disorders especially epileptic *** signals are highly chaotic compared to normal brain signals and thus can be identified from EEG *** the current seizure detection and classification landscape,most models primarily focus on binary classification—distinguishing between seizure and non-seizure *** effective for basic detection,these models fail to address the nuanced stages of seizures and the intervals between *** identification of per-seizure or interictal stages and the timing between seizures is crucial for an effective seizure alert *** granularity is essential for improving patient-specific interventions and developing proactive seizure management *** study addresses this gap by proposing a novel AI-based approach for seizure stage classification using a Deep Convolutional Neural Network(DCNN).The developed model goes beyond traditional binary classification by categorizing EEG recordings into three distinct classes,thus providing a more detailed analysis of seizure *** enhance the model’s performance,we have optimized the DCNN using two advanced techniques:the Stochastic Gradient Algorithm(SGA)and the evolutionary Genetic Algorithm(GA).These optimization strategies are designed to fine-tune the model’s accuracy and ***,k-fold cross-validation ensures the model’s reliability and generalizability across different data *** and validated on the Bonn EEG data sets,the proposed optimized DCNN model achieved a test accuracy of 93.2%,demonstrating its ability to accurately classify EEG *** summary,the key advancement of the present research lies in addressing the limitations of existing models by providing a more detailed seizure classification system,thus potentially enhancing the effectiveness of real-time seizure prediction and management systems in clinic
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