This paper introduces an artificial intelligence (AI) methodology designed to enhance the output of two-dimensional (2D) electromagnetic imaging systems, specifically tailored for the imaging of conductive objects uti...
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Wireless communication has grown tremendously in recent years, impacting nearly every feature of our lives. The increased exigency for wireless broadband services leads to a huge demand for dynamic spectrum access, su...
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In the process of coping with energy and environmental protection issues,technologies such as energy materials,energy devices,and energy systems have made great *** excellent performance,film capacitors play an increa...
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In the process of coping with energy and environmental protection issues,technologies such as energy materials,energy devices,and energy systems have made great *** excellent performance,film capacitors play an increasingly important role in energyrelated *** the increase of application scenarios and the continuous development of film material technology,it is urgent to establish a better theoretical connection from films to ***,the main components of the capacitor including the film and the positional relationships among them are ***,from the two perspectives of indirect calculation according to the volume and the direct calculation according to the winding process,the equation between the dielectric constant of films and the corresponding capacitance of capacitors is ***,the measurement data and error analysis results of the built test platform prove the accuracy and great potential of the proposed calculation *** addition,error sources,including film thickness uniformity,are ***,the challenges faced by the proposed calculation methods and the paths that can be referenced for future research are summarised and discussed.
Understanding the electrical energy consumption load profile of the rural household is a necessary first step in the economical and reliable sizing of standalone renewable-based power supply systems for rural communit...
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This work proposes a model reference adaptive control based on recursive neural networks. This secondary-level controller corrects the deviations on the voltage and frequency setpoints of a simple primary control in a...
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Battery management systems require mathematical models of the battery cells that they monitor and control. Commonly, equivalent circuit models are used. We would like to be able to determine the parameter values of th...
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Quantifying the common information between random variables is a fundamental problem with a long history in information theory. Traditionally, common information is measured in number of bits and thus such measures ar...
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To better enhance the network service for different user devices in various scenarios, autonomous aerial vehicles (AAVs) are increasingly used as aerial base stations (ABSs). However, optimizing coverage for user devi...
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Learning representations and extracting meaningful patterns from electroencephalogram (EEG) recordings is critical for analyzing cognitive events (e.g., predicting cognitive load). The primary challenges include indiv...
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Learning representations and extracting meaningful patterns from electroencephalogram (EEG) recordings is critical for analyzing cognitive events (e.g., predicting cognitive load). The primary challenges include individual variability, technical noise from unreliable sensor-skin contacts, and rapid temporal changes in the EEG recordings. Given the multi-factorial nature of the problems, deep learning models are natural choices for learning representations from the data. However, the extensive time required for data collection limits the number of subjects (samples) available, which is essential for building robust deep learning models. We introduce an ensemble generative adversarial network (EGAN) to generate high-fidelity EEG data. The EGAN generates multichannel EEG recordings and their spatial-spectral representation. Key design constraints were preserving topological structure and maintaining proper bias-variance trade-offs for building robust models. To ensure the quality of the synthetic data, we visually inspected the data generated by EGAN. We conducted spectral analyses to confirm that the quality and spectral similarity were comparable to EEG recordings. To illustrate the efficacy of data generated by EGAN, we developed a convolutional neural network (CNN) model to predict four levels of cognitive load. We used spatial-spectral representations (Topomap) from three frequency bands (i.e., θ, α, and β). We trained our model on real data and a mixture of original and EGAN-generated data with varying proportions (e.g., 50%, 70%, 100%) of real data for training. We compared the performance of models trained solely on original data to those trained on mixed data. Our results indicate that CNN models trained on EGAN-supplemented data significantly outperformed those trained using only real data across all three frequency bands and all training set proportions. For example, the model trained on the mixed data with 50% real data achieved classification F1 and accura
The next generation of Quantum Internet of Things (QIoT) has the potential to revolutionize various sectors, including smart homes, healthcare, and smart cities, by enabling more sophisticated and interconnected syste...
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