Electric vehicles(EVs)are becoming more popular worldwide due to environmental concerns,fuel security,and price *** performance of EVs relies on the energy stored in their batteries,which can be charged using either A...
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Electric vehicles(EVs)are becoming more popular worldwide due to environmental concerns,fuel security,and price *** performance of EVs relies on the energy stored in their batteries,which can be charged using either AC(slow)or DC(fast)***,EVs can also be used as mobile power storage devices using vehicle-to-grid(V2G)*** electronic converters(PECs)have a constructive role in EV applications,both in charging EVs and in ***,this paper comprehensively investigates the state of the art of EV charging topologies and PEC solutions for EV *** examines PECs from the point of view of their classifications,configurations,control approaches,and future research prospects and their impacts on power *** can be classified into various topologies:DC-DC converters,AC-DC converters,DC-AC converters,and AC-AC *** address the limitations of traditional DC-DC converters such as switching losses,size,and high-electromagnetic interference(EMI),resonant converters and multiport converters are being used in high-voltage EV ***,power-train converters have been modified for high-efficiency and reliability in EV *** paper offers an overview of charging topologies,PECs,challenges with solutions,and future trends in the field of the EV charging station applications.
Automated detection of plant diseases is crucial as it simplifies the task of monitoring large farms and identifies diseases at their early stages to mitigate further plant degradation. Besides the decline in plant he...
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Utilizing interpolation techniques (IT) within reversible data hiding (RDH) algorithms presents the advantage of a substantial embedding capacity. Nevertheless, prevalent algorithms often straightforwardly embed confi...
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This paper presents a lightweight and accurate convolution neural network (CNN) based on encoder in vision transformer structure, which uses multigroup convolution rather than multilayer perceptron and multiheaded sel...
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Object detection has become an increasingly important application for mobile devices. However, state-of-the-art object detection relies heavily on deep neural network, which is often burdensome to compute on mobile de...
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A sustainably governed water-ecosystem at village-level is crucial for the community's well-being. It requires understanding natures’ limits to store and yield water and balance it with the stakeholders’ needs, ...
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Positioning clothing parts ($\mathcal {S} \mathbf{s}$) such as sleeves and collars has been in the realm of manual task that had to be done meticulously in order to prevent unnecessary tanglements during the simulatio...
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Positioning clothing parts ($\mathcal {S} \mathbf{s}$) such as sleeves and collars has been in the realm of manual task that had to be done meticulously in order to prevent unnecessary tanglements during the simulation. This paper proposes an optimization-based method to computerize the above $\mathcal {S}$-positioning task. For that, we embed each $\mathcal {S}$ to an abstracting cylinder $\mathcal {C}$ such that $\mathcal {S}$-positioning can be done by adjusting only 3$\sim$4 DOFs (e.g., translating/rotating $\mathcal {C}$ or adjusting its radius) instead of per-vertex-full-DOFs. Then, we formulate an objective function E by scoring undesirableness of $\mathcal {S}$'$\mathbf{s}$ position (e.g., $\mathcal {S}$ penetrating the body, $\mathcal {S}$ making cloth-to-cloth intersection). In organizing E into the loop of the Newton's method, the main challenge was to calculate the symbolic gradient and hessian, for which this paper makes several novel contributions. The resultant $\mathcal {S}$-positioning method works quite successfully;$\mathcal {S}$*$\mathbf{s}$ (the output of the S-positioning method ) are tanglement-free thus running the simulator to that configuration produces acceptable draping quickly;Experiments show that, in obtaining acceptable draping, the proposed method produces about ×9.7 speed up compared to when not using it. IEEE
The increasing dependence on smartphones with advanced sensors has highlighted the imperative of precise transportation mode classification, pivotal for domains like health monitoring and urban planning. This research...
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The increasing dependence on smartphones with advanced sensors has highlighted the imperative of precise transportation mode classification, pivotal for domains like health monitoring and urban planning. This research is motivated by the pressing demand to enhance transportation mode classification, leveraging the potential of smartphone sensors, notably the accelerometer, magnetometer, and gyroscope. In response to this challenge, we present a novel automated classification model rooted in deep reinforcement learning. Our model stands out for its innovative approach of harnessing enhanced features through artificial neural networks (ANNs) and visualizing the classification task as a structured series of decision-making events. Our model adopts an improved differential evolution (DE) algorithm for initializing weights, coupled with a specialized agent-environment relationship. Every correct classification earns the agent a reward, with additional emphasis on the accurate categorization of less frequent modes through a distinct reward strategy. The Upper Confidence Bound (UCB) technique is used for action selection, promoting deep-seated knowledge, and minimizing reliance on chance. A notable innovation in our work is the introduction of a cluster-centric mutation operation within the DE algorithm. This operation strategically identifies optimal clusters in the current DE population and forges potential solutions using a pioneering update mechanism. When assessed on the extensive HTC dataset, which includes 8311 hours of data gathered from 224 participants over two years. Noteworthy results spotlight an accuracy of 0.88±0.03 and an F-measure of 0.87±0.02, underscoring the efficacy of our approach for large-scale transportation mode classification tasks. This work introduces an innovative strategy in the realm of transportation mode classification, emphasizing both precision and reliability, addressing the pressing need for enhanced classification mechanisms in an eve
The adversarial wiretap channel of type II (AWTC-II) is a communication channel that can a) read a fraction of the transmitted symbols up to a given bound and b) induce both errors and erasures in a fraction of the sy...
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In an era dominated by information dissemination through various channels like newspapers,social media,radio,and television,the surge in content production,especially on social platforms,has amplified the challenge of...
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In an era dominated by information dissemination through various channels like newspapers,social media,radio,and television,the surge in content production,especially on social platforms,has amplified the challenge of distinguishing between truthful and deceptive *** news,a prevalent issue,particularly on social media,complicates the assessment of news *** pervasive spread of fake news not only misleads the public but also erodes trust in legitimate news sources,creating confusion and polarizing *** the volume of information grows,individuals increasingly struggle to discern credible content from false narratives,leading to widespread misinformation and potentially harmful *** numerous methodologies proposed for fake news detection,including knowledge-based,language-based,and machine-learning approaches,their efficacy often diminishes when confronted with high-dimensional datasets and data riddled with noise or *** study addresses this challenge by evaluating the synergistic benefits of combining feature extraction and feature selection techniques in fake news *** employ multiple feature extraction methods,including Count Vectorizer,Bag of Words,Global Vectors for Word Representation(GloVe),Word to Vector(Word2Vec),and Term Frequency-Inverse Document Frequency(TF-IDF),alongside feature selection techniques such as Information Gain,Chi-Square,Principal Component Analysis(PCA),and Document *** comprehensive approach enhances the model’s ability to identify and analyze relevant features,leading to more accurate and effective fake news *** findings highlight the importance of a multi-faceted approach,offering a significant improvement in model accuracy and ***,the study emphasizes the adaptability of the proposed ensemble model across diverse datasets,reinforcing its potential for broader application in real-world *** introduce a pioneering ensemble
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