Sentiment analysis aims to extract people's underlying attitudes, opinions and thoughts toward various topics, products and services. Sentiment analysis is a popularly adopted technique that involves applications ...
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Data-free Class-incremental Learning (CIL) is a challenging problem because rehearsing data from previous phases is strictly prohibited, causing catastrophic forgetting of Deep Neural Networks (DNNs). In this paper, w...
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This paper uses the neural network to realize a photovoltaic (PV) system's maximum power point tracking. After Bayesian optimization of hyperparameters, the model can accurately determine the maximum power point v...
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Several advancements were made in wireless charging for e-transportation however, the knowledge acquired in the context of wireless charging of electric cars are not sufficient to apply to electric bicycle (e-bikes). ...
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As the application of smart contracts in blockchain technology becomes increasingly widespread, their security issues have emerged as a focal point of both research and practice. Although symbolic execution technology...
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The high penetration of electronic-coupled distributed energy sources, especially photovoltaic systems into distribution networks has created new challenges for traditional non-directional protection systems. Fault cu...
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Bias is a common problem in both human cognition and machine learning tasks. However, machines struggle more than humans with bias reduction, mainly because most algorithms rely on the assumption that the training dat...
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Multi-label text classification is a special type of natural language processing tasks, which is more complex than traditional single-label classification. Moreover, due to the key strength of pre-trained language mod...
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Keyword extraction has a wide range of applications in the field of natural language processing. Many research results on keyword extraction at present. Among them, keyword extraction methods are based on complex netw...
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Time-series data provide important information in many fields,and their processing and analysis have been the focus of much ***,detecting anomalies is very difficult due to data imbalance,temporal dependence,and ***,m...
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Time-series data provide important information in many fields,and their processing and analysis have been the focus of much ***,detecting anomalies is very difficult due to data imbalance,temporal dependence,and ***,methodologies for data augmentation and conversion of time series data into images for analysis have been *** paper proposes a fault detection model that uses time series data augmentation and transformation to address the problems of data imbalance,temporal dependence,and robustness to *** method of data augmentation is set as the addition of *** involves adding Gaussian noise,with the noise level set to 0.002,to maximize the generalization performance of the *** addition,we use the Markov Transition Field(MTF)method to effectively visualize the dynamic transitions of the data while converting the time series data into *** enables the identification of patterns in time series data and assists in capturing the sequential dependencies of the *** anomaly detection,the PatchCore model is applied to show excellent performance,and the detected anomaly areas are represented as heat *** allows for the detection of anomalies,and by applying an anomaly map to the original image,it is possible to capture the areas where anomalies *** performance evaluation shows that both F1-score and Accuracy are high when time series data is converted to ***,when processed as images rather than as time series data,there was a significant reduction in both the size of the data and the training *** proposed method can provide an important springboard for research in the field of anomaly detection using time series ***,it helps solve problems such as analyzing complex patterns in data lightweight.
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