This paper presents an approach for comparing the results of Web service rankings based on quality characteristics obtained using the multi-criteria decision-making method, Logic Scoring of Preference (LSP), with rank...
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The recent years' progress in deep learning (DL) technology has resulted in convolutional neural networks (CNNs) capable of producing fast and accurate results, with minimal data preprocessing. Currently, gait ana...
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Pool localization is an essential prerequisite for swimmer analysis or performance measurement. Automated analysis studies on swimming pools have often been proposed due to the increase in broadcast videos and the dev...
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The increasing adoption of autonomous vehicles has driven the need for robust data management solutions that support real-time operations and ensure vehicle safety and efficiency. This work introduces a cloud-based fr...
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This paper explores modulation recognition technology based on feature extraction, which is a key means of identifying different modulation types of signals in modern communication systems. By preprocessing and featur...
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The large-scale use of CNC machine tools has become a necessity both in the industrial environment and in workshops and smaller businesses. The precision in processing, the productivity and the diversity of the work t...
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As high-performance computing (HPC) systems advance towards Exascale computing, their size and complexity increase, introducing new maintenance challenges. Modern HPC systems feature data monitoring infrastructures th...
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The COVID-19 pandemic has intensified the need for home-based cardiac health monitoring systems. Despite advancements in electrocardiograph (ECG) and phonocardiogram (PCG) wearable sensors, accurate heart sound segmen...
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ISBN:
(纸本)9798350345025;9798350345018
The COVID-19 pandemic has intensified the need for home-based cardiac health monitoring systems. Despite advancements in electrocardiograph (ECG) and phonocardiogram (PCG) wearable sensors, accurate heart sound segmentation algorithms remain understudied. Existing deep learning models, such as convolutional neural networks (CNN) and recurrent neural networks (RNN), struggle to segment noisy signals using only PCG data. We propose a two-step heart sound segmentation algorithm that analyzes synchronized ECG and PCG signals. The first step involves heartbeat detection using a CNN-LSTM-based model on ECG data, and the second step focuses on beat-wise heart sound segmentation with a 1D U-Net that incorporates multi-modal inputs. Our method leverages temporal correlation between ECG and PCG signals to enhance segmentation performance. To tackle the label-hungry issue in AI-supported biomedical studies, we introduce a segment-wise contrastive learning technique for signal segmentation, overcoming the limitations of traditional contrastive learning methods designed for classification tasks. We evaluated our two-step algorithm using the PhysioNet 2016 dataset and a private dataset from Bayland Scientific, obtaining a 96.43 F1 score on the former. Notably, our segment-wise contrastive learning technique demonstrated effective performance with limited labeled data. When trained on just 1% of labeled PhysioNet data, the model pre-trained on the full unlabeled dataset only dropped 2.88 in the F1 score, outperforming the SimCLR method. Overall, our proposed algorithm and learning technique present promise for improving heart sound segmentation and reducing the need for labeled data.
Microgrids are among the most important sources of energy in developing countries, and their operation and management have become a key issue in the transition towards a sustainable and resilient energy future. The in...
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The educational resource proposed in this work is intended for remote study of some combinatorial block designs and communication protocols of secure cyber-physical systemsbased on them, taking into account possible ...
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