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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Human action recognition (HAR) systems need to process large volumes of data posing several challenges including, but not limited to, accurately identifying the actions and classifying them in near real time. Most of ...
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In this paper the various computational approaches for automatic generation control (AGC) of power systems is covers, with the goal of improving the automatic voltage regulator (AVR) and load frequency control (LFC) t...
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Advanced Persistent Threats (APT) in the current network environment are becoming increasingly complex and diverse. Most existing APT anomaly detection is based on attack knowledge bases and preset rules, which are di...
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ISBN:
(纸本)9798350386783;9798350386776
Advanced Persistent Threats (APT) in the current network environment are becoming increasingly complex and diverse. Most existing APT anomaly detection is based on attack knowledge bases and preset rules, which are difficult to design and cannot make good use of the rich semantic information in the original log data. This results in poor detection of unknown attacks. This paper proposes an anomaly detection method based on meta-path and heterogeneous provenance graph. We design a heterogeneous graph structure to represent provenance graph, and define the meta-paths of the PROCESS nodes. Then we use Heterogeneous Graph Attention Network (HAN) to learn the embedding representation of the nodes based on meta-paths. The resulting graph's node embedding is used as node features, and then we apply SVDD algorithm to identify anomalous nodes. A series of experiments were conducted on the Unicorn SC-2 dataset to validate the proposed method. The final results demonstrate that our method outperforms two current anomaly detection systems.
In the context of modern services that use multiple Deep Neural Networks (DNNs), managing workloads on embedded devices presents unique challenges. These devices often incorporate diverse architectures, necessitating ...
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