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检索条件"主题词=autoencoder"

共 4244 条记录，以下是401-410 订阅

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A Generalization-Enhanced Method for Encrypted Proxy Traffic Identification Based on autoencoder 2

A Generalization-Enhanced Method for Encrypted Proxy Traffic...

引用

2nd International Conference on Mobile Internet, Cloud Computing and Information Security (MICCIS)

作者： Zhou, Nanxin Liu, Yiwei Li, Yujun Xue, Ruini Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu Peoples R China

ISBN: (纸本)9798350389913;9798350389906

While encrypted proxy services like Shadowsocks and Vmess safeguard user privacy by encrypting traffic data, they also provide anonymity tools for cybercriminals, increasing the difficulty of regulating and combating activities such as online fraud and cyberattacks. This presents significant challenges for network management, as these services can obfuscate and disguise traffic with continually updated tools, leading to dynamic traffic features that traditional detection methods struggle to identify. However, the feature engineering and model designing of existing methods is based on previously commonly used encrypted proxy traffic and cannot cope with new types, which means that existing methods are poor in generalization ability. To overcome these challenges, this paper introduces a novel method for identifying encrypted proxy traffic. Initially, by analyzing encrypted proxy traffic, we extract features related to the information entropy, TLS protocol, and length of traffic packets. In this way, we obtain a comprehensive feature vector of length 325. Second, we propose a new identification model, called BiAE-MLP, consisting of an encoding layer with two autoencoders and a classification layer with a MLP classifier. We evaluate our method with four experiments. As a result, our method achieves a 98% F1-score on a test dataset with the same distribution as the training dataset and a 91% F1-score on a dataset with a different distribution. It indicates the effectiveness and generalization ability of our method. Additionally, in Experiment 4, we demonstrate that the encoding layer of the BiAE-MLP plays a crucial role in enhancing the model's generalization ability.

关键词： encrypted proxy traffic autoencoder MLP classifier Shadowsocks

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Medical Image Synthesis using autoencoder with Vision Transformer 39

Medical Image Synthesis using Autoencoder with Vision Transf...

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39th International Conference on Image and Vision Computing New Zealand

作者： Choudhury, Zakia Zinat McCane, Brendan Coffey, Sean Univ Otago Dept Comp Sci Dunedin New Zealand Univ Otago Dept Med Dunedin New Zealand

ISBN: (纸本)9798331518783;9798331518776

Medical imaging plays a crucial role in clinical diagnostics, with modalities like Transthoracic Echocardiography (TTE) and Cardiac Magnetic Resonance (CMR) offering distinct advantages and limitations in cardiovascular assessment. While TTE provides real-time, non-invasive imaging, it is operator-dependent and may yield incomplete views. In contrast, CMR offers comprehensive evaluations but is time-consuming and costly. This paper proposes a novel architecture for synthesizing CMR images from TTE inputs using an integrated autoencoder and vision transformer. The autoencoder captures TTE patterns and transforms them into CMR-like representations, enhanced by the vision transformer's attention mechanisms. Evaluation through quantitative and qualitative metrics demonstrates the system's ability to generate realistic CMR images, potentially enhancing diagnostic accuracy and workflow efficiency in cardiac imaging.

关键词： TTE CMR autoencoder Vision Transformer Image Synthesis

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MANEUVERING TARGET TRACKING USING GAT autoencoder WITH CLUTTERS

MANEUVERING TARGET TRACKING USING GAT AUTOENCODER WITH CLUTT...

引用

IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Chen, Xingyu Wang, Yumiao Zang, Chuanfei Wang, Xiang Cui, Guolong Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu 611731 Peoples R China

ISBN: (纸本)9798350360332;9798350360325

This paper considers the problem of maneuvering target tracking in the clutter environment and proposes a graph attention autoencoder tracking network. Initially, the proposed method constructs the graph by using historical track points and measurement data of the current frame, including the target measurement and clutters, as vertices. Then, the graph is fed into the designed graph attention autoencoder (GAE) network to learn the relationships among the historical trajectory and the measurements of the current frame. Finally, the GAE outputs the tracking result of the current frame. The experimental results highlight that the proposed GAE can outperform the classic maneuvering target tracking algorithms in the clutter environment with low and high maneuverability.

关键词： Maneuvering target tracking clutter environment graph attention network (GAT) autoencoder

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A Data Collection Scheme Using Transformer autoencoder in Wireless Sensor Networks

A Data Collection Scheme Using Transformer Autoencoder in Wi...

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International Conference on Ubiquitous Communication (Ucom)

作者： Xu, Jiahui Li, Cuiran Lanzhou Jiaotong Univ Sch Elect & Informat Engn Lanzhou 730070 Peoples R China

ISBN: (纸本)9798350374223;9798350374216

Data collection is one of the key operations in wireless sensor networks (WSNs). An efficient data collection scheme is designed using a transformer autoencoder for WSNs. During the model training stage, the model is trained using historical monitoring data to obtain an optimized measurement matrix and reconstruction matrix. In the data collection stage, sensor nodes compress the data using the measurement matrix and enable efficient data transmission via a clustered network. Finally, the sink node utilizes the reconstruction matrix to restore the original data. Simulation and verification analysis show that the proposed data reconstruction algorithm outperforms existing algorithms such as OMP, DAE, SSDAE-CS, and ISTA-1DNet in terms of reconstruction accuracy and reconstruction efficiency at a compression ratio of 30%.

关键词： wireless sensor networks compressed sensing data collection transformer autoencoder data reconstruction

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Reconstruction of EEG and ECG from Single Channel Mixture using Branched autoencoder based Separable Representations

Reconstruction of EEG and ECG from Single Channel Mixture us...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Datta, Shreyasi Gubbi, Jayavardhana Pal, Arpan TCS Research India

ISBN: (纸本)9798350368741

The growing use of wearable devices requires accurate and compact representations of high dimensional physiological signals. This work presents a UNet inspired autoencoder to represent and reconstruct multiple neuro-physiological signals from single channel data. The architecture comprises single-encoder/dual-branched decoders to obtain self-attention enabled compact embeddings of mixed ExG (EEG/ECG) signals through decaying encoder-decoder skip connections, for improved representation capability. The embeddings are separable into individual ExG components enabling simultaneous reconstruction of high fidelity EEG and ECG sources. The pretrained encoder can be used for a complex downstream task with minimum fine-tuning. Using the proposed method on a large corpus of single-channel mixed ExG generated from overnight Polysomnography (PSG) recordings, we show subject- and class- independent EEG/ECG reconstructions validated by multiple domain-specific metrics, and evaluate the classification performance of the encoded EEG embeddings into five sleep stages as a downstream task. © 2025 IEEE.

关键词： attention autoencoder ECG EEG reconstruction representation

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DP-DCAN: Differentially Private Deep Contrastive autoencoder Network for Single-Cell Clustering 20th

DP-DCAN: Differentially Private Deep Contrastive Autoencoder...

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20th International Conference on Intelligent Computing (ICIC)

作者： Li, Huifa Fu, Jie Chen, Zhili Yang, Xiaomin Liu, Haitao Ling, Xinpeng East China Normal Univ Shanghai Key Lab Trustworthy Comp Shanghai 200241 Peoples R China Shanghai Jiao Tong Univ Sch Med Xinhua Hosp Shanghai 200241 Peoples R China

ISBN: (纸本)9789819756889;9789819756896

Single-cell RNA sequencing (scRNA-seq) is important to transcriptomic analysis of gene expression. Recently, deep learning has facilitated the analysis of high-dimensional single-cell data. Unfortunately, deep learning models may leak sensitive information about users. As a result, Differential Privacy (DP) is increasingly being used to protect privacy. However, existing DP methods usually perturb whole neural networks to achieve differential privacy, and hence result in great performance overheads. To address this challenge, in this paper, we take advantage of the uniqueness of the autoencoder that it outputs only the dimension-reduced vector in the middle of the network, and design a Differentially Private Deep Contrastive autoencoder Network (DP-DCAN) by partial network perturbation for single-cell clustering. Firstly, we use contrastive learning to enhance the feature extraction of the autoencoder. And then, since only partial network is added with noise, the performance improvement is obvious and twofold: one part of network is trained with less noise due to a bigger privacy budget, and the other part is trained without any noise. Experimental results of 8 datasets have verified that DP-DCAN is superior to the traditional DP scheme with whole network perturbation. The code is available at https://***/LFDbyte/DP-DCAN.

关键词： scRNA-seq data autoencoder Differential privacy Contrastive learning

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Acoustic Anomaly Detection of Machinery using autoencoder based Deep Learning 32

Acoustic Anomaly Detection of Machinery using Autoencoder ba...

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32nd Southern African Universities Power Engineering Conference (SAUPEC)

作者： Chinnasamy, Mark Damien Sumbwanyambe, Mbuyu Hlalele, Tlotlollo Sidwell Univ South Africa Dept Elect Engn Johannesburg South Africa

ISBN: (纸本)9798350371345

This research paper focuses on the domain of Acoustic Anomaly Detection (AAD) in industrial machinery using Deep Learning techniques. The primary objective of this study is to develop a reliable system for detecting anomalies in the acoustic patterns of industrial machines. In large-scale industrial environments, the early detection of faults and anomalies is critical to ensure the uninterrupted operation of machinery, minimize downtime, and optimize maintenance efforts. The datasets used in this research encompass a range of Signal-to-Noise Ratios (SNR) to simulate diverse operating conditions for industrial fans and pumps. After preprocessing each audio sample was transformed into 9 segments of shape 128 x 32 log mel-spectrograms. The study encompasses a comprehensive analysis of Convolutional autoencoders (CAE) and Dense autoencoders (DAE). These models are trained and evaluated against real-world industrial datasets (MIMII dataset), and their performance is meticulously compared. The results for the DAE and CAE had shown over 0.80 at the 6 dB SNR level and decaying results as the SNR level worsens. This research confirms some of the trends pointed out by the literature and provides detailed insight into how the autoencoders are developed and their properties could be used in order to detect anomalous behavior in audio data.

关键词： autoencoder Acoustic Detection Convolutional autoencoder Dense autoencoder ML Unsupervised Anomaly Detection

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Semi-supervised graphical autoencoder regression modeling for soft sensors 39

Semi-supervised graphical autoencoder regression modeling fo...

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39th Youth Academic Annual Conference of Chinese-Association-of-Automation (YAC)

作者： Wang, Xiaoyu Zhu, Li Chen, Junghui Xu, Xirong Dalian Univ Technol Sch Control Sci & Engn Dalian Peoples R China Chung Yuan Christian Univ Dept Chem Engn Chungli Taiwan Dalian Univ Technol Sch Comp Sci & Technol Dalian Peoples R China

ISBN: (纸本)9798350390780;9798350379228

For soft sensor in chemical processes, process variables are usually measured at a higher frequency while quality variables are often measured at a lower rate. This discrepancy in measurement frequencies results in a large number of unlabeled data points. In addition, there are instances where the daily recorded component variables are more readily available, contrasting with the difficulty in obtaining data for the quality variable data. To address these issues, this paper proposes a novel semi-supervised learning framework, TGAE-STN(Transfer entropy based Graph autoencoder-Spatiotemporal Networks), which employs an encoder-decoder architecture to efficiently handle both unlabeled and labeled data. For unlabeled data, transfer entropy is computed from the original sampling data to facilitate the conversion into graph data and subsequent reconstruction of graph structure. For labeled data, the latent variable obtained from the reconstruction process of the codec is used as the input to the prediction model, and the adjacency matrix is dynamically constructed during the training process. The prediction network uses a spatiotemporal graph model to extract features across both temporal and spatial dimensions. Moreover, the encoder part is updated asynchronously based on the performance of the decoder and the prediction network, facilitating effective utilization of the information from both unlabeled and labeled data. The effectiveness of the proposed method is verified through a numerical case and a chemical process. The results show that the proposed TGAE-STN method provides a promising solution for soft sensor technology under semi-supervised conditions.

关键词： soft sensor semi-supervised graph convolutional network autoencoder

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Online Error Evaluation of Capacitive Voltage Transformer Based on Improved Temporal Convolutional Network autoencoder 10

Online Error Evaluation of Capacitive Voltage Transformer Ba...

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10th International Conference on Electrical Engineering, Control and Robotics (EECR)

作者： Zhou, Kun Wei, Hongtao Wang, Wenshuo Wuhan Univ Technol Sch Informat Engn Wuhan Peoples R China

ISBN: (纸本)9798350370010;9798350370003

The measurement accuracy of capacitive voltage transformers (CVTs) can deteriorate during prolonged operation due to environmental factors and other factors. The existing regular offline calibration method is difficult to implement, so a large number of CVTs cannot be repaired sufficiently or at all. Therefore, online data-driven assessment methods for CVTs have attracted researchers' attention. In this paper, an autoencoder network based on an improved temporal convolutional network (TCN-AE) is proposed, which combines the advantages of the autoencoder network for detecting anomalous CVTs through data reconstruction and the parallel and fast operation of the temporal convolutional network. Reconstruction loss is used to obtain anomaly values for the evaluation of training measures and to determine whether the training measure under investigation is anomalous by setting a threshold value. Comparative experiments have shown that this method outperforms conventional autoencoders and other prediction-based models and thus has significant technical application value.

关键词： capacitive voltage transformers online evaluation autoencoder temporal convolutional network

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Power terminal anomaly monitoring technology based on autoencoder and multi-layer perceptron 24

Power terminal anomaly monitoring technology based on autoen...

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3rd International Conference on Networks, Communications and Information Technology (CNCIT)

作者： Li Yong Chen Mu Li Nige Yu Chengfei Zhang Junfeng State Grid Smart Grid Res Inst Co Ltd Nanjing 210003 Peoples R China State Grid Lab Power Cyber Secur Protect & Monito Nanjing 210003 Peoples R China State Grid JiangXi Elect Power Co Ltd Nanchang 330077 Jiangxi Peoples R China

ISBN: (纸本)9798400717048

A terminal anomaly monitoring method based on an improved autoencoder and multi-layer perceptron (MLP) is proposed to address the complex problem of determining user side business ownership in a new power system. This method uses Minimum Gating Unit (MGU) to replace ordinary neural networks in traditional autoencoders for data feature extraction, improving the model's temporal modeling ability. Then, multi-layer perceptrons are used to complete terminal traffic analysis tasks and achieve terminal abnormal behavior detection. The experimental results show that this method is significantly superior to traditional classification methods in performance indicators such as accuracy, recall, precision, and F1 score.t.

关键词： terminal anomaly monitoring autoencoder MLP feature extraction

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