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检索条件"主题词=Autoencoder"

共 4258 条记录，以下是381-390 订阅

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A Hardware Accelerated autoencoder for RF Communication using Short-Time-Fourier-Transform Assisted Convolutional Neural Network

A Hardware Accelerated Autoencoder for RF Communication usin...

引用

27th Design, Automation and Test in Europe Conference and Exhibition (DATE)

作者： Jung, Kuchul Woo, Jongseok Mukhopadhyay, Saibal Georgia Inst Technol Atlanta GA 30332 USA

ISBN: (纸本)9798350348606;9783981926385

This paper presents a hardware-accelerated autoencoder (AE) for wireless communication using a Short-Time-Fourier-Transform Assisted Convolutional Neural Network (STFT-CNN-AE). The design aims to reduce the autoencoder's resource requirements and power dissipation while maintaining its performance even in low Signal-to-Noise Ratio (SNR) wireless channels. The STFT-CNN-AE was implemented and tested on a Zynq UltraScale+ FPGA platform. Prototype measurements show that the STFT-CNN-AE achieves 3.5 times higher throughput at 2.6 times faster frequency, consumes 59% less power, and requires 76% fewer hardware resources (LUT and DSP) compared to a prior Multi-Layer Perceptron-based AE (MLP-AE). These improvements were achieved while maintaining comparable performance in low SNR (<7.5dB) channels.

关键词： autoencoder CNN STFT HW Implementation

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Multi-Classification Decision Fusion Based on Stacked Sparse Shrink autoencoder and GS-Tabnet for Network Intrusion Detection 10

Multi-Classification Decision Fusion Based on Stacked Sparse...

引用

10th International Conference on Control, Decision and Information Technologies (CoDIT)

作者： Wang, Ziqi Guan, Ziyue Wu, Xiangxi Bi, Jing Yuan, Haitao Zhou, MengChu Beijing Univ Technol Fac Informat Technol Beijing 100124 Peoples R China Beihang Univ Sch Automat Sci & Elect Engn Beijing 100191 Peoples R China New Jersey Inst Technol Dept Elect & Comp Engn Newark NJ 07102 USA

ISBN: (纸本)9798350373981;9798350373974

With the rapid development of the Internet, various network invasive behaviors are increasing rapidly. This seriously threatens the economic development of individuals, enterprises, and society. Network intrusion detection is important in network security systems, which can be regarded as a classification problem. It aims to distinguish between the specific categories of various network behaviors and determine whether the behavior belongs to network intrusion. However, network intrusions present a diverse and fast-changing trend, making categorizing difficult. Due to feature redundancy, uneven distribution of sample numbers, and inefficient parameter optimization, traditional rule-based approaches fail to achieve satisfying classification accuracy. This work proposes a multi-classification intrusion detection model based on Stacked Sparse Shrink autoencoder (SSSAE), Genetic Simulated annealing-based particle swarm optimization optimized Tabnet classifier (GS-Tabnet), and Decision Fusion (DF), called for SGTD short. Among them, SSSAE extracts multiple feature sets from the input data. Then GS-Tabnet trains a classifier for each feature set. Finally, the decision fusion fuses the results from these classifiers to obtain the final classification result. SGTD is compared with eight multi-classification benchmark models, and its intrusion detection accuracy is superior to its peers.

关键词： Feature learning autoencoder network intrusion detection intelligent optimization algorithm

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3D-HYBRID CONVOLUTIONAL autoencoder MODEL FOR HYPERSPECTRAL SATELLITE DATA COMPRESSION

3D-HYBRID CONVOLUTIONAL AUTOENCODER MODEL FOR HYPERSPECTRAL ...

引用

IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Kuester, J. Gross, W. Michel, A. Schreiner, S. Middelmann, W. Heimann, M. Fraunhofer IOSB Image Anal Grp Gutleuthausstr 1 DE-76275 Ettlingen Germany Karlsruhe Inst Technol Inst Ind Informat Technol Hertzstr 16 DE-76187 Karlsruhe Germany

ISBN: (纸本)9798350360332;9798350360325

This work addresses the challenge of including the spatial dimension into the autoencoder models for lossy compression of different spatially independent and unknown hyperspectral datasets acquired by space-borne hyperspectral sensors. We propose two different 3D-Hybrid Convolutional autoencoder models with increased compression rates compared to 1D methods that can compress and reconstruct hyperspectral data with arbitrary spectral dimensionality. The architecture of the first 3D-Hybrid model consists of the A1D-CAE in combination with the 2D-CAE. The second 3D-Hybrid model includes the adaptive 1D-CAE and a 3D-CAE. The evaluation of the reconstruction accuracy is measured by comparing the spectral angle and the peak signal-to-noise ratio between the original and the reconstructed data and structural similarity index measure. We show the high transferability and generalizability of our 3D-Hybrid models on different PRISMA datasets. The 3D-Hybrid model is compared with the SSCNet(2D) based on a 2D-CAE and a 3D-CAE model. The findings of this study contribute to understanding the strengths and limitations of machine learning-based compression methods for jointly compressing spectral and spatial information.

关键词： hyperspectral image (HSI) compression spectral compression entropy coding feature extraction dimensionality reduction autoencoder spectral analysis

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Zoom-Based autoencoder for Origin-Destination Demand Prediction 20th

Zoom-Based AutoEncoder for Origin-Destination Demand Predict...

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20th Pacific Rim International Conference on Artificial Intelligence

作者： Ma, Xiaojian Han, Liangzhe Wang, Gang Liu, Xu Zhu, Tongyu Beihang Univ State Key Lab Software Dev Environm Beijing 100191 Peoples R China Minist Transport PRC Highway Monitoring & Emergency Response Ctr Beijing 100029 Peoples R China Tsinghua Univ Sch Vehicle & Mobil Beijing 100084 Peoples R China

ISBN: (纸本)9789819970186;9789819970193

The use of deep neural networks for traffic demand forecasting has garnered significant attention from both academic and industrial communities. Compared with the traditional traffic flow forecasting task, the Origin-Destination(OD) demand prediction task is more valuable and challenging, and several methods have been proposed for OD demand prediction. However, most existing methods follow a general technical route to aggregate historical information spatially and temporally. This paper proposes an alternative approach to predict Origin-Destination demand, named Zoom-based autoencoder for Origin-Destination demand prediction (ODZAE). The main objective of our research is to enhance the integration of diverse inherent patterns in real-world OD demand data in a more efficient manner. Besides, we proposed a zoom operation to learn spatial relationships between traffic nodes and 3DGCN to simultaneously model spatial and temporal dependencies. We have conducted experiments on two real-world datasets from Beijing Subway and New York Taxi, and the results demonstrate the superiority of our model against the state-of-art approaches.

关键词： Origin Destination Demand Prediction autoencoder Spatio-Temporal Data Mining Graph Neural Network Intelligent Transportation System

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Multi-objective topology optimization of synchronous reluctance motor with autoencoder simultaneously considering material selection and shape change 21

Multi-objective topology optimization of synchronous relucta...

引用

IEEE 21st Biennial Conference on Electromagnetic Field Computation (IEEE CEFC)

作者： Kishi, Masahiro Wakao, Shinji Murata, Noboru Makino, Hiroaki Takeuchi, Katsutoku Matsushita, Makoto Waseda Univ Dept Elect Engn & Biosci Tokyo Japan Toshiba Infrastruct Syst & Solut Corp Tokyo Japan

ISBN: (纸本)9798350348958;9798350348965

Generally, the optimal shape depends on the characteristics of the magnetic materials used. In this study, we propose an efficient multi-objective topology optimization method that simultaneously considers material selection and shape change. The proposed approach utilizes an autoencoder, a type of deep learning model, to compress the information of both the device's shape and material characteristics into a latent space. The method enables us to efficiently derive superior combinations of shapes and materials for improving device's performances as pareto solutions. Some numerical examples demonstrate we can successfully carry out the practical device design by combining the proposed approach with the level set method.

关键词： multi-objective optimization synchronous reluctance motor multimaterial autoencoder level set method multimodal deep learning

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A Hybrid LSTM-autoencoder Based Approach for Network Anomaly Detection System in IoT Environments

A Hybrid LSTM-Autoencoder Based Approach for Network Anomaly...

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IEEE International Mediterranean Conference on Communications and Networking (IEEE MeditCom)

作者： Touileb, Lyna Zekri, Kaouthar Bradai, Abbas Pousset, Yannis Point, Jean Charles Univ Poitiers XLIM Lab Poitiers France SODIRA Connect Co Cesson Sevigne France Univ Cote dAzur Nice France

ISBN: (纸本)9798350309492;9798350309485

In the evolving landscape of the Internet of Things (IoT), the susceptibility to cyber threats is widespread, emphasizing the critical need for robust security measures. This paper introduces an innovative anomaly detection system based on a hybrid LSTM-autoencoder approach. Focused on protocol headers analysis in Packet Capture (PCAP) datasets, for robust anomaly detection, our model demonstrates high F1-score in anomalies detection with 99% and 96% on CICIDS2017 and on real network traffic, respectively. Refining our strategy, we address the intricacies of IoT environments, presenting a significant leap forward in intrusion detection for IoT networks.

关键词： Network anomaly detection protocols' headers semi-supervised learning LSTM autoencoder IoT

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Design Considerations for Denoising Quantum Time Series autoencoder 24th

Design Considerations for Denoising Quantum Time Series Auto...

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24th International Conference on Computational Science (ICCS)

作者： Cybulski, Jacob L. Zajac, Sebastian Deakin Univ Melbourne Vic Australia SGH Warsaw Sch Econ Warsaw Poland

ISBN: (纸本)9783031637773;9783031637780

This paper explains the main design decisions in the development of variational quantum time series models and denoising quantum time series autoencoders. Although we cover a specific type of quantum model, the problems and solutions are generally applicable to many other methods of time series analysis. The paper highlights the benefits and weaknesses of alternative approaches to designing a model, its data encoding and decoding, ansatz and its parameters, measurements and their interpretation, and quantum model optimization. Practical issues in training and execution of quantum time series models on simulators, including those that are CPU and GPU based, as well as their deployment on quantum machines, are also explored. All experimental results are evaluated, and the final recommendations are provided for the developers of quantum models focused on time series analysis.

关键词： Quantum machine learning autoencoder Quantum encoding Quantum measurement

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ARFA: AN ASYMMETRIC RECEPTIVE FIELD autoencoder MODEL FOR SPATIOTEMPORAL PREDICTION 49

ARFA: AN ASYMMETRIC RECEPTIVE FIELD AUTOENCODER MODEL FOR SP...

引用

49th IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Zhang, Wenxuan Zou, Xuechao Wu, Li Wang, Xiaoying Huang, Jianqiang Xing, Junliang Qinghai Univ Dept Comp Technol & Applicat Xining Peoples R China Tsinghua Univ Dept Comp Sci & Technol Beijing Peoples R China

ISBN: (纸本)9798350344868;9798350344851

Spatiotemporal prediction aims to generate future sequences by paradigms learned from historical contexts. It is essential in numerous domains, such as traffic flow prediction and weather forecasting. Recently, research in this field has been predominantly driven by deep neural networks based on autoencoder architectures. However, existing methods commonly adopt autoencoder architectures with identical receptive field sizes. To address this issue, we propose an Asymmetric Receptive Field autoencoder (ARFA) model, which introduces corresponding sizes of receptive field modules tailored to the distinct functionalities of the encoder and decoder. In the encoder, we present a large kernel module for global spatiotemporal feature extraction. In the decoder, we develop a small kernel module for local spatiotemporal information reconstruction. Experimental results demonstrate that ARFA consistently achieves state-of-the-art performance on popular datasets. Additionally, we construct the RainBench, a large-scale radar echo dataset for precipitation prediction, to address the scarcity of meteorological data in the domain.

关键词： Asymmetric receptive field autoencoder spatiotemporal prediction

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An autoencoder-Based Constellation Design for AirComp in Wireless Federated Learning 59

An Autoencoder-Based Constellation Design for AirComp in Wir...

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59th Annual IEEE International Conference on Communications (IEEE ICC)

作者： Mu, Yujia Wei, Xizixiang Shen, Cong Univ Virginia Charles L Brown Dept Elect & Comp Engn Charlottesville VA 22903 USA

ISBN: (纸本)9781728190549

Wireless federated learning (FL) relies on efficient uplink communications to aggregate model updates across distributed edge devices. Over-the-air computation (a.k.a. AirComp) has emerged as a promising approach for addressing the scalability challenge of FL over wireless links with limited communication resources. Unlike conventional methods, AirComp allows multiple edge devices to transmit uplink signals simultaneously, enabling the parameter server to directly decode the average global model. However, existing AirComp solutions are intrinsically analog, while modern wireless systems predominantly adopt digital modulations. Consequently, careful constellation designs are necessary to accurately decode the sum model updates without ambiguity. In this paper, we propose an end-to-end communication system supporting AirComp with digital modulation, aiming to overcome the challenges associated with accurate decoding of the sum signal with constellation designs. We leverage autoencoder network structures and explore the joint optimization of transmitter and receiver components. Our approach fills an important gap in the context of accurately decoding the sum signal in digital modulation-based AirComp, which can advance the deployment of FL in contemporary wireless systems.

关键词： Federated Learning Constellation Design autoencoder

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AEROBLADE: Training-Free Detection of Latent Diffusion Images Using autoencoder Reconstruction Error

AEROBLADE: Training-Free Detection of Latent Diffusion Image...

引用

IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Ricker, Jonas Lukovnikov, Denis Fischer, Asja Ruhr Univ Bochum Bochum Germany

ISBN: (纸本)9798350353006

With recent text-to-image models, anyone can generate deceptively realistic images with arbitrary contents, fueling the growing threat of visual disinformation. A key enabler for generating high- resolution images with low computational cost has been the development of latent diffusion models (LDMs). In contrast to conventional diffusion models, LDMs perform the denoising process in the low-dimensional latent space of a pre-trained autoencoder ( AE) instead of the high- dimensional image space. Despite their relevance, the forensic analysis of LDMs is still in its infancy. In this work we propose AEROBLADE, a novel detection method which exploits an inherent component of LDMs: the AE used to transform images between image and latent space. We find that generated images can be more accurately reconstructed by the AE than real images, allowing for a simple detection approach based on the reconstruction error. Most importantly, our method is easy to implement and does not require any training, yet nearly matches the performance of detectors that rely on extensive training. We empirically demonstrate that AEROBLADE is effective against state-of-the-art LDMs, including Stable Diffusion and Midjourney. Beyond detection, our approach allows for the qualitative analysis of images, which can be leveraged for identifying inpainted regions. We release our code and data at https://***/jonasricker/aeroblade.

关键词： autoencoder deepfake detection diffusion models fake image detection latent diffusion models stable diffusion

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