检索结果-内蒙古大学图书馆

An autoencoder for compressing angle-resolved photoemission spectroscopy data

MACHINE LEARNING-SCIENCE AND TECHNOLOGY 2025年第1期6卷

作者： Agustsson, Steinn ymir Haque, Mohammad Ahsanul Truong, Thi Tam Bianchi, Marco Klyuchnikov, Nikita Mottin, Davide Karras, Panagiotis Hofmann, Philip Aarhus Univ Dept Phys & Astron DK-8000 Aarhus Denmark Aarhus Univ Dept Comp Sci DK-8000 Aarhus C Denmark

Angle-resolved photoemission spectroscopy (ARPES) is a powerful experimental technique to determine the electronic structure of solids. Advances in light sources for ARPES experiments are currently leading to a vast increase of data acquisition rates and data quantity. On the other hand, access time to the most advanced ARPES instruments remains strictly limited, calling for fast, effective, and on-the-fly data analysis tools to exploit this time. In response to this need, we introduce ARPESNet, a versatile autoencoder network that efficiently summmarises and compresses ARPES datasets. We train ARPESNet on a large and varied dataset of 2-dimensional ARPES data extracted by cutting standard 3-dimensional ARPES datasets along random directions in k. To test the data representation capacity of ARPESNet, we compare k-means clustering quality between data compressed by ARPESNet, data compressed by discrete cosine transform, and raw data, at different noise levels. ARPESNet data excels in clustering quality despite its high compression ratio.

关键词： autoencoder angle-resolved photoemission spectroscopy data compression

来源：评论

学校读者我要写书评

暂无评论

Optimized design for absorption metasurface based on autoencoder (AE) and BiLSTM-Attention-FCN-Net

引用

PHYSICA SCRIPTA 2024年第3期99卷 036002-036002页

作者： Zhu, Lei Du, Wenchen Dong, Liang Wei, Jinxu Qiqihar Univ Commun & Elect Engn Inst Qiqihar 161006 Peoples R China Qiqihar Univ Sch Comp & Control Engn Qiqihar 161006 Peoples R China Qiqihar Univ Heilongjiang Key Lab Big Data Network Secur Detect Qiqihar 161006 Peoples R China

In order to speed up the process of optimizing design of metasurface absorbers, an improved design model for metasurface absorbers based on autoencoder (AE) and BiLSTM-Attention-FCN-Net (including bidirectional long-short-term memory network, attention mechanism, and fully-connection layer network) is proposed. The metasurface structural parameters can be input into the forward prediction network to predict the corresponding absorption spectra. Meantime, the metasurface structural parameters can be obtained by inputting the absorption spectra into the inverse prediction network. Specially, in the inverse prediction network, the bidirectional long-short-term memory (BiLSTM) network can effectively capture the context relationship between absorption spectral sequence data, and the attention mechanism can enhance the BiLSTM output sequence features, which highlight the critical feature information. After the training, the mean square error (MSE) value on the validation set of the reverse prediction network converges to 0.0046, R2 reaches 0.975, and our network can accurately predict the metasurface structure parameters within 1.5 s with a maximum error of 0.03 mm. Moreover, this model can achieve the optimal design of multi-band metasurface absorbers, including the single-band, dual-band, and three-band absorptions. The proposed method can also be extended to other types of metasurface optimization design.

关键词： absorption metasurface deep learning attention mechanism autoencoder BiLSTM

来源：评论

学校读者我要写书评

暂无评论

Deep autoencoder-Based Z-Interference Channels With Perfect and Imperfect CSI

引用

IEEE TRANSACTIONS ON COMMUNICATIONS 2024年第2期72卷 861-873页

作者： Zhang, Xinliang Vaezi, Mojtaba Villanova Univ Dept Elect & Comp Engn Villanova PA 19085 USA

A deep autoencoder (DAE)-based structure for end-to-end communication over the two-user Z-interference channel (ZIC) with finite-alphabet inputs is designed in this paper. The proposed structure jointly optimizes the two encoder/decoder pairs and generates interference-aware constellations that dynamically adapt their shape based on interference intensity to minimize the bit error rate (BER). An in-phase/quadrature-phase (I/Q) power allocation layer is introduced in the DAE to guarantee an average power constraint and enable the architecture to generate constellations with nonuniform shapes. This brings further gain compared to standard uniform constellations such as quadrature amplitude modulation. The proposed structure is then extended to work with imperfect channel state information (CSI). The CSI imperfection due to both the estimation and quantization errors are examined. The performance of the DAE-ZIC is compared with two baseline methods, i.e., standard and rotated constellations. The proposed structure significantly enhances the performance of the ZIC both for the perfect and imperfect CSI. Simulation results show that the improvement is achieved in all interference regimes (weak, moderate, and strong) and consistently increases with the signal-to-noise ratio (SNR). For instance, more than an order of magnitude BER reduction is obtained with respect to the most competitive conventional method at weak interference when SNR > 15 dB$ and two bits per symbol are transmitted. The improvements reach about two orders of magnitude when quantization error exists, indicating that the DAE-ZIC is more robust to the interference compared to the conventional methods.

关键词： Interference channel Z-interference imperfect CSI autoencoder constellation design

来源：评论

学校读者我要写书评

暂无评论

Homodyned K-Distribution Parameter Estimation in Quantitative Ultrasound: autoencoder and Bayesian Neural Network Approaches

引用

IEEE TRANSACTIONS ON ULTRASONICS FERROELECTRICS AND FREQUENCY CONTROL 2024年第3期71卷 354-365页

作者： Tehrani, Ali K. Z. Cloutier, Guy Tang, An Rosado-Mendez, Ivan M. Rivaz, Hassan Concordia Univ Dept Elect & Comp Engn Montreal PQ H3G 1M8 Canada Univ Montreal Dept Radiol Radiooncol & Nucl Med Montreal PQ H2L 2W5 Canada Univ Montreal Inst Biomed Engn Montreal PQ H2L 2W5 Canada Univ Wisconsin Dept Med Phys Madison WI 53792 USA

Quantitative ultrasound (QUS) analyzes the ultrasound (US) backscattered data to find the properties of scatterers that correlate with the tissue microstructure. Statistics of the envelope of the backscattered radio frequency (RF) data can be utilized to estimate several QU Sparameters. Different distributions have been proposed to model envelope data. The homodyned K-distribution(HK-distribution) is one of the most comprehensive distributions that can model US backscattered envelope data under diverse scattering conditions (varying scatterer number density and coherent scattering). The scatterer clustering parameter (alpha) and the ratio of the coherent to diffuse scattering power (k) are the parameters of this distribution that have been used extensively for tissue characterization in diagnostic US. The estimation of these two parameters (which we refer to as HK parameters) is done using optimization algorithms in which statistical features such as the envelope point-wise signal-to-noise ratio (SNR), skewness, kurtosis, and the log-based moments have been utilized as input tosuch algorithms. The optimization methods minimize the difference between features and their theoretical value from the HK model. We propose that the true value of these statistical features is a hyperplane that covers a small portion of the feature space. In this article, we follow two approaches to reduce the effect of sample features'error. We propose a model projection neural network based on denoising autoencoders to project the noisy features into this space based on this assumption. We also investigate if the noise distribution can be learned by the deep estimators. We compare the proposed methods with conventional methods using simulations, an experimental phantom, and data from an in vivo animal model of hepatic steatosis. The network weight and a demo code are available online at ***://***.

关键词： autoencoder deep learning (DL) homodyned K-distribution quantitative ultrasound (QUS)

来源：评论

学校读者我要写书评

暂无评论

Abnormal State Detection using Memory-augmented autoencoder technique in Frequency-Time Domain

引用

KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS 2024年第2期18卷 348-369页

作者： Zhong, Haoyi Zhao, Yongjiang Lim, Chang Gyoon Chonnam Natl Univ Dept Comp Engn Yeosu 59626 South Korea

With the advancement of Industry 4.0 and Industrial Internet of Things (IIoT), manufacturing increasingly seeks automation and intelligence. Temperature and vibration monitoring are essential for machinery health. Traditional abnormal state detection methodologies often overlook the intricate frequency characteristics inherent in vibration time series and are susceptible to erroneously reconstructing temperature abnormalities due to the highly similar waveforms. To address these limitations, we introduce synergistic, end -to -end, unsupervised Frequency-Time Domain Memory -Enhanced autoencoders (FTD-MAE) capable of identifying abnormalities in both temperature and vibration datasets. This model is adept at accommodating time series with variable frequency complexities and mitigates the risk of overgeneralization. Initially, the frequency domain encoder processes the spectrogram generated through Short -Time Fourier Transform (STFT), while the time domain encoder interprets the raw time series. This results in two disparate sets of latent representations. Subsequently, these are subjected to a memory mechanism and a limiting function, which numerically constrain each memory term. These processed terms are then amalgamated to create two unified, novel representations that the decoder leverages to produce reconstructed samples. Furthermore, the model employs Spectral Entropy to dynamically assess the frequency complexity of the time series, which, in turn, calibrates the weightage attributed to the loss functions of the individual branches, thereby generating definitive abnormal scores. Through extensive experiments, FTD-MAE achieved an average ACC and F1 of 0.9826 and 0.9808 on the CMHS and CWRU datasets, respectively. Compared to the best representative model, the ACC increased by 0.2114 and the F1 by 0.1876.

关键词： Abnormal State Detection Memory Augmentation autoencoder Frequency- Time Domain

来源：评论

学校读者我要写书评

暂无评论

Operational performance evaluation of bridges using autoencoder neural network and clustering

引用

SMART STRUCTURES AND SYSTEMS 2024年第3期33卷 189-199页

作者： Jiang, Huachen Xie, Liyu Fang, Da Wan, Chunfeng Gao, Shuai Yang, Kang Ding, Youliang Xue, Songtao Shanghai Key Lab Engn Struct Safety SRIBS Shanghai 200032 Peoples R China Tongji Univ Dept Disaster Mitigat Struct Shanghai 200092 Peoples R China Southeast Univ Key Lab Concrete & Prestressed Concrete Struct Minist Educ Nanjing 210096 Peoples R China Shanghai Inst Technol Sch Railway Transportat Shanghai 201418 Peoples R China Tohoku Inst Technol Dept Architecture Sendai Miyagi 9828577 Japan

To properly extract the strain components under varying operational conditions is very important in bridge health monitoring. The abnormal sensor readings can be correctly identified and the expected operational performance of the bridge can be better understood if each strain components can be accurately quantified. In this study, strain components under varying load conditions, i.e., temperature variation and live-load variation are evaluated based on field strain measurements collected from a real concrete box-girder bridge. Temperature-induced strain is mainly regarded as the trend variation along with the ambient temperature, thus a smoothing technique based on the wavelet packet decomposition method is proposed to estimate the temperature-induced strain. However, how to effectively extract the vehicle-induced strain is always troublesome because conventional threshold setting-based methods cease to function: if the threshold is set too large, the minor response will be ignored, and if too small, noise will be introduced. Therefore, an autoencoder framework is proposed to evaluate the vehicleinduced strain. After the elimination of temperature and vehicle-induced strain, the left of which, defined as the model error, is used to assess the operational performance of the bridge. As empirical techniques fail to detect the degraded state of the structure, a clustering technique based on Gaussian Mixture Model is employed to identify the damage occurrence and the validity is verified in a simulation study.

关键词： autoencoder early warning Gaussian mixture model strain measurement structural health monitoring

来源：评论

学校读者我要写书评

暂无评论

ABAE: Auxiliary Balanced autoencoder for class-imbalanced semi-supervised learning

引用

PATTERN RECOGNITION LETTERS 2024年 182卷 118-124页

作者： Tang, Qianying Wei, Xiang Su, Qi Zhang, Shunli Beijing Jiaotong Univ Sch Software Engn 3 Shangyuancun Beijing 100044 Peoples R China

Semi -supervised learning has achieved extraordinary success in prevalent image -classification benchmarks. However, a class -balanced distribution that differs notably from real -world data distribution is required. In general, models trained under class-imbalanced semi -supervised learning conditions are severely biased towards the majority classes. To address this issue, we propose a novel framework called ABAE by implanting an Auxiliary Balanced autoencoder branch into existing semi -supervised learning algorithms. Considering that adaptive feature augmentation for different classes can alleviate confirmation bias, we devise a class -aware reconstruction loss to train the autoencoder module. To further smooth the output, we adopt a graph -based label propagation scheme at the end of the autoencoder. Extensive experiments on CIFAR-10/100-LT, SVHN-LT and Small demonstrate the effectiveness of ABAE.

关键词： Class-imbalanced learning Semi-supervised learning autoencoder Label propagation

来源：评论

学校读者我要写书评

暂无评论

Graph autoencoder Anomaly Detection for E-Commerce Application by Contextual Integrating Contrast With Reconstruction and Complementarity

引用

IEEE TRANSACTIONS ON CONSUMER ELECTRONICS 2024年第1期70卷 1623-1630页

作者： Wu, Jinjie Qiu, Zhipeng Zeng, Zhixia Xiao, Ruliang Rida, Imad Zhang, Shi Fujian Normal Univ Coll Comp & Cyber Secur Fuzhou 350117 Peoples R China Univ Technol Compiegne Ctr Rech Royallieu Lab Biomecan & Bioingn UMR7338 F-60200 Compiegne France

Considering the personalized characteristics and behavior patterns of users, quickly and accurately identifying abnormal users is crucial in the realm of E-commerce applications. However, existing methods fall short in integrating local and global structural information. Meanwhile, contextual information can serve effectively as a supervisory signal for anomaly detection. This paper proposes a Graph autoencoder Anomaly Detection model, GAAD, that leverages context-integrated contrast and reconstruction complementarity. This model utilizes local information of target nodes and adopts a contrastive learning module based on graph neural networks to capture anomalies in the structure space;simultaneously, a context subgraph of the global information of the target node is constructed and anomalies in the attribute space are captured through the autoencoder-based reconstruction module. The GAAD model integrates two complementary modules, contrast and reconstruction, to enhance the accuracy of anomaly node detection. The experimental results showcase the superior performance of the proposed model over existing baseline methods across all seven benchmark datasets, thereby highlighting its efficiency in anomaly detection within E-commerce applications. Our code is available at https://***/jinjiewu2000/GAAD.

关键词： Anomaly detection Graph neural networks Context modeling Self-supervised learning Feature extraction Electronic commerce Decoding autoencoder graph neural network contextual integrating anomaly detection

来源：评论

学校读者我要写书评

暂无评论

A Wavelet-Based Memory autoencoder for Noncontact Fingerprint Presentation Attack Detection

引用

IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 2024年 19卷 8717-8730页

作者： Liu, Yi-Peng Yu, Hangtao Fang, Haonan Li, Zhanqing Chen, Peng Liang, Ronghua Zhejiang Univ Technol Coll Comp Sci & Technol Hangzhou 310023 Peoples R China

Fingerprint presentation attack detection (FPAD) is essential in fingerprint identification systems. Noncontact methods such as fingerprint biometrics are becoming popular because they are not affected by skin conditions and there are no hygiene issues. However, most of the existing noncontact FPAD methods are supervised methods with poor generalizability and poor performance during events such as unseen presentation attacks (PAs). Moreover, easily overlooked frequency domain information contributes to the fingerprint antispoofing task. Therefore, we propose a wavelet-based memory-augmented autoencoder that fully utilizes the frequency domain information. Specifically, the model first decomposes the input image into high- and low-frequency information and extracts features separately. Subsequently, we propose a frequency complementary connection (FCC) module to realize the fusion and complementation of frequency domain information at the feature level. Moreover, a memory distance expansion loss is proposed to keep the memory module diverse. Experiments are conducted to verify the effectiveness of the method. The code of our model is available on https://***/SuperIOyht/WaveMemAE.

关键词： Fingerprint recognition Feature extraction Frequency-domain analysis Image reconstruction Memory modules Image matching Biological system modeling Presentation attack detection biometrics noncontact fingerprint anomaly detection autoencoder wavelet frequency memory

来源：评论

学校读者我要写书评

暂无评论

A hybrid model of CNN and LSTM autoencoder-based short-term PV power generation forecasting

引用

ELECTRICAL ENGINEERING 2024年第4期106卷 4239-4255页

作者： Ibrahim, Mohamed Sayed Gharghory, Sawsan Morkos Kamal, Hanan Ahmed Cairo Univ Fac Engn Elect & Commun Engn Dept Giza Egypt Elect Res Inst Comp & Syst Dept Cairo Egypt

Solar energy is one of the main renewable energies available to fulfill global clean energy targets. The main issue of solar energy like other renewable energies is its randomness and intermittency which affects power grids stability. As a solution for this issue, energy storage units could be used to store surplus energy and reuse it during low solar generation intervals. Also, in order to sustain stable power grid and better grid operation and energy storage management, photovoltaic (PV) power forecasting is inevitable. In this paper, new hybrid model based on deep learning techniques is proposed to predict short-term PV power generation. The proposed model incorporates convolutional neural network (CNN) and long short-term memory (LSTM) autoencoder network. The new model differentiates itself in accomplishing high prediction accuracy by extracting spatial features in time series via CNN layers and temporal features between the time series data through LSTM. The introduced model is tested on dataset of power generation from southern UK solar farm and the weather data corresponding to same location and time intervals;the forecasting performance of the suggested model is evaluated in metrics of root-mean-square error (RMSE) and mean absolute error (MAE). The used model is compared with different models from the literature either of pure type of network such as LSTM and gated recurrent unit (GRU) or hybrid combination of different networks like CNN-LSTM and CNN-GRU. The results show that proposed model provides enhanced results and reduces training time significantly compared to other competitive models, where the performance of the proposed model improved averagely by 5% to 25% in terms of RMSE and MAE performance metrics, and the execution time of training significantly reduced with almost 70% less compared to other models.

关键词： CNN LSTM GRU autoencoder PV

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：