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A Learned-SVD Approach to the Electromagnetic Inverse Source Problem

SENSORS 2024年第14期24卷 4496页

作者： Capozzoli, Amedeo Catapano, Ilaria Cinotti, Eliana Curcio, Claudio Esposito, Giuseppe Gennarelli, Gianluca Liseno, Angelo Ludeno, Giovanni Soldovieri, Francesco Univ Napoli Federico II Dipartimento Ingn Elettr & Tecnol Informaz DIETI Via Claudio 21 I-80125 Naples Italy CNR Ist Rilevamento Elettromagnet Ambiente IREA Via Diocleziano 328 I-80124 Naples Italy

We propose an artificial intelligence approach based on deep neural networks to tackle a canonical 2D scalar inverse source problem. The learned singular value decomposition (L-SVD) based on hybrid autoencoding is considered. We compare the reconstruction performance of L-SVD to the Truncated SVD (TSVD) regularized inversion, which is a canonical regularization scheme, to solve an ill-posed linear inverse problem. Numerical tests referring to far-field acquisitions show that L-SVD provides, with proper training on a well-organized dataset, superior performance in terms of reconstruction errors as compared to TSVD, allowing for the retrieval of faster spatial variations of the source. Indeed, L-SVD accommodates a priori information on the set of relevant unknown current distributions. Different from TSVD, which performs linear processing on a linear problem, L-SVD operates non-linearly on the data. A numerical analysis also underlines how the performance of the L-SVD degrades when the unknown source does not match the training dataset.

关键词： autoencoder deep neural networks inverse source singular value decomposition learned singular value decomposition

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High-dimensional multi-objective optimization of coupled cross-laminated timber walls building using deep learning

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2024年 136卷

作者： Das, Sourav Teweldebrhan, Biniam Tekle Tesfamariam, Solomon Univ Waterloo Dept Civil & Environm Engn 200 Univ Ave Waterloo ON N2L 3G1 Canada Univ British Columbia Sch Engn Okanagan Campus3333 Univ Way Kelowna BC V1V 1V7 Canada

This paper introduces a deep learning-based multi-objective optimization framework, applying for advancing the design of the Cross-Laminated Timber Coupled Wall system. While traditional optimization methods often struggle with the curse of dimensionality in high-dimensional problems, the approach proposed in this study employs an autoencoder to effectively reduce the dimensionality of the design space. Subsequently, a neural network establishes a mapping between input variables and latent spaces, with another neural network forming the crucial link between these latent variables and the output responses. The proposed framework is integrated into the design process of a 20-story Cross-Laminated Timber Coupled Wall system, where uncertainties inherent in connection elements are systematically addressed to optimize the structural parameters. The non- dominated sorting genetic algorithm-II is utilized to estimate optimal design variables by minimizing three conflicting objective functions, thereby generating a Pareto front. This optimized design is then bench-marked against three deterministic models with varying coupling beam shear strengths. A two-dimensional numerical model developed in OpenSees facilitates nonlinear time history analysis using 50 bi-directional ground motion records, representative of the seismicity of Vancouver, Canada. The results of this study not only highlight the efficacy of the deep learning-based framework in enhancing the structural integrity and resilience of highrise timber structures in seismic regions but also significantly contribute to the evolution of computational approaches in structural engineering.

关键词： Deep learning autoencoder Dimension reduction Multi-objective optimization Cross-laminated timber (CLT) Coupling beams Uncertainty

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Risk factor aggregation and stress testing

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QUANTITATIVE FINANCE 2024年第9期24卷 1327-1340页

作者： Packham, Natalie Berlin Sch Econ & Law D-10825 Berlin Germany

Stress testing refers to the application of adverse financial or macroeconomic scenarios to a portfolio. For this purpose, financial or macroeconomic risk factors are linked with asset returns, typically via a factor model. We expand the range of risk factors by adapting dimension-reduction techniques from unsupervised learning, namely PCA and autoencoders. This results in aggregated risk factors, encompassing a global factor, factors representing broad geographical regions, and factors specific to cyclical and defensive industries. As the adapted PCA and autoencoders provide an interpretation of the latent factors, this methodology is also valuable in other areas where dimension-reduction and explainability are critical.

关键词： Factor model Principal component analysis autoencoder (Explainable) machine learning Financial stress testing C53 C63 G17

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End-to-End Auto-Encoder System for Deep Residual Shrinkage Network for AWGN Channels

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Journal of Computer and Communications 2023年第5期11卷 161-176页

作者： Wenhao Zhao Shengbo Hu School of Mathematical Sciences Guizhou Normal University Guiyang China School of Big Data and Computer Science Guizhou Normal University Guiyang China

With the rapid development of deep learning methods, the data-driven approach has shown powerful advantages over the model-driven one. In this paper, we propose an end-to-end autoencoder communication system based on Deep Residual Shrinkage Networks (DRSNs), where neural networks (DNNs) are used to implement the coding, decoding, modulation and demodulation functions of the communication system. Our proposed autoencoder communication system can better reduce the signal noise by adding an “attention mechanism” and “soft thresholding” modules and has better performance at various signal-to-noise ratios (SNR). Also, we have shown through comparative experiments that the system can operate at moderate block lengths and support different throughputs. It has been shown to work efficiently in the AWGN channel. Simulation results show that our model has a higher Bit-Error-Rate (BER) gain and greatly improved decoding performance compared to conventional modulation and classical autoencoder systems at various signal-to-noise ratios.

关键词： Deep Residual Shrinkage Network autoencoder End-To-End Learning Communication Systems

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Improving Non-Line-of-Sight Identification in Cellular Positioning Systems Using a Deep Autoencoding and Generative Adversarial Network Model

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SENSORS 2024年第19期24卷 6494页

作者： Gao, Yanbiao Deng, Zhongliang Huo, Yuqi Chen, Wenyan Beijing Univ Posts & Telecommun Sch Elect Engn Beijing 100876 Peoples R China Northeastern Univ Sch Mech Engn & Automat Shenyang 110819 Peoples R China

Positioning service is a critical technology that bridges the physical world with digital information, significantly enhancing efficiency and convenience in life and work. The evolution of 5G technology has proven that positioning services are integral components of current and future cellular networks. However, positioning accuracy is hindered by non-line-of-sight (NLoS) propagation, which severely affects the measurements of angles and delays. In this study, we introduced a deep autoencoding channel transform-generative adversarial network model that utilizes line-of-sight (LoS) samples as a singular category training set to fully extract the latent features of LoS, ultimately employing a discriminator as an NLoS identifier. We validated the proposed model in 5G indoor and indoor factory (dense clutter, low base station) scenarios by assessing its generalization capability across different scenarios. The results indicate that, compared to the state-of-the-art method, the proposed model markedly diminished the utilization of device resources and achieved a 2.15% higher area under the curve while reducing computing time by 12.6%. This approach holds promise for deployment in future positioning terminals to achieve superior localization precision, catering to commercial and industrial Internet of Things applications.

关键词： non-line-of-sight generative adversarial network autoencoder localization

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Radar Emitter Structure Inversion Method Based on Metric and Deep Learning

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REMOTE SENSING 2023年第19期15卷 4844-4844页

作者： Liu, Lutao Zhang, Wei Jiang, Yilin Yang, Yaozu Song, Yu Harbin Engn Univ Key Lab Adv Marine Commun & Informat Technol Minist Ind & Informat Technol Harbin 150001 Peoples R China Harbin Inst Technol Harbin 150001 Peoples R China

With the rapid development of modern military countermeasure technology, deep distinguish hostile radar is essential in electronic warfare. However, traditional radio frequency (RF) feature extraction methods can easily be interfered by signal information and fail due to the lack of research on RF feature extraction techniques for complex situations. Therefore, in this paper, first, the generation mechanism of RF structure information is discussed, and the influence of different signal information introduced by different operating parameters on RF structure feature extraction is analyzed. Then, an autoencoder (AE) network and an autoencoder metric (AEM) network are designed, introducing metric learning ideas, so that the extracted deep RF structure features have good stability and divisibility. Finally, radar emitter structure (RES) inversion is realized using the centroid-matching method. The experimental results demonstrate that this method exhibits good inversion performance under variable operating parameters (modulation type, frequency, bandwidth, input power). RES inversion including unknown operating parameters is realized for the first time, and it is shown that metric learning has the advantage of separability of RF feature extraction, which can provide an idea in emitter and RF feature extraction.

关键词： radar emitter structure autoencoder metric learning deep learning radio frequency

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A novel teacher-student hierarchical approach for learning primitive information

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 246卷

作者： Zhang, Haoke Huang, Yiyong Han, Wei Chen, Hongbo Xiong, Dan Zhang, Chuanfu Medeiros, Elias Paulino de Albuquerque, Victor Hugo C. Sun Yat Sen Univ Sch Syst Sci & Engn Guangzhou 510006 Peoples R China Acad Mil Sci Natl Innovat Inst Def Technol Beijing 100071 Peoples R China Univ Fed Ceara Dept Teleinformat Engn Fortaleza CE Brazil

In this study, we focus on the challenge of robot motion generation, which is critical for improving the ability of robots to perform downstream tasks. To this end, inspired by the idea of variational inference, we propose a novel teacher-student hierarchical approach for learning continuous primitive spaces to generate robot motions. We first learn a variational auto-encoder (VAE) based on Gated Recurrent Unit (GRU) from demonstrations, the encoder is used as a teacher model to encode trajectories to primitive information, and the decoder aims to decode latent information to joint trajectories. To learn a primitive action space for the high-level model, we propose a novel learning method to train a student model. We utilize the trained teacher to encode trajectories to labels, and use the Kullback-Leibler (KL) divergence and the reconstruction errors between the label and the predicted primitive information as the loss to train a student model. We then utilize trained student models to guide high-level policy learning. We evaluate our approach on three different robot datasets. Experiments demonstrate that our method obtains the ability to generate new motion and discover common information across tasks, it also can be used to accelerate downstream task learning.

关键词： Hierarchical learning Motion primitives Variational inference autoencoder Teacher and student

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Anomaly Detection Based on Time Series Data of Hydraulic Accumulator

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SENSORS 2022年第23期22卷 9428-9428页

作者： Park, Min-Ho Chakraborty, Sabyasachi Vuong, Quang Dao Noh, Dong-Hyeon Lee, Ji-Woong Lee, Jae-Ung Choi, Jae-Hyuk Lee, Won-Ju Korea Maritime & Ocean Univ Div Marine Engn Busan 49112 South Korea Korea Maritime & Ocean Univ Interdisciplinary Major Maritime & AI Convergence Busan 49112 South Korea Terenz Co Ltd Busan 48060 South Korea Korea Maritime & Ocean Univ Div Marine Syst Engn Busan 49112 South Korea Hwajin Enterprise Co Ltd 25Mieumsandan 2 Ro Busan 46748 South Korea

Although hydraulic accumulators play a vital role in the hydraulic system, they face the challenges of being broken by continuous abnormal pulsating pressure which occurs due to the malfunction of hydraulic systems. Hence, this study develops anomaly detection algorithms to detect abnormalities of pulsating pressure for hydraulic accumulators. A digital pressure sensor was installed in a hydraulic accumulator to acquire the pulsating pressure data. Six anomaly detection algorithms were developed based on the acquired data. A threshold averaging algorithm over a period based on the averaged maximum/minimum thresholds detected anomalies 2.5 h before the hydraulic accumulator failure. In the support vector machine (SVM) and XGBoost model that distinguish normal and abnormal pulsating pressure data, the SVM model had an accuracy of 0.8571 on the test set and the XGBoost model had an accuracy of 0.8857. In a convolutional neural network (CNN) and CNN autoencoder model trained with normal and abnormal pulsating pressure images, the CNN model had an accuracy of 0.9714, and the CNN autoencoder model correctly detected the 8 abnormal images out of 11 abnormal images. The long short-term memory (LSTM) autoencoder model detected 36 abnormal data points in the test set.

关键词： accumulator pulsating pressure data CNN autoencoder anomaly detection

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A New Intrusion Detection Algorithm AE-3WD for Industrial Control Network

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Journal of New Media 2022年第4期4卷 205-217页

作者： Yongzhong Li Cong Li Yuheng Li Shipeng Zhang School of Computer Science and Engineering Taizhou Institute of Sci.and Tec.NJUSTTaizhou225300China School of Computer Science Jiangsu University of Science and TechnologyZhenjiang212003China Suzhou Institute of Technology Jiangsu University of Science and TechnologySuzhou215600China

In this paper,we propose a intrusion detection algorithm based on auto-encoder and three-way decisions(AE-3WD)for industrial control networks,aiming at the security problem of industrial control *** ideology of deep learning is similar to the idea of intrusion *** learning is a kind of intelligent algorithm and has the ability of automatically *** uses self-learning to enhance the experience and dynamic classification *** use deep learning to improve the intrusion detection rate and reduce the false alarm rate through learning,a denoising autoencoder and three-way decisions intrusion detection method AE-3WD is proposed to improve intrusion detection *** the processing,deep learning autoencoder is used to extract the features of high-dimensional data by combining the coefficient penalty and reconstruction loss function of the encode layer during the training mode.A multi-feature space can be constructed by multiple feature extractions from autoencoder,and then a decision for intrusion behavior or normal behavior is made by three-way ***-KDD data sets are used to the *** experiment results prove that our proposed method can extract meaningful features and effectively improve the performance of intrusion detection.

关键词： Industrial control network security intrusion detection deep learning autoencoder three-way decision

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Scalable Data-based Diagnostic Concept -Introduction and application in automotive field

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IFAC-PapersOnLine 2024年第4期58卷 240-245页

作者： Andreas Schmitz Marco Decker Clemens Gühmann Roland Serway Chair of Electronic Measurement and Diagnostic Technology Technische Universität Berlin Einsteinufer 17 10587 Berlin Germany Software and Systems IAV GmbH Carnotstr. 1 10587 Berlin Germany

This paper introduces and applies the Scalable Data-based Diagnostic Concept. At its core, the concept consists of (Kernel) Principal Component Analysis (PCA) and autoencoder (AE), which are used to perform accurate fault diagnosis in technical systems, e.g. in automotive or railroad sectors, including various sub-methods for fault detection, identification and isolation. The analysis of real automotive fault cases is done, where a new smoothed comparative detection chart is presented. The findings prove the necessity of choosing the right method, regarding efficiency and the inherent data structure, which is one of the main objectives of the comprehensive scalable diagnostic concept.

关键词： Fault Detection Fault Diagnosis Scalability (Measures) Process Monitoring PCA KPCA autoencoder Area Chart Visualization

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