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Radar pre-sorting algorithm based on autoencoder and LSTM

AEU-INTERNATIONAL JOURNAL OF ELECTRONICS AND COMMUNICATIONS 2024年 187卷

作者： Jiang, Yilin Shi, Shaoxiong Zhang, Fangyuan Huang, Wuqi Harbin Engn Univ Coll Informat & Commun Engn Harbin 150001 Peoples R China Harbin Univ Sci & Technol Harbin Peoples R China AVIC United Technol Ctr Electromagnet Spectrum Col Harbin Peoples R China

As the electromagnetic environment becomes increasingly complex, most current radar signal sorting methods are unsustainable. They often perform poorly when dealing with unknown radar types and low-frequency radar pulse data. This paper introduces a radar pre-sorting algorithm based on autoencoder and LSTM. The algorithm utilizes multi-dimensional information such as pulse width, carrier frequency, and time of arrival. The autoencoder network is employed to achieve automatic feature extraction and clustering, enhancing the extraction of latent features in the data. The proposed network model mainly consists of three parts: an encoding module composed of a convolutional neural network (CNN), a feature aggregation module composed of long short-term memory (LSTM), and a decoding module obtained through a convolutional autoencoder, referred to as CLDE (CNN-LSTM-Decode). The encoding module extracts features from multi-dimensional data to obtain compressed features, the feature accumulation module processes the compressed features, further extracting hidden features between pulses. Subsequently, the decoding module determines the pulse modulation type of each pulse, achieving the purpose of radar pulse signal pre-sorting. Simulation results show that this network structure effectively pre-classifies unknown radar signals and has a high recognition rate for low-frequency pulses. Additionally, CLDE exhibits high reliability and stability in environments with pulse loss.

关键词： CLDE Low-frequency autoencoder LSTM

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Itakura-Saito distance based autoencoder for dimensionality reduction of mass spectra

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CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 2017年 167卷 63-68页

作者： Nozaki, Yuji Nakamoto, Takamichi Tokyo Inst Technol Midori Ku 4259 Nagatsuta Yokohama Kanagawa 2268503 Japan

Small signals may contain important information. Mass spectra of chemical compounds are usually given in a format of sparse high-dimensional data-of large dynamic range. As peaks at high m/z (mass to charge ratio) region of a mass spectrum contribute to sensory information, they should not be ignored during the dimensionality reduction process even if the peak is small. However, in most of dimensionality reduction techniques, large peaks in a dataset are typically more emphasized than tiny peaks when Euclidean space is assessed. autoencoders are widely used nonlinear dimensionality reduction technique, which is known as one special form of artificial neural networks to gain a compressed, distributed representation after learning. In this paper, we present an autoencoder which uses IS (Itakura-Saito) distance as its cost function to achieve a high capability of approximation of small target inputs in dimensionality reduction. The result of comparative experiments showed that our new autoencoder achieved the higher performance in approximation of small targets than that of the autoencoders with conventional cost functions such as the mean squared error and the cross-entropy.

关键词： Dimensionality reduction Mass spectra autoencoder Itakura-Saito distance

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Efficient Blind Source Separation Method for fMRI Using autoencoder and Spatiotemporal Sparsity Constraints

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IEEE ACCESS 2023年 11卷 50364-50381页

作者： Khalid, Muhammad Usman Khawaja, Bilal A. Nauman, Malik Muhammad Imam Mohammad Ibn Saud Islamic Univ Coll Comp & Informat Sci Riyadh 11564 Saudi Arabia Islamic Univ Madinah Fac Engn Dept Elect Engn Madinah 42351 Saudi Arabia Univ Brunei Darussalam Fac Integrated Technol Bandar Seri Begawan BE1410 Brunei

Diversity measures exploited by blind source separation (BSS) methods are usually based on either statistical attributes/geometrical structures or sparse/overcomplete (underdetermined) representations of the signals. This leads to some inefficient BSS methods that are derived from either a mixing matrix (mm), sparse weight vectors (sw), or sparse code (sc). In contrast, the proposed efficient method, sparse spatiotemporal BSS (ssBSS), avoids computational complications associated with lag sets, deflation strategy, and repeated error matrix computation using the whole dataset. It solves the spatiotemporal data reconstruction model (STEM) with l(1)-norm penalization and l(0)-norm constraints using Neumann's alternating projection lemma and block coordinate descent approach to yield the desired bases. Its specific solution allows incorporating a three-step autoencoder and univariate soft thresholding for a block update of the source/mixing matrices. Due to the utilization of both spatial and temporal information, it can better distinguish between the sources and yield interpretable results. These steps also make ssBSS unique because, to the best of my knowledge, no mixing matrix based BSS method incorporates sparsity of both features and a multilayer network structure. The proposed method is validated using synthetic and various functional magnetic resonance imaging (fMRI) datasets. Results reveal the superior performance of the proposed ssBSS method compared to the existing methods based on mmBSS and swBSS. Specifically, overall, a 14% increase in the mean correlation value and 91% reduction in computation time over the ssICA algorithm was discovered.

关键词： Sparse representation autoencoder BSS fMRI activation maps PCA l(0)-norm l(1)-norm

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Deep autoencoder-based community detection in complex networks with particle swarm optimization and continuation algorithms

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021年第3期40卷 4517-4533页

作者： Al-Andoli, Mohammed Cheah, Wooi Ping Tan, Shing Chiang Multimedia Univ Fac Informat Sci & Technol Jalan Ayer Keroh Lama Melaka 75450 Melaka Malaysia Univ Nottingham Ningbo China Fac Sci & Engn Sch Comp Sci Ningbo Peoples R China

Detecting communities is an important multidisciplinary research discipline and is considered vital to understand the structure of complex networks. Deep autoencoders have been successfully proposed to solve the problem of community detection. However, existing models in the literature are trained based on gradient descent optimization with the backpropagation algorithm, which is known to converge to local minima and prove inefficient, especially in big data scenarios. To tackle these drawbacks, this work proposed a novel deep autoencoder with Particle Swarm Optimization (PSO) and continuation algorithms to reveal community structures in complex networks. The PSO and continuation algorithms were utilized to avoid the local minimum and premature convergence, and to reduce overall training execution time. Two objective functions were also employed in the proposed model: minimizing the cost function of the autoencoder, and maximizing the modularity function, which refers to the quality of the detected communities. This work also proposed other methods to work in the absence of continuation, and to enable premature convergence. Extensive empirical experiments on 11 publically-available real-world datasets demonstrated that the proposed method is effective and promising for deriving communities in complex networks, as well as outperforming state-of-the-art deep learning community detection algorithms.

关键词： Complex networks community detection autoencoder particle swarm optimization continuation method

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Iris Recognition using Multi Objective Artificial Bee Colony Optimization Algorithm with autoencoder Classifier

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NEURAL PROCESSING LETTERS 2022年第4期54卷 3489-3505页

作者： Sheela, S., V Radhika, K. R. BMS Coll Engn Dept Informat Sci & Engn Bangalore Karnataka India

In recent decades, iris recognition is a trustworthy and important biometric model for human recognition. Criminal to commercial products, citizen confirmation and border control are few application areas. The research work is a deep learning based integrated model for accurate iris detection and recognition. Initially, eye images are considered from two datasets, the Chinese Academy of Sciences Institute of Automation (CASIA) and the Indian Institute of Technology (IIT) Delhi v1.0. Iris region is accurately segmented using Daugman's algorithm and Circular Hough Transform (CHT). Feature extraction is hybrid that is performed using Dual Tree Complex Wavelet Transform (DTCWT), Gabor filter, Local Binary Pattern (LBP) and Gray Level Co-occurrence Matrix (GLCM) from the segmented iris regions. A Multiobjective Artificial Bee Colony (MABC) algorithm is proposed to eliminate noisy and redundant feature vectors by estimating consistent information. In MABC algorithm, two multi-objective functions are formulated as reduction in number of features and classification error rate. The selected active feature vectors are given as input to autoencoder classification for iris recognition. The experimental outcome shows that MABC-autoencoder model obtained 99.67% and 98.73% accuracy on CASIA-Iris, and IIT Delhi v1.0 iris datasets. Performance evaluation is based on accuracy, specificity, Critical Success Index (CSI), sensitivity, Fowlkes Mallows (FM) index, and Mathews Correlation Coefficient (MCC).

关键词： autoencoder Circular Hough transform Daugman's algorithm Iris recognition Local binary pattern Multi objective artificial bee colony

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Improved autoencoder for unsupervised anomaly detection

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INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS 2021年第12期36卷 7103-7125页

作者： Cheng, Zhen Wang, Siwei Zhang, Pei Wang, Siqi Liu, Xinwang Zhu, En Natl Univ Def Technol Sch Comp 109 Deya Rd Changsha 410073 Hunan Peoples R China

Deep autoencoder-based methods are the majority of deep anomaly detection. An autoencoder learning on training data is assumed to produce higher reconstruction error for the anomalous samples than the normal samples and thus can distinguish anomalies from normal data. However, this assumption does not always hold in practice, especially in unsupervised anomaly detection, where the training data is anomaly contaminated. We observe that the autoencoder generalizes so well on the training data that it can reconstruct both the normal data and the anomalous data well, leading to poor anomaly detection performance. Besides, we find that anomaly detection performance is not stable when using reconstruction error as anomaly score, which is unacceptable in the unsupervised scenario. Because there are no labels to guide on selecting a proper model. To mitigate these drawbacks for autoencoder-based anomaly detection methods, we propose an Improved autoencoder for unsupervised Anomaly Detection (IAEAD). Specifically, we manipulate feature space to make normal data points closer using anomaly detection-based loss as guidance. Different from previous methods, by integrating the anomaly detection-based loss and autoencoder's reconstruction loss, IAEAD can jointly optimize for anomaly detection tasks and learn representations that preserve the local data structure to avoid feature distortion. Experiments on five image data sets empirically validate the effectiveness and stability of our method.

关键词： autoencoder deep learning unsupervised anomaly detection

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Heterogeneous domain adaptation network based on autoencoder

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JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING 2018年 117卷 281-291页

作者： Wang, Xuesong Ma, Yuting Cheng, Yuhu Zou, Liang Rodrigues, Joel J. P. C. China Univ Min & Technol Sch Informat & Control Engn Xuzhou 221116 Jiangsu Peoples R China Univ British Columbia Dept Elect & Comp Engn Vancouver BC V6T 1Z4 Canada Natl Inst Telecommun Inatel BR-37540000 Santa Rita Do Sapucai MG Brazil Univ Beira Interior Inst Telecomunicacoes P-6201001 Covilha Portugal ITMO Univ St Petersburg 191002 Russia Univ Fortaleza UNIFOR BR-60811905 Fortaleza Ceara Brazil

Heterogeneous domain adaptation is a more challenging problem than homogeneous domain adaptation. The transfer effect is not ideally caused by shallow structure which cannot adequately describe the probability distribution and obtain more effective features. In this paper, we propose a heterogeneous domain adaptation network based on autoencoder, in which two sets of autoencoder networks are used to project the source-domain and target-domain data to a shared feature space to obtain more abstractive feature representations. In the last feature and classification layer, the marginal and conditional distributions can be matched by empirical maximum mean discrepancy metric to reduce distribution difference. To preserve the consistency of geometric structure and label information, a manifold alignment term based on labels is introduced. The classification performance can be improved further by making full use of label information of both domains. The experimental results of 16 cross-domain transfer tasks verify that HDANA outperforms several state-of-the-art methods. (C) 2017 Elsevier Inc. All rights reserved.

关键词： Heterogeneous domain adaptation autoencoder Maximum mean discrepancy Manifold alignment

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Neighborhood preserving embedding with autoencoder

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DIGITAL SIGNAL PROCESSING 2024年 145卷

作者： Rana, Ruisheng Wang, Jinping Fang, Bin Yang, Weiming Chongqing Normal Univ Coll Comp & Informat Sci Chongqing 401331 Peoples R China Chongqing Univ Coll Comp Sci Chongqing 400044 Peoples R China

Neighborhood preserving embedding (NPE) is a classical method for dimensionality reduction (DR), and it is a linear version of the locally linear embedding method. However, NPE and all its variants only consider the one-way mapping from high-dimensional space to low-dimensional space. The projected low-dimensional data may not accurately and effectively "represent" the original samples. To address this problem, we improve NPE based on linear autoencoder. The conventional projection of NPE is considered as the encoding stage, and the decoder stage is a reconstruction from the low-dimensional space to the original high-dimensional space, which is the key to maintaining more significant information. Based on this, we propose a new NPE method called NPEAE (neighborhood preserving embedding with autoencoder) in this paper. NPEAE performs excellently in face recognition, handwritten character categorization, object classification, etc. The experiments on MNIST, COIL-20, the Extended Yale B, Olivetti Research Laboratory (ORL), and FERET show that NPEAE has a higher recognition accuracy than other comparative methods.

关键词： Neighborhood preserving embedding autoencoder Dimensionality reduction Manifold learning

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Bilingual autoencoder-based efficient harmonization of multi-source private data for accurate predictive modeling

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INFORMATION SCIENCES 2021年 568卷 403-426页

作者： Lee, Taek-Ho Lee, Junghye Jun, Chi-Hyuck Pohang Univ Sci & Technol POSTECH Dept Ind & Management Engn 77 Cheongam Ro Pohang 37673 South Korea Ulsan Natl Inst Sci & Technol UNIST Dept Ind Engn 50 UNIST Gil Ulsan 44919 South Korea

Sharing electronic health record data is essential for advanced analysis, but may put sensitive information at risk. Several studies have attempted to address this risk using contextual embedding, but with many hospitals involved, they are often inefficient and inflexible. Thus, we propose a bilingual autoencoder-based model to harmonize local embeddings in different spaces. Cross-hospital reconstruction of embeddings makes encoders map embeddings from hospitals to a shared space and align them spontaneously. We also suggest two-phase training to prevent distortion of embeddings during harmonization with hospitals that have biased information. In experiments, we used medical event sequences from the Medical Information Mart for Intensive Care-III dataset and simulated the situation of multiple hospitals. For evaluation, we measured the alignment of events from different hospitals and the prediction accuracy of a patient's diagnosis in the next admission in three scenarios in which local embeddings do not work. The proposed method efficiently harmonizes embeddings in different spaces, increases prediction accuracy, and gives flexibility to include new hospitals, so is superior to previous methods in most cases. It will be useful in predictive tasks to utilize distributed data while preserving private information. (c) 2021 The Authors. Published by Elsevier Inc. This is an open access article under the CC BYNC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Distributed EHR Contextual embedding Space alignment autoencoder Predictive tasks

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ARCADE: Adversarially Regularized Convolutional autoencoder for Network Anomaly Detection

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IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT 2023年第2期20卷 1305-1318页

作者： Lunardi, Willian Tessaro Lopez, Martin Andreoni Giacalone, Jean-Pierre Technol & Innovat Inst Secure Syst Res Ctr Abu Dhabi U Arab Emirates

As the number of heterogenous IP-connected devices and traffic volume increase, so does the potential for security breaches. The undetected exploitation of these breaches can bring severe cybersecurity and privacy risks. Anomaly-based Intrusion Detection Systems (IDSs) play an essential role in network security. In this paper, we present a practical unsupervised anomaly-based deep learning detection system called ARCADE (Adversarially Regularized Convolutional autoencoder for unsupervised network anomaly DEtection). With a convolutional autoencoder (AE), ARCADE automatically builds a profile of the normal traffic using a subset of raw bytes of a few initial packets of network flows so that potential network anomalies and intrusions can be efficiently detected before they cause more damage to the network. ARCADE is trained exclusively on normal traffic. An adversarial training strategy is proposed to regularize and decrease the AE's capabilities to reconstruct network flows that are out-of-the-normal distribution, thereby improving its anomaly detection capabilities. The proposed approach is more effective than state-of-the-art deep learning approaches for network anomaly detection. Even when examining only two initial packets of a network flow, ARCADE can effectively detect malware infection and network attacks. ARCADE presents 20 times fewer parameters than baselines, achieving significantly faster detection speed and reaction time.

关键词： Unsupervised anomaly detection autoencoder generative adversarial networks automatic feature extraction deep learning cybersecurity

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