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autoencoder-Embedded Iterated Local Search for Energy-Minimized Task Schedules of Human-Cyber-Physical Systems

IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2025年 22卷 512-522页

作者： Lin, Chengran Cao, ZhengCai Zhou, MengChu Beijing Univ Chem Technol Coll Informat Sci & Technol Beijing 100029 Peoples R China New Jersey Inst Technol Dept Elect & Comp Engn Newark NJ 07102 USA

This work considers a task scheduling problem with deadline constraints in human-cyber-physical systems. To find its energy-efficient schedules in a short time, an autoencoder-embedded iterated local search algorithm is proposed to solve it. Iterated local search is selected as a main scheduler. In order to handle real-time requirements and high computational load involved in the problem solution, a Long Short-Term Memory-based autoencoder model (LSTM-AE) is constructed to capture the relevant and complementary features of the considered problem. The model is used to find low-order and high-fit sub-solutions via unsupervised end-to-end learning, and generates promising solutions in an informative low-dimensional solution space. To further reduce computational burden, a two-stage optimization framework is constructed, which includes an off-line training phase and an online optimization one. The former trains LSTM-AE by using expert knowledge and historical data. The latter designs optimal resource allocation strategies to build a high-quality initial solution. Then, LSTM-AE-assisted local search operators are proposed and used to reform the initial solution and generate better ones. Various numerical experiments are performed to compare the proposed method with several classic heuristics and some recently-developed methods. The results show its superiority over them. Note to Practitioners-In human-cyber-physical systems, a task scheduling problem is usually solved by using heuristics due to the limited computational resources. Nevertheless, fast dispatching rules tend to perform poorly. Meta-heuristics can find a relatively high-quality schedule but are time-consuming, especially for a population-based algorithm that requires to evaluate a fitness function for many candidate solutions at each iteration. To balance computational burden and solution quality, our idea is to combine machine-learning methods with meta-heuristics. Specially, we integrate a long short-term

关键词： Task scheduling energy minimization iterated local search long short term memory autoencoder human-cyber-physical systems machine learning

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autoencoder-Based Discriminant Locality-Preserving Projections for Fault Diagnosis

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2025年 74卷

作者： Ran, Ruisheng Wang, Ting Zhang, Wenfeng Fang, Bing Chongqing Normal Univ Coll Comp & Informat Sci Coll Intelligent Sci Chongqing 401331 Peoples R China Chongqing Univ Coll Comp Sci Chongqing Peoples R China

In complex industrial production environments, the efficacy of fault diagnostic techniques has become increasingly important and can enhance the reliability and safety of systems. In recent years, the discriminant locality-preserving projection (DLPP) algorithm has shown significant effectiveness in extracting meaningful features from complex industrial data. However, DLPP involves only the operation of projecting high-dimensional data onto a lower dimensional space, which is a one-way mapping process and lacks the verification of whether the low-dimensional data projected can accurately and effectively represent the original data. This might result in the loss of vital information in the original data, consequently limiting the performance of DLPP. In this article, we introduce a novel DLPP approach denoted as autoencoder-based DLPP (DLPP-AE), which is predicated upon the autoencoder. DLPP-AE establishes a bidirectional mapping process: in the encoding stage, DLPP serves as a mapping mechanism that transforms the initial high-dimensional data into a low-dimensional embedded representation;whereas in the decoding stage, the low-dimensional embedded data generated in the encoding stage are remapped back to its original high-dimensional form. This effectively resolves the issue of DLPP's inability to perform reverse validation on low-dimensional embeddings. To evaluate the performance of the proposed method, we conducted a comprehensive case study using three laboratory datasets and one real industrial dataset. The experimental results confirm the superior fault diagnosis capability of the DLPP-AE method.

关键词： Fault diagnosis Feature extraction autoencoders Principal component analysis Mathematical models Data mining Encoding Decoding Manifold learning Training autoencoder discriminant locality-preserving projections (DLPPs) fault diagnosis feature extraction manifold learning

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autoencoder-based drug-virus association prediction with reliable negative sample selection: A case study with COVID-19

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BIOPHYSICAL CHEMISTRY 2025年 322卷 107434页

作者： Aruna, A. S. Babu, K. R. Remesh Deepthi, K. Govt Engn Coll Palakkad Dept Informat Technol Palakkad 678633 Kerala India APJ Abdul Kalam Technol Univ Thiruvananthapuram Kerala India Coll Engn Vadakara Dept Comp Sci Kozhikode 673105 Kerala India Cent Univ Kerala Govt India Dept Comp Sci Kasaragod 671320 Kerala India

Emergence of viruses cause unprecedented challenges and thus leading to wide-ranging consequences today. The world has faced massive disruptions like COVID-19 and continues to suffer in terms of public health and world economy. Fighting with this emergence of viruses and its reemergence plays a critical role in the health care industry. Identification of novel virus-drug associations is a vital step in drug discovery. Prediction and prioritization of novel virus-drug associations through computational approaches is an alternative and best choice considering the cost and risk of biological experiments. This study proposes a method, KR-AEVDA that relies on k-nearest neighbor based reliable negative sample selection and autoencoder based feature extraction to explore promising virus-drug associations for further experimental validation. The method analyzes complex relationships among drugs and viruses by investigating similarity and association data between drugs and viruses. It generates feature vectors from the similarity data, and reliable negative samples are extracted through an effective distance-based algorithm from the unlabeled samples in the dataset. Then high level features are extracted via an autoencoder and is fed to an ensemble classifier for inferring novel associations. Experimental results on three different datasets showed that KR-AEVDA reliably attained better performance than other stateof-the-art methods. Molecular docking is carried out between the top-predicted drugs and the crystal structure of the SARS-CoV-2's main protease to further validate the predictions. Case studies for SARS-CoV-2 illustrate the effectiveness of KR-AEVDA in identifying potential virus-drug associations.

关键词： Drug repurposing autoencoder K -nearest neighbor COVID-19 Molecular docking

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Reconstruction of pile-up events using a one-dimensional convolutional autoencoder for the NEDA detector array

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Nuclear Science and Techniques 2025年第2期36卷 62-70页

作者： J.M.Deltoro G.Jaworski A.Goasduff V.González A.Gadea M.Palacz J.J.Valiente-Dobón J.Nyberg S.Casans A.E.Navarro-Antón E.Sanchis G.de Angelis A.Boujrad S.Coudert T.Dupasquier S.Ertürk O.Stezowski R.Wadsworth Department of Electronic Engineering University of ValenciaBurjassotValenciaSpain Heavy Ion Laboratory University of Warsaw02-093 WarsawPoland Istituto Nazionale di Fisica Nucleare Laboratori Nazionali di LegnaroLegnaroItaly Instituto de Física Corpuscular CSIC-Universidad de Valencia46980 PaternaValenciaSpain Department of Physics and Astronomy Uppsala UniversityUppsalaSweden GANIL CEA/DRF-CNRS/IN2P3Bvd.Henri Becquerel14076 CaenFrance CNRS IN2P3IPN LyonUniversitéLyon 169622 VilleurbanneFrance Department of Physics Nigde Omer Halisdemir University51240 NigdeTurkey Department of Physics University of YorkHeslingtonYork YO105DDUK

Pulse pile-up is a problem in nuclear spectroscopy and nuclear reaction studies that occurs when two pulses overlap and distort each other,degrading the quality of energy and timing *** methods have been used for pile-up rejection,both digital and analogue,but some pile-up events may contain pulses of interest and need to be *** paper proposes a new method for reconstructing pile-up events acquired with a neutron detector array(NEDA)using an one-dimensional convolutional autoencoder(1D-CAE).The datasets for training and testing the 1D-CAE are created from data acquired from the *** new pile-up signal reconstruction method is evaluated from the point of view of how similar the reconstructed signals are to the original ***,it is analysed considering the result of the neutron-gamma discrimination based on charge comparison,comparing the result obtained from original and reconstructed signals.

关键词： 1D-CAE autoencoder CAE Convolutional neural network(CNN) Neutron detector Neutron-gamma discrimination(NGD) Machine learning Pulse shape discrimination Pile-up pulse

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MSS-PAE: Saving autoencoder-based Outlier Detection from Unexpected Reconstruction

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PATTERN RECOGNITION 2025年 163卷

作者： Tan, Xu Yang, Jiawei Chen, Junqi Rahardja, Sylwan Rahardja, Susanto Northwestern Polytech Univ Sch Marine Sci & Technol Xian Peoples R China Univ Turku Fac Technol Turku Finland Univ Eastern Finland Sch Comp Joensuu Finland Singapore Inst Technol Engn Cluster Singapore Singapore

The autoencoder (AE) is popular in Outlier Detection (OD) now due to its strong modeling ability. However, AE-based OD methods face the unexpected reconstruction problem: outliers are reconstructed with low errors, impeding their distinction from inliers. This stems from two aspects. First, AE may overconfidently produce good reconstructions in regions where outliers or potential outliers exist while using the mean squared error. To address this, the aleatoric uncertainty was introduced to construct the Probabilistic autoencoder (PAE), and the Weighted Negative Log-Likelihood (WNLL) was proposed to enlarge the score disparity between inliers and outliers. Second, AE focuses on global modeling yet lacks the perception of local information. Therefore, the Mean-Shift Scoring (MSS) method was proposed to utilize the local relationship of data to reduce the false inliers caused by AE. Moreover, experiments on 32 real-world OD datasets proved the effectiveness of the proposed methods. The combination of WNLL and MSS achieved 45% relative performance improvement compared to the best baseline. In addition, MSS improved the detection performance of multiple AE-based outlier detectors by an average of 20%. The proposed methods have the potential to advance AE's development in OD.

关键词： Outlier detection autoencoder Uncertainty estimation Mean-shift

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Dictionary trained attention constrained low rank and sparse autoencoder for hyperspectral anomaly detection

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NEURAL NETWORKS 2025年 181卷 106797页

作者： Hu, Xing Li, Zhixuan Luo, Lingkun Karimi, Hamid Reza Zhang, Dawei Univ Shanghai Sci & Technol Sch Opt Elect & Comp Engn 516 Jungong Rd Shanghai 200093 Peoples R China Shanghai Jiao Tong Univ Sch Aeronaut & Astronaut 800 Dongchuang Rd Shanghai 200240 Peoples R China Politecn Milan Dept Mech Engn I-20156 Milan Italy

Dictionary representations and deep learning autoencoder (AE) models have proven effective in hyperspectral anomaly detection. Dictionary representations offer self-explanation but struggle with complex scenarios. Conversely, autoencoders can capture details in complex scenes but lack self-explanation. Complex scenarios often involve extensive spatial information, making its utilization crucial in hyperspectral anomaly detection. To effectively combine the advantages of both methods and address the insufficient use of spatial information, we propose an attention constrained low-rank and sparse autoencoder for hyperspectral anomaly detection. This model includes two encoders: an attention constrained low-rank autoencoder (AClrAE) trained with a background dictionary and incorporating a Global Self-Attention Module (GAM) to focus on global spatial information, resulting in improved background reconstruction;and an attention constrained sparse autoencoder (ACsAE) trained with an anomaly dictionary and incorporating a Local Self-Attention Module (LAM) to focus on local spatial information, resulting in enhanced anomaly reconstruction. Finally, to merge the detection results from both encoders, a nonlinear fusion scheme is employed. Experiments on multiple real and synthetic datasets demonstrate the effectiveness and feasibility of the proposed method.

关键词： Hyperspectral Anomaly Detection Attention mechanisms autoencoder Deep learning Sparse Representation

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DMGAE: An interpretable representation learning method for directed scale-free networks based on autoencoder and masking

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INFORMATION PROCESSING & MANAGEMENT 2025年第3期62卷

作者： Yang, Qin-Cheng Yang, Kai Hu, Zhao-Long Li, Minglu Zhejiang Normal Univ Sch Comp Sci & Technol Jinhua 321004 Peoples R China Yangzhou Univ Sch Informat Engn Yangzhou 225127 Peoples R China

Although existing graph self-supervised learning approaches have paid attention to the directed nature of networks, they have often overlooked the ubiquitous scale-free attributes. This oversight has resulted in a theoretical gap in understanding graph self-supervised learning from the perspective of network structure. In this paper, we study the degree distribution characteristics of source and target nodes in directed scale-free networks, encompassing node and edge dimensions. Our theoretical analysis reveals the relationship between the average degree of nodes and their average in-degree and out-degree, which is instrumental in discerning positive and negative edges, as well as edge directionality. Here positive edges are the ones that exists in the original graph, and negative edges are the ones that not exists in the original graph. Furthermore, we uncover negative edges connecting to central nodes and positive edges to peripheral nodes to be less predictable. Based on these crucial theoretical insights, we propose DMGAE (Directed Masked Graph autoencoder), a novel representation learning method for directed scale-free networks that offers interpretability. The DMGAE method employs a weighted graph based on edges to replace the original graph structure. It integrates a masking approach based on the weight of the edges. Additionally, it incorporates an adaptive negative sampling method, edge decoder and a degree decoder based on the difference between the in- degree and out-degree of the node. This enhances the model's capability to learn edges and discern their directions. Empirical studies on extensive real-world network data show that, compared to the state-of-the-art methods, DMGAE not only generally has superior learning performance on directed networks, but also performs exceedingly well on undirected networks.

关键词： Complex networks Representation learning Directed graph Scale-free networks Masking autoencoder

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Supervised autoencoder denoiser for non-stationarity in multi-session EEG-based BCI

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JOURNAL OF NEURAL ENGINEERING 2025年第2期22卷 026043-026043页

作者： Ofer, Avin Ophir, Almagor Yoav, Noah Roman, Rosipal Oren, Shriki Ben Gurion Univ Negev Dept Cognit & Brain Sci Beer Sheva Israel Slovak Acad Sci Inst Measurement Sci Bratislava Slovakia

Objective. Non-stationarity in electroencephalogram (EEG) signals poses significant challenges for the performance and implementation of brain-computer interfaces (BCIs). Approach. In this study, we propose a novel method for cross-session BCI tasks that employs a supervised autoencoder to reduce session-specific information while preserving task-related signals. Our approach compresses high-dimensional EEG inputs and reconstructs them, thereby mitigating non-stationary variability in the data. In addition to unsupervised minimization of the reconstruction error, the objective function of the network includes two supervised terms to ensure that the latent representations exclude session identity information and are optimized for subsequent classification. Main results. Evaluation across three different motor imagery datasets demonstrates that our approach effectively addresses domain adaptation challenges, outperforming both na & iuml;ve cross-session and within-session methods. Significance. Our method eliminates the need for data from new sessions, making it fully unsupervised concerning new session data and reducing the necessity for recalibration with each session. Furthermore, the reduction of session-specific information in the reconstructed signals indicates that our approach effectively denoises non-stationary signals, thereby enhancing the accuracy of BCI models. Future applications could extend this model to a broader range of BCI tasks and explore the residual signals to investigate sources of non-stationary brain components and other cognitive processes.

关键词： motor-imagery BCI EEG non-stationarity autoencoder denoising

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Leveraging Mask autoencoder and Crossover Binary Sand Cat Algorithm for Early Detection of Glaucoma

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MICROSCOPY RESEARCH AND TECHNIQUE 2025年第0期 2025 Feb 17页

作者： Rekha, C. Jayashree, K. Panimalar Engn Coll Dept Informat Technol Chennai India Panimalar Engn Coll Dept Artificial Intelligence & Data Sci Chennai India

Glaucoma, a leading cause of irreversible blindness worldwide, can be effectively managed if detected early. Glaucoma is directly associated with aging as it commonly occurs in people over the age of 40 and in elderly people. Glaucoma detection in retinal fundus images typically involves utilizing image processing and machine learning techniques. By leveraging advancements in computer vision, a robust and automated system is developed to assist ophthalmologists in screening and diagnosing glaucoma from retinal fundus images. Furthermore, fundus images can vary significantly in quality due to factors like illumination variations, focus, and artifacts. Ensuring consistent image quality across different datasets and acquisition devices is essential for reliable detection. Addressing these challenges requires interdisciplinary collaboration between ophthalmologists to develop robust and reliable solutions for the detection of glaucoma. Hence a novel mask autoencoder-based crossover binary sand cat (MA-CBSC) algorithm is proposed to detect glaucoma. In this algorithm, the mask autoencoder recognizes the features indicating the presence of glaucoma in the input images and the crossover binary sand cat algorithm is used to fine tune the overall performance of the algorithm by selecting the most appropriate features escaped due to overfitting issues. Preprocessing steps such as image enhancement, filtering, and data cleaning are applied to the extracted ROI for the purpose of increasing the image quality and enhancing the visibility of features relevant to glaucoma detection. ROI extraction attributes namely optic disc, cup-to-disc ratio, bean-pot cupping, and vertical enlargement are derived from the ROI along with some other relevant features. In this work, the crossover-based binary sand cat optimization algorithm is utilized for hyperparameter tuning to enhance the efficiency of the MA-CBSC method. Extensive experimental assessments are conducted, comparing the effectiv

关键词： autoencoder binary sand cat optimization crossover strategy filters fundus image glaucoma image enhancement mask RCNN

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Improved network anomaly detection system using optimized autoencoder - LSTM

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 273卷

作者： Narmadha, S. Balaji, N. V. Karpagam Acad Higher Educ Dept Comp Sci Coimbatore 641021 India

Intrusion detection (ID) gives security in network traffic or system activities monitors to detect suspicious activities, behavior, potential attacks, or unauthorized access. IDs are crucial in cybersecurity, as organizations identify and respond to threats before they cause harm. The anomaly-based detection method is a popular and challenging research area in identifying new threats. So, this study focuses on developing an efficient network anomaly-based detection approach. It can establish a baseline for normal behavior and flag deviations from this baseline as potential threats. So, it can detect new or unknown attacks that deviate from standard traffic patterns. The study's main objective is to reduce the false positive rate and improve the class imbalance issues in the data. So, an optimized Deep Learning (DL) model is developed to detect new threats and reduce the false positive rate in the present ID systems. The DL model combines the Long Short-Term Memory (LSTM) with the autoencoder model, where the auto-encoder learns normal patterns, while LSTM handles sequential dependencies in the data. Moreover, the LSTM model performance is optimized using Particle Swarm Optimization (PSO). The performance of the DL model is evaluated with existing IDS methods and the results shows that the proposed model achieves maximum detection accuracy rate of 0.9989.

关键词： Anomaly detection autoencoder Deep learning Intrusion detection system Long short-term memory Particle swarm optimization

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