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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 DIFAR Sonobuoy Signal Transmission and Reception Method Incorporating Residual Vector Quantization and Compensation Module: Validation Through Air Channel Modeling

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APPLIED SCIENCES-BASEL 2025年第1期15卷 92-92页

作者： Park, Yeonjin Hong, Jungpyo Changwon Natl Univ Dept Informat & Commun Engn Chang Won 51140 South Korea

This paper proposes a novel autoencoder-based neural network for compressing and reconstructing underwater acoustic signals collected by Directional Frequency Analysis and Recording sonobuoys. To improve both signal compression rates and reconstruction performance, we integrate Residual Vector Quantization and a Compensation Module into the decoding process to effectively compensate for quantization errors. Additionally, an unstructured pruning technique is applied to the encoder to minimize computational load and parameters, addressing the battery limitations of sonobuoys. Experimental results demonstrate that the proposed method reduces the data transmission size by approximately 31.25% compared to the conventional autoencoder-based method. Moreover, the spectral mean square errors are reduced by 60.58% for continuous wave signals and 55.25% for linear frequency modulation signals under realistic air channel simulations.

关键词： DIFAR sonobuoy autoencoder air channel modeling signal reconstruction

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autoencoder-based physical layer authentication in a real indoor environment

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PHYSICAL COMMUNICATION 2025年 70卷

作者： Senigagliesi, Linda Ciattaglia, Gianluca Gambi, Ennio Univ Politecn Marche Dept Informat Engn Via Brecce Bianche 12 I-60131 Ancona Italy

Authentication of wireless nodes, as in fifth-generation (5G) and Internet of Things (IoT) networks, is an increasingly pressing issue, in order to limit the required computational effort and the necessary overhead. A simplification of the authentication process may therefore be of interest to achieve the satisfaction of stringent performance requirements, such as those envisaged for sixth-generation (6G) networks. This paper provides a study on the feasibility of physical layer authentication (PLA) in a real indoor environment, as an alternative solution to the traditional authentication schemes. To ensure the reliability of the proposed approach a simulated scenario is firstly tested. Subsequently, real-world data are collected through a laboratory setup using a Vectorial Signal Transceiver (VST) and two Universal Software Radio Peripherals (USRPs) to emulate the behavior of the receiver, the legitimate transmitter, and the potential adversary. A machine learning (ML) algorithm is then exploited to act as authenticator. This means that channel fingerprint is extracted from signals to create a dataset used to train a sparse autoencoder. To emulate areal authentication scenario, the autoencoder is trained only on the class of the legitimate user. Once anew message arrives, the autoencoder task is to discern authentic signals from those forged by the adversary. It is shown that a geometric mean of accuracy of more than 90%, with corresponding low levels of false alarm and missed detection, is achievable irrespective of the nodes location, underlining the robustness and versatility of the proposed ML-based PLA approach.

关键词： Machine learning autoencoder Physical layer authentication Channel measurements Universal Software Radio Peripherals

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autoencoder-Based MIMO Cooperative Communications With Quantize-Forward Relaying

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2024年第12期73卷 19229-19239页

作者： Shin, Juin Jin, Xianglan Jeonbuk Natl Univ Div Elect Engn Jeonju 54896 South Korea Jeonbuk Natl Univ IT Convergence Res Ctr Jeonju 54896 South Korea

This paper investigates an autoencoder-based quantize-forward (QF) relay system that includes a source, a destination, and a relay, each equipped with multiple antennas. The existing phase quantization (PQ) algorithm at the relay has limitations in capturing the amplitude differences of received signals, leading to performance saturation with increasing quantization bits. To address these limitations, we propose a novel relay algorithm, amplitude-phase quantization (APQ), which quantizes both the phase and the amplitude. Moreover, we introduce neural networks into the relay process, resulting in PQ with neural networks (PQNN) and APQ with neural networks (APQNN), which is expected to further improve system performance at the expense of additional computational load at the relay. We also propose a sub-message one-hot encoding method and a retraining approach for the worst-performing sub-message to reduce computational complexity and improve performance in autoencoder-based systems. Simulation results demonstrate that the autoencoder-based QF relay system, with various relay algorithms and the sub-message one-hot encoding method, achieves excellent performance with reduced memory usage at the relay and significantly reduced complexity at the source and destination.

关键词： Relays Quantization (signal) Neural networks Communication systems System performance Encoding MIMO communication autoencoder deep learning multiple-input multiple-output (MIMO) phase quantization relay

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autoencoder-Based Eggshell Crack Detection Using Acoustic Signal

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JOURNAL OF FOOD PROCESS ENGINEERING 2024年第11期47卷

作者： Yabanova, Ismail Balci, Zekeriya Yumurtaci, Mehmet Unler, Tarik Manisa Celal Bayar Univ HFT Technol Fac Elect Engn Dept Manisa Turkiye Van Yuzuncu Yil Univ Caldiran Vocat Sch Elect & Automat Dept Van Turkiye Afyon Kocatepe Univ Dept Elect & Elect Engn Afyon Turkiye Necmettin Erbakan Univ Aeronaut & Astronaut Fac Avionics Dept Konya Turkiye

Breaks or cracks in eggshells offer substantial food safety issues. Bacteria and viruses, in particular, are more likely to enter the egg through breaks and cracks, increasing the risk of food poisoning. Furthermore, deformations in the shell may compromise the integrity of the protective shell, exposing the egg to more external variables and causing it to lose freshness and decay faster. To reduce such hazards, this research created an innovative crack detection system based on an autoencoder (AE) that uses acoustic signals from eggshells. A system that creates an acoustic effect by hitting the eggshell without damaging it was designed, and these effects were recorded through a microphone. Acoustic signal data of size 1 x 1000 was fed into k nearest neighbor (kNN), decision tree (DT), and support vector machine (SVM) classifiers. AE was employed to reduce data size in order to accommodate the raw data's unique features. This AE model, which reduces data size, was used with many classifiers and was able to accurately distinguish between intact and cracked eggs. The built AE-based classifier model completed the classification procedure with 100% accuracy, including microcracks that are invisible to the naked eye.

关键词： acoustic signal autoencoder classification crack eggshell

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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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autoencoder-Based fault detection using building automation system data

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ADVANCED ENGINEERING INFORMATICS 2024年 62卷

作者： El Mokhtari, Karim McArthur, J. J. Toronto Metropolitan Univ Dept Architectural Sci Toronto ON Canada

This paper explores the application of autoencoder algorithms in Automated Fault Detection (AFD) for Heating, Ventilation, and Air Conditioning (HVAC) systems, specifically focusing on Fan Coil Units (FCUs). The begins by reviewing the current state of Fault Detection and Diagnostics (FDD), emphasizing the limitations the potential of unsupervised learning techniques like autoencoders and transfer learning to fill these gaps. data from a full-scale building case study featuring five Fan Coil Units (FCUs), the research develops and uates autoencoder-based AFD models that models effectively compress multivariate inputs into a reduced space, enabling accurate and efficient fault detection. The paper makes two novel contributions: (1) It introduces a methodology to distinguish between equipment-level and system-level faults;and (2) It demonstrates generalizability of the approach across different types of FCUs through cross-testing and transfer learning. results indicate that autoencoders outperform other dimensionality reduction algorithms and separate predictors in fault detection accuracy and efficiency. The paper concludes by discussing the implications of these findings for future research and practical applications in building management.

关键词： Fault detection autoencoder Building automation system HVAC

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autoencoder-CatBoost Model for Accurate Hyperspectral Quality Assessment of Yunling Snowflake Beef

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IEEE ACCESS 2024年 12卷 184701-184713页

作者： Gao, Lutao Zhang, Lilian Chen, Jian Peng, Lin Guo, Lujiale Yang, Linnan Yunnan Agr Univ Coll Food Sci & Technol Kunming 650201 Yunnan Peoples R China Yunnan Agr Univ Coll Big Data Kunming 650201 Yunnan Peoples R China Yunnan Engn Technol Res Ctr Agr Big Data Kunming 650201 Yunnan Peoples R China Yunnan Engn Res Ctr Big Data Intelligent Informat Kunming 650201 Yunnan Peoples R China Fudan Univ Zhongshan Hosp Shanghai 200032 Peoples R China

Snowflake beef is highly valued for its nutritional benefits and exquisite taste, yet the inconsistent quality in the market poses challenges for consumers in distinguishing genuine products, leading to economic losses and trust issues. This study aims to develop a rapid, accurate, and non-destructive method for evaluating the quality of snowflake beef to enhance consumer satisfaction and confidence. We propose a novel autoencoder-Catboost model based on an autoencoder and CatBoost classifier, utilizing hyperspectral imaging (HSI) technology to assess the quality grades of Yunling Cattle snowflake beef. A total of 250 beef samples, scanned in the 900-2500 nm wavelength range, were processed using seven data preprocessing techniques and two feature extraction methods. The autoencoder, comprising three layers of Transformer units, effectively extracted features from the hyperspectral data, which were then classified by the CatBoost classifier. The model demonstrated superior accuracy, precision, recall, and F1-score compared to traditional machine learning methods, achieving an average accuracy of 82.42%. This research introduces an innovative approach by integrating Transformer-based autoencoders with CatBoost for hyperspectral data analysis, providing a new methodology for non-destructive snowflake beef quality evaluation and offering valuable insights for future research in food quality assessment.

关键词： Hyperspectral imaging Feature extraction Accuracy Spectroscopy Cows Transformers Noise Information filters Data models Data mining Snowflake beef quality hyperspectral imaging autoencoder CatBoost Yunling cattle

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autoencoder-enabled eye-tracking data analytics for objective assessment of user preference in humanoid robot appearance design

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EXPERT SYSTEMS WITH APPLICATIONS 2024年第PartC期249卷

作者： Li, Fan Chen, Chun-Hsien Liu, Yisi Chang, Danni Cui, Jian Sourina, Olga Hong Kong Polytech Univ Dept Aeronaut & Aviat Engn Hong Kong Peoples R China Nanyang Technol Univ Sch Mech & Aerosp Engn Singapore Singapore Fraunhofer Singapore Singapore Shanghai Jiao Tong Univ Sch Design Shanghai Peoples R China

Humanoid robots have been extensively utilized in service industries to provide information and product delivery through direct interactions with users. As the design of humanoid robot appearance significantly impacts human-robot interactions, it is crucial to assess user preference towards it. Traditional evaluation tools, such as surveys, field observations, and interviews, are often time-consuming and subjective. Therefore, this study aims to develop a novel eye-tracking-based assessment tool to investigate user preference towards humanoid robot appearance design. We analyze the critical factors influencing user preference from two perspectives: the attributes of robot appearance and users' selective attention distribution. Accordingly, we propose an integrated machine learning method, combining an autoencoder neural network with a support vector machine to handle the collected visual data. This method, named ASVM, extracts several novel indicators from the eye-tracking data via an unsupervised autoencoder neural network and manual entropy analysis. The proposed ASVM achieves an accuracy of 91%, outperforming other classical machine learning methods, including decision tree, naive Bayes, and support vector machine. ASVM can objectively assess user preference towards humanoid robot appearance design with high time resolution. Furthermore, it can enhance humanoid robot design by revealing the visual attention distribution in assessing robot appearance.

关键词： autoencoder Eye -tracking Robot appearance design User preference

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