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Domain Adaptation for Localization Using Combined autoencoder and Gradient Reversal Layer in Dynamic IoT Environment

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IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2024年第1期11卷 685-695页

作者： Prasad, Gaurav Pandey, Ankur Kumar, Sudhir Indian Inst Technol Patna Dept Elect Engn Patna 801106 Bihar India Rajiv Gandhi Inst Petr Technol Dept Elect & Elect Engn Amethi 229304 India

The popularity of Received Signal Strength (RSS) fingerprint-based indoor localization is mainly due to ubiquitous nature of Wi-Fi signals. However, environment changes, device heterogeneity and change in Access Points (APs) results in domain shift between offline and online RSS fingerprints. This article proposes a novel Domain Adversarial Neural Network for Regression (DANN-R) over a compressed RSS representation derived from autoencoders used as a dimension reduction technique to alleviate the challenges of a dynamic IoT environment. In addition, adversarially learn domain-invariant representation in DANN-R using gradient reversal layer (GRL) mitigates these RSS fluctuations by learning a common representation, where source domain (offline RSS data) and target domain (online RSS data) cannot be distinguished. The proposed method outperforms both state-of-art machine learning algorithms and deep domain adaptation frameworks on two public localization testbeds.

关键词： Feature extraction Location awareness Fingerprint recognition Measurement Task analysis Estimation Training Localization deep learning unsupervised domain adaptation autoencoder

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autoencoder-based Data Compression Model Experiment for Semantic Communication 38

Autoencoder-based Data Compression Model Experiment for Sema...

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38th International Conference on Information Networking (ICOIN)

作者： Oh, Jinyoung Choi, Yunkyung Oh, Chanyoung Na, Woongsoo Kongju Natl Univ Dept Software Cheonan South Korea

ISBN: (纸本)9798350330946;9798350330953

In recent years, the convergence of neural network architectures and semantic communication has led to innovative strides in representation learning. This paper explores the application of autoencoders, a subset of neural networks designed for unsupervised learning, in encoding and decoding data to capture semantic nuances. Additionally, we discuss future research directions, proposing a semantic communication model learned from time series data and presenting experimental results. Our experiments involve encoding and decoding time series data using autoencoders, evaluating the feasibility of integrating autoencoder technology into semantic communication. Following the experiment, our proposed model exhibited a loss rate of approximately 0.15-0.2% for the time series data. This outcome represents a notable result, especially when compared to the compression rate of transmission data. It hints at the potential for a future autoencoder-based semantic communication model.

关键词： Semantic communication autoencoder encoding/decoding data compression

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Graph convolutional multi-mesh autoencoder for steady transonic aircraft aerodynamics

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MACHINE LEARNING-SCIENCE AND TECHNOLOGY 2024年第2期5卷 025006页

作者： Massegur, David Da Ronch, Andrea Univ Southampton Fac Engn & Phys Sci Southampton SO16 7QF England

Calculating aerodynamic loads around an aircraft using computational fluid dynamics is a user's and computer-intensive task. An attractive alternative is to leverage neural networks (NNs) bypassing the need of solving the governing fluid equations at all flight conditions of interest. NNs have the ability to infer highly nonlinear predictions if a reference dataset is available. This work presents a geometric deep learning based multi-mesh autoencoder framework for steady-state transonic aerodynamics. The framework builds on graph NNs which are designed for irregular and unstructured spatial discretisations, embedded in a multi-resolution algorithm for dimensionality reduction. The test case is for the NASA common research model wing/body aircraft configuration. Thorough studies are presented discussing the model predictions in terms of vector fields, pressure and shear-stress coefficients, and scalar fields, total force and moment coefficients, for a range of nonlinear conditions involving shock waves and flow separation. We note that the cost of the model prediction is minimal having used an existing database.

关键词： geometric deep learning autoencoder CFD aerodynamics graph convolutional network multi-mesh scheme

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Multimodal Imputation-Based Multimodal autoencoder Framework for AQI Classification and Prediction of Indian Cities

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IEEE ACCESS 2024年 12卷 108350-108363页

作者： Rao, Routhu Srinivasa Kalabarige, Lakshmana Rao Holla, M. Raviraja Sahu, Aditya Kumar Gandhi Inst Technol & Management Dept Comp Sci & Engn Visakhapatnam 530045 Andhra Pradesh India GMRIT Dept Comp Sci & Engn AI Res Lab Rajam 532127 India Manipal Acad Higher Educ MAHE Manipal Inst Technol Dept Informat & Commun Technol Manipal 576104 Karnataka India Amrita Vishwa Vidyapeetham Amrita Sch Comp Amaravati 522503 Andhra Pradesh India

Rising urbanization necessitates robust air quality monitoring and prediction systems, particularly in developing nations like India, to mitigate adverse health impacts. Previous research primarily focused on machine learning algorithms for Air Quality Index (AQI) prediction and classification. We propose a novel MI-MMA-XGB which coupled features of multimodal imputer(MI) with the features of multi-modal autoencoder (MMA) and fed to an XGBoost(XGB) algorithm for AQI prediction and classification. Moreover, imputation approaches namely, KNN, MICE, and SVD were employed to address problems with null values and outliers. Furthermore, SMOTE is employed to balance the imputed data and then the model was trained on both balanced and unbalanced imputed data to extract predictive features. In this process, our model MI-MMA-XGB achieves significant accuracy, reaching 97.14% and 93.87% with and without SMOTE, respectively. Additionally, it attains an R-2 score of 0.9578 and an RMSE of 0.203 for AQI prediction in Indian cities. The proposed model outperforms baseline models in both classification and regression tasks across various evaluation metrics.

关键词： AQI India environmental sustainability PM2.5 PM10 imputation autoencoder

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Convolutional variational autoencoder and multi-scale attention convolutional neural network based diagnostics on filament current sensors for mass spectrometers

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2024年 138卷

作者： Li, Xinshuo Zhou, Wenxing Yin, Jiancheng Zhang, Zhen Huang, Gang Sheng, Yunlong Li, Pinghua Zhuang, Xuye Shandong Univ Technol Mech Engn Sch Zibo 255049 Shandong Peoples R China China Astronaut Res & Training Ctr Beijing 100094 Peoples R China Xi An Jiao Tong Univ Fac Elect & Informat Engn Sch Automat Sci & Engn Xian 710049 Shaanxi Peoples R China

Fault detection of the filament current sensor of the spaceborne mass spectrometer is of great significance to the safe operation of the mass spectrometer. Neglecting sensor failures may affect the normal operation of the mass spectrometer, which in turn affects the health of the astronauts and the space missions. This paper proposes an enhanced intelligent diagnosis method based on filament current sensor for mass spectrometer via improved deep learning network, aiming to improve the accuracy and reliability of the filament current sensor when the fault samples are insufficient. The improved convolutional variational autoencoder (CVAE) model not only enhances the data for four typical sensor fault signals, namely bias, drift, missing and random, but also solves the problem of insufficient samples when spike, Precision degradation and stuck fault occur in sensors. Short-time Fourier transform (STFT) is utilized to transform one-dimensional data into two-dimensional spectrograms, which gives more fault characteristics to the data set. The improved multi-scale attention mechanism convolutional neural network (MSAM-CNN) model is constructed to perform fault diagnosis on the generated spectrogram, which solves the problem of low accuracy of fault identification in traditional convolutional neural network (CNN) models. The results of ablation and comparison experiments show that the samples generated by CVAE have smaller Mean Absolute Error (MAE), Mean Square Error (MSE), and Root Mean Square Error (RMSE), the enhanced samples improve the classification and clustering of MSAM-CNN, and compared to other diagnostic models, MSAM-CNN achieves the highest accuracy, precision, recall, and F1-score of 99.2%, 99.3%, 98.8%, and 0.991.

关键词： Filament current sensor Data enhancement Convolutional variational autoencoder Fault diagnosis Multi-scale attention mechanism Convolutional neural network

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LSTM-autoencoder Based Detection of Time-Series Noise Signals for Water Supply and Sewer Pipe Leakages

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WATER 2024年第18期16卷 2631页

作者： Shin, Yungyeong Na, Kwang Yoon Kim, Si Eun Kyung, Eun Ji Choi, Hyun Gyu Jeong, Jongpil Sungkyunkwan Univ Dept Smart Factory Convergence 2066 Seobu Ro Suwon 16419 South Korea SC Solut Global Co Ltd AI Res Lab 13 Heungdeok 1 roGyeonggi do Yongin 16954 South Korea

The efficient management of urban water distribution networks is crucial for public health and urban development. One of the major challenges is the quick and accurate detection of leaks, which can lead to water loss, infrastructure damage, and environmental hazards. Many existing leak detection methods are ineffective, especially in complex and aging pipeline networks. If these limitations are not overcome, it can result in a chain of infrastructure failures, exacerbating damage, increasing repair costs, and causing water shortages and public health risks. The leak issue is further complicated by increasing urban water demand, climate change, and population growth. Therefore, there is an urgent need for intelligent systems that can overcome the limitations of traditional methodologies and leverage sophisticated data analysis and machine learning technologies. In this study, we propose a reliable and advanced method for detecting leaks in water pipes using a framework based on Long Short-Term Memory (LSTM) networks combined with autoencoders. The framework is designed to manage the temporal dimension of time-series data and is enhanced with ensemble learning techniques, making it sensitive to subtle signals indicating leaks while robustly dealing with noise signals. Through the integration of signal processing and pattern recognition, the machine learning-based model addresses the leak detection problem, providing an intelligent system that enhances environmental protection and resource management. The proposed approach greatly enhances the accuracy and precision of leak detection, making essential contributions in the field and offering promising prospects for the future of sustainable water management strategies.

关键词： leak detection water supply LSTM autoencoder machine learning time series noise signals urban water management

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Convergent autoencoder approximation of low bending and low distortion manifold embeddings

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ESAIM-MATHEMATICAL MODELLING AND NUMERICAL ANALYSIS 2024年第1期58卷 335-361页

作者： Braunsmann, Juliane Rajkovic, Marko Rumpf, Martin Wirth, Benedikt Univ Munster Orleans Ring 10 D-48149 Munster Germany Univ Bonn Inst Numer Simulat Endenicher Allee 60 D-53115 Bonn Germany

autoencoders are widely used in machine learning for dimension reduction of high-dimensional data. The encoder embeds the input data manifold into a lower-dimensional latent space, while the decoder represents the inverse map, providing a parametrization of the data manifold by the manifold in latent space. We propose and analyze a novel regularization for learning the encoder component of an autoencoder: a loss functional that prefers isometric, extrinsically flat embeddings and allows to train the encoder on its own. To perform the training, it is assumed that the local Riemannian distance and the local Riemannian average can be evaluated for pairs of nearby points on the input manifold. The loss functional is computed via Monte Carlo integration. Our main theorem identifies a geometric loss functional of the embedding map as the Gamma-limit of the sampling-dependent loss functionals. Numerical tests, using image data that encodes different explicitly given data manifolds, show that smooth manifold embeddings into latent space are obtained. Furthermore, due to the promotion of extrinsic flatness, interpolation between not too distant points on the manifold is well approximated by linear interpolation in latent space.

关键词： Manifold learning manifold embedding autoencoder latent space Monte Carlo sampling Gamma-convergence

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Model-Free Multiphase AC Signals Downscaling Method Using Orthogonal Phase-Lock autoencoder and Circular Harmonic Decomposition

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2024年第7期72卷 6573-6582页

作者： Qi, Xing Qiu, Tingting Aarniovuori, Lassi Zhang, Qian Xu, Jiazi Cao, Wenping Anhui Univ Dept Elect Engn Hefei 230601 Peoples R China LUT Univ Elect Drives Lab Lappeenranta 53850 Finland

Reference frame transformation methods are generally introduced in multiphase ac systems, so as to simplify system's high dimensional ac signals into low dimensional dc ones. Classical methods, such as Clark/Park transformations, are used in balanced systems whose models are known, but not suitable for model-unknown systems which may contain unbalanced components. To address this issue, this article proposes a model-free method, that can downscale both balanced and unbalanced multiphase ac signals into dc ones, without any access to prior model knowledge. The proposed method follows the principles of low rank and sparsity, and consists of two components: 1) a phase-lock orthogonal autoencoder is designed, to transform multiphase ac signals into two-phase orthogonal ac signals;2) a circular harmonic decomposition is introduced, to further simplify two-phase orthogonal ac signals into some dc constant values. The effectiveness of the proposed method is validated on a real-world model-unknown system, and the experimental results also indicate that the proposed method benefits of discovering some potential patterns of the system, which is helpful for an in-depth study of model-unknown systems. Comparative studies show that the proposed method is superior to classical methods in terms of interpretability, information integrity and scalability in model-unknown scenarios.

关键词： Harmonic analysis Vectors Power system harmonics Standards Induction motors Transforms Rotors Permanent magnets Gold Feature extraction autoencoder circular harmonic function decomposition model-free multiphase systems signal downscaling

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A fusion autoencoder model and piecewise anomaly index for aero-engine fault diagnosis

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APPLIED INTELLIGENCE 2024年第20期54卷 10148-10160页

作者： Feng, Kun Xiao, Yuan Li, Zhouzheng Miao, Dongyan Beijing Univ Chem Technol State Key Lab High end Compressor & Syst Technol 15 Beisanhuan East Rd Beijing 100029 Peoples R China

Safety, efficiency, and reliability are essential requirements for aero-engines. Timely and accurate diagnosis of engine faults enables effective planning of maintenance operations and reduces downtime. Although traditional physics-based methods perform well under controlled test bench scenarios, their effectiveness in handling very noisy data and missing values is limited, constraining their utility in real-world settings. To address these gaps, we propose a fusion autoencoder that combines physics-informed and pattern-informed techniques, augmented with a Beta-Variational autoencoder learning backbone to enhance the robustness of the model. Additionally, a novel health index called the piecewise anomaly index is proposed that can detect and classify faults simultaneously. To evaluate the efficacy of the novel framework, we modified the New Commercial Modular Aero-Propulsion System Simulation (N-CMAPSS) dataset to simulate real-world scenarios and conducted experiments. The results show that the proposed method can detect faults earlier than common techniques, while also achieving accurate fault classification and degree determination with the new index.

关键词： Aero-engine Fault diagnosis autoencoder Health index

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Joint loss learning-enabled semi-supervised autoencoder for bearing fault diagnosis under limited labeled vibration signals

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JOURNAL OF VIBRATION AND CONTROL 2024年第19-20期30卷 4537-4550页

作者： Liang, Mingxuan Zhou, Kai China Jiliang Univ Coll Mech & Elect Engn Hangzhou Peoples R China Hong Kong Polytech Univ Dept Civil & Environm Engn Hong Kong Peoples R China Michigan Technol Univ Dept Mech Engn & Engn Mech Houghton MI USA Hong Kong Polytech Univ Dept Civil & Environm Engn Hung Hom 11 Yuk Choi Rd Hong Kong Peoples R China

Rolling bearing is a critical component of machinery that has been widely applied in manufacturing, transportation, aerospace, and power and energy industries. The timely and accurate bearing fault detection thus is of vital importance. Computational data-driven deep learning has recently become a prevailing approach for bearing fault detection. Despite the progress of the deep learning approach, the deep learning performance is hinged upon the size of labeled data, the acquisition of which is expensive in actual implementation. Unlabeled data, on the other hand, are inexpensive. In this research, we develop a new semi-supervised learning method built upon the autoencoder to fully utilize a large amount of unlabeled data together with limited labeled data to enhance fault detection performance. Compared with the state-of-the-art semi-supervised learning methods, this proposed method can be more conveniently implemented with fewer hyperparameters to be tuned. In this method, a joint loss is established to account for the effects of labeled and unlabeled data, which is subsequently used to direct the backpropagation training. Systematic case studies using the Case Western Reserve University (CWRU) rolling bearing dataset are carried out, in which the effectiveness of this new method is verified by comparing it with other well-established baseline methods. Specifically, nearly all emulation runs using the proposed methodology can lead to around 2%-5% accuracy increase, indicating its robustness in performance enhancement.

关键词： Rolling bearing deep learning bearing fault diagnosis semi-supervised learning autoencoder joint loss

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