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Energy Conservation in Wireless Sensor Networks Using Partly-Informed Sparse autoencoder

IEEE ACCESS 2019年 7卷 63346-63360页

作者： Ayinde, Babajide O. Barnawi, Abdulaziz Y. King Fahd Univ Petr & Minerals Dept Syst Engn Dhahran 31261 Saudi Arabia King Fahd Univ Petr & Minerals Dept Comp Engn Dhahran 31261 Saudi Arabia Univ Louisville Dept Elect & Comp Engn Louisville KY 40292 USA

The energy in wireless sensor networks is considered a scarce commodity, especially in scenarios where it is difficult or impossible to provide supplementary energy sources once the initially available energy is used up. Even in cases where energy harvesting is feasible, effective energy utilization is still a crucial step for prolonging the network lifetime. Enhancement of life-time through efficient energy management is one of the essential ingredients underlining the design of any credible wireless sensor network. In this paper, we propose a sensor selection method using a novel and unsupervised neural network structure referred to as partly-informed sparse autoencoder (PISAE) that aims to reconstruct all sensor readings from a select few. The PISAE comprises three submodules, namely: the gate (which selects the most important sensors), encoder (encodes and compresses the data from select sensors), and decoder (decodes the output of the encoder and regenerates the readings of all initial sensors). Our approach relies on the premise that many sensors are redundant because their readings are spatially and temporally correlated and are predictable from the readings of a few other sensors in the network. Thus, overall network reliability and lifetime are enhanced by putting sensors with redundant readings to sleep without losing significant information. We evaluate the efficacy of the proposed method on three benchmark datasets and compare with existing results. The experimental results indicate the superiority of our approach compared with existing approaches in terms of accuracy and lifetime extension factor.

关键词： autoencoder energy conservation energy management unsupervised feature extraction feature importance feature ranking lifetime extension wireless sensor network deep learning

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An interpretable health indicator for bearing condition monitoring based on semi-supervised autoencoder latent space variance maximization

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MEASUREMENT SCIENCE AND TECHNOLOGY 2023年第12期34卷 125135-125135页

作者： Chen, Xieyi Wang, Yi Meng, Lihua Qin, Yi Tang, Baoping Chongqing Univ Coll Mech & Vehicle Engn Chongqing 400044 Peoples R China Chongqing Univ State Key Lab Mech Transmiss Chongqing 400044 Peoples R China

Effective health indicator (HI) construction can help equipment managers detect the abnormal state of rotating machinery quickly. However, although the current deep learning-based HI construction methods have good life prediction value, most of them lose the ability to detect device anomalies and little work has been done on model interpretability. Therefore, an interpretable HI construction method based on semi-supervised autoencoder (AE) latent space variance maximization (SSALSVM) was proposed to monitor the health status of bearings. In order to fully excavate degradation features inside the device and make the model focus on the encoding process, a deep convolutional neural network (DCNN) is used as the encoding layer, while only a layer of fully-connected layer is used as the decoding layer. In addition, to enable the latent space to capture the device early degradation point (EDP) successfully, an auxiliary layer is added to the output of the encoder layer. Simultaneously, for improving the sensitivity of the indicator to capture equipment abnormal state and highlight the difference between equipment health state and degradation state, the constraint of variance maximization is added into the latent space. The model optimizing process was presented by observing the projected variance of the test set in latent space of each epoch model. The validity of the proposed HI was verified by comparison experiments on two datasets.

关键词： interpretable health indicator semi-supervised autoencoder hidden space variance maximization autoencoder early degradation point

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Multi-task deep autoencoder to predict Alzheimer's disease progression using temporal DNA methylation data in peripheral blood

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COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL 2022年 20卷 5761-5774页

作者： Chen, Li Saykin, Andrew J. Yao, Bing Zhao, Fengdi Univ Florida Dept Biostat Gainesville FL 32603 USA Indiana Univ Sch Med Dept Radiol & Imaging Sci Indianapolis IN 46202 USA Emory Univ Dept Human Genet Atlanta GA 30322 USA

Traditional approaches for diagnosing Alzheimer's disease (AD) such as brain imaging and cerebrospinal fluid are invasive and expensive. It is desirable to develop a useful diagnostic tool by exploiting biomark-ers obtained from peripheral tissues due to their noninvasive and easily accessible characteristics. However, the capacity of using DNA methylation data in peripheral blood for predicting AD progression is rarely known. It is also challenging to develop an efficient prediction model considering the complex and high-dimensional DNA methylation data in a longitudinal study. Here, we develop two multi-task deep autoencoders, which are based on the convolutional autoencoder and long short-term memory autoencoder to learn the compressed feature representation by jointly minimizing the reconstruction error and maximizing the prediction accuracy. By benchmarking on longitudinal DNA methylation data collected from the peripheral blood in Alzheimer's Disease Neuroimaging Initiative, we demonstrate that the proposed multi-task deep autoencoders outperform state-of-the-art machine learning approaches for both predicting AD progression and reconstructing the temporal DNA methylation profiles. In addition, the proposed multi-task deep autoencoders can predict AD progression accurately using only the histor-ical DNA methylation data and the performance is further improved by including all temporal DNA methylation data. Availability:: https://***/lichen-lab/MTAE. (c) 2022 The Author(s). Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Deep learning Alzheimer?s disease Longitudinal data autoencoder DNA methylation

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A semisupervised autoencoder-based approach for anomaly detection in high performance computing systems

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2019年 85卷 634-644页

作者： Borghesi, Andrea Bartolini, Andrea Lombardi, Michele Milano, Michela Benini, Luca Univ Bologna DISI Viale Risorgimento 2 I-40123 Bologna Italy Univ Bologna DEI Viale Risorgimento 2 I-40123 Bologna Italy Swiss Fed Inst Technol Integrated Syst Lab Zurich Switzerland

High Performance Computing (HPC) systems are complex machines with heterogeneous components that can break or malfunction. Automated anomaly detection in these systems is a challenging and critical task, as HPC systems are expected to work 24/7. The majority of the current state-of-the-art methods dealing with this problem are Machine Learning techniques or statistical models that rely on a supervised approach, namely the detection mechanism is trained to recognize a fixed number of different states (i.e. normal and anomalous conditions). In this paper a novel semi-supervised approach for anomaly detection in supercomputers is proposed, based on a type of neural network called autoencoder. The approach learns the normal state of the supercomputer nodes and after the training phase can be used to discern anomalous conditions from normal behavior;in doing so it relies only on the availability of data characterizing only the normal state of the system. This is different from supervised methods that require data sets with many examples of anomalous states, which are in general very rare and/or hard to obtain. The approach was tested on a real-life High Performance Computing system equipped with a monitoring infrastructure capable to generate large amount of data describing the system state. The proposed approach definitely outperforms the best current techniques for semi-supervised anomaly detection, with an increase in accuracy detection of around 12%. Two different implementations are discussed: one where each supercomputer node has a specific model and one with a single, generalized model for all nodes, in order to explore the trade-off between accuracy and ease of deployment.

关键词： Anomaly detection High performance computing autoencoder Machine learning

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Spatial-Aware Hyperspectral Nonlinear Unmixing autoencoder With Endmember Number Estimation

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2022年 15卷 20-41页

作者： Shahid, Kazi Tanzeem Schizas, Ioannis D. Univ Texas Arlington Dept Elect Engn Arlington TX 76019 USA

In this article, we develop a novel fully unsupervised autoencoder-based scheme for nonlinear hyperspectral pixel unmixing. A unique approach is derived where high noise and unresponsive pixels are accounted for, by a unique averaging approach based on spatially aware filters built using radial basis function (RBF) kernels. A novel technique is implemented via calculating rank-equivalent kernel covariance matrices in order to estimate the unknown number of endmembers contributing to the data. Utilization of spatial information is done via RBF-based weighted averaging, which is then followed by endmember estimation via K-means clustering. The RBF distances from the cluster centers are determined to measure the position of the mixed pixels in relation to the centers, which is utilized as a preliminary estimation of the abundances. The proposed framework is robust in the presence of unresponsive pixels, while highly versatile working with different nonlinear unmixing models. Extensive numerical tests establish the superiority of the novel approach with respect to state-of-the-art methods.

关键词： Hyperspectral imaging Estimation Kernel Spatial resolution Earth Recurrent neural networks Meteorology autoencoder hyperspectral imaging neural networks nonlinear unmixing radial basis function (RBF) kernel unsupervised

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LTScoder: Long-Term Time Series Forecasting Based on a Linear autoencoder Architecture

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IEEE ACCESS 2024年 12卷 98623-98633页

作者： Kim, Geunyong Yoo, Hark Kim, Chorwon Kim, Ryangsoo Kim, Sungchang Elect & Telecommun Res Inst Honam Res Div Gwangju 34129 South Korea

A long-term time series forecasting (LTSF) model named LTScoder based on a linear autoencoder architecture is presented in this paper. LTScoder performs feature extraction through an encoder to generate a latent vector and conducts time series prediction based on this latent vector through a decoder. The latent vector, which is mapped to a lower dimensionality by extracting features from the input time series, not only reduces the complexity of the linear regression operations performed in the decoder but also captures essential temporal dependencies for forecasting. Experimental results conducted on popular univariate and multivariate datasets with diverse patterns demonstrate that LTScoder achieves up to 16.95 times faster inference time than PatchTST, the state-of-the-art transformer-based model. LTScoder exhibits minimal degradation in prediction accuracy while using only 2.11% of PatchTST's parameters. Furthermore, we show that the LTScoder model is well-suited for edge computing in time series data prediction tasks. The architectural flexibility of LTScoder allows the encoder and decoder to be deployed in separate systems, enhancing scalability and efficiency. We measure the performance of LTScoder in terms of its prediction accuracy and computational complexity using the ETRI edge gateway system.

关键词： Time series analysis Predictive models Transformers Data models Computational modeling Forecasting Feature extraction autoencoders autoencoder edge computing long-term time series forecasting LTScoder

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Analysis of different RNN autoencoder variants for time series classification and machine prognostics

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MECHANICAL SYSTEMS AND SIGNAL PROCESSING 2021年 149卷 107322-107322页

作者： Yu, Wennian Kim, Il Yong Mechefske, Chris Chongqing Univ Coll Mech Engn Chongqing 400044 Peoples R China Queens Univ Dept Mech & Mat Engn Kingston ON K7L 3N6 Canada

Recurrent neural network (RNN) based autoencoders, trained in an unsupervised manner, have been widely used to generate fixed-dimensional vector representations or embeddings for varying length multivariate time series. These embeddings have been demonstrated to be useful for time series reconstruction, classification, and creation of health index (HI) curves of machines being used in industrial applications, based on which the remaining useful life (RUL) of machines can be estimated. In this study, we extend the traditional form of RNN autoencoders as a feature extractor for multivariate time series to a more general form in terms of arranging the order of input or output sequences and the hidden unit architecture. We apply the embeddings obtained by different variants of RNN autoencoders for a time series classification task and a machine RUL estimation problem using two publicly available datasets. A random research strategy is used to find the optimal hyperparameters of all variants for each task in order to give a fair comparison of the general performance among different variants over a large hyperparameter space, as well as the best performance that each variant can achieve compared with the best reported values in the literature. Our results show that traditional reversing the order of output time series while maintaining the order of input time series when training an RNN autoencoder does not show improved performance for the two studied cases. Thus, intentionally arranging the input or output order seems unnecessary for training the RNN autoencoder as a feature extractor of time series. We further observe that only the RNN architectures with gating mechanism can achieve the functionality of encoding for the time series, and none of the three common gated architectures we studied shows significantly and consistently improved performance compared to the others on the two studied cases. However, the bidirectional RNN autoencoders yield slightly better perform

关键词： Recurrent neural network autoencoder Time series Remaining useful life Random search

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Multitask Air-Quality Prediction Based on LSTM-autoencoder Model

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IEEE TRANSACTIONS ON CYBERNETICS 2021年第5期51卷 2577-2586页

作者： Xu, Xinghan Yoneda, Minoru Kyoto Univ Dept Environm Engn Kyoto 6158540 Japan

With the development of the data-driven modeling techniques, using the neural network to simulate the transport process of atmospheric pollutants and constructing PM2.5 time-series prediction model have become a hot topic. The existing data-driven approaches often ignore the dynamical relationships among multiple sites in urban areas, which results in nonideal prediction accuracy. In response to this problem, this article proposes a long short-term memory (LSTM) autoencoder multitask learning model to predict PM2.5 time series in multiple locations city wide. The model could implicitly and automatically excavate the intrinsic relevance among the pollutants in different stations. And the meteorological information from the monitoring stations is fully utilized, which is beneficial for the performance of the proposed model. Specifically, multilayer LSTM networks can simulate the spatiotemporal characteristics of urban air pollution particles. And using the stacked autoencoder to encode the key evolution pattern of urban meteorological systems could provide important auxiliary information for PM2.5 time-series prediction. In addition, multitask learning could automatically discover the dynamical relationship between multiple key pollution time series and solve the problem of insufficient use of multisite information in the modeling process of the traditional data-driven methods. The simulation results of PM2.5 prediction in Beijing indicate the effectiveness of the proposed method.

关键词： autoencoder long short-term memory (LSTM) multitask learning PM2.5

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Application of Extreme Learning Machine- autoencoder to Medium Term Electricity Price Forecasting

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IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS 2023年第6期59卷 7214-7223页

作者： Najafi, Arsalan Homaee, Omid Golshan, Mehdi Jasinski, Michal Leonowicz, Zbigniew Wroclaw Univ Sci & Technol Fac Elect Engn Dept Elect Engn Fundamentals PL-50370 Wroclaw Poland Islamic Azad Univ Dept Comp Engn Sepidan Branch Sepidan *** Iran VSB Tech Univ Ostrava Fac Elect Engn & Comp Sci Dept Elect Power Engn Ostrava 70800 Czech Republic

Electricity market prices are highly volatile, highly frequent, non-linear, and non-stationary which makes forecasting very complicated. Although accurate forecasting plays a crucial role in the electricity market for traders, retailers, large consumers as well as generation companies in terms of economic efficiency and power systems safety. Hence, this article proposes a new forecasting approach for medium-term electricity market prices based on an extreme learning machine-autoencoder (ELM-AE). The main idea behind this is to use trained weights for hidden layers instead of randomly generated weights. The input hidden layer weights are obtained by solving a network with the same input outputs by the autoencoder method. Then, the obtained output weights are used again as the input weights for a new ELM network. To do so, a data-set is created using input data, where the ahead 24 hours are forecasted based on the previous 168 data. The simulations have been performed on New York Independent System Operator prices and compared with the classic ELM demonstrating the high accuracy of the proposed method in both training and testing.

关键词： Forecasting electricity price extreme learning machine autoencoder electricity market

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Perturbation AUTOVC: Voice Conversion From Perturbation and autoencoder Loss

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IEEE ACCESS 2023年 11卷 140174-140185页

作者： Park, Hwa-Young Lee, Young Han Chun, Chanjun Chosun Univ Dept Comp Engn Gwangju 61452 South Korea Korea Elect Technol Inst KETI Intelligent Image Proc Res Ctr Seongnam 13509 South Korea

AUTOVC is a voice-conversion method that performs self-reconstruction using an autoencoder structure for zero-shot voice conversion. AUTOVC has the advantage of being easy and simple to learn because it only uses the autoencoder loss for learning. However, it performs voice conversion by disentangling speech information from speakers and linguistic information by adjusting the bottleneck dimension;this requires highly meticulous fine tuning of the bottleneck dimension and involves a tradeoff between speech quality and speaker similarity. To address these issues, neural analysis and synthesis (NANSY)-a fully self-supervised learning system that uses perturbations to extract speech features-is proposed. NANSY solves the problem of the adjustment of the bottleneck dimension by utilizing perturbation and exhibits high-reconstruction performance. In this study, we propose perturbation AUTOVC, a voice conversion method that utilizes the structure of AUTOVC and the perturbation of NANSY. The proposed method applies perturbations to speech signals (such as NANSY signals) to solve the problem of the voice conversion method using bottleneck dimensions. Perturbation is applied to remove the speaker-dependent information present in the speech, leaving only the linguistic information, which is then passed through a content encoder and modeled as a content embedding containing only the linguistic information. To obtain speaker information, we used x-vectors, which are extensively used in pretrained speaker recognition. The concatenated linguistic and speaker information extracted from the encoder and additional energy information is used as input to the decoder to perform self-reconstruction. Similar to AUTOVC, it is easy and simple to learn using only the autoencoder loss. For the evaluation, we measured three objective evaluation metrics: character error rate (%), cosine similarity, and short-time objective intelligibility, as well as a subjective evaluation metric: mean opinio

关键词： autoencoder information perturbation speech signal processing voice conversion

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