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A combination model based on wavelet transform for predicting the difference between monthly natural gas production and consumption of US

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ENERGY 2021年 235卷 121216-121216页

作者： Qiao, Weibiao Liu, Wei Liu, Enbin Yan Shan Univ Sch Vehicle & Energy Qinhuangdao 066004 Hebei Peoples R China Chongqing Univ State Key Lab Coal Mine Disaster Dynam & Control Chongqing 400044 Peoples R China Southwest Petr Univ Petr Engn Sch Chengdu 61000 Peoples R China

The prediction model's performance in view of the wavelet transform (WT) is affected because the wavelet basis function (WBF) and its orders and layers are determined randomly. To solve this problem, this research devises a crossover experiment with 160 components of each WBF (Coiflets and Symlets) and forecasts 320 schemes with sparse autoencoder (SAE) and long short-term memory (LSTM), developing a combination model with WT, SAE, and LSTM. Furthermore, to verify the performance of the combination prediction model, the difference between the natural gas production and consumption in the U.S. is determined which is taken as an example. The results indicate that the SAE-LSTM exceeds other AI models (e.g. ELM), and WT outperforms other preprocessing algorithms (e.g. EMD) based on forecasting accuracy. The best performance of the established model is obtained by using the two orders six layers of Coiflets, and six orders seven layers of Symflets for natural gas production and consumption. In addition, the average difference between consumption and production of natural gas is 10.6809 Bcfpd. To make up for such a gap, some import methods can be adopted. It can be concluded that this study can provide a reference for other time-series prediction and natural gas policymakers. (c) 2021 Elsevier Ltd. All rights reserved.

关键词： Natural gas production and consumption Long short-term memory Wavelet transform sparse autoencoder Difference prediction

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A hybrid neural network based on KF-SA-Transformer for SOC prediction of lithium-ion battery energy storage systems

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FRONTIERS IN ENERGY RESEARCH 2024年 12卷

作者： Xiong, Yifei Shi, Qinglian Shen, Lingxu Chen, Chen Lu, Wu Xu, Cong Shanghai Univ Elect Power Coll Elect Engn Shanghai Peoples R China State Grid Xiongan New Area Elect Power Supply Co Baoding Hebei Peoples R China Shanghai Univ Intellectual Property Acad Shanghai Peoples R China

With the widespread application of energy storage stations, BMS has become an important subsystem in modern power systems, leading to an increasing demand for improving the accuracy of SOC prediction in lithium-ion battery energy storage systems. Currently, common methods for predicting battery SOC include the Ampere-hour integration method, open circuit voltage method, and model-based prediction techniques. However, these methods often have limitations such as single-variable research, complex model construction, and inability to capture real-time changes in SOC. In this paper, a novel prediction method based on the KF-SA-Transformer model is proposed by combining model-based prediction techniques with data-driven methods. By using temperature, voltage, and current as inputs, the limitations of single-variable studies in the Ampere-hour integration method and open circuit voltage method are overcome. The Transformer model can overcome the complex modeling process in model-based prediction techniques by implementing a non-linear mapping between inputs and SOC. The presence of the Kalman filter can eliminate noise and improve data accuracy. Additionally, a sparse autoencoder mechanism is integrated to optimize the position encoding embedding of input vectors, further improving the prediction process. To verify the effectiveness of the algorithm in predicting battery SOC, an open-source lithium-ion battery dataset was used as a case study in this paper. The results show that the proposed KF-SA-Transformer model has superiority in improving the accuracy and reliability of battery SOC prediction, playing an important role in the stability of the grid and efficient energy allocation.

关键词： state-of-charge Transformer Kalman filter sparse autoencoder lithium-ion battery

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Anomaly Feature Learning for Unsupervised Change Detection in Heterogeneous Images: A Deep sparse Residual Model

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2020年 13卷 588-600页

作者： Touati, Redha Mignotte, Max Dahmane, Mohamed Univ Montreal Fac Arts & Sci DIRO Vis Lab Montreal PQ H3C 3J7 Canada CRIM R&D Vis Dept Montreal PQ H3N 1M3 Canada

In this article, we propose a novel and simple automatic model based on multimodal anomaly feature learning in a residual space, aiming at solving the binary classification problem of temporal change detection (CD) between pairs of heterogeneous remote sensing images. The model starts by learning from image pairs the normal existing patterns in the before and after images to come up with a suitable representation of the normal (nonchange) class. To achieve this, we employ a stacked sparse autoencoder trained on a large number of temporal image features (training data) in an unsupervised manner. To classify pixels of new unseen image-pairs, the built anomaly detection model reconstructs the input from its representation in the latent space. First, the probe (new) image (i.e., the bitemporal heterogeneous image pair as the input request) is encoded in this compact normal space from a stacked hidden representation. The reconstruction error is computed using the L2 norm in what we call the residual normal space. In which, the nonchange patterns are characterized by small reconstruction errors as a normal class while the change patterns are quantified by high reconstruction errors categorizing the abnormal class. The dichotomic (changed/unchanged) classification map is generated in the residual space by clustering the reconstructed errors using a Gaussian mixture model. Experimental results on different real heterogeneous images, reflecting a mixture of imaging and land surface CD conditions, confirm the robustness of the proposed anomaly detection model.

关键词： Anomalous patterns change detection (CD) deep learning feature space reconstruction heterogeneous remote sensing multimodal anomaly detector reconstruction error sparse autoencoder

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Predicting the potential associations between circRNA and drug sensitivity using a multisource feature-based approach

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JOURNAL OF CELLULAR AND MOLECULAR MEDICINE 2024年第19期28卷 e18591页

作者： Yin, Shuaidong Xu, Peng Jiang, Yefeng Yang, Xin Lin, Ye Zheng, Manyu Hu, Jinpeng Zhao, Qi Univ Sci & Technol Liaoning Sch Comp Sci & Software Engn Anshan 114051 Peoples R China Univ Sci & Technol Liaoning Sch Elect & Informat Engn Anshan Peoples R China Jilin Univ Coll Comp Sci & Technol Changchun Peoples R China

The unique non-coding RNA molecule known as circular RNA (circRNA) is distinguished from conventional linear RNA by having a longer half-life, greater degree of conservation and inherent solidity. Extensive research has demonstrated the profound impact of circRNA expression on cellular drug sensitivity and therapeutic efficacy. There is an immediate need for the creation of efficient computational techniques to anticipate the potential correlations between circRNA and drug sensitivity, as classical biological research approaches are time-consuming and costly. In this work, we introduce a novel deep learning model called SNMGCDA, which aims to forecast the relationships between circRNA and drug sensitivity. SNMGCDA incorporates a diverse range of similarity networks, enabling the derivation of feature vectors for circRNAs and drugs using three distinct calculation methods. First, we utilize a sparse autoencoder for the extraction of drug characteristics. Subsequently, the application of non-negative matrix factorization (NMF) enables the identification of relationships between circRNAs and drugs based on their shared features. Additionally, the multi-head graph attention network is employed to capture the characteristics of circRNAs. After acquiring the characteristics from these three separate components, we combine them to form a unified and inclusive feature vector for each cluster of circRNA and drug. Finally, the relevant feature vectors and labels are inputted into a multilayer perceptron (MLP) to make predictions. The outcomes of the experiment, obtained through 5-fold cross-validation (5-fold CV) and 10-fold cross-validation (10-fold CV), demonstrate SNMGCDA outperforms five other state-of-art methods in terms of performance. Additionally, the majority of case studies have predominantly confirmed newly discovered correlations by SNMGCDA, thereby emphasizing its reliability in predicting potential relationships between circRNAs and drugs.

关键词： circRNA drug graph neural network non-negative matrix factorization sparse autoencoder

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Multi-frequency and multi-domain human activity recognition based on SFCW radar using deep learning

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NEUROCOMPUTING 2021年 444卷 274-287页

作者： Jia, Yong Guo, Yong Wang, Gang Song, Ruiyuan Cui, Guolong Zhong, Xiaoling Chengdu Univ Technol Coll Informat Sci & Technol Chengdu 610059 Peoples R China Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu 611731 Peoples R China

Deep learning and radar make it feasible to automatically recognize human activities in various lighting conditions, even occlusion case, which significantly promotes the application of activity recognition in the fields of security surveillance, health care, and so on. In this paper, an approach for human activity recognition (HAR) using deep learning is proposed based on stepped frequency continues wave (SFCW) radar. Specifically, SFCW radar is utilized to generate two types of characteristic representation domains, namely multiple frequencies of spectrograms in time-frequency domain and range maps in range domain. On the one hand, spectrograms and range maps provide different types of features. On the other hand, multi-frequency spectrograms furnish same type of features while with different scattering properties and frequency resolutions. Then a specific deep learning network including multiple parallel deep convolutional neural networks (DCNNs) and a sparse autoencoder is designed to extract and fuse these features associated with human activities from the multi-frequency spectrograms and rang map. In particular, each DCNN is aimed at extracting the detailed micro-Doppler features from a spectrogram, while sparse autoencoder learns prime range distribution features by compressing each range map to reduce complexity and improve robustness. Experimental results verify that the proposed deep learning scheme achieves 96.42% recognition accuracy about six types of activities by incorporating three frequencies of spectrograms and range map, and surpasses two existed methods depending on single-frequency spectrogram and combination of single-frequency spectrogram and range map. (c) 2020 Elsevier B.V. All rights reserved.

关键词： Human activity recognition SFCW radar Multi-frequency spectrograms Range maps Deep convolutional neural networks sparse autoencoder

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Automatic detection of phishing pages with event-based request processing, deep-hybrid feature extraction and light gradient boosted machine model

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TELECOMMUNICATION SYSTEMS 2021年第1期78卷 103-115页

作者： Kasim, Omer Kutahya Dumlupinar Univ Simav Technol Fac Dept Elect & Elect Engn TR-43500 Kutahya Turkey

Cyber attackers target unconscious users with phishing methods is a serious threat to cyber security. It is important to quickly detect benign web pages according to legitimate ones. Despite the successful detection of phishing in the studies suggested in the literature, the problems of high false positive rate after the web page request is processed should be resolved. The novelty of the study is that classification of deep-hybrid features with the Light Gradient Boosted Machine model is evaluated as an event when the web address is entered on the address bar of the browser. Thus, phishing can be detected at every request entry before the process is completed. In the proposed approach, normalized features from requests of web pages are applied to sparse autoencoder and Principal Component Analysis methods. These methods contribute to encoding of the deep-hybrid feature extraction. Light Gradient Boosted Machine model classifier can effectively distinguish legitimate pages and phishing attacks using these features. The ISCX-URL phishing dataset is used to measure performance of the proposed approach and validate it. The proposed method classifies the features that are encoded with SAE-PCA by using the Light Gradient Boosted Machine model at the rate of 99.6% within the event. The obtained results show that the proposed approach performs better classification performance metrics than most others. This accuracy contributed to the solution of the false-positives problem before requests are processed compared to other models.

关键词： Event-based request processing Phishing detection sparse autoencoder Principal component analysis Light gradient boosted machine model

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A Duo autoencoder-SVM based Approach for Secure Performance Monitoring of Industrial Conveyor Belt System

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COMPUTERS & CHEMICAL ENGINEERING 2023年第1期177卷

作者： Santhi, Thulasi M. Srinivasan, K. Natl Inst Technol Instrumentat & Control Engn Dept Tiruchirappalli 620015 India

Process industries are fascinated by cyber-physical systems because of the potential to integrate physical systems and the cyber realm, resulting in efficient remote monitoring and control. The conveyor belt system has many critical parameters that require continuous attention, necessitating cyber-physical remote monitoring. Due to cloud-based monitoring of parameters, the system is vulnerable to cyber threats. The proposed technique combines a sparse autoencoder and support vector machine (SVM) to detect false data injection attacks (FDIAs) in the presence of sensor bias fault. The sparse autoencoder extracts sparse features and learns anomaly-free dynamics from the input sensor readings. Then, the trained SVM distinguishes attacks and fault by analysing reconstruction residuals of each measurement reading. The residuals also give an idea about the magnitude of abnormality. The proposed method's efficacy is evaluated in terms of accuracy, precision and false-alarm rate with the help of fault and FDIAs models.

关键词： Conveyor belt Cyber-physical system False data injection attacks sparse autoencoder Support vector machine

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Unsupervised dimension-contribution-aware embeddings transformation for anomaly detection

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KNOWLEDGE-BASED SYSTEMS 2023年第1期262卷

作者： Xi, Liang Liang, Chenchen Liu, Han Li, Ao Harbin Univ Sci & Technol 52 Xuefu Rd Harbin 150080 Peoples R China

Deep-learning-based anomaly detection methods achieve excellent performance with the help of powerful feature extraction capabilities. However, the existing methods ignore the fine-grained dimension contributions of low-dimensional embeddings to anomaly analysis from different perspectives. Therefore, we propose an unsupervised Dimension-Contribution-aware Embeddings Transformation method for Anomaly Detection (DCETAD), focusing on the different contributions of the crucial dimensional information of low-dimensional embeddings to anomaly detection from different perspectives. Specifically, we design the Dimension-Contribution-aware sparse autoencoder (DC-SAE) to learn more discriminative embeddings on the basis of suppressing the sub-optimal and useless dimensions and highlight the dimension contributions of low-dimensional embeddings;then, perform the affine transformation to analyze the dimension contributions of low-dimensional embeddings in different transformation subspaces;finally, construct these transformation subspaces as multiple hyperspheres to comprehensively evaluate the anomaly score. Experimental results demonstrate that DCETAD not only achieves excellent detection performance but also outperforms recent state-of-the-art models.(c) 2022 Elsevier B.V. All rights reserved.

关键词： Anomaly detection Unsupervised sparse autoencoder Dimension contribution Affine transformation

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Unsupervised rail vehicle running instability detection algorithm for passenger trains (iVRIDA)

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MEASUREMENT 2023年 216卷

作者： Kulkarni, Rohan Qazizadeh, Alireza Berg, Mats KTH Royal Inst Technol Engn Mech Dept S-10044 Stockholm Sweden

Intelligently identifying rail vehicle faults instigating running instability from carbody floor acceleration is essential to ensure operational safety and reduce maintenance costs. However, the vehicle-track interaction's nonlinearities and scarcity of running instability occurrences complicate the task. The running instability is an anomaly in the vehicle-track interaction. Thus, we propose unsupervised anomaly detection and clustering al-gorithms based iVRIDA framework to detect and identify running instability and corresponding root cause. We deploy and compare the performance of the PCA-AD (baseline), sparse autoencoder (SAE-AD), and LSTM-Encoder-Decoder (LSTMEncDec-AD) model to detect the running instability ***, we deploy a k-means algorithm on latent space to identify clusters associated with root causes instigating instability. We deployed the iVRIDA framework on simulated and measured accelerations of European high-speed rail vehicles where SAE-AD and LSTMEncDec-AD models showed 97% accuracy. The proposed method contributes to smart maintenance by intelligently identifying anomalous vehicle-track interaction events.

关键词： Vehicle hunting Unsupervised machine learning sparse autoencoder LSTM encoder decoder Worn wheel Failed yaw damper

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Simultaneous fault detection and isolation based on multi-task long short-term memory neural networks

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CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 2023年第1期240卷

作者： Qin, Ken Sinkou Du, Yegang Dalian Maritime Univ Informat Sci & Technol Coll Dalian 116026 Peoples R China Waseda Univ Future Robot Org Tokyo 1620041 Japan

Fault detection and isolation are two significant problems in process monitoring. However, due to the complicated relationships between the process variables, it becomes a challenging problem to build a model to capture the complicated relationship between process variables and perform fault detection and isolation based on the model. This paper proposes an advanced model, called multi-task long short-term memory (MTLSTM) neural networks, that can capture the most complicated relationships between process variables and a simultaneous fault detection and isolation approach based on MTLSTM neural networks. The proposed simultaneous fault detection and isolation approach takes a sparse autoencoder (SAE) to extract features from the measurements of process variables. Then, a long short-term memory neural network is trained on the extracted features with a multi-task learning strategy. Compared with the traditional multivariate statistical models (MVSM) such as principal component analysis and independent component analysis models, the MTLSTM neural networks can capture the nonlinear autocorrelations of process variables and correlations between process variables and perform fault detection and isolation for an industrial process simultaneously. To demonstrate the advantages and superiority of the proposed simultaneous fault detection and isolation approach, a case study on fault detection and isolation for the penicillin fermentation process is carried out. Case study results show that the proposed approach significantly outperforms existing fault detection and isolation approaches.

关键词： Fault detection and isolation sparse autoencoder Long short-term memory Penicillin fermentation process

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