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SALLoc: An Accurate Target Localization in WiFi-Enabled Indoor Environments via SAE-ALSTM

IEEE ACCESS 2024年 12卷 19694-19710页

作者： Ayinla, Shehu Lukman Abd Aziz, Azrina Drieberg, Micheal Univ Teknol PETRONAS Dept Elect & Elect Engn Seri Iskandar 32610 Perak Malaysia Univ Ilorin Dept Comp Engn Ilorin 240003 Nigeria

Developing a reliable and accurate indoor localization system is a crucial step for creating a seamless and interactive user-device experience in nearly all intelligent internet of things (IIoTs) and smart applications. Indoor localization systems based on WiFi fingerprinting have been considered as a promising alternative to model-based approaches owing to their accuracy, low cost, availability, and ease of configuration. However, recent studies have revealed that in complex environments, WiFi fingerprinting techniques are faced with a lot of challenges as the coverage area increases. These challenges include fingerprint spatial uncertainty, instability in the received signal strength indicator (RSSI) and discrepancy in fingerprint distribution. Furthermore, there is frequent need for database upgrades or even recreation whenever there is a change in the architecture of the location. These challenges have questioned the robustness and efficiency of most of the existing schemes. In this paper, we present an indoor localization architecture for complex multi-building multi-floor location prediction and subsequently propose SALLoc (SAE-ALSTM Localization), a WiFi fingerprinting indoor localization scheme based on stacked autoencoder (SAE) and Attention-based Long Short-Time Memory (ALSTM) framework. Firstly, stratified sampling technique is used to separate validation set from the entire uneven RSSI training set which ensures that the same proportion of RSSI samples are present in both sets. Secondly, SAE is utilized to select core features and decrease the dimensions of the RSSI samples. Finally, ALSTM is trained to focus on these features to achieve robust location prediction. Extensive investigations were conducted using UJIIndoorLoc, Tampere and UTSIndoorLoc datasets, and the results obtained demonstrated the superiority of the proposed scheme in terms of prediction accuracy, robustness, and generalizations when compared to state-of-the-art methods. The mean local

关键词： Location awareness Fingerprint recognition Wireless fidelity Feature extraction Robustness Target tracking Databases WiFi fingerprinting indoor localization mean localization error stacked autoencoder long short-time memory attention mechanism

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A novel hybrid deep learning model for early stage diabetes risk prediction

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JOURNAL OF SUPERCOMPUTING 2024年第13期80卷 19462-19484页

作者： Bulbul, Mehmet Akif Kayseri Univ Dept Software Engn TR-38280 Kayseri Turkiye

Diabetes is a prevalent global disease that significantly diminishes the quality of life and can even lead to fatalities due to its complications. Early detection and treatment of diabetes are crucial for mitigating and averting associated risks. This study aims to facilitate the prompt and straightforward diagnosis of individuals at risk of diabetes. To achieve this objective, a dataset for early stage diabetes risk prediction from the University of California Irvine (UCI) database, widely utilized in the literature, was employed. A hybrid deep learning model comprising genetic algorithm, stacked autoencoder, and Softmax classifier was developed for classification on this dataset. The performance of this model, wherein both the model architecture and all hyperparameters were specifically optimized for the given problem, was compared with commonly used methods in the literature. These methods include K-nearest neighbor, decision tree, support vector machine, and convolutional neural network, utilizing tenfold cross-validation. The results obtained with the proposed method surpassed those obtained with other methods, with higher accuracy rates than previous studies utilizing the same dataset. Furthermore, based on the study's findings, a web-based application was developed for early diabetes diagnosis.

关键词： Genetic algorithm stacked autoencoder Softmax classification Hyperparameter optimization Hybrid deep learning model

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Intelligent Fault Detection Scheme for Rolling Bearing Based on Generative Adversarial Network and autoencoders Using Convolutional Neural Network

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JOURNAL OF VIBRATION ENGINEERING & TECHNOLOGIES 2024年第8期12卷 8979-8991页

作者： Rathore, Maan Singh Harsha, S. P. Indian Inst Technol Roorkee Dept Mech & Ind Engn Roorkee 247667 Uttarakhand India

Fault detection in early operational stages of rolling bearing is crucial for reliable and safe functioning of rotating machinery. Implementation of intelligent fault detection techniques involving deep learning methods enable automatic feature extraction and selection from raw vibration data provides accurate results. The shortage of enough historical data limits the application of deep learning. Therefore, to solve this problem, in this paper data augmentation method is implemented to generate new data that having greater similitude with the real data for better training of deep learning model for fault detection. For this purpose, WGAN (Wasserstein generative adversarial network) is implemented as imbalanced data augmentation method. Also SAE (stacked autoencoder) is implemented to obtain the latent representation of raw vibration data which is used as noise vector to train WGAN. This has greatly improved the quality of data generation from WGAN. The quality assessment of generated samples is quantified by implementing metrics such as KLD (Kullback-Leibler divergence) and NCC (normalized cross-correlation). The comparison with conventional data generation methods such as VAE, and GAN proves the superior quality of data generation by SAE-WGAN. Test rig experiments are used to gather the vibration data, and deep convolutional neural networks are used to classify the faults (DCNN). The ROC (receiver operating characteristic) curve and performance evaluation metrics like precision, recall, and F1-score amply demonstrated the excellent discriminative power of the suggested methodology for fault detection. Hence the proposed work successfully implemented as condition monitoring tool for early fault detection in rotating machinery.

关键词： Deep convolutional neural network Normalized cross-correlation stacked autoencoder Generative adversarial network ROC AUC (area under Curve)

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The CESAE multiple objection optimization network of the ACO-OFDM VLC system

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OPTICS COMMUNICATIONS 2024年 558卷

作者： Hao, Lili Cao, Pan Li, Chengdong Wang, Dongyi Shandong Jianzhu Univ Sch Informat & Elect Engn Intelligent Architecture Lab Shandong Key Lab Inte Jinan Shandong Peoples R China Univ Maryland Fischell Dept Bioengn Bioimaging & Machine Vis Lab College Pk MD 20740 USA Shandong Jianzhu Univ Sch Informat & Elect Engi neering Intelligent Architecture Lab Shandong Key Lab Inte 1000 Fengming Rd Jinan 250101 Shandong Peoples R China

Visible light communication (VLC) is a foreground technology in the sixth generation (6G) communication system. Simultaneously deep learning network is a novel approach to improve the properties of VLC system. In this paper, we put forward an asymmetric clipping optical orthogonal frequency division multiplexing (ACOOFDM) system based on the constant envelope stacked autoencoder (CESAE) network, which is a combination method of phase modulated constant envelope OFDM (CE-OFDM) and the SAE deep learning network. The CESAE network systematically improves the character of the whole system through multiple layers and the loss function. It is shown by the simulation results that tuning the phase modulation index can availably maintain the peak -to -average -power ratio (PAPR) to about 3 dB. In addition, a detailed comparison with traditional method reveals that the presented method fulfils superior BER performance and spectral efficiency.

关键词： Constant envelope stacked autoencoder Peak -to -average -power ratio reduction Visible light communication

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Thrust online fusion estimation of high-flow dual variable cycle engine

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AEROSPACE SCIENCE AND TECHNOLOGY 2024年 152卷

作者： Ma, Ansheng Zhou, Xin Zou, Zelong Huang, Jinquan Lu, Feng Nanjing Univ Aeronaut & Astronaut State Key Lab Mech & Control Aerosp Struct Nanjing 210016 Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Energy & Power Engn Nanjing 210016 Peoples R China

For the aeroengines, thrust is a crucial performance parameter which is closely related to the mission accessibility. Accurate thrust control is conducive to excavate the potential of the variable cycle engine (VCE) in different operation modes, which meets the requirements of future engine control system. However, the thrust is unmeasured in flight. Besides, the traditional thrust estimation method is difficult to meet the wide-area thrust control requirements due to the strong nonlinearity and large flight envelope for a neoconfiguration VCE of the high-flow triple-bypass, named high-flow dual variable cycle engine (HDVCE). This paper proposes a novel online fusion thrust estimation method mainly under single operation mode for the HDVCE. The problem of individual engine differences is also focused, and the combination of model and data-driven is to generate the thrust estimation with fusion strategy. At first, a multi-combustion chamber coupled dynamic model is established for the HDVCE, and an adaptive network is used for model optimization to improve the model accuracy. On this basis, an EKF-based thrust estimator is built. Secondly, a data-driven thrust estimator is pre-trained by a stacked autoencoder (SAE) and then tuned by back propagation (BP) neural network. Subsequently, the thrust integration is incorporated based on estimation results from the EKF-based and data-driven one using Euclidean distance fusion strategy. Simulation results show that the fusion estimation method proposed in this paper, on the one hand, reduces the effect of model instability on EKF. On the other hand, it ensures the estimation accuracy of SAE-BP far from the training point. The comparisons also reveal that the fusion one produces more than 97% in thrust estimation accuracy and is better than the remaining ones, which is promising for thrust control applications.

关键词： High-flow dual variable cycle engine Thrust fusion estimation Extended Kalman Filter stacked autoencoder Euclidean distance

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Framework for imbalanced fault diagnosis of rolling bearing using autoencoding generative adversarial learning

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JOURNAL OF THE BRAZILIAN SOCIETY OF MECHANICAL SCIENCES AND ENGINEERING 2023年第1期45卷 1-11页

作者： Rathore, Maan Singh Harsha, S. P. Indian Inst Technol Roorkee Dept Mech & Ind Engn Adv Mech Vibrat Lab Roorkee 247667 Uttarakhand India

This paper proposes a data augmentation model SAE-WGAN (stacked autoencoder with Wasserstein generative adversarial network), to reduce data imbalance condition of bearing fault diagnosis. To stabilize Wasserstein generative adversarial network training, both Wasserstein distance and informative noise vectors from stacked autoencoder are utilized to improve the quality of generated samples. For the quantitative evaluation of generated samples, both normalized cross-correlation and Kullback-Leibler divergence metrics are employed. Experimental validation and comparisons with state-of-art methods are presented to verify the effectiveness of the generation model. The results show an improvement of 6.58%, and 10.23% compared to Generative adversarial network, and Variational autoencoder, respectively. Furthermore, one-dimensional convolutional neural network is utilized for fault classification, and its performance is assessed using the receiver operating characteristic curve and area under curve values. The comparison results revealed the superior performance of the proposed generation model for bearing intelligent fault diagnosis under paucity of faulty data.

关键词： Generative adversarial network stacked autoencoder Deep convolutional neural network Normalized cross-correlation Receiver operating characteristic

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A novel multi-module integrated intrusion detection system for high-dimensional imbalanced data

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APPLIED INTELLIGENCE 2023年第1期53卷 272-288页

作者： Cui, Jiyuan Zong, Liansong Xie, Jianhua Tang, Mingwei Xihua Univ Sch Comp & Software Engn Chengdu 610039 Sichuan Peoples R China

The high dimension, complexity, and imbalance of network data are hot issues in the field of intrusion detection. Nowadays, intrusion detection systems face some challenges in improving the accuracy of minority classes detection, detecting unknown attacks, and reducing false alarm rates. To address the above problems, we propose a novel multi-module integrated intrusion detection system, namely GMM-WGAN-IDS. The system consists of three parts, such as feature extraction, imbalance processing, and classification. Firstly, the stacked autoencoder-based feature extraction module (SAE module) is proposed to obtain a deeper representation of the data. Secondly, on the basis of combining the clustering algorithm based on gaussian mixture model and the wasserstein generative adversarial network based on gaussian mixture model, the imbalance processing module (GMM-WGAN) is proposed. Thirdly, the classification module (CNN-LSTM) is designed based on convolutional neural network (CNN) and long short-term memory (LSTM). We evaluate the performance of GMM-WGAN-IDS on the NSL-KDD and UNSW-NB15 datasets, comparing it with other intrusion detection methods. Finally, the experimental results show that our proposed GMM-WGAN-IDS outperforms the state-of-the-art methods and achieves better performance.

关键词： Intrusion detection system stacked autoencoder Gaussian mixture model Wasserstein generative adversarial network Imbalance processing Feature extraction Deep learning

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ROBUST FEATURE LEARNING BY stacked autoencoder WITH MAXIMUM CORRENTROPY CRITERION

ROBUST FEATURE LEARNING BY STACKED AUTOENCODER WITH MAXIMUM ...

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IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Qi, Yu Wang, Yueming Zheng, Xiaoxiang Wu, Zhaohui Zhejiang Univ Qiushi Acad Adv Studies Hangzhou 310003 Zhejiang Peoples R China

ISBN: (纸本)9781479928934

Unsupervised feature learning with deep networks has been widely studied in the recent years. Despite the progress, most existing models would be fragile to non-Gaussian noises and outliers due to the criterion of mean square error (MSE). In this paper, we propose a robust stacked autoencoder (R-SAE) based on maximum correntropy criterion (MCC) to deal with the data containing non-Gaussian noises and outliers. By replacing MSE with MCC, the anti-noise ability of stacked autoencoder is improved. The proposed method is evaluated using the MNIST benchmark dataset. Experimental results show that, compared with the ordinary stacked autoencoder, the R-SAE improves classification accuracy by 14% and reduces the reconstruction error by 39%, which demonstrates that R-SAE is capable of learning robust features on noisy data.

关键词： Unsupervised feature learning stacked autoencoder correntropy deep learning

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NOTE: non-parametric oversampling technique for explainable credit scoring

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SCIENTIFIC REPORTS 2024年第1期14卷 1-18页

作者： Han, Seongil Jung, Haemin Yoo, Paul D. Cali, Andrea Provetti, Alessandro Univ London Birkbeck Coll Sch Comp & Math Sci London England Korea Natl Univ Transportat Dept Ind & Management Engn Chungju South Korea

Credit scoring models are critical for financial institutions to assess borrower risk and maintain profitability. Although machine learning models have improved credit scoring accuracy, imbalanced class distributions remain a major challenge. The widely used Synthetic Minority Oversampling TEchnique (SMOTE) struggles with high-dimensional, non-linear data and may introduce noise through class overlap. Generative Adversarial Networks (GANs) have emerged as an alternative, offering the ability to model complex data distributions. Conditional Wasserstein GANs (cWGANs) have shown promise in handling both numerical and categorical features in credit scoring datasets. However, research on extracting latent features from non-linear data and improving model explainability remains limited. To address these challenges, this paper introduces the Non-parametric Oversampling Technique for Explainable credit scoring (NOTE). The NOTE offers a unified approach that integrates a Non-parametric stacked autoencoder (NSA) for capturing non-linear latent features, cWGAN for oversampling the minority class, and a classification process designed to enhance explainability. The experimental results demonstrate that NOTE surpasses state-of-the-art oversampling techniques by improving classification accuracy and model stability, particularly in non-linear and imbalanced credit scoring datasets, while also enhancing the explainability of the results.

关键词： Conditional Wasserstein generative adversarial networks stacked autoencoder Explainable AI Imbalanced class Oversampling Credit scoring

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A deep learning-based intrusion detection approach for mobile Ad-hoc network

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SOFT COMPUTING 2023年第14期27卷 9425-9439页

作者： Meddeb, Rahma Jemili, Farah Triki, Bayrem Korbaa, Ouajdi Univ Sousse MARS Res Lab ISITCom LR17ES05 Hammam Sousse 4011 Tunisia

The goal of the paper is to present a stacked autoencoder approach for enhancing Intrusion Detection Systems (IDSs) in Mobile Ad-Hoc Networks (MANETs). The paper proposes a stacked autoencoder-based approach for MANET (stacked AE-IDS) to reduce correlation and model relevant features with high-level representation. This method reproduces the input with a reduced correlation, and the output of the autoencoder is used as the input of the Deep Neural Network (DNN) classifier (DNN-IDS). The proposed Deep Learning-based IDS focuses on Denial of Services (DoS) attacks within labeled datasets, which are available for intrusion detection, and employs the most potential attacks that impact routing services in Mobile Networks. The proposed stacked AE-IDS method enhances the effectiveness of IDSs in detecting attacks in MANETs by reducing the correlation and modeling high-level representations of relevant features. The focus on DoS attacks and their impact on routing services in Mobile Networks makes the proposed approach particularly relevant for MANET security. The proposed stacked AE-IDS approach has potential applications in enhancing the security of MANETs by improving the effectiveness of IDSs. This approach can be used to detect different types of attacks, particularly DoS attacks, and their impact on routing services in Mobile Networks.

关键词： Mobile Ad-Hoc networks Intrusion detection systems Semi-supervised learning stacked autoencoder Deep neural network Denial of service attack

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