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3-D BLE Indoor Localization Based on denoising autoencoder

IEEE ACCESS 2017年 5卷 12751-12760页

作者： Xiao, Chao Yang, Daiqin Chen, Zhenzhong Tan, Guang Wuhan Univ Sch Remote Sensing & Informat Engn Wuhan 430079 Hubei Peoples R China Chinese Acad Sci SIAT Shenzhen 518005 Peoples R China

Bluetooth low energy (BLE)-based indoor localization has attracted increasing interests for its low-cost, low-power consumption, and ubiquitous availability in mobile devices. In this paper, a novel denoising autoencoder-based BLE indoor localization (DABIL) method is proposed to provide high-performance 3-D positioning in large indoor places. A deep learning model, called denoising autoencoder, is adopted to extract robust fingerprint patterns from received signal strength indicator measurements, and a fingerprint database is constructed with reference locations in 3-D space, rather than traditional 2-D plane. Field experiments show that 3-D space fingerprinting can effectively increase positioning accuracy, and DABIL performs the best in terms of both horizontal accuracy and vertical accuracy, comparing with a traditional fingerprinting method and a deep learning-based method. Moreover, it can achieve stable performance with incomplete beacon measurements due to unpredictable BLE beacon lost.

关键词： Bluetooth low energy indoor localization fingerprinting denoising autoencoder

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An enhancement denoising autoencoder for rolling bearing fault diagnosis

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MEASUREMENT 2018年 130卷 448-454页

作者： Meng, Zong Zhan, Xuyang Li, Jing Pan, Zuozhou Yanshan Univ Qinhuangdao Peoples R China

denoising autoencoders can automatically learn in-depth features from complex data and extract concise expressions, which are used in fault diagnosis. However, they still have many drawbacks: (1) unsatisfactory results when the input data is not substantial;(2) difficulty in optimising the hyperparameter;(3) inability of existing regularisation methods to combine the advantages of L1 and L2 regularisation. To overcome the aforementioned challenges, here, a new data preprocessing method was proposed to obtain the training data. By reusing the data points between the adjacent samples, the fault identifying rate was significantly improved. Considering the different resilience of each layer after regularisation, the proposed method could alter the hyperparameter by changing the unit numbers of each layer. For a better sparse representation, the norm penalty combined L1 and L2 norm penalties, motivated by the elastic net. Comparison with a normal denoising autoencoder verified the superiority of the proposed method. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： denoising autoencoder Fault diagnosis Hyperparameter Regularisation Elastic net

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Feature learning using convolutional denoising autoencoder for activity recognition

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NEURAL COMPUTING & APPLICATIONS 2021年第17期33卷 10909-10922页

作者： Mohd Noor, Mohd Halim Univ Sains Malaysia Sch Comp Sci George Town 11800 Malaysia

Wearable technology offers a prospective solution to the increasing demand for activity monitoring in pervasive healthcare. Feature extraction and selection are crucial steps in activity recognition since it determines the accuracy of activity classification. However, existing feature extraction and selection methods involve manual feature engineering, which is time-consuming, laborious and prone to error. Therefore, this paper proposes an unsupervised feature learning method that automatically extracts and selects the features without human intervention. Specifically, the proposed method jointly trains a convolutional denoising autoencoder with a convolutional neural network to learn the underlying features and produces a compact feature representation of the data. This allows not only more accurate and discriminative features to be extracted but also reduces the computational cost and improves generalization of the classification models. The proposed method was evaluated and compared with deep learning convolutional neural networks on a public dataset. Results have shown that the proposed method can learn a salient feature representation and subsequently recognize the activities with an accuracy of 0.934 and perform comparably well to the convolutional neural networks.

关键词： Activity recognition denoising autoencoder Deep learning Feature learning

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Elimination of Random Mixed Noise in ECG Using Convolutional denoising autoencoder With Transformer Encoder

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IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2024年第4期28卷 1993-2004页

作者： Chen, Meng Li, Yongjian Zhang, Liting Liu, Lei Han, Baokun Shi, Wenzhuo Wei, Shoushui Shandong Univ Sch Control Sci & Engn Jinan 250061 Peoples R China Shandong Univ Shandong Prov Hosp Dept Cardiol Jinan 250061 Peoples R China Shandong Univ Inst Marine Sci & Technol Qingdao 266237 Peoples R China

Electrocardiogram (ECG) signals frequently encounter diverse types of noise, such as baseline wander (BW), electrode motion (EM) artifacts, muscle artifact (MA), and others. These noises often occur in combination during the actual data acquisition process, resulting in erroneous or perplexing interpretations for cardiologists. To suppress random mixed noise (RMN) in ECG with less distortion, we propose a Transformer-based Convolutional denoising autoencoder model (TCDAE) in this study. The encoder of TCDAE is composed of three stacked gated convolutional layers and a Transformer encoder block with a point-wise multi-head self-attention module. To obtain minimal distortion in both time and frequency domains, we also propose a frequency weighted Huber loss function in training phase to better approximate the original signals. The TCDAE model is trained and tested on the QT Database (QTDB) and MIT-BIH Noise Stress Test Database (NSTDB), with the training data and testing data coming from different records. All the metrics perform the most robust in overall noise and separate noise intervals for RMN removal compared with the baseline methods. We also conduct generalization tests on the Icentia11k database where the TCDAE outperforms the state-of-the-art models, with a 55% reduction of the false positives in R peak detection after denoising. The TCDAE model approximates the short-term and long-term characteristics of ECG signals and has higher stability even under extreme RMN corruption. The memory consumption and inference speed of TCDAE are also feasible for its deployment in clinical applications.

关键词： Electrocardiography Convolution Transformers Feature extraction Logic gates Adaptive filters Wiener filters denoising autoencoder ECG denoising multi-head self-attention point-wise convolution random mixed noise transformer encoder

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Taxonomy-aware collaborative denoising autoencoder for personalized recommendation

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APPLIED INTELLIGENCE 2019年第6期49卷 2101-2118页

作者： Zhang, Chunhong Li, Tiantian Ren, Zhibin Hu, Zheng Ji, Yang Beijing Univ Posts & Telecommun Sch Informat & Commun Engn Beijing Peoples R China Beijing Univ Posts & Telecommun Beijing Peoples R China Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing Peoples R China

Taxonomies are ubiquitous in many real-world recommendation scenarios where each item is classified into a category of a predefined hierarchical taxonomy and provide important auxiliary information for inferring user preferences. However, traditional collaborative filtering approaches have focused on user-item interactions (e.g., ratings) and neglected the impact of taxonomy information on recommendation. In this paper, we present a taxonomy-aware denoising autoencoder based model which incorporates taxonomy-aware side information into denoising autoencoder based recommendation models to enhance recommendation accuracy and alleviate data sparsity and cold start problems in recommendation systems. We propose two types of taxonomic side information, namely the topological representation of tree-structured taxonomy and the statistical properties of the taxonomy. By integrating taxonomic side information, our model can learn more effective user latent vectors which are not only determined by user ratings but also rely on the taxonomy information. We conduct a comprehensive set of experiments on two real-world datasets which provide several outcomes: first, our proposed taxonomy-aware method outperforms the baseline method on RMSE metric. Next, information extracted from taxonomy can help alleviate data sparsity and cold start problems. Moreover, we conduct supplementary experiments to explore the reason why our proposed taxonomic side information improves recommendation performance.

关键词： Recommender systems Taxonomy denoising autoencoder Collaborative filtering

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Trust-Aware Collaborative Filtering with a denoising autoencoder

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NEURAL PROCESSING LETTERS 2019年第2期49卷 835-849页

作者： Wang, Meiqi Wu, Zhiyuan Sun, Xiaoxin Feng, Guozhong Zhang, Bangzuo Northeast Normal Univ Sch Informat Sci & Technol Changchun 130117 Jilin Peoples R China

Collaborative filtering is one of the most successful and extensive methods used by recommender systems for predicting the preferences of users. However, traditional collaborative filtering only uses rating information to model the user, the data sparsity problem and the cold start problem will severely reduce the recommendation performance. To overcome these problems, we propose two neural network models to improve recommendations. The first one called TDAE uses a denoising autoencoder to integrate the ratings and the explicit trust relationships between users in the social networks in order to model the preferences of users more accurately. However, the explicit trust information is very sparse, which limits the performance of this model. Therefore, we propose a second method called TDAE++ for extracting the implicit trust relationships between users with similarity measures, where we employ both the explicit and implicit trust information together to improve the quality of recommendations. Finally, we inject the trust information into both the input and the hidden layer in order to fuse these two types of different information to learn more reliable semantic representations of users. Comprehensive experiments based on three popular data sets verify that our proposed models perform better than other state-of-the-art approaches in common recommendation tasks.

关键词： Collaborative filtering denoising autoencoder Social networks Trust information

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Condition monitoring and performance forecasting of wind turbines based on denoising autoencoder and novel convolutional neural networks

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ENERGY REPORTS 2021年 7卷 6354-6365页

作者： Jia, Xiongjie Han, Yang Li, Yanjun Sang, Yichen Zhang, Guolei Harbin Engn Univ Coll Power & Energy Engn Harbin Peoples R China

With the proportion of wind power in the grid increasing, the monitoring and maintenance of wind turbines is becoming more and more important for the reliability of the grid. In this study, a data-driven modelling framework based on deep convolutional neural networks is constructed for wind turbines condition monitoring (CM) and performance forecasting (PF). For CM, a robust denoising autoencoder (DAE) model is introduced to output the reconstruction error (RE) of raw signals. The RE is processed to a state indicator by exponentially weighted moving average (EWMA) and monitored on a control chart. For PF, two multi-steps ahead forecasting models are constructed for the forecasting of generator bearing and transformer temperature. To prevent overfitting caused by abundant features, the marginal effect analysis based on random forests is implemented to measure the importance of features. Besides, novel residual attention module (RAM) and training strategies are used improve their representation power of DAE and PF models. Experiments on a real wind turbine dataset prove the effectiveness of the proposed models and methods. (C) 2021 The Authors. Published by Elsevier Ltd.

关键词： Wind turbine Condition monitoring Performance forecasting denoising autoencoder Residual attention module

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Intrusion Detection in IoT Systems Using denoising autoencoder

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IEEE ACCESS 2024年 12卷 122401-122425页

作者： Alrayes, Fatma S. Zakariah, Mohammed Amin, Syed Umar Khan, Zafar Iqbal Helal, Maha Princess Nourah Bint Abdulrahman Univ Coll Comp & Informat Sci Informat Syst Dept Riyadh 11671 Saudi Arabia King Saud Univ Coll Comp & Informat Sci Riyadh 11362 Saudi Arabia Prince Sultan Univ Coll Comp & Informat Sci Riyadh 11586 Saudi Arabia Saudi Elect Univ Coll Comp & Informat Riyadh 11673 Saudi Arabia

Protection against unwanted intrusions is crucial for preserving the integrity and security of connected devices in the context of Internet of Things (IoT) networks. The growing number of IoT devices has made several industries more vulnerable to cyberattacks and security breaches, including smart homes, industrial automation, and healthcare. In response to this pressing dilemma, the goal of this project is to create a novel method for intrusion detection in Internet of Things systems utilizing denoising autoencoder (DAE) models. Traditional intrusion detection methods often prove inadequate in Internet of Things scenarios due to resource restrictions, dynamic network topologies, and a diversity of communication protocols. By utilizing DAEs' unsupervised learning and feature extraction skills, our suggested approach creates a system that can identify and stop intrusion attempts in real-time. The evaluation of the study additionally makes use of the NSL-KDD and CICIDS 2017 datasets. DAE integration yields an unequaled accuracy of 99.991% when the CICIDS 2017 dataset is used, and an accuracy of 99.4% when the NSL-KDD dataset is used. The CICIDS 2017 dataset analysis reveals several notable performance measures, including an accuracy of 1.0, a precision of 0.995, and an F1-score of 0.998. Analyses of the NSL-KDD dataset also produce outstanding results, with an F1-score of 0.989, recall of 0.991, accuracy of 0.994, and precision of 0.984. The results also show how well the suggested DAE-based intrusion detection method works to stop unauthorized users from accessing IoT devices, which lowers the risk of issues with system integrity, privacy, and security. By strengthening resilience against evolving cyber threats in the networked Internet of Things landscape, this research enhances cybersecurity strategies tailored to address the unique challenges encountered by IoT ecosystems.

关键词： Internet of Things Intrusion detection system Internet of Things Intrusion detection system machine learning machine learning CICIDS2017 dataset CICIDS2017 dataset denoising autoencoder denoising autoencoder

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Trust-aware denoising autoencoder with spatial-temporal activity for cross-domain personalized recommendations

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NEUROCOMPUTING 2022年 511卷 477-494页

作者： Ahmed, Adeel Saleem, Khalid Khalid, Osman Gao, Jiechao Rashid, Umer Quaid i Azam Univ Dept Comp Sci Islamabad 45320 Pakistan COMSATS Univ Dept Comp Sci Abbottabad Campus Islamabad Pakistan Univ Virginia Dept Comp Sci Charlottesville VA USA

Recently, cross-domain recommendation systems have been very helpful in improving the quality of rec-ommendation and solving the problem of cold start and data sparsity. Cross-domain recommender sys-tems allow the transfer of knowledge from one domain with dense ratings to other domain with sparse ratings. Such transfer of knowledge helps in addressing the data sparseness and cold start issues in tra-ditional recommender systems. Although cross-domain recommendations have evolved significantly, yet employment of various factors, such as time, trust, and location remains a challenge. Most of the existing approaches ignore the important fact that at what specific time the user may be interested in the recom-mended item. Moreover, a person's trust level and sentiments may be influenced by the variation in the location and time, thereby affecting the decision making. In this paper, we propose a cross-domain rec-ommender system that not only takes into account the time at finer granularity levels (e.g., hours, days, weeks, etc.), but also considers a persons location, trust level, and sentiment analysis while computing recommendations. Our proposed model, named as, Trust-Aware Spatial-Temporal Activity based denoising autoencoder (TSTDAE), employs autoencoder-based deep-learning models to generate top-N recommendations for a given user and addresses the cold-start problem in the cross-domain scenario of 'User Overlap'. The proposed work is fivefold: i) Filter out the users' biased profiles based on sentiment analysis. ii) Learn the features using autoencoder and then perform clustering among the users of source and target domains to discover the best neighbors. iii) Compute the trust and ratio of preference bias between active user (the user to whom top-N items are recommended) and their neighbors and grade the neighbors based on unbiased preferences iv) Project the best time for recommending the items to an active user and generate the top-N recommendations. We have eva

关键词： Cross-domain information Collaborative filtering K-means clustering denoising autoencoder Implicit trust Latent Dirichlet allocation Sentiment analysis

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SwinDAE: Electrocardiogram Quality Assessment Using 1D Swin Transformer and denoising autoencoder

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IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2023年第12期27卷 5779-5790页

作者： Chen, Guanyu Shi, Tianyi Xie, Baoxing Zhao, Zhicheng Meng, Zhu Huang, Yadong Dong, Jin Beijing Acad Blockchain & Edge Comp Beijing 100080 Peoples R China Beijing Univ Posts & Telecommun Beijing 100088 Peoples R China Beijing Key Lab Network Syst & Network Culture Beijing 100124 Peoples R China Zhoushan Acad Marine Data Sci Zhoushan 316021 Peoples R China

Objective: Electrocardiogram (ECG) signals have wide-ranging applications in various fields, and thus it is crucial to identify clean ECG signals under different sensors and collection scenarios. Despite the availability of a variety of deep learning algorithms for ECG quality assessment, these methods still lack generalization across different datasets, hindering their widespread use. Methods: In this paper, an effective model named Swin denoising autoencoder (SwinDAE) is proposed. Specifically, SwinDAE uses a DAE as the basic architecture, and incorporates a 1D Swin Transformer during the feature learning stage of the encoder and decoder. SwinDAE was first pre-trained on the public PTB-XL dataset after data augmentation, with the supervision of signal reconstruction loss and quality assessment loss. Specially, the waveform component localization loss is proposed in this paper and used for joint supervision, guiding the model to learn key information of signals. The model was then fine-tuned on the finely annotated BUT QDB dataset for quality assessment. Results: SwinDAE achieved 0.02-0.13 mean F1 score improvement on the BUT QDB dataset compared to multiple deep learning methods, and demonstrated applicability on two other datasets. Conclusion: The proposed SwinDAE shows strong generalization ability on different datasets, and surpasses other state-of-the-art deep learning methods on multiple evaluation metrics. In addition, the statistical analysis for SwinDAE prove the significance of the performance and the rationality of the prediction. Significance: SwinDAE can learn the commonality between high-quality ECG signals, exhibiting excellent performance in the application of cross-sensors and cross-collection scenarios.

关键词： Electrocardiography Quality assessment Feature extraction Deep learning Data models Sensitivity Machine learning denoising autoencoder ECG quality assessment swin Transfromer transfer learning waveform component localization

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