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Driving Maneuver Anomaly Detection Based on deep auto-encoder and Geographical Partitioning

ACM TRANSACTIONS ON SENSOR NETWORKS 2023年第2期19卷 1-22页

作者： Liu, Miaomiao Yang, Kang Fu, Yanjie Wu, Dapeng Du, Wan Univ Calif Merced Dept Comp Sci & Engn Merced CA 95343 USA Univ Cent Florida Dept Comp Sci Orlando FL 32816 USA City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China

This paper presents GeoDMA, which processes the GPS data from multiple vehicles to detect anomalous driving maneuvers, such as rapid acceleration, sudden braking, and rapid swerving. First, an unsupervised deep auto-encoder is designed to learn a set of unique features from the normal historical GPS data of all drivers. We consider the temporal dependency of the driving data for individual drivers and the spatial correlation among different drivers. Second, to incorporate the peer dependency of drivers in local regions, we develop a geographical partitioning algorithm to partition a city into several sub-regions to do the driving anomaly detection. Specifically, we extend the vehicle-vehicle dependency to road-road dependency and formulate the geographical partitioning problem into an optimization problem. The objective of the optimization problem is to maximize the dependency of roads within each sub-region and minimize the dependency of roads between any two different sub-regions. Finally, we train a specific driving anomaly detection model for each sub-region and perform in-situ updating of these models by incremental training. We implement GeoDMA in Pytorch and evaluate its performance using a large real-world GPS trajectories. The experiment results demonstrate that GeoDMA achieves up to 8.5% higher detection accuracy than the baseline methods.

关键词： Anomaly detection deep auto-encoder peer dependency geographical partitioning

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Segmentation of shoulder muscle MRI using a new Region and Edge based deep auto-encoder

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MULTIMEDIA TOOLS AND APPLICATIONS 2023年第10期82卷 14963-14984页

作者： Khan, Saddam Hussain Khan, Asifullah Lee, Yeon Soo Hassan, Mehdi Jeong, Woong Kyo Pakistan Inst Engn & Appl Sci Dept Comp & Informat Sci Pattern Recognit Lab Islamabad 45650 Pakistan Univ Engn & Appl Sci UEAS Dept Comp Syst Engn Swat 19060 Pakistan Pakistan Inst Engn & Appl Sci PIEAS Artificial Intelligence Ctr PAIC Islamabad 45650 Pakistan Pakistan Inst Engn & Appl Sci Ctr Math Sci Islamabad 45650 Pakistan Catholic Univ Daegu Coll Med Sci Dept Biomed Engn Daegu South Korea Air Univ Dept Comp Sci Islamabad Pakistan Korea Univ Coll Med Dept Orthopaed Surg Seoul South Korea

automatic segmentation of shoulder muscle MRI is challenging due to the high variation in muscle size, shape, texture, and spatial position of tears. Manual segmentation of tear and muscle portion is hard, time-consuming, and subjective to pathological expertise. This work proposes a new Region and Edge-based deep auto-encoder (RE-DAE) for shoulder muscle MRI segmentation. The proposed RE-DAE harmoniously employs average and max-pooling operations in the Convolutional encoder and decoder blocks. The DAE's region and edge-based segmentation encourage the network to extract homogenous and anatomical information, respectively. These two concepts, systematically combined in a DAE, generate a discriminative and sparse hybrid feature space (exploiting both region homogeneity and boundaries). Moreover, the concept of static attention is exploited in the proposed RE-DAE that helps effectively learn the tear region. The performances of the proposed RE-DAE architectures have been tested using a 3D MRI shoulder muscle dataset using the hold-out cross-validation technique. The MRI data has been collected from the Korea University Anam Hospital, Seoul, South Korea. Experimental comparisons have been conducted by employing innovative custom-made and existing pre-trained CNN architectures using transfer learning and fine-tuning. Objective evaluation on the muscle datasets using the proposed SA-RE-DAE showed a dice similarity of 85.58% and 87.07%, an accuracy of 81.57% and 95.58% for tear and muscle regions, respectively. The high visual quality and the objective result suggest that the proposed SA-RE-DAE can correctly segment tear and muscle regions in shoulder muscle MRI for better clinical decisions.

关键词： MRI Shoulder muscle deep auto-encoder Segmentation Transfer learning CNN

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Real-time drive-by bridge damage detection using deep auto-encoder

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STRUCTURES 2023年 47卷 1167-1181页

作者： Li, Zhenkun Lin, Weiwei Zhang, Youqi Aalto Univ Dept Civil Engn Rakentajanaukio 4 Espoo 02150 Finland

Structural health condition monitoring of bridge structures has been a concern in the last decades due to their aging and deterioration, in which the core task is damage detection. Recently, the drive-by method has gained much attention as it only needs several sensors installed on the passing vehicle. In this paper, we proposed an automatic damage detection method, which can be exploited in real time when the vehicle is passing the bridge. There are three steps in the proposed method: (1) The vehicle's framed short-time vibrations instead of full-length data are utilized for training a deep auto-encoder model;at this stage, not commonly used time-domain accelerations of the passing vehicle, but its selected frequency-domain responses are employed to circumvent the influence of noises, (2) For the bridge with unknown health conditions, damage indicators can be extracted from its passing vehicle's short-time vibration data using the trained model, and (3) The bridge's health states are determined by real-time extracted damage indicators. To verify the proposed idea, a U-shaped continuous beam and a model truck are used to simulate the vehicle bridge interaction system in engineering. Results showed that the proposed method could identify the bridge's damage with an accuracy of 86.2% when different severity was considered. In addition, it was observed that higher damage severity could not be revealed by greater values of damage indicators in the laboratory test. Instead, a novel index called identified damage ratios was employed as a reference for assessing the severity of the bridge's damage. It was shown that with the increase in damage severity, the index would increase and gradually approach 100%.

关键词： Structural health monitoring Damage detection Drive-by Short-time Fourier transform deep auto-encoder

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Integrated deep auto-encoder and Q-Learning-Based Scheme to Detect Anomalies and Supporting Forensics in Cloud Computing Environments

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WIRELESS PERSONAL COMMUNICATIONS 2022年第3期127卷 2247-2265页

作者： Savaridassan, P. Maragatham, G. SRM Inst Sci & Technol Sch Comp Dept Informat Technol Chennai Tamil Nadu India

The cloud computing environment when deployed correctly is responsible for delivering scalability, cost efficiency, reliability, security and interoperability to the end users. Log analysis is considered to be an indispensable component of security regulations and framework, since these computer-generated records help the organizations, businesses and networks to respond to different kinds of risks that are possible to cloud environment in a reactive and proactive manner. In this paper, an Integrated deep auto-encoder and Q-learning-based deep Learning (IDEA-QLDL) Scheme is proposed for attaining maximum prediction accuracy during the process of exploring log data and classifying them into genuine and anomalous. It initiates the process of acceptance or denial based on the continuous investigation of behavioral patterns that are highly applicable for classification. The results of the proposed IDEA-QLDL Scheme confirmed its predominance in improving the classification accuracy, precision, recall and detection time compared to the benchmarked schemes considered for investigation.

关键词： Bio-inspired algorithm Q-Learning deep auto-encoder Anomaly detection

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Collaborative filtering in dynamic networks based on deep auto-encoder

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JOURNAL OF SUPERCOMPUTING 2022年第5期78卷 7410-7427页

作者： Jalali, Shiva Hosseini, Monireh KN Toosi Univ Technol Dept Ind Engn Tehran Iran

The collaborative filtering method in recommender systems can produce suggestions with good quality. However, this method suffers from the issues of cold start and data sparsity. To overcome these problems, other data sources must be used to identify users. As a source of information, social relationships between users can help solve these problems. Recommender systems, on the other hand, are set in environments where data are generated continuously and with high dimension over time. For example, users have new rates and new relationships. These are indicative of the dynamic nature of system data, the changes in which classical models are unable to manage in order to provide appropriate suggestions to users. Then a dynamic model is needed. In this research, a new hybrid dynamic recommender model, which utilizes deep auto-encoder networks, is described to close these gaps. In this model, users' rating information and social relationships between them are used simultaneously to calculate similarity matrices between users at different timestamps. In each timestamp, the similarity matrix is given as input to the deep neural network, in which users are divided into different clusters. Users' new behaviors over time will help update matrices values. The use of social relationships data and the consideration of users' new behaviors over time solves the problems of classical methods, reduces recommending error, and increases user satisfaction. The proposed model is compared with state-of-the-art models;based on evaluation metrics. The results of execution on the real dataset show that the proposed model outperforms them.

关键词： Collaborative filtering Social network Dynamic networks deep auto-encoder Clustering

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Rolling element bearing fault diagnosis based on multi-objective optimized deep auto-encoder

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MEASUREMENT SCIENCE AND TECHNOLOGY 2024年第9期35卷 096007-096007页

作者： Chang, Xiaoxin Yang, Shaopu Li, Shaohua Gu, Xiaohui Shijiazhuang Tiedao Univ State Key Lab Mech Behav & Syst Safety Traff Engn Shijiazhuang Peoples R China

Bearing fault diagnosis holds significant importance, with widespread attention focused on enhancing its accuracy and efficiency. Existing diagnostic methods based on deep learning and transfer learning typically tackle this issue by introducing new function modules and diagnostic strategies, such as attention mechanism, adversarial domain adaptation, etc. However, most studies do not consider the structure and hyperparameters optimization of the network to improve the diagnostic performance of the network itself. To address this limitation, a novel multi-objective optimized deep auto-encoder is proposed in this paper. The optimal network structure and hyperparameters is determined by a multi-objective particle swarm optimization algorithm. Crucially, the method is based on a data-driven approaches to automatically search for network structures with stronger generalization and feature extraction capabilities to address engineering problems in different scenarios. Finally, this method is examined in both multi-fault classification diagnosis and transfer diagnosis scenarios, demonstrating strong self-adaptability through experimental results. In comparison with typical deep learning fault diagnosis methods, the proposed method demonstrates higher diagnostic accuracy and superior generalization ability.

关键词： bearing fault diagnosis multi-objective optimization deep auto-encoder transfer diagnosis

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A deep auto-encoder satellite anomaly advance warning framework

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AIRCRAFT ENGINEERING AND AEROSPACE TECHNOLOGY 2021年第6期93卷 1085-1096页

作者： Chen, Junfu Zhao, Xiaodong Pi, Dechang Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing Peoples R China

Purpose The purpose of this paper is to ensure the stable operation of satellites in orbit and to assist ground personnel in continuously monitoring the satellite telemetry data and finding anomalies in advance, which can improve the reliability of satellite operation and prevent catastrophic losses. Design/methodology/approach This paper proposes a deep auto-encoder (DAE) satellite anomaly advance warning framework for satellite telemetry data. Firstly, this study performs grey correlation analysis, extracts important feature attributes to construct feature vectors and builds the variational auto-encoder with bidirectional long short-term memory generative adversarial network discriminator (VAE/BLGAN). Then, the Mahalanobis distance is used to measure the reconstruction score of input and output. According to the periodic characteristic of satellite operation, a dynamic threshold method based on periodic time window is proposed. Satellite health monitoring and advance warning are achieved using reconstruction scores and dynamic thresholds. Findings Experiment results indicate DAE methods can probe that satellite telemetry data appear abnormal, trigger a warning before the anomaly occurring and thus allow enough time for troubleshooting. This paper further verifies that the proposed VAE/BLGAN model has stronger data learning ability than other two auto-encoder models and is sensitive to satellite monitoring data. Originality/value This paper provides a DAE framework to apply in the field of satellite health monitoring and anomaly advance warning. To the best of the authors' knowledge, this is the first paper to combine DAE methods with satellite anomaly detection, which can promote the application of artificial intelligence in spacecraft health monitoring.

关键词： Satellite Health monitoring Anomaly advance warning deep auto-encoder Dynamic threshold

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Unsupervised deep learning approach using a deep auto-encoder with an one-class support vector machine to detect structural damage

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STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL 2021年第1期20卷 406-425页

作者： Wang, Zilong Cha, Young-Jin Univ Manitoba Dept Civil Engn SP427 Stanley Pauley Engn Bldg15 Gillson St Winnipeg MB R3T 6B3 Canada

This article proposes an unsupervised deep learning-based approach to detect structural damage. Supervised deep learning methods have been proposed in recent years, but they require data from an intact structure and various damage scenarios of monitored structures for their training processes. However, the labeling work on the training data is typically time-consuming and costly, and sometimes collecting sufficient training data from various damage scenarios of infrastructures in service is impractical. In this article, the proposed unsupervised deep learning method based on a deep auto-encoder with an one-class support vector machine only uses the measured acceleration response data acquired from intact or baseline structures as training data, which enables future structural damage to be detected. The major contributions and novelties of the proposed method are as follows. First, an appropriate deep auto-encoder is carefully designed through comparative studies on the depth of neural networks. Second, the designed deep auto-encoder is taken as an extractor to obtain damage-sensitive features from the measured acceleration response data, and an one-class support vector machine is used as a damage detector. Third, experimental and numerical studies validate the high accuracy of the proposed method for damage detection: a 97.4% mean average for a 12-story numerical building model and a 91.0% accuracy for a laboratory-scaled steel bridge. Fourth, the proposed method also detects light damage (i.e. a 10% reduction in stiffness) with 96.9% to 99.0% accuracy, which shows its superior performance compared with the current state of the art. Fifth, it provides stable and more robust damage detection performance with reduced tuning parameters.

关键词： Unsupervised learning deep learning deep auto-encoder one-class support vector machine structural damage detection

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A new deep auto-encoder using multiscale reconstruction errors and weight update correlation

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INFORMATION SCIENCES 2021年 559卷 130-152页

作者： Song, Wei Li, Wei Hua, Ziyu Zhu, Fuxin Jiangnan Univ Sch Artificial Intelligence & Comp Sci Wuxi 214122 Jiangsu Peoples R China Jiangnan Univ Jiangsu Prov Engn Lab Pattern Recognit & Computat Wuxi 214122 Jiangsu Peoples R China

deep auto-encoder (DAE) is a promising deep learning model to extract the features of data. However, it is difficult to achieve the desired network parameters by training due to the issue of inappropriate learning rate, which usually affects its performance of extracting features. In order to solve such an issue, in this paper we propose a new Variable Learning Speed DAE (VLSDAE) using Multiscale Reconstruction Errors (MRE) and Weights Update Correlation (WUC) to adaptively adjust its learning rate. Specifically, the reconstruction errors based on the multiscales, i.e. mini-batch, epoch and phase, are considered to reflect the macroscopic training effect. Moreover, for the sake of further representing the microscopic training state, WUC is proposed to depict the training effect of each neuron. We first give the complexity analysis and the theoretical proof for the convergence of VLSDAE. To further exhibit its performance, a parameter sensitivity analysis is then provided to investigate the effects of two key parameters. In addition, the proposed MRE, WUC and our integrated method are studied individually to reveal their learning effects. Finally, we compare VLSDAE with eight state-of-the-art algorithms. The experimental results demonstrate that VLSDAE outperforms the other eight competitors in terms of training error, classification accuracy, precision, recall and macro-F1. (C) 2021 Elsevier Inc. All rights reserved.

关键词： deep learning deep auto-encoder Multiscale reconstruction errors Weights update correlation Learning rate

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deep auto-encoder and deep Forest-Assisted Failure Prognosis for Dynamic Predictive Maintenance Scheduling

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SENSORS 2021年第24期21卷 8373-8373页

作者： Yu, Hui Chen, Chuang Lu, Ningyun Wang, Cunsong 38 Res Inst CETC Integrated Syst Integrat Dept Hefei 230088 Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Automat Engn Nanjing 211106 Peoples R China Nanjing Tech Univ Inst Intelligent Mfg Nanjing 210009 Peoples R China

Prognostics and health management (PHM) with failure prognosis and maintenance decision-making as the core is an advanced technology to improve the safety, reliability, and operational economy of engineering systems. However, studies of failure prognosis and maintenance decision-making have been conducted separately over the past years. Key challenges remain open when the joint problem is considered. The aim of this paper is to develop an integrated strategy for dynamic predictive maintenance scheduling (DPMS) based on a deep auto-encoder and deep forest-assisted failure prognosis method. The proposed DPMS method involves a complete process from performing failure prognosis to making maintenance decisions. The first step is to extract representative features reflecting system degradation from raw sensor data by using a deep auto-encoder. Then, the features are fed into the deep forest to compute the failure probabilities in moving time horizons. Finally, an optimal maintenance-related decision is made through quickly evaluating the costs of different decisions with the failure probabilities. Verification was accomplished using NASA's open datasets of aircraft engines, and the experimental results show that the proposed DPMS method outperforms several state-of-the-art methods, which can benefit precise maintenance decisions and reduce maintenance costs.

关键词： failure prognosis maintenance decision-making deep auto-encoder deep forest maintenance cost

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