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Process Monitoring for the Flow Field of Wind Tunnel Systems with a one-class classifier Ensemble

JOURNAL OF AEROSPACE ENGINEERING 2025年第2期38卷

作者： Zhang, Meng Chen, Yiheng Ren, Weijie Shenyang Aerosp Univ Sch Artificial Intelligence 37Daoyi South St Shenyang 110136 Peoples R China Harbin Engn Univ Coll Intelligent Syst Sci & Engn 145 Nantong St Harbin 150001 Peoples R China

The process monitoring system for wind tunnel flow fields enables real-time monitoring of anomalies and faults within the flow field, allowing for the implementation of necessary adjustments to ensure the normal operation of the wind tunnel. Because of the performance limitations of single models, many ensemble monitoring models based on ensemble learning have been proposed. To ensure diversity within the ensemble, a large number of base learners are generated. However, it has been discovered that the redundancy resulting from a large number of base learners not only harms the performance of the ensemble model but also increases computational burden and storage overhead. To address this, this paper presents an ensemble monitoring model based on ensemble pruning. Specifically, given the unavailability of data labels in the wind tunnel flow-field data sets, one-class classifiers are employed as the base learners. After the ensemble generation stage, the performance of each base learner is estimated based on its correlation with the proxy model. Then, using the estimated performance, a dedicated base learner selection mechanism is proposed based on statistical testing to filter out redundant individuals. To validate the effectiveness of the proposed monitoring model, nine data sets from real wind tunnels were used for model training, and an additional nine data sets were used for testing. Experimental results demonstrated the significance of ensemble pruning in enhancing ensemble performance.

关键词： Wind tunnel Flow field Process monitoring one-class classifier Ensemble learning

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Detection of Small Targets in Sea Clutter Using Dual-Polarization Correlation Features and one-class classifier

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2025年 22卷

作者： Liu, Lichao Guo, Qiang Huang, Shuai Kaliuzhnyi, Mykola Harbin Engn Univ Coll Informat & Commun Engn Harbin 150001 Peoples R China Kharkiv Natl Univ Radio Elect Sci & Res Lab UA-61166 Kharkiv Ukraine

Due to the nonstationary, time-varying, and target-like characteristics of sea clutter, detection of small targets embedded within it has been a long-standing and formidable challenge in the field of remote sensing. Traditional adaptive detectors based on sea clutter modeling often struggle to achieve satisfactory detection performance. To address this issue, this letter proposed a novel small target detector embedded in sea clutter that leverages dual-polarization correlation features and one-class classifier. Initially, three distinct features are extracted from the significant differences between the target echoes and sea clutter in the dual-polarization correlation domain. Each individual feature possesses a certain level of discriminative power. Furthermore, under the framework of anomaly detection, a one-class classifier for small sea surface target detection is constructed based on the fast convex hull learning (FCHL) algorithm to make the final decisions. Experimental results based on the IPIX datasets demonstrate that the proposed detector outperforms several existing detectors.

关键词： Clutter Feature extraction Detectors Correlation Object detection Eigenvalues and eigenfunctions Sea surface Radar Time series analysis Scattering Feature-based detection one-class classifier polarization fusion sea clutter small target detection

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Sea-Surface Floating Small Target Detection by one-class classifier in Time-Frequency Feature Space

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2018年第11期56卷 6395-6411页

作者： Shi, Sai-Nan Shui, Peng-Lang Xidian Univ Natl Lab Radar Signal Proc Xian 710071 Shaanxi Peoples R China

This paper presents one feature-based detector to find sea-surface floating small targets. In integration time of the order of seconds, target returns exhibit time-frequency (TF) characteristics different from sea clutter. The normalized smoothed pseudo-Wigner-Ville distribution (SPWVD) is proposed to enhance TF characteristics of target returns, which is computed from the SPWVDs of time series at the cell under test (CUT) and reference cells around the CUT. The differences between target returns and the TF pattern of sea clutter are congregated on the normalized SPWVD. From that the ridge integration (RI) is computed and significant TF points from each time slice form a binary image. The number of connected regions and the maximum size of connected regions in the binary image are extracted and are combined with the RI into a 3-D feature vector. Due to the unavailability of the feature vector samples of radar returns with target, a one-class classifier with a controllable false alarm rate is constructed from the feature vector samples of sea clutter by the fast convex hull learning algorithm. As a result, a new feature-based detector is designed. It is compared with the tri-feature-based detector using amplitude and Doppler features and the fractal-based detector using the Hurst exponent of amplitude time series on the recognized IPIX radar database for floating small target detection. The results show that a significant improvement in detection performance is attained.

关键词： Fast convex hull learning algorithm feature-based detection floating small target detection normalized smoothed pseudo-Wigner-Ville distribution (SPWVD) one-class classifier

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Towards Real-Time Prediction of Freezing of Gait in Patients With Parkinson's Disease: A Novel Deep one-class classifier

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IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2022年第4期26卷 1726-1736页

作者： Naghavi, Nader Wade, Eric Univ Tennessee Dept Mech Aerosp & Biomed Engn Knoxville TN 37996 USA

Freezing of gait (FoG) is a common motor dysfunction in individuals with Parkinson's disease. FoG impairs walking and is associated with increased fall risk. On-demand external cueing systems can detect FoG and provide stimuli to help individuals overcome freezing. Predicting FoG before onset enables preemptive cueing and may prevent FoG. However, detection and prediction remain challenging. If FoG data are not available for an individual, patient-independent models have been used to detect FoG, which have shown great sensitivity and poor specificity, or vice versa. In this study, we introduce a Deep Gait Anomaly Detector (DGAD) using a transfer learning-based approach to improve FoG detection accuracy. We also evaluate the effect of data augmentation and additional pre-FoG segments on prediction rate. Seven individuals with PD performed a series of daily walking tasks wearing inertial measurement units on their ankles. The DGAD algorithm demonstrated average sensitivity and specificity of 63.0% and 98.6% (3.2% improvement compared with the highest specificity in the literature). The target models identified 87.4% of FoG onsets, with 21.9% predicted. This study demonstrates our algorithm's potential for accurate identification of FoG and delivery of cues for patients for whom no FoG data is available for model training.

关键词： Data models Training Sensitivity Predictive models Diseases Mathematical model Legged locomotion Anomaly detection freezing of gait one-class classifier Parkinson's disease transfer learning wearable sensors

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Preliminary study of learning individual thermal complaint behavior using one-class classifier for indoor environment control

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BUILDING AND ENVIRONMENT 2014年第Feb.期72卷 201-211页

作者： Zhao, Qianchuan Zhao, Yin Wang, Fulin Jiang, Yi Jiang, Yi Zhang, Fan Tsinghua Univ Dept Automat Beijing 100084 Peoples R China Tsinghua Univ TNList Beijing 100084 Peoples R China Tsinghua Univ Dept Bldg Sci Beijing 100084 Peoples R China United Technol Res Ctr China Ltd Shanghai 200120 Peoples R China

This paper proposes a data-driven learning method to describe the personal thermal complaint behavior in a complaint-driven environment control system. The complaint-driven system only uses personal human complaints to control the personal office environment. It avoids the user's direct control on the set-point of the room, which usually results in unreasonable and uncomfortable set-point. A two-stage classifier model is proposed, using personal thermal compliant data with respect to the transient and steady complaint behaviors. The classifier structure is developed based on the properties of human thermal perception with parameters to learn for different users. Quantitative results using experimental data show that the model has lower false negative rate than traditional data-driven classification model and acceptable false detection rate. Practical implementation and subjects' questionnaire evaluation demonstrate the satisfying performance of the model in real environment control. We also discuss the limitations and potential extensions of the model at the end of this paper. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Human complaint one-class classifier Two-stage classifier Pareto-frontier False detection rate Miss detection rate

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Condition Monitoring of Wind Turbine Generator Based on Transfer Learning and one-class classifier

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IEEE SENSORS JOURNAL 2022年第24期22卷 24130-24139页

作者： Jin, Xiaohang Pan, Hengtuo Ying, Chengzuo Kong, Ziqian Xu, Zhengguo Zhang, Bin Zhejiang Univ Technol Coll Mech Engn Minist Educ & Zhejiang Prov Hangzhou 310023 Peoples R China Zhejiang Univ Technol Key Lab Special Purpose Equipment & Adv Mfg Techno Minist Educ & Zhejiang Prov Hangzhou 310023 Peoples R China Zhejiang Univ Technol Coll Mech Engn Hangzhou 310023 Peoples R China Zhejiang Univ Coll Control Sci & Engn Hangzhou 310027 Peoples R China Univ South Carolina Dept Elect Engn Columbia SC 29208 USA

The healthy operation conditions of wind tur-bines (WTs) with insufficient data need attention, but they face the problems of data imbalance and lack of labels. Aiming at the condition monitoring (CM) of these WTs, a CM method based on transfer learning and one-class classification (OCC) is proposed. This method uses the source WT data to help learn information about monitoring data from the target WT. First, the data of the target and source WTs are preprocessed to construct a training set. Second, to improve the TrAd-aBoost algorithm for the OCC task, a novel weighting method based on an autoencoder (AE)& mdash;an unsupervised one-class classifier & mdash;is designed to assign weights for samples in the source and target domain dynamically and then the CM model of the target WT is established. Finally, its effectiveness is verified by using the monitoring data to detect the generator fault of the target WT. The comparison with the nontransfer method shows that the proposed method can significantly reduce the number of false alarms and can issue early warnings.

关键词： Condition monitoring (CM) generator one-class classifier transfer learning wind turbine (WT)

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Application of deep autoencoder as an one-class classifier for unsupervised network intrusion detection: a comparative evaluation

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PEERJ COMPUTER SCIENCE 2020年 6卷 e327页

作者： Vaiyapuri, Thavavel Binbusayyis, Adel Prince Sattam bin Abdulaziz Univ Coll Comp Engn & Sci Alkharj Saudi Arabia

The ever-increasing use of internet has opened a new avenue for cybercriminals, alarming the online businesses and organization to stay ahead of evolving thread landscape. To this end, intrusion detection system (IDS) is deemed as a promising defensive mechanism to ensure network security. Recently, deep learning has gained ground in the field of intrusion detection but majority of progress has been witnessed on supervised learning which requires adequate labeled data for training. In real practice, labeling the high volume of network traffic is laborious and error prone. Intuitively, unsupervised deep learning approaches has received gaining momentum. Specifically, the advances in deep learning has endowed autoencoder (AE) with greater ability for data reconstruction to learn the robust feature representation from massive amount of data. Notwithstanding, there is no study that evaluates the potential of different AE variants as one-class classifier for intrusion detection. This study fills this gap of knowledge presenting a comparative evaluation of different AE variants for one-class unsupervised intrusion detection. For this research, the evaluation includes five different variants of AE such as Stacked AE, Sparse AE, Denoising AE, Contractive AE and Convolutional AE. Further, the study intents to conduct a fair comparison establishing a unified network configuration and training scheme for all variants over the common benchmark datasets, NSL-KDD and UNSW-NB15. The comparative evaluation study provides a valuable insight on how different AE variants can be used as one-class classifier to build an effective unsupervised IDS. The outcome of this study will be of great interest to the network security community as it provides a promising path for building effective IDS based on deep learning approaches alleviating the need for adequate and diverse intrusion network traffic behavior.

关键词： Network intrusion detection Deep learning algorithms Deep autoencoders one-class classifier Unsupervised deep learning

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HRRP Clutter Rejection Via one-class classifier With Hausdorff Distance

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2020年第4期56卷 2517-2526页

作者： Du, Lan Liu, Xu Li, Bo Xu, Shuwen Xidian Univ Natl Lab Radar Signal Proc Xian 710071 Peoples R China

Lots of detectors for high resolution range profile (HRRP) data are mainly based on the intensities of target echoes. When the intensities of outliers are as large as those of targets of interest, some false alarms will emerge during the detection stage. In this article, we propose a rejection algorithm for HRRP data between the detection stage and recognition stage to eliminate the false alarms occurring during the detection stage. In this method, the intensities and positions of the dominant scatterers are extracted as a joint feature. Then, the feature is used to develop the K-center one-class classifier based on Hausdorff distance. Experimental results on the measured HRRP data indicate that the proposed rejection algorithm can eliminate the false alarms remarkably well, and keep most of the target data as well. Meanwhile, the proposed method has better performance under different values of signal-to-noise ratio and different values of parameters than some other rejection methods.

关键词： Feature extraction Clutter Detectors Training Radar Flowcharts Target recognition Feature extraction Hausdorff distance (HD) high resolution range profile (HRRP) one-class classifier rejection

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one-class classifier for Chest X-Ray Anomaly Detection via Contrastive Patch-Based Percentile

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IEEE ACCESS 2021年 9卷 168496-168510页

作者： Kim, Kyung-Su Oh, Seong Je Cho, Hyun Bin Chung, Myung Jin Samsung Med Ctr Med AI Res Ctr Seoul 135710 South Korea Sungkyunkwan Univ Sch Med Seoul 06355 South Korea Sungkyunkwan Univ SAIHST Dept Hlth Sci & Technol Seoul 06355 South Korea Sungkyunkwan Univ Samsung Med Ctr Sch Med Dept Radiol Seoul 06355 South Korea

Given its low dose and compactness, chest radiography has been widely used as the first-line test to determine the presence of lung anomalies. Nevertheless, a high-performance diagnosis for initial screening to detect shadows in lungs due to general lung diseases is not available. During initial screening, chest radiography can be used to distinguish any diseased lung shadowing caused by lung diseases. Thus, chest radiography can contribute to the early diagnosis and prevention of novel lung infectious diseases if training for a specific disease is not required. Accordingly, we propose a deep-learning-based diagnostic system called contrast-shifted instances via patch-based percentile (CSIP) to automatically detect diseased lung shadowing via training only on chest X-ray data from healthy subjects. CSIP is the first application of a patch-based percentile approach to state-of-the-art one-class classifiers (OCCs). This application improves the sensitivity of the network to recognize shadowing density differences in each local area of the lung, thereby considerably improving the diagnostic performance of average area under the curve (AUC) by more than 20% and achieving a sufficiently high diagnostic performance (average AUC of 0.96 for various lung diseases), compared to the existing OCC case without applying our patch-based approach (average AUC of 0.74). Therefore, CSIP may contribute to the early detection of anomalies caused by novel infectious diseases such as variants of the coronavirus disease, for whom training data are scarce. The code is available at https://***/kskim-phd/CSIP.

关键词： Lung Diseases Anomaly detection Training Computed tomography COVID-19 Pulmonary diseases Artificial intelligence unsupervised learning diagnostic radiography X-ray detection anomaly detection contrastive learning one-class classifier patch-based percentile shifted instances

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DOC-NAD: A Hybrid Deep one-class classifier for Network Anomaly Detection 23

DOC-NAD: A Hybrid Deep One-class Classifier for Network Anom...

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23rd IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGrid)

作者： Sarhan, Mohanad Kulatilleke, Gayan Lo, Wai Weng Layeghy, Siamak Portmann, Marius Univ Queensland ITEE Brisbane Australia

ISBN: (纸本)9798350302080

Machine Learning (ML) approaches have been used to enhance the detection capabilities of Network Intrusion Detection Systems (NIDSs). Recent work has achieved nearperfect performance by following binary- and multi-class network anomaly detection tasks. Such systems depend on the availability of both (benign and malicious) network data classes during the training phase. However, attack data samples are often challenging to collect in most organisations due to security controls preventing the penetration of known malicious traffic to their networks. Therefore, this paper proposes a Deep oneclass (DOC) classifier for network intrusion detection by only training on benign network data samples. The novel one-class classification architecture consists of a histogram-based deep feed-forward classifier to extract useful network data features and use efficient outlier detection. The DOC classifier has been extensively evaluated using two benchmark NIDS datasets. The results demonstrate its superiority over current state-of-the-art one-class classifiers in terms of detection and false positive rates.

关键词： one-class classifier intrusion detection machine learning anomaly detection

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