• Title/Summary/Keyword: anomaly detection

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Motor Anomaly Detection Using LSTM Autoencoder (LSTM Autoencoder를 활용한 전동기 이상 탐지)

  • Jun-Seok Park;Yoo-Jin Ha;Jae-Chern Yoo
    • Proceedings of the Korean Society of Computer Information Conference
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    • 2023.01a
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    • pp.307-309
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    • 2023
  • 본 논문에서는 LSTM Autoencoder를 활용한 전동기의 Anomaly Detection을 제안한다. 전동기의 Anomaly Detection를 통해 전동킥보드의 고장을 예방하여 이용자의 안전을 보장한다. 전동기로부터 얻은 시계열 진동 데이터와 시계열 데이터 분석에 유의미한 LSTM을 활용한 Autoencoder를 통해 Anomaly Detection을 구현했다. 그 결과 99.9%의 정확도를 기록하였다.

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Semi-Supervised Learning Based Anomaly Detection for License Plate OCR in Real Time Video

  • Kim, Bada;Heo, Junyoung
    • International journal of advanced smart convergence
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    • v.9 no.1
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    • pp.113-120
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    • 2020
  • Recently, the license plate OCR system has been commercialized in a variety of fields and preferred utilizing low-cost embedded systems using only cameras. This system has a high recognition rate of about 98% or more for the environments such as parking lots where non-vehicle is restricted; however, the environments where non-vehicle objects are not restricted, the recognition rate is about 50% to 70%. This low performance is due to the changes in the environment by non-vehicle objects in real-time situations that occur anomaly data which is similar to the license plates. In this paper, we implement the appropriate anomaly detection based on semi-supervised learning for the license plate OCR system in the real-time environment where the appearance of non-vehicle objects is not restricted. In the experiment, we compare systems which anomaly detection is not implemented in the preceding research with the proposed system in this paper. As a result, the systems which anomaly detection is not implemented had a recognition rate of 77%; however, the systems with the semi-supervised learning based on anomaly detection had 88% of recognition rate. Using the techniques of anomaly detection based on the semi-supervised learning was effective in detecting anomaly data and it was helpful to improve the recognition rate of real-time situations.

Power Plant Turbine Blade Anomaly Detection using Deep Neural Network-based Object Detection (깊은 신경망 기반 객체 검출을 이용한 발전 설비 터빈 블레이드 이상 탐지)

  • Yu, Jongmin;Lee, Jangwon;Oh, Hyeontaek;Park, Sang-Ki;Yang, Jinhong
    • The Journal of Korea Institute of Information, Electronics, and Communication Technology
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    • v.15 no.1
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    • pp.69-75
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    • 2022
  • Due to the increase in the demand for anomaly detection according to the ageing of power generation facilities, the need for developing an anomaly detection method that can provide high-reliability turbine blade anomaly detection performance has been continuously raised. Additionally, the false detection results caused by a human error accelerates the increase of the need. In this paper, we propose an anomaly detection technique for turbine blades in power plants using deep neural networks. Experimental results prove that the proposed technique achieves stable anomaly detection performance while minimizing human factor intervention.

Tropospheric Anomaly Detection in Multi-Reference Stations Environment during Localized Atmospheric Conditions-(2) : Analytic Results of Anomaly Detection Algorithm

  • Yoo, Yun-Ja
    • Journal of Navigation and Port Research
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    • v.40 no.5
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    • pp.271-278
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    • 2016
  • Localized atmospheric conditions between multi-reference stations can bring the tropospheric delay irregularity that becomes an error terms affecting positioning accuracy in network RTK environment. Imbalanced network error can affect the network solutions and it can corrupt the entire network solution and degrade the correction accuracy. If an anomaly could be detected before the correction message was generated, it is possible to eliminate the anomalous satellite that can cause degradation of the network solution during the tropospheric delay anomaly. An atmospheric grid that consists of four meteorological stations was used to detect an inhomogeneous weather conditions and tropospheric anomaly applied AWSs (automatic weather stations) meteorological data. The threshold of anomaly detection algorithm was determined based on the statistical weather data of AWSs for 5 years in an atmospheric grid. From the analytic results of anomaly detection algorithm it showed that the proposed algorithm can detect an anomalous satellite with an anomaly flag generation caused tropospheric delay anomaly during localized atmospheric conditions between stations. It was shown that the different precipitation condition between stations is the main factor affecting tropospheric anomalies.

CutPaste-Based Anomaly Detection Model using Multi Scale Feature Extraction in Time Series Streaming Data

  • Jeon, Byeong-Uk;Chung, Kyungyong
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.16 no.8
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    • pp.2787-2800
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    • 2022
  • The aging society increases emergency situations of the elderly living alone and a variety of social crimes. In order to prevent them, techniques to detect emergency situations through voice are actively researched. This study proposes CutPaste-based anomaly detection model using multi-scale feature extraction in time series streaming data. In the proposed method, an audio file is converted into a spectrogram. In this way, it is possible to use an algorithm for image data, such as CNN. After that, mutli-scale feature extraction is applied. Three images drawn from Adaptive Pooling layer that has different-sized kernels are merged. In consideration of various types of anomaly, including point anomaly, contextual anomaly, and collective anomaly, the limitations of a conventional anomaly model are improved. Finally, CutPaste-based anomaly detection is conducted. Since the model is trained through self-supervised learning, it is possible to detect a diversity of emergency situations as anomaly without labeling. Therefore, the proposed model overcomes the limitations of a conventional model that classifies only labelled emergency situations. Also, the proposed model is evaluated to have better performance than a conventional anomaly detection model.

TCN-USAD for Anomaly Power Detection (이상 전력 탐지를 위한 TCN-USAD)

  • Hyeonseok Jin;Kyungbaek Kim
    • Smart Media Journal
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    • v.13 no.7
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    • pp.9-17
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    • 2024
  • Due to the increase in energy consumption, and eco-friendly policies, there is a need for efficient energy consumption in buildings. Anomaly power detection based on deep learning are being used. Because of the difficulty in collecting anomaly data, anomaly detection is performed using reconstruction error with a Recurrent Neural Network(RNN) based autoencoder. However, there are some limitations such as the long time required to fully learn temporal features and its sensitivity to noise in the train data. To overcome these limitations, this paper proposes the TCN-USAD, combined with Temporal Convolution Network(TCN) and UnSupervised Anomaly Detection for multivariate data(USAD). The proposed model using TCN-based autoencoder and the USAD structure, which uses two decoders and adversarial training, to quickly learn temporal features and enable robust anomaly detection. To validate the performance of TCN-USAD, comparative experiments were performed using two building energy datasets. The results showed that the TCN-based autoencoder can perform faster and better reconstruction than RNN-based autoencoder. Furthermore, TCN-USAD achieved 20% improved F1-Score over other anomaly detection models, demonstrating excellent anomaly detection performance.

Research Trends on Deep Learning for Anomaly Detection of Aviation Safety (딥러닝 기반 항공안전 이상치 탐지 기술 동향)

  • Park, N.S.
    • Electronics and Telecommunications Trends
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    • v.36 no.5
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    • pp.82-91
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    • 2021
  • This study reviews application of data-driven anomaly detection techniques to the aviation domain. Recent advances in deep learning have inspired significant anomaly detection research, and numerous methods have been proposed. However, some of these advances have not yet been explored in aviation systems. After briefly introducing aviation safety issues, data-driven anomaly detection models are introduced. Along with traditional statistical and well-established machine learning models, the state-of-the-art deep learning models for anomaly detection are reviewed. In particular, the pros and cons of hybrid techniques that incorporate an existing model and a deep model are reviewed. The characteristics and applications of deep learning models are described, and the possibility of applying deep learning methods in the aviation field is discussed.

The Impacts of Decomposition Levels in Wavelet Transform on Anomaly Detection from Hyperspectral Imagery

  • Yoo, Hee Young;Park, No-Wook
    • Korean Journal of Remote Sensing
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    • v.28 no.6
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    • pp.623-632
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    • 2012
  • In this paper, we analyzed the effect of wavelet decomposition levels in feature extraction for anomaly detection from hyperspectral imagery. After wavelet analysis, anomaly detection was experimentally performed using the RX detector algorithm to analyze the detecting capabilities. From the experiment for anomaly detection using CASI imagery, the characteristics of extracted features and the changes of their patterns showed that radiance curves were simplified as wavelet transform progresses and H bands did not show significant differences between target anomaly and background in the previous levels. The results of anomaly detection and their ROC curves showed the best performance when using the appropriate sub-band decided from the visual interpretation of wavelet analysis which was L band at the decomposition level where the overall shape of profile was preserved. The results of this study would be used as fundamental information or guidelines when applying wavelet transform to feature extraction and selection from hyperspectral imagery. However, further researches for various anomaly targets and the quantitative selection of optimal decomposition levels are needed for generalization.

Emerging Topic Detection Using Text Embedding and Anomaly Pattern Detection in Text Streaming Data (텍스트 스트리밍 데이터에서 텍스트 임베딩과 이상 패턴 탐지를 이용한 신규 주제 발생 탐지)

  • Choi, Semok;Park, Cheong Hee
    • Journal of Korea Multimedia Society
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    • v.23 no.9
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    • pp.1181-1190
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    • 2020
  • Detection of an anomaly pattern deviating normal data distribution in streaming data is an important technique in many application areas. In this paper, a method for detection of an newly emerging pattern in text streaming data which is an ordered sequence of texts is proposed based on text embedding and anomaly pattern detection. Using text embedding methods such as BOW(Bag Of Words), Word2Vec, and BERT, the detection performance of the proposed method is compared. Experimental results show that anomaly pattern detection using BERT embedding gave an average F1 value of 0.85 and the F1 value of 1 in three cases among five test cases.

Anomaly detection of isolating switch based on single shot multibox detector and improved frame differencing

  • Duan, Yuanfeng;Zhu, Qi;Zhang, Hongmei;Wei, Wei;Yun, Chung Bang
    • Smart Structures and Systems
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    • v.28 no.6
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    • pp.811-825
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    • 2021
  • High-voltage isolating switches play a paramount role in ensuring the safety of power supply systems. However, their exposure to outdoor environmental conditions may cause serious physical defects, which may result in great risk to power supply systems and society. Image processing-based methods have been used for anomaly detection. However, their accuracy is affected by numerous uncertainties due to manually extracted features, which makes the anomaly detection of isolating switches still challenging. In this paper, a vision-based anomaly detection method for isolating switches, which uses the rotational angle of the switch system for more accurate and direct anomaly detection with the help of deep learning (DL) and image processing methods (Single Shot Multibox Detector (SSD), improved frame differencing method, and Hough transform), is proposed. The SSD is a deep learning method for object classification and localization. In addition, an improved frame differencing method is introduced for better feature extraction and a hough transform method is adopted for rotational angle calculation. A number of experiments are conducted for anomaly detection of single and multiple switches using video frames. The results of the experiments demonstrate that the SSD outperforms the You-Only-Look-Once network. The effectiveness and robustness of the proposed method have been proven under various conditions, such as different illumination and camera locations using 96 videos from the experiments.