• Journal of Environmental Science International
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• v.2 no.3
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• pp.179-191
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• 1993

This paper aims to know the characteristics of occurrence of the anomaly level and variability of the monthly precipitation in Kyeongnam, Korea. For this study, it was investigated 주e distribution of the annual and cont비y mean precipitation, the precipitation variability and its annual change, and the characteristics of occurrence of the anomaly level in Kyeongnam area the results were summarized as follows : 1) she mean of annual total precipitation averaged over Kyeongnam area is 1433.3mm. I'he spatial distribution of the annual total precipitation shows that in Kyeongnam area, the high rainfall area locates in the southwest area and south coast and the low rainfall area in an inland area. 2) Monthly mean precipitation in llyeongnam area was the highest in July(266.4mm) 각lowed by August(238.0mm), June(210.2mm) in descending order. In summer season, rainfall was concentrated and accounted for 49.9 percent of the annual total precipitation. Because convergence of the warm and humid southwest current which was influenced by Changma and typhoon took place well in this area. 3) The patterns of annual change of precipitaion variability can be divided into two types; One is a coast type and the other an inland type. The variability of precipitation generally appears low in spring and summer season and high in autumn and winter season. This is in accord with the large and small of precipitation. 4) The high frequency of anomaly level was N( Normal)-level and the next was LN( Low Informal) -level and 25(Extremely Subnormal)-level was not appeared in all stations. The occurrence frequency of N level was high in high rainfall area and distinguish성 in spring and summer season but the low rainfall area was not. hey Words : anomaly level, variability, precipitation, coast type, inland type.

• Smart Structures and Systems
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• v.30 no.6
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• pp.613-626
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• 2022

Guaranteeing the quality and integrity of structural health monitoring (SHM) data is very important for an effective assessment of structural condition. However, sensory system may malfunction due to sensor fault or harsh operational environment, resulting in multiple types of data anomaly existing in the measured data. Efficiently and automatically identifying anomalies from the vast amounts of measured data is significant for assessing the structural conditions and early warning for structural failure in SHM. The major challenges of current automated data anomaly detection methods are the imbalance of dataset categories. In terms of the feature of actual anomalous data, this paper proposes a data anomaly detection method based on data-level and deep learning technique for SHM of civil engineering structures. The proposed method consists of a data balancing phase to prepare a comprehensive training dataset based on data-level technique, and an anomaly detection phase based on a sophisticatedly designed network. The advanced densely connected convolutional network (DenseNet) and Transformer encoder are embedded in the specific network to facilitate extraction of both detail and global features of response data, and to establish the mapping between the highest level of abstractive features and data anomaly class. Numerical studies on a steel frame model are conducted to evaluate the performance and noise immunity of using the proposed network for data anomaly detection. The applicability of the proposed method for data anomaly classification is validated with the measured data of a practical supertall structure. The proposed method presents a remarkable performance on data anomaly detection, which reaches a 95.7% overall accuracy with practical engineering structural monitoring data, which demonstrates the effectiveness of data balancing and the robust classification capability of the proposed network.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.47 no.1
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• pp.9-19
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• 2024

Deep learning-based computer vision anomaly detection algorithms are widely utilized in various fields. Especially in the manufacturing industry, the difficulty in collecting abnormal data compared to normal data, and the challenge of defining all potential abnormalities in advance, have led to an increasing demand for unsupervised learning methods that rely on normal data. In this study, we conducted a comparative analysis of deep learning-based unsupervised learning algorithms that define and detect abnormalities that can occur when transparent contact lenses are immersed in liquid solution. We validated and applied the unsupervised learning algorithms used in this study to the existing anomaly detection benchmark dataset, MvTecAD. The existing anomaly detection benchmark dataset primarily consists of solid objects, whereas in our study, we compared unsupervised learning-based algorithms in experiments judging the shape and presence of lenses submerged in liquid. Among the algorithms analyzed, EfficientAD showed an AUROC and F1-score of 0.97 in image-level tests. However, the F1-score decreased to 0.18 in pixel-level tests, making it challenging to determine the locations where abnormalities occurred. Despite this, EfficientAD demonstrated excellent performance in image-level tests classifying normal and abnormal instances, suggesting that with the collection and training of large-scale data in real industrial settings, it is expected to exhibit even better performance.

• ETRI Journal
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• v.43 no.3
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• pp.511-523
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• 2021

The World Health Organization provides guidelines for managing the particulate matter (PM) level because a higher PM level represents a threat to human health. To manage the PM level, a procedure for measuring the PM value is first needed. We use a PM sensor that collects the PM level by laser-based light scattering (LLS) method because it is more cost effective than a beta attenuation monitor-based sensor or tapered element oscillating microbalance-based sensor. However, an LLS-based sensor has a higher probability of malfunctioning than the higher cost sensors. In this paper, we regard the overall malfunctioning, including strange value collection or missing collection data as anomalies, and we aim to detect anomalies for the maintenance of PM measuring sensors. We propose a novel architecture for solving the above aim that we call the hypothesis pruning generative adversarial network (HP-GAN). Through comparative experiments, we achieve AUROC and AUPRC values of 0.948 and 0.967, respectively, in the detection of anomalies in LLS-based PM measuring sensors. We conclude that our HP-GAN is a cutting-edge model for anomaly detection.

• 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.

• International Journal of Contents
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• v.18 no.2
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• pp.68-80
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• 2022

The health of the human heart is commonly measured using ECG (Electrocardiography) signals. To identify any anomaly in the human heart, the time-sequence of ECG signals is examined manually by a cardiologist or cardiac electrophysiologist. Lightweight anomaly detection on ECG signals in an embedded system is expected to be popular in the near future, because of the increasing number of heart disease symptoms. Some previous research uses deep learning networks such as LSTM and BiLSTM to detect anomaly signals without any handcrafted feature. Unfortunately, lightweight LSTMs show low precision and heavy LSTMs require heavy computing powers and volumes of labeled dataset for symptom classification. This paper proposes an ECG anomaly detection system based on two level BiLSTM for acceptable precision with lightweight networks, which is lightweight and usable at home. Also, this paper presents a new threshold technique which considers statistics of the current ECG pattern. This paper's proposed model with BiLSTM detects ECG signal anomaly in 0.467 ~ 1.0 F₁ score, compared to 0.426 ~ 0.978 F₁ score of the similar model with LSTM except one highly noisy dataset.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1413-1414
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• 2003

Interannual variability in the patterns of satellitederived pigment concentrations, sea-level height anomaly, sea surface temperature anomaly, and zonal wind anomaly are observed during the 2002-2003 El Ni${\tilde{n}}$o. The largest spatial extent of the phytoplankton bloom was recovery from El Ni${\tilde{n}}$o over the equatorial Pacific. The evolution towards a warm episode (El Ni${\tilde{n}}$o) started from spring of 2002 and continued during January 2003, while equatorial Sea Surface Temperature Anomaly (SSTA) remained greater than +1$^{\circ}$C in the central equatorial Pacific. The EOS (Earth Observing System) and OSMI (Ocean Scanning Multispectral Imager) data are used for detection of dramatic changes in the patterns of pigment concentration during El Ni${\tilde{n}}$o.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2022.11a
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• pp.171-174
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• 2022

Weakly-supervised learning is a widely adopted approach in video anomaly detection whereby only video labels are utilized instead of expensive frame-level annotations. Since the success of multi-instance learning (MIL), almost all recent approaches are based on maximizing the margin between the set of abnormal video snippets and those of normal video snippets. In this work, we present a simple contrastive approach for weakly supervised video anomaly detection (WS-VAD) with aims to enhance the performance of existing models. The method is generic in nature and introduces a loss function to encourage attraction of output features from the same video class and repel those from different video classes. Experimental results demonstrate our method can be applied to existing algorithms to improve detection accuracy in public video anomaly dataset.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.1
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• pp.55-58
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• 2024

In this paper, we propose a design and implementation of big data-based fine dust anomaly detection machine learning system. The proposed is system that classifies the fine dust air quality index through meteorological information composed of fine dust and big data. This system classifies fine dust through the design of an anomaly detection algorithm according to the outliers for each air quality index classification categories based on machine learning. Depth data of the image collected from the camera collects images according to the level of fine dust, and then creates a fine dust visibility mask. And, with a learning-based fingerprinting technique through a mono depth estimation algorithm, the fine dust level is derived by inferring the visibility distance of fine dust collected from the monoscope camera. For experimentation and analysis of this method, after creating learning data by matching the fine dust level data and CCTV image data by region and time, a model is created and tested in a real environment.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.37 no.4
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• pp.372-382
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• 2009

A GNSS software for processing the SBAS correction data is developed, and Japan MSAS correction data is analyzed. MSAS orbit correction data is analyzed and compared with WAAS data. MSAS ionosphere correction data is analyzed and the effect of the equatorial anomaly on the correction accuracy is discussed. Degradation due to receive delay of correction information and effect of the degradation on protection level analyzed using partial remove of MSAS correction information. Integrity and availability for precision approch using the MSAS system analyzed.