• The Journal of Society for e-Business Studies
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• v.20 no.4
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• pp.41-59
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• 2015

Information systems has been developed and used in various business area, therefore there are abundance of history data (log data) stored, and subsequently, it is required to analyze those log data. Previous studies have been focusing on the discovering of relationship between events and no identification of anomaly instances. Previously, anomaly instances are treated as noise and simply ignored. However, this kind of anomaly instances can occur repeatedly. Hence, a new methodology to detect the anomaly instances is needed. In this paper, we propose a methodology of LAPID (Local Anomaly Process Instance Detection) for discriminating an anomalous process instance from the log data. We specified a distance metric from the activity relation matrix of each instance, and use it to detect API (Anomaly Process Instance). For verifying the suggested methodology, we discovered characteristics of exceptional situations from log data. To demonstrate our proposed methodology, we performed our experiment on real data from a domestic port terminal.

• Journal of Information Processing Systems
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• v.15 no.6
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• pp.1392-1405
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• 2019

The cathode voltage of aluminum electrolytic cell is relatively stable under normal conditions and fluctuates greatly when it has an anomaly. In order to detect the abnormal range of cathode voltage, an anomaly detection algorithm based on sliding window was proposed. The algorithm combines the time series segmentation linear representation method and the k-nearest neighbor local anomaly detection algorithm, which is more efficient than the direct detection of the original sequence. The algorithm first segments the cathode voltage time series, then calculates the length, the slope, and the mean of each line segment pattern, and maps them into a set of spatial objects. And then the local anomaly detection algorithm is used to detect abnormal patterns according to the local anomaly factor and the pattern length. The experimental results showed that the algorithm can effectively detect the abnormal range of cathode voltage.

• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.229-247
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• 2023

With the increasing emphasis on anomaly detection across various fields, diverse anomaly detection algorithms have been developed for various data types and anomaly patterns. However, the performance of anomaly detection algorithms is generally evaluated on publicly available datasets, and the specific performance of each algorithm on anomalies of particular types remains unexplored. Consequently, selecting an appropriate anomaly detection algorithm for specific analytical contexts poses challenges. Therefore, in this paper, we aim to investigate the types of anomalies and various attributes of data. Subsequently, we intend to propose approaches that can assist in the selection of appropriate anomaly detection algorithms based on this understanding. Specifically, this study compares the performance of anomaly detection algorithms for four types of anomalies: local, global, contextual, and clustered anomalies. Through further analysis, the impact of label availability, data quantity, and dimensionality on algorithm performance is examined. Experimental results demonstrate that the most effective algorithm varies depending on the type of anomaly, and certain algorithms exhibit stable performance even in the absence of anomaly-specific information. Furthermore, in some types of anomalies, the performance of unsupervised anomaly detection algorithms was observed to be lower than that of supervised and semi-supervised learning algorithms. Lastly, we found that the performance of most algorithms is more strongly influenced by the type of anomalies when the data quantity is relatively scarce or abundant. Additionally, in cases of higher dimensionality, it was noted that excellent performance was exhibited in detecting local and global anomalies, while lower performance was observed for clustered anomaly types.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.45 no.7
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• pp.550-557
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• 2017

Ionospheric delay, which affect the accuracy of GNSS positioning, is generated by electrons in Ionosphere. Solar activity level, region and time could make change of this delay level. Dual frequency receiver could effectively eliminate the delay using difference of refractive index between L1 to L2 frequency. But, Single frequency receiver have to use limited correction such as ionospheric model in standalone GNSS or PRC(pseudorange correction) in Differential GNSS. Generally, these corrections is effective in normal condition. but, they might be useless, when TEC(total electron content) extremely increase in local area. In this paper, monitoring algorithm is proposed for local ionospheric anomaly using multiple reference stations. For verification, the algorithm was performed with specific measurement data in Ionospheric storm day (20. Nov. 2003). this algorithm would detect local ionospheric anomaly and improve reliability of ionospheric corrections for standalone receiver.

• Korean Journal of Remote Sensing
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• v.28 no.4
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• pp.357-367
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• 2012

This paper presents a two-stage methodology for anomaly detection from hyperspectral imagery that consists of transform-based feature extraction and selection, and computation of a local spatial auto-correlation statistic. First, principal component transform and 3D wavelet transform are applied to reduce redundant spectral information from hyperspectral imagery. Then feature selection based on global skewness and the portion of highly skewed sub-areas is followed to find optimal features for anomaly detection. Finally, a local indicator of spatial association (LISA) statistic is computed to account for both spectral and spatial information unlike traditional anomaly detection methodology based only on spectral information. An experiment using airborne CASI imagery is carried out to illustrate the applicability of the proposed anomaly detection methodology. From the experiments, anomaly detection based on the LISA statistic linked with the selection of optimal features outperformed both the traditional RX detector which uses only spectral information, and the case using major principal components with large eigen-values. The combination of low- and high-frequency components by 3D wavelet transform showed the best detection capability, compared with the case using optimal features selected from principal components.

• ETRI Journal
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• v.46 no.3
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• pp.513-525
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• 2024

Deep neural networks trained on labeled medical data face major challenges owing to the economic costs of data acquisition through expensive medical imaging devices, expert labor for data annotation, and large datasets to achieve optimal model performance. The heterogeneity of diseases, such as Alzheimer's disease, further complicates deep learning because the test cases may substantially differ from the training data, possibly increasing the rate of false positives. We propose a reconstruction-based self-supervised anomaly detection model to overcome these challenges. It has a dual-subnetwork encoder that enhances feature encoding augmented by skip connections to the decoder for improving the gradient flow. The novel encoder captures local and global features to improve image reconstruction. In addition, we introduce an entropy-based image conversion method. Extensive evaluations show that the proposed model outperforms benchmark models in anomaly detection and classification using an encoder. The supervised and unsupervised models show improved performances when trained with data preprocessed using the proposed image conversion method.

• Korean Journal of Head & Neck Oncology
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• v.33 no.1
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• pp.31-34
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• 2017

First branchial cleft anomaly is a very rare disease and exhibits various clinical presentations. Therefore, the diagnosis of first branchial cleft anomaly may be difficult; the condition is often misdiagnosed and mismanaged. Accurate diagnosis is very important, because if not diagnosed correctly, patients with first branchial cleft anomaly would be treated with local incision and drainage repeatedly. We report two cases of first branchial cleft anomaly. The first patient visited for recurrent swell and discharge in the infra-auricular area with a history of previous incision and drainage. The other patient showed a cystic mass in the infra-auricular area and all of them were misdiagnosed initially by their treating specialists elsewhere. The objective of this study is to share our experiences of first branchial cleft anomaly, and emphasize its various clinical patterns and the significance of accurate diagnosis.

• Journal of The Korean Astronomical Society
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• v.46 no.2
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• pp.75-91
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• 2013

We estimate the power spectra of the cosmic microwave background radiation (CMB) temperature anisotropy in localized regions of the sky using the Wilkinson Microwave Anisotropy Probe (WMAP) 7-year data. We find that the north and south hat regions at high Galactic latitude ($|b|{\geq}30^{\circ}C$) show an anomaly in the power spectrum amplitude around the third peak, which is statistically significant up to 3. We try to explain the cause of the observed anomaly by analyzing the low Galactic latitude ($|b|$ < $30^{\circ}C$) regions where the galaxy contamination is expected to be stronger, and the regions weakly or strongly dominated byWMAP instrument noise. We also consider the possible effect of unresolved radio point sources. We find another but less statistically significant anomaly in the low Galactic latitude north and south regions whose behavior is opposite to the one at high latitude. Our analysis shows that the observed north-south anomaly at high latitude becomes weaker on regions with high number of observations (weak instrument noise), suggesting that the anomaly is significant at sky regions that are dominated by the WMAP instrument noise. We have checked that the observed north-south anomaly has weak dependences on the bin-width used in the power spectrum estimation, and on the Galactic latitude cut. We also discuss the possibility that the detected anomaly may hinge on the particular choice of the multipole bin around the third peak. We anticipate that the issue of whether or not the anomaly is intrinsic one or due to WMAP instrument noise will be resolved by the forthcoming Planck data.

• Current Optics and Photonics
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• v.4 no.3
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• pp.210-220
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• 2020

Aiming at the problem that the Local Sparse Difference Index algorithm has low accuracy and low efficiency when detecting target anomalies in a hyperspectral image, this paper proposes a Weighted Collaborative Representation and Sparse Difference-Based Hyperspectral Anomaly Detection algorithm, to improve detection accuracy for a hyperspectral image. First, the band subspace is divided according to the band correlation coefficient, which avoids the situation in which there are multiple solutions of the sparse coefficient vector caused by too many bands. Then, the appropriate double-window model is selected, and the background dictionary constructed and weighted according to Euclidean distance, which reduces the influence of mixing anomalous components of the background on the solution of the sparse coefficient vector. Finally, the sparse coefficient vector is solved by the collaborative representation method, and the sparse difference index is calculated to complete the anomaly detection. To prove the effectiveness, the proposed algorithm is compared with the RX, LRX, and LSD algorithms in simulating and analyzing two AVIRIS hyperspectral images. The results show that the proposed algorithm has higher accuracy and a lower false-alarm rate, and yields better results.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.2
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• pp.494-510
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• 2024

Internet users are exposed to sophisticated cyberattacks that intrusion detection systems have difficulty detecting. Therefore, research is increasing on intrusion detection methods that use artificial intelligence technology for detecting novel cyberattacks. Unsupervised learning-based methods are being researched that learn only from normal data and detect abnormal behaviors by finding patterns. This study developed an anomaly-detection method based on unsupervised machines and deep learning for a network intrusion detection system (NIDS). We present a hybrid anomaly detection approach based on unsupervised learning techniques using the autoencoder (AE), Isolation Forest (IF), and Local Outlier Factor (LOF) algorithms. An oversampling approach that increased the detection rate was also examined. A hybrid approach that combined deep learning algorithms and traditional machine learning algorithms was highly effective in setting the thresholds for anomalies without subjective human judgment. It achieved precision and recall rates respectively of 88.2% and 92.8% when combining two AEs, IF, and LOF while using an oversampling approach to learn more unknown normal data improved the detection accuracy. This approach achieved precision and recall rates respectively of 88.2% and 94.6%, further improving the detection accuracy compared with the hybrid method. Therefore, in NIDS the proposed approach provides high reliability for detecting cyberattacks.