• Journal of the korean academy of Pediatric Dentistry
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• v.42 no.4
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• pp.319-326
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• 2015

Short root anomaly (SRA) is very rare, but can be problematic for physicians because patients with SRA are more vulnerable to root resorption with orthodontic forces. During the mixed dentition period, it may be difficult to diagnose generalized SRA. This article reports the treatment of an orthodontic patient with SRA at the early mixed dentition stage. Despite local tooth loss, a relatively favorable outcome was obtained without excessive root resorption. Ultimately, orthodontic therapy is possible for patients with generalized SRA, but precautions should be taken to avoid complications, such as tooth loss or root resorption.

• Geophysics and Geophysical Exploration
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• v.22 no.1
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• pp.37-43
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• 2019

We present gravity anomaly maps based on KIGAM's gravity data measured from 2000 to 2018. Until 2016, we acquired gravity data on about 6,400 points for the purpose of regional mapping covering the whole country with data density of at least one point per $4km{\times}4km$ for reducing the time of the data acquisition. In addition, we have performed local gravity surveys for the purpose of mining development in and around the NMC Moland Mine at Jecheon in 2013 and in the Taebaeksan mineralized zone from 2015 to 2018 with data interval of several hundred meters to 2 km. Meanwhile, we carried out precise gravity explorations with data interval of about 250 m on and around epicenter areas of Gyeongju and Pohang earthquakes of relatively large magnitude which occurred in 2016 and in 2017, respectively. Thus we acquired in total about 9,600 points data as the result. We also used additional data acquired by Pusan National University for some local areas. Finally, gravity data more than 16,000 points except for the repetition and temporal control points were available to calculate free-air, Bouguer, and isostatic gravity anomalies. Therefore, the presented anomaly maps are most advanced in spatial distribution and the number of used data so far in Korea.

• Economic and Environmental Geology
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• v.31 no.1
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• pp.59-68
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• 1998

Gravity, magnetic and VLF surveys were carried out to investigate the dimension, nature and stability of the waste materials filled in the Seokdae landfill, Pusan. The Seokdae landfill, which is located in a former valley, was used as a dump for mainly domestic-type waste materials for 6 years from 1987. The landfill site is classfied into A, B, C and D areas according to the sequence of dumping period. The Bouguer gravity anomaly map shows maximum variation of 3.1 mgals on the landfill and its general appearance has close relation with the thickness of waste filled. The local variation of anomaly, however, reflect the degree of compactness of waste materials which may be affected by the nature of waste and dumping time. In the case of area A, where dumping process was terminated at the very last stage, most part show negative anomaly compared to other areas. We think that the composition of the waste materials in the area A is high in leftover food and paper trash and they are still in uncompacted condition. In area B, the general trend of variation of gravity anomaly is appeared to be high anomaly in northern part and decrease to the southern part. This is well matched with the prelandfill topography of the landfill site. The southern part of area B is located in the center of valley and its present surface is comparatively rugged, which may be due to the differential settlement of deep burried waste. The thickness of waste in area C is relatively thin, but the gravity anomaly appears to be low. Considering the present condition of surface, it can be inferred that low density wastes such as leftover food were mainly filled in this area. Area D, as in the case of area B, shows gravity anomaly that has close relation with the prelandfill topography. Magnetic data show the variation of total field intensity varies in the range of 46600~51000 nT, and reach maximum anomaly of 4400 nT. The overall pattern of magnetic anomaly well reflects the distribution of magnetic materials in the landfill. The result of VLF survey reveals several low resistivity zones, which may serve as underground passages for contaminant flow, in the area C located near the small Village.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.27 no.1
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• pp.8-14
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• 2023

The existing self-supervised learning-based CutPaste generated synthetic data by cutting and attaching specific patches from normal images and then performed anomaly detection. However, this method has a problem in that there is a clear difference in the boundary of the patch. NSA for solving these problems have achieved higher anomaly detection performance by generating natural synthetic data through Poisson Blending. However, NSA has the disadvantage of having many hyperparameters that need to be adjusted for each class. In this paper, synthetic data similar to normal were generated by a simple method of making the size of the synthetic patch very small. At this time, since the patches are so locally synthesized, models that learn local features can easily overfit synthetic data. Therefore, we performed anomaly detection using gMLP, which learns global features, and even with simple synthesis methods, we were able to achieve higher performance than conventional self-supervised learning techniques.

• Journal of Information Processing Systems
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• v.20 no.3
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• pp.375-390
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• 2024

Edge computing architecture has effectively alleviated the computing pressure on cloud platforms, reduced network bandwidth consumption, and improved the quality of service for user experience; however, it has also introduced new security issues. Existing anomaly detection methods in big data scenarios with cloud-edge computing collaboration face several challenges, such as sample imbalance, difficulty in dealing with complex network traffic attacks, and difficulty in effectively training large-scale data or overly complex deep-learning network models. A lightweight deep-learning model was proposed to address these challenges. First, normalization on the user side was used to preprocess the traffic data. On the edge side, a trained Wasserstein generative adversarial network (WGAN) was used to supplement the data samples, which effectively alleviates the imbalance issue of a few types of samples while occupying a small amount of edge-computing resources. Finally, a trained lightweight deep learning network model is deployed on the edge side, and the preprocessed and expanded local data are used to fine-tune the trained model. This ensures that the data of each edge node are more consistent with the local characteristics, effectively improving the system's detection ability. In the designed lightweight deep learning network model, two sets of convolutional pooling layers of convolutional neural networks (CNN) were used to extract spatial features. The bidirectional long short-term memory network (BiLSTM) was used to collect time sequence features, and the weight of traffic features was adjusted through the attention mechanism, improving the model's ability to identify abnormal traffic features. The proposed model was experimentally demonstrated using the NSL-KDD, UNSW-NB15, and CIC-ISD2018 datasets. The accuracies of the proposed model on the three datasets were as high as 0.974, 0.925, and 0.953, respectively, showing superior accuracy to other comparative models. The proposed lightweight deep learning network model has good application prospects for anomaly traffic detection in cloud-edge collaborative computing architectures.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.57-73
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• 2021

Maintenance and prevention of failure through anomaly detection of ICT infrastructure is becoming important. System monitoring data is multidimensional time series data. When we deal with multidimensional time series data, we have difficulty in considering both characteristics of multidimensional data and characteristics of time series data. When dealing with multidimensional data, correlation between variables should be considered. Existing methods such as probability and linear base, distance base, etc. are degraded due to limitations called the curse of dimensions. In addition, time series data is preprocessed by applying sliding window technique and time series decomposition for self-correlation analysis. These techniques are the cause of increasing the dimension of data, so it is necessary to supplement them. The anomaly detection field is an old research field, and statistical methods and regression analysis were used in the early days. Currently, there are active studies to apply machine learning and artificial neural network technology to this field. Statistically based methods are difficult to apply when data is non-homogeneous, and do not detect local outliers well. The regression analysis method compares the predictive value and the actual value after learning the regression formula based on the parametric statistics and it detects abnormality. Anomaly detection using regression analysis has the disadvantage that the performance is lowered when the model is not solid and the noise or outliers of the data are included. There is a restriction that learning data with noise or outliers should be used. The autoencoder using artificial neural networks is learned to output as similar as possible to input data. It has many advantages compared to existing probability and linear model, cluster analysis, and map learning. It can be applied to data that does not satisfy probability distribution or linear assumption. In addition, it is possible to learn non-mapping without label data for teaching. However, there is a limitation of local outlier identification of multidimensional data in anomaly detection, and there is a problem that the dimension of data is greatly increased due to the characteristics of time series data. In this study, we propose a CMAE (Conditional Multimodal Autoencoder) that enhances the performance of anomaly detection by considering local outliers and time series characteristics. First, we applied Multimodal Autoencoder (MAE) to improve the limitations of local outlier identification of multidimensional data. Multimodals are commonly used to learn different types of inputs, such as voice and image. The different modal shares the bottleneck effect of Autoencoder and it learns correlation. In addition, CAE (Conditional Autoencoder) was used to learn the characteristics of time series data effectively without increasing the dimension of data. In general, conditional input mainly uses category variables, but in this study, time was used as a condition to learn periodicity. The CMAE model proposed in this paper was verified by comparing with the Unimodal Autoencoder (UAE) and Multi-modal Autoencoder (MAE). The restoration performance of Autoencoder for 41 variables was confirmed in the proposed model and the comparison model. The restoration performance is different by variables, and the restoration is normally well operated because the loss value is small for Memory, Disk, and Network modals in all three Autoencoder models. The process modal did not show a significant difference in all three models, and the CPU modal showed excellent performance in CMAE. ROC curve was prepared for the evaluation of anomaly detection performance in the proposed model and the comparison model, and AUC, accuracy, precision, recall, and F1-score were compared. In all indicators, the performance was shown in the order of CMAE, MAE, and AE. Especially, the reproduction rate was 0.9828 for CMAE, which can be confirmed to detect almost most of the abnormalities. The accuracy of the model was also improved and 87.12%, and the F1-score was 0.8883, which is considered to be suitable for anomaly detection. In practical aspect, the proposed model has an additional advantage in addition to performance improvement. The use of techniques such as time series decomposition and sliding windows has the disadvantage of managing unnecessary procedures; and their dimensional increase can cause a decrease in the computational speed in inference.The proposed model has characteristics that are easy to apply to practical tasks such as inference speed and model management.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.5
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• pp.1301-1316
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• 2024

In modern warfare, missiles play a pivotal role but typically spend the majority of their lifecycle in long-term storage or standby mode, making it difficult to detect failures. Preemptive detection of missiles that will fail is crucial to preventing severe consequences, including safety hazards and mission failures. This study proposes a contamination-based stacking ensemble model, employing the local outlier factor (LOF), to detect such missiles. The proposed model creates multiple base LOF models with different contamination values and combines their anomaly scores to achieve a robust anomaly detection. A comparative performance analysis was conducted between the proposed model and the traditional single LOF model, using production-related inspection data from missiles deployed in the military. The experimental results showed that, with the contamination parameter set to 0.1, the proposed model exhibited an increase of approximately 22 percentage points in accuracy and 71 percentage points in F1-score compared to the single LOF model. This approach enables the preemptive identification of potential failures, undetectable through traditional statistical quality control methods. Consequently, it contributes to lower missile failure rates in real battlefield scenarios, leading to significant time and cost savings in the military industry.

• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.57-59
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• 2004

This paper reports the observation of the interferometric synthetic aperture radar (InSAR) phase anomaly on a newly reclaimed mudflat, Hwaong, in west coast of Korea, detected by a series of Radarsat-l SAR data obtained mostly during 2003. The observed phase anomaly could be from subsidence of mud land caused by volumetric contraction of mud in dry season. This process must have been initiated from March 2002 when tidal water supply to this region was permanently blocked by the newly constructed embankment. The maximum subsidence rate measured from InSAR signal is about 3 cm per month. The local heterogeneity of the subsidence rate over the reclaimed mudflat may indicate various mud composition, surface-subsurface hydrological processes, or subsurface information of the mud and basement rock structure. In-situ measurement must follow to support this observation from space.

• Journal of Communications and Networks
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• v.13 no.5
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• pp.463-471
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• 2011

Cooperative communications using relays in wireless networks have similar effects of multiple-input and multiple-output without the need of multiple antennas at each node. To implement cooperation into a system, efficient protocols are desired. In IEEE 802.11 families such as a/b/g, mobile stations can automatically adjust transmission rates according to channel conditions. However throughput performance degradation is observed by low-rate stations in multi-rate circumstances resulting in so-called performance anomaly. In this paper, we propose active relay-based cooperative medium access control (AR-CMAC) protocol, in which active relays desiring to transmit their own data for cooperation participate in relaying, and it is designed to increase throughput as a solution to performance anomaly. We have analyzed the performance of the simplified AR-CMAC using an embedded Markov chain model to demonstrate the gain of AR-CMAC and to verify it with our simulations. Simulations in an infrastructure network with an IEEE 802.11b/g access point show noticeable improvement than the legacy schemes.

• International Journal of Computer Science & Network Security
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• v.21 no.4
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• pp.131-139
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• 2021

In current time, anomaly detection is the primary concern of the administrative authorities. Suspicious activity identification is shifting from a human operator to a machine-assisted monitoring in order to assist the human operator and react to an unexpected incident quickly. These automatic surveillance systems face many challenges due to the intrinsic complex characteristics of video sequences and foreground human motion patterns. In this paper, we propose a novel approach to detect anomalous human activity using a hybrid approach of statistical model and Genetic Programming. The feature-set of local motion patterns is generated by a statistical model from the video data in an unsupervised way. This features set is inserted to an enhanced Genetic Programming based classifier to classify normal and abnormal patterns. The experiments are performed using publicly available benchmark datasets under different real-life scenarios. Results show that the proposed methodology is capable to detect and locate the anomalous activity in the real time. The accuracy of the proposed scheme exceeds those of the existing state of the art in term of anomalous activity detection.