• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.4
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• pp.169-176
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• 2021

Maritime monitoring requirements have been beyond human operators capabilities due to the broadness of the coverage area and the variety of monitoring activities, e.g. illegal migration, or security threats by foreign warships. Abnormal vessel movement can be defined as an unreasonable movement deviation from the usual trajectory, speed, or other traffic parameters. Detection of the abnormal vessel movement requires the operators not only to pay short-term attention but also to have long-term trajectory trace ability. Recent advances in deep learning have shown the potential of deep learning techniques to discover hidden and more complex relations that often lie in low dimensional latent spaces. In this paper, we propose a deep autoencoder-based clustering model for automatic detection of vessel movement anomaly to assist monitoring operators to take actions on the vessel for more investigation. We first generate gridded trajectory images by mapping the raw vessel trajectories into two dimensional matrix. Based on the gridded image input, we test the proposed model along with the other deep autoencoder-based models for the abnormal trajectory data generated through rotation and speed variation from normal trajectories. We show that the proposed model improves detection accuracy for the generated abnormal trajectories compared to the other models.

• Journal of Korea Multimedia Society
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• v.21 no.3
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• pp.348-357
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• 2018

The topic of anomaly detection is one of the emerging research themes in computer vision, computer interaction, video analysis and monitoring. Observers focus attention on behaviors that vary in the magnitude or direction of the motion and behave differently in rules of motion with other objects. In this paper, we use this information and propose a system that detects abnormal behavior by using simple features extracted by optical flow. Our system can be applied in real life. Experimental results show high performance in detecting abnormal behavior in various videos.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.317-322
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• 2002

Dramatic changes in the patterns of satellite-derived pigment concentrations, sea-level height anomaly, sea surface temperature anomaly, and zonal wind anomaly are observed during the 1997-1998 El Nino. By some measures, the 1997-1998 El Nino was the strongest of the 20$^{th}$ century. A very strong El Nino developed during 1997 and matured late in the year. A dramatic recovery occurred in mid-1998 and led to a La Nina conditions. The largest spatial extent of the phytoplankton bloom was followed recovery from El Nino over the equatorial Pacific. The evolution towards a warm episode (El Nino) continued in the equatorial Pacific from March 2002 and further development toward mature El Nino conditions may be possible in late 2002. The OSMI (Ocean Scanning Multispectral Imager) data can be used for detection of dramatic changes in the patterns of pigment concentration during next El Nino.

• The Journal of the Korea institute of electronic communication sciences
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• v.9 no.2
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• pp.261-266
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• 2014

The current status of domestic monitoring centers was reviewed and the pattern-based security monitoring system and the centralized security monitoring system, both of which are the characteristics of security monitoring systems, were analyzed together with their advantages and disadvantages. In addition, as for a development plan of domestic security monitoring systems, in order to improve the problems of the existing pattern-based centralized monitoring system, Honeynet and Darknet, which are based on anomalous behavior detection, were analyzed and their application plans were described.

• Journal of Space Technology and Applications
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• v.4 no.2
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• pp.153-168
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• 2024

Sounding rockets are cost-effective and rapidly deployable tools for directly exploring the ionosphere and microgravity environments. These rockets achieve their target altitudes quickly and are equipped with various scientific instruments to collect real-time data. Perigee Aerospace plans its inaugural test launch in the first half of 2024, followed by a second performance test launch in January 2025. The second launch, scheduled off the coast of Jeju Island, aims to reach an altitude of approximately 150 km with a payload of 30 kg, conducting various experiments in the suborbital region. Particularly in mid-latitude regions, the ionosphere sporadically exhibits increased electron densities in the sporadic E layers and magnetic fluctuations caused by the equatorial electrojet. To measure these phenomena, the sounding rocket version of ionospheric anomaly monitoring by magnetometer and plasma-probe (IAMMAP), currently under development at the KAIST Satellite Research Center, will be onboard. This study focuses on enhancing our understanding of the mid-latitude ionosphere and designing observable missions for the forthcoming performance tests.

• Proceedings of the Korean Geotechical Society Conference
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• 2005.03a
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• pp.1211-1218
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• 2005

Temperature variation according to space and time on the inner parts of engineering constructions(e.g.: dam, slope) can be a basic information for diagnosing their safety problem. In general, as constructions become superannuated, structural deformation(e.g.: cracks, defects) could be occurred by various factors. Seepage or leakage of water through these cracks or defects in old dams will directly cause temperature anomaly. Groundwater level also can be easily observed by abrupt change of temperature on the level. This study shows that the position of seepage or leakage in dam body can be detected by multi-channel temperature monitoring using thermal line sensor. For this, diverse temperature monitoring experiments for a leakage physical model were performed in the laboratory. In field application of an old dam, temperature variations for water depth and for inner parts of boreholes located at downstream slope were measured. Temperature monitoring results for a long time at the bottom of downstream slope of the dam showed the possibility that temperature monitoring can provide the synthetic information about flowing path and quantity of seepage of leakage in dam body.

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.11
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• pp.997-1004
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• 2012

In enterprise software projects, performance issues have become more critical during recent decades. While developing software products, many performance tests are executed in the earlier development phase against the newly added code pieces to detect possible performance regressions. In our previous research, we introduced the framework to enable automated performance anomaly detection and reduce the analysis overhead for identifying the root causes, and showed Statistical Process Control (SPC) can be successfully applied to anomaly detection. In this paper, we explain the special performance trend in which the existing anomaly detection system can hardly detect the noticeable performance change especially when a performance regression is introduced and recovered again a while later. Within the fixed number of sampling period, the fluctuation gets aggravated and the lower and upper control limit get relaxed so that sometimes the existing system hardly detect the noticeable performance change. To resolve the issue, we apply dynamically tuned sampling window size based on the performance trend, and Fuzzy theory to find an appropriate size of the moving window.

• Journal of the Korean Data and Information Science Society
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• v.21 no.6
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• pp.1125-1136
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• 2010

Lately there exist increasing demands for online abnormality monitoring over trajectory stream, which are obtained from moving object tracking devices. This problem is challenging due to the requirement of high speed data processing within limited space cost. In this paper, we present a FADA (Fuzzy Anomaly Detection Algorithm) which constructs normal profile by computing mobility feature information from the GPS (Global Positioning System) logs of mobile devices in MANETs (Mobile Ad-hoc Networks), computes a fuzzy dissimilarity between the current mobility feature information of the mobile device and the mobility feature information in the normal profile, and detects effectively the anomaly behaviors of mobile devices on the basis of the computed fuzzy dissimilarity. The performance of proposed FADA is evaluated through simulation.

• Smart Structures and Systems
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• v.29 no.1
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• pp.63-75
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• 2022

The raw data collected by structural health monitoring (SHM) systems may suffer multiple patterns of anomalies, which pose a significant barrier for an automatic and accurate structural condition assessment. Therefore, the detection and classification of these anomalies is an essential pre-processing step for SHM systems. However, the heterogeneous data patterns, scarce anomalous samples and severe class imbalance make data anomaly detection difficult. In this regard, this study proposes a convolutional neural network-based data anomaly detection method. The time and frequency domains data are transferred as images and used as the input of the neural network for training. ResNet18 is adopted as the feature extractor to avoid training with massive labelled data. In addition, the focal loss function is adopted to soften the class imbalance-induced classification bias. The effectiveness of the proposed method is validated using acceleration data collected in a long-span cable-stayed bridge. The proposed approach detects and classifies data anomalies with high accuracy.

• Journal of Navigation and Port Research
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• v.39 no.6
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• pp.479-484
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• 2015

Network RTK is a precise positioning technique using carrier phase correction data from reference stations within the network, and is constantly being researched for improved performance. However, the study for the system accuracy has been performed but system integrity research has not been done as much as system accuracy, because network RTK has been mainly used on surveying for static or kinematic positioning. In this paper, adequate monitoring system for network RTK is designed as basis research for integrity monitoring on network RTK. To this, fault tree on network RTK is analyzed, and a countermeasure is prepared to detect and identify the each fault items. Based these algorithms, monitoring system to use on central processing facility is designed for network RTK service.