• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.34 no.2
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• pp.90-95
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• 2006

For single engine application like T-50, advanced supersonic jet trainer, airstart capability is very important. This paper presents the results of airstart tests performed to verify T-50 airstart capability for various flight condition. The tests include spooldown, APU assisted and auto-relight airstart tests. Except for the auto-relight tests T-50 engine was successfully restarted for all airstart tests. After modifying FADEC flameout detection schedule, auto-relight tests also were successfully demonstrated. Through T-50 engine airstart tests excellent T-50 airstart capability was validated.

• Convergence Security Journal
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• v.21 no.3
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• pp.13-23
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• 2021

The attacker's techniques and tools are becoming intelligent and sophisticated. Existing Anti-Virus cannot prevent security accident. So the security threats on the endpoint should also be considered. Recently, EDR security solutions to protect endpoints have emerged, but they focus on visibility. There is still a lack of detection and responsiveness. In this paper, we use real-world EDR event logs to aggregate knowledge-based MITRE ATT&CK and autoencoder-based anomaly detection techniques to detect anomalies in order to screen effective analysis and analysis targets from a security manager perspective. After that, detected anomaly attack signs show the security manager an alarm along with log information and can be connected to legacy systems. The experiment detected EDR event logs for 5 days, and verified them with hybrid analysis search. Therefore, it is expected to produce results on when, which IPs and processes is suspected based on the EDR event log and create a secure endpoint environment through measures on the suspicious IP/Process.

• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.303-304
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• 2006

This paper proposes detection method of vehicles using camera for auto-vehicle-system. Detection method is based on shadow detection and symmetric feature of vehicle. This method consists of three part. First is lane detection. By lane detection, we can reduce the area for vehicle detection. Second part is shadow detection. Shadow has information of vehicle width and position. Third part is symmetry. This feature is helpful for confirming the vehicle.

• The Bulletin of The Korean Astronomical Society
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• v.46 no.2
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• pp.46.2-46.2
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• 2021

We present solar event auto detection using deep-learning-based object detection algorithms and DeepSDO event dataset. DeepSDO event dataset is a new detection dataset with bounding boxed as ground-truth for three solar event (coronal holes, sunspots and prominences) features using Solar Dynamics Observatory data. To access the reliability of DeepSDO event dataset, we compared to HEK data. We train two representative object detection models, the Single Shot MultiBox Detector (SSD) and the Faster Region-based Convolutional Neural Network (R-CNN) with DeepSDO event dataset. We compared the performance of the two models for three solar events and this study demonstrates that deep learning-based object detection can successfully detect multiple types of solar events. In addition, we provide DeepSDO event dataset for further achievements event detection in solar physics.

• 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.

• International Journal of Precision Engineering and Manufacturing
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• v.7 no.1
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• pp.30-34
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• 2006

Recently, there has been an increasing demand for computer-vision-based inspection and/or measurement system as a part of factory automation equipment. In general, it is almost impossible to check the fault of all parts, coming from part-feeding system, with only manual inspection because of time limitation. Therefore, most of manual inspection is applied to specific samples, not all coming parts, and manual inspection neither guarantee consistent measuring accuracy nor decrease working time. Thus, in order to improve the measuring speed and accuracy of the inspection, a computer-aided measuring and analysis method is highly needed. In this paper, a computer-vision-based pipe inspection system is proposed, where the front and side-view profiles of three different kinds of pipes, coming from a forming line, are acquired by computer vision. And the edge detection is processed by using Laplace operator. To reduce the vision processing time, modified Hough transform is used with clustering method for straight line detection. And the center points and diameters of inner and outer circle are found to determine eccentricity of the parts. Also, an inspection system has been built so that the data and images of faulted parts are stored as files and transferred to the server.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.10 no.6
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• pp.113-118
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• 2010

The split spectrum processing algorithm of an ultrasonic wave on the tire was studied for the damage detection of a driving vehicle's tire. The processing results of normal and damaged tire was compared using the split spectrum algorithm to estimate the maximum value. The result that used Auto-correlation in case of damaged tire, the damage feature point is detected during 81ms intervals at a speed of 100km/h and during 162ms periodicity at a speed of 50km/h. This results was meaned the possibility for the tire's damage decision by damaging material with using periodicity feature point of tire damage according to vehicle speed.

• Journal of IKEEE
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• v.20 no.4
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• pp.454-458
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• 2016

Generally, fix the end of the fabric to pin with the fabric tenter process. At this time, the pin fixing part of the fiber fabric bulges and deforms. The deformation of the textile causes deterioration of the quality of the textile product. Detection of fiber fabric selvage portion is always required in the processing of the fabric. This research is a non-contact sensor for sensing fiber selvage. In this study, Developed a non-contact fabric selvage detecting sensor for use in automatic selvage cutting system. For the production of the fabric selvage detecting sensor prototype it was produced by placing thirty two sensor 2.5 mm interval. The selvage sensor system experimentally confirmed that actual selvage detection is possible.

• Journal of the Korean Data and Information Science Society
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• v.22 no.3
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• pp.449-458
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• 2011

Illegal waste dumping is one of the major problems that the government agency monitoring water quality has to face. Recently government agency installed COD (chemical oxygen demand) auto-monitering machines in river. In this article we provide an outlier detection algorithm using R based on the time series intervention model that detects some outlier values among those COD time series values generated from an auto-monitering machine. Through this algorithm using R, we can achieve an automatic algorithm that does not need manual intervention in each step, and that can further be used in simulation study.

• Proceedings of the KSR Conference
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• 2007.11a
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• pp.872-898
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• 2007

Pantograph sliding plate abrasion auto-detect system, one of the electric rail car auto-detecting devices, is a system that decides how much abrasion and when to replace without an inspector physically looking at the abrasion on the wet plate using machine vision, a cutting-edge technology. This paper covers the cause of deteriorating reliability that affects pantograph wet plate edge detection due to noise added to the video when it rains. In order to remove such noise, problems should be checked through Smoothing, Averaging mask and Median filter using filtering technique and stable edge detection without being affected by noise should be induced in video measurement used in machine vision technology.