• Nuclear Medicine and Molecular Imaging
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• v.42 no.sup1
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• pp.110-112
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• 2008

Endometrial carcinoma is one of the most common gynecologic malignancies and which is predominant in postmenopausal women. Clinically many patients are hospitalized in early stage due to clinical sign and symptom such as vaginal bleeding and in this case, patient's prognosis is known to be good. However, considerable number of patients with advanced and relapsed disease reveal poor prognosis. Therefore, exact staging work up is essential for proper treatment as is primary lesion detection. $^{18}F-FDG-PET$ has been widely used for the evaluation of gynecologic malignancies such as cervical carcinoma and ovarian cancer. In contrast, FDG PET application to endometrial carcinoma is limited until now and there is no sufficient data to validate the usefulness of FDG PET for this disease yet. However, several studies showed promising results that FDG PET is sensitive and specific in detection of recurrent or metastatic lesions. Therefore further active investigation in this field can facilitate the use of FDG PET for endometrial carcinoma.

• Journal of the Korean Geophysical Society
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• v.8 no.3
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• pp.137-143
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• 2005

Early detection of premonitory symptom of slope movement ensures tremendous saving of lives and repair costs from catastrophic disaster. Therefore, it is essential to constantly monitor the performance and integrity of both reinforced and un-reinforced cut slopes. We developed a novel monitoring system by using tension wire sensors. It's advantages are highly sensitivity, simple installation, large displacement measurement, durability of system, capability of remote sensing. Real-time measurement of slope surface movement is shown graphically and it gives a warning when the monitored value exceeds a given threshold level so that any sign of abnormal slope movement can be easily perceived.

• Proceedings of the Korean Geotechical Society Conference
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• 2000.11b
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• pp.60-76
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• 2000

Early detection in real-time response of slope movements ensures tremendous saving of lives and repair costs from catastrophic disaster Therefore, it is essential to constantly monitor the performance and integrity of slope-stabilizing structures such as Rock bolt, Nail and Pile during or after installation. We developed a novel monitoring system using Fiber Bragg Grating (FBG)sensor. It's advantages are highly sensitivity, small dimension and electro-magnetic immunity. capability of multiplexing, system integrity, remote sensing - these serve real-time health monitoring of the structures. Real-time strain measurement by the signal processing program is shown graphically and it gives a warning sound when the monitored strain state exceeds a given threshold level so that any sign of abnormal disturbance on the spot can be easily perceived.

• Proceedings of the KIEE Conference
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• 1988.07a
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• pp.709-712
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• 1988

Ultrasonic Doppler Diagnostic System utilizes the Doppler effect for measurement of blood velocity. The sign of the Doppler frequency shift represents blood flow direction. CW(Continuous-Wave) Doppler System uses quadrature detection and phase rotation method to produce simultaneous independent audio and velocity signals for forward and reverse blood flow direction in the time-domain, had been fabricated. But time-domain analyzing such as audio evaluation and zero- crossing detection for instantaneous and mean frequnecy measurement do not provide both an accurate and quantitative result. Therefore, it is necessary to adopt frequency-domain technique to improve system performance. In this paper, we describe a unit which is composed of CW Doppler System and real-time spectrum analyzer (installed TMS 32010 DSP Chip). This unit shows time-dependent spectrum variation and mean velocity of Blood signal.

• Journal of the Korean Geophysical Society
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• v.8 no.1
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• pp.7-14
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• 2005

Early detection in real-time response of slope movements ensures tremendous saving of lives and repair costs from catastrophic disaster. Therefore, it is essential to constantly monitor the performance and integrity of slope-stabilizing structures such as Rock bolt, Nail and Pile during or after installation. We developed a novel monitoring system using Fiber Bragg Grating (FBG) sensor. It's advantages are highly sensitivity, small dimension and electro-magnetic immunity. capability of multiplexing, system integrity, remote sensing - these serve real-time health monitoring of the structures. Real-time strain measurement by the signal processing program is shown graphically and it gives a warning sound when the monitored strain state exceeds a given threshold level so that any sign of abnormal disturbance on the spot can be easily perceived.

• Journal of Applied Reliability
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• v.18 no.1
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• pp.20-32
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• 2018

Purpose: The purpose of this study is to set up an anomaly detection criteria for sensor data coming from a motorcycle. Methods: Five sensor values for accelerator pedal, engine rpm, transmission rpm, gear and speed are obtained every 0.02 second from a motorcycle. Exploratory data analysis is used to find any pattern in the data. Traditional process control methods such as X control chart and time series models are fitted to find any anomaly behavior in the data. Finally unsupervised learning algorithm such as k-means clustering is used to find any anomaly spot in the sensor data. Results: According to exploratory data analysis, the distribution of accelerator pedal sensor values is very much skewed to the left. The motorcycle seemed to have been driven in a city at speed less than 45 kilometers per hour. Traditional process control charts such as X control chart fail due to severe autocorrelation in each sensor data. However, ARIMA model found three abnormal points where they are beyond 2 sigma limits in the control chart. We applied a copula based Markov chain to perform statistical process control for correlated observations. Copula based Markov model found anomaly behavior in the similar places as ARIMA model. In an unsupervised learning algorithm, large sensor values get subdivided into two, three, and four disjoint regions. So extreme sensor values are the ones that need to be tracked down for any sign of anomaly behavior in the sensor values. Conclusion: Exploratory data analysis is useful to find any pattern in the sensor data. Process control chart using ARIMA and Joe's copula based Markov model also give warnings near similar places in the data. Unsupervised learning algorithm shows us that the extreme sensor values are the ones that need to be tracked down for any sign of anomaly behavior.

• Convergence Security Journal
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• v.12 no.6
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• pp.31-37
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• 2012

Today, the core technology of an automobile is becoming to IT-based convergence system technology. To cope with many kinds of situations and provide the convenience for drivers, various IT technologies are being integrated into automobile system. In this paper, we propose an convergence system, which is called Augmented Driving System (ADS), to provide high safety and convenience of drivers based on image information processing. From imaging sensor, the image data is acquisited and processed to give distance from the front car, lane, and traffic sign panel by the proposed methods. Also, a converged interface technology with camera for gesture recognition and microphone for speech recognition is provided. Based on this kind of system technology, car accident will be decreased although drivers could not recognize the dangerous situations, since the system can recognize situation or user context to give attention to the front view. Through the experiments, the proposed methods achieved over 90% of recognition in terms of traffic sign detection, lane detection, and distance measure from the front car.

• KIPS Transactions on Software and Data Engineering
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• v.4 no.11
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• pp.521-528
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• 2015

Road-view object classification methods are mostly influenced by weather and illumination conditions, thus the most of the research activities are based on dataset in clean weathers. In this paper, we present a road-view object classification method based on color segmentation that works for all kinds of weathers. The proposed method first classifies the weather and illumination conditions and then applies the weather-specified color models to find the road traffic signs. Using 5 different features of the road-view images, we classify the weather and light conditions as sunny, cloudy, rainy, night, and backlight. Based on the classified weather and illuminations, our model selects the weather-specific color ranges to generate Gaussian Mixture Model for each colors, Green, Yellow, and Blue. The proposed method successfully detects the traffic signs regardless of the weather and illumination conditions.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.6
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• pp.228-241
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• 2021

This study verified the performance change of a LiDAR when it detects road signs, which are potential cooperation targets for an autonomous vehicle. In particular, road signs of different colors and materials were produced and tested in controlled rainfall on the real road environment. The NPC and intensity were selected as the performance indicators, and a T-Test was used for comparison. The study results show that the performance of LiDAR for the detection of road signs was reduced with the increase of rainfall. The degradation of performance in retroreflective sheets was lesser than painted road signs, but at the amount of 40 mm/h or more, the detection performance of retroreflective sheets deteriorates to an extent that data cannot be collected. The performance level of black paint was lower than that of other colors on a clear day. In addition, the white sheet was most sensitively degraded with the increase in precipitation. These performance verification results are expected to be utilized in the manufacturing of road facilities that improve the visibility of sensors in the future.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.6
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• pp.907-917
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• 2023

Recently, a self-driving car have applied deep learning technology to advanced driver assistance system can provide convenience to drivers, but it is shown deep that learning technology is vulnerable to adversarial evasion attacks. In this paper, we performed five adversarial evasion attacks, including MI-FGSM(Momentum Iterative-Fast Gradient Sign Method), targeting the object detection algorithm YOLOv5 (You Only Look Once), and measured the object detection performance in terms of mAP(mean Average Precision). In particular, we present a method applying morphology operations for YOLO to detect objects normally by removing noise and extracting boundary. As a result of analyzing its performance through experiments, when an adversarial attack was performed, YOLO's mAP dropped by at least 7.9%. The YOLO applied our proposed method can detect objects up to 87.3% of mAP performance.