• 한국산학기술학회논문지
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• 제19권3호
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• pp.693-698
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• 2018

본 논문은 최근 물체탐지 분야에서 실시간 물체 탐지 알고리즘으로 주목을 받고 있는 YOLOv2(You Only Look Once) 알고리즘을 이용하여 밀집 영역에 주차되어 있는 자동차 탐지 방법을 제안한다. YOLO의 컨볼루션 네트워크는 전체 이미지에서 한 번의 평가를 통해서 직접적으로 경계박스들을 예측하고 각 클래스의 확률을 계산하고 물체 탐지 과정이 단일 네트워크이기 때문에 탐지 성능이 최적화 되며 빠르다는 장점을 가지고 있다. 기존의 슬라이딩 윈도우 접근법과 R-CNN 계열의 탐지 방법은 region proposal 방법을 사용하여 이미지 안에 가능성이 많은 경계박스를 생성하고 각 요소들을 따로 학습하기 때문에 최적화 및 실시간 적용에 어려움을 가지고 있다. 제안하는 연구는 YOLOv2 알고리즘을 적용하여 기존의 알고리즘이 가지고 있는 물체 탐지의 실시간 처리 문제점을 해결하여 실시간으로 지상에 있는 자동차를 탐지하는 방법을 제안한다. 제안하는 연구 방법의 실험을 위하여 오픈소스로 제공되는 Darknet을 사용하였으며 GTX-1080ti 4개를 탑재한 Deep learning 서버를 이용하여 실험하였다. 실험결과 YOLO를 활용한 자동차 탐지 방법은 기존의 알고리즘 보다 물체탐지에 대한 오버헤드를 감소 할 수 있었으며 실시간으로 지상에 존재하는 자동차를 탐지할 수 있었다.

• Journal of Positioning, Navigation, and Timing
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• 제9권4호
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• pp.397-403
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• 2020

In order to synchronize a remote system time to the reference time like Coordinated Universal Time (UTC), it is required to compare the time difference between the two clocks. The time comparison data may have some outliers and the time synchronization performance can be significantly degraded if the outliers are not removed. Therefore, it is required to employ an effective outlier detection algorithm for keeping high accurate system time. In this paper, an outlier detection method is presented for the time difference data of GNSS time transfer receivers. The time difference data between the system time and the GNSS usually have slopes because the remote system clock is under free running until synchronized to the reference clock time. For investigating the outlier detection performance of the proposed algorithm, simulations are performed by using the time difference data of a GNSS time transfer receiver corrected to a free running Cesium clock with intentionally inserted outliers. From the simulation, it is investigated that the proposed algorithm can effectively detect the inserted outliers while conventional methods such as modified Z-score and adjusted boxplot cannot. Furthermore, it is also observed that the synchronization performance can be degraded to more than 15% with 20 outliers compared to that of original data without outliers.

• 한국군사과학기술학회지
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• 제15권5호
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• pp.601-614
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• 2012

Since the optimized use of sonar systems available for detection is a very practical problem for a given ocean environment, the measure of mission achievability is needed for operating the sonar system efficiently. In this paper, a theory on Measure Of Effectiveness(MOE) for specific mission such as detection is described as the measure of mission achievability, and a recursive Cumulative Detection Probability(CDP) algorithm is found to be most efficient from comparing three CDP algorithms for discrete glimpses search to reduce computation time and memory for complicated scenarios. The three CDPs which are MOE for sonar-maneuver pattern are calculated as time evolves for comparison, based on three different formula depending on the assumptions as follows; dependent or independent glimpses, unimodal or non-unimodal distribution of Probability of Detection(PD) as a function of observation time interval for detection. The proposed CDP algorithm which is made from unimodal formula is verified and applied to OASPP(Optimal Acoustic Search Path Planning) with complicated scenarios.

• 한국멀티미디어학회논문지
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• 제22권1호
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• pp.44-54
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• 2019

Recently, image-based object detection has made great progress with the introduction of Convolutional Neural Network (CNN). Many trials such as Region-based CNN, Fast R-CNN, and Faster R-CNN, have been proposed for achieving better performance in object detection. YOLO has showed the best performance under consideration of both accuracy and computational complexity. However, these data-driven detection methods including YOLO have the fundamental problem is that they can not guarantee the good performance without a large number of training database. In this paper, we propose a data sampling method using CycleGAN to solve this problem, which can convert styles while retaining the characteristics of a given input image. We will generate the insufficient data samples for training more robust object detection without efforts of collecting more database. We make extensive experimental results using the day-time and night-time road images and we validate the proposed method can improve the object detection accuracy of the night-time without training night-time object databases, because we converts the day-time training images into the synthesized night-time images and we train the detection model with the real day-time images and the synthesized night-time images.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제8권10호
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• pp.3475-3489
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• 2014

Here we present a simple flame detection method for an infrared (IR) thermal camera based real-time fire surveillance digital signal processor (DSP) system. Infrared thermal cameras are especially advantageous for unattended fire surveillance. All-weather monitoring is possible, regardless of illumination and climate conditions, and the data quantity to be processed is one-third that of color videos. Conventional IR camera-based fire detection methods used mainly pixel-based temporal correlation functions. In the temporal correlation function-based methods, temporal changes in pixel intensity generated by the irregular motion and spreading of the flame pixels are measured using correlation functions. The correlation values of non-flame regions are uniform, but the flame regions have irregular temporal correlation values. To satisfy the requirement of early detection, all fire detection techniques should be practically applied within a very short period of time. The conventional pixel-based correlation function is computationally intensive. In this paper, we propose an IR camera-based simple flame detection algorithm optimized with a compact embedded DSP system to achieve early detection. To reduce the computational load, block-based calculations are used to select the candidate flame region and measure the temporal motion of flames. These functions are used together to obtain the early flame detection algorithm. The proposed simple algorithm was tested to verify the required function and performance in real-time using IR test videos and a real-time DSP system. The findings indicated that the system detected the flames within 5 to 20 seconds, and had a correct flame detection ratio of 100% with an acceptable false detection ratio in video sequence level.

• 한국수정란이식학회지
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• 제32권3호
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• pp.153-157
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• 2017

This study was conducted to investigate the optimal artificial insemination (AI) time with diagnostic kit at ovulation time. We already applied the patent about the protein in the cow heat mucose in external reproductive tract. And we would examine the accuracy for detection of cow heat by the kit produced with the protein. Evaluation of optimal heat detection was tried two time at 12 hrs and 24 hrs after the heat. And then, AI service also performed two times with no relation to the results of heat diagnosis by heat detection kit and pregnancy rates were checked with rectal palpation on $60^{th}$ day after AI. Heat diagnostic results by kit in natural heat after 12 hrs in Hanwoo cows were showed 31.3~75.0% on positive in first heat detection and 33.3~100.0% on positve in second heat detection. In the $1^{st}$ positive results were significant different (p<0.05), but $2^{nd}$ positive were not. The results of heat detection showed different result on regional influence and individual cow effects. The pregnancy rates of first trial of heat detection were showed 34.4~78.7% on positive and 21.3~68.8% on negative after the diagnosis by heat detection kit. And the pregnancy rates of next trial of heat detection were showed 33.3~85.7% on positive and 14.3~66.6% on negative after the heat diagnosis. Both positive results of first trial and next trial also were showed significant different (p<0.05), but negative results were not. In positive result, first trial of total pregnancy rates was higher than the next trial of pregnancy, but there showed opposite results on negative results. In conclusion, the optimal heat detection kit is suitable to ordinary Hanwoo cows and it suggested that we have to improve the kit's accuracy by detecting the materials like proteins related optimal AI time.

• Journal of Electrical Engineering and Technology
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• 제8권6호
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• pp.1283-1295
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• 2013

This study proposed an improvement in synchronously rotating reference frame-based voltage sag detection under distorted grid voltages. In the past, the conventional synchronously rotating reference frame (CSRRF)-based voltage sag detection was generally used in the voltage sag compensation applications. Its disadvantage is a long delay of detection time. The modified synchronously rotating reference frame (MSRRF)-based voltage sag detection is able to detect the voltage sag with only a short delay in detection time. However, its operation under distorted grid voltage conditions is unavailable. This paper proposed the improvement of modified synchronously rotating reference frame (IMSRRF)-based voltage sag detection for use in distorted grid voltages with very fast operation of voltage sag detection. The operation of the proposed voltage sag detections is investigated via simulations and experimentations to verify the performance of the IMSRRF-based voltage sag detection.

• 한국컴퓨터정보학회논문지
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• 제15권2호
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• pp.47-53
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• 2010

본 논문은 동영상에서 실시간 얼굴검출을 위하여 Residual Image 검출과 색상정보를 이용한 얼굴검출 기법을 제안하였다. 제안된 기법은 동영상에서 빠른 처리 속도와 높은 얼굴 검출율을 나타냈으며 기울어진 얼굴영상에 대한 보정작업을 통하여 검출 에러율을 줄였다. 실시간으로 전송된 동영상에서 검출의 대상이 되는 정지영상을 추출한다. 추출된 영상은 기울어진 얼굴검출을 위한 window회전 알고리즘을 사용하고 이렇게 보정된 영상은 얼굴 검출에 필요한 특징을 추출하기 위해 AdaBoost알고리즘을 사용하여 실시간으로 얼굴이 검출된 영상을 획득하게 된다.

• 대한의생명과학회지
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• 제16권2호
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• pp.127-131
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• 2010

Mycobacterium leprae detection is difficult even with molecular biological techniques due to the low sensitivity of current methodologies. In this report, real-time PCR targeting the M. leprae-specific repetitive element (RLEP) sequence was developed as a new diagnostic tool and evaluated using clinical specimens. For this, M. leprae DNAs were extracted from skin biopsy specimens from 80 patients and analyzed by real-time PCR using TaqMan probe. Then, the detection efficiency of the real-time PCR was compared with that of standard PCR. In brief, the rate of positive detection by the standard PCR and real-time PCR was 32.50% and 66.25%, respectively. The results seemed to clearly show that the TaqMan real-time PCR developed in this study may be a useful tool for sensitive detection of M. leprae from clinical specimens.

• 제어로봇시스템학회논문지
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• 제4권2호
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• pp.246-255
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• 1998

This paper presents a novel method for real-time malfunction detection of plasma etching process using EPD signal traces. First, many reference EPD signal traces are collected using monochromator and data acquisition system in normal etching processes. Critical points are defined by applying differentiation and zero-crossing method to the collected reference signal traces. Critical parameters such as intensity, slope, time, peak, overshoot, etc., determined by critical points, and frame attributes transformed signal-to symbol of reference signal traces are saved. Also, UCL(Upper Control Limit) and LCL(Lower Control Limit) are obtained by mean and standard deviation of critical parameters. Then, test EPD signal traces are collected in the actual processes, and frame attributes and critical parameters are obtained using the above mentioned method. Process malfunctions are detected in real-time by applying SPC(Statistical Process Control) method to critical parameters. the Real-time malfunction detection method presented in this paper was applied to actual processes and the results indicated that it was proved to be able to supplement disadvantages of existing quality control check inspecting or testing random-selected devices and detect process malfunctions correctly in real-time.