• 대한임베디드공학회논문지
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• 제17권1호
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• pp.41-49
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• 2022

This paper proposes a novel lane detection algorithm based on inverse perspective transformation and machine learning in lightweight embedded system. The inverse perspective transformation method is presented for obtaining a bird's-eye view of the scene from a perspective image to remove perspective effects. This method requires only the internal and external parameters of the camera without a homography matrix with 8 degrees of freedom (DoF) that maps the points in one image to the corresponding points in the other image. To improve the accuracy and speed of lane detection in complex road environments, machine learning algorithm that has passed the first classifier is used. Before using machine learning, we apply a meaningful first classifier to the lane detection to improve the detection speed. The first classifier is applied in the bird's-eye view image to determine lane regions. A lane region passed the first classifier is detected more accurately through machine learning. The system has been tested through the driving video of the vehicle in embedded system. The experimental results show that the proposed method works well in various road environments and meet the real-time requirements. As a result, its lane detection speed is about 3.85 times faster than edge-based lane detection, and its detection accuracy is better than edge-based lane detection.

• 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.

• 한국멀티미디어학회논문지
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• 제19권2호
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• pp.180-188
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• 2016

In this paper, we propose a fire detection algorithm based on motion information and color information analysis. Conventional fire detection algorithms have as main problem the difficulty to detect fire due to external light, intensity, background image complexity, and little fire diffusion. So we propose a fire detection algorithm that accurate and fast. First, it analyzes the motion information in video data and then set the first candidate. Second, it determines this domain after analyzing the color and the domain. This algorithm assures a fast fire detection and a high accuracy compared with conventional fire detection algorithms. Our algorithm will be useful to real-time fire detection in real world.

• 한국철도학회:학술대회논문집
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• 한국철도학회 2011년도 정기총회 및 추계학술대회 논문집
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• pp.69-74
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• 2011

In the curving detection method by using an accelerometer, the ride comfort in the first car is worse than one in the others due to spend the time to calculate the tilting command and drive the tilting mechanism after entering in the curve. In order to enhance the ride comfort in the first car, the preditive curve detection method which predicts the distance from a train to the starting point of curve by using the GPS, Tachometer, Ground balise and position DB for track. In this study, we predicted and evaluated the ride comfort for predictive curve detection method in transient curves according to the shape and dimension of transient curve and the various driving speed. Also, we predicted the improvement of the ride comfort for predictive curve detection method by comparing with the result of the ride comfort for predictive curve detection method and for curve detection method using an accelerometer in the short transient curve.

• 한국멀티미디어학회논문지
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• 제7권12호
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• pp.1692-1699
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• 2004

Many researchers have developed various techniques for detection of human faces in ordinary still images. Face detection is the first imperative step of human face recognition systems. The two main problems of human face detection are how to cutoff the running time and how to reduce the number of false positives. In this paper, we present frontal and near-frontal face detection algorithm in still gray images using a thick edge image and neural network. We have devised a new filter that gets the thick edge image. Our overall scheme for face detection consists of two main phases. In the first phase we describe how to create the thick edge image using the filter and search for face candidates using a whole face detector. It is very helpful in removing plenty of windows with non-faces. The second phase verifies for detecting human faces using component-based eye detectors and the whole face detector. The experimental results show that our algorithm can reduce the running time and the number of false positives.

• 한국인터넷방송통신학회논문지
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• 제15권6호
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• pp.141-146
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• 2015

다양하게 복합된 소리 및 음성신호를 FPGA의 마이크로 입력받아서 신호를 분류하고 분석하여 이상 신호를 감지할 수 있는 많은 시스템이 있으나, 효율적이며 효과적으로 이상 신호를 감지하는 시스템을 구현하는데 있어서는 많은 문제점들을 가지고 있다. 따라서 이 문제를 해결하고 감지율을 높이기 위하여 본 연구에서 제안된 방법에서는 소리 신호가 입력되는 마이크 센서를 사용하여 FIFO(First-in First-out) 구조에 적용하고, 통계학적으로 분산과 변동계수를 적용한 알고리즘을 기반으로 이상 신호를 효과적으로 분류하고, 효율적으로 감지 여부를 출력하는 시스템을 제안하고 구현하였다. 제안된 알고리즘을 적용한 시스템을 통하여 100회 이상의 실험을 반복한 결과 96.3%의 감지율을 보였다.

• 반도체디스플레이기술학회지
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• 제17권4호
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• pp.105-108
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• 2018

Face Detection is an essential first step of the face recognition, and this is significant effects on face feature extraction and the effects of face recognition. Face detection has extensive research value and significance. In this paper, we present and analysis the principle, merits and demerits of the classic AdaBoost face detection and ASM algorithm based on point distribution model, which ASM solves the problems of face detection based on AdaBoost. First, the implemented scheme uses AdaBoost algorithm to detect original face from input images or video stream. Then, it uses ASM algorithm converges, which fit face region detected by AdaBoost to detect faces more accurately. Finally, it cuts out the specified size of the facial region on the basis of the positioning coordinates of eyes. The experimental result shows that the method can detect face rapidly and precisely, with a strong robustness.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2003년도 Proceedings of ACRS 2003 ISRS
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• pp.461-463
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• 2003

The information on the cloud presence within a instantaneous field of view is the first step toward the derivation of many other geophysical parameters. Here, we first applied the current MODIS cloud detection algorithm developed by University of Wisconsin and compared the results to a visual interpretation of composite data, especially during the daytime. Most of cases, the detection algorithm performs very well, except a few cases with over-detection. One of the reasons for the false detection is due to the time independent use of land information which affects the threshold values of visible channel test. In the presentation, we show detailed analysis of the current cloud detection algorithm and suggest possible way to overcome the current shortfall.

• Communications for Statistical Applications and Methods
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• 제19권3호
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• pp.333-344
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• 2012

We consider some statistical properties of the first order difference-based error variance estimator in nonparametric regression models with a single outlier. So far under an outlier(s) such difference-based estimators has been rarely discussed. We propose the first order difference-based estimator using the leave-one-out method to detect a single outlier and simulate the outlier detection in a nonparametric regression model with the single outlier. Moreover, the outlier detection works well. The results are promising even in nonparametric regression models with many outliers using some difference based estimators.

• 전자공학회논문지
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• 제50권7호
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• pp.258-266
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• 2013

본 논문에서는 진동센서를 기반으로 하는 탐지 시스템에서 불규칙적으로 변화는 잡음의 특성을 고려하여 허위경보(false alarm)를 감소하기 위한 적응형 탐지 알고리즘을 제안한다. 제안된 알고리즘은 커널 함수(Kerenl function)을 이용한 1차 검출과 탐지 확정 단계를 적용한 2차 검출로 구성된다. 1차 검출기의 커널 함수는 측정된 신호로부터 잡음의 확률적 모수를 이용하여 잡음 변화에 따라 Neyman-Pearson 결정법으로 문턱 값을 찾아 구한다. 그리고 2차 탐지기는 1차 탐지된 표본수를 이용하여 발걸음 신호의 점유시간을 계산한 후 4단계의 탐지 확정 단계로 구성된다. 본 논문에서 제안된 알고리즘을 검증하기 위해 측정된 걷기와 뛰기 진동 신호를 이용하여 발걸음 신호에 대한 탐지 실험을 수행 하였으며 고정 문턱 값을 이용한 탐지 결과와 비교 하였다. 제안된 1차 검출기의 목표물 탐지 결과 사람의 걷기와 뛰기에 대하여 10m 구간까지 95%의 높은 탐지 성능을 획득하였다. 또한, 허위경보 확률은 고정 문턱 값과 비교할 때 40%에서 20%로 감소하였으며 탐지 확정 단계를 적용한 결과 4%미만으로 크게 감소한 결과를 얻었다.