• Proceedings of the Korean Information Science Society Conference
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• 2003.10b
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• pp.328-330
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• 2003

최근 운영시스템. 제어프로그램, 적용프로그램과 같은 여러 가지 소프트웨어 시스템이 더욱 더 복잡화 및 대형화되고 있기 때문에 신뢰도가 높은 소프트웨어 시스템을 개발하는 일이 매우 중요하며, 따라서 소프트웨어 제품 개발에 있어서 소프트웨어의 신뢰도가 핵심사항이라고 할 수 있다. 소프트웨어가 주어진 시간동안 고장이 발생하지 않을 확률 즉, 신뢰도는 소프트웨어의 테스트 과정을 계속하면서 반복해서 결함을 발견 및 수정하면 더욱 더 향상될 것이다. 그러한 검출현상을 설명해주는 소프트웨어 신뢰도 모델을 소프트웨어 신뢰도 성장모델(SRGM)이라 한다.

• Proceedings of the Korean Information Science Society Conference
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• 1999.10a
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• pp.569-571
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• 1999

소프트웨어의 신뢰도는 하드웨어의 신뢰도와 고장메타니즘이 다르므로 하드웨어의 신뢰도 모델을 그대로 이용할 수 없다. 소프트웨어의 신뢰도를 추정하기 위한 방법은 그동안 Jelinski-Moranda(JM) 모델을 비롯하여 많은 기법이 연구되었다. 그러나, 아직까지 만족하다고 인정할 만한 신뢰도모델링은 개발되지 않았다. 본 연구에서는 소프트웨어의 테스트를 통하여 검출되는 에러 개수의 추세를 가지고 비제차포아송과정(NHPP)의 파라미터를 찾아 신뢰도함수를 구하고자 하며, 아울러, 테스트중단시간을 결정하고자 한다. 파라미터를 찾는 방법은 maximum likelihood estimate(MLE) 기법을 이용하며, 테스트 중단시간은 구해진 파라미터를 신뢰도 함수에 대입하여 결정한다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2022.07a
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• pp.343-344
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• 2022

간암에는 크게 두 종류가 있는데 하나는 간에서 생긴 종양이 악성종양으로 진행된 것이고 다른 하나는 다른 장기에서 생긴 암이 간으로 전이되는 것이다. 본 논문에서는 간에서 생긴 종양이 악성종양으로 진행되는 것을 조기 발견하고 막고자 Object Detect 모델인 YOLO v5의 다섯 가지 모델을 비교하여 악성 종양으로의 발전 가능성이 있는 간의 lesion을 찾아보았다.

• Annual Conference of KIPS
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• 2021.11a
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• pp.606-609
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• 2021

최근 한국에서도 이루다의 윤리 이슈를 기점으로 딥러닝 모델의 윤리적 언어학습 필요성이 대두되었다. 그럼에도 불구하고 영어 데이터에 비해 한국어 데이터는 Korean Hate Speech Detection Dataset 이 유일하다. 이번 연구에서는 기존 데이터 세트의 유연성이 떨어지고 세부 라벨이 제한적이라는 문제를 개선한 새로운 데이터 세트를 제안하고, 해당 데이터 세트에 대하여 다양한 신경망 분류 모델을 적용한 벤치마크 결과를 공개한다.

• Journal of the Institute of Electronics and Information Engineers
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• v.49 no.9
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• pp.347-352
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• 2012

In this paper, an 2D spatial-map construction for workers identification and avoidance of AGV using the detection scheme of the spatial coordinates based on stereo camera is proposed. In the proposed system, face area of a moving person is detected from a left image among the stereo image pairs by using the YCbCr color model and its center coordinates are computed by using the centroid method and then using these data, the stereo camera embedded on the mobile robot can be controlled for tracking the moving target in real-time. Moreover, using the disparity map obtained from the left and right images captured by the tracking-controlled stereo camera system and the perspective transformation between a 3-D scene and an image plane, depth map can be detected. From some experiments on AGV driving with 240 frames of the stereo images, it is analyzed that error ratio between the calculated and measured values of the worker's width is found to be very low value of 2.19% and 1.52% on average.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.19 no.7
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• pp.726-735
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• 2009

In this paper, we propose an approach to signal model-based fault detection and diagnosis system for induction motors. The current fault detection techniques used in the industry are limit checking techniques, which are simple but cannot predict the types of faults and the initiation of the faults. The system consists of two consecutive processes: fault detection process and fault diagnosis process. In the fault detection process, the system extracts the significant features from sound signals using combination of variance, cross-correlation and wavelet. Consequently, the pattern classification technique is applied to the fault diagnosis process to recognize the system faults based on faulty symptoms. The sounds generated from different kinds of typical motor's faults such as motor unbalance, bearing misalignment and bearing loose are examined. We propose two approaches for fault detection and diagnosis system that are waveletand-variance-based and wavelet-and-crosscorrelation-based approaches. The results of our experiment show more than 95 and 78 percent accuracy for fault classification, respectively.

• Journal of the Korean Society of Marine Environment & Safety
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• v.26 no.6
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• pp.601-605
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• 2020

In large ships with complex structures, it is difficult to locate workers. In particular, it is not easy to detect when a worker falls down, making it difficult to respond quickly. Thus, research is being conducted to detect fallen workers using a camera or by attaching a device to the body. Existing image-based fall detection systems have been designed to detect a person's body parts; hence, it is difficult to detect them in various ships and postures. In this study, the entire fall area was extracted and deep learning was used to detect the fallen shipworker based on the image. The data necessary for learning were obtained by recording falling states at the shipyard. The amount of learning data was augmented by flipping, resizing, and rotating the image. Performance evaluation was conducted with precision, reproducibility, accuracy, and a low error rate. The larger the amount of data, the better the precision. In the future, reinforcing various data is expected to improve the effectiveness of camera-based fall detection models, and thus improve safety.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.4
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• pp.86-97
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• 2011

This paper presents a novel algorithm to detect more accurate road lane with image sensor-based color classification and directional edge clustering. With treatment of road region and lane as a recognizable color object, the classification of color cues is processed by an iterative optimization of statistical parameters to each color object. These clustered color objects are taken into considerations as initial kernel information for color object detection and recognition. In order to improve the limitation of object classification using the color cues, the directional edge cures within the estimated region of interest in the lane boundary (ROI-LB) are clustered and combined. The results of color classification and directional edge clustering are optimally integrated to obtain the best detection of road lane. The characteristic of the proposed system is to obtain robust result to all real road environments because of using non-parametric approach based only on information of color and edge clustering without a particular mathematical road and lane model. The experimental results to the various real road environments and imaging conditions are presented to evaluate the effectiveness of the proposed method.

• Journal of IKEEE
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• v.22 no.3
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• pp.723-728
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• 2018

This paper proposes a signal detection method for IoT door lock system which is a new application field of VLC (Visible Light Communication). This paper describes the signal detection technique for user recognition that needs to be overcome in order to apply VLC to door lock system which has a demand for new technology due to security issue. This system has security and high signal detection characteristics because it uses existing infrastructure to communicate with visible light. In order to detect the signal using FFT, the signal of the user who accesses the authentication channel based on the pilot signal is detected, and the performance of the false alarm probability and detection probability is shown in the channel model.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.12
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• pp.1978-1984
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• 2016

This paper proposes a robust vehicle detecting method by using Adaboost and CLAHE(Contrast-Limit Adaptive Histogram Equalization). We propose two method to detect vehicle effectively. First, we are able to judge rainy and night by converting RGB value to brightness. Second, we can detect a taillight, designate a ROI(Region Of Interest) by using CLAHE. And then, we choose an Adaboost algorithm by comparing traditional vehicle detecting method such as GMM(Gaussian Mixture Model), Optical flow and Adaboost. In this paper, we use proposed method and get better performance of detecting vehicle. The precision and recall score of proposed method are 0.85 and 0.87. That scores are better than GMM and optical flow.