• Journal of Korean Society of Industrial and Systems Engineering
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• v.43 no.3
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• pp.95-100
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• 2020

Machines and facilities are physically or chemically degenerated by continuous usage. One of the results of this degeneration is the process mean shift. The representative type of the degeneration is wear of tool or machine. According to the increasing wear level, non-conforming products cost and quality loss cost are increasing simultaneously. Therefore a periodic preventive resetting the process is necessary. The total cost consists of three items: adjustment cost (or replacement cost), non-conforming cost due to product out of upper or lower limit specification, and quality loss cost due to difference from the process target value and the product characteristic value among the conforming products. In this case, the problem of determining the adjustment period or wear limit that minimizes the total cost is called the 'process mean shift' problem. It is assumed that both specifications are set and the wear level can be observed directly. In this study, we propose a new model integrating the quality loss cost, process variance, and production volume, which has been conducted in different fields in previous studies. In particular, for the change in production volume according to the increasing in wear level, we propose a generalized production quantity function g(w). This function can be applied to most processes and we fitted the g(w) to the model. The objective equation of this model is the total cost per unit wear, and the determining variables are the wear limit and initial process setting position that minimize the objective equation.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.1
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• pp.45-50
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• 2021

Machines and facilities are physically or chemically degenerated by continuous usage. The representative type of the degeneration is the wearing of tools, which results in the process mean shift. According to the increasing wear level, non-conforming products cost and quality loss cost are increasing simultaneously. Therefore, a preventive maintenance is necessary at some point. The problem of determining the maintenance period (or wear limit) which minimizes the total cost is called the 'process mean shift problem'. The total cost includes three items: maintenance cost (or adjustment cost), non-conforming cost due to the non-conforming products, and quality loss cost due to the difference between the process target value and the product characteristic value among the conforming products. In this study, we set the production volume as a decreasing function rather than a constant. Also we treat the process variance as a function to the increasing wear rather than a constant. To the quality loss function, we adopted the Cpm+, which is the left and right asymmetric process capability index based on the process target value. These can more reflect the production site. In this study, we presented a more extensive maintenance model compared to previous studies, by integrating the items mentioned above. The objective equation of this model is the total cost per unit wear. The determining variables are the wear limit and the initial process setting position that minimize the objective equation.

• Journal of information and communication convergence engineering
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• v.14 no.2
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• pp.78-83
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• 2016

In underwater acoustic communication, the transmitted signals are severely influenced by the reflections from both the sea surface and the sea bottom. As very large reflection signals from these boundaries cause an inter-symbol interference (ISI) effect, the communication quality worsens. A channel estimation-based equalizer is usually adopted to compensate for the reflected signals under the acoustic communication channel. In this study, a feed-forward equalizer (FFE) with the least mean squares (LMS) algorithm was applied to a quadrature phase-shift keying (QPSK) transmission system. Two different types of equalizers were adopted in the QPSK system, namely a real-coefficient equalizer and a complex-coefficient equalizer. The performance of the complex-coefficient equalizer was better than that of two real-coefficient equalizers. Therefore, a Hilbert transform was applied to the real-coefficient binary phase-shift keying (BPSK) system to obtain a complex-coefficient BPSK system. Consequently, we obtained better results than those of a real-coefficient equalizer.

• Journal of Communications and Networks
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• v.16 no.6
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• pp.583-591
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• 2014

In wireless communications, knowledge of the signal-to-noise ratio is required in diverse communication applications. In this paper, we derive the variance of the maximum likelihood estimator in the data-aided and non-data-aided schemes for determining the optimal shrinkage factor. The shrinkage factor is usually the constant that is multiplied by the unbiased estimate and it increases the bias slightly while considerably decreasing the variance so that the overall mean squared error decreases. The closed-form biased estimators for binary-phase-shift-keying and quadrature phase-shift-keying systems are then obtained. Simulation results show that the mean squared error of the proposed method is lower than that of the maximum likelihood method for low and moderate signal-to-noise ratio conditions.

• Journal of Electrical Engineering and Technology
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• v.12 no.5
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• pp.2067-2078
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• 2017

In this paper, a new face tracking algorithm is proposed. The CamShift (Continuously adaptive mean SHIFT) algorithm shows unstable tracking when there exist objects with similar color to that of face in the background. This drawback of the CamShift is resolved by the proposed algorithm using Kinect's pixel-by-pixel depth information and the skin detection method to extract candidate skin regions in HSV color space. Additionally, even when the target face is disappeared, or occluded, the proposed algorithm makes it robust to this occlusion by the feature point matching. Through experimental results, it is shown that the proposed algorithm is superior in tracking performance to that of existing TLD (Tracking-Learning-Detection) algorithm, and offers faster processing speed. Also, it overcomes all the existing shortfalls of CamShift with almost comparable processing time.

• Journal of the Chungcheong Mathematical Society
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• v.32 no.1
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• pp.157-164
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• 2019

In this article, we study expansiveness of the shift maps on orbital inverse limit spaces which consist of two cross bonding mappings. On orbital inverse limit systems, horizontal directions express inverse limit systems and vertical directions mean orbits based on horizontal axes. We characterize the c-expansiveness of functions on orbital spaces. We also prove that the c-expansiveness of the functions is equivalent to the expansiveness of the shift maps on orbital inverse limit spaces.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2010.11a
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• pp.175-177
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• 2010

본 논문에서는 MPEG 의 3차원 비디오 시스템의 표준 깊이정보 맵에 대한 효율적인 부호화를 위하여 전처리 방법을 제안한다. 현재 3차원 비디오 부호화(3DVC)에 대한 표준화가 진행 중에 있지만 아직 깊이정보 맵의 부호화 방법에 대한 표준이 확정되지 않은 상태이다. 제안하는 기법에서는 우선, 입력된 깊이정보 맵에 대하여 원래의 히스토그램 분포를 가우시안 혼합모델(GMM)기반의 EM 군집화 기법에 의한 방법으로 분리 후, 분리된 히스토그램을 기반으로 깊이정보 맵을 여러 개의 영상으로 분리한다. 그 후 분리된 각각의 영상을 배경과 객체에 따라 다른 조건의 mean shift filter로 필터링한다. 결과적으로 영상내의 각 영역 경계는 최대한 살리면서 영역내의 화소 값에 대해서는 평균 연산을 취하여 부호화시 효율을 극대화 하고자 하였다. 실험조건은 $1024{\times}768$ 영상에 대해서 50 프레임으로 H.264/AVC base 프로파일로 부호화를 진행하였다. 최종 실험결과 bit rate는 대략 23% ~ 26% 정도 감소하고 부호화 시간도 다소 줄어드는 것을 확인 할 수 있었다.

• Journal of Korea Multimedia Society
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• v.18 no.5
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• pp.610-619
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• 2015

*In this paper, we propose a real-time face tracking system using an adaptive face detector and a tracking algorithm. An image is divided into the regions of background and face candidate by a real-time updated skin color identifying system in order to accurately detect facial features. The facial characteristics are extracted using the five types of simple Haar-like features. The extracted features are reinterpreted by Principal Component Analysis (PCA), and the interpreted principal components are processed by Support Vector Machine (SVM) that classifies into facial and non-facial areas. The movement of the face is traced by Kalman filter and Mean shift, which use the static information of the detected faces and the differences between previous and current frames. The proposed system identifies the initial skin color and updates it through a real-time color detecting system. A similar background color can be removed by updating the skin color. Also, the performance increases up to 20% when the background color is reduced in comparison to extracting features from the entire region. The increased detection rate and speed are acquired by the usage of Kalman filter and Mean shift.

• The Journal of Korea Robotics Society
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• v.4 no.4
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• pp.257-264
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• 2009

This paper proposes a novel face detection method that finds tiny faces located at a long range even with low-resolution input images captured by a mobile robot. The proposed approach can locate extremely small-sized face regions of $12{\times}12$ pixels. We solve a tiny face detection problem by organizing a system that consists of multiple detectors including a mean-shift color tracker, short- and long-rage face detectors, and an omega shape detector. The proposed method adopts the long-range face detector that is well trained enough to detect tiny faces at a long range, and limiting its operation to only within a search region that is automatically determined by the mean-shift color tracker and the omega shape detector. By focusing on limiting the face search region as much as possible, the proposed method can accurately detect tiny faces at a long distance even with a low-resolution image, and decrease false positives sharply. According to the experimental results on realistic databases, the performance of the proposed approach is at a sufficiently practical level for various robot applications such as face recognition of non-cooperative users, human-following, and gesture recognition for long-range interaction.

• Journal of the Institute of Convergence Signal Processing
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• v.14 no.1
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• pp.33-38
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• 2013

In this paper, we propose an algorithm to track multi-small targets in infrared image sequences in case of dissipation or creation of targets by using the background estimation filter, Kahnan filter and mean shift algorithm. We detect target candidates in a still image by subtracting an original image from an background estimation image, and we track multi-targets by using Kahnan filter and target selection. At last, we adjust specific position of targets by using mean shift algorithm In the experiments, we compare the performance of each background estimation filters, and verified that proposed algorithm exhibits better performance compared to classic methods.