• 한국지능시스템학회논문지
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• 제4권3호
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• pp.32-48
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• 1994

This paper proposes the compensation algorithm of conventional shift pattern using fuzzy reasoning in automatic transmission vehicles. Recently, automatic transimssion vehicles are continually increasing because of theire ease to drive. Also users require the high performance which includes the smooth shift quality and shift scheduling that matches driver;s intentions. So the shift scheduling has been inproved significantly through the application of electronic control. But, in spite of this development, vehicles using conventional shift pattern are sometimes in discord with driver's intention on roads. In this paper, the paper, the proposed compensation algorithm makes a automatic transmission vehicle be able to select an optimal gear shifting time and position using fuzzy reasoning and make a vehicle driver feel confortable even when the vehicle runs on roads which is extremely changed. Therefore, a vehicle driver can expect to reduce the nimber of unnecessary gear shifting and expect the fuel efficiency high. To show usefulness of the proposed method, some simulation are made to compared with conventional gear shifting. Paper prosposes the compensation mehtod of conventional shift pattern using fuzzy reasoning for the purpose that a vehicle can select an optimal gerar shifting time and position in automatic vehicle. Though the conventional shift pattern has no pliability, vehicle driver can feel comfortable and can reduce the number of unnecessary gear shifting using the proposed method on variable road condition. Therefore, it can be expected the fuel efficiency.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2015년도 춘계학술대회
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• pp.579-582
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• 2015

Mean-Shift 알고리즘은 객체 모델과 객체 후보 영상에서 색상 히스토그램 분포의 유사도를 이용하여 객체를 추적하는 강인한 알고리즘이다. 그러나 색상정보를 이용한 Mean-Shift 알고리즘은 객체와 배경이 비슷한 색상 분포를 가질 경우에 추적에 실패할 수 있는 단점이 있다. 이러한 단점을 보완하기 위해 배경과 객체를 분리할 정보를 색상(hue)과 채도(saturation) 영상에서 각각 4비트의 bit-plane을 조합한 새로운 영상을 사용한 강인한 객체 추적 알고리즘을 구현한다.

• 한국정밀공학회지
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• 제27권3호
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• pp.118-126
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• 2010

The automated mechanical transmission(AMT) is composed of electronic control management(ECM) and automatic shift gear(ASG). The AMT has advantages which are high efficiency of manual transmissions(MT) and offer operation convenience similar to automatic transmissions(AT). However, it has defects that are the torque gap during gear shift transients and shift time is long. To reduce such defects, it is necessary practically to evaluate error and characteristics as developing simulation model before the control algorithm is applied. In this paper, models are composed of vehicle model and AMT shift control model. Particularly AMT shift control model consists of main clutch management model (MCM) and shift control management model(SCM). The developed models were verified by comparing the simulated and experimental results under the same operational conditions. It can also be used to evaluate shift algorithm.

• ETRI Journal
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• 제31권3호
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• pp.282-291
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• 2009

An online mean-shift object tracking algorithm, which consists of a learning stage and an estimation stage, is proposed in this work. The learning stage selects the features for tracking, and the estimation stage composes a likelihood image and applies the mean shift algorithm to it to track an object. The tracking performance depends on the quality of the likelihood image. We propose two schemes to generate and integrate likelihood images: one based on the discrete AdaBoost (DAB) and the other based on the real AdaBoost (RAB). The DAB scheme uses tuned feature values, whereas RAB estimates class probabilities, to select the features and generate the likelihood images. Experiment results show that the proposed algorithm provides more accurate and reliable tracking results than the conventional mean shift tracking algorithms.

• 한국통신학회논문지
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• 제39C권8호
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• pp.637-642
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• 2014

본 논문은 블록 LDPC(low density parity check) 부호 설계를 위한 순환 천이 값(shift index)을 탐색하는 효율적인 알고리즘을 제안한다. 여기에는 메시지-패싱(message-passing) 기반의 순환 주기(cycle) 탐색 알고리즘과 ACE(approximate cycle extrinsic message degree) 알고리즘이 결합되어 있다. LDPC 부호 성능에 영향을 미치는 요인들에 우선순위를 두어 효율적으로 순환 천이 값을 찾을 수 있도록 했다. 이 알고리즘을 통해 기존의 탐색 알고리즘 보다 훨씬 낮은 복잡도로 행렬 저장 공간을 절약하면서 좋은 성능의 패리티 검사 행렬(parity check matrix)을 만들 수 있다.

• 한국정보통신학회논문지
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• 제18권11호
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• pp.2733-2737
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• 2014

실시간 영상을 이용하여 움직임 검출을 하는데 사용하는 배경 차영상 기법에 의한 움직임 및 변화 영역 검출 방법과 움직임 히스토리에 의한 움직임 검출법, 광류에 의한 움직임 검출법, 움직임 추적을 위한 추적하려는 물체의 히스토그램의 역투영을 이용하면서 물체의 중심점을 추적하는 MeanShift와 물체의 중심, 크기, 방향을 함께 추적하는 CamShift, Kalman 필터에 의한 움직임 추적 알고리즘 등이 있다. 본 논문에서는 물체의 색상과 모양 정보를 이용한 움직임 검출 알고리즘을 구현하고 검증하였다.

• Journal of applied mathematics & informatics
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• 제39권5_6호
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• pp.859-882
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• 2021

In the proposed paper, a new algorithm based on Nonlinear Feedback Shift Register (NLFSR) and modified RC4A (Rivest Cipher 4A) cipher is introduced. NLFSR is used for image pixel scrambling while modified RC4A algorithm is used for pixel substitution. NLFSR used in this algorithm is of order 27 with maximum period 2^27-1 which was found using Field Programmable Gate Arrays (FPGA), a searching method. Modified RC4A algorithm is the modification of RC4A and is modified by introducing non-linear rotation operator in the Key Scheduling Algorithm (KSA) of RC4A cipher. Analysis of occlusion attack (up to 62.5% pixels), noise (salt and pepper, Poisson) attack and key sensitivity are performed to assess the concreteness of the proposed method. Also, some statistical and security analyses are evaluated on various images of different size to empirically assess the robustness of the proposed scheme.

• 한국항행학회논문지
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• 제23권1호
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• pp.77-83
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• 2019

본 논문에서는 컬러 히스토그램과 CNN 모델을 이용한 객체 추적 기법 알고리즘을 제안한다. CNN (convolutional neural network) 모델기반 객체 추적 알고리즘인 GOTURN (generic object tracking using regression network)의 정확도를 높이기 위해 컬러 히스토그램 기반 mean-shift 추적 알고리즘을 합성하였다. 두 알고리즘을 SVM (support vector machine)을 통해 분류하여 추적 정확도가 더 높은 알고리즘을 선택하도록 설계하였다. Mean-shift 추적 알고리즘은 객체 추적에 실패할 때 경계 박스가 큰 범위로 움직이는 경향이 있어 경계 박스의 이동거리에 제한을 두어 정확도를 향상시켰다. 또한 영상 평균 밝기, 히스토그램 유사도를 고려하여 두 알고리즘의 추적 시작 위치를 초기화하여 성능을 높였다. 결과적으로 기존 GOTURN 알고리즘보다 본 논문에서 제안한 알고리즘이 전체적으로 정확도가 1.6% 향상되었다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제8권8호
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• pp.2675-2690
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• 2014

We study the problem of optimal opportunistic spectrum access with unknown and heterogeneous channel dynamics in cognitive radio networks. There is neither statistic information about the licensed channels nor information exchange among secondary users in the respective systems. We formulate the problem of maximizing network throughput. To achieve the desired optimization, we propose a win-shift lose-stay algorithm based only on rewards. The key point of the algorithm is to make secondary users tend to shift to another channel after receiving rewards from the current channel. The optimality and the convergence of the proposed algorithm are proved. The simulation results show that for both heterogeneous and homogenous systems the proposed win-shift lose-stay algorithm has better performance in terms of throughput and fairness than an existing algorithm.

• 유공압시스템학회논문집
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• 제4권2호
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• pp.7-20
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• 2007

TCU is a shift controller far automatic transmission of which major functions are to determine the shift point and manage the shifting process based on the various input signals. As the recent digital control technologies advance, it plays a key-role to improve a transmission performance and its algorithm becomes more complicated. This paper describes the development of transmission simulator fur wheel loader that enables a TCU for normal stand-alone operation by the real-time emulation of TCU interface signals. It can be utilized for the analysis of shift control algorithm implemented in a commercial TCU as well as for the development of brand new TCU.