• Journal of the Korean Institute of Intelligent Systems
• /
• v.11 no.8
• /
• pp.709-714
• /
• 2001

Recently, most vehicles has the Automatic transmission system as their transmission system. The automatic transmission system operates with fixed shift patterns. In the opposite of manual operation, it is easy and convenient for driving. Though these merit, the system can not evaluate the driver s intension because of usage of fixed shift pattern. To consider driver s intension, we must consider both the driving intensity of driver and the status of vehicle. In this paper, we developed flexible automatic transmission system by using the proposed moduled neural networks which can learn the status of th vehicle and driver s intensity. As a results, we compare the transmission system using fixed shift pattern and the proposed transmission system and show the good performance in the change of shift position.

• Journal of Drive and Control
• /
• v.14 no.4
• /
• pp.29-36
• /
• 2017

Electronic control technologies that have long been developed for passenger cars spread to construction equipment and agricultural vehicles because of its outstanding performance achieved by embedded software. Especially, system program of transmission control unit (TCU) plays a crucial role for the superb shift quality, driving performance and fuel efficiency, etc. Since the control algorithm is embedded in software that is rarely analyzed, development of such a TCU cannot be conducted by conventional reverse engineering. Transmission simulator is a kind of electronic device that simulates the electric signals including driver operation command and output of various sensors installed in transmission. Standalone TCU can be run in normal operation mode with the signals provided by transmission simulator. In this research, transmission simulator for the tracked vehicle TCU is developed for the analysis of shift control algorithm from the experiments with standalone TCU. It was confirmed that shift experimental data for the simulator setup conditions can be used for the analysis of control algorithms on proportional solenoid valves and shift map.

• Journal of the Korean Magnetic Resonance Society
• /
• v.1 no.2
• /
• pp.71-78
• /
• 1997

The magnetic anisotropy effects of peptide group in structured protein on proton chemical shift have been investigated using trialanine modeling. The structure dependent part of chemical shift of C${\alpha}$H of the second amino acid residue was assumed to come purely from the magnetic anisotropy effects of C=O and C-N bonds of peptide in the direct neighborhood and thus to be dependent on and $\psi$ angle of this dipeptide. A set of dipeptide models with different and $\psi$angles were generated and from these models the chemical shift values were calculated using known algorithm to emphasize the role of parameters used in the equation. Comparison of sets of different parameters resulted in an optimized parameters which could reproduce the statistical chemical shift values observed in proteins with respect ot the secondary conformation.

• Journal of information and communication convergence engineering
• /
• v.9 no.5
• /
• pp.556-561
• /
• 2011

This paper presents a new method for shift keying using the combination of scaling function and wavelet named scaling wavelet shift keying (SWSK). An algorithm for SWSK modulation is carried out where the scaling function and the wavelet are encoded to 1 and 0 in accordance with the binary input, respectively. Signal energy, correlation coefficient and error probability of SWSK are derived from error probability of frequency shift keying(FSK). The performance is analyzed in terms of error probability and it is simulated in accordance with the kind of the wavelet. Based on the results, we can conclude that the proposed scheme is superior to the performance of the conventional schemes.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2011.04a
• /
• pp.384-385
• /
• 2011

본 논문은 중요도 맵과 Mean Shift 알고리즘을 이용하여 모바일 폰 영상 내의 와인 라벨 검출 방법을 제안한다. Mean Shift 알고리즘은 비모수적 클러스터링 기술로 클러스터의 수에 대한 사전 지식이 없이도 클러스터링이 가능한 알고리즘인데 실행 시간이 많이 필요한 단점이 있다. 이러한 문제를 해결하기 위해서 입력 칼라 와인 영상에 Saliency Map을 먼저 적용하고 영상의 두드러진 영역을 찾는다. 다음으로 Mean Shift 알고리즘을 이용한 분할 결과에서 얻은 칼라 마스크를 따라 빈도가 가장 높은 칼라 영역을 찾고 와인 라벨 영역을 검출한다. 실험결과를 통하여 제안된 방법을 모바일 폰을 이용하여 획득된 다양한 와인 영상의 라벨 영역을 효율적으로 검출할 수 있음을 볼 수 있다.

• Journal of Broadcast Engineering
• /
• v.19 no.3
• /
• pp.329-341
• /
• 2014

Kernel-based mean-shift object tracking has gained more interests nowadays, with the aid of its feasibility of reliable real-time implementation of object tracking. This algorithm calculates the best mean-shift vector based on the color histogram similarity between target model and target candidate models, where the color histograms are usually produced after uniform color-space quantization for the implementation of real-time tracker. However, when the image of target model has a reduced contrast, such uniform quantization produces the histogram model having large values only for a few histogram bins, resulting in a reduced accuracy of similarity comparison. To solve this problem, a non-uniform quantization algorithm has been proposed, but it is hard to apply to real-time tracking applications due to its high complexity. Therefore, this paper proposes a fast non-uniform color-space quantization method using the histogram equalization, providing an adjusted histogram distribution such that the bins of target model histogram have as many meaningful values as possible. Using the proposed method, the number of bins involved in similarity comparison has been increased, resulting in an enhanced accuracy of the proposed mean-shift tracker. Simulations with various test videos demonstrate the proposed algorithm provides similar or better tracking results to the previous non-uniform quantization scheme with significantly reduced computation complexity.

• Journal of Digital Contents Society
• /
• v.16 no.1
• /
• pp.157-164
• /
• 2015

The existing input device is limited to keyboard and mouse. However, recently new type of input device has been developed in response to requests from users. To reflect this trend we propose the new type of input device that gives instruction as analyzing the hand motion of image without special device. After binarizing the skin color area using Cam-Shift method and tracking, it recognizes the hand motion by inputting the finger areas and the angles from the palm center point, which are separated through labeling, into four cardinal directions and counting them. In cases when specific background was not set and without gloves, the recognition rate remained approximately at 75 percent. However, when specific background was set and the person wore red gloves, the recognition rate increased to 90.2 percent due to reduction in noise.

• Journal of the Korea Institute of Military Science and Technology
• /
• v.24 no.3
• /
• pp.264-271
• /
• 2021

Raman spectroscopy is an equipment that is widely used for classifying chemicals in chemical defense operations. However, the classification performance of Raman spectrum may deteriorate due to dark current noise, background noise, spectral shift by vibration of equipment, spectral shift by pressure change, etc. In this paper, we compare the classification accuracy of various machine learning algorithms including k-nearest neighbor, decision tree, linear discriminant analysis, linear support vector machine, nonlinear support vector machine, and convolutional neural network under noisy and spectral shifted conditions. Experimental results show that convolutional neural network maintains a high classification accuracy of over 95 % despite noise and spectral shift. This implies that convolutional neural network can be an ideal classification algorithm in a real combat situation where there is a lot of noise and spectral shift.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.10 no.6
• /
• pp.617-625
• /
• 2017

In this paper, an effective reversible data embedding algorithm was proposed to embed secrete data into image. In the proposed algorithm, prediction image is generated by accurately predicting pixel values using local similarity existing in image, difference sequence is generated using the generated prediction image and original cover image, and then histogram shift technique is applied to create a stego-image with secrete data hidden. Applying the proposed algorithm, secrete data can be extracted from the stego-image and the original cover image can be restored without loss. Experimental results show that it is possible to embed more secrete data into cover image than APD algorithm by applying the proposed algorithm.

• Journal of Korea Society of Industrial Information Systems
• /
• v.12 no.5
• /
• pp.39-46
• /
• 2007

Research on human identification in ubiquitous environment has recently attracted a lot of attention. As one of those research, gait recognition is an efficient method of human identification using physical features of a walking person at a distance. In this paper, we present a human shape extraction and tracking for gait recognition using geodesic active contour models(GACMs) combined with mean shift algorithm The active contour models (ACMs) are very effective to deal with the non-rigid object because of its elastic property. However, they have the limitation that their performance is mainly dependent on the initial curve. To overcome this problem, we combine the mean shift algorithm with the traditional GACMs. The main idea is very simple. Before evolving using level set method, the initial curve in each frame is re-localized near the human region and is resized enough to include the targe region. This mechanism allows for reducing the number of iterations and for handling the large object motion. The proposed system is composed of human region detection and human shape tracking modules. In the human region detection module, the silhouette of a walking person is extracted by background subtraction and morphologic operation. Then human shape are correctly obtained by the GACMs with mean shift algorithm. In experimental results, the proposed method show that it is extracted and tracked efficiently accurate shape for gait recognition.