• International Journal of Control, Automation, and Systems
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• 제4권5호
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• pp.660-667
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• 2006

This paper presents a novel region-based snake method for vessel segmentation. According to geometric shape analysis of the vessel structure with different scale, an efficient statistical estimation of vessel branches is introduced into the energy objective function, which applies not only the vessel intensity information, but also geometric information of line-like structure in the image. The defined energy function is minimized using the gradient descent method and a new region-based speed function is obtained, which is more accurate to the vessel structure and not sensitive to the initial condition. The narrow band algorithm in the level set framework implements the proposed method, the solution of which is steady. The segmentation experiments are shown on several images. Compared with other geometric active contour models, the proposed method is more efficient and robust.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2005년도 추계학술대회 논문집
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• pp.901-905
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• 2005

It is very important to arrange landmarks when a mobile robot needs to measure its own location. So, it has been discussed often how to arrange landmarks in the optimal way until now. We, there, chose the RFID (Radio frequency Identification) tags as landmarks which can be observed by a mobile robot, and demonstrated the possibility of the optimal arrangement of them. For this work first, we defined the optimization problem and its parameters for the arrangement of tags. Second, we proposed the algorithm which can be applied to the optimization problem. Finally we could obtain closely optimal and practical arrangement with the minimum number of landmarks which satisfied the necessary condition by experimentation.

• 한국항공우주학회지
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• 제41권6호
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• pp.440-447
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• 2013

본 연구에서는 능동적인 블레이드 제어기법인 고조파제어(Higher Harmonic Control, HHC) 기법을 적용하여 로터의 허브 진동하중을 억제하기 위한 최적 제어입력을 탐색하였다. 통합 공탄성 해석 프로그램인 CAMRAD II를 이용하여 HART II 로터를 모델링하고 다양한 HHC 입력 조건에 대하여 파라미터 연구를 수행하여 최대의 진동하중을 감소시키기 위한 제어입력을 찾고, HART II 시험에서 정한 최소 진동 조건에서의 허브 진동하중과 비교하였다. 파라미터 연구를 통하여 HART II 시험에서의 최소 진동 조건에서는 진동하중이 증가하는 것을 확인하였다. 허브 진동하중을 감소시키기 위한 최적의 제어입력은 3/rev의 가진주파수에서 찾을 수 있었고 0.8도의 진폭과 350도의 위상각을 갖는 제어입력을 이용하는 경우에 기준조건대비 약 45%의 허브 진동하중 감소효과를 얻었다. HHC 기법을 이용하는 경우의 로터의 파워 감소는 5% 미만으로 나타나고, 성능이 향상되는 경우에는 대부분 진동이 증가하는 경향을 보였다.

• 전자공학회논문지B
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• 제31B권7호
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• pp.170-178
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• 1994

The nearest neighbor classification rule is widely used because it is not only simple but the error rate is asymptotically less than twice Bayes theoretical minimum error. But the method basically use the whole training patterns as the reference vectors. so that both storage and classification time increase as the number of training patterns increases. LVQ(Learning Vector Quantization) resolved this problem by training the reference vectors instead of just storing the whole training patterns. But it is a heuristic algorithm which has no theoretic background there is no terminating condition and it requires a lot of iterations to get to meaningful result. This paper is to propose a new training method of the reference vectors. which minimize the given error function. The nearest neighbor network,the network version of the nearest neighbor classification rule is proposed. The network is funtionally identical to the nearest neighbor classification rule is proposed. The network is funtionally identical to the nearest neighbor classification rule and the reference vectors are represented by the weights between the nodes. The network is trained to minimize the error function with respect to the weights by the steepest descent method. The learning algorithm is derived and it is shown that the proposed method can adjust more reference vectors than LVQ in each iteration. Experiment showed that the proposed method requires less iterations and the error rate is smaller than that of LVQ2.

• Journal of Microbiology and Biotechnology
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• 제20권4호
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• pp.835-843
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• 2010

The objectives of this study were to investigate the effect of fermentation medium on the hydroxyl radical scavenging activity of exopolysaccharides from Inonotus obliquus by response surface methodology (RSM). A two-level fractional factorial design was used to evaluate the effect of different components of the medium. Corn flour, peptone, and $KH_2PO_4$ were important factors significantly affecting hydroxyl radical scavenging activity. These selected variables were subsequently optimized using path of steepest ascent (descent), a central composite design, and response surface analysis. The optimal medium composition was (% w/v): corn flour 5.30, peptone 0.32, $KH_2PO_4$ 0.26, $MgSO_4$ 0.02, and $CaCl_2$ 0.01. Under the optimal condition, the hydroxyl radical scavenging rate (49.4%) was much higher than that using either basal fermentation medium (10.2%) and single variable optimization of fermentation medium (35.5%). The main monosaccharides components of the RSM optimized polysaccharides are rhamnose, arabinose, xylose, mannose, glucose, and galactose with molar proportion at 1.45%, 3.63%, 2.17%, 15.94%, 50.00%, and 26.81%.

• 한국추진공학회:학술대회논문집
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• 한국추진공학회 2011년도 제37회 추계학술대회논문집
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• pp.135-138
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• 2011

한국형 달착륙선 개발을 위한 기초연구로 달착륙선 지상시험용 추진시스템의 개발을 진행 중이다. 착륙선의 하강을 위한 추력기는 200 N 급으로 설계유량 100 g/s, 연소실 압력 200 psi, 진공추력 220 N을 목표로 설계/제작 하였다. 연소시험을 위해 LM guide(Linear Motion Guide)를 이용한 추력시험장치를 꾸며 연소시험을 수행하였으며, 그 결과 연소실 압력 210 psi 일 때 유량은 96.1 g/s가 흘렀으며 그에 따른 추력은 약 160 N으로 측정되었다.

• 한국컴퓨터정보학회논문지
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• 제23권7호
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• pp.81-90
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• 2018

In this paper, reducing lense Vignetting effect and adaptive learning rate method are proposed to complement Scribner's neural network for nuc algorithm which is the effective algorithm in statistic SBNUC algorithm. Proposed reducing vignetting effect method is updated weight and bias each differently using different cost function. Proposed adaptive learning rate for updating weight and bias is using sobel edge detection method, which has good result for boundary condition of image. The ordinary statistic SBNUC algorithm has problem to compensate lense vignetting effect, because statistic algorithm is updated weight and bias by using gradient descent method, so it should not be effective for global weight problem same like, lense vignetting effect. We employ the proposed methods to Scribner's neural network method(NNM) and Torres's reducing ghosting correction for neural network nuc algorithm(improved NNM), and apply it to real-infrared detector image stream. The result of proposed algorithm shows that it has 10dB higher PSNR and 1.5 times faster convergence speed then the improved NNM Algorithm.

• 한국지능시스템학회논문지
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• 제14권6호
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• pp.752-758
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• 2004

본 논문에서는 고전적인 슬라이딩 모드 제어에서 단점으로 나타나는 채터링을 감소시키고 도달 시간을 줄이기 위한 적응 퍼지 슬라이딩 모드 제어기를 제안하였다. 전문가의 경험이나 직관에 주로 의존하는 제어 규칙의 획득을 쉽게 하기 위해 최대 경사법을 이용하여 슬라이딩 모드 조건이 만족되면서 채터링 현상을 줄이기 위한 제어 규칙의 결론부 파라미터를 수정하는 적응 알고리즘을 제안하였다. 제안된 알고리즘은 고전적인 가변 구조 제어 시스템의 장점인 외란, 매개변수 변화와 불확실성에 둔감한 성질을 그대로 간직하고 있다. 제안한 알고리즘의 유용성을 보이기 위해 도립 진자 시스템에 적용한 결과 채터링 감소와 빠른 응답 특성을 얻을 수 있다.

• Journal of the Korean Wood Science and Technology
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• 제38권1호
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• pp.43-48
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• 2010

톱밥과 왕겨로 제조된 혼합보드로 세라믹을 제조한 후 수지함침율 및 소성온도에 따른 세라믹의 표면온도를 조사하였다. 경과시간 초기에 표면온도가 급상승 하였으며 수지함침율 60~70%와 소성온도 $1200^{\circ}C$로 제조된 세라믹의 표면온도가 가장 높았다. 히터의 표면온도보다 혼합세라믹의 표면온도 하강속도가 느려 혼합세라믹의 열 보존력이 높음을 알 수 있었다.

• 한국소음진동공학회논문집
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• 제16권1호
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• pp.27-33
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• 2006

Fault diagnosis and condition monitoring for rotating machines are important for efficiency and accident prevention. The process of fault diagnosis is to extract the feature of signals and to classify each state. Conventionally, fault diagnosis has been developed by combining signal processing techniques for spectral analysis and pattern recognition, however these methods are not able to diagnose correctly for certain rotating machines and some faulty phenomena. In this paper, we add a minimum detection error algorithm to the previous method to reduce detection error rate. Vibration signals of the induction motor are measured and divided into subband signals. Each subband signal is processed to obtain the RMS, standard deviation and the statistic data for constructing the feature extraction vectors. We make a study of the fault diagnosis system that the feature extraction vectors are applied to K-means clustering algorithm and minimum detection error algorithm.