• Imaging Science in Dentistry
• /
• 제43권3호
• /
• pp.153-161
• /
• 2013

Purpose: To prevent low bone mineral density (BMD), that is, osteoporosis, in postmenopausal women, it is essential to diagnose osteoporosis more precisely. This study presented an automatic approach utilizing a histogram-based automatic clustering (HAC) algorithm with a support vector machine (SVM) to analyse dental panoramic radiographs (DPRs) and thus improve diagnostic accuracy by identifying postmenopausal women with low BMD or osteoporosis. Materials and Methods: We integrated our newly-proposed histogram-based automatic clustering (HAC) algorithm with our previously-designed computer-aided diagnosis system. The extracted moment-based features (mean, variance, skewness, and kurtosis) of the mandibular cortical width for the radial basis function (RBF) SVM classifier were employed. We also compared the diagnostic efficacy of the SVM model with the back propagation (BP) neural network model. In this study, DPRs and BMD measurements of 100 postmenopausal women patients (aged >50 years), with no previous record of osteoporosis, were randomly selected for inclusion. Results: The accuracy, sensitivity, and specificity of the BMD measurements using our HAC-SVM model to identify women with low BMD were 93.0% (88.0%-98.0%), 95.8% (91.9%-99.7%) and 86.6% (79.9%-93.3%), respectively, at the lumbar spine; and 89.0% (82.9%-95.1%), 96.0% (92.2%-99.8%) and 84.0% (76.8%-91.2%), respectively, at the femoral neck. Conclusion: Our experimental results predict that the proposed HAC-SVM model combination applied on DPRs could be useful to assist dentists in early diagnosis and help to reduce the morbidity and mortality associated with low BMD and osteoporosis.

• Journal of Electrical Engineering and Technology
• /
• 제12권6호
• /
• pp.2388-2398
• /
• 2017

In this study, we propose a robust face recognition system to pose variations based on automatic pose estimation. Radial basis function neural network is applied as one of the functional components of the overall face recognition system. The proposed system consists of preprocessing and recognition modules to provide a solution to pose variation and high-dimensional pattern recognition problems. In the preprocessing part, principal component analysis (PCA) and 2-dimensional 2-directional PCA ($(2D)^2$ PCA) are applied. These functional modules are useful in reducing dimensionality of the feature space. The proposed RBFNNs architecture consists of three functional modules such as condition, conclusion and inference phase realized in terms of fuzzy "if-then" rules. In the condition phase of fuzzy rules, the input space is partitioned with the use of fuzzy clustering realized by the Fuzzy C-Means (FCM) algorithm. In conclusion phase of rules, the connections (weights) are realized through four types of polynomials such as constant, linear, quadratic and modified quadratic. The coefficients of the RBFNNs model are obtained by fuzzy inference method constituting the inference phase of fuzzy rules. The essential design parameters (such as the number of nodes, and fuzzification coefficient) of the networks are optimized with the aid of Particle Swarm Optimization (PSO). Experimental results completed on standard face database -Honda/UCSD, Cambridge Head pose, and IC&CI databases demonstrate the effectiveness and efficiency of face recognition system compared with other studies.

• 전기학회논문지
• /
• 제64권6호
• /
• pp.922-934
• /
• 2015

In this paper, The classification between precipitation echo(PRE) and non-precipitation echo(N-PRE) (including ground echo and clear echo) is carried out from weather radar data using neuro-fuzzy algorithm. In order to classify between PRE and N-PRE, Input variables are built up through characteristic analysis of radar data. First, the event classifier as the first classification step is designed to classify precipitation event and non-precipitation event using input variables of RBFNNs such as DZ, DZ of Frequency(DZ_FR), SDZ, SDZ of Frequency(SDZ_FR), VGZ, VGZ of Frequency(VGZ_FR). After the event classification, in the precipitation event including non-precipitation echo, the non-precipitation echo is completely removed by the echo classifier of the second classifier step that is built as Type-2 FCM based RBFNNs. Also, parameters of classification system are acquired for effective performance using PSO(Particle Swarm Optimization). The performance results of the proposed echo classifier are compared with CZ. In the sequel, the proposed model architectures which use event classifier as well as the echo classifier of Interval Type-2 FCM based RBFNN show the superiority of output performance when compared with the conventional echo classifier based on RBFNN.

• 한국융합학회논문지
• /
• 제8권10호
• /
• pp.1-8
• /
• 2017

정확한 예측은 미래에 일어날 현상에 대해 효과적으로 준비 혹은 대처 할 수 있게 해준다. 특히, 기상 현상은 인간의 생활과 밀접한 연관이 있으며, 발생할 수 있는 기상 및 재난 예측을 통해 인명, 재산 등의 피해로부터 예방 할 수 있게 해준다. 해상에서 발생할 수 있는 재난 중 하나인 유류유출 사고에 대해 빠르고 효과적으로 대응하기 위해서는 유출유의 이동과 주변 해역의 기상을 정확하게 예측하는 것이 중요하다. 본 논문에서는 분류 및 회귀 예측과 관련된 연구에서 준수한 성능 및 예측 가능성을 보여준 기계학습 기법으로 서포트 벡터 머신, 가우시안 프로세스, 다층 퍼셉트론, 방사기저함수 네트워크의 총 4 개의 기계학습 기법을 선별하였다. 선별한 기계학습 기법을 이용하여 유류유출의 탐지와 바람, 강우량, 오존 등의 기상 데이터를 예측하는 연구들의 연구 방법과 결과 등을 설명하며 이를 활용한 기계학습 기반 유류유출 예측 모델의 적용 가능성을 제시한다.

• 한국전산유체공학회:학술대회논문집
• /
• 한국전산유체공학회 2010년 춘계학술대회논문집
• /
• pp.530-533
• /
• 2010

This paper presents numerical analysis and design optimization of various turbine blade cooling techniques with three-dimensional Reynolds-averaged Navier-Stokes(RANS) analysis. The fluid flow and heat transfer have been performed using ANSYS-CFX 11.0. A fan-shaped hole for film-cooling has been carried out to improve film-cooling effectiveness with the radial basis neural network method. The injection angle of hole, lateral expansion angle of hole and ratio of length-to-diameter of the hole are chosen as design variables and spatially averaged film-cooling effectiveness is considered as an objective function which is to be maximized. The impingement jet cooling has been performed to investigate heat transfer characteristic with geometry variables. Distance between jet nozzle exit and impingement plate, inclination of nozzle and aspect ratio of nozzle hole are considered as geometry variables. The area averaged Nusselt number is evaluated each geometry variables. A rotating rectangular channel with staggered array pin-fins has been investigated to increase heat transfer performance ad to decrease friction loss using KRG modeling. Two non-dimensional variables, the ratio of the eight diameter of the pin-fins and ratio of the spacing between the pin-fins to diameter of the pin-fins selected as design variables. A rotating rectangular channel with staggered dimples on opposite walls are formulated numerically to enhance heat transfer performance. The ratio of the dimple depth and dimple diameter are selected as geometry variables.

• 전기학회논문지
• /
• 제64권7호
• /
• pp.1114-1124
• /
• 2015

In this paper, precipitation / non-precipitation pattern classification of meteorological radar data is conducted by using neuro-fuzzy algorithm. Structure expression of meteorological radar data information is analyzed in order to effectively classify precipitation and non-precipitation. Also diverse input variables for designing pattern classifier could be considered by exploiting the quantitative as well as qualitative characteristic of meteorological radar data information and then each characteristic of input variables is analyzed. Preferred pattern classifier can be designed by essential input variables that give a decisive effect on output performance as well as model architecture. As the proposed model architecture, neuro-fuzzy algorithm is designed by using FCM-based radial basis function neural network(RBFNN). Two parts of classifiers such as instance classifier part and echo classifier part are designed and carried out serially in the entire system architecture. In the instance classifier part, the pattern classifier identifies between precipitation and non-precipitation data. In the echo classifier part, because precipitation data information identified by the instance classifier could partially involve non-precipitation data information, echo classifier is considered to classify between them. The performance of the proposed classifier is evaluated and analyzed when compared with existing QC method.

• International Journal of Fluid Machinery and Systems
• /
• 제9권4호
• /
• pp.370-381
• /
• 2016

As a single-channel pump is used for wastewater treatment, this particular pump type can prevent performance reduction or damage caused by foreign substances. However, the design methods for single-channel pumps are different and more difficult than those for general pumps. In this study, a design optimization method to improve the hydrodynamic performance of a single-channel pump impeller is implemented. Numerical analysis was carried out by solving three-dimensional steady-state incompressible Reynolds-averaged Navier-Stokes equations using the shear stress transport turbulence model. As a state-of-the-art impeller design method, two design variables related to controlling the internal cross-sectional flow area of a single-channel pump impeller were selected for optimization. Efficiency was used as the objective function and was numerically assessed at twelve design points selected by Latin hypercube sampling in the design space. An optimization process based on a radial basis neural network model was conducted systematically, and the performance of the optimum model was finally evaluated through an experimental test. Consequently, the optimum model showed improved performance compared with the base model, and the unstable flow components previously observed in the base model were suppressed remarkably well.

• 한국해양공학회지
• /
• 제35권2호
• /
• pp.141-149
• /
• 2021

The A60 class deck penetration piece is a fire-resistance apparatus installed on the deck compartment to protect lives and to prevent flame diffusion in the case of a fire accident in a ship or offshore plant. In this study, the sensitivity of the fire-resistance performance and approximation characteristics for the A60 class penetration piece was evaluated by conducting a transient heat-transfer analysis and fire test. The transient heat-transfer analysis was conducted to evaluate the fire-resistance design of the A60 class deck penetration piece, and the analysis results were verified via the fire test. The penetration-piece length, diameter, material type, and insulation density were used as the design factors (DFs), and the output responses were the weight, temperature, cost, and productivity. The quantitative effects of each DF on the output responses were evaluated using the design-of-experiments method. Additionally, an optimum design case was identified to minimize the weight of the A60 class deck penetration piece while satisfying the allowable limits of the output responses. According to the design-of-experiments results, various approximate models, e.g., a Kriging model, the response surface method, and a radial basis function-based neural network (RBFN), were generated. The design-of-experiments results were verified by the approximation results. It was concluded that among the approximate models, the RBFN was able to explore the design space of the A60 class deck penetration piece with the highest accuracy.

• 대한토목학회논문집
• /
• 제29권2B호
• /
• pp.111-120
• /
• 2009

교각주위의 국부세굴은 교량붕괴의 주원인 중 하나로 알려져 있다. 세굴심을 산정하는 방법에는 경험식에 의한 방법과 수치모형을 이용한 시뮬레이션이 있다. 하지만 경험식에 의한 방법은 공식이 적용될 수 있는 유사한 상황에서만 제한적으로 사용가능하며, 수치모형을 이용한 방법은 비용이 많이 든다는 단점을 가지고 있다. 그러므로 본 연구에서는 세굴심 예측을 위한 CSU 공식, 다중회귀분석, 다양한 인공신경망 모형의 유용성을 비교분석하였다. 또한 세굴심을 산정하는데 있어 넓은 범위의 오차를 발생시키는 인공신경망 모형의 단점을 보완하기 위하여 본 연구에서는 인공신경망 모형에 군집분석을 결합하여 오차를 감소시키고자 하였다. 세굴심 예측을 위해 CSU 공식, 다중회귀분석, 다양한 인공신경망 모형을 적용해 본 결과 역전파알고리즘을 이용하는 인공신경망 모형이 가장 높은 정확성을 보였으며, 인공신경망 모형에 군집분석을 적용한 세굴심 예측에서는 군집수가 3일 때 가장 높은 정확도를 보였다. 군집분석을 적용한 인공신경망 모형의 정확도는 다른 모형과 비교할 때 최고 42.73%가 향상된 결과를 보여 인공신경망 모형내의 군집분석의 적용이 인공신경망의 오차를 줄이는데 큰 역할을 할 수 있음을 알 수 있었다.

• 한국음향학회지
• /
• 제30권8호
• /
• pp.446-460
• /
• 2011

최근 항공 산업, 자동차 산업 등의 산업 현장에서 유도 전동기의 사용이 증대되고 있으며, 유도 전동기는 산업 현장에서 중요한 역할을 하고 있다. 따라서 유도 전동기의 고장으로 인한 피해를 최소화하기 위해 유도 전동기의 고장 검출 및 분류 시스템의 개발이 중요한 문제로 대두되고 있다. 이와 같은 이유로 본 논문에서는 유도 전동기의 고장을 조기에 검출하고 진단하기 위해 에너지 (short-time energy)와 특이치 분해와 이산 코사인 변환과 특이치 분해를 이용한 특징 벡터 추출 방법을 제안하였고, 이를 역 전파 신경 회로망과 다층 서포트 벡터 머신의 입력으로 이용하여 유도 전동기의 고장을 유형별로 분류하였다. 하지만 본 논문에서는 역 전파 신경 회로망과 다층 서포트 벡터 머신을 분류기로 사용함에 있어 역 전파 신경 회로망은 신경망을 구성하는 입력 뉴런 수, 은닉 뉴런 수, 학습 알고리즘에 의해 분류 성능이 달라지며, 다층 서포트 벡터 머신은 커널 함수로 사용한 가우시안 방사 기저 함수의 표준 편차 값에 따라 분류 성능이 달라지는 점을 고려하여 여러 가지 조건하에서의 실험을 통해 높은 분류 성능을 보이는 설정 방법을 제시하였다.