• 천문학회지
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• 제49권6호
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• pp.255-259
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• 2016

We estimate the fractal dimension of the ${\rho}$ Ophiuchus Molecular Cloud Complex, associated with star forming regions. We selected a cube (${\upsilon}$, l, b) database, obtained with J = 1-0 transition lines of $^{12}CO$ and $^{13}CO$ at a resolution of 22" using a multibeam receiver system on the 14-m telescope of the Five College Radio Astronomy Observatory. Using a code developed within IRAF, we identified slice-clouds with two threshold temperatures to estimate the fractal dimension. With threshold temperatures of 2.25 K ($3{\sigma}$) and 3.75 K ($5{\sigma}$), the fractal dimension of the target cloud is estimated to be D = 1.52-1.54, where $P{\propto}A^{D/2}$, which is larger than previous results. We suggest that the sampling rate (spatial resolution) of observed data must be an important parameter when estimating the fractal dimension, and that narrower or wider dispersion around an arbitrary fit line and the intercepts at NP = 100 should be checked whether they relate to firms noise level or characteristic structure of the target cloud. This issue could be investigated by analysing several high resolution databases with different quality (low or moderate sensitivity).

• 한국강구조학회 논문집
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• 제24권1호
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• pp.119-128
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• 2012

본 연구에서는 실험적 모드해석 기법을 이용하여 외부환경에 직접 노출되어 있는 실제 철도판형교의 full-scale 동적 테스트가 수행되었다. 충격해머 모드실험에 의해 얻어진 철도판형교의 모드 매개변수를 유한요소해석으로부터 구한 고유진동수와 모드형상과 비교, 분석하였다. 실험적 모드해석에 의해 측정된 실험 데이터와 해석적 진동분석에서 얻어지는 출력만의 데이터를 교량 부재의 기하학적 특성 및 재료적 특성을 다양하게 고려하여 모델보정 테크닉에 적용하였다. 철도판형교의 실험적 모드해석 결과를 검증하기 위한 유한요소모델이 모드인식 기법을 이용하여 보정되었다. 실험 데이터와 유한요소해석 기준모델의 모델보정과정의 결과와 함께 부재특성의 변화를 통하여 이루질 수 있는 손상평가에 대한 기초적 데이터베이스가 제공된다.

• 대한의용생체공학회:의공학회지
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• 제28권4호
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• pp.494-502
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• 2007

Ubiquitous location based services (u-LBS) will be interested to an important services. They can easily recognize object position at anytime, anywhere. At present, many researchers are making a study of the position recognition and tracking. This paper consists of postion recognition and user identification system. The position recognition is based on location under services (LBS) using a signal strength map, a database is previously made use of empirical measured received signal strength indicator (RSSI). The user identification system automatically controls instruments which is located in home. Moreover users are able to measures body signal freely. We implemented the multi-hop routing method using the Star-Mesh networks. Also, we use the sensor devices which are satisfied with the IEEE 802.15.4 specification. The used devices are the Nano-24 modules in Octacomm Co. Ltd. A RSSI is very important factor in position recognition analysis. It makes use of the way that decides position recognition and user identification in narrow indoor space. In experiments, we can analyze properties of the RSSI, draw the parameter about position recognition. The experimental result is that RSSI value is attenuated according to increasing distances. It also derives property of the radio frequency (RF) signal. Moreover, we express the monitoring program using the Microsoft C#. Finally, the proposed methods are expected to protect a sudden death and an accident in home.

• 한국정보과학회논문지:소프트웨어및응용
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• 제36권10호
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• pp.816-824
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• 2009

얼굴영상 공간에서 얼굴영상들은 조명이나 포즈에 의해 비선형적 분포를 갖는다. 이들을 선형모델에 기반을 둔 AAM으로 모델링 하는 것은 한계가 있다. 본 논문에서는 얼굴영상에 대한 몇 개의 군집이 주어졌다고 가정하고, 각 군집 별로 지역적인 AAM 모델을 구축하여 정합과정 중에 적합한 모델이 선택되도록 한다. 정합과정에서 발생하는 모델변경에 따른 모델간의 정합 인자 갱신의 문제는 인자 공간에서 모델간의 선형 관계를 미리 학습하여 해결한다. 심각한 정합 실패에 따른 잘못된 모델 선택을 줄이기 위해 점진적으로 모델변경이 이루어지도록 한다. 실험에서는 제안하는 방법을 Yale-B 조명얼굴 영상에 적용하여 모델을 생성하고 기존 방법과 정합 성능을 비교한다. 제안 방법은 심각한 그림자가 발생하는 강도 높은 조명얼굴 영상에서 성공적인 정합 결과를 보여주었다.

• 한국추진공학회:학술대회논문집
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• 한국추진공학회 2006년도 제27회 추계학술대회논문집
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• pp.281-284
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• 2006

PW206C 터보 축 엔진을 위해 신경회로망을 이용한 지능형 성능 진단 프로그램이 제안되었다. 이 엔진은 항공우주연구원에서 개발 중에 있는 틸트 로터 타입 스마트 무인기의 추진시스템으로 선정되었다. 스마트 무인기 추진시스템에서 계측되는 성능변수는 가스발생기 회전속도, 동력터빈 회전속도, 배기가스 온도, 토크 등 4개이다. 그러나 이러한 4개 계측변수로는 각 구성품의 손상 진단이 어려운 관계로 압축기 출구 압력 및 압축기 터빈 출구 온도를 포함한 6개의 계측변수를 진단에 이용하였다. 구성품 성능저하량을 판단하는 성능파라미터는 압축기, 압축기 터빈, 동력 터빈의 유량함수 및 효율이다. 신경망을 훈련하고 테스트하기 위한 데이터베이스는 가스터빈 성능모사 프로그램을 이용하여 구성하였다. 훈련된 신경망을 PW206C 터보 축 엔진의 진단에 적용한 결과 제안된 진단 알고리즘이 압축기 오염과 압축기 터빈의 침식과 같은 단일 손상을 탐지하는데 유용함을 확인하였다.

• 대한토목학회논문집
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• 제32권2C호
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• pp.49-60
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• 2012

본 연구에서는 국내 지반조건에 적합한 대심도 마찰무리말뚝의 주면하중전이 해석 및 주면 무리효율을 고찰하였으며, 여러 현장재하시험 자료와 3차원 유한요소해석 및 이론적인 방법을 통해 대심도 마찰말뚝의 f-w곡선을 제안하였다. 현장재하시험 사례와의 비교분석 결과, 제안된 해석방법은 기존 f-w곡선에 비해 대심도 마찰말뚝의 거동 및 변형 특성을 적절히 예측함을 알 수 있었다. 또한 마찰무리말뚝의 무리효과 및 무리효율을 정량적으로 평가하기 위하여 말뚝 배열, 간격, 지반조건 및 말뚝캡의 위치에 따른 수치해석을 수행하여 다양한 조건 별 마찰무리말뚝의 주면 무리효율을 제시하였다.

• Structural Engineering and Mechanics
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• 제57권4호
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• pp.657-680
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• 2016

Reinforced concrete (RC) deep beams are structural members that predominantly fail in shear. Therefore, determining the shear strength of these types of beams is very important. The strut-and-tie method is commonly used to design deep beams, and this method has been adopted in many building codes (ACI318-14, Eurocode 2-2004, CSA A23.3-2004). In this study, the efficiency of artificial neural networks (ANNs) in predicting the shear strength of RC deep beams is investigated as a different approach to the strut-and-tie method. An ANN model was developed using experimental data for 214 normal and high-strength concrete deep beams from an existing literature database. Seven different input parameters affecting the shear strength of the RC deep beams were selected to create the ANN structure. Each parameter was arranged as an input vector and a corresponding output vector that includes the shear strength of the RC deep beam. The ANN model was trained and tested using a multi-layered back-propagation method. The most convenient ANN algorithm was determined as trainGDX. Additionally, the results in the existing literature and the accuracy of the strut-and-tie model in ACI318-14 in predicting the shear strength of the RC deep beams were investigated using the same test data. The study shows that the ANN model provides acceptable predictions of the ultimate shear strength of RC deep beams (maximum $R^2{\approx}0.97$). Additionally, the ANN model is shown to provide more accurate predictions of the shear capacity than all the other computed methods in this study. The ACI318-14-STM method was very conservative, as expected. Moreover, the study shows that the proposed ANN model predicts the shear strengths of RC deep beams better than does the strut-and-tie model approaches.

• Steel and Composite Structures
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• 제17권3호
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• pp.215-236
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• 2014

The flexural behaviour of steel beams significantly affects the structural performance of the steel frame structures. In particular, the flexural overstrength (namely the ratio between the maximum bending moment and the plastic bending strength) that steel beams may experience is the key parameter affecting the seismic design of non-dissipative members in moment resisting frames. The aim of this study is to present a new formulation of flexural overstrength factor for steel beams by means of artificial neural network (NN). To achieve this purpose, a total of 141 experimental data samples from available literature have been collected in order to cover different cross-sectional typologies, namely I-H sections, rectangular and square hollow sections (RHS-SHS). Thus, two different data sets for I-H and RHS-SHS steel beams were formed. Nine critical prediction parameters were selected for the former while eight parameters were considered for the latter. These input variables used for the development of the prediction models are representative of the geometric properties of the sections, the mechanical properties of the material and the shear length of the steel beams. The prediction performance of the proposed NN model was also compared with the results obtained using an existing formulation derived from the gene expression modeling. The analysis of the results indicated that the proposed formulation provided a more reliable and accurate prediction capability of beam overstrength.

• Earthquakes and Structures
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• 제7권4호
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• pp.485-510
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• 2014

In performance-based seismic design procedures Peak Ground Acceleration (PGA) and pseudo-Spectral acceleration ($S_a$) are commonly used to predict the response of structures to earthquake. Recently, research has been carried out to evaluate the predictive capability of these standard Intensity Measures (IMs) with respect to different types of structures and Engineering Demand Parameter (EDP) commonly used to measure damage. Efforts have been also spent to propose alternative IMs that are able to improve the results of the response predictions. However, most of these IMs are not usually employed in probabilistic seismic demand analyses because of the lack of reliable Ground Motion Prediction Equations (GMPEs). In order to define seismic hazard and thus to calculate demand hazard curves it is essential, in fact, to establish a GMPE for the earthquake intensity. In the light of this need, new GMPEs are proposed here for the elastic input energy spectra, energy-based intensity measures that have been shown to be good predictors of both structural and non-structural damage for many types of structures. The proposed GMPEs are developed using mixed-effects models by empirical regressions on a large number of strong-motions selected from the NGA database. Parametric analyses are carried out to show the effect of some properties variation, such as fault mechanism, type of soil, earthquake magnitude and distance, on the considered IMs. Results of comparisons between the proposed GMPEs and other from the literature are finally shown.

• 산업경영시스템학회지
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• 제39권1호
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• pp.123-129
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• 2016

In the manufacturing industry fields, thousands of quality characteristics are measured in a day because the systems of process have been automated through the development of computer and improvement of techniques. Also, the process has been monitored in database in real time. Particularly, the data in the design step of the process have contributed to the product that customers have required through getting useful information from the data and reflecting them to the design of product. In this study, first, characteristics and variables affecting to them in the data of the design step of the process were analyzed by decision tree to find out the relation between explanatory and target variables. Second, the tolerance of continuous variables influencing on the target variable primarily was shown by the application of algorithm of decision tree, C4.5. Finally, the target variable, loss, was calculated by a loss function of Taguchi and analyzed. In this paper, the general method that the value of continuous explanatory variables has been used intactly not to be transformed to the discrete value and new method that the value of continuous explanatory variables was divided into 3 categories were compared. As a result, first, the tolerance obtained from the new method was more effective in decreasing the target variable, loss, than general method. In addition, the tolerance levels for the continuous explanatory variables to be chosen of the major variables were calculated. In further research, a systematic method using decision tree of data mining needs to be developed in order to categorize continuous variables under various scenarios of loss function.