• 대한공간정보학회지
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• 제11권4호
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• pp.21-27
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• 2003

IKONOS 2호와 QuickBird 2호의 센서 모델로서 제공되는 RPC(rational polynomial coefficients) 모델은 물리적 센서 모델의 대체 모델로 다양한 센서에 적용 가능하다. 고해상도 위성들이 상용화되면서 각기 센서들의 복잡성과 보안성 문제로 인해 물리적 센서모델을 대체할 수 있는 센서 모델로서 RPC의 활용도가 높아지고 있다. 대표적인 상업용 고해상도 위성인 IKONOS 2호는 물리적 센서 모델을 공개하지 않고 각영상에 대한 RPC만을 제공하며 QuckBird 영상은 센서의 기하 정보와 함께 RPC를 제공한다. 이에 본 연구에서는 물리적 센서모델로부터 RPC를 추출하는 원천 기술을 확보하고 RPC의 물리적 센서모델에 대한대체 적합성을 평가해보고자 하였다. 이를 위해 공간해상도가 높은 항공사진과 국내 위성인 KOMPSAT 1호의 기하 모델로부터 분모식과 차수를 달리하는 RPC모델들을 추출하는 실험을 수행하였다. 최소제곱법을 통해 RPC 초기값을 구하고 Levenberg Marquardt 기법을 이용하여 반복 조정한 RPC를 물리적 센서 모델과 비교 평가하여 최적의 RPC를 결정하였다. 그 결과 항공사진은 분모식이 동일한 1차 RPC가 KOMPSAT 1호는 분모식이 상이한 3차 RPC가 가장 정확도가 높았으며 각 오차(RMSE)는 $2{\times}10^{-5}$ 화소 이하로 나타났다.

• 터널과지하공간
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• 제20권2호
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• pp.97-104
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• 2010

가속 크리프 거동을 보이는 재료의 파괴를 설명하기 위하여 재료 파괴식($\ddot{\Omega}=A{(\dot{\Omega})}^\alpha$, $\Omega$는 변위와 같은 측정가능한 양을 나타낸다)이 사용된다. 상수 A와 $\alpha$는 주어진 측정 자료를 곡선적합하여 얻는다. 본 연구에서는 재료 파괴식을 이용하여 터널의 파괴시간을 예측하였고, 재료 파괴식을 적용하기 위하여 4가지 곡선적합기법이 사용되었다. 4가지 곡선적합기법 중 로그속도-로그가속도기법, 로그시간-로그속도기법, 역속도법은 선형최소자승법을 이용하고 비선형최소자승기법은 Levenberg-Marquardt 알고리즘을 이용한다. 로그속도-로그가속도기법은 재료 파괴식을 대수형태로 만들어 해석을 하기 때문에 터널의 파괴시간 예측에 재료 파괴식을 적용하는 것이 타당한지에 대한 근거를 제시한다. 로그속도-로그가속도기법에 따른 자료의 상관계수가 0.84로 비교적 높게 나타났기 때문에 재료 파괴식을 터널의 파괴시간 예측에 적용하는 것이 타당하다고 판단된다. 실제 파괴시간과 4가지 곡선적합기법으로부터 얻은 예측 파괴시간을 비교한 결과 로그시간-로그속도기법이 가장 우수한 결과를 보여주는 것으로 나타났다.

• 지구물리와물리탐사
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• 제21권1호
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• pp.15-25
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• 2018

탄성파 역산은 유가스 집적이 가능한 구조의 탐지에 고해상도의 분해능을 가지는 반면, 인공송신원을 이용한 해양전자탐사 역산은 유가스의 직접적인 탐지가 가능하다. 이런 이종의 물리탐사자료를 함께 이용한 복합역산은 단일 역산의 불확실성을 줄일 수 있고, 각각의 탐사자료가 가지는 장점 또한 함께 이용할 수 있다. 이 연구에서는 암석물리모델을 이용하여 탄성파탐사자료와 전자탐사자료가 동시에 최적화 될 때의 저류층의 물성값을 추출할 수 있는 동시복합역산 알고리듬을 개발하였다. 상호구배(cross-gradient) 방법을 적용하여 구조적인 해상도를 향상시켰으며, 최대우도추정법을 이용한 상대 가중치를 적용하여 자료간의 균형을 조절하였다. 개발된 알고리듬을 단순한 고립 가스층 모델에 적용한 결과, 동시복합역산으로 고해상도의 저류층 물성 추출이 가능함을 확인하였다. 하지만 오일 저류층을 모사한 배사구조의 모델에서는 적용된 모델 가중 행렬에 따라 전혀 다른 결과를 획득할 수 있었다. 따라서, 기존의 알고리듬을 각각의 모델 변수에 적합한 모델 가중 행렬을 사용하도록 수정하여, 평활화 기법과 감쇠항 기법을 수포화율과 공극률에 각각 적용하였다. 개선된 알고리듬을 오일 저류층 모델에 다시 적용한 결과, 저류층의 공극률과 수포화율을 성공적으로 추출할 수 있었다. 개발한 복합역산 알고리듬을 이용하여 획득한 결과는 유가스전 저류층의 매장량 계산에 직접적인 정보로 사용될 수 있을 것이다.

• International Journal of Concrete Structures and Materials
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• 제7권3호
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• pp.225-238
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• 2013

In this study, a two-layer feed-forward neural network was constructed and applied to determine a mapping associating mix design and testing factors of cement-nano silica (NS)-rice husk ash ternary blended concrete samples with their performance in conductance to the water absorption properties. To generate data for the neural network model (NNM), a total of 174 field cores from 58 different mixes at three ages were tested in the laboratory for each of percentage, velocity and coefficient of water absorption and mix volumetric properties. The significant factors (six items) that affect the permeability properties of ternary blended concrete were identified by experimental studies which were: (1) percentage of cement; (2) content of rice husk ash; (3) percentage of 15 nm of $SiO_2$ particles; (4) content of NS particles with average size of 80 nm; (5) effect of curing medium and (6) curing time. The mentioned significant factors were then used to define the domain of a neural network which was trained based on the Levenberg-Marquardt back propagation algorithm using Matlab software. Excellent agreement was observed between simulation and laboratory data. It is believed that the novel developed NNM with three outputs will be a useful tool in the study of the permeability properties of ternary blended concrete and its maintenance.

• 대한조선학회논문집
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• 제52권1호
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• pp.88-95
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• 2015

For the frequency-domain spectral fatigue analysis, the probability density function of stress range needs to be estimated based on the stress spectrum only, which is a frequency domain representation of the response. The probability distribution of the stress range of the narrow-band spectrum is known to follow the Rayleigh distribution, however the PDF of wide-band spectrum is difficult to define with clarity due to the complicated fluctuation pattern of spectrum. In this paper, efforts have been made to figure out the links between the probability density function of stress range to the structural response of wide-band Gaussian random process. An artificial neural network scheme, known as one of the most powerful system identification methods, was used to identify the multivariate functional relationship between the idealized wide-band spectrums and resulting probability density functions. To achieve this, the spectrums were idealized as a superposition of two triangles with arbitrary location, height and width, targeting to comprise wide-band spectrum, and the probability density functions were represented by the linear combination of equally spaced Gaussian basis functions. To train the network under supervision, varieties of different wide-band spectrums were assumed and the converged probability density function of the stress range was derived using the rainflow counting method and all these data sets were fed into the three layer perceptron model. This nonlinear least square problem was solved using Levenberg-Marquardt algorithm with regularization term included. It was proven that the network trained using the given data set could reproduce the probability density function of arbitrary wide-band spectrum of two triangles with great success.

• 한국통신학회논문지
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• 제39C권9호
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• pp.820-827
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• 2014

One of the basic problems in Wireless Sensor Networks (WSNs) is the localization of the sensor nodes based on the known location of numerous anchor nodes. WSNs generally consist of a large number of sensor nodes and recording the location of each sensor nodes becomes a difficult task. On the other hand, based on the application environment, the nodes may be subject to mobility and their location changes with time. Therefore, a scheme that will autonomously estimate or calculate the position of the sensor nodes is desirable. This paper presents an intelligent localization scheme, which is an artificial neural network (ANN) based localization scheme used to estimate the position of the unknown nodes. In the proposed method, three anchors nodes are used. The mobile or deployed sensor nodes request a beacon from the anchor nodes and utilizes the received signal strength indicator (RSSI) of the beacons received. The RSSI values vary depending on the distance between the mobile and the anchor nodes. The three RSSI values are used as the input to the ANN in order to estimate the location of the sensor nodes. A feed-forward artificial neural network with back propagation method for training has been employed. An average Euclidian distance error of 0.70 m has been achieved using a ANN having 3 inputs, two hidden layers, and two outputs (x and y coordinates of the position).

• Structural Engineering and Mechanics
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• 제83권3호
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• pp.327-340
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• 2022

The Collapse Margin Ratio (CMR) is a notable index used for seismic assessment of the structures. As proposed by FEMA P695, a set of analyses including the Nonlinear Static Analysis (NSA), Incremental Dynamic Analysis (IDA), together with Fragility Analysis, which are typically time-taking and computationally unaffordable, need to be conducted, so that the CMR could be obtained. To address this issue and to achieve a quick and efficient method to estimate the CMR, the Artificial Neural Network (ANN), Response Surface Method (RSM), and Adaptive Neuro-Fuzzy Inference System (ANFIS) will be introduced in the current research. Accordingly, using the NSA results, an attempt was made to find a fast and efficient approach to derive the CMR. To this end, 5016 IDA analyses based on FEMA P695 methodology on 114 various Reinforced Concrete (RC) frames with 1 to 12 stories have been carried out. In this respect, five parameters have been used as the independent and desired inputs of the systems. On the other hand, the CMR is regarded as the output of the systems. Accordingly, a double hidden layer neural network with Levenberg-Marquardt training and learning algorithm was taken into account. Moreover, in the RSM approach, the quadratic system incorporating 20 parameters was implemented. Correspondingly, the Analysis of Variance (ANOVA) has been employed to discuss the results taken from the developed model. Additionally, the essential parameters and interactions are extracted, and input parameters are sorted according to their importance. Moreover, the ANFIS using Takagi-Sugeno fuzzy system was employed. Finally, all methods were compared, and the effective parameters and associated relationships were extracted. In contrast to the other approaches, the ANFIS provided the best efficiency and high accuracy with the minimum desired errors. Comparatively, it was obtained that the ANN method is more effective than the RSM and has a higher regression coefficient and lower statistical errors.

• Geomechanics and Engineering
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• 제27권5호
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• pp.527-535
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• 2021

The creep characteristics of rock is of great significance for the study of long-term stability of engineering, so it is necessary to carry out indoor creep test and creep model of rock. First of all, in different water-bearing state and different positive pressure conditions, the granite is graded loaded to conduct indoor shear creep test. Through the test, the shear creep characteristics of granite are obtained. According to the test results, the stress-strain isochronous curve is obtained, and then the long-term strength of granite under different conditions is determined. Then, the fractional-order calculus software element is introduced, and it is connected in series with the spring element and the nonlinear viscoplastic body considering the creep acceleration start time to form a nonlinear viscoplastic creep model with fewer elements and fewer parameters. Finally, based on the shear creep test data of granite, using the nonlinear curve fitting of Origin software and Levenberg-Marquardt optimization algorithm, the parameter fitting and comparative analysis of the nonlinear creep model are carried out. The results show that the test data and the model curve have a high degree of fitting, which further explains the rationality and applicability of the established nonlinear visco-elastoplastic creep model. The research in this paper can provide certain reference significance and reference value for the study of nonlinear creep model of rock in the future.

• 한국자기공명학회논문지
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• 제5권2호
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• pp.99-109
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• 2001

Authors investigated neuronal changes of local cellular metabolism in the cerebral lesions of Parkinsonian symptomatic side between before and after stereotactic neurosurgery by follow-up 1H magnetic resonance spectroscopy (MRS). Patients with Parkinson's disease (PD) (n = 15) and age-matched normal controls (n = 15) underwen MRS examinations using a stimulated echo acquisition mode (STEAM) pulse sequence that provided 2${\times}$2${\times}$2 ㎤ (8ml) volume of interest in the regions of substantia nigra, thalamus, and lentiform nucleus. Spectral parameters were 20 ms TE, 2000 ms TR, 128 averages,2500 Hz spectral width, and 2048 data points. Raw data were processed by the SAGE data analysis package (GE Medical Systems). Peak areas of N-acetylaspartate (NAA), creatine (Cr), choline-containing compounds (Cho), inositols (Ins), and the sum (Glx) of glutamate and GABA were calculated by means of fitting the spectrum to a summation of Lorentzian curves using Marquardt algorithm. After blindly processed, we evaluated neuronal alterations of observable metabolite ratios between before and after stereotactic neurosurgery using Pearson product-moment analysis (SPSS, Ver. 6.0). A significant reduction of NAA/Cho ratio was observed in the cerebral lesion in substantia nigra of PD patient related to the symptomatic side after neurosurgery (P : 0.03). In thalamus, NAA/Cho ratio was also significantly decreased in the cerebral lesion including the electrode-surgical region (P : 0.03). A significant reduction of NAA/Cho ratio in lentiform nucleus was not oberved, but tended toward significant reduction after neurosurgery (P = 0.08). In particular, remarkable lactate signal was noted from the surgical thalamic lesions of 6 among 8 patients and internal segments of globus pallidus of 6 among 7 patients, respectively. Significant metabolic alterations of NAA/Cho ratio might reflect functional changes of neuropathological processes in the lesion of substantia nigra, thalamus, and lentiform nucleus, and could be a valuable finding fur evaluation of Parkinson's disease after neurosurgery. Increase of lactate signals, being remarkable in surgical lesions, could be consistent with a common consequence of neurosurgical necrosis. Thus, IH MRS could be a useful modality to evaluate the diagnostic and prognostic implications fur Parkinsons disease after functional neurosurgery.