• Archives of Pharmacal Research
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• 제27권7호
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• pp.806-810
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• 2004

Metformin is a biguanide antihyperglycemic agent often used for the treatment of non-insulin dependent diabetics (NIDDM). In this study, the pharmacokinetics and pharmacodynamics of metformin were investigated in Korean healthy volunteers during a fasting state for over 10 h. In order to evaluate the amount of glucose-lowering effect of metformin, the plasma concentrations of glucose were measured for a period of 10 h followed by the administration of metformin (oral 500 mg) or placebo. In addition, the concentration of metformin in blood samples was determined by HPLC assay for the drug. All volunteers were consumed with 12 g of white sugar 10 minutes after drug intake to maintain initial plasma glucose concentration. The time courses of the plasma concentration of metformin and the glucose-lowering effect were analyzed by nonlinear regression analysis. The estimated $C_{max}$, $T_{max}$, $CL_{t}$/F (apparent clearance), V/F(apparent volume of distribution), and half-life of metformin were 1.42$\{pm}$0.07 $\mu\textrm{g}$/mL, 2.59$\{pm}$0.18h, 66.12$\{pm}$4.6 L/h, 26.63 L, and 1.54 h respectively. Since a significant counterclock-wise hysteresis was found for the metformin concentration in the plasma-effect relationship, indirect response model was used to evaluate pharmacodynamic parameters for metformin. The mean concentration at half-maximum inhibition $IC_{50}$, $k_{in}$, $k_{out}$ were 2.26 $\mu\textrm{g}$/mL, 83.26 $H^{-1}$, and 0.68 $H^{-1}$, respectively. Therefore, the pharmacokinetic-pharmacodynamic model may be useful in the description for the relationship between plasma concentration of metformin and its glucose-lowering effect.

• 융합신호처리학회논문지
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• 제19권4호
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• pp.147-152
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• 2018

수중에서의 일시적인 신호는 복잡하고, 변화가 심하며, 비선형적이므로 신호의 패턴을 정확히 모델링하기 어렵다. 본 논문에서는 수중 신호 중 하나인 고래 소리를 선택하여 음성분석 기법에 많이 사용하는 Cepstral 분석에 의한 MFCC 추출법을 이용하여 분석하였고, MFCC와 $L_2$-norm 최소화 기법을 이용하여 고래소리를 재생하였다 실험 분석에 사용된 고래의 종류는 혹등고래(Humpback whale), 참고래(Right whale), 대왕고래(Blue whale), 귀신고래(Gray whale), 밍크고래(Minke whale) 등 5종으로서 과거 한반도 동해안에 출몰한 적이 있는 고래들이다. 원본 고래소리에서 MATLAB프로그래밍을 이용하여 20차 MFCC계수들을 추출한 후 이를 가중 $L_2$-norm 최소화를 이용한 MFCC역변환을 통해 재생한다. 최종적으로 가중치가 3~4의 값에서 고래소리 재생이 가장 적합함을 알 수 있었다.

• 한국융합학회논문지
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• 제10권1호
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• pp.7-12
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• 2019

신경회로망은 적합한 수학적 모델에 대한 가정 없이 데이터로부터 유용한 정보를 추출해서 예측에 필요한 입출력 관계를 정의할 수 있어서 주가 예측에 널리 사용되어 왔다. 본 논문에서는 신경회로망 모델을 사용하여 일별 KOrea composite Stock Price Index (KOSPI) 종가를 예측한다. 예측된 종가를 기반으로 KOSPI에 연동해 변동하는 Exchange Traded Funds (ETFs)의 거래를 위한 알파 매매를 제안한다. 본 논문에 제안된 방법으로 KOSPI 예측 신경회로망 모델들을 구현하고 예측 정확도를 평가한다. 구현된 신경회로망 모델(NN1)의 학습 오차(MAPE)는 0.427, 평가 오차는 0.627이다. 평가용 데이터를 사용해 알파 매매를 시뮬레이션하면 수익률은 7.16 ~ 15.29 %를 보인다. 이는 125 거래일 데이터로 거둔 수익률로 제안된 알파 매매가 효과적임을 보인다.

• Steel and Composite Structures
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• 제30권1호
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• pp.13-29
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• 2019

This work presents the buckling investigation of functionally graded plates resting on two parameter elastic foundations by using a new hyperbolic plate theory. The main advantage of this theory is that, in addition to including the shear deformation effect, the displacement field is modelled with only four unknowns and which is even less than the first order shear deformation theory (FSDT) and higher-order shear deformation theory (HSDT) by introducing undetermined integral terms, hence it is unnecessary to use shear correction factors. The governing equations are derived using Hamilton's principle and solved using Navier's steps. The validation of the proposed theoretical model is performed to demonstrate the efficacy of the model. The effects of various parameters like the Winkler and Pasternak modulus coefficients, inhomogeneity parameter, aspect ratio and thickness ratio on the behaviour of the functionally graded plates are studied. It can be concluded that the present theory is not only accurate but also simple in predicting the critical buckling loads of functionally graded plates on elastic foundation.

• Advances in concrete construction
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• 제11권5호
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• pp.357-365
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• 2021

High deformable concrete (HDC) elements have compressive strength rates equal to conventional concrete and have got a high compressive strain at about 20% to 50%. These types of concrete elements as prefabricated parts have an abundance of applications in the construction industry which is the most used in the construction of tunnels in squeezing grounds, tunnel passwords from fault zones or swelling soils as soft supports. HDC elements after reaching to compressive yield stress, in nonlinear behavior have hardening combined with increasing strain and compressive strength. The main aim of this laboratory and numerical research is to construct concrete elements with the above properties so the compressive stress-strain behavior of different concrete elements with four categories of mix designs have been discussed and finally one of them has been defined as HDC element mix design. Furthermore, two columns with and without implementing of HDC elements have been made and stress-strain curves of them have been investigated experimentally. An analysis model is presented for columns using finite element method adopted by ABAQUS. The results obtained from the ABAQUS finite element method are compared with experimental data. The main comparison is made for stress-strain curve. The stress-strain curves from the finite element method agree well with experimental results. The results show that the dimension of the HDC samples is significant in the stress-strain behavior. The use of the element greatly increases energy absorption and ductility.

• 한국인터넷방송통신학회논문지
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• 제21권3호
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• pp.7-14
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• 2021

자동화된 스마트 그리드의 등장은 이러한 문제에 대응을 위한 필수적인 장치가 되고 있으며 스마트 그리드 기반 사회로의 진전을 가져오고 있다. 스마트 그리드는 전기 공급 업체와 소비자 간의 양방향 통신을 가능하게 하는 새로운 패러다임이다. 스마트 그리드는 전력 그리드를 보다 안정적이고 신뢰할 수 있으며 효율적이고 안전하게 만들기 위한 엔지니어의 이니셔티브로 인해 등장했다. 스마트 그리드는 전력 소비자가 전력 사용에서 더 큰 역할을 할 수 있는 기회를 창출하고 전력을 현명하고 효율적으로 사용하도록 동기를 부여한다. 이에 본 연구에서는 기계 학습을 통한 전력 수요 관리에 중점을 둔다. 기계 학습을 사용한 수요 예측과 관련하여 현재 다양한 기계 학습 모델이 소개되어 적용되고 있는 데 이에 관한 체계적인 접근이 요구되고 있다. 특히 GP 학습 모델의 경우에 일반 소비 예측 및 데이터의 가시화와 관련해서 다른 학습 모델보다 장점이 있지만, 스마트 미터 데이터의 예측과 관련해서는 데이터 독립성에 강한 영향을 받는다.

• Structural Engineering and Mechanics
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• 제77권2호
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• pp.197-215
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• 2021

Under a severe environment of multiple hazards such as earthquakes and winds, the life-cycle performance of engineering structures may inevitably be deteriorated due to the fatigue effect caused by long-term exposure to wind loads, which would further increase the structural vulnerability to earthquakes. This paper presents a framework for evaluating the lifetime structural seismic performance under the effect of wind-induced fatigue considering different sources of uncertainties. The seismic behavior of a high-rise steel-concrete composite frame with buckling-restrained braces (FBRB) during its service life is systematically investigated using the proposed approach. Recorded field data for the wind hazard of Fuzhou, Fujian Province of China from Jan. 1, 1980 to Mar. 31, 2019 is collected, based on which the distribution of wind velocity is constructed by the Gumbel model after comparisons. The OpenSees platform is employed to establish the numerical model of the FBRB and conduct subsequent numerical computations. Allowed for the uncertainties caused by the wind generation and structural modeling, the final annual fatigue damage takes the average of 50 groups of simulations. The lifetime structural performance assessments, including static pushover analyses, nonlinear dynamic time history analyses and fragility analyses, are conducted on the time-dependent finite element (FE) models which are modified in lines with the material deterioration models. The results indicate that the structural performance tends to degrade over time under the effect of fatigue, while the influencing degree of fatigue varies with the duration time of fatigue process and seismic intensity. The impact of wind-induced fatigue on structural responses and fragilities are explicitly quantified and discussed in details.

• 응용통계연구
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• 제35권2호
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• pp.189-201
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• 2022

본 연구는 외부 요인을 모형에 강화시켜 암호화폐 수익률 예측력을 향상시키는 방법에 대해서 다루고 있다. 고려한 요인으로는 크게 나누어 금융 경제적 요인 및 심리적 요인을 고려하였다. 먼저 금융 경제적 요인을 반용하기 위해서 주성분 요인을 사용하여 수 많은 변수를 차원축소를 통해서 모형에 반영하였다. 또한 심리적 요인을 위해서는 뉴스 기사 데이터를 활용하여 산출해낸 감성지수를 활용하였다. 이러한 요인들은 충격반응함수 분석을 통해서 요인들의 의미와 영향력을 시각화하였다. 또한 전통적인 ARIMAX 뿐 만 아니라 랜덤포레스트 및 딥러닝 모형을 활용하여 비선형성을 반영하였다. 그 결과 요인 강화가 암호화폐 수익률 예측력을 향상시킴을 실증분석을 통해 밝혔으며 그 중에서 딥러닝 모형인 GRU가 가장 좋은 예측 성능을 보임을 관찰하였다.

• Nuclear Engineering and Technology
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• 제54권1호
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• pp.36-48
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• 2022

As a valid numerical method to obtain a high-resolution result of a flow field, computational fluid dynamics (CFD) have been widely used to study coolant flow and heat transfer characteristics in fuel rod bundles. However, the time-consuming, iterative calculation of Navier-Stokes equations makes CFD unsuitable for the scenarios that require efficient simulation such as sensitivity analysis and uncertainty quantification. To solve this problem, a reduced-order model (ROM) based on proper orthogonal decomposition (POD) and machine learning (ML) is proposed to simulate the flow field efficiently. Firstly, a validated CFD model to output the flow field data set of the rod bundle is established. Secondly, based on the POD method, the modes and corresponding coefficients of the flow field were extracted. Then, an deep feed-forward neural network, due to its efficiency in approximating arbitrary functions and its ability to handle high-dimensional and strong nonlinear problems, is selected to build a model that maps the non-linear relationship between the mode coefficients and the boundary conditions. A trained surrogate model for modes coefficients prediction is obtained after a certain number of training iterations. Finally, the flow field is reconstructed by combining the product of the POD basis and coefficients. Based on the test dataset, an evaluation of the ROM is carried out. The evaluation results show that the proposed POD-ROM accurately describe the flow status of the fluid field in rod bundles with high resolution in only a few milliseconds.

• Steel and Composite Structures
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• 제42권4호
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• pp.513-538
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• 2022

This paper reports on experiments addressing the buckling and collapse behavior of an innovative built-up cold-formed steel (CFS) columns. The built-up column consists of four individual CFS lipped channels, two of them placed back-to-back at the web using two self-drilling screw fasteners at specified spacing along the column length, while the other two channels were connected flange-to-flange using one self-drilling screw fastener at specified spacing along the column length. In total, 12 experimental tests are reported, covering a wide range of column lengths from stub to slender columns. The initial geometric imperfections and material properties were determined for all test specimens. The effect of screw spacing, load-versus axial shortening behaviour and buckling modes for different lengths and screw spacing were investigated. Nonlinear finite element (FE) models were also developed, which included material nonlinearities and initial geometric imperfections. The FE models were validated against the experimental results, both in terms of axial capacity and failure modes of built-up CFS columns. Furthermore, using the validated FE models, a parametric study was conducted which comprises 324 models to investigate the effect of screw fastener spacing, thicknesses and wide range of lengths on axial capacity of back-to-back and flange-to-flange built-up CFS channel sections. Using both the experimental and FE results, it is shown that design in accordance with the American Iron and Steel Institute (AISI) and Australia/New Zealand (AS/NZS) standards is slightly conservative by 6% on average, while determining the axial capacity of back-to-back and flange-to-flange built-up CFS channel sections.