• Computers and Concrete
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• 제29권 5호
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• pp.279-283
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• 2022

The seismic analytic collapse fragility of soil nail wall structures with a shotcrete concrete covering is investigated in this paper. The finite element modeling process has been well described. The fragility function evaluates the link between ground motion intensities and the likelihood of reaching a specific level of damage. The soil nail wall has been subjected to incremental dynamic analysis (IDA) from medium to strong ground vibrations. The nonlinear dynamic analysis of the soil nail wall uses a set of 20 earthquake ground motions with varying PGAs. PGD is utilized as an intensity measure, the numerical findings demonstrate that the soil nailing wall reaction is particularly sensitive to earthquake intensity measure (IM).

• 경영과학
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• 제12권2호
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• pp.147-164
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• 1995

There have been two dominant paradigms in understanding and modeling an expert's decision-making behavior: output analysis and process-tracing. While the two paradigms are complementary, they have not been used yet in a combined manner. This study extends the previous research work in the two paradigms to inductive modeling research by 1) analyzing individual experts' decision strategies, 2) comparing performance of four popular inductive modeling methods, and 3) matching their performance against the type of decision strategy employed by experts.

• 제어로봇시스템학회논문지
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• 제21권12호
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• pp.1173-1177
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• 2015

This paper deals with estimating the acid concentration of pickling process using the Volterra inputs. To estimate the acid concentration, the whole pickling process is represented by the grey box model consists of the white box dealing with known system and the black box dealing with unknown system. Because there is a possibility of nonlinear term in the unknown system, the Volterra series are used to estimate the acid concentration. For the white box modeling, the acid tank solution level and concentration equations are used, and for the black box modeling, the acid concentration is estimated using the Volterra Least Mean Squares (LMS) algorithm and Least Squares (LS) algorithm. The LMS algorithm has the advantage of the simple structure and the low computation, and the LS algorithm has the advantage of lowest error. The simulation results compared to the measured data are included.

• International Journal of Automotive Technology
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• 제6권3호
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• pp.277-283
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• 2005

Formability simulation of automotive panels at early design phases can reduce product and tooling development time and cost. However, for the simulation to be effective in leading the design process, fast and reliable results should be achieved with limited design definition and minimum modeling effort. In this paper, nonlinear finite element analysis is used to develop an automated process for the formability simulation of automotive body panels at early design phases. Due to the limited design definition at early design phases, the automated simulation process is based on the plane strain analysis for selected number of typical sections along the panel. Therefore, an entire panel can be analyzed with few sections. The state of plane strain can be easily induced, during simulation through symmetry and applied boundary conditions that simplify the modeling process. To study the reliability and effectiveness of the developed simulation process, the analytical results are compared with measured results of production automotive body side panels. The comparison demonstrates that the developed simulation process is reliable and can be effective for analyzing sheet metal formability, in early vehicle development phases.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제2권1호
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• pp.53-58
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• 2002

In the construction of successful fuzzy models for nonlinear systems, the identification of an optimal fuzzy model system is an important and difficult problem. Traditionally, sGA(simple genetic algorithm) has been used to identify structures and parameters of fuzzy model because it has the ability to search the optimal solution somewhat globally. But SGA optimization process may be the reason of the premature local convergence when the appearance of the superior individual at the population evolution. Therefore, in this paper we propose a new method that can yield a successful fuzzy model using VmGA(virus messy genetic algorithms). The proposed method not only can be the countermeasure of premature convergence through the local information changed in population, but also has more effective and adaptive structure with respect to using changeable length string. In order to demonstrate the superiority and generality of the fuzzy modeling using VmGA, we finally applied the proposed fuzzy modeling methodof a complex nonlinear system.

• 한국전산구조공학회:학술대회논문집
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• 한국전산구조공학회 2006년도 정기 학술대회 논문집
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• pp.971-976
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• 2006

This paper presents a design technique of steel structures subjected to static and dynamic loadings using practical nonlinear inelastic analysis software. The beam-column approach using the stability functions and the plastic hinge concept enables the software to suitably predict second-order effects and inelastic behavior of beam-columns. For dynamic analysis. the incremental from of the equation of motion is solved by the use of a step-by-step numerical integration procedure in which the assumption of constant acceleration over a small time step is employed. The accuracy of the analysis program is validated using the results of ABAQUS program and experimental tests. A user-friendly graphic interface of the software is developed to facilitate the modeling process and result interpretation of the problem. A design example of large span bridge is presented to detail the direct design process using the practical advanced analysis software.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1997년도 추계학술대회 학술발표 논문집
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• pp.383-386
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• 1997

In this paper, We proposed a parallel structure of the Neural Network model to nonlinear complex system. Neural Network was used as basic model which has learning ability and high tolerence level. This paper, we used Neural Network which has BP(Error Back Propagation Algorithm) model. But it sometimes has difficulty to append characteristic of input data to nonlinear system. So that, I used HCM(hard c-Means) method of clustering technique to append property of input data. Clustering Algorithms are used extensively not only to organized categorize data, but are also useful for data compression and model construction. Gas furance, a sewage treatment process are used to evaluate the performance of the proposed model and then obtained higher accuracy than other previous medels.

• Transactions on Electrical and Electronic Materials
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• 제6권6호
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• pp.256-261
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• 2005

This paper presents that the modeling to predict the characteristics with respect to the performance of solenoid embedded inductors manufactured by LTCC process via the nonlinear regression model based on the quasi-Newton method. In order to reduce the runs, the design of experiments (DOE) was used to generate the design space. The nonlinear process models were constructed by the piecewise regression model based on the quasi-Newton method for estimating the model coefficient with the break point on the statistical confidence intervals. Those models were verified by the model accuracy checking based on the assumption statistically.

• 한국지능시스템학회논문지
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• 제12권6호
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• pp.577-582
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• 2002

Neural network can be trained to approximate an arbitrary nonlinear function of multivariate data like the mini-mill crown values in Automatic Shape Control. The trained weights of neural network can evaluate or generalize the process data outside the training vectors. Sometimes, the blind modeling of the process data is necessary to compare with the scattered analytical model of mini-mill process in isolated electro-mechanical forms. To come up with a viable model, we propose the blind neural-based range-division domain-clustering piecewise-linear modeling scheme. The basic ideas are: 1) dividing the range of target data, 2) clustering the corresponding input space vectors, 3)training the neural network with clustered prototypes to smooth out the convergence and 4) solving the resulting matrix equations with a pseudo-inverse to alleviate the ill-conditioning problem. The simulation results support the effectiveness of the proposed scheme and it opens a new way to the data analysis technique. By the comparison with the statistical regression, it is evident that the proposed scheme obtains better modeling error uniformity and reduces the magnitudes of errors considerably. Approximatly 10-fold better performance results.

• 정보처리학회논문지:컴퓨터 및 통신 시스템
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• 제13권1호
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• pp.1-9
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• 2024

고분자 분야의 개발 및 양산과정에는 제어가 안되는 많은 변수가 있으며, 화학적 조성, 구조, 가공 조건 등 작은 변화에도 물성편차가 크게 발생하기에 보편적인 환경을 가정한 기존의 선형적 모델링 기법으로는 현장 데이터 적용시 많은 오차가 발생한다. 이에 본 연구에서는 최근 산업용 구동부품의 플라스틱 채용경향에 맞추어 엔지니어링 플라스틱인 Polyacetal 수지의 내마모성 및 내굴곡성 강화 연구에 다변량 분석기법인 구조방정식과 가우시안 프로세스 회귀를 결합한 모델링 방식(GPR-SEM)을 제안하고, 비선형성을 가지는 물질 모델링에 활용 가능성을 고찰하고자 한다.