• 한국전산구조공학회논문집
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• 제18권3호
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• pp.219-226
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• 2005

본 연구에서는 휠체어의 경량화를 위해 기존의 강관으로 제작된 휠체어를 피로파괴 및 손상에 강하고 방진 특성이 우수하며 유지 및 보수가 용이한 복합재료 중공빔으로 구성된 복합재료 휠체어로 대체하기 위하여 복합재료 중공빔 이론과 유전자 알고리즘을 적용하여 최적화된 등가 강성을 가지는 복합재료 중공빔의 최적의 단면 치수를 제시하였다. 제시한 최적의 단면치수를 가지는 복합재료 중공빔으로 구성된 휠체어 전체 구조에 Tsai-Wu 파손이론을 이용해 과하중이 가해지는 경우에 대하여 구조해석을 수행한 결과, 휠체어의 파손 유무를 나타내는 Makimum Tsai-Wu Failure Criteria Index가 파손이 발생하는 1.00보다 현저히 낮은 $0.192\times10^{-3}$을 나타내고 있음을 알 수 있었다. 또한 기존의 강관을 동일한 강성을 가지는 복합재료 증공빔으로 대체하였을 경우 중공빔 중량을 최대 45%감소하는 효과를 얻을 수 있음을 확인할 수 있었다.

• 정보과학회 컴퓨팅의 실제 논문지
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• 제22권2호
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• pp.95-100
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• 2016

빅데이터, 클라우드 컴퓨팅 시대가 오면서 데이터 사용량이 점점 증가하고 있고 이러한 빅데이터를 신속히 처리하기 위한 시스템들이 개발되고 있다. 그 중 데이터를 저장하기 위한 시스템으로 분산 파일 시스템이 널리 사용되고 있다, 이러한 분산 파일 시스템 중에는 글러스터 파일 시스템(GlusterFS)이 있다. 또한 기술의 발달로 고성능 장비인 Nand flash SSD (Solid State Drive)의 가격이 하락함에 따라서 데이터센터로 도입이 증가되는 추세이다. 따라서 GlusterFS에서도 SSD를 도입하려고 하지만, GlusterFS는 하드디스크를 기반으로 설계되었기 때문에 SSD를 이용했을 시 구조적인 문제로 성능 저하가 발생하게 된다. 이러한 구조적인 문제점들에는 I/O-cache, Read-ahead, Write-behind Translator들이 있다. 랜덤 I/O에 장점이 있는 SSD에 맞지 않는 기능들을 제거함으로써, 4KB 랜덤 읽기의 경우 255%까지의 성능 향상 결과와, 64KB 랜덤 읽기의 경우 50%까지의 성능 향상 결과를 얻었다.

• Structural Engineering and Mechanics
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• 제75권3호
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• pp.295-309
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• 2020

The construction of Reinforced Concrete (R/C) buildings with unreinforced masonry infills is part of the traditional building practice in many countries with regions of high seismicity throughout the world. When these buildings are subjected to seismic motions the presence of masonry infills and especially their configuration can highly influence the seismic damage state. The capability to avoid configurations of masonry infills prone to seismic damage at the stage of initial architectural concept would be significantly definitive in the context of Performance-Based Earthquake Engineering. Along these lines, the present paper investigates the potential of instant prediction of the damage response of R/C buildings with various configurations of masonry infills utilizing Artificial Neural Networks (ANNs). To this end, Multilayer Feedforward Perceptron networks are utilized and the problem is formulated as pattern recognition problem. The ANNs' training data-set is created by means of Nonlinear Time History Analyses of 5 R/C buildings with a large number of different masonry infills' distributions, which are subjected to 65 earthquakes. The structural damage is expressed in terms of the Maximum Interstorey Drift Ratio. The most significant conclusion which is extracted is that the ANNs can reliably estimate the influence of masonry infills' configurations on the seismic damage level of R/C buildings incorporating their optimum design.

• 대한조선학회논문집
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• 제52권5호
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• pp.372-379
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• 2015

The blade root fillets which have strong influences on the performance of propellers in the both structural and hydrodynamic points of view, are mechanical parts for smooth connection surface with a blade and a hub. A few related researches (Sabol, 1983; Kennedy, 1997) have noted that 3T-T/3 double radius section design would be suitable for reducing Stress Concentration Factor(SCF) and increasing Cavitation Inception Speed(CIS). In this paper, it is confirmed that this compound cross-section design has come close to the optimum solution in the shape optimization standpoint so that it could protect the propeller blade under the frequent and various loading cases. On that basis, we suggest the definite and simple fillet design methodology that has the cross-section with nT-T/n compound radius and elliptic shape which could sustain the given derivatives information as well as the offsets at the boundary and all inner region of the fillet surface. In addition, the result of design is presented in form of IGES file format in order to connect with NC machine seamlessly.

• 한국구조물진단유지관리공학회 논문집
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• 제12권5호
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• pp.179-189
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• 2008

본 연구는 능동텐던을 이용 지진을 받는 구조물의 최적 능동제어 방법에 관한 수치해법 적용 및 프로그램 개발에 목적이 있다. 능동텐던 시스템에 의한 제어이론을 적용하기 위해서 Ricatti 폐회로 알고리즘을 이용하였으며, 시간지연 문제를 고려하였다. 최적제어의 정식화를 위해서 SUMT기법의 최적화에 의해 성능지수를 최소로 하는 최적 가중치행렬을 추정토록 하였다. 구조물에서의 능동텐던의 최적 위치 선정을 위해서 가제어지수에 의한 방법을 소개하였다. 수치 예를 통해, 제어기의 최적 위치선정을 고려한 능동최적제어가 지진하중을 받는 구조물의 성능제어에 우수한 효과를 나타내는 것으로 평가되었다.

• 한국기계가공학회지
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• 제13권6호
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• pp.37-45
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• 2014

Ball valves are used as a key element in the process industries. The industrial development of valves has increased steadily, but continued improvement requires high design reliability and long service life. Currently, the development of high performance valves is not easy because of the lack of relevant technology in Korea. Valves are being imported at a level of up to 58 percent of the domestic market, which represents a value of almost 7 million US dollars. Therefore, in this work, the improvement of the design and performance of industrial valves has been studied in an attempt to achieve valves that will have longer service life and better output during operation. The structural stability was evaluated using the ANSYS FSI (Fluid-Structural Interaction) module. Moreover, to obtain maximum product reliability, torque analysis simulation was performed to compare and experimental results. The simulation results were used to predict the change in torque by changes in shape, thereby reducing the time and cost of manufacturing a number of prototypes for experimental validation.

• Journal of Information Technology Applications and Management
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• 제30권6호
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• pp.91-111
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• 2023

This study aims to identify the influencing factors of intention to use cloud services using the SEM-ANN two-step approach. In previous studies of SEM-ANN, SEM presented R² and ANN presented MSE(mean squared error), so analysis performance could not be compared. In this study, R² and MSE were calculated and presented by SEM and ANN, respectively. Then, analysis performance was compared and feature importances were compared by sensitivity analysis. As a result, the ANN default model improved R² by 2.87 compared to the PLS model, showing a small Cohen's effect size. The ANN optimization model improved R² by 7.86 compared to the PLS model, showing a medium Cohen effect size. In normalized feature importances, the order of importances was the same for PLS and ANN. The contribution of this study, which links structural equation modeling to artificial intelligence, is that it verified the effect of improving the explanatory power of the research model while maintaining the order of importance of independent variables.

• 한국지진공학회논문집
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• 제10권6호
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• pp.19-28
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• 2006

기존의 유한요소모델개선기법들은 측정에 의한 모달 데이터와 해석적으로 계산된 시스템 행렬로 구성된 수학적인 목적함수를 사용하거나 업데이팅 변수에 관한 모달 특성의 미분함수를 사용하여야만 한다. 따라서 교량구조물과 같은 복잡한 구조물에의 적용이 어렵고 역해석에 있어 해의 안정성 문제가 발생할 수 있다. 또한 개선된 모델이 물리적인 의미를 지니지 못할 수도 있다. 본 논문에서는 유전자알고리즘과 Welder-Mead의 심플렉스기법을 사용한 하이브리드 최적화 유한요소모델개선기법을 제안하였다. 하이브리드 최적화 기법의 성능을 검증하기 위해 3개의 국부최소값과 1개의 전체최소값을 갖는 Goldstein-Price 함수를 사용하여 비선형문제에 대한 적용성을 검토하였다. 또한 최적화목적함수의 영향을 검토하기 위해 10개의 자유도를 갖는 스프링-질량 모델을 사용하여 변수연구를 수행하였다. 최종적으로 수치해석을 통해서 질량과 강성을 동시에 개선하기 위한 최적화 목적함수를 제시하고, 제안된 하이브리드 최적화 기법이 유한요소모델개선을 위해 매우 효과적인 방법임을 입증하였다.

• 대한전기학회논문지:시스템및제어부문D
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• 제55권6호
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• pp.264-273
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• 2006

In this paper, we propose a new architecture of Self-Organizing Fuzzy Polynomial Neural Networks (SOFPNN) by means of consecutive optimization and also discuss its comprehensive design methodology involving mechanisms of genetic optimization. The network is based on a structurally as well as parametrically optimized fuzzy polynomial neurons (FPNs) conducted with the aid of information granulation and genetic algorithms. In structurally identification of FPN, the design procedure applied in the construction of each layer of a SOFPNN deals with its structural optimization involving the selection of preferred nodes (or FPNs) with specific local characteristics and addresses specific aspects of parametric optimization. In addition, the fuzzy rules used in the networks exploit the notion of information granules defined over system's variables and formed through the process of information granulation. That is, we determine the initial location (apexes) of membership functions and initial values of polynomial function being used in the premised and consequence part of the fuzzy rules respectively. This granulation is realized with the aid of the hard c-menas clustering method (HCM). For the parametric identification, we obtained the effective model that the axes of MFs are identified by GA to reflect characteristic of given data. Especially, the genetically dynamic search method is introduced in the identification of parameter. It helps lead to rapidly optimal convergence over a limited region or a boundary condition. To evaluate the performance of the proposed model, the model is experimented with using two time series data(gas furnace process, nonlinear system data, and NOx process data).

• 전기학회논문지
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• 제60권2호
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• pp.398-406
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• 2011

In this paper, we introduce a new architecture of PSO-based Polynomial Neural Networks (PNN) and discuss its comprehensive design methodology. The conventional PNN is based on a extended Group Method of Data Handling (GMDH) method, and utilized the polynomial order (viz. linear, quadratic, and modified quadratic) as well as the number of node inputs fixed (selected in advance by designer) at Polynomial Neurons located in each layer through a growth process of the network. Moreover it does not guarantee that the conventional PNN generated through learning results in the optimal network architecture. The PSO-based PNN results in a structurally optimized structure and comes with a higher level of flexibility that the one encountered in the conventional PNN. The PSO-based design procedure being applied at each layer of PNN leads to the selection of preferred PNs with specific local characteristics (such as the number of input variables, input variables, and the order of the polynomial) available within the PNN. In the sequel, two general optimization mechanisms of the PSO-based PNN are explored: the structural optimization is realized via PSO whereas in case of the parametric optimization we proceed with a standard least square method-based learning. To evaluate the performance of the PSO-based PNN, the model is experimented with using Gas furnace process data, and pH neutralization process data. For the characteristic analysis of the given entire data with non-linearity and the construction of efficient model, the given entire system data is partitioned into two type such as Division I(Training dataset and Testing dataset) and Division II(Training dataset, Validation dataset, and Testing dataset). A comparative analysis shows that the proposed PSO-based PNN is model with higher accuracy as well as more superb predictive capability than other intelligent models presented previously.