• 한국추진공학회:학술대회논문집
• /
• 한국추진공학회 2011년도 제36회 춘계학술대회논문집
• /
• pp.397-400
• /
• 2011

소형 가스터빈 엔진에 장착 가능한 저압단 축류 압축기의 공력성능 및 구조적 안정성을 동시에 고려한 최적화 설계를 수행하였다. 근사모델을 구축하여 유전알고리즘을 이용하여 전역 최적화 해를 도출하였다. 최적 설계된 압축기의 동익단은 Hub쪽에서 날개의 부하가 커지되, Tip쪽에서 입사각이 0에 가깝게 설계되었다. 한편 동익의 형상은 허브쪽에서 사다리꼴 모양으로 수렴이 되어 구조적 안정성을 확보하도록 설계가 되었다. 최종적인 수치해석 결과 작동점에서 동익단의 효율은 87.6%이며 구조적 안정성을 나타내는 안전계수는 3이상을 확보하였다.

• Structural Engineering and Mechanics
• /
• 제70권5호
• /
• pp.639-647
• /
• 2019

The performance of precast concrete structures is greatly influenced by the behaviour of beam-to-column connections. A single connection may be required to transfer several loads simultaneously so each one of those loads must be considered in the design. A good connection combines practicality and economy, which requires an understanding of several factors; including strength, serviceability, erection and economics. This research work focuses on the performance aspect of a specific type of beam-to-column connection using partly hidden corbel in precast concrete structures. In this study, the results of experimental assessment of the proposed beam-to-column connection in precast concrete frames was used. The purpose of this research is to develop and apply the Extreme Learning Machine (ELM) for moment-rotation prediction of precast beam-to-column connections. The ELM results are compared with genetic programming (GP) and artificial neural network (ANN). The reliability of the computational models was accessed based on simulation results and using several statistical indicators.

• 산업융합연구
• /
• 제15권2호
• /
• pp.1-6
• /
• 2017

본 논문은 알고리즘을 활용한 최적화를 바탕으로 한 시스템 다이내믹스 피드백 모델을 통해 고용 시뮬레이션의 특징을 제시하는 목적을 가지고 있고 직위, 근무기간, 계급 등의 요소를 중심으로 적정한 고용 인원을 제시하는 3차원 논리적 판단구조를 제공한다. 호주 육군의 고용정책에 대해 보다 신축적인 고용시스템을 제시할 목적으로 시스템 다이내믹스 모델을 통해 국방부의 변화가 심한 정책에 대한 안정적 고용 적정선을 파악한다. 특히 생산성을 최대로 발휘할 수 있는 필요한 고용 패턴 및 외부 인력의 고용, 내부인력의 타 조직으로 이동 등 다양한 가능성을 분석한다.

• 전기전자학회논문지
• /
• 제24권4호
• /
• pp.1034-1038
• /
• 2020

레이더를 통한 표적식별을 효과적으로 처리하기 위해서는 표적에 대한 정확한 신호 정보가 필요하다. 그러나 이러한 표적 신호에는 잡음이 섞여 있는 경우가 일반적이며, 이 부분에 대한 연구가 지속적으로 이루어지고 있다. 특히 표적에 대한 이미지 처리, 표적신호처리, 표적식별 등이 그 예라 할 수 있겠다. 군사적 측면으로 볼 때 표적식별 분야가 중요하므로 본 논문에서는 트리 구조의 퍼지 뉴럴 네트워크를 이용하여 비행체 표적식별에 대한 연구를 수행하였다. 비행체에 대한 반사파 데이터를 활용하여 퍼지 뉴럴 네트워크를 학습시켜 모델에 대한 최적화를 수행하였고, 최적화된 모델에 표적에 대한 테스팅 데이터를 입력하여 표적식별에 대한 실험을 수행하여 그 결과를 통해 제안된 방법의 효용성을 검증하였다.

• International Journal of Computer Science & Network Security
• /
• 제21권1호
• /
• pp.97-106
• /
• 2021

Social networking platforms have become a smart way for people to interact and meet on internet. It provides a way to keep in touch with friends, families, colleagues, business partners, and many more. Among the various social networking sites, Twitter is one of the fastest-growing sites where users can read the news, share ideas, discuss issues etc. Due to its vast popularity, the accounts of legitimate users are vulnerable to the large number of threats. Spam and Malware are some of the most affecting threats found on Twitter. Therefore, in order to enjoy seamless services it is required to secure Twitter against malicious users by fixing them in advance. Various researches have used many Machine Learning (ML) based approaches to detect spammers on Twitter. This research aims to devise a secure system based on Hybrid Similarity Cosine and Soft Cosine measured in combination with Genetic Algorithm (GA) and Artificial Neural Network (ANN) to secure Twitter network against spammers. The similarity among tweets is determined using Cosine with Soft Cosine which has been applied on the Twitter dataset. GA has been utilized to enhance training with minimum training error by selecting the best suitable features according to the designed fitness function. The tweets have been classified as spammer and non-spammer based on ANN structure along with the voting rule. The True Positive Rate (TPR), False Positive Rate (FPR) and Classification Accuracy are considered as the evaluation parameter to evaluate the performance of system designed in this research. The simulation results reveals that our proposed model outperform the existing state-of-arts.

• 전력전자학회논문지
• /
• 제27권4호
• /
• pp.297-304
• /
• 2022

This study compares the performance of artificial intelligence (AI)-based maximum power point tracking (MPPT) methods under partial shading conditions in a photovoltaic generation system. Although many studies on AI-based MPPT have been conducted, few studies comparing the tracking performance of various AI-based global MPPT methods seem to exist in the literature. Therefore, this study compares four representative AI-based global MPPT methods including fuzzy logic control (FLC), particle swarm optimization (PSO), grey wolf optimization (GWO), and genetic algorithm (GA). Each method is theoretically analyzed in detail and compared through simulation studies with MATLAB/Simulink under the same conditions. Based on the results of performance comparison, PSO, GWO, and GA successfully tracked the global maximum power point. In particular, the tracking speed of GA was the fastest among the investigated methods under the given conditions.

• Genomics & Informatics
• /
• 제20권1호
• /
• pp.9.1-9.6
• /
• 2022

Mendelian randomization (MR) uses genetic variation as a natural experiment to investigate the causal effects of modifiable risk factors (exposures) on outcomes. Two-sample Mendelian randomization (2SMR) is widely used to measure causal effects between exposures and outcomes via genome-wide association studies. 2SMR can increase statistical power by utilizing summary statistics from large consortia such as the UK Biobank. However, the first-order term approximation of standard error is commonly used when applying 2SMR. This approximation can underestimate the variance of causal effects in MR, which can lead to an increased false-positive rate. An alternative is to use the second-order approximation of the standard error, which can considerably correct for the deviation of the first-order approximation. In this study, we simulated MR to show the degree to which the first-order approximation underestimates the variance. We show that depending on the specific situation, the first-order approximation can underestimate the variance almost by half when compared to the true variance, whereas the second-order approximation is robust and accurate.

• Communications for Statistical Applications and Methods
• /
• 제29권5호
• /
• pp.591-601
• /
• 2022

In gene set analysis with microarray expression data, a group of genes such as a gene regulatory pathway and a signaling pathway is often tested if there exists either differentially expressed (DE) or differentially co-expressed (DC) genes between two biological conditions. Recently, a statistical test based on covariance estimation have been proposed in order to identify DC genes. In particular, covariance regularization by hard thresholding indeed improved the power of the test when the proportion of DC genes within a biological pathway is relatively small. In this article, we compare covariance thresholding methods using four different regularization penalties such as lasso, hard, smoothly clipped absolute deviation (SCAD), and minimax concave plus (MCP) penalties. In our extensive simulation studies, we found that both SCAD and MCP thresholding methods can outperform the hard thresholding method when the proportion of DC genes is extremely small and the number of genes in a biological pathway is much greater than a sample size. We also applied four thresholding methods to 3 different microarray gene expression data sets related with mutant p53 transcriptional activity, and epithelium and stroma breast cancer to compare genetic pathways identified by each method.

• Structural Engineering and Mechanics
• /
• 제85권3호
• /
• pp.369-379
• /
• 2023

Reliability-Based Design Optimization (RBDO) is an appropriate framework for obtaining optimal designs by taking uncertainties into account. Large-scale problems with implicit limit state functions and problems with discrete design variables are two significant challenges to traditional RBDO methods. To overcome these challenges, this paper proposes a hybrid method to perform RBDO of structures that links Firefly Algorithm (FA) as an optimization tool to advanced (finite element) reliability methods. Furthermore, the Genetic Algorithm (GA) and the FA are compared based on the design cost (objective function) they achieve. In the proposed method, Weighted Simulation Method (WSM) is utilized to assess reliability constraints in the RBDO problems with explicit limit state functions. WSM is selected to reduce computational costs. To performing RBDO of structures with finite element modeling and implicit limit state functions, a First-Order Reliability Method (FORM) based on the Direct Differentiation Method (DDM) is utilized. Four numerical examples are considered to assess the effectiveness of the proposed method. The findings illustrate that the proposed RBDO method is applicable and efficient for RBDO problems with discrete and continuous design variables and finite element modeling.

• Advances in aircraft and spacecraft science
• /
• 제10권3호
• /
• pp.245-256
• /
• 2023

In order to improve aerodynamic performance of multi-stage axial flow turbines used in aircraft engines, a one-dimensional aerodynamic design and optimization framework is constructed. In the method, flow path is generated by solving mass continuation and energy conservation with loss computed by the Craig & Cox model; Also real gas properties has been taken into consideration. To obtain an optimal result, a multi-objective genetic algorithm is used to optimize the efficiencies and determine values of various design variables; Final design can be selected from obtained Pareto optimal solution sets. A three-stage axial turbine is used to verify the effectiveness of the developed optimization framework, and designs are checked by three-dimensional CFD simulation. Results show that the aerodynamic performance of the optimized turbine has been significantly improved at design point, with the total-to-total efficiency increased by 1.17% and the total-to-static efficiency increased by 1.48%. As for the off-design performance, the optimized one is improved at all working points except those at small mass flow.