Study of Hybrid Optimization Technique for Grain Optimum Design

Seok-Hwan Oh(오석환),Yong-Chan Kim,Seung-Won Cha,Tae-Seong Roh 한국항공우주학회 2017 International Journal of Aeronautical and Space Sc Vol.18 No.4

The propellant grain configuration is a design variable that determines the shape and performance of a solid rocket motor. Grain configuration variables have complicated effects on the motor performance; so the global optimization problem has to be solved in order to design the configuration variables. The grain performance has been analyzed by means of the grain burn-back and internal ballistic analysis, and the optimization technique searches for the configuration variables that satisfy the requirements. The deterministic and stochastic optimization techniques have been applied for the grain optimization, but the results are imperfect. In this study, the optimization design of the configuration variables has been performed using the hybrid optimization technique, which combines those two techniques. As a result, the hybrid optimization technique has proved to be efficient for the grain optimization design.

A multi-parameter optimization technique for prestressed concrete cable-stayed bridges considering prestress in girder

Qiong Gao,Meng-Gang Yang,Jian-Dong Qiao 국제구조공학회 2017 Structural Engineering and Mechanics, An Int'l Jou Vol.64 No.5

The traditional design procedure of a prestressed concrete (PC) cable-stayed bridge is complex and time-consuming. The designers have to repeatedly modify the configuration of the large number of design parameters to obtain a feasible design scheme which maybe not an economical design. In order to efficiently achieve an optimum design for PC cable-stayed bridges, a multi-parameter optimization technique is proposed. In this optimization technique, the number of prestressing tendons in girder is firstly set as one of design variables, as well as cable forces, cable areas and cross-section sizes of the girders and the towers. The stress and displacement constraints are simultaneously utilized to ensure the safety and serviceability of the structure. The target is to obtain the minimum cost design for a PC cable-stayed bridge. Finally, this optimization technique is carried out by a developed PC cable-stayed bridge optimization program involving the interaction of the parameterized automatically modeling program, the finite element structural analysis program and the optimization algorithm. A low-pylon PC cable-stayed bridge is selected as the example to test the proposed optimization technique. The optimum result verifies the capability and efficiency of the optimization technique, and the significance to optimize the number of prestressing tendons in the girder. The optimum design scheme obtained by the application can achieve a 24.03% reduction in cost, compared with the initial design.

Study of Hybrid Optimization Technique for Grain Optimum Design

오석환,김용찬,차승원,노태성 한국항공우주학회 2017 International Journal of Aeronautical and Space Sc Vol.18 No.4

The propellant grain configuration is a design variable that determines the shape and performance of a solid rocket motor. Grain configuration variables have complicated effects on the motor performance; so the global optimization problem has to be solved in order to design the configuration variables. The grain performance has been analyzed by means of the grain burnback and internal ballistic analysis, and the optimization technique searches for the configuration variables that satisfy the requirements. The deterministic and stochastic optimization techniques have been applied for the grain optimization, but the results are imperfect. In this study, the optimization design of the configuration variables has been performed using the hybrid optimization technique, which combines those two techniques. As a result, the hybrid optimization technique has proved to be efficient for the grain optimization design.

Study of Hybrid Optimization Technique for Grain Optimum Design

Oh, Seok-Hwan,Kim, Yong-Chan,Cha, Seung-Won,Roh, Tae-Seong The Korean Society for Aeronautical and Space Scie 2017 International Journal of Aeronautical and Space Sc Vol.18 No.4

The propellant grain configuration is a design variable that determines the shape and performance of a solid rocket motor. Grain configuration variables have complicated effects on the motor performance; so the global optimization problem has to be solved in order to design the configuration variables. The grain performance has been analyzed by means of the grain burn-back and internal ballistic analysis, and the optimization technique searches for the configuration variables that satisfy the requirements. The deterministic and stochastic optimization techniques have been applied for the grain optimization, but the results are imperfect. In this study, the optimization design of the configuration variables has been performed using the hybrid optimization technique, which combines those two techniques. As a result, the hybrid optimization technique has proved to be efficient for the grain optimization design.

Optimized Polynomial Neural Network Classifier Designed with the Aid of Space Search Simultaneous Tuning Strategy and Data Preprocessing Techniques

Wei Huang,Sung-Kwun Oh 대한전기학회 2017 Journal of Electrical Engineering & Technology Vol.12 No.2

There are generally three folds when developing neural network classifiers. They are as follows: 1) discriminant function; 2) lots of parameters in the design of classifier; and 3) high dimensional training data. Along with this viewpoint, we propose space search optimized polynomial neural network classifier (PNNC) with the aid of data preprocessing technique and simultaneous tuning strategy, which is a balance optimization strategy used in the design of PNNC when running space search optimization. Unlike the conventional probabilistic neural network classifier, the proposed neural network classifier adopts two type of polynomials for developing discriminant functions. The overall optimization of PNNC is realized with the aid of so-called structure optimization and parameter optimization with the use of simultaneous tuning strategy. Space search optimization algorithm is considered as a optimize vehicle to help the implement both structure and parameter optimization in the construction of PNNC. Furthermore, principal component analysis and linear discriminate analysis are selected as the data preprocessing techniques for PNNC. Experimental results show that the proposed neural network classifier obtains better performance in comparison with some other well-known classifiers in terms of accuracy classification rate.

Optimized Polynomial Neural Network Classifier Designed with the Aid of Space Search Simultaneous Tuning Strategy and Data Preprocessing Techniques

Huang, Wei,Oh, Sung-Kwun The Korean Institute of Electrical Engineers 2017 Journal of Electrical Engineering & Technology Vol.12 No.2

There are generally three folds when developing neural network classifiers. They are as follows: 1) discriminant function; 2) lots of parameters in the design of classifier; and 3) high dimensional training data. Along with this viewpoint, we propose space search optimized polynomial neural network classifier (PNNC) with the aid of data preprocessing technique and simultaneous tuning strategy, which is a balance optimization strategy used in the design of PNNC when running space search optimization. Unlike the conventional probabilistic neural network classifier, the proposed neural network classifier adopts two type of polynomials for developing discriminant functions. The overall optimization of PNNC is realized with the aid of so-called structure optimization and parameter optimization with the use of simultaneous tuning strategy. Space search optimization algorithm is considered as a optimize vehicle to help the implement both structure and parameter optimization in the construction of PNNC. Furthermore, principal component analysis and linear discriminate analysis are selected as the data preprocessing techniques for PNNC. Experimental results show that the proposed neural network classifier obtains better performance in comparison with some other well-known classifiers in terms of accuracy classification rate.

Review: Biological Optimization Techniques in Webpage Classification

Shashank Dixit,Dr. R. K. Gupta 보안공학연구지원센터 2015 International Journal of Database Theory and Appli Vol.8 No.1

With the explosive growth of the data stored in various forms, need for innovative and effective technologies to find and use information and knowledge from a large variety of data sources which is continually increasing. Web information contains a lot of noise. Web Mining is the application of data mining techniques to discover classification of web data. It focuses on techniques that could predict the data’s class while the user interacts with the web. The aim of this paper is to measure, propose and improve the use of advance web page classification techniques which is highly used in the advent of mining large web pages based data sets which allows data analysts to conduct more efficient execution of large scale web pages data searches. Thus in this paper researchers introduce an improved concept which may reduce the search space using classification techniques with optimization technique.

생성형 최적화 설계에 대한 군집화 기법의 적용 방안 연구 - 최적화 툴, 월라시를 활용한 도심형 고층 건축물군의 배치계획 도출을 중심으로 -

이우형 대한건축학회지회연합회 2024 대한건축학회연합논문집 Vol.26 No.1

본 연구는 최근 건축설계 분야에서 활용의 저변이 커지고 있는 생성 기반 최적화 설계가 가지는 문제점인 성능 중심의 평가로 인해 형태 기준이 간과됨과 방대한 수의 솔루션을 해석함에 대한 설계자의 어려움에 주목하였다. 이에 대한 대응으로 기존 최적화 과정에 형태 기준의 군집화 기법을 접목하여 그 문제점을 개선하고자 한다. 이를 위해 라이노 그라스하퍼 기반의 최적화 툴인 월라시를 활용하여 설정된 최적화 예제에 대한 최적화 연산을 수행한다. 이후 도출된 최적 솔루션군에 대하여 다양한 군집화 기법으로 도출된 군집 구성 및 과정적 특성을 분석하였다. 세부적으로 첫 단계로 설정된 예제에 대하여 도출된 최적 솔루션군을 대표적인 군집화 기법인 K-평균 군집화를 적용하여 각 목적별 형태 기준에 따른 군집화의 적정성과 특성을 확인하였다. 두 번째 단계로 K-평균 군집화와 3가지 계층적 군집화 기법의 연속된 군집화로 도출된 다양한 군집화 결과의 변화와 차이를 비교 분석하여 그 적용에 대한 합리성과 효율적 활용 방안을 도출한다. 이를 통해 생성형 디자인에 군집화 기법의 적용은 형태 기준 평가 및 군집화를 통해 방대한 솔루션군을 단순화하므로 솔루션의 구성에 대한 사용자 해석과 그 활용성을 높이고 이를 통해 건축설계가 요구하는 최적화의 완결성을 보완하게 한다. This study focused on the problems of generative-based optimization design, recently become increasingly popular in the field of architectural design, such as overlooking form due to performance-oriented evaluation and designers' difficulties in analyzing huge number of solutions. In response to this, the study aims to incorporate a form-based clustering technique into the existing optimization process. To achieve this, we use Wallacei, an optimization tool, to perform optimization on the set example. Afterwards, the cluster composition and process characteristics of the derived optimal solution group were analyzed using various clustering techniques. In detail, K-means clustering was applied to the optimal solution group derived for the example set as the first step to confirm the adequacy and characteristics of clustering according to the form criteria. Second, we perform comparison analysis on the changes and differences in various clustering results derived from K-means clustering and three hierarchical clustering to derive rationality and efficient utilization of its application. Through this, the application of the clustering technique to generative design simplifies the huge solutions through form-based evaluation and clustering to increase user interpretation of the solutions and its usability, thereby complementing the completeness of optimization process required in architectural design.

PSO 알고리즘을 이용한 퍼지 Extreme Learning Machine 최적화

노석범(Seok-Beom Roh),王繼紅(Jihong Wang),김용수(Yong-Soo Kim),안태천(Tae-Chon Ahn) 한국지능시스템학회 2016 한국지능시스템학회논문지 Vol.26 No.1

본 논문에서는 일반적인 신경회로망의 단점인 느린 학습속도를 획기적으로 개선한 네트워크인 Extreme Learning Machine과 전문가들의 언어적 정보들을 기술 할 수 있는 퍼지 이론을 접목한 퍼지 Extreme Learning Machine을 최적화하기 위하여 Particle Swarm Optimization 알고리즘을 이용하였다. 퍼지 Extreme Learning Machine의 활성화 함수를 일반적인 시그모이드 함수를 사용하지 않고, 퍼지 C-Means 클러스터링 알고리즘의 활성화 레벨 함수를 이용하였다. Particle Swarm Optimization 알고리즘과 같은 최적화 알고리즘을 통하여 퍼지 Extreme Learning Machine의 활성화 함수의 파라미터들을 최적화 한다. Particle Swarm Optimization과 같은 최적화 알고리즘을 통한 제안된 모델의 최적화 하고 최적화된 모델의 분류성능을 평가하기 위하여 다양한 머신 러닝 데이터 집합을 사용하여 평가한다. In this paper, optimization technique such as particle swarm optimization was used to optimize the parameters of fuzzy Extreme Learning Machine. While the learning speed of conventional neural networks is very slow, that of Extreme Learning Machine is very fast. Fuzzy Extreme Learning Machine is composed of the Extreme Learning Machine with very fast learning speed and fuzzy logic which can represent the linguistic information of the field experts. The general sigmoid function is used for the activation function of Extreme Learning Machine. However, the activation function of Fuzzy Extreme Learning Machine is the membership function which is defined in the procedure of fuzzy C-Means clustering algorithm. We optimize the parameters of the membership functions by using optimization technique such as Particle Swarm Optimization. In order to validate the classification capability of the proposed classifier, we make several experiments with the various machine learning datas.

경험적 상수와 최적화 기법을 활용한 솔레노이드 액츄에이터 설계

성백주(Baekju Sung),이은웅(Eunwoong Lee),이재규(Jaegyu Lee) 한국자동차공학회 2006 한국자동차공학회 춘 추계 학술대회 논문집 Vol.- No.-

For the optimal design of solenoid actuator which is used as key components in automobile and aircraft industry, general electromagnetic theory and empirical knowledge are simultaneously needed. Designer must perform the out-line design of item within the framework of macroscopic by basic familiarity with physical law and carry out the optimal design through the detail design of item based on the empirical knowledge. In this paper, we present the optimal design technique of low consumption type DC solenoid actuator using the governing equation for the solenoid actuator based on the electromagnetic theory and empirical coefficient and constrained optimization technique. Then we design the DC 12V, 0.5W solenoid actuator and present the result of design parameters.