• Smart Media Journal
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• v.9 no.3
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• pp.31-40
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• 2020

Currently, black-box-based machine learning algorithms are used to analyze big data in manufacturing. This algorithm has the advantage of having high analytical consistency, but has the disadvantage that it is difficult to interpret the analysis results. However, in the manufacturing industry, it is important to verify the basis of the results and the validity of deriving the analysis algorithms through analysis based on the manufacturing process principle. To overcome the limitation of explanatory power as a result of this machine learning algorithm, we propose a manufacturing big data analysis method using genetic programming. This algorithm is one of well-known evolutionary algorithms, which repeats evolutionary operators such as selection, crossover, mutation that mimic biological evolution to find the optimal solution. Then, the solution is expressed as a relationship between variables using mathematical symbols, and the solution with the highest explanatory power is finally selected. Through this, input and output variable relations are derived to formulate the results, so it is possible to interpret the intuitive manufacturing mechanism, and it is also possible to derive manufacturing principles that cannot be interpreted based on the relationship between variables represented by formulas. The proposed technique showed equal or superior performance as a result of comparing and analyzing performance with a typical machine learning algorithm. In the future, the possibility of using various manufacturing fields was verified through the technique.

• Journal of KIISE:Software and Applications
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• v.32 no.12
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• pp.1229-1237
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• 2005

Combining multiple classifiers has been actively exploited to improve classification performance. It is required to construct a pool of accurate and diverse base classifier for obtaining a good ensemble classifier. Conventionally ensemble learning techniques such as bagging and boosting have been used and the diversify of base classifiers for the training set has been estimated, but there are some limitations in classifying gene expression profiles since only a few training samples are available. This paper proposes an ensemble technique that analyzes the diversity of classification rules obtained by genetic programming. Genetic programming generates interpretable rules, and a sample is classified by combining the most diverse set of rules. We have applied the proposed method to cancer classification with gene expression profiles. Experiments on lymphoma cancer dataset, prostate cancer dataset and ovarian cancer dataset have illustrated the usefulness of the proposed method. h higher classification accuracy has been obtained with the proposed method than without considering diversity. It has been also confirmed that the diversity increases classification performance.

• Proceedings of the Korea Multimedia Society Conference
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• 2003.05b
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• pp.571-574
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• 2003

서열 정렬(sequence alignment)은 새로운 서열의 기능적, 구조적, 진화적 분석을 용이하게 하기 때문에 분자 생물학(molecular biology) 등에서 널리 사용된다. 지금까지 서열 정렬 알고리즘들에 대한 연구는 활발히 진행되어 왔다. 특히, 생물학 데이터양의 기하급수적인 증가와 전체 유전체 서열의 분석이 이루어진 종(species)들이 증가하면서, 보다 빠르고 정확하게 서열 정력을 수행하는 알고리즘이 필요하게 되었다. 본 논문에서는 동적 프로그래밍 방식에서부터 전체 유전체 서열 알고리즘에 이르기까지 서열 정렬 알고리즘의 연구 동향을 분석하고자 한다.

• Proceedings of the Korea Committee for Ocean Resources and Engineering Conference
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• 2003.10a
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• pp.163-166
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• 2003

Hull form designers have to design a ship with satisfying an economical, technical and environmental demand. When it is concerned by a technical and environmental demand, there will be a economical demand left to criticize optimization. In this case, there were used to be requirements which needs to meet only a best performance not concerning about input of Human resource. Life cycle's cost contains building cost and operation cost so that now we need to check Man Hour cost in building a ship. This research shows a correlation between hull form information, i.e. curvature, length, breadth and thickness of surface and Man Hour of the Shell plating manufacture with using Artificial Neural Network and Gentic Programming. This study will support to classify initial work, to have a high assumption possible through predicting a Man Hour and to provide a guide book to infer a building cost and a economical optimization hull form.

• Journal of the Society of Naval Architects of Korea
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• v.52 no.1
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• pp.1-7
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• 2015

The weight estimation of floating offshore structures such as FPSO, TLP, semi-Submersibles, Floating Offshore Wind Turbines etc. in the preliminary design, is one of direct measures of both construction cost and basic performance. Through both literature investigation and internet search, the weight data of floating offshore structures such as FPSO and TLP was collected. In this study, the weight estimation model with the genetic programming was suggested for FPSO. The weight estimation model using genetic programming was established by fixing the independent variables based on this data. In addition, the correlation analysis was performed to make up for the weak points of genetic programming; it is apt to induce over-fitting when the number of data is relatively smaller than that of independent variables. That is, by reducing the number of variables through the analysis of the correlation between the independent variables, the increasing effect in the number of weight data can be expected. The reliability of the developed weight estimation model was within 2% of error rate.

• Journal of Information Technology Application
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• v.3 no.3
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• pp.25-40
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• 2001

This paper deals with the problem of generating optimal polynomials using Genetic Programming(GP). The polynomial should approximate nonlinear response surfaces. Also, there should be a consideration regarding the size of the polynomial, It is not desirable if the polynomial is too large. To build small or medium size of polynomials that enable to model nonlinear response surfaces, we use the low order Tailor series in the function set of GP, and put the constrain on generating GP tree during the evolving process in order to prevent GP trees from becoming too large size of polynomials. Also, GAGPT(Group of Additive Genetic Programming Trees) is adopted to help achieving such purpose. Two examples are given to demonstrate our method.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.6
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• pp.677-682
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• 2006

Multi-domain design is difficult because such systems tend to be complex and include a mixtures of electrical, mechanical, hydraulic, and thermal components. To design an optimal system, unified and automated procedure with efficient search technique is required. This paper introduces design method for multi-domain system to obtain design solutions automatically, combining bond graph which is domain independent modeling tool and genetic programming which is well recognized as a powerful tool for open-ended search. The suggested design methodology has been applied for design of electric fitter, electric printer drive, and and pump system as a proof of concept for this approach.

• Journal of the Society of Naval Architects of Korea
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• v.42 no.5 s.143
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• pp.534-541
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• 2005

Until now, Korean shipyards have accumulated a great amount of data. But they do not have appropriate tools to utilize the data in practical works. Engineering data contains experts' experience and know-how in its own. It is very useful to extract knowledge or information from the accumulated existing data by using data mining technique This paper treats an evolutionary computation based on genetic programming (GP), which can be one of the components to realize data mining. The paper deals with linear models of GP for the regression or approximation problem when given learning samples are not sufficient. The linear model, which is a function of unknown parameters, is built through extracting all possible base functions from the standard GP tree by utilizing the symbolic processing algorithm. In addition to a standard linear model consisting of mathematic functions, one variant form of a linear model, which can be built using low order Taylor series and can be converted into the standard form of a polynomial, is considered in this paper. The suggested model can be utilized as a designing tool to predict design parameters with small accumulated data.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.22 no.12
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• pp.2819-2827
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• 1997

A dielectric waveguide structure using rectangular dielectic strip is analyzed directly in terms of the wave equation for quasi TE and quasi TE and quasiTM modes. This problem can be solved, with no approximation in the wave equation for the electric field $\vec{E}$ and magnetic field $\vec{H}$ inside and outside the dielectric rectangular waveguide matching the boundary conditions between interfaces. This leads to an eigenvalue problem where spurious modes do not appear. Dispersion characeristic examples are presented for square and rectangular waveguides. The formulation is general and can be used for compuarison with ogher methods such as FDM or FEM in various structures.

• Proceedings of the Korean Information Science Society Conference
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• 1999.10b
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• pp.3-5
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• 1999

본 논문에서는 진화 하드웨어에 기반한 자율 이동 로봇의 온라인 학습 기법에 관하여 소개하고자 한다. 진화 하드웨어는 실행 시간중에 하드웨어 회로 구성을 변경시킬 수 있는 새로운 개념의 FPGA이다. 제어 프로그램은 진화 하드웨어상에 트리 형식으로 구현되며 유전자 프로그래밍을 이용하여 학습하게 된다. 로봇의 환경 탐사가 진행됨에 따라 입력되는 센서 정보에 기반하여 제어 프로그램은 학습을 수행하게 되며, 노드 돌연변이의 유전 연산자를 이용하여 진화한다. 제어 프로그램의 게이트 회로는 학습의 진행에 맞추어 실행 시간중에 보다 적합도가 높은 방향으로 발전한다. 본 논문에서는 진화 하드에어를 이용한 학습 방식과 FPGA 구현 및 로봇 제어에의 응용에 대한 실험 결과 등을 설명할 것이다.