유전알고리즘을 이용한 반도체식 가스센서 최적 필터 설계

공정식,Kong, Jung-Shik 한국금형공학회 2022 한국금형공학회지 Vol.16 No.1

This paper is about elimination the situation in which gas sensor data becomes inaccurate due to temperature control when a semiconductor gas sensor is driven. Recently, interest in semiconductor gas sensors is high because semiconductor sensors can be driven with small and low power. Although semiconductor-type gas sensors have various advantages, there is a problem that they must operate at high temperatures. First temperature control was configured to adjust the temperature value of the heater mounted on the gas sensor. At that time, in controlling the heater temperature, gas sensor data are fluctuated despite supplying same gas concentration according to the temperature controlled. To resolve this problem, gas and temperature are extracted as a data. And then, a relation function is constructed between gas and temperature data. At this time, it is included low pass filter to get the stable data. In this paper, we can find optimal gain and parameters between gas and temperature data by using genetic algorithm.

기호 코딩을 이용한 유전자 알고리즘 기반 퍼지 다항식 뉴럴네트워크의 설계

이인태(In-Tae Lee),오성권(Sung-Kwun Oh),최정내(Jeoung-Nae Choi) 대한전기학회 2006 정보 및 제어 심포지엄 논문집 Vol.2006 No.1

In this paper, we discuss optimal design of Fuzzy Polynomial Neural Networks by means of Genetic Algorithms(GAs) using symbolic coding for non-linear data. One of the major subject of genetic algorithms is representation of chromosomes. The proposed model optimized by the means genetic algorithms which used symbolic code to represent chromosomes. The proposed gFPNN used a triangle and a Gaussian-like membership function in premise part of rules and design the consequent structure by constant and regression polynomial (linear, quadratic and modified quadratic) function between input and output variables. The performance of the proposed model is quantified through experimentation that exploits standard data already used in fuzzy modeling. These results reveal superiority of the proposed networks over the existing fuzzy and neural models.

Genetically optimized self-tuning Fuzzy-PI controller for HVDC system

왕중선(Zhongxian Wang),양정제(Juengje Yang),안태천(Taechon Ahn) 대한전기학회 2006 정보 및 제어 심포지엄 논문집 Vol.2006 No.1

In this paper, we study an approach to design a self-tuning Fuzzy-PI controller in HVDC(High Voltage Direct Current) system. In the rectifier of conversional HVDC system, turning on, turning off, triggering and protections of thyristors have lots of problems that can make the dynamic instability and cannot damp the dynamic disturbance efficiently. The above problems are solved by adapting Fuzzy-PI controller for the fire angle control of rectifier.[7] The performance of the Fuzzy-PI controller is sensitive to the variety of scaling factors. The design procedure dwells on the use of evolutionary computing(Genetic Algorithms, GAs). Then we can obtain the optimal scaling factors of the Fuzzy-PI controller by Genetic Algorithms. In order to improve Fuzzy-PI controller, we adopt FIS to tune the scaling factors of the Fuzzy-PI controller on line. A comparative study has been performed between Fuzzy-PI and self-tuning Fuzzy-PI controller. to prove the superiority of the proposed scheme.

Genetic algorithm based optimization of the process parameters for gas metal arc welding of AISI 904 L stainless steel

P. Sathiya,P. M. Ajith,R. Soundararajan 대한기계학회 2013 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.27 No.8

The present study is focused on welding of super austenitic stainless steel sheet using gas metal arc welding process with AISI 904 L super austenitic stainless steel with solid wire of 1.2 mm diameter. Based on the Box - Behnken design technique, the experiments are carried out. The input parameters (gas flow rate, voltage, travel speed and wire feed rate) ranges are selected based on the filler wire thickness and base material thickness and the corresponding output variables such as bead width (BW), bead height (BH) and depth of penetration (DP) are measured using optical microscopy. Based on the experimental data, the mathematical models are developed as per regression analysis using Design Expert 7.1 software. An attempt is made to minimize the bead width and bead height and maximize the depth of penetration using genetic algorithm.

천연가스 액화공정의 최적화와 분석

이태영(T. Y. LEE),서연미(Y. M. SEO),한경호(K. H. HAN),남기일(K. I. Nam) 대한기계학회 2013 대한기계학회 춘추학술대회 Vol.2013 No.12

Natural gas liquefaction is one of highly energy-intensive processes throughout the overall natural gas supply chain. The optimization of the liquefaction processes offers a lot of opportunities to reduce the operational and capital costs. Thus, many previous studies have tackled this kind of problems of optimizing natural gas liquefaction process conditions and have also suggested new liquefaction processes. In this study a single mixed refrigerant (SMR) process on the LNG FPSO is optimized using genetic algorithm. The objective function value (OFV) of the problem is the total energy consumption, and the variables including operational conditions (pressures, temperatures and flowrates) and the composition values of the mixed refrigerant used in this process are considered to solve this optimization problem. In addition, an analysis procedure fully using the merits of genetic algorithm is suggested to evaluate the optimization results.

GMA용접에서 유전자 알고리즘을 이용한 비드높이 예측 모델 개발에 관한 연구

손준식,김일수,장경천,이동길 한국공작기계학회 2006 한국공작기계학회 추계학술대회논문집 Vol.2006 No.-

Gas metal arc welding process has been chosen as a metal joining technique due to the wide range of usable applications, cheap consumables and easy handling. Three main indicators such as arc voltage, welding speed and welding current have a big influence in the quality welding. Since all these factors affect the quality of the welded joining parts, the effect of these parameters was investigated experimentally. In this paper, an attempt has been made to develop the predicted models (quadratic and cubic) for bead height using genetic algorithm. Performance of the developed models were proved to be compared to the regression equation.

Evaluating the Performance of Four Selections in Genetic Algorithms-Based Multispectral Pixel Clustering

Abdullah Al Rahat Kutubi,홍민기,김천 대한원격탐사학회 2018 大韓遠隔探査學會誌 Vol.34 No.1

This paper compares the four selections of performance used in the application of genetic algorithms (GAs) to automatically optimize multispectral pixel cluster for unsupervised classification from KOMPSAT-3 data, since the selection among three main types of operators including crossover and mutation is the driving force to determine the overall operations in the clustering GAs. Experimental results demonstrate that the tournament selection obtains a better performance than the other selections, especially for both the number of generation and the convergence rate. However, it is computationally more expensive than the elitism selection with the slowest convergence rate in the comparison, which has less probability of getting optimum cluster centers than the other selections. Both the ranked-based selection and the proportional roulette wheel selection show similar performance in the average Euclidean distance using the pixel clustering, even the ranked-based is computationally much more expensive than the proportional roulette. With respect to finding global optimum, the tournament selection has higher potential to reach the global optimum prior to the ranked-based selection which spends a lot of computational time in fitness smoothing. The tournament selection-based clustering GA is used to successfully classify the KOMPSAT-3 multispectral data achieving the sufficient thematic accuracy assessment (namely, the achieved Kappa coefficient value of 0.923).

유전자 알고리즘을 이용한 GMA 필릿 용접 비드형상 예측에 관한 연구

김영수,김일수,이지혜,정성명,이종표,박민호,Kim, Young-Su,Kim, Ill-Soo,Lee, Ji-Hye,Jung, Sung-Myoung,Lee, Jong-Pyo,Park, Min-Ho,Chand, Reenal Ritesh 대한용접접합학회 2012 대한용접·접합학회지 Vol.30 No.6

The GMA welding process involves large number of interdependent variables which may affect product quality, productivity and cost effectiveness. The relationships between process parameters for a fillet joint and bead geometry are complex because a number of process parameters are involved. To make the automated GMA welding, a method that predicts bead geometry and accomplishes the desired mechanical properties of the weldment should be developed. The developed method should also cover a wide range of material thicknesses and be applicable for all welding position. For the automatic welding system, the data must be available in the form of mathematical equations. In this study a new intelligent model with genetic algorithm has been proposed to investigate interrelationships between welding parameters and bead geometry for the automated GMA welding process. Through the developed model, the correlation between process parameters and bead geometry obtained from the actual experimental results, predicts that data did not show much of a difference, which means that it is quite suitable for the developed genetic algorithm. Progress to be able to control the process parameters in order to obtain the desired bead shape, as well as the systematic study of the genetic algorithm was developed on the basis of the data obtained through the experiments in this study can be applied. In addition, the developed genetic algorithm has the ability to predict the bead shape of the experimental results with satisfactory accuracy.

스테인레스강의 용접품질 제어

손성우,김일수,정재원,김지선,나현호 한국공작기계학회 2009 한국공작기계학회 춘계학술대회논문집 Vol.2009 No.-

The weld-bead geometry in stainless steel produced by GTAW(Gas Tungsten Arc Welding) plays an important role in determining the mechanical properties of the weld and its quality. However, due to various welding parameter of GTAW(Gas Tungsten Arc Welding) process, it was difficult that optimum welding condition conclusion satisfied welding quality. In this paper we describe an experimental method for optimum welding properties using Genetic algorithm. Genetic algorithm was applied to decide optimum welding parameters. Genetic algorithm was created random welding condition and selected probability concept for optimization of welding condition.

Evaluating the Performance of Four Selections in Genetic Algorithms-Based Multispectral Pixel Clustering

Kutubi, Abdullah Al Rahat,Hong, Min-Gee,Kim, Choen The Korean Society of Remote Sensing 2018 大韓遠隔探査學會誌 Vol.34 No.1

This paper compares the four selections of performance used in the application of genetic algorithms (GAs) to automatically optimize multispectral pixel cluster for unsupervised classification from KOMPSAT-3 data, since the selection among three main types of operators including crossover and mutation is the driving force to determine the overall operations in the clustering GAs. Experimental results demonstrate that the tournament selection obtains a better performance than the other selections, especially for both the number of generation and the convergence rate. However, it is computationally more expensive than the elitism selection with the slowest convergence rate in the comparison, which has less probability of getting optimum cluster centers than the other selections. Both the ranked-based selection and the proportional roulette wheel selection show similar performance in the average Euclidean distance using the pixel clustering, even the ranked-based is computationally much more expensive than the proportional roulette. With respect to finding global optimum, the tournament selection has higher potential to reach the global optimum prior to the ranked-based selection which spends a lot of computational time in fitness smoothing. The tournament selection-based clustering GA is used to successfully classify the KOMPSAT-3 multispectral data achieving the sufficient the matic accuracy assessment (namely, the achieved Kappa coefficient value of 0.923).