• Journal of the Korean Society for Precision Engineering
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• v.20 no.11
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• pp.117-126
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• 2003

GA(genetic algorithm) has a powerful searching ability and is comparatively easy to use and to apply as well. By that reason, GA is in the spotlight these days as an optimization skill for mechanical systems.$^1$However, GA has a low efficiency caused by a huge amount of repetitive computation and an inefficiency that GA meanders near the optimum. It also can be shown a phenomenon such as genetic drifting which converges to a wrong solution.$^{8}$ These defects are the reasons why GA is not widdy applied to real world problems. However, the low efficiency problem and the meandering problem of GA can be overcomed by introducing parallel computation$^{7}$ and gray code$^4$, respectively. Standard GA(SGA)$^{9}$ works fine on small to medium scale problems. However, SGA done not work well for large-scale problems. Large-scale problems with more than 500-bit of sere's have never been tested and published in papers. In the result of using the SGA, the powerful searching ability of SGA doesn't have no effect on optimizing the problem that has 96 design valuables and 1536 bits of gene's length. So it converges to a solution which is not considered as a global optimum. Therefore, this study proposes ExpGA(experience GA) which is a new genetic algorithm made by applying a new probability parameter called by the experience value. Furthermore, this study finds the solution throughout the whole field searching, with applying ExpGA which is a optimization technique for the structure having genetic drifting by the standard GA and not making a optimization close to the best fitted value. In addition to them, this study also makes a research about the possibility of GA as a optimization technique of large-scale design variable problems.

• Korean Chemical Engineering Research
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• v.55 no.5
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• pp.652-659
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• 2017

In the present research, in order to predict activity coefficient of inorganic ions in electrolyte solution of a petroleum system, we studied 13 components in the electrolyte solution, including $H_2O$, $CO_2$ (aq), $H^+$, $Na^+$, $Ba^{2+}$, $Ca^{2+}$, $Sr^{2+}$, $Mg^{2+}$, $SO_4$, $CO_3$, $OH^-$, $Cl^-$, and $HCO_3$. To predict the activity coefficient of the components of the petroleum system (a solid/liquid equilibrium system), activity coefficient model of Extended UNIQUAC was studied, along with its adjustable parameters optimized based on a genetic algorithm. The total calculated error associated with optimizing the adjustable parameters of Extended UNIQUAC model considering the 13 components under study at three temperature levels (298.15, 323.15, and 373.15 K) using the genetic algorithm is found to be 0.07.

• Journal of Korean Society for Geospatial Information Science
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• v.20 no.2
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• pp.23-29
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• 2012

Existing pedestrian and evacuation models generally seek to find locally optimal solutions for the shortest or the least time paths to exits from individual locations considering pedestrian's characteristics (eg. speed, direction, sex, age, weight and size). These models are not designed to produce globally optimal solutions that reduce the total evacuation time of the entire pedestrians in a building when all of them evacuate at the same time. In this study, we suggest a globally optimal model for indoor pedestrian evacuation to minimize the total evacuation time of occupants in a building considering different distributions of them. We used the genetic algorithm, one of meta-heuristic techniques because minimizing the total evacuation time can not be easily solved by polynomial expressions. We found near-optimal evacuation path and time by expressing varying pedestrians distributions using chromosomes and repeatedly filtering solutions. In order to express and experiment our suggested algorithm, we used CA(cellular automata)-based simulator and applied to different indoor distributions and presented the results.

• Journal of the Korea Society of Computer and Information
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• v.14 no.10
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• pp.159-166
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• 2009

A nature immunity system responds sensitively to an external invasion with various functions in a lot of bodies, besides it there is a function to remember information to have been currently infected. we propose a hybrid model similar to immune system which combine with the antibody which applied genetic algorithm as select antibody and the arbitrary abnormal system call sequence that applied negative selection of a nature immunity system. A proposed model uses an arbitrary abnormal system Kol sequence in order to reduce a positive defect and a negative defect. Data used to experiment are send mail data processed UNM (University of New Maxico). The negative defect that an experiment results proposal model judged system call more abnormal than the existing negative selection to normal system call appeared 0.55% low.

• The Transactions of the Korea Information Processing Society
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• v.7 no.7
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• pp.2116-2124
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• 2000

In this paper we propose combustion control of refuse incineration plant using fuzzy model and genetic algorithm. At first fuzzy modelling is performed to obtain fuzzy model of the refuse incineration plant and obtained fuzzy model predicts outputs of the plant when inputs are given. Fuzzy model ca be used to obtain control strategy, and train and enhance operators' skill by simulating the plant. Then genetic algorithms search and find out optimal control inputs over all possible solutions in respect to desired outputs and these are inserted to plant. In order to testify proposed control method, computer simulation was carried out. As a result, ISE of fuzzy model of refuse incineration plant is 0.015 and ITAE of control by proposed method, 352 which is better than that by manual operation.

• Proceedings of the Korea Water Resources Association Conference
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• 2005.05b
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• pp.679-683
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• 2005

The technique which connects Generalized Regression Neural Networks Model(GRNNM) with Genetic Algorithm (CA) is used to derive rating curve in the river basin. GRNNM architecture consists of 4 layers ; input, hidden, summation and output layer. GA method is applied to estimate the optimal smoothing factor when GRNNM is trained. The derivation of rating curve using GRNNM is considered different kinds of hydraulic characteristics such as water stage, area and mean velocity and is applied two stage stations; Sunsan and Jungam. Furthermore, it is compared with conventional curve-fitting method. Through the training and validation performance, the results show that GRNNM is much superior as compared to the conventional curve-fitting method.

• Journal of Environmental Health Sciences
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• v.42 no.4
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• pp.280-292
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• 2016

Objectives: The objective of this study was to build COD regression models for the Han River and evaluate water quality. Methods: Water quality data sets for the dry season (as of January) during a four-year period (2012-2015) were collected from the database of the Han River automatic water quality monitoring stations. Statistical techniques, including combined genetic algorithm-multiple linear regression (GA-MLR) were used to build five-descriptor COD models. Multivariate statistical techniques such as principal component analysis (PCA) and cluster analysis (CA) are useful tools for extracting meaningful information. Results: The $r^2$ of the best COD models provided significant high values (> 0.8) between 2012 and 2015. Total organic carbon (TOC) was a surrogate indicator for COD (as COD/TOC) with high reliability ($r^2=0.63$ in 2012, $r^2=0.75$ for 2013, $r^2=0.79$ for 2014 and $r^2=0.85$ for 2015). The ratios of COD/TOC were calculated as 2.08 in 2012, 1.79 in 2013, 1.52 and 1.45 in 2015, indicating that biodegradability in the water body of the Han River was being sustained, thereby further improving water quality. The BOD/COD ratio supported these findings. The cluster analysis revealed higher annual levels of microorganisms and phosphorous at stations along the Hangang-Seoul and Hantangang areas. Nevertheless, the overall water quality over the last four years showed an observable trend toward continuous improvement. These findings also suggest that non-point pollution control strategies should consider the influence of upstreams and downstreams to protect water quality in the Han River. Conclusion: This data analysis procedure provided an efficient and comprehensive tool to interpret complex water quality data matrices. Results from a trend analysis provided much important information about sources and parameters for Han River water quality management.

• Journal of Environmental Health Sciences
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• v.41 no.2
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• pp.90-101
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• 2015

Objective: This study was conducted to develop a chemical oxygen demand (COD) regression model using water quality monitoring data (January, 2014) obtained from the Han River auto-monitoring stations. Methods: Surface water quality data at 198 sampling stations along the six major areas were assembled and analyzed to determine the spatial distribution and clustering of monitoring stations based on 18 WQPs and regression modeling using selected parameters. Statistical techniques, including combined genetic algorithm-multiple linear regression (GA-MLR), cluster analysis (CA) and principal component analysis (PCA) were used to build a COD model using water quality data. Results: A best GA-MLR model facilitated computing the WQPs for a 5-descriptor COD model with satisfactory statistical results ($r^2=92.64$,$Q{^2}_{LOO}=91.45$,$Q{^2}_{Ext}=88.17$). This approach includes variable selection of the WQPs in order to find the most important factors affecting water quality. Additionally, ordination techniques like PCA and CA were used to classify monitoring stations. The biplot based on the first two principal components (PCs) of the PCA model identified three distinct groups of stations, but also differs with respect to the correlation with WQPs, which enables better interpretation of the water quality characteristics at particular stations as of January 2014. Conclusion: This data analysis procedure appears to provide an efficient means of modelling water quality by interpreting and defining its most essential variables, such as TOC and BOD. The water parameters selected in a COD model as most important in contributing to environmental health and water pollution can be utilized for the application of water quality management strategies. At present, the river is under threat of anthropogenic disturbances during festival periods, especially at upstream areas.