• Proceedings of the Korea Inteligent Information System Society Conference
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• 2007.05a
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• pp.389-398
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• 2007

Case-based reasoning (CBR) is one of the most popular prediction techniques for medical diagnosis because it is easy to apply, has no possibility of overfitting, and provides a good explanation for the output. However, it has a critical limitation - its prediction performance is generally lower than other artificial intelligence techniques like artificial neural networks (ANNs). In order to obtain accurate results from CBR, effective retrieval and matching of useful prior cases for the problem is essential, but it is still a controversial issue to design a good matching and retrieval mechanism for CBR systems. In this study, we propose a novel approach to enhance the prediction performance of CBR. Our suggestion is the simultaneous optimization of feature weights, instance selection, and the number of neighbors that combine using genetic algorithms (GAs). Our model improves the prediction performance in three ways - (1) measuring similarity between cases more accurately by considering relative importance of each feature, (2) eliminating redundant or erroneous reference cases, and (3) combining several similar cases represent significant patterns. To validate the usefulness of our model, this study applied it to a real-world case for evaluating cytological features derived directly from a digital scan of breast fine needle aspirate (FNA) slides. Experimental results showed that the prediction accuracy of conventional CBR may be improved significantly by using our model. We also found that our proposed model outperformed all the other optimized models for CBR using GA.

• Journal of the Earthquake Engineering Society of Korea
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• v.12 no.6
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• pp.13-23
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• 2008

In this study, a finite element (FE) model updating method based on the hybrid genetic algorithm (HGA) is proposed to improve the grillage FE model for plate girder bridges. HGA consists of a genetic algorithm (GA) and direct search method (DS) based on a modification of Nelder & Mead's simplex optimization method (NMS). Fitness functions based on natural frequencies, mode shapes, and static deflections making use of the measurements and analytical results are also presented to apply in the proposed method. In addition, a multi-objective function has been formulated as a linear combination of fitness functions in order to simultaneously improve both stiffness and mass. The applicability of the proposed method to girder bridge structures has been verified through a numerical example on a two-span continuous grillage FE model, as well as through an experimental test on a simply supported plate girder skew bridge. In addition, the effect of measuring error is considered as random noise, and its effect is investigated by numerical simulation. Through numerical and experimental verification, it has been proven that the proposed method is feasible and effective for FE model updating on plate girder bridges.

• Journal of the Institute of Convergence Signal Processing
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• v.13 no.1
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• pp.12-18
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• 2012

Stereo correspondence is the central problem of stereo vision. In this paper, we propose a stereo matching scheme based on a genetic algorithm using a multi-resolution method and AD-Census. The proposed approach considers the matching environment as an optimization problem and finds the disparity by using a genetic algorithm And adaptive chronosome structure using edge pixels and crossover mechanism are employed in this technique. A cost function is composes of certain constraints whice are commonly used in stereo matching. AD-Census measure is applied to reduce disparity error. To increase the efficiency of process, we apply image pyramid method to stereo matching and calculate the initial disparity map at the coarsest resolution. Then initial disparity map is propagated to the next finer resolution, interpolated and performed disparity refinement using local feature vector. We valid our method not only reduces the search time for correspondence compared with conventional GA-based method but also ensures the validity of matching.

• Geomechanics and Engineering
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• v.33 no.3
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• pp.279-289
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• 2023

Brittleness index (BI) is an important property of rocks because it is a good index to predict rockburst. Due to its importance, several empirical and soft computing (SC) models have been proposed in the literature based on the punch penetration test (PPT) results. These models are very important as there is no clear-cut experimental means for measuring BI asides the PPT which is very costly and time consuming to perform. This study used a novel Multivariate Adaptive regression spline (MARS), M5P, and white-box ANN to predict the BI of rocks using the available data in the literature for an improved BI prediction. The rock density, uniaxial compressive strength (σ_c) and tensile strength (σ_t) were used as the input parameters into the models while the BI was the targeted output. The models were implemented in the MATLAB software. The results of the proposed models were compared with those from existing multilinear regression, linear and nonlinear particle swarm optimization (PSO) and genetic algorithm (GA) based models using similar datasets. The coefficient of determination (R²), adjusted R² (Adj R²), root-mean squared error (RMSE) and mean absolute percentage error (MAPE) were the indices used for the comparison. The outcomes of the comparison revealed that the proposed ANN and MARS models performed better than the other models with R² and Adj R² values above 0.9 and least error values while the M5P gave similar performance to those of the existing models. Weight partitioning method was also used to examine the percentage contribution of model predictors to the predicted BI and tensile strength was found to have the highest influence on the predicted BI.

• Journal of Korea Water Resources Association
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• v.54 no.2
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• pp.93-104
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• 2021

Due to climate change the sustainable water resources management of agricultural reservoirs, the largest number of reservoirs in Korea, has become important. However, the DIROM, rainfall-runoff model for calculating agricultural reservoir inflow, has used regression equation developed in the 1980s. This study has optimized the parameters of the DIROM using the genetic algorithm (GA) based on historical inflow data for some agricultural reservoirs that recently begun to observe inflow data. The result showed that the error between the historical inflow and simulated inflow using the optimal parameters was decreased by about 80% compared with the annual inflow with the existing parameters. The correlation coefficient and root mean square error with the historical inflow increased to 0.64 and decreased to 28.2 × 103 ㎥, respectively. As a result, if the DIROM uses the optimal parameters based on the historical inflow of agricultural reservoirs, it will be possible to calculate the long-term reservoir inflow with high accuracy. This study will contribute to future research using the historical inflow of agricultural reservoirs and improvement of the rainfall-runoff model parameters. Furthermore, the reliable long-term inflow data will support for sustainable reservoir management and agricultural water supply.

• Korean Journal of Construction Engineering and Management
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• v.16 no.5
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• pp.54-64
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• 2015

The modular construction has several advantages such as high quality of product, safe work condition and short construction duration. The manufacturing planning of modular construction should consider time frame of manufacturing, transport and erection process with limited resources (e.g., modular units, transporter and workers). The manufacturing planning of multi modular construction project manages the modular construction's characteristics and diversity of projects, as a type of modular unit, modular unit quantities, and date for delivery. However, current modular manufacturing planning techniques are weak in dealing with resource interactions and each project requirement in multi modular construction project environments. Inefficient allocation of resources during multi modular construction project may cause delays and cost overruns to construction operation. In this circumstance, this research suggest a manufacturing planning model for schedule optimization of multi project of modular construction, using genetic algorithm as one of the powerful method for schedule optimization with multiple constrained resources. Comparing to the result of the existed schedule of case study, setting optimized scheduling for multi project decrease the total factory producing schedule. By using proposed optimization tool, efficient allocation of resource and saving project time is expected.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.17 no.3
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• pp.319-332
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• 2004

Due to their high strength to weight ratios and excellent durability, fiber reinforced polymer(FRP) is widely used in construction industries. In this paper, a shape optimum design of FRP bridge decks haying pultruded cellular cross-section is presented. In the problem formulation, an objective function is selected to minimize the volumes. The cross-sectional dimensions and material properties of the deck of FRP bridges are used as the design variables. On the other hand, deflection limits in the design code, material failure criteria, buckling load, minimum height, and stress are selected as the design constraints to enhance the structural performance of FRP decks. In order to efficiently treat the optimization process, the cross-sectional shape of bridge decks is assumed to be a tube shape. The optimization process utilizes an improved Genetic Algorithms incorporating indexing technique. For the structural analysis using a three-dimensional finite element, a commercial package(ABAQUS) is used. Using a computer program coded for this study, an example problem is solved and the results are presented with sensitivity analysis. The bridge consists of a deck width of 12.14m and is supported by five 40m long steel girders spaced at 2.5m. The bridge is designed to carry a standard DB-24 truck loading according to the Standard Specifications for Highway Bridges in Korea. Based on the optimum design, viable cross-sectional dimensions for FRP decks, suitable for pultrusion process are proposed.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.1
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• pp.79-86
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• 2017

This research focused on deciding optimal manufacturing WIP (Work-In-Process) limit for a small production system. Reducing WIP leads to stable capacity, better manufacturing flow and decrease inventory. WIP is the one of the important issue, since it can affect manufacturing area, like productivity and line efficiency and bottlenecks in manufacturing process. Several approaches implemented in this research. First, two strategies applied to decide WIP limit. One is roulette wheel selection and the other one is elite strategy. Second, for each strategy, JIT (Just In Time), CONWIP (Constant WIP), Gated Max WIP System and CWIPL (Critical WIP Loops) system applied to find a best material flow mechanism. Therefore, pull control system is preferred to control production line efficiently. In the production line, the WIP limit has been decided based on mathematical models or expert's decision. However, due to the complexity of the process or increase of the variables, it is difficult to obtain optimal WIP limit. To obtain an optimal WIP limit, GA applied in each material control system. When evaluating the performance of the result, fitness function is used by reflecting WIP parameter. Elite strategy showed better performance than roulette wheel selection when evaluating fitness value. Elite strategy reach to the optimal WIP limit faster than roulette wheel selection and generation time is short. For this reason, this study proposes a fast and reliable method for determining the WIP level by applying genetic algorithm to pull system based production process. This research showed that this method could be applied to a more complex production system.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.8 no.3
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• pp.100-111
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• 2009

Cognitive radio is an advanced technology for efficient utilization of under-utilized spectrum via spectrum sensing. CR users should move from current allocating channel to empty channel to avoid the interference to the primary user if the primary user is allocating that channel. Thus, CR system cannot support the CR user's QoS(Quaiity of Service). In this paper, we propose dynamic frequency selection method based on Genetic Algorithm with power control. It is to find the optimization channel for satisfying various CR user's needs with the power control method to minimize the CR user's interference to the primary user. And, we propose the Genetic Algorithm(GA) which determines the best configuration for CR communication systems. The computer simulation results show that the proposed method guaranteed the primary user's decodability and the optimized solution for various channel status.

• Journal of Intelligence and Information Systems
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• v.23 no.1
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• pp.47-67
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• 2017

Steel plate faults is one of important factors to affect the quality and price of the steel plates. So far many steelmakers generally have used visual inspection method that could be based on an inspector's intuition or experience. Specifically, the inspector checks the steel plate faults by looking the surface of the steel plates. However, the accuracy of this method is critically low that it can cause errors above 30% in judgment. Therefore, accurate steel plate faults diagnosis system has been continuously required in the industry. In order to meet the needs, this study proposed a new steel plate faults diagnosis system using Simultaneous MTS (S-MTS), which is an advanced Mahalanobis Taguchi System (MTS) algorithm, to classify various surface defects of the steel plates. MTS has generally been used to solve binary classification problems in various fields, but MTS was not used for multiclass classification due to its low accuracy. The reason is that only one mahalanobis space is established in the MTS. In contrast, S-MTS is suitable for multi-class classification. That is, S-MTS establishes individual mahalanobis space for each class. 'Simultaneous' implies comparing mahalanobis distances at the same time. The proposed steel plate faults diagnosis system was developed in four main stages. In the first stage, after various reference groups and related variables are defined, data of the steel plate faults is collected and used to establish the individual mahalanobis space per the reference groups and construct the full measurement scale. In the second stage, the mahalanobis distances of test groups is calculated based on the established mahalanobis spaces of the reference groups. Then, appropriateness of the spaces is verified by examining the separability of the mahalanobis diatances. In the third stage, orthogonal arrays and Signal-to-Noise (SN) ratio of dynamic type are applied for variable optimization. Also, Overall SN ratio gain is derived from the SN ratio and SN ratio gain. If the derived overall SN ratio gain is negative, it means that the variable should be removed. However, the variable with the positive gain may be considered as worth keeping. Finally, in the fourth stage, the measurement scale that is composed of selected useful variables is reconstructed. Next, an experimental test should be implemented to verify the ability of multi-class classification and thus the accuracy of the classification is acquired. If the accuracy is acceptable, this diagnosis system can be used for future applications. Also, this study compared the accuracy of the proposed steel plate faults diagnosis system with that of other popular classification algorithms including Decision Tree, Multi Perception Neural Network (MLPNN), Logistic Regression (LR), Support Vector Machine (SVM), Tree Bagger Random Forest, Grid Search (GS), Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The steel plates faults dataset used in the study is taken from the University of California at Irvine (UCI) machine learning repository. As a result, the proposed steel plate faults diagnosis system based on S-MTS shows 90.79% of classification accuracy. The accuracy of the proposed diagnosis system is 6-27% higher than MLPNN, LR, GS, GA and PSO. Based on the fact that the accuracy of commercial systems is only about 75-80%, it means that the proposed system has enough classification performance to be applied in the industry. In addition, the proposed system can reduce the number of measurement sensors that are installed in the fields because of variable optimization process. These results show that the proposed system not only can have a good ability on the steel plate faults diagnosis but also reduce operation and maintenance cost. For our future work, it will be applied in the fields to validate actual effectiveness of the proposed system and plan to improve the accuracy based on the results.