• Journal of the Earthquake Engineering Society of Korea
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• v.11 no.3 s.55
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• pp.43-52
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• 2007

This study suggests a method to determine optimal seismic retrofitting schemes for existing bridges based on weighting-factored evaluation. According to the recognition for potential seismic risk, various kinds of retrofitting methods are applied to improve the seismic performance of existing bridges. However, the relevant technique is not available to select optimal retrofitting scheme for bridges now. This suggested method weights five factors, structural compatibility, economic efficacy, environmental factor, consturctability and maintenance, and draws out optimal seismic retrofitting schemes. The application of the developed method to one hundred sixty existing bridges verifies the adaptability of this method. As a result, this study provides an idealized retrofitting schemes, and the suggested method could be a guideline to determine the more cost-effective and optimal retrofitting schemes for existing bridges in Korea.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.12
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• pp.1542-1550
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• 2019

Legacy studies for classifying arrhythmia have been studied to improve the accuracy of classification, Neural Network, Fuzzy, etc. Deep learning is most frequently used for arrhythmia classification using error backpropagation algorithm by solving the limit of hidden layer number, which is a problem of neural network. In order to apply a deep learning model to an ECG signal, it is necessary to select an optimal model and parameters. In this paper, we propose optimal parameter extraction method based on a deep learning. For this purpose, R-wave is detected in the ECG signal from which noise has been removed, QRS and RR interval segment is modelled. And then, the weights were learned by supervised learning method through deep learning and the model was evaluated by the verification data. The detection and classification rate of R wave and PVC is evaluated through MIT-BIH arrhythmia database. The performance results indicate the average of 99.77% in R wave detection and 97.84% in PVC classification.

• Fibers and Polymers
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• v.7 no.4
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• pp.344-351
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• 2006

This paper is intended to determine the optimal processing parameters applied to the dyeing procedure so that the desired color strength of a raw fabric can be achieved. Moreover, the processing parameters are also used for constructing a system to predict the fabric quality. The fabric selected is the nylon and Lycra blend. The dyestuff used for dyeing is acid dyestuff and the dyeing method is one-bath-two-section. The Taguchi quality method is applied for parameter design. The analysis of variance (ANOVA) is applied to arrange the optimal condition, significant factors and the percentage contributions. In the experiment, according to the target value, a confirmation experiment is conducted to evaluate the reliability. Furthermore, the genetic algorithm (GA) is combined with the back propagation neural network (BPNN) in order to establish the forecasting system for searching the best connecting weights of BPNN. It can be shown that this combination not only enhances the efficiency of the learning algorithm, but also decreases the dependency of the initial condition during the network training. Most of all, the robustness of the learning algorithm will be increased and the quality characteristic of fabric will be precisely predicted.

• Microbiology and Biotechnology Letters
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• v.24 no.3
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• pp.274-281
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• 1996

A bacterium producing Cyclodextrin Glucanotransferase (CGTase) and Cyclodextrinase (CDase) was isolated from soil, and named as Bacillus stearothermophilus KJ16. The growth of the isolated strain occurred in two steps, and syntheses of CGTase and CDase were dependedt on the growth cycle of the cell. CGTase was constitutively synthesized during the 1st growing phase, while CDase was synthesized inducibly during the 2nd growing phase. When the midium pH was controlled at 7.0 the maximum enzyme activities of CGTase and CDase were increased by 12-fold (1300 mU/ml) and 2-fold (225 mU/ml), respectively, compared with the pH-uncontrolled batch culture. The CGTase of the isolate converted soluble starch to CDs with the ratio of $\alpha$-CD:$\beta$-CD:$\gamma$-CD=42:46:12 at $55^{\circ}C$.The optimal pH and temperature of CGTase were 6.0 and $60^{\circ}C$, respectively and the optimal pH and temperature of CDase were 6.0 and $55^{\circ}C$. The molecular weights of the purified CGTase and CDase were estimated to be 65, 000 and 68, 000 dalton, respectively. Among several substrates, $\gamma$-CD was most rapidly hydrolyzed by the purified CDase.

• Genomics & Informatics
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• v.14 no.4
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• pp.173-180
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• 2016

The meta-analysis has become a widely used tool for many applications in bioinformatics, including genome-wide association studies. A commonly used approach for meta-analysis is the fixed effects model approach, for which there are two popular methods: the inverse variance-weighted average method and weighted sum of z-scores method. Although previous studies have shown that the two methods perform similarly, their characteristics and their relationship have not been thoroughly investigated. In this paper, we investigate the optimal characteristics of the two methods and show the connection between the two methods. We demonstrate that the each method is optimized for a unique goal, which gives us insight into the optimal weights for the weighted sum of z-scores method. We examine the connection between the two methods both analytically and empirically and show that their resulting statistics become equivalent under certain assumptions. Finally, we apply both methods to the Wellcome Trust Case Control Consortium data and demonstrate that the two methods can give distinct results in certain study designs.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.13 no.1
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• pp.39-49
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• 2013

This paper presents a method of improving the performance of a day-ahead 24-h load curve and peak load forecasting. The next-day load curve is forecasted using radial basis function (RBF) neural network models built using the best design parameters. To improve the forecasting accuracy, the load curve forecasted using the RBF network models is corrected by the weighted sum of both the error of the current prediction and the change in the errors between the current and the previous prediction. The optimal weights (called "gains" in the error correction) are identified by differential evolution. The peak load forecasted by the RBF network models is also corrected by combining the load curve outputs of the RBF models by linear addition with 24 coefficients. The optimal coefficients for reducing both the forecasting mean absolute percent error (MAPE) and the sum of errors are also identified using differential evolution. The proposed models are trained and tested using four years of hourly load data obtained from the Korea Power Exchange. Simulation results reveal satisfactory forecasts: 1.230% MAPE for daily peak load and 1.128% MAPE for daily load curve.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.7 no.4
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• pp.767-778
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• 2003

This paper presents a method for generating optimal fuzzy rules using a genetic algorithm. Fuzzy rules are generated from the training data in the first stage. In this stage, fuzzy c-Means clustering method and cluster validity are used to determine the structure and initial parameters of the fuzzy inference system. A cluster validity is used to determine the number of clusters, which can be the number of fuzzy rules. Once the structure is figured out in the first stage, parameters relating the fuzzy rules are optimized in the second stage. Weights and variance parameters are tuned using genetic algorithms. Variance parameters are also managed with left and right for asymmetrical Gaussian membership function. The method ensures convergence toward a global minimum by using genetic algorithms in weight and variance spaces.

• Proceedings of the Korea Contents Association Conference
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• 2009.05a
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• pp.467-471
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• 2009

A road is a fundammental public traffic facility for transporting people and goods. Road designer should determine the best route considering various conditions to minimize environmental bad effect due to road construction and show road functions sufficiently. In this research, we tried to select the optimal route location by comparing candidate routes considering weights of various items such as land use, slope, aspect, land price and so forth. The candidate routes were analyzed and visualized from built data using a GIS software and then compared with the existing route.

• Journal of the Society of Naval Architects of Korea
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• v.48 no.5
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• pp.435-444
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• 2011

This paper deals with the structural assessment of metallic and non-metallic stiffened/monocoque plated marine structures under a lateral pressure load to identify appropriate combination of material and section configuration, especially at the preliminary marine structural design stage. A generic rectangular plated structure is exemplified from the metallic superstructure of a marine vessel and its structural topology is varied for the structural assessment. In total 13 different structural topologies are proposed and assessed using appropriate elastic solutions in conjunction with a set of stress and deflection limits obtained from practice. The geometry dimensions and weights of the structural topologies are calculated, and subsequently, the costs of the materials used in the structural topologies are reviewed to discuss the cost-effectiveness of the materials. Finally, conclusions are made with the aim of suggesting suitable structural topology for the marine structural member considered in this paper.

• Structural Monitoring and Maintenance
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• v.4 no.1
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• pp.85-105
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• 2017

Monitoring is the most important part of the construction and operation of the embankment dams. Applied instruments in these dams should be determined based on dam requirements and specifications. Instruments selection considered as one of the most important steps of monitoring plan. Competent instruments selection for dams is very important, as inappropriate selection causes irreparable loss in critical condition. Lack of a systematic method for determining instruments has been considered as a problem for creating an efficient selection. Nowadays, decision making methods have been used widely in different sciences for optimal determination and selection. In this study, the Multi-Attribute Decision Making is applied by considering 9 criteria and categorisation of 8 groups of geotechnical instruments. Therefore, the Analytic Hierarchy Process and Multi-Criteria Optimisation and Compromise Solution methods are employed in order to determine the attributes' importance weights and to prioritise of instruments for embankment dams, respectively. This framework was applied for a rock fill with clay core dam. The results indicated that group decision making optimizes the selection and prioritisation of monitoring instruments for embankment dams, and selected instruments are reliable based on the dam specifications.