• The Journal of Engineering Research
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• v.4 no.1
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• pp.137-149
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• 2002

Time passages could deteriorate the flow ability and hold the flow in the water distribution facilities because of their erosion and breakdown. It is necessary that the study to determine the optimal change time and the improvement plan for the continuous management using optimization methods or decision support systems. But, the present study tendency only aware the changes of hydraulic characteristics without industrial management plans. This study shows the pipe replacement program in these two concepts and the elementary process to apply it to Daejeon city.

• Annual Conference of KIPS
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• 2012.11a
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• pp.42-45
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• 2012

A network processor (NP) is an application-specific instruction-set processor for fast and efficient packet processing. There are many issues in compiler's code generation and optimization due to NP's hardware constraints and special hardware support. In this paper, we describe in detail how to resolve the issues. Our compiler was developed on LLVM 3.0 and the NP target was our in-house network processor which consists of 32 64-bit RISC processors and supports multi-context with special hardware structures. Our compiler incurs only 9.36% code size overhead over hand-written code while satisfying QoS, and the generated code was tested on a real packet processing hardware, called S20 for code verification and performance evaluation.

• International Journal of Computer Science & Network Security
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• v.21 no.4
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• pp.272-276
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• 2021

Presently, Online / Offline Users are facing cyber attacks every day. These cyber attacks affect user's performance, resources and various daily activities. Due to this critical situation, attention must be given to prevent such users through cyber attacks. The objective of this research paper is to improve the IDS systems by using machine learning approach to develop a hybrid model which controls the cyber attacks. This Hybrid model uses the available KDD 1999 intrusion detection dataset. In first step, Hybrid Model performs feature optimization by reducing the unimportant features of the dataset through decision tree, support vector machine, genetic algorithm, particle swarm optimization and principal component analysis techniques. In second step, Hybrid Model will find out the minimum number of features to point out accurate detection of cyber attacks. This hybrid model was developed by using machine learning algorithms like PSO, GA and ELM, which trained the system with available data to perform the predictions. The Hybrid Model had an accuracy of 99.94%, which states that it may be highly useful to prevent the users from cyber attacks.

• Journal of Korean Academy of Dental Administration
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• v.10 no.1
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• pp.1-8
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• 2022

This study introduces research on the quality of medical services, optimization of medical services, dental medical services, implant medical services, and time schedules, as well as the effective process of dental implant medical services, which is expensive and requires a long treatment period. For improvement, it is suggested to evaluate using a time schedule. In this method, a time schedule is prepared in which each step, starting from the patients appointment until the completion of the treatment process, is allotted a certain time. This schedule was finalized in consultation with the employees. When performing all implant operations, the starting time of each item was checked to evaluate the degree of compliance and to understand any reasons for delay in each step. After identifying the causes for delay at each step, suitable steps to rectify the drawbacks were developed, and an optimal plan for patient management was determined. Changes in waiting time and human resource utilization were shown as concrete data, suggesting that such a schedule is meaningful as a decision-making support tool.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2023.05a
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• pp.254-255
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• 2023

This paper discovers a robust managerial strategy for a stochastic inventory of perishable products, where the model experiences changing factors including inner parameters and an external disturbance with unknown form. An analytical solution for the optimization problem can be obtained by applying the Hamilton-Bellman-Jacobi equation, however the policy result cannot completely suppress the oscillation from the external disturbance. Therefore, an intelligent approach named Radial Basis Function Neural Networks is applied to estimate the unknown disturbance and provide a robust controller to manipulate the inventory level more effective. The final results show the outstanding performance of RBFNN controller, where both the estimation error and control error are guaranteed in the predefined limit.

• Advances in concrete construction
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• v.17 no.1
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• pp.27-36
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• 2024

Reinforced concrete (RC) flat slabs should be designed based on punching shear strength. As part of this study, machine learning (ML) algorithms were developed to accurately predict the punching shear strength of RC flat slabs without shear reinforcement. It is based on Bayesian optimization (BO), combined with four standard algorithms (Support vector regression, Decision trees, Random forests, Extreme gradient boosting) on 446 datasets that contain six design parameters. Furthermore, an analysis of feature importance is carried out by Shapley additive explanation (SHAP), in order to quantify the effect of design parameters on punching shear strength. According to the results, the BO method produces high prediction accuracy by selecting the optimal hyperparameters for each model. With R² = 0.985, MAE = 0.0155 MN, RMSE = 0.0244 MN, the BO-XGBoost model performed better than the original XGBoost prediction, which had R² = 0.917, MAE = 0.064 MN, RMSE = 0.121 MN in total dataset. Additionally, recommendations are provided on how to select factors that will influence punching shear resistance of RC flat slabs without shear reinforcement.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.5
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• pp.102-108
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• 2011

Support vector machines are well known for their outstanding performance in pattern recognition fields. One example of their applications is music/speech classification for a standardized codec such as 3GPP2 selectable mode vocoder. In this paper, we propose a novel scheme that improves the speech/music classification of support vector machines based on the second-order conditional maximum a priori. While conventional support vector machine optimization techniques apply during training phase, the proposed technique can be adopted in classification phase. In this regard, the proposed approach can be developed and employed in parallel with conventional optimizations, resulting in synergistic boost in classification performance. According to experimental results, the proposed algorithm shows its compatibility and potential for improving the performance of support vector machines.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.32 no.6
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• pp.501-506
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• 2019

To overcome the limitations of the conventional Ni anode-supported SOFCs, various types of ceramic anodes have been studied. However, these ceramic anodes are difficult to commercialize because of their low cell performances and difficulty in manufacturing anode-support typed SOFCs. Therefore, in this study, to use these ceramic anodes and take advantage of anode-supported SOFC, which can minimize ohmic loss from the thin electrolyte, we fabricated cathode support-typed SOFC. The cathode-support of LSCF-YSZ was prepared by the acid treatment of conventional Ni-YSZ (Yttria-stabilized Zirconia) anode-support, followed by the infiltration of LSCF to YSZ scaffold. The composite of $La(Sr)Ti(Ni)O_3$ and $Ce(Mn,Fe)O_2$ was used as the ceramic anode. The fabricated cathode-supported button cell showed a relatively low power density of $0.207Wcm^{-2}$ at $850^{\circ}C$; however, it is expected to show better performance through the optimization of the infiltration rate and thickness of LSCF-YSZ cathode-support layer.

• Proceedings of the Korean Geotechical Society Conference
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• 2003.03a
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• pp.265-272
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• 2003

In this study, the rock mass classification results from the face mapping and the resistivity inversion data are compared and analyzed for the reliability investigation of the determination of the rock support type based on the surface electrical survey. To get the quantitative correlation, rock engineering indices such as RCR(rock condition rating), N(Rock mass number), Q-system based on RMR(rock mass rating) are calculated. Kriging method as a post processing technique for global optimization is used to improve its resolution. The result of correlation analysis shows that the geological condition estimated from 2D electrical resistivity survey is coincident globally with the trend of rock type except for a few local areas. The correlation between the results of 3D electrical resistivity survey and the rock mass classification turns out to be very high. It can be concluded that 3D electrical resistivity survey is powerful to set up the reliable rock support type.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.686-689
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• 2004

Genetic algorithm is well known for stochastic searching method in imitating natural phenomena. In recent times, studies have been conducted in improving conventional evolutionary computation speed and promoting precision. This paper presents an approach to optimize PID controller gains with the application of modified Genetic Algorithm using Support Vector Machine (SVMGA). That is, we aim to explore optimum parameters of PID controller using SVMGA. Simulation results are given to compare to those of tuning methods, based on Simple Genetic Algorithm and Ziegler-Nicholas tuning method.