• Journal of information and communication convergence engineering
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• v.7 no.4
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• pp.469-474
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• 2009

Q-algorithm is proposed at EPC global Class-1 Generation-2 RFID systems to determine the frame size of next query round. In Q-algorithm, the reader calculates the frame size without estimating the number of tags. But, it uses only the slot conditions: empty, success, or collision. Therefore, it wastes less computational cost and is simpler than other algorithms. However, the constant parameter C value, which is used for calculating the next frame size, is not optimized. In this paper, we propose the optimized C values of Q-algorithm according to the number of tags within the identification range of reader through a lot of computer simulations.

• Proceedings of the IEEK Conference
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• 2009.05a
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• pp.384-386
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• 2009

This paper introduces an effectively optimized Fuzzy model identification by means of complex and nonlinear system applying PSO algorithm. In other words, we use PSO(Particle Swarm Optimization) for identification of Fuzzy model structure and parameter. PSO is an algorithm that follows a collaborative population-based search model. Each particle of swarm flies around in a multidimensional search space looking for the optimal solution. Then, Particles adjust their position according to their own and their neighboring-particles experience. This paper identifies the premise part parameters and the consequence structures that have many effects on Fuzzy system based on PSO. In the premise parts of the rules, we use triangular. Finally we evaluate the Fuzzy model that is widely used in the standard model of gas data and sew data.

• Korean Journal of Computational Design and Engineering
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• v.12 no.6
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• pp.405-412
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• 2007

It's difficult for manufacturers to derive a new design from the demands of consumers as quickly as possible and a designer carries out design operation using insufficient resources in initial design. To carry out initial design process efficiently for an excavator front group, it is necessary for a designer to manage lots of parameter with an existing knowledge or with in-house know-how and develop function module that calculates working range and excavator force. By doing so, it will bring up the optimized values of parameters based on the DOE in the early design stage. In this paper, a new approach to improve the process with optimized parameters is proposed to reduce a product development time of the excavator front design.

• Proceedings of the Korean Geotechical Society Conference
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• 2010.03a
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• pp.862-870
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• 2010

This thesis has been researched on optimized design method for the total cross section of embankment considering the fact that the size of embankment cross section is directly related with economic efficiency when dam designing. In general, embankment cross section of fill dam is either determined by cohesion and angle of internal friction, a strength parameter of embankment materials or by permeability of embankment. Therefore the size of embankment cross section depending on strength parameter of embankment materials was determined by using MIDAS-GTS program through stress-seepage coupled analysis at the time of fill dam design. As a result, determination of embankment cross section was more affected by the size of central core and permeability rather than by slope stability by shear strength and it was revealed that in case of embankment height being over 20m, stability against infiltration and slope action could be secured only when embankment slope is at least over 1:2.5. In addition, it was also revealed that in case of making the size of central core exceeding specification standard, total cross section of embankment could be reduced considerably and at the time of embankment design, adequate size and appropriateness of embankment cross section could be determined with referring the table suggested by this study.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.1
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• pp.177-182
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• 2010

The forward convener is one of power supplies used widely. This paper presents parameter tuning methods to obtain optimal circuit element values for the forward converter to minimize the output voltage variation under various load changing environments. The conventional method using the concept of the phase margin is extended to have optimal phase margin that gives slightly improved performance in the output voltage response. For this, the phase margin becomes the tuning parameter and is optimized with the genetic algorithm. Next, the circuit element values are directly chosen as the tuning parameters and also optimized using the genetic algorithm to have very improved performance in the output voltage control of the forward converter.

• The Journal of Korean Association of Computer Education
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• v.11 no.5
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• pp.57-66
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• 2008

An adaptive optimization of parametric cubic convolution for image interpolation is derived in this paper. The proposed technique is based on optimizing the standard cubic convolution interpolation formula at each interpolated pixel. Conventional parametric cubic convolution methods use a fixed parameter in an image, so properties of each pixel cannot be incorporated into the interpolation. The proposed method optimizes the interpolation kernel by obtaining parameters adaptively on each pixel. A new cost function is introduced to reflect frequency properties of the original data. The proposed technique produces noticeably sharper edges than traditional techniques and exhibits an average PSNR improvement of traditional techniques.

• Journal of Ocean Engineering and Technology
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• v.18 no.5
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• pp.29-35
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• 2004

A new approach, referred to as a successive zooming genetic algorithm (SZGA), is proposed for identifying a global solution, using continuous zooming factors for optimization problems. In order to improve the local fine-tuning of the GA, we introduced a new method whereby the search space is zoomed around the design variable with the best fitness per 100 generation, resulting in an improvement of the convergence. Furthermore, the reliability of the optimized solution is determined based on the theory of probability, and the parameter used for the successive zooming method is optimized. With parameter optimization, we can eliminate the time allocated for deciding parameters used in SZGA. To demonstrate the superiority of the proposed theory, we tested for the minimization of a multiple function, as well as simple functions. After testing, we applied the parameter optimization to a truss problem and wicket gate servomotor optimization. Then, the proposed algorithm identifies a more exact optimum value than the standard genetic algorithm.

• Proceedings of the Korea Society for Energy Engineering kosee Conference
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• 2003.11a
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• pp.323-330
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• 2003

• Geomechanics and Engineering
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• v.36 no.3
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• pp.259-276
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• 2024

The undrained shear strength is widely acknowledged as a fundamental mechanical property of soil and is considered a critical engineering parameter. In recent years, researchers have employed various methodologies to evaluate the shear strength of soil under undrained conditions. These methods encompass both numerical analyses and empirical techniques, such as the cone penetration test (CPT), to gain insights into the properties and behavior of soil. However, several of these methods rely on correlation assumptions, which can lead to inconsistent accuracy and precision. The study involved the development of innovative methods using extreme gradient boosting (XGB) to predict the pile set-up component "A" based on two distinct data sets. The first data set includes average modified cone point bearing capacity (q_t), average wall friction (f_s), and effective vertical stress (σ_vo), while the second data set comprises plasticity index (PI), soil undrained shear cohesion (S_u), and the over consolidation ratio (OCR). These data sets were utilized to develop XGBoost-based methods for predicting the pile set-up component "A". To optimize the internal hyperparameters of the XGBoost model, four optimization algorithms were employed: Particle Swarm Optimization (PSO), Social Spider Optimization (SSO), Arithmetic Optimization Algorithm (AOA), and Sine Cosine Optimization Algorithm (SCOA). The results from the first data set indicate that the XGBoost model optimized using the Arithmetic Optimization Algorithm (XGB - AOA) achieved the highest accuracy, with R2 values of 0.9962 for the training part and 0.9807 for the testing part. The performance of the developed models was further evaluated using the RMSE, MAE, and VAF indices. The results revealed that the XGBoost model optimized using XGBoost - AOA outperformed other models in terms of accuracy, with RMSE, MAE, and VAF values of 0.0078, 0.0015, and 99.6189 for the training part and 0.0141, 0.0112, and 98.0394 for the testing part, respectively. These findings suggest that XGBoost - AOA is the most accurate model for predicting the pile set-up component.

• Journal of Korea Multimedia Society
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• v.6 no.1
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• pp.136-147
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• 2003

To achieve an optimal performance for a given problem, we need an integrative process of the parameter optimization via learning and the structure optimization via model selection. In this paper, we propose an efficient optimization method for improving generalization performance by considering the property of each sub-method and by combining them with common theoretical properties. First, weight parameters are optimized by natural gradient teaming with adaptive regularization, which uses a diverse error function. Second, the network structure is optimized by eliminating unnecessary parameters with natural pruning. Through iterating these processes, candidate models are constructed and evaluated based on the Bayesian Information Criterion so that an optimal one is finally selected. Through computational experiments on benchmark problems, we confirm the weight parameter and structure optimization performance of the proposed method.