• Journal of the Korean Society of Costume
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• v.62 no.8
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• pp.118-133
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• 2012

A historical research on the costume of memorial service officials during the period of late 18th century, King Jeongjo period, is performed for an accurate reconstruction of the memorial service for King Danjong, which is a part of King Danjong Cultural Festival held at Yeongwol. This paper summarizes the results of the research as follows: 1. Jangneung memorial service is held only on Hansik day and the procedure follows the rule based on Gukjooryeui(國朝五禮儀). The service was first held in 1791 for King Danjong and his royal followers. 2. The rule based on Chunkwantonggo(春官通考), defines the king's tomb memorial services and attendees. King Danjong's memorial service follows the rule regarding its attendees and the attendees include high-ranking local government officials, local confucian scholars, and service men. 3. The costume of attendees for the King Danjong's memorial service is as follows: 1)Dangsangkwan(堂上官) and Danghakwan(堂下官) wear Sangbok(常服), which consisted of Samo(紗帽), Heukdanllyeong(黑團領), Pumdae(品帶), and black boots(黑靴). 2)Local confucian scholars wear their uniform consisting of Yukeon(儒巾), Dopo(道袍), Sejodae, and black boots. 3)Service men wear their uniform consisting of Jeonjakeon(典字巾), red-robe with rounded collar called Hongui(紅衣), Kwangdaw hoe(廣多繪), and Uuhae(雲鞋).

• Journal of applied mathematics & informatics
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• v.41 no.6
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• pp.1341-1364
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• 2023

This article introduces a method for estimating the conditional hazard function of a real-valued response variable based on a functional variable. The method uses local linear estimation of the conditional density and cumulative distribution function and is applied to a functional stationary ergodic process where the explanatory variable is in a semi-metric space and the response is a scalar value. We also examine the uniform almost complete convergence of this estimation technique.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.7
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• pp.1217-1233
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• 2015

The paper studies characteristics of the minimum mean-square error symmetric scalar quantizers for the generalized gamma, Bucklew-Gallagher and Hui-Neuhoff probability density functions. Toward this goal, asymptotic formulas for the inner- and outermost thresholds, and distortion are derived herein for nonuniform quantizers for the Bucklew-Gallagher and Hui-Neuhoff densities, parallelling the previous studies for the generalized gamma density, and optimal uniform and nonuniform quantizers are designed numerically and their characteristics tabulated for integer rates up to 20 and 16 bits, respectively, except for the Hui-Neuhoff density. The assessed asymptotic formulas are found consistently more accurate as the rate increases, essentially making their asymptotic convergence to true values numerically acceptable at the studied bit range, except for the Hui-Neuhoff density, in which case they are still consistent and suggestive of convergence. Also investigated is the uniqueness problem of the differentiation method for finding optimal step sizes of uniform quantizers: it is observed that, for the commonly studied densities, the distortion has a unique local minimizer, hence showing that the differentiation method yields the optimal step size, but also observed that it leads to multiple solutions to numerous generalized gamma densities.

• ETRI Journal
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• v.44 no.5
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• pp.805-815
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• 2022

The model-based evolutionary algorithms are divided into three groups: estimation of distribution algorithms, inverse modeling, and surrogate modeling. Existing inverse modeling is mainly applied to solve multi-objective optimization problems and is not suitable for many-objective optimization problems. Some inversed-model techniques, such as the inversed-model of multi-objective evolutionary algorithm, constructed from the Pareto front (PF) to the Pareto solution on nondominated solutions using a random grouping method and Gaussian process, were introduced. However, some of the most efficient inverse models might be eliminated during this procedure. Also, there are challenges, such as the presence of many local PFs and developing poor solutions when the population has no evident regularity. This paper proposes inverse modeling using random forest regression and uniform reference points that map all nondominated solutions from the objective space to the decision space to solve many-objective optimization problems. The proposed algorithm is evaluated using the benchmark test suite for evolutionary algorithms. The results show an improvement in diversity and convergence performance (quality indicators).

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.14-19
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• 1993

In this paper, we proposed an indirect learning and direct adaptive control schemes using neural networks, i.e., composite adaptive neural control, for a class of continuous nonlinear systems. With the indirect learning method, the neural network learns the nonlinear basis of the system inverse dynamics by a modified backpropagation learning rule. The basis spans the local vector space of inverse dynamics with the direct adaptation method when the indirect learning result is within a prescribed error tolerance, as such this method is closely related to the adaptive control methods. Also hash addressing technique, similar to the CMAC functional architecture, is introduced for partitioning network hidden nodes according to the system states, so global neuro control properties can be organized by the local ones. For uniform stability, the sliding mode control is introduced when the neural network has not sufficiently learned the system dynamics. With proper assumptions on the controlled system, global stability and tracking error convergence proof can be given. The performance of the proposed control scheme is demonstrated with the simulation results of a nonlinear system.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.4
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• pp.416-426
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• 2003

In this paper we propose a new method for choosing the initial values of Expectation-Maximization(EM) algorithm that has been used in various applications for clustering. Conventionally, the initial values were chosen randomly, which sometimes yields undesired local convergence. Later, K-means clustering method was employed to choose better initial values, which is currently widely used. However the method using K-means still has the same problem of converging to local points. In order to resolve this problem, a new method of initializing values for the EM process. The proposed method not only strengthens the characteristics of EM such that the number of iteration is reduced in great amount but also removes the possibility of falling into local convergence.

• The KIPS Transactions:PartB
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• v.11B no.7 s.96
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• pp.841-848
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• 2004

Evolutionary a1gorithms are well-suited for multi-objective optimization problems involving several, often conflicting objectives. Pareto-based evolutionary algorithms, in particular, have shown better performance than other multi-objective evolutionary algorithms in comparison. However, generalized evolutionary multi-objective optimization algorithms have a weak point, in which the distribution of solutions are not uni-formly distributed onto Pareto optimal front. In this paper, we propose an evolutionary a1gorithm for multi-objective optimization which uses seed individuals in order to overcome weakness of algorithms Published. Seed individual means a solution which is not located in the crowded region on Pareto front. And the idea of our algorithm uses seed individuals for reproducing individuals for next generation. Thus, proposed a1go-rithm takes advantage of local searching effect because new individuals are produced near the seed individual with high probability, and is able to produce comparatively uniform distributed pareto optimal solutions. Simulation results on five testbed problems show that the proposed algo-rithm could produce uniform distributed solutions onto pareto optimal front, and is able to show better convergence compared to NSGA-II on all testbed problems except multi-modal problem.

• Proceedings of the KIEE Conference
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• 1990.11a
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• pp.23-27
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• 1990

Adaptive mesh refinement scheme is incorporated with the Boundary Element Method (BEM) in order to get accurate solution with relatively fewer unknowns for the case of magnetostatic field analysis and A new and simple posteriori local error estimation method is presented. The local error is defined as integration over the element of the difference between solutions acquired us ing second order and first order interpolation function and is used as the criterion for mesh refinement at given grid. Case study for two dimensional problems with singular point reveals that meshes are concentrated on the neighbor of singular point and the error is decreased gradually and the solutions calculated on the domain are converged to the analytic solution as the number of unknowns increases. The adaptive mesh gives much better rate of convergence in global errors than the uniform mesh.

• The Journal of the Convergence on Culture Technology
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• v.9 no.3
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• pp.551-556
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• 2023

The study is to analyze the success strategies of the Cooperative Integrated Arts Activities in Seoul and to explore ways to implement it to the local public school. According to research, Cooperative Integrated Arts Activities fulfill the purpose of providing universal arts education by being implemented in the school curriculum. They are also characterized by active administrative and financial support and interactive participatory arts education with assured diversity. Therefore, when applying to a local public school, it is necessary to consider factors such as active administrative support, sustainable allocation of human and material resources, customized arts education that engages the all students and reflects their unique characteristics, social distribution through sharing, and continuous monitoring.

• Journal of The Korean Society of Agricultural Engineers
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• v.64 no.2
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• pp.1-13
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• 2022

Agrivoltaic facility is the composite system that the solar panel is installed above the farmland, and it enables crop and electricity production simultaneously. Solar panels of the agrivoltaic facilities can block and reduce the amount of solar irradiance arriving at the farmland, but it can help the crop growth by preventing excessive solar irradiance. Therefore, to clarify how the agrivoltaic facilities affect the crop growth, precise solar irradiance distribution under the solar panel should be modeled. In this study, PAR (photosynthetically active radiation), radiation from 400 to 700 nm, which crops usually use to grow, was extracted from the total irradiance and its distribution model under various conditions was developed. Monthly irradiance distributions varied because the elevation of the sun was changed over time, which made the position changed that the local maximum and minimum irradiance appear. The higher panel height did not cause any significant difference in the amount of irradiance reaching below the solar panel, but its distribution became more uniform. Furthermore, the panel angles with the most irradiance arriving below the solar panel were different by month, but its difference was up to 2%p between the irradiance with 30° angle which is usually recommended in Korea. Finally, the interval between panels was adjusted; when the ratio of the length of the panel to the empty space was 1:2, the irradiance of 0.719 times was reached compared to when there was no panel, 0.579 times for 1:1 and 0.442 times for 2:1.