• Journal of the Korean Data and Information Science Society
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• v.28 no.6
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• pp.1229-1244
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• 2017

In recent years, as demand for data-based analytical methodologies increases in various fields, optimization methods have been developed to handle them. In particular, various constraints required for problems in statistics and machine learning can be solved by convex optimization. Alternating direction method of multipliers (ADMM) can effectively deal with linear constraints, and it can be effectively used as a parallel optimization algorithm. ADMM is an approximation algorithm that solves complex original problems by dividing and combining the partial problems that are easier to optimize than original problems. It is useful for optimizing non-smooth or composite objective functions. It is widely used in statistical and machine learning because it can systematically construct algorithms based on dual theory and proximal operator. In this paper, we will examine applications of ADMM algorithm in various fields related to statistics, and focus on two major points: (1) splitting strategy of objective function, and (2) role of the proximal operator in explaining the Lagrangian method and its dual problem. In this case, we introduce methodologies that utilize regularization. Simulation results are presented to demonstrate effectiveness of the lasso.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.12
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• pp.2761-2768
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• 2010

In this paper, we studied the use of the foreground and background features and SVM classifier to improve the accuracy of offline handwritten numeral recognition. The foreground features are two directional features: directional gradient feature by Kirsch operators and directional stroke feature by projection runlength, and the background feature is concavity feature which is extracted from the convex hull of the numeral, where concavity feature functions as complement to the directional features. During classification of the numeral, these three features are combined to obtain good discrimination power. The efficiency of our feature sets was tested by recognition experiments on the handwritten numeral database CENPARMI, where we used SVM with RBF kernel as a classifier. The experimental results showed that each combination of two or three features gave a better performance than a single feature. This means that each single feature works with a different discriminating power and cooperates with other features to enhance the recognition accuracy. By using the composite feature of the three features, we achieved a recognition rate of 98.90%.

• Journal of Environmental Policy
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• v.6 no.4
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• pp.55-81
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• 2007

Most studies on $CO_2$ abatement cost with a computational general equilibrium(CGE) model focus on a specific country. On the contrary, this study compares and analyses the $CO_2$ abatement cost functions across 20 countries, consisting of OECD countries, China and Brazil, with a CGE model. For this purpose, we estimate the GDP loss from $CO_2$ emission reduction, assuming the 4 sector model. Our findings show that those cost curves are convex but different among the countries. However, despite of the difference in the cost curios, we have found that one group of countries has the relatively constant average abatement cost and the other group has the increasing average cost. The reason why such a pattern occurs is explained in terms of the variations of value-added and $Co_2$ emission coefficient by sector across the countries. As an environmental policy implication, this study presents information about which country is similar to one another in terms of the abatement cost.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.34 no.4
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• pp.199-204
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• 2021

In this study, an isogoemetric analysis (IGA) method that uses NURBS (Non-Uniform Rational B-Spline) basis functions in computer-aided design (CAD) systems is employed to account for the geometric exactness of curved electrodes constituting an electro-adhesive pad in electrostatic problems. The IGA is advantageous for obtaining precise normal vectors when computing the electro-adhesive forces on curved surfaces. By performing parametric studies using numerical examples, we demonstrate the superior performance of the curved electrodes, which is attributed to the increase in the normal component of the electro-adhesive forces. In addition, concave curved electrodes exhibit better performance than their convex counterparts.

• Korean Journal of Heritage: History & Science
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• v.55 no.4
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• pp.196-209
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• 2022

This study compared and analyzed the spatial division function and role of partitions by comparing the entire space and the spatial changes before and after the installation of partitions in <Donggwoldo>, which was manufactured in the late Joseon Dynasty. As a research method, a set standard was prepared to decompose the space of <Donggwoldo> into a unit space, and the standard was set according to the role and height of the space by classifying it into a main space, sub space, and transition space. Two convex maps were prepared according to before and after the installation of the Panjang, and the values of connectivity, control, and integration, which are spatial syntax variables, were calculated and analyzed. The results of the study are as follows. First, the partition in <Donggwoldo(東闕圖)> did not affect the overall spatial arrangement and control or connection of Donggwol, but the movement and access of space is limited to specific areas. Second, the partition was a facility intensively distributed in Naejeon(內殿) and Donggung(東宮) to be used actively in the way of space utilization. It shows that the unit space increased rapidly due to the installation of the partition. Since the partition was installed in the spaces that were open and under high control in the case of Naejeon(內殿), it helped to secure private spaces as closed ones under low control. On the other hand, for Donggung(東宮), the spaces were compartmented and divided with the partition to guide the movement path through narrow gates of the partition and increase the depth of the space. This helped to create spaces that are free and can be hidden as it increased the number of spaces coming through. Third, In addition to the functions of "eye blocking, space division, and movement path control" revealed in prior research, the partition has created a "space that is easy to control" within a specific area. The installation of the partition reduced the scale through the separation of spaces, but it occurred the expansion of the movement path and space. Also, the partition functioned to strengthen hiding and closure or increase openness as well through space division. This study is significant in that it revealed the value of the spatial control function of panjang through the analysis of spatial control and depth by analyzing the function of the partition with a mathematical model in addition to the analysis and study of the function and role of panjang. In addition, it is valuable in that it has prepared a framework for analysis tools that can be applied to traditional residential complexes similar to palaces by applying space syntax to <Donggungdo> to create convex spaces according to unit space division and connection types of palace architecture and landscape elements.

• Journal of Animal Science and Technology
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• v.66 no.5
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• pp.949-961
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• 2024

The annual forage crop production system, enclosing silage corn (Zea mays L.) and following cool-season annual forage, can enhance forage production efficiency where available land is limited for pasture production. In this forage production system, successful silage corn cultivation has a significant value due to the great yield of highly digestible forage. However, some untimely planting or harvesting of corn due to changing weather often reduces biomass and feeding values. Therefore, a study was conducted to quantify the corn silage biomass reductions by the deviations from optimum planting soil temperature and optimum growing degree day (GDD). The approximations of maximum corn production were estimated based on field trial data conducted between 1978 and 2018 with early, medium, and late-maturity corn groups. Based on weather data, the recorded planting dates and harvest dates were converted into the corresponding trials' soil temperatures at planting (STP) and the GDD. The silage corn biomass data were regressed against STP and GDD using a quadratic function. The maximum biomass point was modeled in a convex upward quadratic yield curve and the optimum STP and GDD were defined as those values at the maximum biomass for each maturity group. Optimized STP was at 16.6℃, 16.2℃, and 15.6℃ for early, medium, and late maturity corn groups, respectively, while optimized GDD at harvest was at 1424, 1363, and 1542℃. The biomass reductions demonstrated quadratic functions by the departures of STP or GDD. The 5% reductions were anticipated when STP departed from the optimum temperature by 2.2℃, 2.4℃, and 1.4℃ for early, medium, and late maturity corns, respectively; the same degree of reductions were estimated when the GDD departed by 200, 180, and 130℃ in the same order of the maturity groups. This result indicates that biomass reductions of late-maturity corn were more sensitive to the departures of STP or GDD than the early-maturity corn. Therefore, early maturing cultivars are more stable in biomass production in a silage corn-winter annual forage crop production system to enhance forage-based livestock production efficiency.

• Journal of Korea Water Resources Association
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• v.52 no.10
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• pp.681-695
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• 2019

A Transient Storage Model (TSM) is one of the most widely used model accounting for complex solute transport in natural river to understanding natural river properties with four TSM key parameters. The TSM parameters are estimated via inverse modeling. Parameter estimation of the TSM is carried out by solving optimization problem about finding best fitted simulation curve with measured curve obtained from tracer test. Several studies have reported uncertainty in parameter estimation from non-convexity of the problem. In this study, we assessed best combination of optimization method and objective function for TSM parameter estimation using Cheong-mi Creek tracer test data. In order to find best optimization setting guaranteeing convergence and speed, Evolutionary Algorithm (EA) based global optimization methods, such as CCE of SCE-UA and MCCE of SP-UCI, and error based objective functions were compared, using Shuffled Complex-Self Adaptive Hybrid EvoLution (SC-SAHEL). Overall results showed that multi-EA SC-SAHEL with Percent Mean Squared Error (PMSE) objective function is the best optimization setting which is fastest and stable method in convergence.