• Laser Solutions
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• v.11 no.1
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• pp.1-6
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• 2008

We proposed new light guide panel (LGP) fabrication method exploiting laser-processed inner scatterers and surface pattern. The proposed method has achieved LGP performance improvement in both brightness and uniformity. The inner scatterers and surface pattern of grid type were fabricated with a 2nd harmonic Nd:YAG pulse laser engraving system and a $CO_2$ laser scanning system, respectively. In the implementation of LGP, inner scatterers was arranged in accordance with linear or curved pattern with changing density and surface pattern was engraved on the surface of an inner-scatterers embedded LGP. The increase of scatterers' density and the use of surface patterns in both linear and curved pattern provided high luminance and uniformity enhancement. While thecurved pattern incorporated with increased scatterers' density and surface patterns yielded brightness improvement with preserving good uniformity, the linear pattern showed highly localized brightness near the light entrance of the LGP. We can also observe that the uniformity was mainly determined by pattern of inner scatterers, and the brightness was improved by the higher density and the utilization of surface patterns. From the results, the use of laser-processed inner and surface patterns can be a potential alternative for efficient and simple LGP fabrication method.

• Journal of Korea Water Resources Association
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• v.55 no.1
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• pp.71-84
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• 2022

The objective of this study is to develop a two-dimensional (2D) flood model that can perform accurate flood analysis with simple input data. The 2D flood inundation models currently used to create flood forecast maps require complex input data and grid generation tools. This sometimes requires a lot of time and effort for flood modeling, and there may be difficulties in constructing input data depending on the situation. In order to compensate for these shortcomings, in this study, a grid-based model that can derive accurate and rapid flood analysis by reflecting correct topography as simple input data was developed. The calculation efficiency was improved by extending the existing 2×2 sub-grid model to a 5×5. In order to examine the accuracy and applicability of the model, it was applied to the Gamcheon Basin where both urban and river flooding occurred due to Typhoon Rusa. For efficient flood analysis according to user's selection, flood wave propagation patterns, accuracy and execution time according to grid size and number of sub-grids were investigated. The developed model is expected to be highly useful for flood disaster mapping as it can present the results of flooding analysis for various situations, from the flood inundation map showing accurate flooding to the flood risk map showing only approximate flooding.

• Journal of the Korea Society of Computer and Information
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• v.12 no.2 s.46
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• pp.231-242
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• 2007

The existing pattern mining methods[1,2,3,4,5,6,11,12,13] do not use location generalization method on the set of location history data of moving object, but even so they simply do extract only frequent patterns which have no spatio-temporal constraint in moving patterns on specific space. Therefore, it is difficult for those methods to apply to frequent pattern mining which has spatio-temporal constraint such as optimal moving or scheduling paths among the specific points. And also, those methods are required more large memory space due to using pattern tree on memory for reducing repeated scan database. Therefore, more effective pattern mining technique is required for solving these problems. In this paper, in order to develop more effective pattern mining technique, we propose new location generalization method that converts data of detailed level into meaningful spatial information for reducing the processing time for pattern mining of a massive history data set of moving object and space saving. The proposed method can lead the efficient spatial moving pattern mining of moving object using by creating moving sequences through generalizing the location attributes of moving object into 2D spatial area based on $R^*$-Tree and Area Grid Hash Table(AGHT) in preprocessing stage of pattern mining.

• Applied Microscopy
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• v.34 no.1
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• pp.23-29
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• 2004

In this study, we introduce the structural analysis of amorphous silica nanoparticles by EF-TEM electron diffraction and in-situ heating experiments. Three diffused rings were observed on the electron diffraction patterns of initial silica nanoparticles, while crystalline spot patterns were gradually appeared during the insitu heating process at $900^{\circ}C$. These patterns indicate the basic unit of $SiO_4$ tetrahedra consisting amorphous silica and gradual crystallization into the ideal layer structure of tridymite by heating. Under high vacuum condition in TEM, SiO nanoparticles were redeposited on the carbon grid after evaporation of SiO gas from $SiO_2$ above $850^{\circ}C$ and the remaining $SiO_2$ were crystallized into orthorhombic tridymite, consistent with ex-situ heating results in furnace at $900^{\circ}C$.

• JSTS:Journal of Semiconductor Technology and Science
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• v.4 no.2
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• pp.100-105
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• 2004

The influencing factors on the OPC (optical proximity correction) results are quantitatively analyzed using OPCed L/S patterns. ${\sigma}$ values of proximity variations are measured to be 9.3 nm and 15.2 nm for PR-A and PR-B, respectively. The effect of post exposure bake condition is assessed. 16.2 nm and 13.8 nm of variations are observed. Proximity variations of 11.6 nm and 15.2 nm are measured by changing the illumination condition. In order not to seriously deteriorate the OPC, these factors should be fixed after the OPC rules are extracted. Proximity variations of 11.4, 13.9, and 15.2 nm are observed for the mask mean-to-targets of 0, 2 and 4 nm, respectively. The decrease the OPC grid size from 1 nm to 0.5 nm enhances the correction resolution and the OCV is reduced from 14.6 nm to 11.4 nm. The enhancement amount of proximity variations are 9.2 nm corresponding to 39% improvement. The critical dimension (CD) uniformity improvement for adopting the small grid size is confirmed by measuring the CD uniformity on real SRAM pattern. CD uniformities are measured 9.9 nm and 8.7 nm for grid size of 1 nm and 0.5 nm, respectively. 22% improvement of the CD uniformity is achieved. The decrease of OPC grid size is shown to improve not only the proximity correction, but also the uniformity.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39C no.1
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• pp.72-81
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• 2014

Recently, because of irregular power demand, we have suffered from an electric power shortage. The necessity of the adoption of smart grid which makes effective supply of power by using the two-way communication across the grid between the customers and electric energy providers is growing more and more. If smart grid set up in our country, the third-parties which provide services to customer using the information acquired from smart grid, might be revved up. In this paper, we suggest a methodology how classify the types of family by analysing an power consumption pattern using data mining technique. To make a classifier for categorizing the household types, we need power consumption data and their family type. However, it is hard to get both of them. Therefore we develop the simulator that generates power consumption patterns of the household and classify the types of family. Also, we present a potential for application services such as customized services for a specific family or goods marketing.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.1
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• pp.93-104
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• 2024

Demand response is a strategy that encourages customers to adjust their consumption patterns at times of peak demand with the aim to improve the reliability of the power grid and minimize expenses. The integration of renewable energy sources into smart grids poses significant challenges due to their intermittent and unpredictable nature. Demand response strategies, coupled with reinforcement learning techniques, have emerged as promising approaches to address these challenges and optimize grid operations where traditional methods fail to meet such kind of complex requirements. This research focuses on investigating the application of reinforcement learning algorithms in demand response for renewable energy integration. The objectives include optimizing demand-side flexibility, improving renewable energy utilization, and enhancing grid stability. The results emphasize the effectiveness of demand response strategies based on reinforcement learning in enhancing grid flexibility and facilitating the integration of renewable energy.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.28 no.4
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• pp.395-405
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• 2004

The filling pattern and an adaptive grid refinement based on the finite element method and Eulerian mesh advancement approach have been developed to analyze incompressible transient viscous flow with free surfaces. The governing equation for flow analysis is Navier-Stokes equation including inertia and gravity effects. The mixed FE formulation and predictor-corrector method are used effectively for unsteady numerical simulation. The flow front surface and the volume inflow rate are calculated using the filling pattern technique to select an adequate pattern among four filling patterns at each triangular control volume. By adaptive grid refinement, the new flow field that renders better prediction in flow surface shape is generated and the velocity field at the flow front part is calculated more exactly. In this domain the elements in the surface region are made finer than those in the remaining regions for more efficient computation. Using the proposed numerical technique, the collapse of a water dam has been analyzed to predict flow phenomenon of fluid and the predicted front positions with respect to time have been compared with the reported experimental results.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.12
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• pp.565-572
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• 2017

Traditional method for time series analysis, autoregressive integrated moving average (ARIMA) allows to mine significant patterns from the past observations using autocorrelation and to forecast future sequences. However, Korean baseball games do not have regular intervals to analyze relationship among the past attendance observations. To address this issue, we propose artificial neural network (ANN) based attendance prediction model using various measures including performance, team characteristics and social influences. We optimized ANNs using grid search to construct optimal model for regression problem. The evaluation shows that the optimal and ensemble model outperform the baseline model, linear regression model.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.9B
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• pp.1407-1415
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• 2010

In recent years, the smart grid technique for maximizing the energy efficiency of power networks has received a great deal of attentions. In particular, the Demand Response is a core technology differentiated from the present power network under the smart grid paradigm. To minimize the electric cost and maximize users' satisfaction, this paper proposes a unique scheduling algorithm derived by using optimization where the characteristics of various home appliances are taken into account. For this goal, we represent mathematical consumption patterns of the electric loads and propose the optimal scheduling scheme based on the importance factor of each device during one day. In the simulation results, we demonstrate the effectiveness of the proposed algorithm in the viewpoint of the minimal electric costs utilizing real statistical figures.