• Communications of the Korean Mathematical Society
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• v.18 no.1
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• pp.87-94
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• 2003

We use geometric properties of Gromov-Hausdorff-convergence to present a way to construct rough but natural invariants of metric geometry.

• Advances in nano research
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• v.17 no.2
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• pp.137-147
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• 2024

Graphene nanoribbons (GNRs) are considered a promising alternative to graphene for future nanoelectronic applications. However, GNRs-based device modeling is still at an early stage. This research models the electronic properties of n-doped rough-edged 13-armchair graphene nanoribbons (13-AGNRs) and quantum transport properties of n-doped rough-edged 13-armchair graphene nanoribbon field-effect transistors (13-AGNRFETs) at different doping concentrations. Step-up and edge doping are used to incorporate doping within the nanostructure. The numerical real-space nearest-neighbour tight-binding (NNTB) method constructs the Hamiltonian operator matrix, which computes electronic properties, including the sub-band structure and bandgap. Quantum transport properties are subsequently computed using the self-consistent solution of the two-dimensional Poisson and Schrödinger equations within the non-equilibrium Green's function method. The finite difference method solves the Poisson equation, while the successive over-relaxation method speeds up the convergence process. Performance metrics of the device are then computed. The results show that highly doped, rough-edged 13-AGNRs exhibit a lower bandgap. Moreover, n-doped rough-edged 13-AGNRFETs with a channel of higher doping concentration have better gate control and are less affected by leakage current because they demonstrate a higher current ratio and lower off-current. Furthermore, highly n-doped rough-edged 13-AGNRFETs have better channel control and are less affected by the short channel effect due to the lower value of subthreshold swing and drain-induced barrier lowering. The inclusion of dopants enhances the on-current by introducing more charge carriers in the highly n-doped, rough-edged channel. This research highlights the importance of optimizing doping concentrations for enhancing GNRFET-based device performance, making them viable for applications in nanoelectronics.

• Journal of Digital Convergence
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• v.11 no.11
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• pp.343-349
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• 2013

Rough set theory comes to derive optimal rules through the effective selection of features from the redundancy of lots of information in data mining using the concept of equivalence relation and approximation space in rough set. The reduction of attributes is one of the most important parts in its applications of rough set. This paper purports to define a information-theoretic measure for determining the most important attribute within the association of attributes using rough entropy. The proposed method generates the effective reduct set and formulates the core of the attribute set through the elimination of the redundant attributes. Subsequently, the control rules are generated with a subset of feature which retain the accuracy of the original features through the reduction.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1995.09a
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• pp.325-333
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• 1995

Back-propagation is the most widely used algorithm for supervised learning in multi-layer feed-forward networks. However, back-propagation is very slow in convergence. In this paper, a new weight initialization method, called rough map initialization, in multi-layer perceptrons is proposed. To overcome the long convergence time, possibly due to the random initialization of the weights of the existing multi-layer perceptrons, the rough map initialization method initialize weights by utilizing relationship of input-output features with singular value decomposition technique. The results of this initialization procedure are compared to random initialization procedure in encoder problems and xor problems.

• The Korean Journal of Food & Health Convergence
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• v.4 no.3
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• pp.12-21
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• 2018

This research was conducted at agric physical lab, Department of Animal science, Faculty of Agriculture to determines the effects of marinating spent layer meat with basil leaf paste on drip loss and sensory attributes under different post mortem conditions. In the light of this, the poultry industry is obliged to continuously grow for a steady supply of quality poultry meat. Marinating the spent layer hen's meat with fresh basil leaves (Ocimum basilicum) in addition to subjecting the meat to 0, 6, 12, and at 24 hours post mortem aging before cooking increased it's organoleptic attributes which was readily acceptable to consumers. Marination of meat with herbs or spices like basil leaves paste had enhanced consumer's preference for taste, texture aroma, colour and overall acceptance. Marination improved consumer acceptance of spent layer meat irrespective of parts and post mortem aging. However, the majority of the respondents preferred meat marinated and subjected to 12 hours of post mortem aging. It is recommended that more quantity of marinate should be added further studies should in order to determine more effect of fresh basil leaves rough paste. And more hours of postmortem aging should be increased in order to determine more effect of fresh basil leaves rough paste marinate.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.31 no.8
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• pp.680-687
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• 2007

The paper presents a numerical simulation of flow of a turbulent boundary layer, representing a typical wind environment and matching a series of wind tunnel observations. The simulations are carried out at a Reynolds number of 20,000, based on the velocity U at a pseudo-height h, and large enough that the flow be effectively Reynolds number independent. Some wall models are proposed for the LES(Large Eddy Simulation) of the turbulent boundary layer over a rough surface. The Jenson number, $J=h/z_0$, based on the roughness length $z_0$, is 600 to match the wind tunnel data. The computational mesh is uniform with a spacing of h/32, as this aids rapid convergence of the multigrid solver, and the governing equations are discretised using second order finite differences within a parallel multiblock environment. The results presented include the comparison between wind tunnel measurements and LES computations of the turbulent boundary layer over rough surface.

• Journal of Integrative Natural Science
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• v.12 no.4
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• pp.105-115
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• 2019

In this paper, a prediction system is proposed to control the brightness of smart street lamps by predicting the moving path through the reduction of consumption power and information of pedestrian's past moving direction while meeting the function of existing smart street lamps. The brightness of smart street lamps is adjusted by utilizing the walk tracking vector and soft hand-off characteristics obtained through the motion sensing sensor of smart street lamps. In addition, the motion vector is used to analyze and predict the pedestrian path, and the GPU is used for high-speed computation. Pedestrians were detected using adaptive Gaussian mixing, weighted difference imaging, and motion vectors, and motions of pedestrians were analyzed using the extracted motion vectors. The preprocessing process using linear interpolation is performed to improve the performance of the proposed prediction system. Fuzzy prediction system and neural network prediction system are designed in parallel to improve efficiency and rough set is used for error correction.

• Journal of information and communication convergence engineering
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• v.9 no.1
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• pp.55-58
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• 2011

In this paper we introduce an improved method to measure saliency values of pixels from an image. The proposed saliency measure is formulated using local features of color and a statistical framework. In the preprocessing step, rough salient pixels are determined as the local contrast of an image region with respect to its neighborhood at various scales. Then, the saliency value of each pixel is calculated by Bayes' rule using rough salient pixels. The experiments show that our approach outperforms the current Bayes' rule based method.

• Journal of the Korean Data and Information Science Society
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• v.25 no.2
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• pp.281-292
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• 2014

As the importance of algorithm trading is getting stronger, researches for artificial intelligence (AI) based trading strategy is also being more important. However, there are not enough studies about using more than two AI methodologies in one trading system. The main aim of this study is development of algorithm trading strategy based on the rough set theory that is one of rule-based AI methodologies. Especially, this study used genetic algorithm for optimizing profit of rough set based strategy rule. The most important contribution of this study is proposing efficient convergence of two different AI methodology in algorithm trading system. Target of purposed trading system is KOPSI200 futures market. In empirical study, we prove that purposed trading system earns significant profit from 2009 to 2012. Moreover, our system is evaluated higher shape ratio than buy-and-hold strategy.

• Journal of Korea Water Resources Association
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• v.56 no.1
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• pp.45-52
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• 2023

Due to natural disasters such as heavy rain that occurred in the metropolitan area in August 2022, human casualties and property damage are increasing. Accordingly, the government is making efforts to respond to natural disasters, but due to the absence of related standards and standardized standards, problems such as increased construction costs and deterioration in construction quality for disaster prevention facility maintenance projects are occurring. Accordingly, a rough construction cost estimation module was developed and applied to 25 new pumping stations in Korea. As a result of the analysis, the accuracy of the rough construction cost derived through the module recorded 70% of the detailed design cost, which is 4% higher than the previously used rough construction cost accuracy of 66% by the Ministry of Environment. Accordingly, it is expected that the efficiency of the disaster prevention project can be increased if the developed module is used to calculate the rough construction cost for storm and flood disaster prevention in the future.