• Journal of Multimedia Information System
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• v.4 no.2
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• pp.57-64
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• 2017

Numerous software vulnerabilities have been found in the popular operating systems. And recently, robust linear behaviors in software vulnerability discovery process have been noticeably observed among the many popular systems having multi-versions released. Software users need to estimate how much their software systems are risk enough so that they need to take an action before it is too late. Security vulnerabilities are discovered throughout the life of a software system by both the developers, and normal end-users. So far there have been several vulnerability discovery models are proposed to describe the vulnerability discovery pattern for determining readiness for patch release, optimal resource allocations or evaluating the risk of vulnerability exploitation. Here, we apply a linear vulnerability discovery model into Windows operating systems to see the linear discovery trends currently observed often. The applicability of the observation form the paper show that linear discovery model fits very well with aggregate version rather than each version.

• Bulletin of the Korean Chemical Society
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• v.26 no.1
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• pp.139-145
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• 2005

Artificial neural networks (ANNs), for a first time, were successfully developed for the modeling and prediction of solvent effects on rate constant of [2+2] cycloaddition reaction of diethyl azodicarboxylate with ethyl vinyl ether in various solvents with diverse chemical structures using quantitative structure-activity relationship. The most positive charge of hydrogen atom (q$^+$), dipole moment ($\mu$), the Hildebrand solubility parameter (${\delta}_H^2$) and total charges in molecule (q$_t$) are inputs and output of ANN is log k$_2$ . For evaluation of the predictive power of the generated ANN, the optimized network with 68 various solvents as training set was used to predict log k$_2$ of the reaction in 16 solvents in the prediction set. The results obtained using ANN was compared with the experimental values as well as with those obtained using multi-parameter linear regression (MLR) model and showed superiority of the ANN model over the regression model. Mean square error (MSE) of 0.0806 for the prediction set by MLR model should be compared with the value of 0.0275 for ANN model. These improvements are due to the fact that the reaction rate constant shows non-linear correlations with the descriptors.

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.50 no.6
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• pp.273-281
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• 2001

In order to overcome the demerit and to improve the operation characteristics of Moving Coil type Linear Oscillatory Actuator(MC-LOA) with single-pole permanent magnet, this paper presents two models having the balanced magnetic circuit by multi-pole permanent magnet. They are short coil type with two-pole single-sided and two-ple double-sided permanent magnet. The characteristics between single-pole and multi-pole permanent magnet type MC-LOA are compared. As a result, multi-pole type MC-LOA has more merits than single-pole type about operation characteristics improvement and machine volume. The characteristics analysis is performed by their dynamic analysis composed of kinetic and electric equations and Finite Element Method(FEM). The propriety of multi-pole type MC-LOA model is verified with analysis results.

• KIEAE Journal
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• v.3 no.2
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• pp.17-24
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• 2003

The purpose of this study is to present rational methods of multi-criteria optimization of the envelope of buildings. The object is to determine the optimum R-value of the envelope of a building, based on the following criteria: minimum building costs (including the cost of materials and construction) and yearly heating costs. Mathematical model described heat losses and gains in a building during the heating season. It takes into consideration heat losses through wall, roof, floor and windows. Particular attention was paid to have a more detailed description of heat gains due to solar radiation. On the assumption that shape of building is rectangle in order to solve the problem, optimum R-value of the envelope of a building is determined by using non-linear programing methods(Kuhn-Tucker Conditions). The results constitute information for designers on the optimum R-value of a building envelope for energy saving buildings.

• Structural Engineering and Mechanics
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• v.53 no.2
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• pp.373-392
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• 2015

In the present work, structural joints have been modeled as a pair of translational and rotational springs and frequency equation of the overall system has been developed using sub-structure synthesis. It is shown that using first few natural frequencies of the system, one can obtain a set of over-determined system of equations involving the unknown stiffness parameters. Method of multi-linear regression is then applied to obtain the best estimate of the unknown stiffness parameters. The estimation procedure has been developed first for a two parameter joint model and then for a three parameter model, in which cross coupling terms are also included. Two cases of structural connections have been considered, first with a cantilever beam with support flexibility and then a pair of beams connected through lap joint. The validity of the proposed method is demonstrated through numerical simulation and by experimentation.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.9A
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• pp.1299-1305
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• 2000

In B-ISDN, to realize ATM, the optimum control method of multi-media traffic must be proposed. Because there is not the traffic model of multi-media to make clear, the realization of optimum ATM congestion control is very difficult. In this paper, the traffic model is assumed to be slowly time-variable non-linear function and for real-time prediction of it, new model which is composed with parallel triple neural networks is proposed. And the simulation to predict assumed ATM traffic is executed. From the result, it's capability is shown that the proposed neural network model can be used in ATM congestion control.

• Proceedings of the KIEE Conference
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• 2001.11c
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• pp.420-423
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• 2001

In this paper, the Multi-FNNs(Fuzzy-Neural Networks) architecture is identified and optimized using HCM(Hard C-Means) clustering method and genetic algorithms. The proposed Multi-FNNs architecture uses simplified inference and linear inference as fuzzy inference method and error back propagation algorithm as learning rules. Here, HCM clustering method, which is carried out for the process data preprocessing of system modeling, is utilized to determine the structure of Multi-FNNs according to the divisions of input-output space using I/O process data. Also, the parameters of Multi-FNNs model such as apexes of membership function, learning rates and momentum coefficients are adjusted using genetic algorithms. An aggregate performance index with a weighting factor is used to achieve a sound balance between approximation and generalization abilities of the model. To evaluate the performance of the proposed model we use the time series data for gas furnace and the NOx emission process data of gas turbine power plant.

• 제어로봇시스템학회:학술대회논문집
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• 1986.10a
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• pp.321-325
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• 1986

A new control scheme for the state space model of the robot system using the theory of optimal multi-variable structure is presented in this paper. It is proposed to optimize multi-dimensional variable structure systems for obtaining the required stabilizing signal by minimizing a performance index with respect to the state vector in the sliding mode. It is concluded the proposed variable structure controller yields better system dynamic performance than that obtained by using the only linear optimal controller inthat responses for a step disturbance have a shorter setting time, no matter what overshoot values and rising time.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.32 no.4
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• pp.53-62
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• 2009

Lee[15] examined quantity discount contracts between a manufacturer and a retailer in a stochastic, two-period inventory model where quantity discounts are provided based on the previous order size. During the two periods, the retailer faces stochastic (truncated Poisson distributed) demands and he/she places orders to meet the demands. The manufacturer provides for the retailer a price discount for the second period order if its quantity exceeds the first period order quantity. In this paper we extend the above two-period model to a k-period one (where k < 2) and propose a stochastic nonlinear mixed binary integer program for it. In order to make the program tractable, the nonlinear term involving the sum of truncated Poisson cumulative probability function values over a certain range of demand is approximated by an i-interval piecewise linear function. With the value of i selected and fixed, the piecewise linear function is determined using an evolutionary algorithm where its fitness to the original nonlinear term is maximized. The resulting piecewise linear mixed binary integer program is then transformed to a mixed binary integer linear program. With the k-period model developed, we suggest a solution procedure of receding horizon control style to solve n-period (n < k) order decision problems. We implement Lee's two-period model and the proposed k-period model for the use in receding horizon control style to solve n-period order decision problems, and compare between the two models in terms of the pattern of order quantities and the total profits. Our computational study shows that the proposed model is superior to the two-period model with respect to the total profits, and that order quantities from the proposed model have higher fluctuations over periods.

• Journal of Korean Society of Water and Wastewater
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• v.13 no.1
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• pp.95-112
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• 1999

In this research, it is proposed that optimization method is introduced and applied to the design of pipeline system in multi-regional water supply project, which has been constructed to settle the regional unbalance problems of available water resources. For the purpose, interface programs are developed to integrate linear programming model and KYPIPE model which is used for optimization and hydraulic analysis, respectively. The developed program is applied to the pipeline system design of multi-regional water supply project. The optimal diameters from the application of linear programming technique are compared with those from conventional method that is time-consuming and tedious trail and error process. Since the conventional design largely depends upon the experience of designers and the results of general hydraulic analysis, it can not be reasonable and consistent. The application of linear programming technique can make it possible to design pipeline system optimally by using same design factors of general hydraulic models. The model can select commercial discrete pipe diameter as optimal size by using pipe length as decision variables. The developed model is applied to Pohang multi-regional water supply system design with two different objective functions, which are initial construction cost and annual cost including electric cost. As results, it is calculated that the initial construction cost of 1,449,740 thousand won is saved and annual cost of 128,951 thousand won is saved for a year within study year. Also, the optimal site of pump station is selected on 5th pipe, which is located between the diverging junction to Kangdong(2) province and the diverging junction to Cheonbuk province. It is explained that pump cost is less than pipe cost in this application case study due to little pump station scale. In the case of water supply with large pump capacity, it is reasonal that the increase of pipe size is more efficient instead the increase of pump station capacity to save annual cost.