• Journal of the Korea Society of Computer and Information
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• v.21 no.4
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• pp.81-88
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• 2016

The optimal solution of quadratic assignment problem (QAP) cannot get done in polynomial time. This problem is called by NP-complete problem. Therefore the meta-heuristic techniques are applied to this problem to get the approximated solution within polynomial time. This paper proposes an algorithm for a random type QAP, in which the instance of two nodes are arbitrary. The proposed algorithm employs what is coined as a max flow-min distance rule by which the maximum flow node is assigned to the minimum distance node. When applied to the random type QAP, the proposed algorithm has been found to obtain optimal solutions superior to those of the genetic algorithm.

• Journal of Korean Society for Quality Management
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• v.34 no.3
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• pp.19-30
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• 2006

For highly reliable products, it is difficult to assess the lifetime of the products with traditional life tests. Accordingly, a recent approach is to observe the performance degradation of product during the test rather than regular failure time. This study compares performances of three methods(i.e. the approximation, analytical and numerical methods) to estimate the parameters and quantiles of the lifetime when the time-to-failure distribution follows Weibull and lognormal distributions under a random coefficient degradation rate model. Numerical experiments are also conducted to investigate the effects of model error such as measurements in a random coefficient model.

• Journal of Applied and Pure Mathematics
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• v.4 no.3_4
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• pp.107-122
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• 2022

This manuscript aims to investigate the existence, uniqueness, and stability of non-local random impulsive neutral stochastic differential time delay equations (NRINSDEs) with Poisson jumps. First, we prove the existence of mild solutions to this equation using the Banach fixed point theorem. Next, we demonstrate the stability via continuous dependence initial value. Our study extends the work of Wang, and Wu [16] where the time delay is addressed by the prescribed phase space 𝓑 (defined in Section 3). To illustrate the theory, we also provide an example of our methods. Using our results, one could investigate the controllability of random impulsive neutral stochastic differential equations with finite/infinite states. Moreover, one could extend this study to analyze the controllability of fractional-order of NRINSDEs with Poisson jumps as well.

• Journal of the Korean Operations Research and Management Science Society
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• v.12 no.1
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• pp.19-26
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• 1987

In analyzing time series, estimating the level or the current mean of the process plays an important role in understanding its structure and in being able to make forecasts. The studies the class of time series models where the level of the process is assumed to follow a random walk and the deviation from the level follow an ARMA process. The estimation and forecasting problem in a Bayesian framework and uses the Kalman filter to obtain forecasts based on estimates of level. In the analysis of time series, we usually make the assumption that the time series is generated by one model. However, in many situations the time series undergoes a structural change at one point in time. For example there may be a change in the distribution of random variables or in parameter values. Another example occurs when the level of the process changes abruptly at one period. In order to study such problems, the assumption that level follows a random walk process is relaxed to include a major level change at a particular point in time. The major level change is detected by examining the likelihood raio under a null hypothesis of no change and an alternative hypothesis of a major level change. The author proposes a method for estimation the size of the level change by adding one state variable to the state space model of the original Kalman filter. Detailed theoretical and numerical results are obtained for th first order autoregressive process wirth level changes.

• Journal of the Korean Data and Information Science Society
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• v.8 no.2
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• pp.271-276
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• 1997

A dynamic discount approach is proposed for the estimation of the Poisson parameter and the forecasting of the Poisson random variable, where the parameter of the Poisson distribution varies over time intervals. The recursive estimation procedure of the Poisson parameter is provided. Also the forecasted distribution of the Poisson random variable in the next time interval based on the information gathered until the current time interval is provided.

• Proceedings of the Korean Institute of Communication Sciences Conference
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• 1991.10a
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• pp.100-102
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• 1991

In this paper, we use LFSR(Linear Feedback Shift Register) as a kind of pseudo-random one-time pad. Key generator is constructed using r separate LFSR's with IP(Irreducible Polynominal) which are relatively prime. Key generated in this method has high linear complexity. And also, file cryptosystem for file encryption and decryption is constructed.

• Journal of Korean Society of Transportation
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• v.20 no.5
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• pp.67-79
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• 2002

In this paper, observed trajectories of a vehicle platoon are viewed as one realization of a finite sequence of random trajectories. In this point of view, we develop novel and mathematically rigorous concept of traffic flow variables such as local traffic density, instantaneous traffic flow, and velocity field and investigate their nature on a general probability space of a sequence of random trajectories which represent vehicle trajectories. We present a simple model of random trajectories as an illustrative example and, derive the values of traffic flow variables based on the new definitions in this model. In particular, we construct the model for the sequence of random vehicle trajectories with a system of stochastic differential equations. Each equation of the system nay represent microscopic random maneuvering behavior of each vehicle with properly designed drift coefficient functions and diffusion coefficient functions. The system of stochastic differential equations nay generate a well-defined probability space of a sequence of random vehicle trajectories. We derive the partial differential equation for the expected cumulative plot with appropriate initial conditions. By solving the equation with numerical methods, we obtain the values of expected cumulative plot, local traffic density, and instantaneous traffic flow. In addition, we derive the partial differential equation for the expected travel time to a certain location with appropriate initial and/or boundary conditions, which is solvable numerically. We apply this model to a case of single vehicle trajectory.

• Journal of the Korean Data and Information Science Society
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• v.22 no.2
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• pp.287-296
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• 2011

Thirty six female students participated in the experiment of the fat mass weight loss. they kept diary for foods they ate every day, took a picture of the foods, transmitted the picture to the experimenter by the camera phone, and consulted him about fat mass loss once a week for 8 weeks period. Fat mass weight and its related factors of the students had been measured repeatedly every week during 8 weeks, The repeated measurement data were used for applying various random coefficient models. And hence optimal random coefficient model was selected. From the optimal model, the baseline, body mass index, diastolic blood pressure, total cholesterol and time of the fixed factors were very significant. The fixed quadratic time effect existed. The variance components corresponding to the subject effect, linear time effect of the random coefficients were all positive. Thus random coefficients up to the linear terms were considered as the optimal model. The treatment effect reduced the weight loss to an average of 2.1kg at the end of the period.

• Journal of Institute of Control, Robotics and Systems
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• v.10 no.7
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• pp.583-589
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• 2004

Modem control systems often use a communication network to send measurement and control signals between nodes. Communication delays can be time varying. The length of the time delays is often hard to predict and modeled as being random. This paper proposes a combined controller used to compensate network time delay by estimating the delay with the interacting multiple model (IMM). The network delay is modeled as a Markov chain and 3 modes representing heavy, medium, and low network loads are used in the IMM. The proposed method is applied to an optimal control system with double integrators and the results are compared with the existing control methods.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.915-920
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• 2003

Modern control systems often use a communication network to send measurement and control signals between nodes. Communication delays can be time varying. The length of the time delays is often hard to predict and are modeled as being random. This paper proposes a combined controller used to compensate network time delay by estimating the delay with the interacting multiple model (IMM). The network delay is modeled as a Markov chain and 3 modes representing heavy, medium, and low network loads are used in the IMM. The proposed method is applied to an optimal control system with double integrators and the results are compared with the existing control methods.