• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.444-446
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• 2021

MANETs(Mobile Ad-hoc Networks) are composed of mobile nodes that are not subordinate to fixed networks and have the feature that can form their own networks. they are used in various fields for specific goals. The mobility model in MANET can be applied in various ways depending on the purpose of usage. The random mobility model has the advantage of being simple and easy to implement, so it is being used the most. In a MANET, it is assumed that each node moves independently. The random movement model is a good model for expressing this independence of each node. However, it is insufficient to express the characteristics of all nodes with only random properties of individual nodes. This paper limits the stochastic mobility model applicable in MANET. we compare the proposed stochastic mobility model and the random mobility model. We confirm that the proposed mobility model is applied to the routing protocol to show improved characteristics in terms of energy consumption efficiency.

• Journal of Korean Institute of Industrial Engineers
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• v.24 no.4
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• pp.485-492
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• 1998

Most forecasting models often fail to produce appropriate forecasts because we build a model based on the assumption of the data being generated from the only one stochastic process. However, in many real problems, the time series data are generated from one stochastic process for a while and then abruptly undergo certain structural changes. In this paper, we assume the basic underlying process is the simple state-space model with random level and deterministic drift but interrupted by three types of exogenous shocks: level shift, drift change, outlier. A Bayesian procedure to detect, estimate and adapt to the structural changes is developed and compared with simple, double and adaptive exponential smoothing using simulated data and the U.S. leading composite index.

• International Journal of Computer Science & Network Security
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• v.21 no.4
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• pp.84-92
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• 2021

Care services are a significant asset in human life. Care in its overall nature focuses on human needs and covers several aspects such as health care, homes, personal care, and education. In fact, care deals with many dimensions: physical, psychological, and social interconnections. Very little information is available on estimating the cost of care services that provided to orphans and abandoned children. Prediction of the cost of the care system delivered by governmental or non-governmental organizations to support orphans and abandoned children is increasingly needed. The purpose of this study is to analyze the care cost for orphanage organizations in Saudi Arabia to forecast the cost as well as explore the most influence factor on the cost. By using business analytic process that applied statistical and machine learning techniques, we proposed a model includes simple linear regression, Naive Bayes classifier, and Random Forest algorithms. The finding of our predictive model shows that Naive Bayes has addressed the highest accuracy equals to 87% in predicting the total care cost. Our model offers predictive approach in the perspective of business analytics.

• The Journal of Korea Robotics Society
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• v.9 no.1
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• pp.11-19
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• 2014

In this paper, an external torque estimation problem in one-degree-of-freedom (1-DOF) flexible-joint robot equipped with a joint-torque sensor is revisited. Since a sensor torque from the joint-torque sensor is distorted by two dynamics having a spring connection, i.e., motor dynamics and link dynamics of a flexible-joint robot, a model-based estimation, rather than a simple linear spring model, should be required to extract external torques accurately. In this paper, an external torque estimation algorithm for a 1-DOF flexible-joint robot is proposed. This algorithm estimates both an actuating motor torque from the motor dynamics and an external link torque from the link dynamics simultaneously by utilizing the flexible-joint robot model and the Kalman filter estimation based on random-walk model. The basic structure of the proposed algorithm is explained, and the performance is investigated through a custom-designed experimental testbed for a vertical situation under gravity.

• The Journal of Korea Robotics Society
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• v.4 no.1
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• pp.56-61
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• 2009

A probability-based data generation is a typical context-generation method that is a not only simple and strong data generation method but also easy to update generation conditions. However, the probability-based context-generation method has been found its natural-born ambiguousness and confliction problems in generated context data. In order to compensate for the disadvantages of the probabilistic random data generation method, a situation propagation network is proposed in this paper. The situation propagating network is designed to update parameters of probability functions are included in probability-based data generation model. The proposed probability-based context-generation model generates two kinds of contexts: one is related to independent contexts, and the other is related to conditional contexts. The results of the proposed model are compared with the results of the probabilitybased model with respect to performance, reduction of ambiguity, and confliction.

• Journal of electromagnetic engineering and science
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• v.5 no.4
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• pp.183-188
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• 2005

A simple microwave backscattering model for vegetation canopies on earth surfaces is developed in this study. A natural earth surface is modeled as a two-layer structure comprising a vegetation layer and a ground layer. This scattering model includes various scattering mechanisms up to the first-order multiple scattering( double-bounce scattering). Radar backscatter from ground surface has been modeled by the polarimetric semi-empirical model (PSEM), while the backscatter from the vegetation layer modeled by the vector radiative transfer model. The vegetation layer is modeled by random distribution of mixed scattering particles, such as leaves, branches and trunks. The number of input parameters has been minimized to simplify the scattering model. The computation results are compared with the experimental measurements, which were obtained by ground-based scatterometers and NASA/JPL air-borne synthetic aperture radar(SAR) system. It was found that the scattering model agrees well with the experimental data, even though the model used only ten input parameters.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.10 no.2
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• pp.73-82
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• 1998

In order to simulate surface discharged heat dispersion in costal area, a 2-dimensional Eulerian-Lagrangian model for far field and semi-active particle tracking random walk model in near field has been combined. The mass of discharged heat water in near field has treated as particles with buoyancy and this is eventually converted to horizontal additive dispersion in random walk equations. This model is applied to both a simplified coastal geometry and a real site. In simple application it can simulate plume-like characteristics around discharging point than a near field-model, CORMIX/3. Actual application in the Chonsu Bay shows farther spreading of heat water in near field comparing the observed data, and this shows that the developed model might be applied with satisfaction.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.8
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• pp.1082-1087
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• 2021

In MANET(mobile ad hoc network), Mobility models vary according to the application-specific goals. The most widely used Random WayPoint Mobility Model(RWPMM) is advantageous because it is simple and easy to implement, but the random characteristic of nodes' movement is not enough to express the mobile characteristics of the entire sensor nodes' movements. The random mobility model is insufficient to express the inherent movement characteristics of the entire sensor nodes' movements. In the proposed Stochastic mobility model, To express the overall nodes movement characteristics of the network, the moving nodes are treated as random variables having a specific probability distribution characteristic. The proposed Stochastic mobility model is more stable and energy-efficient than the existing random mobility model applies to the routing protocol to ensure improved performances in terms of energy efficiency.

• Journal of Mechanical Science and Technology
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• v.14 no.8
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• pp.900-911
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• 2000

A simple Lagrangian pdf model is proposed with a new numerical algorithm for application in wall-bounded turbulent flows. To investigate the performance of the Lagrangian model, we minimize model's dependence on empirical constants by selecting the simplest model for turbulent dissipation rate. The effect of viscosity is also included by adding a Brownian random walk calculate the position of a particle. For the no-slip condition at the wall and correct nearwall behavior of velocity, we develop a new boundary treatment for the particles that strike the wall. By applying the model to a fully developed turbulent channel flow at low Reynolds number, we investigate the model's performance through comparison with direct numerical simulation result.

• Journal of electromagnetic engineering and science
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• v.12 no.1
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• pp.70-76
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• 2012

We compare the detection probabilities of the time-reversal(TR) detection system and the conventional radar system. The target is assumed to be hidden inside a random medium such as a forest. We propose a TR detection system based on the SAR(Synthetic Aperture Radar) algorithm. Unlike the conventional SAR images, the proposed TR-SAR system has an interesting property. Specifically, the target-related signal components due to the time-reversal refocusing characteristics, as well as some of clutter-related signal components are concentrated at the time-reversal reference point. The remaining clutter-related signal components are scattered around that reference point. In this paper, we model the random media as a collection of point scatterers to avoid unnecessary complexities. We calculate the detection probability of the TR radar system based on the proposed simple random media model.