• Journal of Korean Society for Quality Management
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• v.51 no.4
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• pp.691-700
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• 2023

Purpose: This study aims to analyze the diffusion pattern of the Mongolian mobile phone market. In particular, we used a generalized diffusion model to explore the factors affecting market potenial. Methods: We used three diffusion models to estimate the number of mobile subscribers in Mongolia. Based on the Logistic model with the best fitness, we introduced time-varying market potential and explored the influence of various independent variables such as GDP and inflation. Results: Among the basic diffusion models, the Logistic model was the best in terms of estimation performance and statistical significance. The estimation results of the Generalized Logistic model confirm that investment in the telecommunication sector has a significant positive effect on market potential. The estimation of the Generalized Logistic model effectively describes the continuous growth of the Mongolian telecommunications market until recently. Conclusion: We have analyzed the diffusion pattern of the Mongolian telecommunications market and found that the amount of investment in the sector leads to the growth of the market size. This study is original in terms of its subject - Mongolian telecommunications market and methodology - time-varying market potential.

• The Transactions of the Korea Information Processing Society
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• v.5 no.5
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• pp.1083-1097
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• 1998

The conventional geographic information systems(GIS) is a software which handles spatial and aspatial information of objects in the real world. The system can not support users time-varying information because it manipulates their snapshot data in the spatial database. Also even though it supports time-varying information, it is very limited and hs many difficulties in presenting and processing queries. This paper therefore describes an integrated spatiotemporal data model using loosely-coupled approach which is extended a time dimension for the previous spatial database and which handles time-varying historical information of spatial objects. Conclusionally this paper not only designed a data structure for spatiotemporal database, but also implemented spatial comparison operations varying over time.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.23 no.11 s.170
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• pp.1982-1992
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• 1999

This paper presents a novel concept of cargo handling system adapted for a sea port subjected to severe time-varying tide. The proposed system can perform loading or unloading by using a sort of hydraulic elevator associated with real-time position control. In order to achieve a proof-of-concept, a small-sized laboratory model of the cargo handling system is designed and built. The model consists of three principal components container palette transfer (CPT) car, platform with lifting columns, and cargo ship. The platform activated by electro-rheological (ER) valve-cylinders is actively controlled to track the position of the cargo ship subjected to be varied due to the time-varying tide and wave motion. Following the derivation of the dynamic model for the platform and cargo ship motions, an appropriate control scheme is formulated and implemented. The location of the CPT car is sensed by a set of photoelectric switches and controlled via sequence controller. On the other hand, a sliding mode controller (SMC) is adopted as the position controller for the platform. Both simulated and measured control results are presented to demonstrate the effectiveness of the proposed cargo system.

• Structural Engineering and Mechanics
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• v.75 no.4
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• pp.425-434
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• 2020

Practical non-synoptic fluctuating wind often exhibits nonstationary features and should be modeled as nonstationary random processes. Generally, the coherence function of the fluctuating wind field has time-varying characteristics. Some studies have shown that there is a big difference between the fluctuating wind field of the coherent function model with and without time variability. Therefore, it is of significance to simulate nonstationary fluctuating wind field with time-varying coherent function. However, current studies on the numerical simulation of nonstationary fluctuating wind field with time-varying coherence are very limited, and the proposed approaches are usually based on the traditional spectral representation method with low simulation efficiency. Especially, for the simulation of multi-variable wind field of large span structures such as transmission tower-line, not only the simulation is inefficient but also the matrix decomposition may have singularity problem. In this paper, it is proposed to conduct the numerical simulation of nonstationary fluctuating wind field in one-spatial dimension with time-varying coherence based on the wavenumber-frequency spectrum. The simulated multivariable nonstationary wind field with time-varying coherence is transformed into one-dimensional nonstationary random waves in the simulated spatial domain, and the simulation by wavenumber frequency spectrum is derived. So, the proposed simulation method can avoid the complicated Cholesky decomposition. Then, the proper orthogonal decomposition is employed to decompose the time-space dependent evolutionary power spectral density and the Fourier transform of time-varying coherent function, simultaneously, so that the two-dimensional Fast Fourier transform can be applied to further improve the simulation efficiency. Finally, the proposed method is applied to simulate the longitudinal nonstationary fluctuating wind velocity field along the transmission line to illustrate its performances.

• Journal of the Korean Operations Research and Management Science Society
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• v.34 no.4
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• pp.15-26
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• 2009

Bass diffusion model have played a central role in studying the diffusion of the new products since 1969, the year of publication of Bass model. Almost 750 publications based on the Bass diffusion model have explored extensions and applications. Extension models can be divided into two types. One is the model containing marketing-mix variables and the other is the model containing additional parameters. This paper presents another extension model of the latter type. Our model allows the time varying coefficients of innovation and imitation. Two pieces approximation of time varying coefficients is introduced and it's parameters are estimated based on NLS(Non-Linear Mean Square) method. Empirical studies are performed and the results show that our model is superior to the basic Bass model and the NUI(Non-Uniform Influence) model which is the well-known extension of the Bass model. The model developed in this paper is, also, transformed into the Bass model with the ready potential adopters in order to enhance the descriptive power.

• Environmental and Resource Economics Review
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• v.13 no.3
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• pp.417-440
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• 2004

For the behavior of the wholesale spot price, a regime switching model with time-varying transition probabilities was estimated using the data from the PJM (Pennsylvania-New Jersey-Maryland) market. By including the temperature as an explanatory variable in the transition probability equations, the threshold effect of changing regime is clearly enhanced. And hence the predictability of the price spikes was improved. This means that the model showed a very clear threshold effect, with a low probability of switching for low loads and low temperatures and a high probability for high loads and high temperatures. And temperature showed a clearer threshold effect than load does. This implies that weather-related contracts may help to hedge against the risk in the cost of buying electricity during a summer.

• Journal of the Korean Society for Heat Treatment
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• v.36 no.6
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• pp.374-381
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• 2023

This study develops and evaluates a deep learning model for predicting oxide and nitride layers based on plasma process data. We introduce a novel deep learning-based Varying Coefficient Regressor (VCR) by adapting the VCR, which previously relied on an existing unique function. This model is employed to forecast the oxide and nitride layers within the plasma. Through comparative experiments, the proposed VCR-based model exhibits superior performance compared to Long Short-Term Memory, Random Forest, and other methods, showcasing its excellence in predicting time series data. This study indicates the potential for advancing prediction models through deep learning in the domain of plasma processing and highlights its application prospects in industrial settings.

• Proceedings of the KOR-KST Conference
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• 2002.02a
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• pp.97-139
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• 2002

The aim of the present study is to find a good quality user equilibrium assignments under time varying condition. For this purpose, this study introduces a dynamic network loading method that can maintain correct flow propagation as well as flow conservation, and it develops a novel solution algorithm that does not need evaluation of the objective function by modifying the Schittenhelm (1990)'s algorithm. This novel algorithm turns out to be efficient and convenient compared to the conventional Frank-Wolfe (1956) algorithm because the former finds solutions based on routes rather than links so that it can maintain correct flow propagation intrinsically in the time-varying network conditions. The application of dynamic user equilibrium (DUE) assignment model with this novel solution algorithm to test networks including medium-sized one shows that the present DUE assignment model gives rise to high quality discrete time solutions when we adopt the deterministic queuing model for a link performance function, and we associate flows and costs in a proper way.

• Communications of the Korean Mathematical Society
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• v.21 no.2
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• pp.375-384
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• 2006

We consider a class of bilinear models with periodic regime switching and find easy-to-check sufficient conditions that ensures the existence of a stationary process obtained from given difference equation. Existence of a higher order moments is examined.

• Proceedings of the KSME Conference
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• 2008.11a
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• pp.1331-1336
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• 2008

This paper suggests a method using Bayesian inference to estimate the parameters of Weibull distribution and acceleration parameters under the condition that the stresses are time-dependent functions. A Bayesian model based on the discrete time approximation is formulated to infer the parameters of interest from the failure data of the virtual tests and a statistical analysis is considered to decide the most probable mean values of the parameters for reasoning of the failure data.