• ETRI Journal
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• v.28 no.2
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• pp.247-249
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• 2006

In the development of a new wireless communications system, a versatile and accurate radio channel for indoor communications is needed. In particular, the investigation of radio transmission into buildings is very important. In this letter, we present an improved three-dimensional electromagnetic wave propagation prediction model for indoor wireless communications that takes into consideration building penetration loss. A ray tracing technique based on an image method is also employed in this study. Three-dimensional models can predict any complex indoor environment composed of arbitrarily shaped walls. A speed-up algorithm, which is a modified deterministic ray tube method, is also introduced for efficient prediction and computation.

• Journal of Advanced Marine Engineering and Technology
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• v.31 no.5
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• pp.599-605
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• 2007

This paper presents a method for identifying various systems based on input-output data and a real-coded genetic algorithm(RCGA). The advantages of this technique are, first, it is not dependent on the deterministic or stochastic nature of the systems and, second, the globally optimized models for the original systems can be identified without the need of a differentiable measure function of linearly separable parameters. Under suitable hypotheses, the estimation error is shown to converge in probability to zero. The performance of the proposed algorithm is demonstrated through several simulations.

• Proceedings of the Korean Society of Computer Information Conference
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• 2014.01a
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• pp.5-8
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• 2014

This paper presents a parallel approach of formant synthesis method for haegeum on graphics processing units (GPU) using spectral modeling. Spectral modeling synthesis (SMS) is a technique that models time-varying spectra as a combination of sinusoids and a time-varying filtered noise component. A second-order digital resonator by the impulse-invariant transform (IIT) is applied to generate deterministic components and the results are band-pass filtered to adjust magnitude. The noise is calculated by first generating the sinusoids with formant synthesis, subtracting them from the original sound, and then removing some harmonics remained. The synthesized sounds are consequently by adding sinusoids, which are shown to be similar to the original Haegeum sounds. Furthermore, GPU accelerates the synthesis process enabling- real time music synthesis system development, supporting more sound effect, and multiple musical sound compositions.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.3
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• pp.197-202
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• 1999

Electric arc furnaces (EAFs) use bulk electrical energy to create heat in metal refining industries. The electric arc process is a main cause of the degradation of the electric power quality such as voltage flicker due to the interaction of the high demand currents of the load with the supply system impedance. The stochastic models have described the aperiodic physical phenomena of EAFs. An alternative approach is to include deterministic chaos in the characterization of the arc currents. In this parer, a chaotic approach to such modeling is described and justified. At the same time, a DLL(Dynamic Link Library) module, which is a FORTRAN interface with TACS (Transient Analysis of Control Systems), is developed to implement the chaotic load model in the Electromagnetic Transients Program (EMTP). The details of the module and the results of tests performed on the module to verify the model and to illustrate its capabilities are presented in this paper.

• Proceedings of the KOSOMBE Conference
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• v.1996 no.05
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• pp.27-30
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• 1996

PR interval variability has been proposed as a noninvasive tool for in-vestigating the autonomic nervous system as welt as heart rate variability. The goal of this paper is to determine whether PR interval variability is generated from deterministic nonlinear dynamics. The data used in this study is a 24-hour bolter ECGs of 20 healthy adults. We developed an automatic PR interval measurement algorithm, and tested it using MIT ECG Databases. The general discriminants of nonlinear dynamics, such as, correlation dimension and phase space reconstruction are used. Surrogate data is generated from simpler linear models to have similar statistical characteristics with the original data. Nonlinear discriminants are applied to both data, and compared for any significant results. It was concluded that PR interval variability shows non-linear characteristics.

• Industrial Engineering and Management Systems
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• v.11 no.1
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• pp.18-25
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• 2012

IChinese postman problem is one of the classical combinatorial optimization problems with many applications. However, in application, some uncertain factors are frequently encountered. This paper employs uncertain programming to deal with Chinese postman problem with uncertain weight Within the framework of uncertainty theory, the concepts of expected shortest route, ${\alpha}$-shortest route, and distribution shortest route are proposed. After that, expected shortest model, and ${\alpha}$-shortest model are constructed. Taking advantage of properties of uncertainty theory, these models can be transf-ormed into their corresponding deterministic forms, which can be solved by classical algorithm..

• Wind and Structures
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• v.10 no.2
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• pp.153-176
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• 2007

The Proper Orthogonal Decomposition (POD) is a statistical method particularly suitable and versatile for dealing with many problems concerning wind engineering and several other scientific and humanist fields. POD represents a random process as a linear combination of deterministic functions, the POD modes, modulated by uncorrelated random coefficients, the principal components. It owes its popularity to the property that only few terms of the series are usually needed to capture the most energetic coherent structures of the process, and a link often exists between each dominant mode and the main mechanisms of the phenomenon. For this reason, POD modes are normally used to identify low-dimensional subspaces appropriate for the construction of reduced models. This paper provides a state-of-the-art and some prospects on POD, with special regard to its framework and applications in wind engineering. A wide bibliography is also reported.

• Journal of Industrial Technology
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• v.43 no.1
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• pp.57-62
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• 2023

In this review, we introduce the non-parametric Bayesian filtering algorithm known as the point-mass filter (PMF) and discuss recent studies related to it. PMF realizes Bayesian filtering by placing a deterministic grid on the state space and calculating the probability density at each grid point. PMF is known for its robustness and high accuracy compared to other nonparametric Bayesian filtering algorithms due to its uniform sampling. However, a drawback of PMF is its inherently high computational complexity in the prediction phase. In this review, we aim to understand the principles of the PMF algorithm and the reasons for the high computational complexity, and summarize recent research efforts to overcome this challenge. We hope that this review contributes to encouraging the consideration of PMF applications for various systems.

• Journal of Preventive Medicine and Public Health
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• v.22 no.4 s.28
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• pp.542-554
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• 1989

The skyrocketing inflation of medical costs has become a major health problem among most developed countries. Korea, which recently covered the entire population with National Health Insurance, is facing the same problem. The proportion of health expenditure to GNP has increased from 3% to 4.8% during the last decade. This was remarkable, if we consider the rapid economic growth during that time. A few policy analysts began to raise cost containment as an agenda, after recognizing the importance of medical cost inflation. In order to Prepare an appropriate alternative for the agenda, it is necessary to find out reasons for the cost inflation. Then, we should focus on the reasons which are controllable, and those whose control are socially desirable. This study is designed to articulate the theory of medical cost inflation through literature reviews, to find out reasons for cost inflation, by analyzing aggregated data with a deterministic model. Finally to identify determinants of changes in both medical demand and service intensity which are major reasons for cost inflation. The reasons for cost inflation are classified into cost push inflation and demand pull inflation, The former consists of increases in price and intensity of services, while the latter is made of consumer derived demand and supplier induced demand. We used a time series (1983-1987), and cross sectional (over regions) data of health insurance. The deterministic model reveals, that an increase in service intensity is a major cause of inflation in the case of inpatient care, while, more utilization, is a primary attribute in the case of physician visits. Multiple regression analysis shows that an increase in hospital beds is a leading explanatory variable for the increase in hospital care. It also reveals, that an introduction of a deductible clause, an increase in hospital beds and degree of urbanization, are statistically significant variables explaining physician visits. The results are consistent with the existing theory, The magnitude of service intensity is influenced by the level of co-payment, the proportion of old age and an increase in co-payment. In short, an increase in co-payment reduced the utilization, but it induced more intensities or services. We can conclude that the strict fee regulation or increase in the level of co-payment can not be an effective measure for cost containment under the fee for service system. Because the provider can react against the regulation by inducing more services.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.7A
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• pp.697-705
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• 2010

In Wireless Sensor Networks (WSNs), blanket (area) coverage analysis is generally carried to find the minimum number of active sensor nodes required to cover a monitoring interest area with the desired fractional coverage-threshold. Normally, the coverage analysis is performed using the stochastic geometry as a tool. The major component of such coverage analysis is the assumed sensing model. Hence, the accuracy of such analysis depends on the underlying assumption of the sensing model: how well the assumed sensing model characterizes the real sensing phenomenon. In this paper, we review the coverage analysis for different deterministic and probabilistic sensing models like Boolean and Shadow-fading model; and extend the analysis for Exponential and hybrid Boolean-Exponential model. From the analytical performance comparison, we demonstrate the redundancy (in terms of number of sensors) that could be resulted due to the coverage analysis based on the detection capability mal-characterizing sensing models.