• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.8B
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• pp.667-680
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• 2008

In this work, we present the results of our empirical study on 802.11 wireless LAN network traffic. We collect the packet trace from existing campus wireless LAN infra-structure. We analyzed four different data sets: aggregate traffic, upstream traffic, downstream traffic, tcp only packet trace from aggregate traffic. We analyze the time series aspect of underlying traffic (byte count process and packet count process), marginal distribution of time series, and packet size distribution. We found that in all four data sets there exist long-range dependent property in byte count and packet count process. Inter-arrival distribution is well fitted with Pareto distribution. Upstream traffic, i.e. from the user to Internet, exhibits significant difference in its packet size distribution from the rests. Average packet size of upstream traffic is 151.7 byte while average packet size of the rest of the data sets are all greater than 260 bytes. Packets with full data payloads constitutes 3% and 10% in upstream traffic and the downstream traffic, respectively. Despite the significant difference in packet size distribution, all four data sets have similar Hurst values. The Hurst alone does not properly explain the stochastic characteristics of the underlying traffic. We model the underlying traffic using fractional-ARIMA (FARIMA) and fractional Gaussian Noise (FGN). While the fractional Gaussian Noise based method is computationally more efficient, FARIMA exhibits superior performance in accurately modeling the underlying traffic.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.1
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• pp.40-49
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• 2004

In this paper, we propose a new architecture of Genetic Algorithms(GAs)-based Self-Organizing Polynomial Neural Networks(SOPNN), discuss a comprehensive design methodology and carry out a series of numeric experiments. The conventional SOPNN is based on the extended Group Method of Data Handling(GMDH) method and utilized the polynomial order (viz. linear, quadratic, and modified quadratic) as well as the number of node inputs fixed (selected in advance by designer) at Polynomial Neurons (or nodes) located in each layer through a growth process of the network. Moreover it does not guarantee that the SOPNN generated through learning has the optimal network architecture. But the proposed GA-based SOPNN enable the architecture to be a structurally more optimized network, and to be much more flexible and preferable neural network than the conventional SOPNN. In order to generate the structurally optimized SOPNN, GA-based design procedure at each stage (layer) of SOPNN leads to the selection of preferred nodes (or PNs) with optimal parameters- such as the number of input variables, input variables, and the order of the polynomial-available within SOPNN. An aggregate performance index with a weighting factor is proposed in order to achieve a sound balance between approximation and generalization (predictive) abilities of the model. A detailed design procedure is discussed in detail. To evaluate the performance of the GA-based SOPNN, the model is experimented with using two time series data (gas furnace and NOx emission process data of gas turbine power plant). A comparative analysis shows that the proposed GA-based SOPNN is model with higher accuracy as well as more superb predictive capability than other intelligent models presented previously.

• Journal of Korean Society of Transportation
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• v.23 no.2
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• pp.37-50
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• 2005

This study analyzes the effect of airport investment on GRDP(Gross Regional Domestic Product) using Regional Production Function with public investment on social infrastructure. Particularly it includes the spillover effect of airport investment on the economies of neighbor regions beyond border. We estimate regional production function with the independent variable of airport investment stock using panel data with regional cross-section and time-series data. In the analysis with aggregate data of all industries, it shows the positive relationship between airport investment and GRDP which implies the affirmative effect of airport investment on regional economy in the aspects of direct and indirect spill-over effects. On the contrary, the research results of each industry do not appear to be the same. With the different characteristics of each industry, the direct and indirect effect may not be the same and the SOC investment contributes to the restructuring of regional economy by altering the industrial organizations of any specific region and its neighbors.

• Journal of the Korea Concrete Institute
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• v.21 no.2
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• pp.147-152
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• 2009

The compressive strength test, bulk density and mortar absorption ratio were carried out to utilize the data as the basic sources for the lightweight mortar and the lightweight concrete, through the study on the physical characteristics of the artificial lightweight aggregate (ALA) made of waste glasses, which was developed for the first time in the country. On the basis of these experiments, the density and the unit volume weight of the ALA showed the value less than 50% of the common aggregate due to the independent pore structure, and the mortar that contains ALA had no big difference from the Control mortar in the test of the absorption ratio. It is judged that this happens based on the internal independent pore structure of the ALA. In case of the mortar containing ALA, there was a tendency of declination in the compressive strength and the bending strength as the mixing rate is increasing, but all mortar showed more than 70% of the Control mortar compressive strength except for the La50 mortar. Hereafter, it is judged that according to the control of the mixing ratio of mineral admixing agent, water and cement, it will realize the equal strength to the control mortar, and the long term edurance is needed to be considered together.

• International journal of advanced smart convergence
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• v.13 no.3
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• pp.150-155
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• 2024

Genomic data plays a transformative role in medicine, biology, and forensic science, offering insights that drive advancements in clinical diagnosis, personalized medicine, and crime scene investigation. Despite its potential, the integration and analysis of diverse genomic datasets remain challenging due to compatibility issues and the specialized nature of existing tools. This paper presents the GenomeSync system, designed to overcome these limitations by utilizing the Hadoop framework for large-scale data handling and integration. GenomeSync enhances data accessibility and analysis through SQL-based search capabilities and machine learning techniques, facilitating the identification of genetic traits and the resolution of forensic cases. By pre-processing DNA profiles from crime scenes, the system calculates similarity scores to identify and aggregate related genomic data, enabling accurate prediction models and personalized treatment recommendations. GenomeSync offers greater flexibility and scalability, supporting complex analytical needs across industries. Its robust cloud-based infrastructure ensures data integrity and high performance, positioning GenomeSync as a crucial tool for reliable, data-driven decision-making in the genomic era.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2008.10a
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• pp.319-323
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• 2008

"How does advertising affect sales?" is the fundamental issue of modern advertising research. There is an interesting issue for estimating carry over effects of advertising on sales, and the aggregated data biases exist in the duration of advertising effect. This research suggests a modified model at micro-data using Koyck model (Koyck 1954) by estimated model the aggregate data, and empirically shows the aggregated data bias. Our modified model with the aggregated level of actual data is more appropriate than the base model for micro-data. The result shows that it is very important to consider the disaggregated data level in the analysis of dynamic effects of adverting such as lagged effects.

• International Journal of Control, Automation, and Systems
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• v.2 no.4
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• pp.423-434
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• 2004

In this paper, we introduce a new topology of Self-Organizing Polynomial Neural Networks (SOPNN) based on genetically optimized Multi-Layer Perceptron (MLP) and discuss its comprehensive design methodology involving mechanisms of genetic optimization. Let us recall that the design of the 'conventional' SOPNN uses the extended Group Method of Data Handling (GMDH) technique to exploit polynomials as well as to consider a fixed number of input nodes at polynomial neurons (or nodes) located in each layer. However, this design process does not guarantee that the conventional SOPNN generated through learning results in optimal network architecture. The design procedure applied in the construction of each layer of the SOPNN deals with its structural optimization involving the selection of preferred nodes (or PNs) with specific local characteristics (such as the number of input variables, the order of the polynomials, and input variables) and addresses specific aspects of parametric optimization. An aggregate performance index with a weighting factor is proposed in order to achieve a sound balance between the approximation and generalization (predictive) abilities of the model. To evaluate the performance of the GA-based SOPNN, the model is experimented using pH neutralization process data as well as sewage treatment process data. A comparative analysis indicates that the proposed SOPNN is the model having higher accuracy as well as more superb predictive capability than other intelligent models presented previously.reviously.

• Journal of the Korea Institute of Building Construction
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• v.13 no.5
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• pp.491-497
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• 2013

The objective of the present study is to establish a straightforward mixture proportioning procedure for structural lightweight aggregate concrete (LWAC), and evaluate the selection range of the targeted dry density of concrete against the designed concrete compressive strength. In developing this procedure, mathematical models were formulated based on a nonlinear regression analysis over 347 data sets and two boundary conditions of the absolute volume and dry density of concrete. The proposed procedure demonstrated the appropriate water-to-cement ratio and dry density of concrete to achieve the designed strength decrease with the increase in volumetric ratio of coarse aggregates. This trend was more significant in all-LWAC than in sand-LWAC. Overall, the selection range of the dry density of LWAC exists within a certain range according to the designed strength, which can be obtained using the proposed procedure.

• Proceedings of the KSR Conference
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• 2010.06a
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• pp.2166-2178
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• 2010

The Korea Transportation Database (KTDB) is used to obtain data on the origin and destination (OD) of inter-city travel, which are currently used in railroad planning when estimating traffic demand. The KTDB employs the trip assignment method, whereby the total traffic volume researched for inter-city travel in Korea is divided into road, rail and air traffic, etc. However, as regards rail travel, the railroad stations are not identical to the existing zones or the connector has not been established because there are several stations in one zone as such, certain problems with the applicable methods have been identified. Therefore, estimates of the volume of railroad traffic using the KTDB display low reliability compared to other modes of transportation. In this study, these problems are reviewed and analyzed, and use of the aggregate model method to estimate the direct demand for rail travel is proposed in order to improve the reliability of estimation. In addition, a method of minimizing error in traffic demand estimation for the railroad field is proposed via an analysis of the relationship between the aggregate model and various social-economic indicators including population, distances, numbers of industrial employees, numbers of automobiles, and the extension of roads between cities.

• Structural Engineering and Mechanics
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• v.41 no.4
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• pp.495-508
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• 2012

The strength theory of concrete is significant to structure design and nonlinear finite element analysis of concrete structures because concrete utilized in engineering is usually subject to the action of multi-axial stress. Experimental results have revealed that lightweight aggregate (LWA) concrete exhibits plastic flow plateau under high compressive stress and most of the lightweight aggregates are crushed at this stage. For the purpose of safety, therefore, in the practical application the strength of LWA concrete at the plastic flow plateau stage should be regarded as the ultimate strength under multi-axial compressive stress state. With consideration of the strength criterion, the ultimate strength surface of LWA concrete under multi-axial stress intersects with the hydrostatic stress axis at two different points, which is completely different from that of the normal weight concrete as that the ultimate strength surface is open-ended. As a result, the strength criteria aimed at normal weight concrete do not fit LWA concrete. In the present paper, a multi-axial strength criterion for LWA concrete is proposed based on the Unified Twin-Shear Strength (UTSS) theory developed by Prof Yu (Yu et al. 1992), which takes into account the above strength characteristics of LWA under high compressive stress level. In this strength criterion model, the tensile and compressive meridians as well as the ultimate strength envelopes in deviatoric plane under different hydrostatic stress are established just in terms of a few characteristic stress states, i.e., the uniaxial tensile strength $f_t$, the uniaxial compressive strength $f_c$, and the equibiaxial compressive $f_{bc}$. The developed model was confirmed to agree well with experimental data under different stress ratios of LWA concrete.