• Proceedings of the KIEE Conference
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• 2005.05a
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• pp.12-14
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• 2005

In this paper, we propose a new architecture of Self-Organizing Fuzzy Polynomial Neural Networks(SOFPNN) by means of genetically optimized fuzzy polynomial neuron(FPN) and discuss its comprehensive design methodology involving mechanisms of genetic optimization, especially genetic algorithms(GAs). The conventional SOFPNNs hinges on an extended Group Method of Data Handling(GMDH) and exploits a fixed fuzzy inference type in each FPN of the SOFPNN as well as considers a fixed number of input nodes located in each layer. The design procedure applied in the construction of each layer of a SOFPNN deals with its structural optimization involving the selection of preferred nodes (or FPNs) with specific local characteristics (such as the number of input variables, the order of the polynomial of the consequent part of fuzzy rules, a collection of the specific subset of input variables, and the number of membership function) and addresses specific aspects of parametric optimization. Therefore, the proposed SOFPNN gives rise to a structurally optimized structure and comes with a substantial level of flexibility in comparison to the one we encounter in conventional SOFPNNs. To evaluate the performance of the genetically optimized SOFPNN, the model is experimented with using two time series data(gas furnace and chaotic time series).

• KIPS Transactions on Software and Data Engineering
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• v.6 no.9
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• pp.457-464
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• 2017

Indoor air quality analysis is conducted to understand abnormal atmospheric phenomena and the external factor affecting indoor air quality. By recording indoor air quality measurements periodically, we are able to observe patterns in air quality. However, it difficult to predict the number of potential parameters, set parameters for a given observation and find the coefficients. Moreover, the results are time-dependent. Thus to address these issues, we introduce a microchip capable of periodically recording indoor air quality and a model that estimates atmospheric changes based on time series data.

• The Korean Journal of Applied Statistics
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• v.6 no.1
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• pp.145-162
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• 1993

We review sampling schemes on successive occasions with partial replacement of units and propose a Rao-Hartley-Cochran(RHC) type's sampling scheme over two successive occasions with probability proportionate to observations on the previous occasion. For comparison of the reviewed and proposed sampling schemes, optimal estimator of population mean on second occasion and its variance are derived. The relative efficiency of the proposed sampling scheme is compared with other equal and unequal probability sampling scheme by theoretical and numerical simulation study. For simulation study, three artificial populations are generated by a time series model. It is observed that RHC type's sampling scheme has small variance and deviation in general.

• The Korean Journal of Applied Statistics
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• v.31 no.2
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• pp.229-239
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• 2018

This paper introduces how to adopt the concept of temporal hierarchies to forecast time series data. Similarly as in hierarchical cross-sectional data, temporal hierarchies can be constructed for any time series data by means of non-overlapping temporal aggregation. Reconciliation forecasts with temporal hierarchies result in more accurate and robust forecasts when compared with the independent base and bottom-up forecasts. As an empirical example, we forecast traffic accident counts with temporal hierarchies and observe that reconciliation forecasts are superior to the base and bottom-up forecasts in terms of forecast accuracy.

• Environmental Sciences Bulletin of The Korean Environmental Sciences Society
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• v.3 no.2
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• pp.81-89
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• 1999

This study estimates the correlation dimensions of the land-sea breeze phenomenon, that has a clear diurnal cycle, in order to gain a more detailed understanding of this phenomenon. The data adopted include north-south wind velocity component(v) and temperature(T) time series that were observed at Kimhae Airport and Inje University over a period of 5 days, from the 4th to the 8th of August, 1994. The embedding phase space of the time series were reconstructed from 2 to 14 dimensions, and the correlation dimensions of the attractors were then estimated. The results show that the land-sea breeze phenomenon exhibits a deterministic chaos with non-integer correlation dimension values between 2 and 3. Accordingly, 3 is the minimum number of independent variables required to model the dynamics of the landsea breeze phenomenon in the Kimhae area. Since the saturated embedding dimension, when the correlation dimension remains unchanged, is larger for the wind velocity v-component than for temperature, this indicates that wind velocity is susceptible to topology.

• The Transactions of the Korean Institute of Power Electronics
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• v.6 no.4
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• pp.317-324
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• 2001

This paper proposes a SSSC based on 3-level half-bridge inverters. The dynamic characteristic of the proposed SSSC was analyzed by EMTP simulation and a scaled hardware model, assuming that the SSSC is inserted in the transmission line of the one-machine-infinite-but power system. The proposed SSC has six 3-level half-bridge inverters per phase, which operates in PWM mode. The proposed SSSC generates a quasi-sinusoidal output voltage by 90 degree phase shift to the line current. The proposed SSSC does not require the coupling transformer for voltage injection, and has a flexibility in operation voltage by increasing the number of series connection.

• Proceedings of the Korean Geotechical Society Conference
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• 2005.03a
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• pp.1520-1527
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• 2005

In order to investigate the effect of improvement with bamboo mats on dredged fills, several tests were performed with different widthes (0.0B, 0.5B, 1.0B, 2.0B). The results indicate that the failure modes of the models without bamboo mats are close to local shear failure, and change to general shear failure as the width of a bamboo mat increases. A series of tests with four different widths (0.0B, 0.5B, 1.0B, 2.0B) were carried out on the models which have no desiccated surface layers, to find out the width that the general shear failure might occur. Finally, a series of tests with this width were performed on the models with three different desiccated surface layers (3cm, 5cm, 10cm). Throughout the tests, strength and deformation according to the thickness of desiccated surface layer was investigated.

• Proceedings of the Korean Geotechical Society Conference
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• 2004.03b
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• pp.197-202
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• 2004

In order to design accurately sand compaction pile (SCP) method with low replacement area ratio, it is important to understand the mechanical interaction between sand piles and clays and its mechanism during consolidation process of the composition ground. In this paper, a series of numerical analyses on composition ground improved by SCP with low replacement area ratio were carried out, in order to investigate the mechanical interaction between sand piles and clays. The applicability of numerical analyses, in which an elasto-viscoplastic consolidation finite element method was applied, could be confirmed comparing with results of a series of model tests on consolidation behaviors of composition ground improved by SCP. And, through the results of the numerical analyses, each mechanical behaviors of sand piles and clays in the composition ground during consolidation was elucidated, together with stress sharing mechanism between sand piles and clays.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.10
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• pp.1272-1278
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• 1999

A fault is considered as a variation of physical parameters; therefore the design of fault detection and identification(FDI) can be reduced to the parameter identification of a non linear system and to the association of the set of the estimated parameters with the mode of faults. Neuro-Fuzzy Inference System which contains multiple linear models as consequent part is used to model nonlinear systems. Generally, the linear parameters in neuro-fuzzy inference system can be effectively utilized to fault diagnosis. In this paper, we proposes an FDI system for nonlinear systems using neuro-fuzzy inference system. The proposed diagnostic system consists of two neuro-fuzzy inference systems which operate in two different modes (parallel and series-parallel mode). It generates the parameter residuals associated with each modes of faults which can be further processed by additional RBF (Radial Basis Function) network to identify the faults. The proposed FDI scheme has been tested by simulation on two-tank system.

• Geomechanics and Engineering
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• v.23 no.5
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• pp.405-418
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• 2020

Deep excavation may have impact on the adjacent tunnels. It is obvious that the excavation will adversely affect and even damage the existing tunnels if the induced deformation exceeds the capacity of tunnel structures. It hence creates a high necessity to predict tunnel displacement induced by nearby excavation to ensure the safety of tunnel. In this paper, a simplified method to evaluate the heave of the underlying tunnel induced by adjacent excavation is presented and verified by field measurement results. In the proposed model, the tunnel is represented by a series of short beams connected by tensile springs, compressional springs and shear springs, so that the rotational effect and shearing effect of the joints between lining rings can be captured. The proposed method is compared with the previous modelling methods (e.g., Euler-Bernoulli beam, a series of short beams connected only by shear springs) based on a field measured longitudinal deformation of subway tunnels. Results of these case studies show a reasonable agreement between the predictions and observations.