• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.13 no.5
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• pp.85-91
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• 2013

For location based service and context awareness services, accurate indoor positioning technology is essential. The TDOA method that uses the range difference between signal source and receivers for estimating the location of the signal source, has estimation error due to measurement error. In this paper, a new algorithm is proposed to select the real root among calculated roots using the range difference information, and the estimated position of the signal source shows good accuracy compared to the existing method.

• The Korean Journal of Applied Statistics
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• v.32 no.6
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• pp.909-922
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• 2019

In this paper, we study the adaptive least absolute shrinkage and selection operator (LASSO) for the unstable autoregressive (AR) model. To identify the existence of the unit root, we apply the adaptive LASSO to the augmented Dickey-Fuller regression model, not the original AR model. We illustrate our method with simulations and a real data analysis. Simulation results show that the adaptive LASSO obtained by minimizing the Bayesian information criterion selects the order of the autoregressive model as well as the degree of differencing with high accuracy.

• Journal of Plant Biotechnology
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• v.36 no.3
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• pp.294-300
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• 2009

Activation tagging that uses T-DNA vectors containing multimerized transcriptional enhancers from the cauliflower mosaic virus (CaMV) 35S gene is a powerful tool to determine gene function in plants. This approach has been successfully applied in screening various types of mutations and cloning the corresponding genes. We generated an activation tagged hairy root pool of ginseng (Panax ginseng C.A. Meyer) in an attempt to isolate genes involved in the biosynthetic pathway of ginsenoside (triterpene saponin), which is known as the major active ingredient of the root. Quantitative and qualitative variation of ginsenoside in activation tagged hairy root lines were profiled using LC/MS. Metabolic profiling data enabled selection of a specific hairy root line which accumulated ginsenoside at a higher level than other lines. The relative expression level of several genes of triterpene biosynthetic pathway in the selected hairy root line was determined by real time RT-PCR. Overall results suggest that the activation tagged ginseng hairy root system described in this study would be useful in isolating genes involved in a complex metabolic pathway from genetically intractable plant species by metabolic profiling.

• Journal of Ginseng Research
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• v.43 no.3
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• pp.482-487
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• 2019

Background: The mixed-cultivation of different Panax ginseng cultivars can cause adverse effects on stability of yield and quality. K-1 is a superior cultivar with good root shape and stronger disease resistance. DNA markers mined from functional genes are clearly desirable for K-1, as they may associate with major traits and can be used for marker-assisted selection to maintain the high quality of Korean ginseng. Methods: Five genes encoding pathogenesis-related (PR) proteins of P. ginseng were amplified and compared for polymorphism mining. Primary, secondary, and tertiary structures of PR5 protein were analyzed by ExPASy-ProtParam, PSSpred, and I-TASSER methods, respectively. A coding single nucleotide polymorphism (SNP)-based specific primer was designed for K-1 by introducing a destabilizing mismatch within the 3' end. Allele-specific polymerase chain reaction (PCR) and real-time allele-specific PCR assays were conducted for molecular discrimination of K-1 from other cultivars and landraces. Results: A coding SNP leading to the modification of amino acid residue from aspartic acid to asparagine was exploited in PR5 gene of K-1 cultivar. Bioinformatics analysis showed that the modification of amino acid residue changed the secondary and tertiary structures of the PR5 protein. Primer KSR was designed for specific discrimination of K-1 from other ginseng cultivars and landraces. The developed real-time allele-specific PCR assay enabled easier automation and accurate genotyping of K-1 from a large number of ginseng samples. Conclusion: The SNP marker and the developed real-time allele-specific PCR assay will be useful not only for marker-assisted selection of K-1 cultivar but also for quality control in breeding and seed programs of P. ginseng.

• Journal of Welding and Joining
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• v.30 no.6
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• pp.42-48
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• 2012

Due to increase in demand of stable and long pipelines in natural gas industry, wide range of researches are being performed on automation welding to improved welding quality with respect to weld process parameters in real time measurement. In particular, the coupling between the pipe manufacturing process and location of the weld seam, the measured size of the gap that exists in the weld position and the weld angle depending on whether the movement of molten weld. This is due to absence of controlling welding penetration position, depending on the required size of the angle of the setting. In addition, the optimum welding conditions must be considered while selecting, the correlation between these variables and the systematic correlation has not yet been identified. Therefore, in most welded pipe root-pass weld solely depends on the experience of workers in relation to secure a stable weld quality. In this study, automation welding system is implemented to select a suitable root-pass STT (Surface Tension Transfer) welding method using the optimal welding conditions. To successfully accomplish this objective, there were various welding conditions used for welding experiment to confirm that the assessment required for construction through the pipe and automatic welding process is proposed to optimize this plan.

• Smart Structures and Systems
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• v.15 no.4
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• pp.997-1018
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• 2015

Structural health monitoring along with damage detection and assessment of its severity level in non-accessible reinforced concrete members using piezoelectric materials becomes essential since engineers often face the problem of detecting hidden damage. In this study, the potential of the detection of flexural damage state in the lower part of the mid-span area of a simply supported reinforced concrete beam using piezoelectric sensors is analytically investigated. Two common severity levels of flexural damage are examined: (i) cracking of concrete that extends from the external lower fiber of concrete up to the steel reinforcement and (ii) yielding of reinforcing bars that occurs for higher levels of bending moment and after the flexural cracking. The purpose of this investigation is to apply finite element modeling using admittance based signature data to analyze its accuracy and to check the potential use of this technique to monitor structural damage in real-time. It has been indicated that damage detection capability greatly depends on the frequency selection rather than on the level of the harmonic excitation loading. This way, the excitation loading sequence can have a level low enough that the technique may be considered as applicable and effective for real structures. Further, it is concluded that the closest applied piezoelectric sensor to the flexural damage demonstrates higher overall sensitivity to structural damage in the entire frequency band for both damage states with respect to the other used sensors. However, the observed sensitivity of the other sensors becomes comparatively high in the peak values of the root mean square deviation index.

• Communications for Statistical Applications and Methods
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• v.24 no.1
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• pp.97-113
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• 2017

This paper proposes a method for sufficient dimension reduction (SDR) of mixture data. We consider mixture data containing more than one component that have distinct central subspaces. We adopt an approach of a model-based sliced inverse regression (MSIR) to the mixture data in a simple and intuitive manner. We employed mixture probabilistic principal component analysis (MPPCA) to estimate each central subspaces and cluster the data points. The results from simulation studies and a real data set show that our method is satisfactory to catch appropriate central spaces and is also robust regardless of the number of slices chosen. Discussions about root selection, estimation accuracy, and classification with initial value issues of MPPCA and its related simulation results are also provided.

• Communications for Statistical Applications and Methods
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• v.31 no.1
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• pp.143-154
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• 2024

This paper considers a combined model of exponential smoothing (ETS) and autoregressive integrated moving average (ARIMA) models that are commonly used to forecast time series data. The combined model is constructed through an innovational state space model based on the level variable instead of the differenced variable, and the identifiability of the model is investigated. We consider the maximum likelihood estimation for the model parameters and suggest the model selection steps. The forecasting performance of the model is evaluated by two real time series data. We consider the three competing models; ETS, ARIMA and the trigonometric Box-Cox autoregressive and moving average trend seasonal (TBATS) models, and compare and evaluate their root mean squared errors and mean absolute percentage errors for accuracy. The results show that the combined model outperforms the competing models.

• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.121-131
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• 2014

Traffic speed is an important measure in transportation. It can be employed for various purposes, including traffic congestion detection, travel time estimation, and road design. Consequently, accurate speed prediction is essential in the development of intelligent transportation systems. In this paper, we present an analysis and speed prediction of a certain road section in Busan, South Korea. In previous works, only historical data of the target link are used for prediction. Here, we extract features from real traffic data by considering the neighboring links. After obtaining the candidate features, linear regression, model tree, and k-nearest neighbor (k-NN) are employed for both feature selection and speed prediction. The experiment results show that k-NN outperforms model tree and linear regression for the given dataset. Compared to the other predictors, k-NN significantly reduces the error measures that we use, including mean absolute percentage error (MAPE) and root mean square error (RMSE).

• The KIPS Transactions:PartD
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• v.8D no.4
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• pp.393-400
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• 2001

This paper presents a stochastic model for the software failure phenomenon based on a nonhomogeneous Poisson process (NHPP). The failure process is analyzed to develop a suitable mean value function for the NHPP ; expressions are given for several performance measure. Actual software failure data are compared with generalized model by Goel dependent on the constant reflecting the quality of testing. The performance measures and parametric inferences of the new models, Rayleigh and Gumbel distributions, are discussed. The results of the new models are applied to real software failure data and compared with Goel-Okumoto and Yamada, Ohba and Osaki models. Tools of parameter inference was used method of the maximun likelihood estimate and the bisection algorithm for the computing nonlinear root. In this paper, using the sum of the squared errors, model selection was employed. The numerical example by NTDS data was illustrated.