• East Asian Journal of Business Economics (EAJBE)
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• v.8 no.1
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• pp.57-69
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• 2020

Purpose - This study examines what are the asset market fluctuations in Europe and how each economic variable affects major variables, and explore the dynamics of housing and stock market through Greece. The variables under consideration are balance on current account (BCA), index of stock (STOCK), gross domestic product (GDP), housing price indices (HOUSING), M3, real rate of interest (IR_REAL) and household credits (LOAN). We investigate the functional and causal relationships between housing and stock market. Research design, data, and methodology - Vector error correction model (VECM) is used to figure out the dynamic relationships among variables. This study also contains the augmented Dickey-Fuller unit root, cointegration, Granger causality test, and impulse response function and variance decomposition analysis by EViews 11.0. Results - The statistical tests show that all variables under consideration have one unit root and there is a longterm equilibrium relationship among variables for Greece. GDP, IR_REAL, M3, STOCK and LOAN can be considered as causal factors to affect real estate market, while GDP, LOAN, M3, BCA and HOUSING can bring direct effects to stock market in Greece. Conclusions - It can be judged that the policy that affects the lending policy of financial institutions may be more effective than the indirect variable such as monetary interest rate.

• Journal of Welding and Joining
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• v.9 no.4
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• pp.40-49
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• 1991

Both impact and fracture toughnesses were investigated with root gaps in FCAW weld metals at room temperature and 0.deg.C. The maximum impact value was obtained at the root gap of 16mm for 1G position weld metal, and the impact value of 3G position weld metals also showed the maximum impact value at the root gap of 12mm. However, the fracture toughnes(CTOD)values tend to decrease with increasing root gaps at both temperatures in 1G weld metal. Bending test also showed satisfactory results with all of root gaps investigated. Based on this result, it becomes possible to apply wide root gaps in real projects in both aspects of toughness and bending resistance.

• Restorative Dentistry and Endodontics
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• v.37 no.3
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• pp.136-141
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• 2012

Objectives: The maintenance of the healthy periodontal ligament cells of the root surface of donor tooth and intimate surface contact between the donor tooth and the recipient bone are the key factors for successful tooth transplantation. In order to achieve these purposes, a duplicated donor tooth model can be utilized to reduce the extra-oral time using the computer-aided rapid prototyping (CARP) technique. Materials and Methods: Briefly, a three-dimensional digital imaging and communication in medicine (DICOM) image with the real dimensions of the donor tooth was obtained from a computed tomography (CT), and a life-sized resin tooth model was fabricated. Dimensional errors between real tooth, 3D CT image model and CARP model were calculated. And extra-oral time was recorded during the autotransplantation of the teeth. Results: The average extra-oral time was 7 min 25 sec with the range of immediate to 25 min in cases which extra-oral root canal treatments were not performed while it was 9 min 15 sec when extra-oral root canal treatments were performed. The average radiographic distance between the root surface and the alveolar bone was 1.17 mm and 1.35 mm at mesial cervix and apex; they were 0.98 mm and 1.26 mm at the distal cervix and apex. When the dimensional errors between real tooth, 3D CT image model and CARP model were measured in cadavers, the average of absolute error was 0.291 mm between real teeth and CARP model. Conclusions: These data indicate that CARP may be of value in minimizing the extra-oral time and the gap between the donor tooth and the recipient alveolar bone in tooth transplantation.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.16 no.1
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• pp.9-18
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• 2007

There have been continuous efforts for automation of joint tracking system. This automation process is mainly used to do in root pass of gas metal arc welding in the field of heavy industry and shipbuilding etc. For automation, it is important using of vision sensor. Welding robot with vision sensor is used for weld seam tracking on welding fabrication. Recently, it is used to on post-weld inspection for weld quality evaluation. For real time seam tracking, it is very important role in vision process technique. Vision process is included in filtering and thinning, segmentation processing, feature extraction and recognition. In this paper, it has shown performance comparison results of seam tracking for real time root pass on gas metal arc welding. It can be concluded better segment splitting method than iterative averaging technique in the performance results of seam tracking.

• Proceedings of the IEEK Conference
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• 2002.06d
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• pp.405-408
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• 2002

LSP(Line Spectrum Pairs) Parameter is used for speech analysis in vocoders or recognizers since it has advantages of constant spectrum sensitivity. low spectrum distortion and easy linear interpolation. However the method of transforming LPC(Linear Predictive Coding) into LSP is so complex that it takes much time to compute. Among conventional methods, the real root method is considerably simpler than others, but nevertheless, it still suffers from its jndeterministic computation time because the root searching is processed sequentially in frequency region. We suggest a method of reducing the LSP transformation time using voice characteristics The proposed method is to apply search order and interval differently according to the distribution of LSP parameters. in comparison with the conventional real root method, the proposed method results in about 46.5% reduction. And, the total computation time is reduce to about 5% in the G.723.1 vocoder.

• East Asian Economic Review
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• v.20 no.4
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• pp.493-517
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• 2016

This paper examines the persistent properties of 12 sectoral relative prices between Korea and Japan obtained following the Classification of Individual Consumption according to Purpose (COICOP) over the period of 1985-2016. Applying a new econometric method developed by Pesaran which controls for the cross-section dependence in a panel, we are not able to reject the hypothesis that the sectoral real exchange rates contain a common stochastic trend. On the other hand, the well-known panel unit root tests such as the IPS and LLC tests widely used by previous studies strongly reject the unit root hypothesis. Since the error term of the regression for our panel exhibits significant cross-section dependence, these opposite results justify that the use of the new econometric method is appropriate.

• Proceedings of the KAIS Fall Conference
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• 2010.05a
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• pp.261-264
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• 2010

Fast algorithm is proposed by using mel scale and the distribution characteristic of LSP parameters, and is to reduce the computational amount. Computational amount means the calculating times of transformation from LPC coefficients to LSP parameters. Among conventional methods, the real root method is considerably simpler than other, but neverthless, it still suffer from its indeterministic computational time. Because the root searching is processed sequentially in frequency region. In this paper, the searching interval is arranged by using mel scale but not it is uniform and searching order is arranged by the distribution characteristic of LSP parameters that is most LSP papameters are occured in specific frequency region. In experimental results, computational amount of the proposed algorithm is reduced about 48.95% in average, but the transformed LSP parameters of the proposed method were the same as those of real root method.

• Nuclear Engineering and Technology
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• v.55 no.12
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• pp.4408-4425
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• 2023

Alarm flood due to abnormality propagation is the most difficult alarm overloading problem in nuclear power plants (NPPs). Root-cause analysis is suggested to help operators in understand emergency events and plant status. Multilevel Flow Modeling (MFM) has been extensively applied in alarm management by virtue of the capability of explaining causal dependencies among alarms. However, there has never been a technique that can identify the actual root cause for complex alarm situations. This paper presents an automated root-cause analysis system based on MFM. The causal reasoning algorithm is first applied to identify several possible root causes that can lead to massive alarms. A novel root-cause ranking algorithm can subsequently be used to isolate the most likely faults from the other root-cause candidates. The proposed method is validated on a pressurized water reactor (PWR) simulator at HAMMLAB. The results show that the actual root cause is accurately identified for every tested operating scenario. The automation of root-cause identification and ranking affords the opportunity of real-time alarm analysis. It is believed that the study can further improve the situation awareness of operators in the alarm flooding situation.

• The Korean Journal of Applied Statistics
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• v.23 no.1
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• pp.73-79
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• 2010

Missing values for unit root processes are imputed by the most recent observations. Treating the imputed observations as if they are complete ones, semiparametric unit root tests are extended to missing value situations. Also, an invariance principle for the partial sum process of the imputed observations is established under some mild conditions, which shows that the extended tests have the same limiting null distributions as those based on complete observations. The proposed tests are illustrated by analyzing an unequally spaced real data set.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.1
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• pp.127-136
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• 2024

Because the hazardous working environments and high labor intensity of the root industry can potentially impact the health of workers, current regulations have focused on measuring and controlling environmental factors, on a semi-annual basis. However, there is a lack of quantitative criteria addressing workers' health conditions other than the physical work environment. This gap makes it challenging to prevent occupational diseases resulting from continuous exposure to harmful substances below regulatory thresholds. Therefore, this paper proposes a machine learning-based method for determining the peak level of risk in root industry work environments and enables real-time safety assessment in workplaces utilizing this approach.