• Journal of Distribution Science
• /
• v.11 no.7
• /
• pp.15-22
• /
• 2013

Purpose - The study investigates the influence of tourism and hospitality industry on economic growth and CO2 emissions. Research design, data, and methodology - In the empirical analysis, unit root tests, cointegration test and vector error correction model regression using time series data of South Korea from the first quarter of 1970 to the third quarter of 2010 are performed to examine the long-run equilibrium relationship and short-run dynamics among the tourism and hospitality industry, CO2 emissions, economic growth and other industry sectors. Results - Results indicate that a long-run equilibrium relationship exists among these variables. Furthermore, the tourism and hospitality industry and CO2 emissions have high significant positive effect on economic growth. The tourism and hospitality industry in Korea, in turns, shows a high significant positive impact on economic growth while the industry sector incursa high significant negative impact on CO2 emissions. Conclusions - The tourism and hospitality industry in Korea may havebeen prompted by several factors such as accelerated process of technological innovation or energy and environmental policies. These findings suggest that the effectively managed tourism and hospitality sector in Korea has resulted in both economic growth and a reduction in CO2 emissions.

• Journal of the Ergonomics Society of Korea
• /
• v.16 no.1
• /
• pp.107-117
• /
• 1997

It is necessary to evaluate HMI inaspects of human factors in the design stage of MMIS(man machine interface system) and feedback the result of evaluation because operators performance is mainly influenced by the HMI. Therefore, the MMIS design should be reflected the operators psychological, behavioral and physiological characteristics in the interaction with human machine interface(HMI) in order to improve the safety and availability of the MMIS of a nuclear power plant(NPP) by reduction of human error. The development of human factors experimental evaluation techniques and integrated test facility(ITF) for the human factors evaluation become an important research field to resolve hi,am factors issues on the design of an advanced control room(ACR). We developed am ITF, which is aimed to experiment with the design of the ACR and the human machine interaction as it relates to the control of NPP. This paper presents the development of an ITF that consists of three rooms such as main test room(MTR), supporting test room(STR) and experiment control room(ECR). And, the ITF has a various facilities such as a human machine simulator(HMS), experimental measurement systems and data analysis and experiment evaluation supporting system(DAEXESS). The HMS consists of full-scope simulation model of Korean standard NPP and advanced HMI based on visual display nits (VDUS) such as touch color CRT, large scale display panel(LSDP), flat panel display unit and so on.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.7 no.7
• /
• pp.1380-1386
• /
• 2003

Speech recognition performance is degraded by the variation in vocal tract length among speakers. In this paper, we have used a vocal tract length normalization method wherein the frequency axis of the short-time spectrum associated with a speaker's speech is scaled to minimize the effects of speaker's vocal tract length on the speech recognition performance In order to normalize vocal tract length, we tried several frequency warping functions such as linear and piece-wise linear function. Variable interval piece-wise linear warping function is proposed to effectively model the variation of frequency axis scale due to the large variation of vocal tract length. Experimental results on TIDIGITS connected digits showed the dramatic reduction of word error rates from 2.15% to 0.53% by the proposed vocal tract normalization.

• International Journal of Automotive Technology
• /
• v.6 no.2
• /
• pp.95-105
• /
• 2005

Diesel engines have low specific fuel consumption, but high particulate emissions, mainly soot. Diesel soot is suspected to have significant effects on the health of living beings and might also affect global warming. Hence stringent measures have been put in place in a number of countries and will be even stronger in the near future. Diesel engines require either advanced integrated exhaust after treatment systems or modified engine models to meet the statutory norms. Experimental analysis to study the emission characteristics is a time consuming affair. In such situations, the real picture of engine control can be obtained by the modeling of trend prediction. In this article, an effort has been made to predict emissions smoke and NO$_{x}$ using cylinder combustion derived parameters and diesel particulate filter data, with artificial neural network techniques in MATLAB environment. The model is based on three layer neural network with a back propagation learning algorithm. The training and test data of emissions were collected from experimental set up in the laboratory for different loads. The network is trained to predict the values of emission with training values. Regression analysis between test and predicted value from neural network shows least error. This approach helps in the reduction of the experimentation required to determine the smoke and NO$_{x}$ for the catalyst coated filters.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
• /
• v.12 no.4
• /
• pp.81-89
• /
• 1998

In the field of industrial drives, the vector control of the induction motor has been widely used to achieve the good control performance. In this paper, to require the information of rotor flux in direct vector control scheme, the flux observer by current model of rotor circuit is used. This flux observer is not only available at low-speed region bt good for the error reduction by feedback properties. Also, employing the flux observer on rotor reference frame, the robustness of decoupling control to the observation of rotor flux can be achieved. Through digital simulation and DSP-based IGBT inverter system, the validity for practical implementation is verified.

• International Journal of Computer Science & Network Security
• /
• v.21 no.1
• /
• pp.40-48
• /
• 2021

Content-Based Image Retrieval (CBIR) system plays a vital role to retrieve the relevant images as per the user perception from the huge database is a challenging task. Images are represented is to employ a combination of low-level features as per their visual content to form a feature vector. To reduce the search time of a large database while retrieving images, a novel image retrieval technique based on feature dimensionality reduction is being proposed with the exploit of metaheuristic optimization techniques based on Genetic Algorithm (GA), Extended Binary Cuckoo Search (EBCS) and Whale Optimization Algorithm (WOA). Each image in the database is indexed using a feature vector comprising of fuzzified based color histogram descriptor for color and Median binary pattern were derived in the color space from HSI for texture feature variants respectively. Finally, results are being compared in terms of Precision, Recall, F-measure, Accuracy, and error rate with benchmark classification algorithms (Linear discriminant analysis, CatBoost, Extra Trees, Random Forest, Naive Bayes, light gradient boosting, Extreme gradient boosting, k-NN, and Ridge) to validate the efficiency of the proposed approach. Finally, a ranking of the techniques using TOPSIS has been considered choosing the best feature selection technique based on different model parameters.

• Structural Monitoring and Maintenance
• /
• v.9 no.1
• /
• pp.81-105
• /
• 2022

The use of concrete filled steel tube (CFST) column is widely accepted due to its property of high axial load carrying capacity, more ductility and more resistant to earthquake specially using in bridges and high-rise buildings. The initial imperfection (δ) that produces during casting or fixing causes the reduction in load carrying capacity, this is the reason, experimental capacity is always less then theoretical one. In this research, the effect of δ on load carrying capacity and behavior of concrete filled steel tube (CFST) column have been investigated by numerically simulation of large number of models with different δ and other geometric parameters that include length (L), width (B), steel tube thickness (t), f'_c and f_y. Finite element analysis software ANSYS v18 is used to develop model of SCFST column to evaluate strength capacity, buckling and failure pattern of member which is applied during experimental study under cyclic axial loading. After validation of results, 42 models with different parameters are evaluated to develop empirical equation predicting axial load carrying capacity for different value of δ. Results indicate that empirical equation shows the 0 to 9% error for finite element analysis Forty-two models in comparison with ANSYS results, respectively. Empirical equation can be used for predicting the axial capacity of early estimating the axial capacity of SCFT column including 𝛿.

• Composites Research
• /
• v.37 no.3
• /
• pp.179-185
• /
• 2024

This study quantitatively evaluated and investigated the changes in transverse tensile strength of unidirectional fiber-reinforced composites with initial void defects using a Representative Volume Element (RVE) model. After calculating the appropriate sample size based on margin of error and confidence level for initial void defects, a sample group of 5000 RVE models with initial void defects was generated. Dimensional reduction and density-based clustering analysis were conducted on the sample group to assess similarity, confirming and verifying that the sample group was unbiased. The validated sample analysis results were represented using a Weibull distribution, allowing them to be applied to the reliability analysis of composite structures.

• Transactions of the Korean Society of Automotive Engineers
• /
• v.22 no.3
• /
• pp.171-178
• /
• 2014

From now on, in order to meet more stringer diesel emission standard, diesel vehicle should be equipped with emission after-treatment devices as NOx reduction catalyst and particulate filters. Urea-SCR is being developed as the most efficient method of reducing NOx emissions in the after-treatment devices of diesel engines, and recent studies have begun to mount the urea-SCR device for diesel passenger cars and light duty vehicles. That is because their operational characteristics are quite different from heavy duty vehicles, urea solution injection should be changed with other conditions. Therefore, the number and diameter of the nozzle, injection directions, mounting positions in front of the catalytic converter are important design factors. In this study, major design parameters concerning urea solution injection in front of SCR are optimized by using a CFD analysis and Taguchi method. The computational prediction of internal flow and spray characteristics in front of SCR was carried out by using STAR-CCM+7.06 code that used to evaluate $NH_3$ uniformity index($NH_3$ UI). The design parameters are optimized by using the $L_{16}$ orthogonal array and small-the-better characteristics of the Taguchi method. As a result, the optimal values are confirmed to be valid in 95% confidence and 5% significance level through analysis of variance(ANOVA). The compared maximize $NH_3$ UI and activation time($NH_3$ UI 0.82) are numerically confirmed that the optimal model provides better conversion efficiency of $NH_3$. In addition, we propose a method to minimize wall-wetting around the urea injector in order to prevent injector blocks caused by solid urea loading. Consequently, the thickness reduction of fluid film in front of mixer is numerically confirmed through the mounting mixer and correcting injection direction by using the trial and error method.

• KIPS Transactions on Computer and Communication Systems
• /
• v.3 no.10
• /
• pp.383-392
• /
• 2014

The research for coatings is one of the most popular and active research in the polymer industry. For the coatings, electronics industry, medical and optical fields are growing more important. In particular, the trend is the increasing of the technical requirements for the performance and accuracy of the coatings by the development of automotive and electronic parts. In addition, the industry has a need of more intelligent and automated system in the industry is increasing by introduction of the IoT and big data analysis based on the environmental information and the context information. In this paper, we propose an optimization model for the design of experiments based coating formulation data objects using the Internet technologies and big data analytics. In this paper, the coating formulation was calculated based on the best data analysis is based on the experimental design, modify the operator with respect to the error caused based on the coating formulation used in the actual production site data and the corrected result data. Further optimization model to correct the reference value by leveraging big data analysis and Internet of things technology only existing coating formulation is applied as the reference data using a manufacturing environment and context information retrieval in color and quality, the most important factor in maintaining and was derived. Based on data obtained from an experiment and analysis is improving the accuracy of the combination data and making it possible to give a LOT shorter working hours per data. Also the data shortens the production time due to the reduction in the delivery time per treatment and It can contribute to cost reduction or the like defect rate reduced. Further, it is possible to obtain a standard data in the manufacturing process for the various models.