• Journal of Ecology and Environment
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• v.43 no.1
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• pp.61-72
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• 2019

Background: Organic carbon stored in coastal wetlands, which comprises the major part of oceanic "blue carbon," is a subject of growing interest and concern. In this study, organic carbon storage in coastal wetlands and its economic value were estimated using the raw data of 25 studies related to soil carbon storage. Data were collected from three tidal flats (one protected and two developed areas) and two estuarine salt marshes (one protected and one restored area). Bulk density, soil organic matter content, and standing biomass of vegetation were all considered, with Monte Carlo simulation applied to estimate the uncertainty. Results: Mean carbon storage in two salt marshes ranged between 14.6 and $25.5kg\;C\;m^{-2}$. Mean carbon storage in tidal flats ranged from 18.2 to $28.6kg\;C\;m^{-2}$, with variability possibly related to soil texture. The economic value of stored carbon was estimated by comparison with the price of carbon in the emission trading market. The value of US $ $6600\;ha^{-1}$ is ~ 45% of previously estimated ecosystem services from fishery production and water purification functions in coastal areas. Conclusions: Although our study sites do not cover all types of large marine ecosystem, this study highlights the substantial contribution of coastal wetlands as carbon sinks and the importance of conserving these habitats to maximize their ecosystem services.

• Wind and Structures
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• v.28 no.4
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• pp.225-238
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• 2019

In this paper a novel and efficient computational framework to estimate the stress range versus number of cycles curves experienced by a cable due to external excitations (e.g., seismic excitations, traffic and wind-induced vibrations, among others) is proposed. This study is limited to the wind-cable interaction governed by the Vortex Shedding mechanism which mainly rules cables vibrations at low amplitudes that may lead to their failure due to bending fatigue damage. The algorithm relies on a stochastic approach to account for the uncertainties in the cable properties, initial conditions, damping, and wind excitation which are the variables that govern the wind-induced vibration phenomena in cables. These uncertainties are propagated adopting Monte Carlo simulations and the concept of importance sampling, which is used to reduce significantly the computational costs when new scenarios with different probabilistic models for the uncertainties are evaluated. A high fidelity cable model is also proposed, capturing the effect of its internal wires distribution and helix angles on the cables stress. Simulation results on a 15 mm diameter high-strength steel strand reveal that not accounting for the initial conditions uncertainties or using a coarse wind speed discretization lead to an underestimation of the stress range experienced by the cable. In addition, parametric studies illustrate the computational efficiency of the algorithm at estimating new scenarios with new probabilistic models, running 3000 times faster than the base case.

• International Journal of Naval Architecture and Ocean Engineering
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• v.12 no.1
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• pp.428-439
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• 2020

The continuous development of information and communication technologies has resulted in an exponential increase in data. Consequently, technologies related to data analysis are growing in importance. The shipbuilding industry has high production uncertainty and variability, which has created an urgent need for data analysis techniques, such as machine learning. In particular, the industry cannot effectively respond to changes in the production-related standard time information systems, such as the basic cycle time and lead time. Improvement measures are necessary to enable the industry to respond swiftly to changes in the production environment. In this study, the lead times for fabrication, assembly of ship block, spool fabrication and painting were predicted using machine learning technology to propose a new management method for the process lead time using a master data system for the time element in the production data. Data preprocessing was performed in various ways using R and Python, which are open source programming languages, and process variables were selected considering their relationships with the lead time through correlation analysis and analysis of variables. Various machine learning, deep learning, and ensemble learning algorithms were applied to create the lead time prediction models. In addition, the applicability of the proposed machine learning methodology to standard work hour prediction was verified by evaluating the prediction models using the evaluation criteria, such as the Mean Absolute Percentage Error (MAPE) and Root Mean Squared Logarithmic Error (RMSLE).

• The Journal of Asian Finance, Economics and Business
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• v.8 no.3
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• pp.561-573
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• 2021

Knowing how to build and maintain consumer trust is crucial for e-commerce. Despite the number of empirical studies that have explored the factors that influence consumer trust, none of them considers the relative importance of different antecedents and how they interact to influence consumer trust. Therefore, based on the integrated Decision Making Trial and Evaluation Laboratory (DEMATEL) and Interpretive Structural Modeling (ISM) approaches, we establish a hierarchical structural model, which not only demonstrates the intensity of the relationships but also identifies the interdependence among the drivers of consumer trust in E-commerce. The findings confirm that propensity to trust is the most important determinant of consumer trust. The brand-related factors and platform-related factors are prominent in the process of building trust as they influence consumer trust indirectly through propensity to trust. Geographic location, demographic variables, and high security are identified as the root causes that affect consumer trust through other trust antecedents. Furthermore, the findings of this study offer valuable insights into an important element of e-commerce and provide a useful platform for future research. More represented samples and factors are encouraged for further research to ensure research fairness and minimize consumer distrust and uncertainty.

• Journal of the Korean Society of Visualization
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• v.20 no.2
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• pp.86-92
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• 2022

Pipe failure due to thermal fatigue and environmental regulations are increasing the importance of pipe monitoring systems in industrial plants. Since most pipe monitoring systems are focus on external crack inspected, it is necessary to temperature and concentration measuring monitoring system inside the pipe. These systems have spatial uncertainty due to sample inspection by one-point measurement. In addition, real-time measurement is not possible due to the limitation of time delay due to contact measurement. In this study, CT-TDLAS (Computed tomography-Tunable diode laser absorption spectroscopy) apply to overcome the limitations of existing methods. Lasers exhibiting an absorption response at a wavelength of 1395 nm were arranged in a lattice pattern on measuring cell. It showed that the inside of the pipe changed to an unstable combustion state over time.

• Journal of Korea Society of Digital Industry and Information Management
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• v.18 no.2
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• pp.61-75
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• 2022

The purpose of this study is to analyze the impact of quality management system requirements, a management innovation technique, on business performance to secure the competitiveness of SMEs in an environment of increasing uncertainty in the global economy and intensifying competition. To this end, a survey was conducted on small and medium-sized enterprises (SMEs) operating a quality management system, and statistical analysis was performed through validity and reliability analysis, regression analysis, and group analysis using IBM SPSS 26.0. As a result of the study, it was found that planning, operation, support, and improvement, which are the core requirements of a quality management system, have a positive effect on business performance. In addition, through group analysis, it was found that the effect of quality management system requirements on business performance varies according to the level of the company. This means that the importance of quality management requirements required for strategy establishment varies according to the quality management introduction level of small and medium-sized manufacturers, and it can be used for small and medium-sized manufacturers to set strategic directions.

• Structural Engineering and Mechanics
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• v.83 no.3
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• pp.401-413
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• 2022

The collapse of civil infrastructure due to natural disasters results in financial losses and many casualties. In particular, the recent increase in earthquake activities has highlighted on the importance of assessing the seismic performance and predicting the seismic risk of a structure. However, the nonlinear behavior of a structure and the uncertainty in ground motion complicate the accurate seismic response prediction of a structure. Artificial intelligence can overcome these limitations to reasonably predict the nonlinear behavior of structures. In this study, a deep learning-based algorithm was developed to estimate the time-history seismic response of bridge structures. The proposed deep neural network was trained using structural and ground motion parameters. The performance of the seismic response prediction algorithm showed the similar phase and magnitude to those of the time-history analysis in a single-degree-of-freedom system that exhibits nonlinear behavior as a main structural element. Then, the proposed algorithm was expanded to predict the seismic response and fragility prediction of a bridge system. The proposed deep neural network reasonably predicted the nonlinear seismic behavior of piers and bearings for approximately 93% and 87% of the test dataset, respectively. The results of the study also demonstrated that the proposed algorithm can be utilized to assess the seismic fragility of bridge components and system.

• International journal of advanced smart convergence
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• v.12 no.3
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• pp.168-174
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• 2023

The purpose of this paper is to analyze entrepreneurship and to find out the impact of CEOs in the restaurant industry on corporate performance when they have entrepreneurship. Entrepreneurs need entrepreneurship to take risks and jump into the market to generate profits. Entrepreneurship is not limited to the abilities or resources held, but it is not limited to the ability or resources held, and entrepreneurship to act means the spirit to take uncertainty and preempt opportunities through innovative activities [1]. In this study, the CEO's entrepreneurship was set as an independent variable and corporate performance as a dependent variable. By applying and analyzing how the CEO's entrepreneurship affects corporate performance in the restaurant industry, the importance of entrepreneurship in the restaurant industry and the impact relationship on corporate performance are analyzed. To this end, 100 CEOs working in the restaurant industry will be surveyed using the Likert 5-point scale[2]. And an empirical analysis will be conducted through the SPSS program[3]. Entrepreneurship is a spirit that can take risks and seize opportunities through bold challenges to generate profits. Therefore, it has been confirmed that it affects corporate performance as a key factor for improving corporate performance, and from related studies, the entrepreneurship of the CEO of the restaurant industry is expected to have a positive (+) effect on corporate performance.

• International conference on construction engineering and project management
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• 2009.05a
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• pp.40-40
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• 2009

Construction firms have long sought success in the global construction market through diversifying revenue sources and project portfolios. The volume of international contracts has contributed to firms' sustained growth by mitigating the impact of the domestic market's cyclical nature. In spite of the importance of international construction, the uncertainty and dynamic changes surrounding global construction pose serious threats to global contactors. Over the last decade, the international construction industry has changed drastically in many ways, particularly including financial resource diversity, competition rules for the selection of contractors, and the terms of delivery systems requiring more competent total service providers. This paper investigates the important changes for global contractors through various documentation analysis as well as in-depth interviews with industry experts. This paper then analyzes the common strategies and lessons obtained from the cases of leading global contractors that have sustained their growth in the competitive global construction during the last decade. In addition, the authors further analyzed the comparisons between those firms and Korean contractors to discern any difference in sustaining their growth in the competitive market. It was found that those leading firms were quite proactive and responsive to changing markets by diversifying their market revenues to stabilize their revenue structure and enhancing their competency through a wide range of 'business convergence'. In addition, they significantly increased their upstream/downstream functional capabilities; hence becoming more competent service providers, able to grow in these rapidly changing market conditions. Finally, this paper benchmarks the critical strategies that support growth, which in turn can provide a strategic guideline for expansion into the global construction market.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2022.06a
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• pp.204-206
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• 2022

The global economy and the air transport business have been affected since the spread of the COVID-19 pandemic. As countries tighten restrictions on international movements, the growing emphasis on air cargo puts pressure on airports to maintain and upgrade their cargo policies, facilities, and operations. Hence, ensuring the competitiveness of cargo airports becomes pivotal for airports survival under the volatile global demand. The study aims to evaluate the importance of the competitiveness factors for cargo airports and identify areas for further improvement. The study applies the Best-Worst Method (BWM) to assess the cargo airports' competitiveness factors: 'Transport Capacity,' 'Airport Operations and Facility Capacity,' 'Economic Growth,' 'Financial Performance,' and 'Airport Brand Value.' The selected airports include Heathrow Airport, Aéroport de Paris-Charles de Gaulle, Hong Kong International Airport, and Incheon International Airport. The results identify 'Transport Capacity' as the most significant competitiveness factor, and Hong Kong International Airport the best performing cargo airport. This research forms a reference framework for evaluating cargo airports' competitive position, which may help identify airports' relative strengths and weaknesses. Moreover, this framework can also serve as a tool facilitating the strategic design of airports that may accommodate both air cargo and passenger demand flexibly under the demand uncertainty.