• Journal of Korea Technology Innovation Society
• /
• v.12 no.1
• /
• pp.212-236
• /
• 2009

This study is an attempt to deduct environmental uncertainties facing next-generation convergence services, in four areas including customer, technology, service provider and regulation. We assess the level of residual uncertainty with regard to key environmental uncertainty factors, and conduct a scenario planning analysis. Based on the results of this analysis, we provide suggestions on market entry strategy for providers of this next-generation convergence service. The strategic assessment of six scenarios developed in this study, each with two levels of residual uncertainty (alternate futures and a range of futures) resulted in two key success factors (KSF), namely, customer demand trends and easing of advertising restrictions. Four types of strategic scenarios were then discerned, for each of which we present response capabilities that may be required of service providers, along with strategic suggestions. The results of this study are rich in implications for both policy-makers and regulators seeking ways to create and stimulate a convergence service market and prospective providers of next-generation convergence services, as they provide concrete tips related to market entry strategy, including efficient resource allocation, types of market entry and time-frames for entry.

• Journal of Technology Innovation
• /
• v.20 no.3
• /
• pp.1-28
• /
• 2012

This study conducted a qualitative analysis on the Korea's nuclear energy sector in 2030 through scenario-based strategic foresight method. Specifically, the relationships between environmental influencing factors of the future nuclear energy sector was examined from a multi-dimensional perspective on the basis of STEEP analysis and network analysis. In addition, scenario planning method was used with key uncertainty factors (KUF) to create three predictable strategic scenarios including optimistic, business as usual, and pessimistic. Common strategies that need to be urgently pursued as well as the maximum risk avoidance strategies for each scenario were also presented. This study further identified energy pricing, global economic trend, competitiveness in nuclear technology, and marketing capability as key uncertainty factors in the future nuclear energy industry sector. In order to furnish effective export strategy in the future nuclear energy sector, it was also suggested that government policy should adopt following measures as top priorities: securing nuclear safety technology, educating nuclear engineers, securing nuclear resources such as uranium, increasing nuclear capability and so on. The implications and limitations of this study were then discussed based on research findings.

• Smart Structures and Systems
• /
• v.29 no.2
• /
• pp.321-336
• /
• 2022

Model uncertainty is a key factor that could influence the accuracy and reliability of numerical model-based analysis. It is necessary to acquire an appropriate updating approach which could search and determine the realistic model parameter values from measurements. In this paper, the Bayesian model updating theory combined with the transitional Markov chain Monte Carlo (TMCMC) method and K-means cluster analysis is utilized in the updating of the structural model parameters. Kriging and polynomial chaos expansion (PCE) are employed to generate surrogate models to reduce the computational burden in TMCMC. The selected updating approaches are applied to three structural examples with different complexity, including a two-storey frame, a ten-storey frame, and the national stadium model. These models stand for the low-dimensional linear model, the high-dimensional linear model, and the nonlinear model, respectively. The performances of updating in these three models are assessed in terms of the prediction uncertainty, numerical efforts, and prior information. This study also investigates the updating scenarios using the analytical approach and surrogate models. The uncertainty quantification in the Bayesian approach is further discussed to verify the validity and accuracy of the surrogate models. Finally, the advantages and limitations of the surrogate model-based updating approaches are discussed for different structural complexity. The possibility of utilizing the boosting algorithm as an ensemble learning method for improving the surrogate models is also presented.

• Journal of the Korea Institute of Building Construction
• /
• v.8 no.4
• /
• pp.53-59
• /
• 2008

Cost estimates are very important to their decision-making in the early stages of a construction project. So Clients have wanted not only to know the results of conceptual cost estimates but also to assess their quality Conceptual cost estimates process is very complex process, so the results of cost estimates are influenced by various factors. So the purpose of this study is to reveal the key factors which influence the reliability of conceptual cost estimates in building construction projects. The analytic hierarchy process is used to determine the relative important weights of elements influencing the conceptual cost estimates. And factor analysis is used to reveal the key factors from the elements that influence the conceptual cost estimates. The results showed that the key factors is an experience level, available data level, level of will for winning the bid, difficulty level of conceptual cost estimate, uncertainty level.

• Korea Trade Review
• /
• v.47 no.1
• /
• pp.25-42
• /
• 2022

This study identified consumers' shopping behavior in live streaming commerce. To this end, this study put the uncertainty issue of live shopping and the transfer of trust at the center of the discussion. The verification of the research model resulted in the following conclusions. First, reduced uncertainty in live shopping was a factor in increasing the level of involvement and attachment in the service. These results showed that resolving uncertainty in newly introduced services is a key factor in determining users' positive attitudes. Second, the trust in shopping sites influenced the current live shopping attitude. This is because the transfer of trust is also valid in live shopping, which demonstrated the importance of building trust. Third, this study proposed and validated a research model that could systematically understand the consumption process of live streaming shopping. Furthermore, this study provides a beneficial implication for those who want to use live shopping in practice.

• Proceedings of the KIEE Conference
• /
• 1996.07b
• /
• pp.859-861
• /
• 1996

This paper presents the novel fault diagnostic expert system based on dissolved gas analysis(DGA) techniques in power transformer. The uncertainty of key gas analysis, norm threshold, and gas ratio boundaries are managed by using a fuzzy set concept. The uncertainty of rules are handled by fuzzy measures. Trend analysis through the monthly increment of key gas and DGA analysis are combined by the Dempster-Shafer theory, and the state of transformer and confidence factor are yielded by using this combined analysis. To verify the effectiveness of the proposed diagnosis technique, the expert system has been tested by using KEPCO's transformer gas records.

• Wind and Structures
• /
• v.22 no.1
• /
• pp.17-41
• /
• 2016

To address the uncertainty of the flight trajectories caused by the turbulence and gustiness of the wind field over the roof and in the wake of a building, a 3-D probabilistic trajectory model of flat-type wind-borne debris is developed in this study. The core of this methodology is a 6 degree-of-freedom deterministic model, derived from the governing equations of motion of the debris, and a Monte Carlo simulation engine used to account for the uncertainty resulting from vertical and lateral gust wind velocity components. The influence of several parameters, including initial wind speed, time step, gust sampling frequency, number of Monte Carlo simulations, and the extreme gust factor, on the accuracy of the proposed model is examined. For the purpose of validation and calibration, the simulated results from the 3-D probabilistic trajectory model are compared against the available wind tunnel test data. Results show that the maximum relative error between the simulated and wind tunnel test results of the average longitudinal position is about 20%, implying that the probabilistic model provides a reliable and effective means to predict the 3-D flight of the plate-type wind-borne debris.

• International Journal of Naval Architecture and Ocean Engineering
• /
• v.10 no.4
• /
• pp.499-507
• /
• 2018

Autonomous Underwater Vehicle (AUV), which are becoming more and more important in ocean exploitation tasks, needs energy conservation urgently when sailing the complex mission path in long time cruise. As hull lines optimization design becomes the key factor, which closely related with resistance, in AUV preliminary design stage, uncertainty parameters need to be considered seriously. In this research, Myring axial symmetry revolution body with parameterized expression is assumed as AUV hull lines, and its travelling resistance is obtained via modified DATCOM formula. The problems of AUV hull lines design for the minimum travelling resistance with uncertain parameters are studied. Based on reliability-based optimization design technology, Design For Six Sigma (DFSS) for high quality level is conducted, and is proved more reliability for the actual environment disturbance.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.9 no.1
• /
• pp.20-33
• /
• 2015

Spectrum sensing is a key component of cognitive radio. The prediction of the primary user status in a low signal-to-noise ratio is an important factor in spectrum sensing. However, because of noise uncertainty, secondary users have difficulty distinguishing between the primary signal and an unauthorized signal when an unauthorized user exists in a cognitive radio network. To resolve the sensitivity to the noise uncertainty problem, we propose an entropy-based spectrum sensing scheme to detect the primary signal accurately in the presence of an unauthorized signal. The proposed spectrum sensing uses the conditional entropy between the primary signal and the unauthorized signal. The ability to detect the primary signal is thus robust against noise uncertainty, which leads to superior sensing performance in a low signal-to-noise ratio. Simulation results show that the proposed spectrum sensing scheme outperforms the conventional entropy-based spectrum sensing schemes in terms of the primary user detection probability.

• Structural Engineering and Mechanics
• /
• v.57 no.2
• /
• pp.369-387
• /
• 2016

In this work, random homogenization analysis for the effective thermal properties of a three-dimensional composite material with unidirectional fibers is presented by combining the equivalent inclusion method with Random Factor Method (RFM). The randomness of the micro-structural morphology and constituent material properties as well as the correlation among these random parameters are completely accounted for, and stochastic effective thermal properties as thermal expansion coefficients as well as their correlation are then sought. Results from the RFM and the Monte-Carlo Method (MCM) are compared. The impact of randomness and correlation of the micro-structural parameters on the random homogenized results is revealed by two methods simultaneously, and some important conclusions are obtained.