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After the Industry 4.0 discussion in Germany in 2011, much attention has been paid to smart factory in Korea. Since 2014, smart factories have been established and expanded in Korea. However, about 80% of them were established at a low level. In this paper, we analyze smart factory statuses in detail through an empirical research on 113 manufacturing companies that have established smart factories in Korea. We build a framework based on the resource-based view (RBV) and IT value creation process and analyze the results of five constructs—manufacturing strategy, organization, system, process, and performance—using basic statistical methodologies to derive the current statuses of manufacturing companies that have established smart factories. Our results show that implementing advanced technologies such as AI technology that can implement semi-finished and finished product quality inspection, manufacturing process optimization and product demand forecast is a challenge, particularly for SMEs. We also find that securing and managing facility data is a difficult problem. In addition, while output and material management ranked high, the utilization of integration systems, which is important when building a smart factory, was found to be extremely low. Lastly, the performance indicator results showed that yield management and defect rate were most important, while job creation through the introduction of smart factories was low. Based on the results of this study, the government may be able to determine effective smart factory policies and provide manufacturing companies with a guide on establishing a smart factory. [ABSTRACT FROM AUTHOR]

• The highest flood probabilities were in river levees following future precipitation. • Flood probability in paddy fields was robust due to presence of drainage structures. • Significantly low flood probabilities within Yedang Reservoir were found. • Downstream rivers and agricultural fields showed variations in flood probabilities. In agricultural reservoir watersheds, the presence of various agricultural hydraulic structures combined with the influence of climate change necessitates effective flood risk analysis. This study aims to develop a flood risk evaluation technique for agricultural basins by incorporating the low temporal resolution of climate change scenarios. The approach utilizes flood probability as a key component and incorporates the Multiplicative Random Cascade model as a probability-based rainfall time decomposition model to capture the inherent uncertainty in climate change data. Reliability analysis with the limit state equation is employed for probability-based risk evaluation. Given that agricultural watersheds typically consist of agricultural reservoirs and farmland, the study develops a hydraulic and hydrological linkage model to comprehensively calculate flood probability, simulating from the agricultural reservoir in the upper side to the farmland located at the bottom edge of the watershed. The flood risk assessment is applied to the Yedang Reservoir watershed in Korea, using the Shared Socio-economic Pathways climate change scenario. Results indicate that flood probability is the highest in river levees during future precipitation events, converging to nearly 100% under 24-hour duration rainfall, irrespective of the rain frequency. Despite the hydraulic structures at the farmland being designed for a lower frequency of rain events, the average probability of flooding in the farmland have reached 50%. The agricultural reservoir exhibits a low risk of flooding even under climate change conditions. The proposed method not only enables the establishment of flood countermeasures but also facilitates the evaluation and prediction of future flood risk for each hydraulic structure located in rural areas affected by climate change. [ABSTRACT FROM AUTHOR]

Weighting criteria methods achieved popularity in the quantification of uncertainty in the first decade of 21 st century. In this study, Reliability Ensemble Averaging (REA) method is used to provide a quantitative estimate of uncertainty range and reliability of future climate projections over Han river basin in Korea simulated by 18 GCMs under CMIP5 Project. The root mean square error (RMSE) was used to measure uncertainty range. The result indicates that historical simulations project 43% decrease in precipitation while future scenarios simulations of GCMs projects a moderate increase in mean annual temperature and precipitation. The future projections showed a reduction in uncertainty range of about 150% as compared to simple ensemble average approach. The results suggest the viability of REA method by incorporating the model performance and model convergence criteria. [ABSTRACT FROM AUTHOR]

In the present study, the overall stability of typical Korean composite caisson breakwaters that were initially designed following the conventional deterministic approach is investigated using reliability approaches. Therefore, the sensitivity of critical uncertainties regarding breakwater safety is analyzed. Uncertainty sources related to the structure, ocean conditions, and properties of the subsoil and rubble mound are considered in the reliability analysis. Sliding and overturning failures are presented as explicit equations, and three reliability methods, i.e., the mean value first-order second-moment, first-order reliability method, and Monte Carlo simulation, are applied in the evaluation process. Furthermore, the bearing capacity of the rubble mound and subsoil are analyzed using the discrete slice method conjugated with the Monte Carlo simulation. The results of this study establish that the sliding failure generally is the most frequent failure occurring among the above-mentioned overall stability failures (around 15 times more common than failures observed in the foundation). Additionally, it is found that the horizontal wave force primarily contributes to the sliding of the caisson body, whereas the friction coefficient is the main factor producing the resistance force. Furthermore, a much small probability of overturning failure implies that the overturning of a caisson around its heels uncommonly occurs during their lifetime, unlike other overall failure modes. Moreover, the failure in foundations may commonly encounter in the breakwater that has a high rubble mound structure compared with sliding mode. Particularly, the performance function of the all foundation bearing capacities presents a nonlinear behavior and positively skewed distribution when using the Monte Carlo simulation method. This phenomenon proves that simulation methods might be an appropriate approach to evaluate the bearing capacity of a breakwater foundation that can overcome several drawbacks of the conventional design approach. [ABSTRACT FROM AUTHOR]

Ensemble verification of low-level wind shear (LLWS) is an important issue in airplane landing operations and management. In this study, we conducted an accuracy and reliability analysis using a rank histogram, Brier score, and reliability diagram to verify LLWS ensemble member forecasts based on grid points over the Jeju area of the Republic of Korea. Thirteen LLWS ensemble member forecasts derived from a limited area ensemble prediction system (LENS) were obtained between 1 July 2016 and 30 May 2018, and 3-h LLWS forecasts for lead times up to 72 h (three days) were issued twice a day at 0000 UTC (9 am local time) and 1200 UTC (9 pm local time). We found that LLWS ensemble forecasts have a weak negative bias in summer and autumn and a positive bias in the spring and winter; the forecasts also have under-dispersion for all seasons, which implies that the ensemble spread of an ensemble is smaller than that of the corresponding observations. Additionally, the reliability curve in the associated reliability diagram indicates an over-forecasting of LLWS events bias. The selection of a forecast probability threshold from the LLWS ensemble forecast was confirmed to be one of the most important factors for issuing a severe LLWS warning. A simple method to select a forecast probability threshold without economic factors was conducted. The results showed that the selection of threshold is more useful for issuing a severe LLWS warning than none being selected. [ABSTRACT FROM AUTHOR]