• International Journal of Advanced Culture Technology
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• v.12 no.2
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• pp.167-179
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• 2024

This study addresses the challenge of objectively evaluating the performance of early-stage startups amidst limited information and uncertainty. Focusing on companies selected by TIPS, a leading private sector-driven startup support policy in Korea, the research develops a new indicator to assess technological efficiency. By analyzing various input and output variables collected from Crunchbase and KIND (Korea Investor's Network for Disclosure System) databases, including technology use metrics, patents, and Crunchbase rankings, the study derives technological efficiency for TIPS-selected startups. A prediction model is then developed utilizing machine learning techniques such as Random Forest and boosting (XGBoost) to classify startups into efficiency percentiles (10th, 30th, and 50th). The results indicate that prediction accuracy improves with higher percentiles based on the technical efficiency index, providing valuable insights for evaluating and predicting startup performance in early markets characterized by information scarcity and uncertainty. Future research directions should focus on assessing growth potential and sustainability using the developed classification and prediction models, aiding investors in making data-driven investment decisions and contributing to the development of the early startup ecosystem.

• Nuclear Engineering and Technology
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• v.56 no.5
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• pp.1725-1732
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• 2024

In nuclear power plants, reactor coolant leakage can occur due to various reasons. Early detection of leaks is crucial for maintaining the safety of nuclear power plants. Currently, a detection system is being developed in Korea to identify reactor coolant system (RCS) leakage of less than 0.5 gpm. Typically, RCS leaks are detected by monitoring temperature, humidity, and radioactivity in the containment, and a water level in the sump. However, detecting small leaks proves challenging because the resulting changes in the containment humidity and temperature, and the sump water level are minimal. To address these issues and improve leak detection speed, it is necessary to quantify the leaks and develop an artificial intelligence-based leak detection system. In this study, we employed bidirectional long short-term memory, which are types of neural networks used in artificial intelligence, to predict the relative humidity in the leakage area for leak quantification. Additionally, an optimization technique was implemented to reduce learning time and enhance prediction performance. Through evaluation of the developed artificial intelligence model's prediction accuracy, we expect it to be valuable for future leak detection systems by accurately predicting the relative humidity in a leakage area.

• Journal of Distribution Science
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• v.13 no.10
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• pp.73-82
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• 2015

Purpose - This paper aims to investigate whether information asymmetry could explain capital structures in Korean corporations. According to Myers (1984), firms prefer internal funding to external financing due to the costs associated with information asymmetry. When external financing is necessary, firms prefer to issue debt rather than equity by the same reasoning. Since Shyam-Sunder and Myers (1999), numerous studies continue to debate the validity of the theory. In this paper, we show how the theory depends on assumptions and incorporated variables. We hope our investigation can provide helpful implications regarding capital structure, information asymmetry, and other firm characteristics. Specifically, our empirical results are complementary to the analysis of Son and Lee's (2015), a recent study that examines the pecking order theory prediction for Korean retail firms. Research design, data, and methodology - We test empirical models that are some variants of model used in Shyam-Sunder and Myers (1999). The financial and accounting data are provided by WISEfn for the firms listed on the KOSPI during 1990 to 2013. Bond ratings are supplied by the Korea Investor Service (KIS). We take into account the heterogeneity in debt capacity; a firm's debt capacity is measured by using the method of Lemmon and Zender (2010) based on its bond ratings. Finally, we estimate empirical models suggested by Shyam-Sunder and Myers (1999), Frank and Goyal (2003), and Lemmon and Zender (2010). Results - First, we find that Shyam-Sunder and Myers' (1999) prediction fails to explain total debt changes of Korean firms. Second, we find a non-monotonic relationship between total debt changes and financial deficits with respect to debt capacity. This contradicts the prediction of Lemmon and Zender (2010) that argues the pecking order theory survives with a monotonically increasing relationship. Third, we estimate a negative correlation coefficient between financial deficit and current debt changes. The result is the complete opposite of the prediction of Lemmon and Zender (2010). Finally, we also confirm the non-monotonic relationship between non-current debt changes and financial deficits with respect to debt capacity. Yet, the slope of coefficient is smaller than that of total debt change case. Indeed, the results are, to some extent, consistent with the prediction of pecking order theory, if we exclude the mid-debt capacity firms. Conclusions - Our empirical results complementary to the analysis of Son and Lee (2015), a recent study focusing on capital structure in Korean retail firms; their paper suggests interesting topics regarding capital structure, information asymmetry, and other firm characteristics in Korean corporations. Contrary to Son and Lee (2015), our results show that total debt changes and current debt changes are inconsistent with the prediction of Shyam-Sunder and Myers (1999). However, similar to Son and Lee (2015), non-current debt changes are consistent with the pecking order prediction, in the case of excluding the mid-level debt capacity firms. This contrast allows us to infer that industry characteristics significantly affect the validity of the pecking order prediction. Further studies are needed to analyze the economics behind this phenomenon, which is beyond the scope of our paper. In addition, the estimation bias potentially matters regarding the firm-level debt capacity calculation. We also reserve this topic for future research.

• Journal of the Korean Society of Marine Environment & Safety
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• v.24 no.5
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• pp.519-526
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• 2018

In this study, an algorithm to predict evacuation routes in support of shipboard lifesaving activities is presented. As the first step of algorithm development, the feasibility and necessity of an evacuation route prediction algorithm are shown numerically. The proposed algorithm can be explained in brief as follows. This system continuously obtains and analyzes passenger movement data from the ship's monitoring system during non-disaster conditions. In case of a disaster, evacuation route prediction information is derived using the previously acquired data and a prediction tool, with the results provided to rescuers to minimize casualties. In this study, evacuation-related data obtained through fire evacuation trials was filtered and analyzed using a statistical method. In a simulation using the conventional evacuation prediction tool, it was found that reliable prediction results were obtained only in the SN1 trial because of the conceptual and structural nature of the tool itself. In order to verify the validity of the algorithm proposed in this study, an industrial engineering tool was adapted for evacuation characteristics prediction. When the proposed algorithm was implemented, the predicted values for average evacuation time and route were very similar to the measured values with error ranges of 0.6-6.9 % and 0.6-3.6 %, respectively. In the future, development of a high-performance evacuation route prediction algorithm is planned based on shipboard data monitoring and analysis.

• Journal of Korea Water Resources Association
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• v.52 no.7
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• pp.451-461
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• 2019

The dam reservoir inflow prediction is utilized to ensure for water supply and prevent future droughts. In this study, we predicted the dam reservoir inflow and analyzed how seasonal weather forecasting affected the accuracy of the inflow for even multi-purpose dams. The hindcast and forecast of GloSea5 from KMA were used as input for rainfall-runoff models. TANK, ABCD, K-DRUM and PRMS models which have individual characteristics were applied to simulate inflow prediction. The dam reservoir inflow prediction was assessed for the periods of 1996~2009 and 2015~2016 for the hindcast and forecast respectively. The results of assessment showed that the inflow prediction was underestimated by comparing with the observed inflow. If rainfall-runoff models were calibrated appropriately, the characteristics of the models were not vital for accuracy of the inflow prediction. However the accuracy of seasonal weather forecasting, especially precipitation data is highly connected to the accuracy of the dam inflow prediction. It is recommended to consider underestimation of the inflow prediction when it is used for operations. Futhermore, for accuracy enhancement of the predicted dam inflow, it is more effective to focus on improving a seasonal weather forecasting rather than a rainfall-runoff model.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2019.11a
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• pp.22-23
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• 2019

The emerging Versatile Video Coding (VVC) standard introduces Bi-prediction with CU-level Weights (BCW) to enhance the bi-predictive prediction. The syntax element of BCW index is adaptively coded according to the value of NoBackwardPredFlag which indicates if there is no future picture in the display order among the reference pictures, and it can violate the flexibility of codec and cause the dependency issue. This paper proposes BCW clean-up design that allows all weights can be parsed without any condition. The experimental results show negligible BD-rate losses while resolving the issues.

• Journal of Ocean Engineering and Technology
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• v.9 no.2
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• pp.41-52
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• 1995

the necessity of development of the Nearshore zone greatly emphasis in recent years. In the wave deformation model, we can get the wave height and wave direction using the hyperbolic mild slope equation considered the reflection wave. Radiation Stress the driving force of flow was calculated by the Watanabe and Maruyama who proposed on the partial standing wave. In the surf zone, applying the Izumiya and Horikawa's turbulent model considered the bottom friction and energy dissipation, we compared and examined with the Numerical model and Hydraulic test result of Watanabe and Maruyama. This model results obtained for Jin-ha Beach agreed well with the Numerical results. This model is expected so helpful to solve the prediction of the wave deformation problems in the development of the Nearshore zone in the future.

• Journal of the Korean Statistical Society
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• v.35 no.2
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• pp.167-177
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• 2006

An important role of statistical analysis in science is interpreting observed data as evidence, that is 'what do the data say?'. Although standard statistical methods (hypothesis testing, estimation, confidence intervals) are routinely used for this purpose, the theory behind those methods contains no defined concept of evidence and no answer to the basic question 'when is it correct to say that a given body of data represent evidence supporting one statistical hypothesis against another?' (Royall, 1997). In this article, we use likelihood ratios to measure evidence provided by record values in favor of a hypothesis and against an alternative. This hypothesis is concerned on mean of an exponential model and prediction of future record values.

• Water for future
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• v.17 no.1
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• pp.31-36
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• 1984

This study has performed to investigate and evaluate the simulation model of steam Water Quality and the simulated results have 매내 been compared with the observed data in the Han River. The predicted BOD, Total-N, Coliform concentrations in the downstream of the Chungrang-Cheon are 8.6m/1, 4.5mg/1 and $3.7X10^5$ respectively. It is interesting to note that the results simulated based on the WQRRS model are extremely in good agreement and also are very much comparable with those observed data reported previously references.

• Proceedings of the KIPE Conference
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• 2012.11a
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• pp.33-34
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• 2012

As the distributed power system with PV and ESS is highlighted to be one of the most prominent structure to replace the traditional electric power system, power flow scheduling is expected to bring better system efficiency. Optimal energy management system (EMS) where the power from PV and the grid is managed in time-domain using ESS needs an optimization process. In this paper, main optimization method is implemented using dynamic programming (DP). To overcome the drawback of DP in which ideal future information is required, prediction stage precedes every EMS execution. A simple auto-regressive moving-average (ARMA) forecasting followed by a PI-controller updates the prediction data. Assessment of the on-line optimal EMS scheme has been evaluated on several cases.