• 유통과학연구
• /
• 제13권10호
• /
• pp.73-82
• /
• 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.

• 환경영향평가
• /
• 제15권6호
• /
• pp.373-383
• /
• 2006

The spatial mapping of risk is very useful data in planning for disaster preparedness. This research presents a methodology for making the landslide life risk map in the Boeun area which had considerable landslide damage following heavy rain in August, 1998. We have developed a three-stage procedure in spatial data analysis not only to estimate the probability of the occurrence of the natural hazardous events but also to evaluate the uncertainty of the estimators of that probability. The three-stage procedure consists of: (i)construction of a hazard prediction map of "future" hazardous events; (ii) validation of prediction results and estimation of the probability of occurrence for each predicted hazard level; and (iii) generation of risk maps with the introduction of human life factors representing assumed or established vulnerability levels by combining the prediction map in the first stage and the estimated probabilities in the second stage with human life data. The significance of the landslide susceptibility map was evaluated by computing a prediction rate curve. It is used that the Bayesian prediction model and the case study results (the landslide susceptibility map and prediction rate curve) can be prepared for prevention of future landslide life risk map. Data from the Bayesian model-based landslide susceptibility map and prediction ratio curves were used together with human rife data to draft future landslide life risk maps. Results reveal that individual pixels had low risks, but the total risk death toll was estimated at 3.14 people. In particular, the dangerous areas involving an estimated 1/100 people were shown to have the highest risk among all research-target areas. Three people were killed in this area when landslides occurred in 1998. Thus, this risk map can deliver factual damage situation prediction to policy decision-makers, and subsequently can be used as useful data in preventing disasters. In particular, drafting of maps on landslide risk in various steps will enable one to forecast the occurrence of disasters.

• 농업과학연구
• /
• 제47권4호
• /
• pp.1029-1037
• /
• 2020

The machine learning algorithm has been widely used in water-related fields such as water resources, water management, hydrology, atmospheric science, water quality, water level prediction, weather forecasting, water discharge prediction, water quality forecasting, etc. However, water quality prediction studies based on the machine learning algorithm are limited compared to other water-related applications because of the limited water quality data. Most of the previous water quality prediction studies have predicted monthly water quality, which is useful information but not enough from a practical aspect. In this study, we predicted the dissolved oxygen (DO) using recurrent neural network with long short-term memory model recurrent neural network long-short term memory (RNN-LSTM) algorithms with hourly- and daily-datasets. Bugok Bridge in Oncheoncheon, located in Busan, where the data was collected in real time, was selected as the target for the DO prediction. The 10-month (temperature, wind speed, and relative humidity) data were used as time prediction inputs, and the 5-year (temperature, wind speed, relative humidity, and rainfall) data were used as the daily forecast inputs. Missing data were filled by linear interpolation. The prediction model was coded based on TensorFlow, an open-source library developed by Google. The performance of the RNN-LSTM algorithm for the hourly- or daily-based water quality prediction was tested and analyzed. Research results showed that the hourly data for the water quality is useful for machine learning, and the RNN-LSTM algorithm has potential to be used for hourly- or daily-based water quality forecasting.

• 한국통신학회논문지
• /
• 제35권12B호
• /
• pp.1219-1226
• /
• 2010

적조 현상에 대한 판별, 예측 분석을 위한 시스템은 현재 개발이 아주 미흡한 상태이고 현재의 적조원인에 대한 연구는 화학 및 생물학적 원인의 규명에 대해 그 초점이 맞추어져 있어 지능적인 의사 결정 알고리즘을 갖는 시스템 구현이 필요하다. 본 논문에서는 사례 기반 추론 기법을 이용하여 적조 현상에 관한 사례를 지식 베이스로 구축하고 추론하는 시스템을 설계하였다. 가장 유사한 사례 추천을 위해 KNN 알고리즘을 이용하였고 적조 사례 베이스를 구축하기 위하여 375 건의 데이터를 입력 받아 실험하였다. 학습 데이터로부터의 영향을 최소화하고 신뢰성을 확보하기 위해 10-Fold 교차검증을 수행한 결과 적조 사례에 대한 평균 정확도는 약 84.2%를 나타냈고 유사도 분류 k 개수가 5인 경우에 최적의 수행 결과를 나타냈다. 또한, 추론된 결과를 이용하여 적조 모니터링 시스템을 구현하였다.

• 한국터널지하공간학회 논문집
• /
• 제24권6호
• /
• pp.553-581
• /
• 2022

TBM (Tunnel Boring Machine)이 터널 산업에 도입된 이후로 안전성과 친환경의 이점으로 TBM의 사용이 전 세계적으로 증가하였다. 암반 및 토사 지반을 굴착하는 TBM 터널에서의 주요 비용 중 하나는 손상되거나 마모된 커터의 교체로 볼 수 있다. 커터의 교체는 시간과 비용에 큰 영향을 끼치는 작업이며 TBM 가동률과 굴진율을 크게 감소시킬 수 있다. 따라서 커터의 수명을 정확하게 평가하는 것은 공기와 비용의 측면에서 매우 중요하다. 그러나 복합 지반을 포함하여 토사 구간, 암반구간에서 커터 마모에 대한 예측은 매우 복잡하고 명확하지 않다. 이에 따라 커터 마모에 대한 다양한 예측 모델이 개발 및 도입되었지만 이러한 불확실성으로 인해 가변적인 결과를 나타낸다. 본 연구에서는 커터 마모 예측 모델을 제시하기 보다는 커터 교체의 설계 및 시공 사례 연구를 소개하고 분석했다. 커터는 지반 조건, TBM 장비 및 운전의 영향을 많이 받으므로 불확실성과 한계를 감안하면 신뢰성 있는 예측 모델을 제안하는 것은 매우 어렵기 때문에 오히려 실제 사례를 분석하고 이에 대한 자료 공유가 더 실용적이다. 커터 교체에 대한 예측과 결과 간의 차이를 확인하고 심도 있게 분석하였다.

• 한국음향학회지
• /
• 제16권4호
• /
• pp.29-34
• /
• 1997

원거리에서 수동소나에 의한 탐지거리를 예측하기 위해서는 소나방정식이 이용된다. 본 연구에서는 거리와 깊이함수의 신호이득 및 탐지확률을 구한 후 이를 거리로 적분하여 거리의존 해양환경에서 탐지거리를 계산하는 탐지거리 예측모델을 개발하였다. 개발된 모델은 기존에 발표된 거리독립 해양환경에서의 결과와 비교하여 검증하였고, 이를 바탕으로 거리의존 해양환경에서 수동소나에 의한 표적탐지에 큰 영향을 주는 난수성 소용돌이 해양환경에 확장 적용하여 표적의 탐지거리를 예측하였으며, 그 결과에 대하여 소개한다.

• 한국IT서비스학회지
• /
• 제17권1호
• /
• pp.91-103
• /
• 2018

This study developed a prediction model using machine learning technology and predicted the success of health consulting by using life log data generated through u-Health service. The model index of the Random Forest model was the highest using. As a result of analyzing the Random Forest model, blood pressure was the most influential factor in the success or failure of metabolic syndrome in the subjects of u-Health service, followed by triglycerides, body weight, blood sugar, high cholesterol, and medication appear. muscular, basal metabolic rate and high-density lipoprotein cholesterol were increased; waist circumference, Blood sugar and triglyceride were decreased. Further, biometrics and health behavior improved. After nine months of u-health services, the number of subjects with four or more factors for metabolic syndrome decreased by 28.6%; 3.7% of regular drinkers stopped drinking; 23.2% of subjects who rarely exercised began to exercise twice a week or more; and 20.0% of smokers stopped smoking. If the predictive model developed in this study is linked with CBR, it can be used as case study data of CBR with high probability of success in the prediction model to improve the compliance of the subject and to improve the qualitative effect of counseling for the improvement of the metabolic syndrome.

• Journal of Electrical Engineering and Technology
• /
• 제7권5호
• /
• pp.797-806
• /
• 2012

Fault detection and isolation (FDI) algorithms provide fault monitoring methods in GPS measurement to isolate abnormal signals from the GPS satellites or the acquired signal in receiver. In order to monitor the occurred faults, FDI generates test statistics and decides the case that is beyond a designed threshold as a fault. For such problem of fault detection and isolation, this paper presents and evaluates position domain integrity monitoring methods by formulating various pseudorange prediction methods and investigating the resulting test statistics. In particular, precise measurements like carrier phase and Doppler rate are employed under the assumption of fault free carrier signal. The presented position domain algorithm contains the following process; first a common pseudorange prediction formula is defined with the proposed variations in pseudorange differential update. Next, a threshold computation is proposed with the test statistics distribution considering the elevation angle. Then, by examining the test statistics, fault detection and isolation is done for each satellite channel. To verify the performance, simulations using the presented fault detection methods are done for an ideal and real fault case, respectively.

• 한국정보과학회논문지:시스템및이론
• /
• 제31권1_2호
• /
• pp.67-77
• /
• 2004

고성능 슈퍼스칼라 프로세서에서 값 예측(value prediction) 방식은 명령의 결과 값을 미리 예측하고, 이 후 데이타 종속 관계가 있는 명령들에게 값을 조기에 공급함으로써 이들 명령들을 모험적으로 실행하여 성능을 향상시키는 방식이다. 값 예측으로 성능을 향상시키기 위해서는 예측 실패 시에 효율적으로 복구하는 과정이 필수적이다. 본 논문에서는 값 예측 실패 시에 잘못 예측된 값을 사용하여 모험적으로 수행된 명령들만을 순차적으로 취소하고 복구한 후에 재이슈하는 값 예측 실패 복구 메커니즘(value misprediction recovery mechanism)을 제안한다. 제안된 복구 방식은 한번에 모든 종속명령들을 검색하지 않음으로써 파이프라인을 정지시키지 않는다. 즉, 파이프라인이 진행되는 순서에 따라 순차적으로 값 예측이 틀린 종속명령만을 선택적으로 취소하고 재이슈하여 불필요한 취소와 재이슈를 줄임으로써 값 예측 실패 시에 손실을 줄인다.

• KIEAE Journal
• /
• 제5권1호
• /
• pp.51-57
• /
• 2005

In this study, time-dependent concentration variations of VOCs from fixed furniture and Ondol floor widely used as finishing material of the floor were measured, and prediction equations were developed based on the measured results. VOCs were measured and analyzed based on EPA TO-17 and NIOSH 1500, 1501 method respectively, and GC/FID were used for the analysis of VOCs concentration. Measurements were carried out for 10 days after the installation of furniture and for 40 days after the installation of the floor in the residence constructed more than 10 years ago. In both case of floor and furniture installation, time-dependent concentration decay of VOCs can be properly converted into logarithmic scale. Especially in case of furniture, toluene showed the highest concentration and took longest time to decay. As a result of the prediction of VOCs concentration decay under different air change rate using estimated equations, concentration decay rate of indoor VOCs increased rapidly as the air change rate also increased.