• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.55-71
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• 2013

The prediction model is the main factor affecting the performance of a knowledge-based system for bankruptcy prediction. Earlier studies on prediction modeling have focused on the building of a single best model using statistical and artificial intelligence techniques. However, since the mid-1980s, integration of multiple techniques (hybrid techniques) and, by extension, combinations of the outputs of several models (ensemble techniques) have, according to the experimental results, generally outperformed individual models. An ensemble is a technique that constructs a set of multiple models, combines their outputs, and produces one final prediction. The way in which the outputs of ensemble members are combined is one of the important issues affecting prediction accuracy. A variety of combination schemes have been proposed in order to improve prediction performance in ensembles. Each combination scheme has advantages and limitations, and can be influenced by domain and circumstance. Accordingly, decisions on the most appropriate combination scheme in a given domain and contingency are very difficult. This paper proposes a confidence-based selection approach as part of an ensemble bankruptcy-prediction scheme that can measure unified confidence, even if ensemble members produce different types of continuous-valued outputs. The present experimental results show that when varying the number of models to combine, according to the creation type of ensemble members, the proposed combination method offers the best performance in the ensemble having the largest number of models, even when compared with the methods most often employed in bankruptcy prediction.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.9
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• pp.5810-5818
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• 2015

The dissertation presents the portfolio construction method using the score sheet so that general investors can utilize it easily. This study draws the significant variables to contribute the enterprise value and suggests the combined models by applying the single methodology, which private investors can easily utilize. The results of the research can be classified into 2 areas. Firstly, the significantly affecting variables were selected for analyzing the enterprise value. The variables and the method for the enterprise value analysis were studied from the existing researches to choose the optimal variables. The variables were identified by using AHP method and the structure equation method from the investigation of the previous researches. And the critical variables were added extracted from the common denominator of variables which the 3 grue investors used for their investment. The final variables identified are dividend yield, PER, PBR, PCR, EV/EBITDA, ROE, net income, sales growth rate, net current asset, debt ratio, current ratio, rate of operating profits, ratio of operating profit to net sales, ratio of net income to net sales, net profit to total assets, EPS growth rate, inventory turnover ratio, and receivables turnover. Second, the new methodologies for forecasting enterprise value modifying the existing methods were developed. The result of the Logistic regression analysis for forecasting showed that the equation could not be suitable as the accuracy with 91.98%.

• Korean Journal of Soil Science and Fertilizer
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• v.33 no.1
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• pp.8-14
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• 2000

Long term cultivation crops like tomato, capsicum, melon etc. demand much amount of continuous supplying of nutrition during the whole growing periods. It is not easy to cover satisfactorily the nutritional demands for them by splitted top dressings, slow release fertilizer applications and fertigation systems. To overcome these problems, the "CULTAN" (Controlled Uptake Long Term Ammonium Nutrition) Beaker Deposit techniques have been developed and it was put into PVC beaker with the combined nitrogen fertilizer type mixed with the ratio of one-third of ammonium sulfate-N and two-thirds of urea-N, in which nitrogen was loaded on the demanding amount of a tomato plant during the growing period. Gypsum was mixed as a binder, and loamy soil and compost were used as a diffusion regulator. It was placed upside down into root zone of tomato at the transplanting. Tomato roots were spreaded into the Deposit beaker by ammonium ions which attract root growth. The tomato fruit yield and nitrogen uptake by plant were increased by application of $NH_4$-Beaker deposit fertilizer rather than those of common fertilizer treatment. In conclusion, it was able to improve economic and ecological benefits through CULTAN system compared with common fertilization systems. CULTAN system was estimated as a prospective alternative to enhance productivity and minimize nutrient lose. In addition, it shows further developing possibility of CULTAN system by the supplement of micro-nutrients and pesticides in the macro-nutrient beaker deposits.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.2
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• pp.12-28
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• 2019

Road geometry is one of the many factors that cause crashes, but the effect on traffic accident depends on weather conditions even under the same road geometry. This study identifies the variables affecting the crash severity by matching the highway accident data and weather data for 14 years from 2001 to 2014. A hierarchical ordered Logit model is used to reflect the effects of road geometry and weather condition interactions on crash severity, as well as the correlation between individual crashes in a region. Among the hierarchical models, we apply a random intercept model including interaction variables between road geometry and weather condition and a random coefficient model including regional weather characteristics as upper-level variables. As a result, it is confirmed that the effects of toll, ramp, downhill slope of 3% or more, and concrete barrier on the crash severity vary depending on weather conditions. It also shows that the combined effects of road geometry and weather conditions may not be linear depending on rainfall or snowfall levels. Finally, we suggest safety improvement measures based on the results of this study, which are expected to reduce the severity of traffic accidents in the future.

• The Korean Journal of Applied Statistics
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• v.32 no.3
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• pp.349-374
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• 2019

A class imbalance problem arises when one class outnumbers the other class by a large proportion in binary data. Studies such as transforming the learning data have been conducted to solve this imbalance problem. In this study, we compared resampling methods among methods to deal with an imbalance in the classification problem. We sought to find a way to more effectively detect the minority class in the data. Through simulation, a total of 20 methods of over-sampling, under-sampling, and combined method of over- and under-sampling were compared. The logistic regression, support vector machine, and random forest models, which are commonly used in classification problems, were used as classifiers. The simulation results showed that the random under sampling (RUS) method had the highest sensitivity with an accuracy over 0.5. The next most sensitive method was an over-sampling adaptive synthetic sampling approach. This revealed that the RUS method was suitable for finding minority class values. The results of applying to some real data sets were similar to those of the simulation.

• The Journal of Korean Academy of Prosthodontics
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• v.59 no.2
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• pp.201-209
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• 2021

In removable partial dentures, many types of retentive systems have been studied and applied in clinical treatment. One of those systems is the double crown denture system which is widely used in European countries such as Germany and Sweden. Telescopic double crown dentures have several advantages such as convenience in maintaining oral hygiene, enabling to transfer occlusal force along the long axis of the abutment, and secondary splinting effect between the abutments which leads to higher clinical performance compared to conventional removal partial dentures. In this clinical case, the patient was initially restored with a maxillary hybrid telescopic double crown denture with friction pin using remaining natural teeth as abutments. After 7 years, due to lack of recall check-up and poor oral hygiene, the abutment teeth were affected by periodontitis and 4 out of 5 of the abutment teeth had to be extracted. 3 additional implants were placed and the original abutment tooth with the inner crown was maintained. The mandible had fixed prostheses including implants but nevertheless, with strategic implant placement, the patient adapted well and was satisfied with the new maxillary tooth-implant combined double crown denture.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.6
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• pp.116-126
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• 2021

Ground Component Command (GCC) has been developing operational planning and execution systems to implement "Decisive Integrated Operations", which is the concept of ground operations execution, and achieved remarkable results. In particular, "Simultaneous Offense-Defense Integrated Operations" is developed mainly to neutralize enemies in deep areas and develop favorable conditions for the allies early by simultaneously attacking and defending from the beginning of the war. On the other hand, it is limited to providing scientific and reasonable support for the commander's decision-making process because analyzing the effects of the deep operation with existing M&S systems is impossible. This study developed a model for analyzing the effects of deep operations that can be used in the KJCCS. Previous research was conducted on the effects of surveillance, physical strike, and non-physical strike, which are components of deep operations to find the characteristics and limitations and suggest a research direction. A methodology for analyzing the effects of deep operations reflecting the interactions of components using data was then developed by the GCC, and input data for each field was calculated through combat experiments and a literature review. Finally, the Deep operations Effect CAlculating Model(DECAM) was developed and distributed to the GCC and Corps battle staff during the ROK-US Combined Exercise. Through this study, the effectiveness of the methodology and the developed model were confirmed and contribute to the development of the GCC and Corps' abilities to perform deep operations.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.5
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• pp.334-342
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• 2021

In Korea, the training is performed through independent environments without interoperability among L-V-C systems. In the L system, training for large units is limited due to civil complaints at the training grounds and road restrictions. The V system is insufficient in training related to tactical training, and the C system lacks practicality due to a lack of combat friction elements. To achieve synchronicity and integration training between upper and lower units, it is necessary to establish a system to ensure integrated training for each unit by interoperating the currently operating L, V, and C systems. The interoperability between the C-C system supports Korea-US Combined Exercise. On the other hand, the actual development of the training system through the interoperability of L, V, and C has not been made. Although efforts are being made to establish the L, V, and C system centering on the Army, the joint composite battlefield and LVC integrated architecture technology are not yet secured. Therefore, this paper proposes a new plan for the future training system by designing and implementing the LVC integrated architecture technology, which is the core technology that can build the L-V-C interoperability training system. In conclusion, a division-level L-V-C interoperability training system can be established in the future by securing the LVC integrated architecture technology.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.42 no.2
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• pp.209-217
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• 2022

Lane management with a central variable lane(s) (or reversible lane) where the traffic flow is temporarily reversed in one or more lanes during peak periods has been evaluated as an effective strategy to alleviate congestion caused by tidal traffic flows. However, due to traffic safety issues, such a movable barrier system can be considered as an alternative to supplement the existing its operation facilities such as static and/or dynamic signs and special pavement markings. In addition, when combined with a bus exclusive lane strategy, its effectiveness could be greatly increased. The objective of this study is to propose a feasibility analysis procedure for operational strategies of a bus-exclusive lanes using a barrier transfer system (BTS) for urban expressways. To this end, a case study was conducted on two urban expressways on the west side of the Han River in Seoul. As a result, temporary operation during rush hour in the morning was found to be most effective. The results presented in this study are expected to serve as a basis for establishing bus-exclusive lane operation strategies using similar systems in the future.

• Journal of Korea Society of Industrial Information Systems
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• v.28 no.5
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• pp.1-14
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• 2023

Soybeans are one of the world's top five staple crops and a major source of plant-based protein. Due to their susceptibility to climate change, which can significantly impact grain production, the National Agricultural Science Institute is conducting research on crop phenotypes through growth analysis of various soybean varieties. While the process of capturing growth progression photos of soybeans is automated, the verification, recording, and analysis of growth stages are currently done manually. In this paper, we designed and trained a YOLOv5s model to detect soybean leaf objects from image data of soybean plants and a Convolution Neural Network (CNN) model to judgement the unfolding status of the detected soybean leaves. We combined these two models and implemented an algorithm that distinguishes layers based on the coordinates of detected soybean leaves. As a result, we developed a program that takes time-series data of soybeans as input and performs growth analysis. The program can accurately determine the growth stages of soybeans up to the second or third compound leaves.