• Journal of Korean Port Research
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• v.11 no.1
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• pp.45-64
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• 1997

Ship's delay caused by port congestion has drawn attention to the necessity for analysis on overall operation in port. But, in order to analyze the complicated port operation which contains large number of variable factors, queueing theory is needed to be adopted, which is applicable to a large scale transportation including ship's arrival in a large port. In this paper, a simulation model for Inchon Port was developed by the software SIMAN V and presented congestion rate under a certain scenario regarding the arrival ship's number and service levels. To develop the simulation model, types of ships and cargoes during the 1995 in Inchon Port was analyzed in advance. The results of the simulation can be summarized as follows : In order to maintain present levels of congestion rate and time with the increasing number of arrival vessels, service rate should be increased at an exponential rate. To improve the current congestion effectively, part of cargoes are needed to be transferred to a newly developing port. Results obtained from simulation can be used properly to prepare improved service levels and to plan appropriate investment strategies.

• Journal of Korea Technology Innovation Society
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• v.10 no.2
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• pp.183-205
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• 2007

An investigation was undertaken of the optimal discriminant model for predicting the likelihood of insolvency in advance for medium-sized firms based on the technology evaluation. The explanatory variables included in the discriminant model were selected by both factor analysis and discriminant analysis using stepwise selection method. Five explanatory variables were selected in factor analysis in terms of explanatory ratio and communality. Six explanatory variables were selected in stepwise discriminant analysis. The effectiveness of linear discriminant model and logistic discriminant model were assessed by the criteria of the critical probability and correct classification rate. Result showed that both model had similar correct classification rate and the linear discriminant model was preferred to the logistic discriminant model in terms of criteria of the critical probability In case of the linear discriminant model with critical probability of 0.5, the total-group correct classification rate was 70.4% and correct classification rates of insolvent and solvent groups were 73.4% and 69.5% respectively. Correct classification rate is an estimate of the probability that the estimated discriminant function will correctly classify the present sample. However, the actual correct classification rate is an estimate of the probability that the estimated discriminant function will correctly classify a future observation. Unfortunately, the correct classification rate underestimates the actual correct classification rate because the data set used to estimate the discriminant function is also used to evaluate them. The cross-validation method were used to estimate the bias of the correct classification rate. According to the results the estimated bias were 2.9% and the predicted actual correct classification rate was 67.5%. And a threshold value is set to establish an in-doubt category. Results of linear discriminant model can be applied for the technology financing banks to evaluate the possibility of insolvency and give the ranking of the firms applied.

• International Journal of Naval Architecture and Ocean Engineering
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• v.6 no.2
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• pp.418-430
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• 2014

In this paper, a modelling technique for simulating self-propelled ships in waves is presented. The flow is modelled using a RANS solver coupled with an actuator disk model for the propeller. The motion of the ship is taken into consideration in the definition of the actuator disk region as well as the advance ratio of the propeller. The RPM of the propeller is controlled using a PID-controller with constraints added on the maximum permissible RPM increase rate. Results are presented for a freely surging model in regular waves with different constraints put on the PID-controller. The described method shows promising results and allows for the studying of several factors relating to self-propulsion. However, more validation data is needed to judge the accuracy of the model.

• Electronics and Telecommunications Trends
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• v.34 no.1
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• pp.98-110
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• 2019

Machine learning has been applied to medical imaging and has shown an excellent recognition rate. Recently, there has been much interest in preventive medicine. If data are accessible, machine learning packages can be used easily in digital healthcare fields. However, it is necessary to prepare the data in advance, and model evaluation and tuning are required to construct a reliable model. On average, these processes take more than 80% of the total effort required. In this study, we describe the basic concepts of machine learning, pre-processing and visualization of datasets, feature engineering for reliable models, model evaluation and tuning, and the latest trends in popular machine learning frameworks. Finally, we survey a explainable machine learning analysis tool and will discuss the future direction of machine learning.

• Journal of the Society of Naval Architects of Korea
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• v.57 no.5
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• pp.262-270
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• 2020

An outdoor free-running model test system was designed for assessing ship maneuverability with test uncertainty. The test model was a surface combatant of tumblehome hull geometry. The straight forward tests were conducted first to obtain the relationship between the propeller revolution rate and advance speed. During the outdoor tests, the propeller revolution rate to achieve a certain Froude number condition was higher than that in the indoor free-running model tests. Turning circle and zigzag tests for evaluating ship maneuverability criteria were carried out at the propeller revolution rate determined by the straight forward test results. The random and systematic standard uncertainties of maneuvering criteria were obtained by repeated tests and comparison with the indoor free-running model test results, respectively. The test uncertainty was largely dominated by the systematic standard uncertainty, while the random standard uncertainty was small with good repeatability.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.6
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• pp.165-171
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• 2022

Human heart rate can be used to measure exercise intensity as an important indicator. If heart rate can be predicted, exercise can be performed more efficiently by regulating the intensity of exercise in advance. In this paper, a FitRec-based prediction model is proposed for estimating running heart rate for users. Endomondo data is utilized for training the proposed prediction model. The processing algorithms for time-series data, such as LSTM(long short term memory) and GRU(gated recurrent unit), are employed to compare their performance. On the basis of simulation results, it was demonstrated that the proposed model trained with running exercise performed better than the model trained with several cardiac exercises.

• Journal of the Korean Geosynthetics Society
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• v.10 no.4
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• pp.61-70
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• 2011

This paper describes prediction results of penetration rate using case history in order to compare empirical models for penetration rate prediction of TBM. The reasonable empirical model is evaluated by comparison with prediction results and measured result. The penetration rate prediction is applied in separate empirical models considering rock characteristics and mechanical characteristics of TBM. The rock of applied filed had almost gneiss and its unconfined compressive strength was irregular due to the exist of weak zones and joint. In prediction results using unconfined compressive strength, Graham's model (1976) had impractical result when it had lower strength. NTNU model (1998) of the separate empirical models used in average penetration rate had the highest accuracy by comparison with the others, because it is a reasonable model which has rock characteristics and mechanical characteristics of TBM. However, Tarkoy's model (1986) based on unconfined compressive strength correspond with the measured values in field. Therefore, it should be considered a rock type, geological characteristic and mechanical characteristic of TBM at prediction of penetration rate.

• Korea Trade Review
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• v.46 no.1
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• pp.23-38
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• 2021

This paper investigated the preference change of payment terms in international trade along with counteroffer or first offer conditions of the other parties. Studies on trade payment terms have mainly focused on payment term determination factors such as firm size, product price level, country credit rate, etc. We tried to find other factors affecting payment terms choice, during the negotiation process. We applied behavioral economics theories such as 'Status Quo Bias' and 'Anchoring effect' to build our research model. To prove the existence of the above effects, we proceeded with field experiments to the exporting companies in Alibaba.com. Both 'Status Quo Bias' and 'Anchoring effect' were found in the field experiment. Most of the exporting companies preferred traditional payment methods to new payment methods. And an initial request for a low advance payment ratio led to a lower advance payment ratio. Also, the experience of using new payment methods could diminish status quo bias. This paper applied behavioral economics theories and field experiment methodology to the payment term studies in international trades. These attempts could contribute to expanding the diversity of methodology and scope of international trade studies.

• Journal of Korean Society of Water and Wastewater
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• v.37 no.5
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• pp.271-279
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• 2023

Fouling is an inevitable problem in membrane water treatment plant. It can be measured by trans-membrane pressure (TMP) in the constant flux operation, and chemical cleaning is carried out when TMP reaches a critical value. An early fouilng alarm is defined as warning the critical TMP value appearance in advance. The alarming method was developed using one of machine learning algorithms, decision tree, and applied to a ceramic microfiltration (MF) pilot plant. First, the decision tree model that classifies the normal/abnormal state of the filtration cycle of the ceramic MF pilot plant was developed and it was then used to make the early fouling alarm method. The accuracy of the classification model was up to 96.2% and the time for the early warning was when abnormal cycles occurred three times in a row. The early fouling alram can expect reaching a limit TMP in advance (e.g., 15-174 hours). By adopting TMP increasing rate and backwash efficiency as machine learning variables, the model accuracy and the reliability of the early fouling alarm method were increased, respectively.

• KEPCO Journal on Electric Power and Energy
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• v.4 no.1
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• pp.13-17
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• 2018

According to the recent blackouts, large blackouts can be described by cascading outages. Cascading outage is defined by sequential outages from an initial disturbance. Sequential and probabilistic approach are necessary to minimize the blackout damage caused by cascading outages. In addition, conventional cascading outage analysis models are computationally complex and have time constraints, it is necessary to develop the new analytical techniques. In this paper, we propose the advance visualization model for probabilistic analysis of cascading failure risks. We introduce the visualization model for identifying size of cascading and potential outages and estimate the propagation rate of sequential outage simulation. The proposed model is applied to Korean power systems.