• Journal of the Korea Society of Computer and Information
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• v.6 no.3
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• pp.91-101
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• 2001

In this paper, we examined the concept and introduction effect of internet E-commerce. We analysed some factors are influenced to the B2B internet E-commerce. According to the result Environment factors divided into uncertainty and industrial competition factors, organization factors divided into scale, concentration of decision making, standardization of work. And information system take in IS/IT infra structure, integration of information technology Throughout this study, Enterprises which was introduced to the B2B internet E-commerce enable to be provided for introduction Judgment, guide of decision making, successfully adaptation, furthermore enlarged e-commerce trade.

• ETRI Journal
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• v.41 no.3
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• pp.358-370
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• 2019

The main aim of this study is to select the optimal set of genes from microarray cancer datasets that contribute to the prediction of specific cancer types. This study proposes the enhancement of the feature selection filter algorithm based on Joe's normalized mutual information and its use for gene selection. The proposed algorithm is implemented and evaluated on seven benchmark microarray cancer datasets, namely, central nervous system, leukemia (binary), leukemia (3 class), leukemia (4 class), lymphoma, mixed lineage leukemia, and small round blue cell tumor, using five well-known classifiers, including the naive Bayes, radial basis function network, instance-based classifier, decision-based table, and decision tree. An average increase in the prediction accuracy of 5.1% is observed on all seven datasets averaged over all five classifiers. The average reduction in training time is 2.86 seconds. The performance of the proposed method is also compared with those of three other popular mutual information-based feature selection filters, namely, information gain, gain ratio, and symmetric uncertainty. The results are impressive when all five classifiers are used on all the datasets.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.42 no.2
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• pp.9-17
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• 2019

In order to deal with high uncertainty and variability in emergency medical centers, many researchers have developed various models for their operational planning and scheduling. However, most of the models just provide static plans without any risk measures as their results, and thus the users often lose the opportunity to analyze how much risk the patients have, whether the plan is still implementable or how the plan should be changed when an unexpected event happens. In this study, we construct a simulation model combined with a risk-based planning and scheduling module designed by Simio LLC. In addition to static schedules, it provides possibility of treatment delay for each patient as a risk measure, and updates the schedule to avoid the risk when it is needed. By using the simulation model, the users can experiment various scenarios in operations quickly, and also can make a decision not based on their past experience or intuition but based on scientific estimation of risks even in urgent situations. An example of such an operational decision making process is demonstrated for a real mid-size emergency medical center located in Seoul, Republic of Korea. The model is designed for temporal short-term planning especially, but it can be expanded for long-term planning also with some appropriate adjustments.

• Journal of the Earthquake Engineering Society of Korea
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• v.25 no.6
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• pp.293-303
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• 2021

Seismic fragility functions for unreinforced masonry buildings were derived based on the incremental dynamic analysis of eight representative inelastic numerical models for application to Korea's earthquake damage estimation system. The effects of panel zones formed between piers and spandrels around openings were taken into account explicitly or implicitly regarding stiffness and inelastic deformation capacity. The site response of ground motion records measured at the rock site was used as input ground motion. Limit states were proposed based on the fraction of structural components that do not meet the required performance from the nonlinear static analysis of each model. In addition to the randomness of ground motion considered in the incremental dynamic analysis explicitly, supplementary standard deviation due to uncertainty that was not reflected in the fragility assessment procedure was added. The proposed seismic fragility functions were verified by applying them to the damage estimation of masonry buildings located around the epicenter of the 2017 Pohang earthquake and comparing the result with actual damage statistics.

• International Journal of Naval Architecture and Ocean Engineering
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• v.12 no.1
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• pp.428-439
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• 2020

The continuous development of information and communication technologies has resulted in an exponential increase in data. Consequently, technologies related to data analysis are growing in importance. The shipbuilding industry has high production uncertainty and variability, which has created an urgent need for data analysis techniques, such as machine learning. In particular, the industry cannot effectively respond to changes in the production-related standard time information systems, such as the basic cycle time and lead time. Improvement measures are necessary to enable the industry to respond swiftly to changes in the production environment. In this study, the lead times for fabrication, assembly of ship block, spool fabrication and painting were predicted using machine learning technology to propose a new management method for the process lead time using a master data system for the time element in the production data. Data preprocessing was performed in various ways using R and Python, which are open source programming languages, and process variables were selected considering their relationships with the lead time through correlation analysis and analysis of variables. Various machine learning, deep learning, and ensemble learning algorithms were applied to create the lead time prediction models. In addition, the applicability of the proposed machine learning methodology to standard work hour prediction was verified by evaluating the prediction models using the evaluation criteria, such as the Mean Absolute Percentage Error (MAPE) and Root Mean Squared Logarithmic Error (RMSLE).

• Journal of Korean Society of Industrial and Systems Engineering
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• v.45 no.2
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• pp.56-64
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• 2022

Many studies have suggested that e-commerce value creation potential depends on four interdependent factors Lock-In, Complementarity, Efficiency, and Novelty. In order to survive in the recent fierce competition, companies have also secured e-Trust that strengthens long-term business relationships by reducing consumer uncertainty. This study, while analyzing the value creation factors (Lock-in, Complementarity, Efficiency, Novelty, e-Trust) of recent e-commerce (online shopping mall) companies from the point of view of purchase intention, customer value (Functional value, Emotional value, Social value) We present an academic proposition that can also examine the mediating effect of value). First, through previous studies on value-based strategy and value creation in e-commerce, various discussions on the theoretical background necessary for effective value-based strategy establishment and strategy execution of e-commerce (online shopping mall) companies were reviewed. Second, it provides academic discussion and practical implications by presenting academic propositions on the value creation factors of e-commerce (online shopping mall) companies, purchase intentions, and customer value, and confirming the basis through empirical analysis.

• East Asian Economic Review
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• v.26 no.1
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• pp.3-25
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• 2022

East Asia's Regional Comprehensive Economic Partnership (RCEP) came into force in 2022 as the world's largest free trade agreement. RCEP was concluded, signed and brought into force in the face of major international uncertainty and is a significant boost to the global trading system. RCEP brings Australia, China, Japan, South Korea and New Zealand into the same agreement with the ten member ASEAN group at its centre. It keeps markets open and updates trade and investment rules in East Asia, a major centre of global economic activity, at a time of rising protectionism when the WTO itself is under threat. The agreement builds on ASEAN's free trade agreements and strengthens ASEAN centrality. One of the pillars of RCEP is an economic cooperation agenda which has its antecedents in ASEAN's approach to bringing along its least developed members and builds on the experience of capacity building in APEC and technical cooperation under the ASEAN Australia-New Zealand Free Trade Agreement. There is an opportunity to create a framework that facilitates deeper economic cooperation that involves experience-sharing, extending RCEP's rules and membership at the same time as strengthening political cooperation. The paper suggests some areas that might be best suited to cooperation - that is confidence and trust building instead of or before negotiation - and discusses how non-members may be engaged and the membership expanded. Options such as multilateralising provisions and becoming a platform for policy convergence and coordinating unilateral reforms are canvassed.

• Nuclear Engineering and Technology
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• v.54 no.10
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• pp.3703-3716
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• 2022

Target alignment technology is one of the most critical technologies in laser fusion experiments and is an important technology related to the success of laser fusion experiments. In this study, by combining the open-loop and closed-loop errors of the target alignment, the Kalman state observer is used to estimate the position of the target, which improves the observation precision of the target alignment. Then the optimized result is used to guide the alignment of the target. This method can greatly optimize the target alignment error and reduce uncertainty. With the improvement of the target alignment precision, it will greatly improve the reliability and repeatability of the experiments' results, thereby improving the success rate of the experiments.

• Journal of the Society of Naval Architects of Korea
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• v.58 no.6
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• pp.424-430
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• 2021

Fire localization is a key mission that must be preceded for an autonomous fire suppression system. Although studies using a variety of sensors for the localization are actively being conducted, the fire localization is still unfinished due to the high cost and low performance. This paper presents the modeling and simulation of the fire localization estimation using Bayesian estimation to determine the probabilistic location of the fire. To minimize the risk of fire accidents as well as the time and cost of preparing and executing live fire tests, a 40m × 40m-virtual space is created, where two ultraviolet sensors are simulated to rotate horizontally to collect ultraviolet signals. In addition, Bayesian estimation is executed to compute the probability of the fire location by considering both sensor errors and uncertainty under fire environments. For the validation of the proposed method, sixteen fires were simulated in different locations and evaluated by calculating the difference in distance between simulated and estimated fire locations. As a result, the proposed method demonstrates reliable outputs, showing that the error distribution tendency widens as the radial distance between the sensor and the fire increases.

• New & Renewable Energy
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• v.18 no.3
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• pp.32-42
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• 2022

Rapid spread of intermittent renewable energy has amplified the instability and uncertainty of power systems. The Korea Power Exchange (KPX) promoted efficient management by opening the power brokerage market in 2019. By combining small-scale intermittent renewable energy with a flexible facility through the power brokerage market, the KPX aims to develop a virtual power plant system that will allow the conversion of existing intermittent renewable energy into collective power plants. However, the participation rate of renewable power owners in the power brokerage market is relatively low because other markets such as the small solar power contract market or the Korea Electric Power Corporation power purchase agreement are more profitable. In this study, we used a choice experiment to determine the attributes affecting the participation rate in the power brokerage market for 113 renewable power owners and estimate the value of the power brokerage market. According to the estimation results, a low smart meter installation cost, low profit variations, long contract periods, and few clearances increased the probability of participation. Moreover, the average value of the power brokerage market was estimated to be 2.63 million KRW per power owner.