• 한국프랜차이즈경영연구
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• 제6권2호
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• pp.67-85
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• 2015

This study attempts to measure the performance of franchise brands using the Enterprise Contract Management(ECM) and Franchise Contract Management Leverage(FCML). The data were collected from 14 chicken franchise brands and 16 coffee franchise brands. This research suggests some implications as follows. Chicken franchise brands are confronted with the slow growth, but coffee franchise brands still have the potential growth. According to FCML, as well, it shows that chicken franchise brands should focus on qualitative performance through franchise store management to achieve sustainability, and coffee franchise brands need the managerial strategies to maximize franchisees' sales profit, which leads increasing franchisor's business performance ultimately.

• 국제학술발표논문집
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• The 6th International Conference on Construction Engineering and Project Management
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• pp.174-178
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• 2015

This paper presents a new method for forecasting construction project cost and time at completion or at any intermediate time horizon of the project duration. The method is designed to overcome identified limitations of current applications of earned value method in forecasting project cost and time. The proposed method usesfuzzy set theory to model uncertainties associated with project performance and it integrates the earned value technique and the contractors' judgement. The fuzzy set theory is applied as an alternative approach to deterministic and probabilistic methods. Using fuzzy set theory allows contractors to: (1) perform risk analysis for different scenarios of project performance indices, and (2) perform different scenarios expressing vagueness and imprecision of forecasted project cost and time using a set of measures and indices. Unlike the current applications of Earned Value Method(EVM), The proposed method has a numberof interesting features: (1) integrating contractors' judgement in forecasting project performance; (2) enabling contractors to evaluate the risk associated with cost overrun in much simpler method comparing with that of simulation, and (3) accounting for uncertainties involved in the forecasting project cost.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2023년도 춘계학술발표대회
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• pp.652-654
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• 2023

The accurate prediction of User Equipment (UE) paths in wireless networks is crucial for improving handover mechanisms and optimizing network performance, particularly in the context of Beyond 5G and 6G networks. This paper presents a comprehensive evaluation of unidirectional and bidirectional recurrent neural network (RNN) architectures for UE path prediction. The study employs a sequence-to-sequence model designed to forecast user paths in a wireless network environment, comparing the performance of unidirectional and bidirectional RNNs. Through extensive experimentation, the paper highlights the strengths and weaknesses of each RNN architecture in terms of prediction accuracy and computational efficiency. These insights contribute to the development of more effective predictive path-based mobility management strategies, capable of addressing the challenges posed by ultra-dense cell deployments and complex network dynamics.

• The Journal of Asian Finance, Economics and Business
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• 제7권12호
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• pp.1-10
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• 2020

This study investigates the problem of outlier detection based on discrete wavelet transform in the context of time series data where the identification and treatment of outliers constitute an important component. An outlier is defined as a data point that deviates so much from the rest of observations within a data sample. In this work we focus on the application of the traditional method suggested by Tukey (1977) for detecting outliers in the closed price series of the Saudi Arabia stock market (Tadawul) between Oct. 2011 and Dec. 2019. The method is applied to the details obtained from the MODWT (Maximal-Overlap Discrete Wavelet Transform) of the original series. The result show that the suggested methodology was successful in detecting all of the outliers in the series. The findings of this study suggest that we can model and forecast the volatility of returns from the reconstructed series without outliers using GARCH models. The estimated GARCH volatility model was compared to other asymmetric GARCH models using standard forecast error metrics. It is found that the performance of the standard GARCH model were as good as that of the gjrGARCH model over the out-of-sample forecasts for returns among other GARCH specifications.

• 응용통계연구
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• 제28권3호
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• pp.467-475
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• 2015

정확한 수요예측은 수입관리(RM)에서 중요한 요소이다. 기 출발편 예약 데이터는 미래 출발편의 수요를 예측하는데 이용되는데, 이 중 일부 데이터에는 예약 요청이 거부된 경우가 포함된다. 거부된 예약 요청은 통계학적 관점에서 중도절단된 것으로 해석될 수 있으며, 이러한 중도절단된 수요를 복원하는 것은 미래 출발편의 참수요 예측을 위해 중요한 사안이다. 현재까지 여러 복원방법들이 소개되었으며, Expectation Maximization 방법이 가장 우수하다고 알려져있다. 본 연구에서는 중도절단된 자료를 복원할 수 있는 회귀모형 기반의 새로운 수요복원 방법을 제시하였다. 그리고 모의실험을 통해 제안된 새로운 방법의 성능을 RM에서 대표적으로 사용되는 두 가지 복원방법들과 비교하였다.

• 한국인공지능학회지
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• 제4권1호
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• pp.11-13
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• 2016

These days, medical technology has been developed rapidly to meet desire of living healthy life. Average lifespan was extended to let people see a doctor because of many reasons. This study has shown rate of leaving of hospitals to investigate the rate of not only department of surgery but also department of internal medicine. Linear model, tree, classification rule, association and algorithm of data mining were used. This study investigated by using J48 and AD tree of decision-making tree In this study, J48 and AD tree of decision-making tree of data mining were used to investigate based on result of both data. Both algorithms were found to have similar performance. Both algorithms were not equivalent to require detailed experiment. Collect more experimental data in the future to apply from various points of view. Development of medical technology gives dream, hope and pleasure. The ones who suffer from incurable diseases need developed medical technology. Environment being similar to the reality shall be made to experiment exactly to investigate data carefully and to let the ones of various ages visit hospital and to increase survival rate.

• 대한조선학회논문집
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• 제42권1호
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• pp.57-63
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• 2005

This paper introduces basic research of optimum routing assessment system as voyage support purpose which can obtain safe and efficient route. In view point of safety, the prediction of ship motion should be evaluated in the condition of rough weather This part includes general seakeeping estimation based on 3 dimensional panel method and parametric roil prediction. For increasing voyage efficiency, ETA(Estimated Time of Arrival) and fuel consumption should be calculated considering speed reduction and power increase due to wave effects based on added resistance calculation and ship performance characteristics. Basically, the weather forecast is assumed to be prepared previously to operate this system. The idea of these factors in this system will be helpful to escape from dangerous voyage situation by wave conditions and to make optimum route planning based on ETA and fuel consumption.

• 대기
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• 제24권4호
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• pp.533-540
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• 2014

Statistical forecast models for the prediction of the summertime Changma precipitation have been developed in this study. As effective predictors for the Changma precipitation, the springtime sea surface temperature (SST) anomalies over the North Atlantic (NA1), the North Pacific (NPC) and the tropical Pacific Ocean (CNINO) has been suggested in Lee and Seo (2013). To further improve the performance of the statistical prediction scheme, we select other potential predictors and construct 2 additional statistical models. The selected predictors are the Northern Indian Ocean (NIO) and the Bering Sea (BS) SST anomalies, and the spring Eurasian snow cover anomaly (EUSC). Then, using the total three statistical prediction models, a simple ensemble-mean prediction is performed. The resulting correlation skill score reaches as high as ~0.90 for the last 21 years, which is ~16% increase in the skill compared to the prediction model by Lee and Seo (2013). The EUSC and BS predictors are related to a strengthening of the Okhotsk high, leading to an enhancement of the Changma front. The NIO predictor induces the cyclonic anomalies to the southwest of the Korean peninsula and southeasterly flows toward the peninsula, giving rise to an increase in the Changma precipitation.

• 대기
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• 제28권3호
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• pp.247-261
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• 2018

The objective of this paper is to compare probabilistic temperature forecasts from different regional and global ensemble prediction systems over PyeongChang area. A statistical post-processing method is used to take into account combination and calibration of forecasts from different numerical prediction systems, laying greater weight on ensemble model that exhibits the best performance. Observations for temperature were obtained from the 30 stations in PyeongChang and three different ensemble forecasts derived from the European Centre for Medium-Range Weather Forecasts, Ensemble Prediction System for Global and Limited Area Ensemble Prediction System that were obtained between 1 May 2014 and 18 March 2017. Prior to applying to the post-processing methods, reliability analysis was conducted to identify the statistical consistency of ensemble forecasts and corresponding observations. Then, ensemble model output statistics and bias-corrected methods were applied to each raw ensemble model and then proposed weighted combination of ensembles. The results showed that the proposed methods provide improved performances than raw ensemble mean. In particular, multi-model forecast based on ensemble model output statistics was superior to the bias-corrected forecast in terms of deterministic prediction.

• 대한산업공학회지
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• 제26권3호
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• pp.193-199
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• 2000

In many studies, considerable attention has been focussed upon choosing a model which represents underlying process of time series and forecasting the future. In the real world, however, there may be some cases that one model can not reflect all the characteristics of original time series. Under such circumstances, we may get better performance by combining the forecasts from several models. The most popular methods for combining forecasts involve taking a weighted average of multiple forecasts. But the weights are usually unstable. In cases the assumptions of normality and unbiasedness for forecast errors are satisfied, a Bayesian method can be used for updating the weights. In the real world, however, there are many circumstances the Bayesian method is not appropriate. This paper proposes a PNN(Probabilistic Neural Net) approach as a method for combining forecasts that can be applied when the assumption of normality or unbiasedness for forecast errors is not satisfied. In this paper, PNN method, which is similar to Bayesian approach, is suggested as an updating method of the unstable weights in the combination of the forecasts. The PNN method has been usually used in the field of pattern recognition. Unlike the Bayesian approach, it requires no assumption of a specific prior distribution because it gets probabilities by using the distribution estimated from given data. Empirical results reveal that the PNN method offers superior predictive capabilities.