• The Korean Journal of Franchise Management
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• v.2 no.1
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• pp.134-151
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• 2011

The strategy for brand alliance is a new type of franchise to iron out the problems like the hotel restaurant's structural contradiction and decreasing profits caused by keen competition with external restaurants. This study is purposed to present the decisive factors for the brand alliance throughexamining the correlations between the brand restaurant designation standards and the expected effects from local low- and mid-priced hotel's brand alliance. The questionnaires were distributed to instructors and professors who have experience in teaching the food and beverage sections at college's hotel and tourism departments and 100 specialists at managerial level of a hotel's food and beverage parts.This survey was conducted for 20 days from December 2 to 22, 2004 and analyzed by independent t-test and canonical correlation analysis. The findings of this survey are as follows.Firstly, the service of the expected effect factors of the brand alliance was recognized relatively high by the specialists in hotel industry, while the sales effect factor of restaurant designation standards was recognized higher by the academic experts.The specialists of the hotel industry recognized the factors of menu and corporate culture higher than the academic experts. Secondly, the entire factors of the brand restaurant designation standards showed a correlation with the whole factors of the restaurant designation standards.In particular, the 'menu' factor presented the most influential to the expected effects of brand alliance.The factors of 'risk reduction' and 'synergy effect' exerted the strongest effect on the restaurant designation standards, which indicated the mutual correlation between the expected effect of brand alliance and the restaurant designation standards. Based on this study, the correlation between the expected effect of brand alliance and brand restaurant designation standards may play a primary role to choose a partner for the brand alliance, a decisive factor for the success.The execution of the brand alliance or the method to designate the alliance partner may vary from the hotel's desirable effects when the brand alliance is determined.In other words, the partner designation standards should be corresponding to the expected effects from the brand alliance between hotel and brand restaurant, and the academic and industrial experts' perceived differences in the expected effects of brand alliance and restaurant designation standards should be clarified to display the direction of decision-making and find the potential risks.

• The Journal of Bigdata
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• v.8 no.1
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• pp.83-97
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• 2023

Project evaluation is the process of evaluating the progress and results of a project. Smart city projects can be divided into system components (infrastructure, services, platforms), or projects can run simultaneously for multiple services. In addition, services are developed and expanded through additional projects. In order to ensure that the smart city, which is composed of various projects, proceeds in accordance with the goals and strategies, periodic project evaluation is required during the project implementation process. The smart city project evaluation system proposed in this paper is designed to provide comprehensive and objective indicators by reflecting various factors that must be considered for projects occurring in all stages of planning, design, construction, and operation of smart cities. The indicators derived from the evaluation system can be used by decision makers to determine the direction of smart city project development. In addition, it is designed so that the performance of the project can be evaluated interim before the end of the project and the feedback obtained from it can be reflected. To introduce the application method of the smart city project evaluation system proposed in this study, the evaluation system developed in this study was applied to the smart city project case of Incheon Free Economic Zone (IFEZ). Based on the evaluation results, items that can maximize the improvement effect of each smart city project item were presented, and the direction of smart city project implementation was suggested. By utilizing a smart city project evaluation system that reflects the characteristics of smart city projects that are composed of multiple projects, comprehensive planning and management of smart city projects will be possible, and this study will serve as a reference for identifying priority improvement factors for projects.

• The Journal of Bigdata
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• v.8 no.1
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• pp.23-34
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• 2023

Nowadays, the Designated Driver Services employ dynamic pricing, which adapts in real-time based on nearby driver availability, service user volume, and current weather conditions during the user's request. The uncertain volatility is the main cause of price increases, leading to customer attrition and service refusal from driver. To make a good Designated Driver Services, development of a demand forecasting model is required. In this study, we propose developing a demand forecasting model using data from the Designated Driver Service by considering normal and peak periods, such as rush hour and rush day, as prior knowledge to enhance the model performance. We propose a new methodology called Time-Series with Conditional Probability(TSCP), which combines conditional probability and time-series models to enhance performance. Extensive experiments have been conducted with real Designated Driver Service data, and the result demonstrated that our method outperforms the existing time-series models such as SARIMA, Prophet. Therefore, our study can be considered for decision-making to facilitate proactive response in Designated Driver Services.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.2B
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• pp.225-236
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• 2008

The techniques to calculate the probability precipitation for the design of hydrological projects can be determined by the point frequency analysis and the regional frequency analysis. Probability precipitation usually calculated by point frequency analysis using rainfall data that is observed in rainfall observatory which is situated in the basin. Therefore, Probability precipitation through point frequency analysis need observed rainfall data for enough periods. But, lacking precipitation data can be calculated to wrong parameters. Consequently, the regional frequency analysis can supplement the lacking precipitation data. Therefore, the regional frequency analysis has weaknesses compared to point frequency analysis because of suppositions about probability distributions. In this paper, rainfall observatory in Korea did grouping by cluster analysis using position of timely precipitation observatory and characteristic time rainfall. Discordancy and heterogeneity measures verified the grouping precipitation observatory by the cluster analysis. So, there divided rainfall observatory in Korea to 6 areas, and the regional frequency analysis applies index-flood techniques and L-moment techniques. Also, the probability precipitation was calculated by the regional frequency analysis using variable kernel density function. At the results, the regional frequency analysis of the variable kernel function can utilize for decision difficulty of suitable probability distribution in other methods.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.2D
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• pp.153-161
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• 2010

The city planning of a local government is a continuous process that does not end with the creation of a plan but proceeds through decision-making, monitoring and evaluation phases. As a new city planning is changed and confirmed, there is a chance to construct a large scale road that is connected with an under constructed road. In this case, the expansion of the width and length of road, the addition of bridges or tunnels, and the change of the size and location of interchanges lead to many changes on road design and construction. In the past, the consignment contracts for a road construction have been made in limited numbers and for limited civil works. Now, it is growing in numbers and is making for large scale multi-works. However, the standard process and guidelines for the consignment contracts have not been suggested yet, so there is difficulty in performing the consigned road construction effectively. In this paper, the important factors for the consignment contracts are determined by construction document reviews and expert interviews. Based on these results, a standard process for the consigned contracts and a guideline for agreeing on construction cost are suggested. The costs that should be paid by a consignor are also defined.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.2B
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• pp.173-179
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• 2009

Industrial water supplied by water resource project is essential input materials along with labor, capital and land for companies. It is very important to stably secure these input materials in order for the industry to generate additional values. If the supply of industrial water is stopped, it is known damage for the industry is greater than domestic water or agriculture water based on same amount of supply. Like this, the actual value of industrial water has been highly acknowledged from the intuitive perspective, but study on the value and benefits of industrial water has been rarely conducted. Therefore, this study verified the value of industrial water supplied from water resource project, and used marginal production value as a measure to estimate the benefits of industrial water in the analysis of economic efficiency. As a result of empirical analysis using Cobb-Douglas production function and Translog production function, industries' average marginal production value was $5,427KRW/m^3$ and $5,583KRW/m^3$ respectively. The marginal production value for eleven industries were estimated by using same method. The marginal production value by industries presented by this study will be used as important data to calculate benefits of industrial water in the future. Moreover, the result of this study will provide reasonable criteria for decision making on the allocation of water in emergency situation, and problem of resource supply from water resource project.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.5D
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• pp.771-781
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• 2006

This Study is about the development of LCC Analysis Model and Evaluation of VE. It was carried out to help the person's intention decision about choosing the pavement construction method that can deal with 'Pavement Life Factor' like Area Character and Traffic Volume efficiently, by considering the total life cycle cost of pavement life cycle happens according to the numbers of public use year. For this, we developed the new LCC Analysis Model by using the Data of Seoul city the representative city in Korea, and carried out VE Evaluation that reflects the opinions of specialists. This Analysis Model consists of cost items that affects directly the choice of pavement construction, except for the common cost items of the various pavement construction. And we investigated the propriety by applying our model to the example line that are used for the public at present. About the base data of cost items that are used for our analysis, we enhanced our model's confidence by using the statistics data of Seoul and the standard data of unit cost calculation.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2022.06a
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• pp.383-384
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• 2022

Following the development of technologies such as digital twin, IoT, and AI after the 4th industrial revolution, decision-making problems are being solved based on high-dimensional data analysis. This has recently been applied to the port logistics sector, and a number of studies on big data analysis, deep learning predictions, and simulations have been conducted on container terminals to improve port productivity. These high-dimensional data analysis techniques generally require a large number of data. However, the global port environment has changed due to the COVID-19 pandemic in 2020. It is not appropriate to apply data before the COVID-19 outbreak to the current port environment, and the data after the outbreak was not sufficiently collected to apply it to data analysis such as deep learning. Therefore, this study intends to present a port data augmentation method for data analysis as one of these problem-solving methods. To this end, we generate the container stowage situation of the yard through a generative adversarial neural network model in terms of container terminal operation, and verify similarity through statistical distribution verification between real and augmented data.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.11
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• pp.481-492
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• 2023

This study explores how to build a Korean dataset to extract information from text using generative large language models. In modern society, mixed information circulates rapidly, and effectively categorizing and extracting it is crucial to the decision-making process. However, there is still a lack of Korean datasets for training. To overcome this, this study attempts to extract information using text-based zero-shot learning using a generative large language model to build a purposeful Korean dataset. In this study, the language model is instructed to output the desired result through prompt engineering in the form of "system"-"instruction"-"source input"-"output format", and the dataset is built by utilizing the in-context learning characteristics of the language model through input sentences. We validate our approach by comparing the generated dataset with the existing benchmark dataset, and achieve 25.47% higher performance compared to the KLUE-RoBERTa-large model for the relation information extraction task. The results of this study are expected to contribute to AI research by showing the feasibility of extracting knowledge elements from Korean text. Furthermore, this methodology can be utilized for various fields and purposes, and has potential for building various Korean datasets.

• Journal of Internet of Things and Convergence
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• v.10 no.1
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• pp.39-45
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• 2024

Container volume is a very important factor in accurate evaluation of port performance, and accurate prediction of effective port development and operation strategies is essential. However, it is difficult to improve the accuracy of container volume prediction due to rapid changes in the marine industry. To solve this problem, it is necessary to analyze the impact on port performance using the Internet of Things (IoT) and apply it to improve the competitiveness and efficiency of Busan Port. Therefore, this study aims to develop a prediction model for predicting the future container volume of Busan Port, and through this, focuses on improving port productivity and making improved decision-making by port management agencies. In order to predict port container volume, this study introduced the Extreme Gradient Boosting (XGBoost) technique of a machine learning model. XGBoost stands out of its higher accuracy, faster learning and prediction than other algorithms, preventing overfitting, along with providing Feature Importance. Especially, XGBoost can be used directly for regression predictive modelling, which helps improve the accuracy of the volume prediction model presented in previous studies. Through this, this study can accurately and reliably predict container volume by the proposed method with a 4.3% MAPE (Mean absolute percentage error) value, highlighting its high forecasting accuracy. It is believed that the accuracy of Busan container volume can be increased through the methodology presented in this study.