• Journal of Navigation and Port Research
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• v.44 no.3
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• pp.211-218
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• 2020

In recent years, the hub and spoke strategy has been strengthened in accordance with the enlargement of ships. As the needs of the port users change, the ports are also becoming larger and modernized. Under these circumstances, changes in existing port operations are expected. One example is the movement to promote economic and operational effectiveness through the joint operation of small and medium-sized operators. This study analyzed the effect of the association of small and medium-sized operators on Busan New Port in terms of economy. Additionally, the issue of cost allocation within the association of operators was presented through the game theory. As a result, in the case of operating jointly rather than divided into five operating companies as of the present, it has been shown to have a cost reduction effect in terms of operating companies. Considering the use of the Proportional method, the Shapley Value, and the Nucleus method in allocating the costs among the operators participating in the coalition, the Shapley Value method was the most suitable method in this study.

• Journal of Korea Port Economic Association
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• v.28 no.4
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• pp.257-274
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• 2012

Since service competition among global supply chains became intensified, market-oriented system, instead of the existing cost-based system, for port service pricing has been strongly recommended in order to enhance their long-term viability and competitiveness. The Owen value of cooperative game frameworks allows us to apply a market-oriented pricing theory for the port pricing in the case of Gwangyang port to verify its usefulness. The analytical results of this paper suggested some solutions in the problem of berth-based cost allocation by a characteristic function and also showed the proper relative weights of factors to derive the quay use index by Budescu(1993). We also suggested a favorable port pricing system to major shipping firms as well as a discount port pricing system for their strategic alliance. To put it differently, the results of this study enable the port managers to make out some strategic port pricing system like the reasonable discount in port charge for the larger ship owners using the ports frequently.

• Journal of Korean Society of Water and Wastewater
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• v.36 no.4
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• pp.239-248
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• 2022

The prediction of algal bloom is an important field of study in algal bloom management, and chlorophyll-a concentration(Chl-a) is commonly used to represent the status of algal bloom. In, recent years advanced machine learning algorithms are increasingly used for the prediction of algal bloom. In this study, XGBoost(XGB), an ensemble machine learning algorithm, was used to develop a model to predict Chl-a in a reservoir. The daily observation of water quality data and climate data was used for the training and testing of the model. In the first step of the study, the input variables were clustered into two groups(low and high value groups) based on the observed value of water temperature(TEMP), total organic carbon concentration(TOC), total nitrogen concentration(TN) and total phosphorus concentration(TP). For each of the four water quality items, two XGB models were developed using only the data in each clustered group(Model 1). The results were compared to the prediction of an XGB model developed by using the entire data before clustering(Model 2). The model performance was evaluated using three indices including root mean squared error-observation standard deviation ratio(RSR). The model performance was improved using Model 1 for TEMP, TN, TP as the RSR of each model was 0.503, 0.477 and 0.493, respectively, while the RSR of Model 2 was 0.521. On the other hand, Model 2 shows better performance than Model 1 for TOC, where the RSR was 0.532. Explainable artificial intelligence(XAI) is an ongoing field of research in machine learning study. Shapley value analysis, a novel XAI algorithm, was also used for the quantitative interpretation of the XGB model performance developed in this study.

• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.147-170
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• 2022

Although the number of patent which is one of the core outputs of technological innovation continues to increase, the number of low-value patents also hugely increased. Therefore, efficient evaluation of patents has become important. Estimation of patent lifespan which represents private value of a patent, has been studied for a long time, but in most cases it relied on a linear model. Even if machine learning methods were used, interpretation or explanation of the relationship between explanatory variables and patent lifespan was insufficient. In this study, patent lifespan (number of renewals) is predicted based on the idea that patent lifespan represents the value of the patent. For the research, 4,033,414 patents applied between 1996 and 2017 and finally granted were collected from USPTO (US Patent and Trademark Office). To predict the patent lifespan, we use variables that can reflect the characteristics of the patent, the patent owner's characteristics, and the inventor's characteristics. We build four different models (Ridge Regression, Random Forest, Feed Forward Neural Network, Gradient Boosting Models) and perform hyperparameter tuning through 5-fold Cross Validation. Then, the performance of the generated models are evaluated, and the relative importance of predictors is also presented. In addition, based on the Gradient Boosting Model which have excellent performance, Accumulated Local Effects Plot is presented to visualize the relationship between predictors and patent lifespan. Finally, we apply Kernal SHAP (SHapley Additive exPlanations) to present the evaluation reason of individual patents, and discuss applicability to the patent evaluation system. This study has academic significance in that it cumulatively contributes to the existing patent life estimation research and supplements the limitations of existing patent life estimation studies based on linearity. It is academically meaningful that this study contributes cumulatively to the existing studies which estimate patent lifespan, and that it supplements the limitations of linear models. Also, it is practically meaningful to suggest a method for deriving the evaluation basis for individual patent value and examine the applicability to patent evaluation systems.

• Journal of Distribution Research
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• v.2 no.1
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• pp.143-174
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• 1997

The model applied in this paper is based on the theory of economic order quantity (EOQ). EOQ model is introduced to explain the improvement of the transaction efficiency through the cooperative purchase. We examine explicitly how horizontal cooperation affects vertical transactions. A result of the analysis is that a seller can prefer transacting with a cooperative rather than with each buyer separately, even if he reduces the selling price of the product. Without increasing the demand for the product, this result is that dealing with a cooperative, rather than separately with each buyer, decreases the transaction cost for the seller-buyers system, the cost reduction more than off-setting the effect of price decrease on the sellers profit. For a coopative consisting of any number of buyers, Pareto efficient ordering policies that maximize the joint cost saving for the seller-buyers system are identified. We then discuss the conditions under which a cooperative under consideration can be modified to increase efficiency gain. Next, we relax the assumption that all buyers participate in a single cooperative and examine the issue of how many cooperatives, each consisting of a subset of the buyers, should be formed to maximize the total cost saving for the seller-buyers system. Finally, the issue of shapley value to divide the cooperatives gain among its members is discussed.

• Journal of Korea Port Economic Association
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• v.24 no.3
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• pp.23-35
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• 2008

The purpose of this paper is to suggest a rational cost allocation method that is efficient and fair. Cost allocation by taking cooperative game theory shows fair allocation considering marginal cost by ship type. Current berth occupancy charging method can not recover quay construction costs. Because it levies charges according to berthing time and tonnage of ships without considering the recovery of quay construction costs. And there are also cross subsidies among ships. This paper suggests the cost allocation method of cooperative game theory as a fair and efficient method.

• Korean Journal of Remote Sensing
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• v.39 no.6_3
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• pp.1721-1730
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• 2023

As awareness of the problem of global warming emerges around the world, the role of carbon sinks in settlement is increasingly emphasized to achieve carbon neutrality in urban areas. In order to manage carbon sinks in settlement, it is necessary to identify the current status of carbon sinks. Identifying the status of carbon sinks requires a lot of manpower and time and a corresponding budget. Therefore, in this study, a map predicting the location of trees was created using already established tree location information and Sentinel-2 satellite images targeting Seoul. To this end, after constructing a tree presence/absence dataset, structured data was generated using 16 types of vegetation indices information constructed from satellite images. After learning this by applying the Extreme Gradient Boosting (XGBoost) model, a tree prediction map was created. Afterward, the correlation between independent and dependent variables was investigated in model learning using the Shapely value of Shapley Additive exPlanations(SHAP). A comparative analysis was performed between maps produced for local parts of Seoul and sub-categorized land cover maps. In the case of the tree prediction model produced in this study, it was confirmed that even hard-to-detect street trees around the main street were predicted as trees.

• Environmental and Resource Economics Review
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• v.28 no.1
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• pp.95-120
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• 2019

As the Nagoya Protocol has been in effect since 2014, firms that invest in projects related with the genetic resources should establish methods to share the benefits arising from using genetic resources with the country providing such resources. The objective of this paper is to investigate the factors that affect the genetic resources related investment decisions under the Nagoya Protocol. Specifically, we construct the model of Sharpley value and benefit sharing rate in order to consider the results of benefit sharing with a providing country under the Real Options, and simulate the model in the context of Madagascar Banana project. The results show that the product time to market, benefit sharing rate, and discount rate significantly influence the investment decisions.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.8
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• pp.2016-2029
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• 2023

Accurate prediction of critical illness is significant for ensuring the lives and health of patients. The selection of indicators affects the real-time capability and accuracy of the prediction for critical illness. However, the diversity and complexity of these indicators make it difficult to find potential connections between them and critical illnesses. For the first time, this study proposes an indicator analysis model to extract key indicators from the preoperative and intraoperative clinical indicators and laboratory results of critical illnesses. In this study, preoperative and intraoperative data of heart failure and respiratory failure are used to verify the model. The proposed model processes the datum and extracts key indicators through four parts. To test the effectiveness of the proposed model, the key indicators are used to predict the two critical illnesses. The classifiers used in the prediction are light gradient boosting machine (LightGBM) and eXtreme Gradient Boosting (XGBoost). The predictive performance using key indicators is better than that using all indicators. In the prediction of heart failure, LightGBM and XGBoost have sensitivities of 0.889 and 0.892, and specificities of 0.939 and 0.937, respectively. For respiratory failure, LightGBM and XGBoost have sensitivities of 0.709 and 0.689, and specificity of 0.936 and 0.940, respectively. The proposed model can effectively analyze the correlation between indicators and postoperative critical illness. The analytical results make it possible to find the key indicators for postoperative critical illnesses. This model is meaningful to assist doctors in extracting key indicators in time and improving the reliability and efficiency of prediction.

• The Korean Journal of Applied Statistics
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• v.37 no.2
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• pp.239-253
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• 2024

This research applies both point-wise and pair-wise learning strategies within the learning-to-rank (LTR) framework to predict horse race rankings in Seoul. Specifically, for point-wise learning, we employ a linear model and random forest. In contrast, for pair-wise learning, we utilize tools such as RankNet, and LambdaMART (XGBoost Ranker, LightGBM Ranker, and CatBoost Ranker). Furthermore, to enhance predictions, race records are standardized based on race distance, and we integrate various datasets, including race information, jockey information, horse training records, and trainer information. Our results empirically demonstrate that pair-wise learning approaches that can reflect the order information between items generally outperform point-wise learning approaches. Notably, CatBoost Ranker is the top performer. Through Shapley value analysis, we identified that the important variables for CatBoost Ranker include the performance of a horse, its previous race records, the count of its starting trainings, the total number of starting trainings, and the instances of disease diagnoses for the horse.