• Journal of Information Technology Applications and Management
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• v.18 no.3
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• pp.91-110
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• 2011

Korea professional baseball industry has grown to take the lion's share of the domestic sports industry, but still does not make break even. The purpose of this study is to examine the financial impact of adopting the Customer Relation Management (CRM) approach on the profitability of Korea professional baseball industry. We use a measuring tool called entropy used in ID3 decision tree algorithm. In the paper, we specify five the most important factors that affect spectator satisfaction based on the previous literature, perform survey analysis, calculate entropy values, and find the results. We predicted the change in revenues when we adopt CRM by checking the spectators' willingness to pay more when the conditions of each factor are improved. We find that we can reap significant fruits of the effect of CRM introduction through enhancing 'game content factor' and 'game promotion factor' among the five factors. We also find that we can increase the revenues of domestic professional baseball teams to 2.4 times or 2.1 times the current level if we manage intensively those two factors respectively. It is very surprising to see that the improvement in total revenues makes both ends meet for domestic professional baseball teams. This clearly demonstrates the effectiveness of CRM approach in improving the profitability of organizations.

• Nuclear Engineering and Technology
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• v.44 no.7
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• pp.755-766
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• 2012

A Binary Decision Diagram (BDD) is a graph-based data structure that calculates an exact top event probability (TEP). It has been a very difficult task to develop an efficient BDD algorithm that can solve a large problem since its memory consumption is very high. Recently, in order to solve a large reliability problem within limited computational resources, Jung presented an efficient method to maintain a small BDD size by a BDD truncation during a BDD calculation. In this paper, it is first identified that Jung's BDD truncation algorithm can be improved for a more practical use. Then, a more efficient truncation algorithm is proposed in this paper, which can generate truncated BDD with smaller size and approximate TEP with smaller truncation error. Empirical results showed this new algorithm uses slightly less running time and slightly more storage usage than Jung's algorithm. It was also found, that designing a truncation algorithm with ideal features for every possible fault tree is very difficult, if not impossible. The so-called ideal features of this paper would be that with the decrease of truncation limits, the size of truncated BDD converges to the size of exact BDD, but should never be larger than exact BDD.

• Nuclear Engineering and Technology
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• v.40 no.5
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• pp.327-348
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• 2008

A risk-informed regulatory approach implies that risk insights be used as supplement of deterministic information for safety decision-making purposes. In this view, the use of risk assessment techniques is expected to lead to improved safety and a more rational allocation of the limited resources available. On the other hand, it is recognized that uncertainties affect both the deterministic safety analyses and the risk assessments. In order for the risk-informed decision making process to be effective, the adequate representation and treatment of such uncertainties is mandatory. In this paper, the risk-informed regulatory framework is considered under the focus of the uncertainty issue. Traditionally, probability theory has provided the language and mathematics for the representation and treatment of uncertainty. More recently, other mathematical structures have been introduced. In particular, the Dempster-Shafer theory of evidence is here illustrated as a generalized framework encompassing probability theory and possibility theory. The special case of probability theory is only addressed as term of comparison, given that it is a well known subject. On the other hand, the special case of possibility theory is amply illustrated. An example of the combination of probability and possibility for treating the uncertainty in the parameters of an event tree is illustrated.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.3
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• pp.85-90
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• 2021

In this paper, fall detection using individual wearable devices for older people is considered. To design a low-cost wearable device for reliable fall detection, we present a comprehensive analysis of two representative models. One is a machine learning model composed of a decision tree, random forest, and Support Vector Machine(SVM). The other is a deep learning model relying on a one-dimensional(1D) Convolutional Neural Network(CNN). By considering data segmentation, preprocessing, and feature extraction methods applied to the input data, we also evaluate the considered models' validity. Simulation results verify the efficacy of the deep learning model showing improved overall performance.

• The Journal of the Acoustical Society of Korea
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• v.21 no.2
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• pp.199-210
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• 2002

In this paper, we carried out the study on speech recognition using the KM-Net topology design algorithm based on decision tree state-clustering to improve the performance of acoustic models in speech recognition. The Korean has many allophonic and grammatical rules compared to other languages, so we investigate the allophonic variations, which defined the Korean phonetics, and construct the phoneme question set for phonetic decision tree. The basic idea of the HM-Net topology design algorithm is that it has the basic structure of SSS (Successive State Splitting) algorithm and split again the states of the context-dependent acoustic models pre-constructed. That is, it have generated. the phonetic decision tree using the phoneme question sets each the state of models, and have iteratively trained the state sequence of the context-dependent acoustic models using the PDT-SSS (Phonetic Decision Tree-based SSS) algorithm. To verify the effectiveness of the above algorithm we carried out the speech recognition experiments for 452 words of center for Korean language Engineering (KLE452) and 200 sentences of air flight reservation task (YNU200). Experimental results show that the recognition accuracy has progressively improved according to the number of states variations after perform the splitting of states in the phoneme, word and continuous speech recognition experiments respectively. Through the experiments, we have got the average 71.5%, 99.2% of the phoneme, word recognition accuracy when the state number is 2,000, respectively and the average 91.6% of the continuous speech recognition accuracy when the state number is 800. Also we haute carried out the word recognition experiments using the HTK (HMM Too1kit) which is performed the state tying, compared to share the parameters of the HM-Net topology design algorithm. In word recognition experiments, the HM-Net topology design algorithm has an average of 4.0% higher recognition accuracy than the context-dependent acoustic models generated by the HTK implying the effectiveness of it.

• Journal of the Korean Society of Systems Engineering
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• v.12 no.1
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• pp.41-58
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• 2016

Reliability Centered Maintenance (RCM) introduces a systematic method and decision logic tree for utilizing previous operating experience focused on reliability and optimization of maintenance activities. In this paper RCM methodology is applied on safety injection system for APR-1400. Functional Failure Mode Effects and Criticality Analysis (FME&CA) are applied to evaluate the failure modes and the effect on the component, system and plant. Logic Tree Analysis (LTA) is used to determine the optimum maintenance tasks. The results show that increasing the condition based maintenance will reduce component failure and improve reliability and availability of the system. Also the extension of the surveillance test interval of Safety Injection Pumps (SIPs) would lead to an improved pump's availability, eliminate the unnecessary maintenance tasks and this will optimize maintenance activities.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.2
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• pp.117-132
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• 2023

Mobility as a Service(MaaS) is an integrated mobility service that enables flexible reservation and payments for various transportation services on one platform, including existing public transportation. To popularize MaaS, users need to be satisfied with the service, and the factors that lower satisfaction need to be improved. In this study, the structural equation model was used to identify the influence of factors on the overall satisfaction level. Improvement plans to increase user satisfaction were derived based on importance-satisfaction analysis and the decision tree model. The analysis revealed the level of connectivity in MaaS apps to be a significant factor that needs to be improved for a higher user satisfaction level of MaaS

• Journal of Korea Multimedia Society
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• v.13 no.6
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• pp.841-848
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• 2010

Conventional filters using email header and body information equally judge whether an incoming email is spam or not. However this is unrealistic in everyday life because each person has different criteria to judge what is spam or not. To resolve this problem, we consider user preference information as well as email category information derived from the email content. In this paper, we have developed a personalized anti-spam system using ontologies constructed from rules derived in a data mining process. The reason why traditional content-based filters are not applicable to the proposed experimental situation is described. In also, several experiments constructing classifiers to decide email category and comparing classification rule learners are performed. Especially, an ID3 decision tree algorithm improved the overall accuracy around 17% compared to a conventional SVM text miner on the decision of email category. Some discussions about the axioms generated from the experimental dataset are given too.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.5
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• pp.325-330
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• 2023

This paper proposes to apply machine learning to the process of predicting the slab thickness based on the structural analysis results or experience and subjectivity of engineers in the design of bridge data construction to enable digital-based decision-making. This study aims to build a reliable design environment by utilizing machine learning techniques to provide guide values to engineers in addition to structural analysis for slab thickness selection. Based on girder bridges, which account for the largest proportion of bridge data, a prediction model process for predicting slab thickness among superstructures was defined. Various machine learning models (Linear Regress, Decision Tree, Random Forest, and Muliti-layer Perceptron) were competed for each process to produce the prediction value for each process, and the optimal model was derived. Through this study, the applicability of machine learning techniques was confirmed in areas where slab thickness was predicted only through existing structural analysis, and an accuracy of 95.4% was also obtained. models can be utilized in a more reliable construction environment if the accuracy of the prediction model is improved by expanding the process

• Journal of the Korea Convergence Society
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• v.9 no.3
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• pp.53-59
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• 2018

In this paper, we propose a sequence mining based manufacturing process using a decision model in cognitive factory. The proposed model is a method to increase the production efficiency by applying the sequence mining decision model in a small scale production process. The data appearing in the production process is composed of the input variables. And the output variable is composed the production rate and the defect rate per hour. We use the GSP algorithm and the REPTree algorithm to generate rules and models using the variables with high significance level through t-test. As a result, the defect rate are improved by 0.38% and the average hourly production rate was increased by 1.89. This has a meaning results for improving the production efficiency through data mining analysis in the small scale production of the cognitive factory.