• Korean Management Science Review
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• v.21 no.1
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• pp.39-55
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• 2004

Efficient crew scheduling for cockpit crew is important in airline industry due to operational safety and cost associated with the flight duty time. Because of complexity of regulations imposed to the cockpit crew. it is complicated to generate an efficient schedule. Schedule of cockpit crew can be generated through two steps; selecting of flight patterns. and scheduling of them to the specific time horizon. Heuristic method is developed and applied with massive data in a limited time of computation. A set of flight patterns is selected from all possible flight patterns. which are generated by composing the flight leg based on regulations. by using the set partitioning problem with objective function of oversea stay cost. The selected set of flight patterns found at the first step is allocated to 4 week crew schedule to minimize the variance of total fight time assigned to each crew. The crew schedules obtained are evaluated and compared with the ones currently used in one of major airline company.

• Journal of Broadcast Engineering
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• v.13 no.1
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• pp.86-91
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• 2008

In the field of network data analysis, the research of how much the estimation data reflects the population data is inevitable. This paper compares and analyzes the well known MIT Lincoln Lab network data, which is composed of collectable standard information from the network with the KDD CUP 99 dataset which was composed from the MIT/LL data. For comparison and analysis, the protocol information of both the data was used. Correspondence analysis was used for analysis, SVD was used for 2 dimensional visualization and weigthed euclidean distance was used for network data quantification.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.30B no.12
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• pp.51-58
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• 1993

This paper presents a design of the fuzzy decision maker which infers set value for fuel rate in the rotary kiln of making CaO. The fuzzy decision maker proposed are divided into two groups whose functions are different each other. The one operates when production demand is constant. The other deals with the status of varying production demand. We have chosen several variables used for composing condition and action part by investigating ingerent features of the rotary kiln and skilled operators`manual method of inferring fuel rate. Membership function of each variable was designed by analyzing experimental data and field data collected during two months. On-line operation with fuzzy rules suggested was done safely like human operators' action.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2005.04a
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• pp.301-304
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• 2005

In this paper, we propose a new fuzzy set-based polynomial neuron (FSPN) involving the information granule, and new fuzzy-neural networks - Fuzzy Set based Polynomial Neural Networks (FSPNN). We have developed a design methodology (genetic optimization using Genetic Algorithms) to find the optimal structure for fuzzy-neural networks that expanded from Group Method of Data Handling (GMDH). It is the number of input variables, the order of the polynomial, the number of membership functions, and a collection of the specific subset of input variables that are the parameters of FSPNN fixed by aid of genetic optimization that has search capability to find the optimal solution on the solution space. We have been interested in the architecture of fuzzy rules that mimic the real world, namely sub-model (node) composing the fuzzy-neural networks. We adopt fuzzy set-based fuzzy rules as substitute for fuzzy relation-based fuzzy rules and apply the concept of Information Granulation to the proposed fuzzy set-based rules.

• Journal of The Korean Society of Agricultural Engineers
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• v.64 no.5
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• pp.17-26
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• 2022

In this study, in order to establish the criteria for evaluation of importance by the type of facility specialized for agricultural reservoirs, an expert group consisting of a total of 167 members who were in management, or specialized in the fields of design, research, and diagnosis were organized, and the importance for facilities was set with application of the AHP technique. The importance of dam body, spillway, and intake structure composing a reservoir were set at 59%, 24%, and 17%, and the importance of dam crest, upstream slope, and downstream slope constituting a dam body was set at 32%, 31%, and 37%, respectively. In addition, the importance of approach channel, regulated channel, chute channel, and stilling basin consisting a spillway was set at 15%, 44%, 26%, and 15%, and the importance of inclined conduit and outlet conduit consisting an intake structure was set at 35% and 65%, respectively. The safety grade of the reservoirs evaluated by applying the newly presented importance values in this study showed the rearrangement of the grades with a change of 11% compared to the previous grades. In this way, the newly established criteria are expected to be utilized as basic data with strategic importance in reservoir safety management and disaster prevention as well as the operation of systems in the future.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.24 no.65
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• pp.41-50
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• 2001

Clustering in data mining is a discovery process that groups a set of data such that the intracluster similarity is maximized and intercluster similarity is minimized. The discovered clusters from clustering process are used to explain the characteristics of the data distribution. In this paper we propose a new methodology for clustering related transactions with categorical attributes. Our approach starts with transforming general relational databases into a transactional databases. We make use of inter-dimensional association rules for composing hypergraph edges, and a hypergraph partitioning algorithm for clustering the values of attributes. The clusters of the values of attributes are used to find the clusters of transactions. The suggested procedure can enhance the interpretation of resulting clusters with allocated attribute values.

• Journal of Korea Society of Digital Industry and Information Management
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• v.20 no.2
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• pp.1-10
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• 2024

For the purpose of predicting credit card customer churn accurately through data analysis, a model can be constructed with various machine learning algorithms, including decision tree. And feature importance has been utilized in selecting better input features that can improve performance of data analysis models for several application areas. In this paper, a method of utilizing feature importance calculated from the MDI method and its effects are investigated in the credit card customer churn prediction problem with classification trees. Compared with several random feature selections from case data, a set of input features selected from higher value of feature importance shows higher predictive power. It can be an efficient method for classifying and choosing input features necessary for improving prediction performance. The method organized in this paper can be an alternative to the selection of input features using feature importance in composing and using classification trees, including credit card customer churn prediction.

• Journal of Intelligence and Information Systems
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• v.13 no.2
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• pp.1-14
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• 2007

Case-based reasoning (CBR) is one of the most widely applied data mining techniques and has proven its effectiveness in various domains. Since CBR is basically based on k-Nearest Neighbors (NN) method, the value of k affects the performance of CBR model directly. Once the value of k is set, it is fixed for the lifetime of the CBR model. However, if the value is set greater or smaller than the optimal value, the performance of CBR model will be deteriorated. In this research, we propose a new method of composing the NN set using similarity scores as themselves, which we shall call s-NN method, rather than using the fixed value of k. In the s-NN method, the different number of nearest neighbors can be selected for each new case. Performance evaluation using the data from UCI Machine Learning Repository shows that the CBR model adopting the s-NN method outperforms the CBR model adopting the traditional k-NN method.

• Proceedings of the KAIS Fall Conference
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• 2003.11a
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• pp.118-126
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• 2003

In data mining, having access to large amount of data sets for the purpose of predictive data does not guarantee good method, even where the size of Real data is Mobile commerce unlimited. In addition to searching expected Goods objects for Users, it becomes necessary to develop a recommendation service based on XML. In this paper, we design the optimized XML Recommender product data. Efficient XML data preprocessing is required, include of formatting, structural, and attribute representation with dependent on User Profile Information. Our goal is to find a relationship among user interested products from E-Commerce and M-Commerce to XDB. Firstly, analyzing user profiles information. In the result creating clusters with analyzed user profile such as with set of sex, age, job. Secondly, it is clustering XML data which are associative products classify from user profile in shopping mall. Thirdly, after composing categories and goods data in which associative objects exist from the first clustering, it represent categories and goods in shopping mall and optimized clustering XML data which are personalized products. The proposed personalized user profile clustering method has been designed and simulated to demonstrate it's efficient.

• Proceedings of the KIEE Conference
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• 1987.07a
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• pp.226-229
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• 1987

This paper was aimed to, using a new data structure, develop a set of algorithms to execute the output function of Digital System. These functions were represented as directed, acyclic graphs. by applying many restrictions on vertices on graph, the efficient manipulation of boolean function was accomplished. The results were as follows; 1. A canonical representation of a boolean function was created by the reduction algorithm. 2. The operation of two functions was accomplished using t he apply algorithm, according to the binary operator. 3. The arguments having 1 as the value nf function were enumerated using the satisfy algorithm. 4. Composing TTL 74181 4-bit ALU and 74182 look-ahead carry generator, the ALU having 4-bit and 16-bit as word size was implemented.