• Journal of KIISE
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• v.44 no.10
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• pp.1105-1111
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• 2017

Influence maximization (IM) is the problem of finding a seed set composed of k nodes that maximizes the influence spread in social networks. However, one of the biggest problems of existing solutions for IM is that it takes too much time to select a k-seed set. This performance issue occurs at the micro and macro levels. In this paper, we propose a fast hybrid method that addresses two issues at micro and macro levels. Furthermore, we propose a path-based community detection method that helps to select a good seed set. The results of our experiment with four real-world datasets show that the proposed method resolves the two issues at the micro and macro levels and selects a good k-seed set.

• Proceedings of the KIEE Conference
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• 2005.05a
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• pp.6-8
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• 2005

In this paper, we discuss model identification of nonlinear data using GAs-based Fuzzy Polynomial Neural Networks(GAs-FPNN). Fuzzy Polynomial Neural Networks(FPNN) is proposed model based Group Method Data Handling(GMDH) and Neural Networks(NNs). Each node of FPNN is expressed Fuzzy Polynomial Neuron(FPN). Network structure of nonlinear data is created using Genetic Algorithms(GAs) of optimal search method. Accordingly, GAs-FPNN have more inflexible than the existing models (in)from structure selecting. The proposed model select and identify its for optimal search of Genetic Algorithms that are no. of input variables, input variable numbers and consequence structures. The GAs-FPNN model is select tuning to input variable number, number of input variable and the last part structure through optimal search of Genetic Algorithms. It is shown that nonlinear data model design using Genetic Algorithms based FPNN is more usefulness and effectiveness than the existing models.

• Composites Research
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• v.25 no.5
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• pp.147-153
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• 2012

In this paper, static perforation tests are conducted to predict the penetration energy for the composite laminates subjected to high velocity impact. Three methods are used to analyze the perforation energy accurately. The first method is to select the perforation point using the AE sensor signal energy, the second method is to retest the tested specimen and use the difference between initial and retested perforation energy, and the third method is to select the perforation point based on the maximum loading point in the retested load-displacement curve of the tested specimen. The predicted perforation energy results are presented and verified by comparing with those by the high velocity tests.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.21 no.2
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• pp.137-143
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• 2022

Composite materials, being light-weight and of high mechanical strength, are increasingly used in various industries such as the aerospace, automobile, sporting-goods manufacturing, and ship-building industries. Recently, manufacturing of composite materials using 3D printers has increased. 3D-printed composite materials are made in free-form and adapted for end-use by adjusting the fiber content and orientation. However, research on the machining of 3D printed composite materials is limited. The aim of this study is to develop a machine learning method to select machining conditions for machining of 3D-printed composite materials. The composite material was composed of Onyx and carbon fibers and stacked sequentially. The experiments were performed using the following machining conditions: spindle speed, feed rate, depth of cut, and machining direction. Cutting forces of the different machining conditions were measured by milling the composite materials. PCA, a method of machine learning, was developed to select the machining conditions and will be used in subsequent experiments under various machining conditions.

• Journal of Korea Society of Digital Industry and Information Management
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• v.19 no.4
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• pp.137-146
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• 2023

The dropout of university freshmen is a very important issue in the financial problems of universities. Moreover, the dropout rate is one of the important indicators among the external evaluation items of universities. Therefore, universities need to predict dropout students in advance and apply various dropout prevention programs targeting them. This paper proposes a method to predict such dropout students in advance. This paper is about a method for predicting dropout students. It proposes a method to select dropouts by applying logistic regression using a shift sigmoid classification function using only quantitative data from the first semester of the first year, which most universities have. It is based on logistic regression and can select the number of prediction subjects and prediction accuracy by using the shift sigmoid function as an classification function. As a result of the experiment, when the proposed algorithm was applied, the number of predicted dropout subjects varied from 100% to 20% compared to the actual number of dropout subjects, and it was found to have a prediction accuracy of 75% to 98%.

• Journal of the Korea Institute of Building Construction
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• v.8 no.6
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• pp.155-160
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• 2008

The BTL project, 4 years since its operation. has benchmarked the PFI Project in Japan and has been introduced. Given the evaluation step to select a preferred bidder, in a technological factor, the basic plans are corrected and complemented, whereas in a price factor, the low price bidding system is being enforced. There is concern that how to select preferred bidders and how to operate project costs during operation and management period may be problematic. Thus, in this study, using the Analytic Hierarchy Process, the method of deciding the priority to select preferred bidders in an early stage of the project and the evaluation model to evaluate the validity of BTL project expenses in process of project enforcement are established. Targeting the group composed of experts who have experiences in the BTL project. Then, the levelling of evaluation factors and grouping have been carried out as following : LCC analysis and disbursement for government including two more detailed factors.

• Journal of KIISE:Computing Practices and Letters
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• v.14 no.3
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• pp.326-330
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• 2008

The study on combining multiple classifiers in the field of pattern recognition has mainly focused on how to combine multiple classifiers, but it has gradually turned to the study on how to select multiple classifiers from a classifier pool recently. Actually, the performance of multiple classifier system depends on the selected classifiers as well as the combination method of classifiers. Therefore, it is necessary to select a classifier set showing good performance, and an approach based on information theory has been tried to select the classifier set. In this paper, a classifier set candidate is made by the selection of classifiers, on the basis of mutual information between classifiers, and the classifier set candidate is compared with the other classifier sets chosen by the different selection methods in experiments.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.520-523
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• 1997

This paper presents FPGA implementation of fuzzy controller using Product-Sum inference method. Product-Sum inference method has much better performance than other inference methods. This fuzzy controller is composed of several digital modules, e.g. fuzzifier, rule base, adder, multiplier, select center and divider, and is operated by error and error variation. We synthesized the fuzzy controller and performed wave simulation using Xilinx VHDL tool(ViewLogic, ViewSim).

• Proceedings of the IEEK Conference
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• 1998.10a
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• pp.747-750
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• 1998

This paper deals with the vehicle arbitration algorithm used in communication system between vehicles and a roadside control unit. To Improve the performance of vehicle arbitration, a random delay counter method is taken into account and modified to select the optimal maximum count value according to the vehicle arrival rate. The suggested algorithm is tested by computer simulation andthe enhanced performance was shown. This method could be applied to various systems which include the communications between transponders and a control unit.

• Proceedings of the IEEK Conference
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• 1998.10a
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• pp.139-142
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• 1998

This paper presents a method to determine the rolloff factors of both the digital terrestrial TV modulator and the bandpass filter (BPF) at the digital TV receiver input, using an analytical power spectral density model satisfying given co-channel and adjacent channel protection ratios for the digital terrestrial broadcasting services. Since the proposed method is very simple and effective to use, also can be used regardless of type of systems or modulation techniques, this method is expected to be applied to select some other digital terrestrial broadcasting system specifications.