• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.9
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• pp.1898-1904
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• 2009

Recently, real documents such as newspapers as well as blogs include newly coined words such as "Wikipedia". However, most previous information processing technologies cannot deal with these newly coined words because they construct their dictionaries based on materials acquired during system development. In this paper, we propose a model to automatically recognize Korean unknown words excluded from the previously constructed dictionary. The proposed model consists of an unknown noun recognition phase based on full text analysis, an unknown verb recognition phase based on web document frequency, and an unknown noun recognition phase based on web document frequency. The proposed model can recognize accurately the unknown words occurred once and again in a document by the full text analysis. Also, the proposed model can recognize broadly the unknown words occurred once in the document by using web documents. Besides, the proposed model fan recognize both a Korean unknown verb, which syllables can be changed from its base form by inflection, and a Korean unknown noun, which syllables are not changed in any eojeol. Experimental results shows that the proposed model improves precision 1.01% and recall 8.50% as compared with a previous model.

• Structural Engineering and Mechanics
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• v.75 no.4
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• pp.477-485
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• 2020

Finite element (FE) model based structural damage detection (SDD) methods play vital roles in effectively locating and quantifying structural damages. Among these methods, structural model updating should be conducted before SDD to obtain benchmark models of real structures. However, the characteristics of updating parameters are not reasonably considered in existing studies. Inspired by the l_∞ norm regularization, a novel anti-sparse representation method is proposed for structural model updating in this study. Based on sensitivity analysis, both frequencies and mode shapes are used to define an objective function at first. Then, by adding l_∞ norm penalty, an optimization problem is established for structural model updating. As a result, the optimization problem can be solved by the fast iterative shrinkage thresholding algorithm (FISTA). Moreover, comparative studies with classical regularization strategy, i.e. the l₂ norm regularization method, are conducted as well. To intuitively illustrate the effectiveness of the proposed method, a 2-DOF spring-mass model is taken as an example in numerical simulations. The updating results show that the proposed method has a good robustness to measurement noises. Finally, to further verify the applicability of the proposed method, a six-storey aluminum alloy frame is designed and fabricated in laboratory. The added mass on each storey is taken as updating parameter. The updating results provide a good agreement with the true values, which indicates that the proposed method can effectively update the model parameters with a high accuracy.

• Journal of Korea Society of Industrial Information Systems
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• v.21 no.4
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• pp.31-41
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• 2016

This paper is to evaluate the performance of a proposed hybrid genetic algorithm (pro-HGA) approach using closed-loop supply chain (CLSC) model. The proposed CLSC model is a integrated supply chain network model both with forward logistics and reverse logistics. In the proposed CLSC model, the reuse, resale and waste disposal using the returned products are taken into consideration. For implementing the proposed CLSC model, two conventional approaches and the pro-HGA are used in numerical experiment and their performances are compared with each other using various measures of performance. The experimental results show that the pro-HGA approach is more efficient in locating optimal solution than the other competing approaches.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.6
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• pp.173-178
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• 2022

Recently, a recommendation system using deep learning in social network services has been actively studied. However, in the case of a recommendation system using deep learning, the cold start problem and the increased learning time due to the complex computation exist as the disadvantage. In this paper, the user-tailored exercise routine recommendation algorithm is proposed using the user's metadata. Metadata (the user's height, weight, sex, etc.) set as the input of the model is applied to the designed model in the proposed algorithms. The exercise recommendation system model proposed in this paper is designed based on the neural collaborative filtering (NCF) algorithm using multi-layer perceptron and matrix factorization algorithm. The learning proceeds with proposed model by receiving user metadata and exercise information. The model where learning is completed provides recommendation score to the user when a specific exercise is set as the input of the model. As a result of the experiment, the proposed exercise recommendation system model showed 10% improvement in recommended performance and 50% reduction in learning time compared to the existing NCF model.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.3
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• pp.9-14
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• 2022

In the IoT(internet of things) where various devices can be connected, failure of essential devices may lead to a lot of economic and life losses. For reducing the losses, fault diagnosis techniques have been considered an essential part of IoT. In this paper, the method based on a graph neural network is proposed for determining fault and classifying types by extracting features from vibration data of systems. For training of the deep learning model, fault dataset are used as input data obtained from the CWRU(case western reserve university). To validate the classification performance of the proposed model, a conventional CNN(convolutional neural networks)-based fault classification model is compared with the proposed model. From the simulation results, it was confirmed that the classification performance of the proposed model outweighed the conventional model by up to 5% in the unevenly distributed data. The classification runtime can be improved by lightweight the proposed model in future works.

• The Transactions of the Korea Information Processing Society
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• v.6 no.11
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• pp.2845-2862
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• 1999

As the technical advances in portable computers and wireless communication technologies, mobile computing environment has been rapidly expanded. The mobile users on mobile host can access information via wireless communication from the distributed heterogeneous multidatabase system in which pre-existing independent local information systems are integrated into one logical system to support mobile applications. Hence, mobile transaction model should include not only the features for heterogeneous multidatabase systems but also the ones for mobile computing environment. In this paper, we proposed a mobile flex transaction model which extends the flexible transaction model that previously proposed for heterogeneous multidatabase systems is extended to support the requirements of mobile heterogeneous multidatabase systems. We also presented the execution control mechanism of the mobile flex transaction model. The proposed mobile flex transaction model allows the definition of location-dependent subtransactions, the effective support of hand-over, and the flexibility of transaction executions. Hence, the proposed mobile flex transaction model can be suit to mobile heterogeneous multidatabase systems that have low power capability, low bandwidth, and high communication failure possibility.

• The Journal of the Korea Contents Association
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• v.6 no.9
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• pp.42-49
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• 2006

In this paper, we proposed a model to evaluate confidence and satisfaction of health information web-sites visited by general health consumers. In order to propose the evaluation model, lots of foreign and domestic researches are investigated and analyzed. Based on these analyses, confidence and satisfaction standards for the model are proposed. A process for evaluation and a method for calculation of evaluation value by the standards are proposed, too. The proposed model can be utilized as a tool for analyzing conventional health information web-sites and can be utilized as check lists for developments in case of health information web-sites under construction.

• Journal of Korean Institute of Industrial Engineers
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• v.37 no.1
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• pp.41-54
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• 2011

A central theme in recent IT (information technology) industry is a mobile ecosystem. While a concept of business ecosystem, which is an economic community of firms and individuals producing and consuming goods and services, has been around for about 20 years now, the recent spotlight is mainly caused by the enormous success of iPhone. Many hand-set makers or platform developers want to mimic Apple's iPhone ecosystem from which both application developers and hand-set users can benefit. In this study, a representation model of the business ecosystem is proposed for supporting systematic design and analysis of ecosystems. Whereas previous studies also proposed some representation models, they emphasized only on the value chain between participating players. The proposed model, which is named relation-based ecosystem model, represents an ecosystem with the requirement relationships between product and service components and the roles of players, as well as their value chain. Such comprehensive representation explicitly reveals the strategic difference between ecosystems. This advantage was illustrated by comparing a Korean traditional mobile ecosystem and an emerging smart-phone ecosystem represented by the proposed model.

• Journal of Korean Institute of Industrial Engineers
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• v.38 no.1
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• pp.57-66
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• 2012

The paper deals with the development of a model for group decision making under multiple criteria. The Multi-criteria group decision making (MCGDM) is the process to determine the best compromise solution in a set of competing alternatives that are evaluated by decision makers having their own preferences on conflicting objectives. For MCGDM, we propose a Mixed-Integer Programming (MIP) model that implements a revised median approach by noticing that the original median approach cannot consider the willingness to reject and the indifferent preference conditions. The proposed MIP model tries to select a common best Pareto-optimal solution by maximizing the overall desirability considering the willingness to reject and the indifferent preference that represent the tolerance measure of each decision maker. To evaluate the effectiveness of the proposed model, we compared the results of the proposed model with those of the median approach. The results showed that the proposed MIP model produces more realistic and better compromised alternative by incorporating the decision maker's willingness to reject and the indifferent preferences over each criteria.

• Geomechanics and Engineering
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• v.22 no.6
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• pp.519-528
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• 2020

A mathematical wetting model is usually used to predict the deformation of core wall rockfill dams induced by the wetting effect. In this paper, a series of wetting triaxial tests on a rockfill was conducted using a large-sized triaxial apparatus, and the wetting deformation behavior of the rockfill was studied. The wetting strains were found to be related to the confining pressure and shear stress levels, and two empirical equations, which are regarded as the proposed mathematical wetting model, were proposed to express these properties. The stress and deformation of a core wall rockfill dam was studied by using finite element analysis and the proposed wetting model. On the one hand, the simulations of the wetting model can estimate well the observed wetting strains of the upstream rockfill of the dam, which demonstrated that the proposed wetting model is applicable to express the wetting deformation behavior of the rockfill specimen. On the other hand, the simulated additional deformation of the dam induced by the wetting effect is thought to be reasonable according to practical engineering experience, which indicates the potential of the model in dam engineering.