• Structural Engineering and Mechanics
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• v.78 no.2
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• pp.219-230
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• 2021

International seismic codes stipulate that adjacent buildings should be separated by a specified minimum distance, otherwise the pounding effect should be considered in the design. Recent researches proposed an alternative method (Double Difference Combination Rule) to estimate seismic gap between structures, as this method considers the cross relation of adjacent buildings behavior during earthquakes. Four different criteria were used to calculate the minimum separation distance using this method and results are compared to the international codes for five separation cases. These cases used four case study buildings classified by different heights, lateral load resisting systems and fundamental periods of vibrations to assess the consistency in results for the alternative methods. Non-linear analysis was performed to calculate the inelastic displacements of the four buildings, and the results were used to evaluate the relation between elastic and inelastic displacements due to the ductility of structural elements resisting seismic loads. A verification analysis was conducted to guarantee that the separation distance calculated is sufficient to avoid pounding. Results shows that the use of two out of the four studied methods yields separation distances smaller than that calculated by the code specified equations without under-estimating the minimum separation distance required to avoid pounding.

• Proceedings of the Safety Management and Science Conference
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• 2007.11a
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• pp.107-119
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• 2007

The purpose of this article is to examine the relationship between unsafe behavior, human factor and human error. For the object, several correlation analyses for those three elements were implemented. Several hypotheses for the relationship between them was suggested. The suggested hypotheses were verified by a comprehensive survey received from 132 safety manager of manufacturing industry. The conclusions were proven from the hypotheses verificaiton as belows; 1) The dependent relation items between unsafe behavior and human factor are dress protection tool, machine(equipment) and working rule have a dependent relation. 2) The dependent relation items between human factor and human error are uncommunication, control, slaps, fatigue, education, system, unmonitoring, failure. 3) The dependent relation items between human error and unsfafe behavior are decline and product/working method,failure and uncommunication have a dependent relation.

• Journal of Korea Multimedia Society
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• v.13 no.9
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• pp.1329-1336
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• 2010

Cellular Automata is a discrete dynamical system which natural phenomena may be specified completely in terms of local relation, can increase and decrease the difference of luminance locally according to transition rule by keeping the characteristic of target image. In this paper, we propose a noise reduction method by keeping the characteristic using transition rule of Cellular Automata, also we propose methods of effective transition rule, the selection of parameters, the selection of number of neighborhood pixels. For uniform distribution noise, Gaussian noise, impulse noise, we do an experiment on adaptive state using different mathematical operations and compare its results. It was confirmed that the proposed transition rule is based on fast convergence speed and has stabile results.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2002.04a
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• pp.675-681
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• 2002

Parametric modeling and configuration design method are a important methods for rapid design in manufacturing. This paper proposes a relation rules which parametrically models a bonnet trimming tool based on Pro/Engineer. The concept of desogn is applied a trimming die of the bonnet outer panel. Trimming die have a many parameters. Each a parameter is related the die face and punch profile. A design system consists of a Pro/Engineer, a Pro/program.

• Journal of KIISE
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• v.42 no.3
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• pp.367-378
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• 2015

This study was directed at the design of a hybrid algorithm for competition relation extraction. Previous works on relation extraction have relied on various lexical and deep parsing indicators and mostly utilize only the machine learning method. We present a new algorithm integrating machine learning with various filtering methods. Some simple but useful features for competition relation extraction are also introduced, and an optimum feature set is proposed. The goal of this paper was to increase the precision of competition relation extraction by combining supervised learning with various filtering methods. Filtering methods were employed for classifying compete relation occurrence, using distance restriction for the filtering of feature pairs, and classifying whether or not the candidate entity pair is spam. For evaluation, a test set consisting of 2,565 sentences was examined. The proposed method was compared with the rule-based method and general relation extraction method. As a result, the rule-based method achieved positive precision of 0.812 and accuracy of 0.568, while the general relation extraction method achieved 0.612 and 0.563, respectively. The proposed system obtained positive precision of 0.922 and accuracy of 0.713. These results demonstrate that the developed method is effective for competition relation extraction.

• Journal of Intelligence and Information Systems
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• v.8 no.2
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• pp.91-103
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• 2002

A collaborative filtering which supports personalized services of users has been common use in existing web sites for increasing the satisfaction of users. A collaborative filtering is demanded that items are estimated more than specified number. Besides, it tends to ignore information of other users as recommending them on the basis of information of partial users who have similar inclination. However, there are valuable hidden information into other users' one. In this paper, we use Association Rule, which is common wide use in Data Mining, with collaborative filtering for the purpose of discovering those information. In addition, this paper proved that Association Rule applied to Recommender System has a effects to recommend users by the relation between groups. In other words, Association Rule based on the history of all users is derived from. and the efficiency of Recommender System is improved by using Association Rule with collaborative filtering.

• Journal of the Korean Data and Information Science Society
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• v.17 no.2
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• pp.455-465
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• 2006

Data mining is the method to find useful information for large amounts of data in database. It is used to find hidden knowledge by massive data, unexpectedly pattern, and relation to new rule. The methods of data mining are decision tree, association rules, clustering, neural network and so on. We analyze industrial waste database using data mining technique. We use k-means algorithm for clustering and C5.0 algorithm for decision tree and Apriori algorithm for association rule. We can use these outputs for environmental preservation and environmental improvement.

• 한국데이터정보과학회:학술대회논문집
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• 2006.04a
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• pp.241-251
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• 2006

Data mining is the method to find useful information for large amounts of data in database It is used to find hidden knowledge by massive data, unexpectedly pattern, relation to new rule. The methods of data mining are decision tree, association rules, clustering, neural network and so on. We analyze industrial waste database using data mining technique. We use k-means algorithm for clustering and C5.0 algorithm for decision tree and Apriori algorithm for association rule. We can use these analysis outputs for environmental preservation and environmental improvement.

• Transactions of Materials Processing
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• v.17 no.3
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• pp.161-169
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• 2008

In the present work, the two-back stress model is proposed and continuum damage mechanics (CDM) is incorporated into the plastic constitutive relation in order to describe the plastic deformation localization and the damage evolution in a deforming continuum body. Coupling between damage mechanics and isothermal rate independent plasticity is performed using the kinematic hardening rule, which in turn is formulated by combining the nonlinear Armstrong-Frederick rule and the Phillips rule. The numerical analyses are carried out within h deformation theory. It is noted that the damage evolution within a work piece accelerates the plastic deformation localization such that the material with lower hardening exponent results in a rapid shear band formation. Moreover, the results from the numerical analysis reflected closely with the micro-structures around the fractured regime. The effects of the various hardening parameters on deformation localization are also investigated. As the nonlinear strain rate description in the back stress evolution becomes dominant, the strain localization becomes intensified as well as the damage evolution.

• Transactions of Materials Processing
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• v.17 no.1
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• pp.19-26
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• 2008

In the present work, the ratcheting behavior under uniaxial cyclic loading is analyzed. A comparison between the published and the results from the present model is also included. In order to simulate the ratcheting behavior, Two-Back Stress model is proposed by combining the non-linear Armstrong-Frederick rule and the non-linear Phillips hardening rule based on kinematic hardening equation. It is shown that some ratcheting behaviors can be obtained by adjusting the control material parameters and various evolutions of the kinematic hardening parameter can be obtained by means of simple combination of hardening rules using simple rule of mixtures. The ultimate back stress is also derived for the present combined kinematic hardening models.