• Journal of the Korean Society for Library and Information Science
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• v.49 no.4
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• pp.203-220
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• 2015

This study was to suggest the creation of hybrid bibliographic records in complicated bibliographic environment applying both AACR2 and RDA for uniform display and correct search. The literature reviews and case studies were used as the research methods. In case studies, Kent State University, University of Houston as the libraries, and Backstage Library Works as algorithm converting to hybrid records were surveyed. For creating the hybrid records, it was needed to differentiate hybrid records and born RDA records, to keep the data that appeared in 250 tag without spelling out edition data, to maintain 260 tag instead of converting 260 tag to 264 tag, and last to design mapping table for converting GMD to CMC. This study would suggest uniform display and search through hybridization of bibliographic records of foreign resources.

• Steel and Composite Structures
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• v.25 no.1
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• pp.1-17
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• 2017

Seismic performance of hybrid steel frames defined as mixture of rigid and semi-rigid connections is investigated in this paper. Three frames with 10, 15 and 20 stories are designed with fully rigid connections and then with 4 patterns for semi-rigid connection placement, some of beam to column rigid connections would turn to semi-rigid. Each semi-rigid connection is considered with 4 different moment capacities and all rigid and semi-rigid frames consisting of 51 models are subjected to 5 selected earthquake records for nonlinear analysis. Maximum story drifts, roof acceleration and base shear are extracted for those 5 earthquake records and average values are obtained for each case. Based on numerical results for the proposed hybrid frames, story drifts remain in allowable range and the reductions in the maximum roof acceleration of 22, 29 and 25% and maximum base shear of 33, 31 and 54% occur in those 10, 15 and 20-story frames, respectively.

• KCI Concrete Journal
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• v.11 no.3
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• pp.191-199
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• 1999

A Precast frame system with hybrid beam-column connections was proposed in this study. An analytical study evaluated the system under seismic loadings. Four buildings with different heights were modeled in which each building had three types of joint details (A. B, C). Thus, twelve buildings were examined with variables such as building height and joint detail. Four earthquake records were applied to the buildings as input ground motions. All the records were normalized to the intensity of 0.25g to assess behavior under the same intensity of seismic excitation. All the joint types showed almost identical results except for the Mexico earthquake which was scaled up from 0. 1g to 0.25g. Buildings with the type C joint exhibited the largest deflection for the Mexico earthquake. It was concluded that type B joint could be used in a high seismic zone and the type C joint could possibly be used in the regions of low to medium seismic activity.

• Journal of Korean Association for Spatial Structures
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• v.17 no.4
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• pp.35-42
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• 2017

This study develops a new hybrid passive energy dissipation device for seismic rehabilitation of an existing structure. The device is composed of a friction damper combined with a steel plate with vertical slits as a hysteretic damper. Analytical model is developed for the device, and the capacity of the hybrid device to satisfy a given target performance is determined based on the ASCE/SEI 7-10 process. The effect of the device is verified by nonlinear dynamic analyses using seven earthquake records. The analysis results show that the dissipated inelastic energy is concentrated on the hybrid damper and the maximum interstory drift of the SMRF with damping system satisfies the requirement of the current code.

• Journal of KIISE:Computing Practices and Letters
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• v.9 no.6
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• pp.693-698
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• 2003

Every baseball game generates various records and on the basis of those records, win/lose prediction about the next game is carried out. Researches on win/lose predictions of professional baseball games have been carried out, but there are not so good results yet. Win/lose prediction is very difficult because the choice of features on win/lose predictions among many records is difficult and because the complexity of a learning model is increased due to overlapping factors among the data used in prediction. In this paper, learning features were chosen by opinions of baseball experts and a heuristic function was formed using the chosen features. We propose a hybrid model by creating a new value which can affect predictions by combining multiple features, and thus reducing a dimension of input value which will be used for backpropagation learning algorithm. As the experimental results show, the complexity of backpropagation was reduced and the accuracy of win/lose predictions on professional baseball games was improved.

• Journal of the Korean Society for Library and Information Science
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• v.47 no.1
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• pp.105-124
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• 2013

Metadata standardization of resources, having a heterogeneous metadata structure for each presidential archive and presidential library and museum is preferentially required for utilizing and sharing presidential records. An integrated operation model of metadata to manage various types of presidential records is then needed. The purpose of this study is to create a design principle of integrated metadata, and to suggest relationships and attributes of metadata, needed for developing integrated metadata operation system on presidential records. The design principle includes "creation of relationship among presidential records", "design of each entity, applicable multiple entity data model", "design to describe various types of presidential records", "design to reflect lifelong management on records of holding institutes", and "designing hybrid metadata for long term preservation". Metadata element set consists of elements for common attributes with all types of presidential records for a unique attribute for a specific presidential record and for reference information among different records related to the production of presidential records.

• Earthquakes and Structures
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• v.15 no.4
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• pp.351-360
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• 2018

Energy-based methodology is utilized to design novel timber-steel hybrid core wall system. The timber-steel core wall system consists of cross laminated timber (CLT), steel columns, angled brackets and t-stub connections. The CLT wall panels are stiff and strong, and ductility is provided through the steel t-stub connections. The structural system was modelled in SAP2000 finite element program. The hybrid system is explained in detail and validated using first principles. To evaluate performance of the hybrid core system, a 7-story building was designed using both forced-based design and energy based design (EBD) approaches. Performance of the structure was evaluated using 10 earthquakes records selected for 2500 return period and seismicity of Vancouver. The results clearly served as a good example of the benefits of EBD compared to conventional forced based design approaches.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.125-140
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• 2013

We call a data set in which the number of records belonging to a certain class far outnumbers the number of records belonging to the other class, 'imbalanced data set'. Most of the classification techniques perform poorly on imbalanced data sets. When we evaluate the performance of a certain classification technique, we need to measure not only 'accuracy' but also 'sensitivity' and 'specificity'. In a customer churn prediction problem, 'retention' records account for the majority class, and 'churn' records account for the minority class. Sensitivity measures the proportion of actual retentions which are correctly identified as such. Specificity measures the proportion of churns which are correctly identified as such. The poor performance of the classification techniques on imbalanced data sets is due to the low value of specificity. Many previous researches on imbalanced data sets employed 'oversampling' technique where members of the minority class are sampled more than those of the majority class in order to make a relatively balanced data set. When a classification model is constructed using this oversampled balanced data set, specificity can be improved but sensitivity will be decreased. In this research, we developed a hybrid model of support vector machine (SVM), artificial neural network (ANN) and decision tree, that improves specificity while maintaining sensitivity. We named this hybrid model 'hybrid SVM model.' The process of construction and prediction of our hybrid SVM model is as follows. By oversampling from the original imbalanced data set, a balanced data set is prepared. SVM_I model and ANN_I model are constructed using the imbalanced data set, and SVM_B model is constructed using the balanced data set. SVM_I model is superior in sensitivity and SVM_B model is superior in specificity. For a record on which both SVM_I model and SVM_B model make the same prediction, that prediction becomes the final solution. If they make different prediction, the final solution is determined by the discrimination rules obtained by ANN and decision tree. For a record on which SVM_I model and SVM_B model make different predictions, a decision tree model is constructed using ANN_I output value as input and actual retention or churn as target. We obtained the following two discrimination rules: 'IF ANN_I output value <0.285, THEN Final Solution = Retention' and 'IF ANN_I output value ${\geq}0.285$, THEN Final Solution = Churn.' The threshold 0.285 is the value optimized for the data used in this research. The result we present in this research is the structure or framework of our hybrid SVM model, not a specific threshold value such as 0.285. Therefore, the threshold value in the above discrimination rules can be changed to any value depending on the data. In order to evaluate the performance of our hybrid SVM model, we used the 'churn data set' in UCI Machine Learning Repository, that consists of 85% retention customers and 15% churn customers. Accuracy of the hybrid SVM model is 91.08% that is better than that of SVM_I model or SVM_B model. The points worth noticing here are its sensitivity, 95.02%, and specificity, 69.24%. The sensitivity of SVM_I model is 94.65%, and the specificity of SVM_B model is 67.00%. Therefore the hybrid SVM model developed in this research improves the specificity of SVM_B model while maintaining the sensitivity of SVM_I model.

• The Transactions of the Korea Information Processing Society
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• v.4 no.4
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• pp.923-932
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• 1997

In this paper, we propose a two-level hybrid signature file method(THM) to dffciently deal with large txt databases that use a term discrimination concept.In addition, we apply Yoo's clustering scheme to the two-level hybeid signature file method. The clustering schme groups similar signatures together according to the similarity of the highly discriminatiory tems so that we may achive better performance on retrival. The space-time ana-lyhtical model of the proposed two-level hybrid method is provided. Based on the analytical model and experiments, we compare it with the exsting methods, i.e. the bit-sliced method(BM), the-level method(TM), and the hybrid method(HM). As a result, we show that THM achives the best retrival performance in a large database with 100,000 records when the mumber fo matching records is less than 160.

• Journal of Electrical Engineering and Technology
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• v.13 no.3
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• pp.1089-1098
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• 2018

This study aims at developing and applying a hybrid model to the wind power prediction (WPP). The hybrid model for a very-short-term WPP (VSTWPP) is achieved through analytical data, multiple linear regressions and least square methods (MLR&LS). The data used in our hybrid model are based on the historical records of wind power from an offshore region. In this model, the WPP is achieved in four steps: 1) transforming historical data into ratios; 2) predicting the wind power using the ratios; 3) predicting rectification ratios by the total wind power; 4) predicting the wind power using the proposed rectification method. The proposed method includes one-step and multi-step predictions. The WPP is tested by applying different models, such as the autoregressive moving average (ARMA), support vector machine (SVM), and artificial neural network (ANN). The results of all these models confirmed the validity of the proposed hybrid model in terms of error as well as its effectiveness. Furthermore, forecasting errors are compared to depict a highly variable WPP, and the correlations between the actual and predicted wind powers are shown. Simulations are carried out to definitely prove the feasibility and excellent performance of the proposed method for the VSTWPP versus that of the SVM, ANN and ARMA models.