• Journal of the Architectural Institute of Korea Planning & Design
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• v.36 no.4
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• pp.41-49
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• 2020

The purpose of this study was to investigate the use of the Deep Neural Networks(DNN) model to classify user's emotions, in particular Electroencephalography(EEG) toward Virtual-Reality(VR) based 3D design alternatives. Four different types of VR Space were constructed to measure a user's emotion and EEG was measured for each stimulus. In addition to the quantitative evaluation based on EEG data, a questionnaire was conducted to qualitatively check whether there is a difference between VR stimuli. As a result, there is a significant difference between plan types according to the normalized ranking method. Therefore, the value of the subjective questionnaire was used as labeling data and collected EEG data was used for a feature value in the DNN model. Google TensorFlow was used to build and train the model. The accuracy of the developed model was 98.9%, which is higher than in previous studies. This indicates that there is a possibility of VR and Fast Fourier Transform(FFT) processing would affect the accuracy of the model, which means that it is possible to classify a user's emotions toward VR based 3D design alternatives by measuring the EEG with this model.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.9 no.1
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• pp.47-56
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• 1999

RBAC(Role Based Access Control) is an access control method based on the user's roles and it provides more flexibility and applicability on the various computer and network security fields than DAC(Discretionary Access Control) or MAC(Mandatory Access Control). In this paper, we newly propose ERBAC$_{0}$(Extended RBAC$_{0}$) model by considering subject's and object's roles additionally to REAC$_{0}$ model which is firstly proposed by Ravi S. Sandhu as a base model. The proposed ERBAC$_{0}$ model provides finer grained access control on the base of subject and object level than RBAC$_{0}$ model.

• International Journal of Computer Science & Network Security
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• v.23 no.9
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• pp.47-54
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• 2023

For Bengali music emotion classification, deep learning models, particularly CNN and RNN are frequently used. But previous researches had the flaws of low accuracy and overfitting problem. In this research, attention-based Conv1D and BiGRU model is designed for music emotion classification and comparative experimentation shows that the proposed model is classifying emotions more accurate. We have proposed a Conv1D and Bi-GRU with the attention-based model for emotion classification of our Bengali music dataset. The model integrates attention-based. Wav preprocessing makes use of MFCCs. To reduce the dimensionality of the feature space, contextual features were extracted from two Conv1D layers. In order to solve the overfitting problems, dropouts are utilized. Two bidirectional GRUs networks are used to update previous and future emotion representation of the output from the Conv1D layers. Two BiGRU layers are conntected to an attention mechanism to give various MFCC feature vectors more attention. Moreover, the attention mechanism has increased the accuracy of the proposed classification model. The vector is finally classified into four emotion classes: Angry, Happy, Relax, Sad; using a dense, fully connected layer with softmax activation. The proposed Conv1D+BiGRU+Attention model is efficient at classifying emotions in the Bengali music dataset than baseline methods. For our Bengali music dataset, the performance of our proposed model is 95%.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.4
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• pp.1200-1215
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• 2023

With the development of IoT and artificial intelligence, location-based services are getting more and more attention. For solving the current problem that indoor positioning error is large and generalization is poor, this paper proposes a Model Stacking Algorithm for Indoor Positioning System using WiFi fingerprinting. Firstly, we adopt a model stacking method based on Bayesian optimization to predict the location of indoor targets to improve indoor localization accuracy and model generalization. Secondly, Taking the predicted position based on model stacking as the observation value of particle filter, collaborative particle filter localization based on model stacking algorithm is realized. The experimental results show that the algorithm can control the position error within 2m, which is superior to KNN, GBDT, Xgboost, LightGBM, RF. The location accuracy of the fusion particle filter algorithm is improved by 31%, and the predicted trajectory is close to the real trajectory. The algorithm can also adapt to the application scenarios with fewer wireless access points.

• 한국전산유체공학회:학술대회논문집
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• 2008.03b
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• pp.542-546
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• 2008

In this study, we developed a whole cardiovascular system model combined with a Laplace heart based on the numerical cardiac cell model and a detailed arterial network structure. The present model incorporates the Laplace heart model and pulmonary model using the lumped parameter model with the distributed arterial system model. The Laplace heart plays a role of the pump consisted of the atrium and ventricle. We applied a cellular contraction model modulated by calcium concentration and action potential in the single cell. The numerical arterial model is based upon a numerical solution of the one-dimensional momentum equations and continuity equation of flow and vessel wall motion in a geometrically accurate branching network of the arterial system including energy losses at bifurcations. For validation of the present method, the computed pressure waves are compared with the existing experimental observations. Using the cell-system-arterial network combined model, the pathophysiological events from cells to arterial network are delineated.

• Journal of Preventive Medicine and Public Health
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• v.32 no.4
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• pp.538-545
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• 1999

Objective : To compare the predictive power of International Classification of Diseases 10th Edition(ICD-10) based International Classification of Diseases based Injury Severity Score(ICISS) with Trauma and Injury Severity Score(TRISS) and International Classification of Diseases 9th Edition Clinical Modification(ICD-9CM) based ICISS in the injury severity measure. Methods : ICD-10 version of Survival Risk Ratios(SRRs) was derived from 47,750 trauma patients from 35 Emergency Centers for 1 year. The predictive power of TRISS, the ICD-9CM based ICISS and ICD-10 based ICISS were compared in a group of 367 severely injured patients admitted to two university hospitals. The predictive power was compared by using the measures of discrimination(disparity, sensitivity, specificity, misclassification rates, and ROC curve analysis) and calibration(Hosmer-Lemeshow goodness-of-fit statistics), all calculated by logistic regression procedure. Results : ICD-10 based ICISS showed a lower performance than TRISS and ICD-9CM based ICISS. When age and Revised Trauma Score(RTS) were incorporated into the survival probability model, however, ICD-10 based ICISS full model showed a similar predictive power compared with TRISS and ICD-9CM based ICISS full model. ICD-10 based ICISS had some disadvantages in predicting outcomes among patients with intracranial injuries. However, such weakness was largely compensated by incorporating age and RTS in the model. Conclusions : The ICISS methodology can be extended to ICD-10 horizon as a standard injury severity measure in the place of TRISS, especially when age and RTS were incorporated in the model. In patients with intracranial injuries, the predictive power of ICD-10 based ICISS was relatively low because of differences in the classifying system between ICD-10 and ICD-9CM.

• Interaction and multiscale mechanics
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• v.1 no.2
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• pp.279-301
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• 2008

This paper develops a 3D homogenization based continuum damage mechanics (HCDM) model for fiber reinforced composites undergoing micromechanical damage under monotonic and cyclic loading. Micromechanical damage in a representative volume element (RVE) of the material occurs by fiber-matrix interfacial debonding, which is incorporated in the model through a hysteretic bilinear cohesive zone model. The proposed model expresses a damage evolution surface in the strain space in the principal damage coordinate system or PDCS. PDCS enables the model to account for the effect of non-proportional load history. The loading/unloading criterion during cyclic loading is based on the scalar product of the strain increment and the normal to the damage surface in strain space. The material constitutive law involves a fourth order orthotropic tensor with stiffness characterized as a macroscopic internal variable. Three dimensional damage in composites is accounted for through functional forms of the fourth order damage tensor in terms of components of macroscopic strain and elastic stiffness tensors. The HCDM model parameters are calibrated from homogenization of micromechanical solutions of the RVE for a few representative strain histories. The proposed model is validated by comparing results of the HCDM model with pure micromechanical analysis results followed by homogenization. Finally, the potential of HCDM model as a design tool is demonstrated through macro-micro analysis of monotonic and cyclic damage progression in composite structures.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.21 no.1
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• pp.51-58
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• 2008

A constitutive model based on a combination of a micromechanics-based weakened interface elastic model (Lee and Pyo, 2007) and a crack nucleation model (Karihaloo and Fu, 1989) is proposed to predict the effective elastic behavior of particle-reinforced composites. The model specifically considers imperfect interfaces in particles and microcracks in the matrix. To exercise the proposed constitutive model and to investigate the influence of model parameters on the behavior of the composites, numerical simulations on uniaxial tension tests were conducted. Furthermore, the present prediction is compared with available experimental data in the literature to verify the accuracy of the proposed constitutive model.

• Proceedings of the KAIS Fall Conference
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• 2006.11a
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• pp.290-293
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• 2006

The goal of research is to propose a framework for drawing data search system which is based on the web. The existing search systems were reviewed in the form of case studies and thereby the limitation were addressed: the unsystematic translation between the presentational building model and the discursive design criteria. besides the limited area in sharing and space. Therefore, a web-based drawing search with common structure which building representational model and building behavior model is proposed. The system contains a number of phases: firstly, a user is required to build a building model with the proposed building representational model and then this model is automatically transformed into an aspect model; secondly, a user is also required to present his query in form of the propose building behavior model by web page; finally, these two models - building representational model and building behavior model - are compared by database data so as to retrieve the proper result.

• Journal of the Korea Society for Simulation
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• v.24 no.3
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• pp.55-60
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• 2015

This paper presents an SBA (simulation-based acquisition) effectiveness analysis environment using the RUP (Relational Unified Process) model. The RUP model has 4 phases which cover the whole development steps such as requirement analysis, design, development and test. By applying the RUP model, SW development can be represented with the iterations of developments for each phase. Such a characteristics of the model would make the model suitable for defense acquisition. In this paper, we show the relation between the RUP model and V process model, which is the foundation for defense acquisition. In order to show how the model could be applied to SBA effectiveness analysis, graphical user interfaces for the analysis are presented at the end of the paper.