• The Journal of Korean Institute of Communications and Information Sciences
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• v.18 no.10
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• pp.1422-1432
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• 1993

Nonlinear mapping function of the HCNN( Hidden Control Neural Network ) can change over time to model the temporal variability of a speech signal by combining the nonlinear prediction of conventional neural networks with the segmentation capability of HMM. We have two things in this paper. first, we showed that the performance of the HCNN is better than that of HMM. Second, the HCNN with its prediction error measure given by weighted distance is proposed to use suitable distance measure for the HCNN, and then we showed that the superiority of the proposed system for speaker-independent speech recognition tasks. Weighted distance considers the differences between the variances of each component of the feature vector extraced from the speech data. Speaker-independent Korean digit recognition experiment showed that the recognition rate of 95%was obtained for the HCNN with Euclidean distance. This result is 1.28% higher than HMM, and shows that the HCNN which models the dynamical system is superior to HMM which is based on the statistical restrictions. And we obtained 97.35% for the HCNN with weighted distance, which is 2.35% better than the HCNN with Euclidean distance. The reason why the HCNN with weighted distance shows better performance is as follows : it reduces the variations of the recognition error rate over different speakers by increasing the recognition rate for the speakers who have many misclassified utterances. So we can conclude that the HCNN with weighted distance is more suit-able for speaker-independent speech recognition tasks.

• Proceedings of the IEEK Conference
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• 2004.06a
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• pp.79-82
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• 2004

In this paper, we analyze the performance of high-rate OFDM(Orthogonal Frequency Division Multiplexing) in the JTC(Joint Technical Committee) channel models using transmit diversity. In this thesis, each independent channel characteristic antennas in the transmitter are analyzed. Also, Equalization method through the channel estimation in the realistic fading channel environments is applied for BEE 802.11a WLAN system performance. From the simulation results, BER through transmit diversity of WLAN system is evaluated in AWGN channel and multipath channel environments.

• Health Policy and Management
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• v.13 no.3
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• pp.129-147
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• 2003

This study is to grasp a trend of profitability classified by characteristics of hospitals and to analyze related factors. Subjects are 145 hospitals which have gotten the standardization audit by Korean Hospital Association during 1998-200l. Profitability was measured in the aspect of operation profit rate with operating margin to gross revenue as proxy variables. Independent variables were classified by general factors (ownership, number of beds, period of establishment, competition), financial factors (liabilities to total assets, current ratio, fixed ratio, total asset turnover, inventories turnover), and factors related to patient treatment (average length of stay, bed occupancy rate, new outpatient ratio, admission ratio of outpatients, number of patients per specialist, personnel costs per adjusted inpatient, administrative costs per adjusted inpatient). Hierarchical multiple regression analysis model was used in this study. As a result of hierarchical multiple regression analyzation of operating margin to gross revenue, adjustive $R^2$ of general factors was relatively more powerful. The factors had significant effect on operating margin to gross revenue were ownership(+), number of beds(+), competition(+), current ratio(+), fixed ratio(+), total asset turnover(+), personnel costs per adjusted inpatient(-).

• 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.

• Speech Sciences
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• v.13 no.4
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• pp.177-186
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• 2006

In an effort to enhance the quality of feature vector classification and thereby reduce the recognition error rate of the speaker-independent speech recognition, we employ the Mahalanobis distance in the calculation of the similarity measure between feature vectors. It is assumed that the metric matrix of the Mahalanobis distance be diagonal for the sake of cost reduction in memory and time of calculation. We propose that the diagonal elements be given in terms of the variations of the feature vector components. Geometrically, this prescription tends to redistribute the set of data in the shape of a hypersphere in the feature vector space. The idea is applied to the speech recognition by hidden Markov model with fuzzy vector quantization. The result shows that the recognition is improved by an appropriate choice of the relevant adjustable parameter. The Viterbi score difference of the two winners in the recognition test shows that the general behavior is in accord with that of the recognition error rate.

• Computers and Concrete
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• v.15 no.4
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• pp.515-545
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• 2015

The paper presents results of FE simulations of the strain-rate sensitive concrete behaviour under dynamic loading at the macroscopic level. To take the loading velocity effect into account, viscosity, stress modifications and inertial effects were included into a rate-independent elasto-plastic formulation. In addition, a decrease of the material stiffness was considered for a very high loading velocity to simulate fragmentation. In order to ensure the mesh-independence and to properly reproduce strain localization in the entire range of loading velocities, a constitutive formulation was enhanced by a characteristic length of micro-structure using a non-local theory. Numerical results were compared with corresponding laboratory tests and available analytical formulae.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.31 no.2D
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• pp.237-245
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• 2011

Asphalt Plug Joint(APJ) is a new type of expansion joint that it's application are increased in USA as well as several European countries. APJ's' advantages are cheap construction and maintenance costs, and simple construction and securing of excellent flatness. However, APJ's usability is hindered because it showed a problem of premature failure. Research for solving this problem has been progressed, and FEM analysis among existing researches was peformed. However, the behavior of APJ was insufficiently analyzed and the reliability of the analysis was much low, since the material showing complicated behavior was oversimplified, Therefore, a material model was proposed and its effectiveness was confirmed by comparing it with actual behavior in order to improve the reliability of FEM analysis in this paper. ABAQUS program was used for FEM analysis, and an elasto-plastic model and a viscous-plastic model as the material model of APJ were suggested on the base of experiment results of APJ material performed by Bramel et al. The elasto-plastic model was defined by time-independent analysis since it didn't consider time and strain rate, and the viscous-plastic model was defined by time-dependent analysis since it considered. Influence of various elements affecting the behavior of APJ was investigated, and it was confirmed that the time-dependent analysis showed better result closed to actual behavior than the time-independent analysis.

• The Journal of Information Systems
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• v.31 no.3
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• pp.177-195
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• 2022

Purpose This paper aims to prepare a full operational readiness by establishing an optimal flight plan considering the weather conditions in order to effectively perform the mission and operation of military aircraft. This paper suggests a flight prediction model and rules by analyzing the correlation between flight implementation and cancellation according to weather conditions by using big data collected from historical flight information of military aircraft supplied by Korean manufacturers and meteorological information from the Korea Meteorological Administration. In addition, by deriving flight rules according to weather information, it was possible to discover an efficient flight schedule establishment method in consideration of weather information. Design/methodology/approach This study is an analytic study using data mining techniques based on flight historical data of 44,558 flights of military aircraft accumulated by the Republic of Korea Air Force for a total of 36 months from January 2013 to December 2015 and meteorological information provided by the Korea Meteorological Administration. Four steps were taken to develop optimal flight prediction models and to derive rules for flight implementation and cancellation. First, a total of 10 independent variables and one dependent variable were used to develop the optimal model for flight implementation according to weather condition. Second, optimal flight prediction models were derived using algorithms such as logistics regression, Adaboost, KNN, Random forest and LightGBM, which are data mining techniques. Third, we collected the opinions of military aircraft pilots who have more than 25 years experience and evaluated importance level about independent variables using Python heatmap to develop flight implementation and cancellation rules according to weather conditions. Finally, the decision tree model was constructed, and the flight rules were derived to see how the weather conditions at each airport affect the implementation and cancellation of the flight. Findings Based on historical flight information of military aircraft and weather information of flight zone. We developed flight prediction model using data mining techniques. As a result of optimal flight prediction model development for each airbase, it was confirmed that the LightGBM algorithm had the best prediction rate in terms of recall rate. Each flight rules were checked according to the weather condition, and it was confirmed that precipitation, humidity, and the total cloud had a significant effect on flight cancellation. Whereas, the effect of visibility was found to be relatively insignificant. When a flight schedule was established, the rules will provide some insight to decide flight training more systematically and effectively.

• Journal of Mechanical Science and Technology
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• v.15 no.1
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• pp.88-97
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• 2001

The present study numerically investigates the fuel-air mixing characteristics, flame structure, and pollutant emission inside a double-swirler combustor. A PSR(Perfectly Stirred Reactor) based microstructural model is employed to account for the effects of finite rate chemistry on the flame structure and NO formation. The turbulent combustion model is extended to nonadiabatic flame condition with radiation by introducing an enthalpy variable, and the radiative heat loss is calculated by a local, geometry-independent model. The effects of turbulent fluctuation are taken into account by the joint assumed PDFs. Numerical model is based on the non-orthogonal body-fitted coordinate system and the pressure/velocity coupling is handled by PISO algorithm in context with the finite volume formulation. The present PSR-based turbulent combustion model has been applied to analyze the highly intense turbulent nonpremixed flame field in the double swirler combustor. The detailed discussions were made for the flow structure, combustion effects on flow structure, flame structure, and emission characteristics in the highly intense turbulent swirling flame of the double swirler burner.

• Computers and Concrete
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• v.24 no.6
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• pp.555-560
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• 2019

During the application of shotcrete, a part of the concrete bounces back after hitting to the surface, the reinforcement or previously sprayed concrete. This rebound material is definitely not added to the mixture and considered as waste. In this study, a deep neural network model was developed to predict the rebound material during shotcrete application. The factors affecting rebound and the datasets of these parameters were obtained from previous experiments. The Long Short-Term Memory (LSTM) architecture of the proposed deep neural network model was used in accordance with this data set. In the development of the proposed four-tier prediction model, the dataset was divided into 90% training and 10% test. The deep neural network was modeled with 11 dependents 1 independent data by determining the most appropriate hyper parameter values for prediction. Accuracy and error performance in success performance of LSTM model were evaluated over MSE and RMSE. A success of 93.2% was achieved at the end of training of the model and a success of 85.6% in the test. There was a difference of 7.6% between training and test. In the following stage, it is aimed to increase the success rate of the model by increasing the number of data in the data set with synthetic and experimental data. In addition, it is thought that prediction of the amount of rebound during dry-mix shotcrete application will provide economic gain as well as contributing to environmental protection.