• The Journal of the Acoustical Society of Korea
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• v.23 no.2
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• pp.173-179
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• 2004

Due to the insufficiency of training data in large vocabulary continuous speech recognition, similar context dependent phones can be clustered by decision trees to share the data. When the decision trees are built and used to predict unseen triphones, a phonetic question set is required. The phonetic question set, which contains categories of the phones with similar co-articulation effects, is usually generated by phonetic or linguistic experts. This knowledge-based approach for generating phonetic question set, however, may reduce the homogeneity of the clusters. Moreover, the experts must adjust the question sets whenever the language or the PLU (phone-like unit) of a recognition system is changed. Therefore, we propose a data-driven method to automatically generate phonetic question set. Since the proposed method generates the phone categories using speech data distribution, it is not dependent on the language or the PLU, and may enhance the homogeneity of the clusters. In large vocabulary speech recognition experiments, the proposed algorithm has been found to reduce the error rate by 14.3%.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.47 no.11
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• pp.795-802
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• 2019

This paper deals with position-attitude coupling motion during spacecraft relative operation, and suggests dual quaternion-based kinematics for the problem. The position-attitude coupling motion can occur when the target point is located at an arbitrary point on the satellite body, not the center of mass. This is especially apparent in close proximity operation case. The dual quaternion-based kinematics directly reflects the angular velocity state, so that the coupling motion in which the change of attitude affects the position can be concisely defined. In this study, a new dual quaternion-based kinematics is presented along with a conventional approach to solve the coupling problem. Numerical simulations show that the position error for the target point is generated by the coupling motion, and verify that the dual quaternion-based kinematics can solve this problem.

• Smart Structures and Systems
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• v.6 no.9
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• pp.1057-1077
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• 2010

An early detection of structural damages is critical for the decision making of repair and replacement maintenance in order to guarantee a specified structural reliability. Consequently, the structural damage detection, based on vibration data measured from the structural health monitoring (SHM) system, has received considerable attention recently. The traditional time-domain analysis techniques, such as the least square estimation (LSE) method and the extended Kalman filter (EKF) approach, require that all the external excitations (inputs) be available, which may not be the case for some SHM systems. Recently, these two approaches have been extended to cover the general case where some of the external excitations (inputs) are not measured, referred to as the adaptive LSE with unknown inputs (ALSE-UI) and the adaptive EKF with unknown inputs (AEKF-UI). Also, new analysis methods, referred to as the adaptive sequential non-linear least-square estimation with unknown inputs and unknown outputs (ASNLSE-UI-UO) and the adaptive quadratic sum-squares error with unknown inputs (AQSSE-UI), have been proposed for the damage tracking of structures when some of the acceleration responses are not measured and the external excitations are not available. In this paper, these newly proposed analysis methods will be compared in terms of accuracy, convergence and efficiency, for damage identification of structures based on experimental data obtained through a series of laboratory tests using a scaled 3-story building model with white noise excitations. The capability of the ALSE-UI, AEKF-UI, ASNLSE-UI-UO and AQSSE-UI approaches in tracking the structural damages will be demonstrated and compared.

• Korean Journal of Biological Psychiatry
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• v.21 no.4
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• pp.141-150
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• 2014

Objectives The purpose of this study is to evaluate the effects of attachment security, social support and health-related burden in the prediction of psychological distress and the mediation effects of social support and health-related burden in relationship between attachment security and psychological distress. Methods Finally, 161 patients were included for the analysis. Chi-square test and independent samples t-test were used for comparing differences between depressive/anxious group and non-depressive/non-anxious group. For evaluating the relationship among attachment security, social support, psychological distress and health-related burden, structural equation modeling analysis were performed. Results 40.7% and 32.0% of the patients have significant depressive symptoms and anxiety symptoms, respectively. In the analysis for testing the differences between groups who have psychological distress and who have not, there were no significant differences of sociodemographic factors and medical characteristics between groups, except for association between depressive symptoms and type of surgery (p = 0.01). Contrary to sociodemographic and medical characteristics, there were significant differences of health-related burden and two coping resources (attachment security and social support) between groups (all p < 0.01), except for the support from medical team in between anxious group and non-anxious group (p = 0.20). In the structural equation model analysis (Model fit : chi-square/df ratio = 0.8, root mean square error of approximation = 0.000, comparative fit index = 1.000, non-normed fit index =0.991), attachment security and social support emerged as an important predictor of psychopathology. Conclusions Attachment security and social support are important factors affecting the psychological distress. We suggest that individual attachment style and the social support state must be considered to approach the newly diagnosed breast cancer patients with psychological distress.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.2
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• pp.265-276
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• 2017

In this paper, a robust and more accurate trajectory tracking control method for a mobile robot is proposed using WIPDC(Weighted Integral Parallel Distributed Compensation) and T-S Fuzzy disturbance observer. WIPDC reduces the steady state error by adding weighted integral term to PDC. And, T-S Fuzzy disturbance observer makes it possible to estimate and cancel disturbances for a T-S fuzzy model system. As a result, the trajectory tracking controller based on T-S Fuzzy disturbance observer shows robust tracking performance. When the initial postures of a mobile robot and the reference trajectory are different, the initial control inputs to the mobile robot become too large to apply them practically. In this study, also, the problem is solved by designing an initial approach path using a path planning method which employs $B\acute{e}zier$ curve with acceleration limits. Performances of the proposed method are proved from the simulation results.

• Journal of Power Electronics
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• v.16 no.1
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• pp.297-309
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• 2016

Grid-connected inverters (GCIs) with an LCL output filter have the ability of attenuating high-frequency (HF) switching ripples. However, by using only grid-current control, the system is prone to resonances if it is not properly damped, and the current distortion is amplified significantly under highly distorted grid conditions. This paper proposes a synchronous reference frame equivalent proportional-integral (SRF-EPI) controller in the αβ stationary frame using the parallel virtual resistance-based active damping (PVR-AD) strategy for grid-interfaced distributed generation (DG) systems to suppress LCL resonance. Although both a proportional-resonant (PR) controller in the αβ stationary frame and a PI controller in the dq synchronous frame achieve zero steady-state error, the amplitude- and phase-frequency characteristics differ greatly from each other except for the reference tracking at the fundamental frequency. Therefore, an accurate SRF-EPI controller in the αβ stationary frame is established to achieve precise tracking accuracy. Moreover, the robustness, the harmonic rejection capability, and the influence of the control delay are investigated by the Nyquist stability criterion when the PVR-based AD method is adopted. Furthermore, grid voltage feed-forward and multiple PR controllers are integrated into the current loop to mitigate the current distortion introduced by the grid background distortion. In addition, the parameters design guidelines are presented to show the effectiveness of the proposed strategy. Finally, simulation and experimental results are provided to validate the feasibility of the proposed control approach.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.2
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• pp.714-720
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• 2021

The ITL (information theoretic learning) based on the kernel density estimation method that has successfully been applied to machine learning and signal processing applications has a drawback of severe sensitiveness in choosing proper kernel sizes. For the maximization of correntropy criterion (MCC) as one of the ITL-type criteria, several methods of adapting the remaining kernel size ( ) after removing the term have been studied. In this paper, it is shown that the main cause of sensitivity in choosing the kernel size derives from the term and that the adaptive adjustment of in the remaining terms leads to approach the absolute value of error, which prevents the weight adjustment from continuing. Thus, it is proposed that choosing an appropriate constant as the kernel size for the remaining terms is more effective. In addition, the experiment results when compared to the conventional algorithm show that the proposed method enhances learning performance by about 2dB of steady state MSE with the same convergence rate. In an experiment for channel models, the proposed method enhances performance by 4 dB so that the proposed method is more suitable for more complex or inferior conditions.

• Journal of Korea Water Resources Association
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• v.54 no.3
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• pp.157-166
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• 2021

Forecasting dam inflow based on high reliability is required for efficient dam operation. In this study, deep learning technique, which is one of the data-driven methods and has been used in many fields of research, was manipulated to predict the dam inflow. The Long Short-Term Memory deep learning with Sequence-to-Sequence model (LSTM-s2s), which provides high performance in predicting time-series data, was applied for forecasting inflow of Soyang River dam. Various statistical metrics or evaluation indicators, including correlation coefficient (CC), Nash-Sutcliffe efficiency coefficient (NSE), percent bias (PBIAS), and error in peak value (PE), were used to evaluate the predictive performance of the model. The result of this study presented that the LSTM-s2s model showed high accuracy in the prediction of dam inflow and also provided good performance for runoff event based runoff prediction. It was found that the deep learning based approach could be used for efficient dam operation for water resource management during wet and dry seasons.

• Journal of the Korean School Mathematics Society
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• v.25 no.4
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• pp.423-446
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• 2022

This study explores and compares Korean and U.S. elementary preservice teachers' analytic approaches of students' addition and subtraction computational strategies. Twenty-six Korean and twenty U.S. elementary preservice teachers participated in the study. Participants were asked to analyze mathematical approaches and errors from students' addition and subtraction operations. Preservice teachers' written documents were analyzed by applying open coding and inductive coding based on the grounded theory. As a result, the pattern of error analysis and interpretation of students' addition computations were similar for both Korean and U.S. preservice teachers whereas there were some differences in the analysis of students' subtraction computations. Both Korean and U.S. preservice teachers had difficulties identifying students' strategies and errors for a complicated and unconventional computational approach. Results also indicated that preservice teachers' noticing and interpretation of students' strategies and errors were influenced by their K-12 mathematics curriculum and teacher education program. This study suggests implications and future directions for teacher education, more contextualized teacher preparation programs and balanced connection to the K-12 curriculum.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.45 no.4
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• pp.157-166
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• 2022

In the case of a die-casting process, defects that are difficult to confirm by visual inspection, such as shrinkage bubbles, may occur due to an error in maintaining a vacuum state. Since these casting defects are discovered during post-processing operations such as heat treatment or finishing work, they cannot be taken in advance at the casting time, which can cause a large number of defects. In this study, we propose an approach that can predict the occurrence of casting defects by defect type using machine learning technology based on casting parameter data collected from equipment in the die casting process in real time. Die-casting parameter data can basically be collected through the casting equipment controller. In order to perform classification analysis for predicting defects by defect type, labeling of casting parameters must be performed. In this study, first, the defective data set is separated by performing the primary clustering based on the total defect rate obtained during the post-processing. Second, the secondary cluster analysis is performed using the defect rate by type for the separated defect data set, and the labeling task is performed by defect type using the cluster analysis result. Finally, a classification learning model is created by collecting the entire labeled data set, and a real-time monitoring system for defect prediction using LabView and Python was implemented. When a defect is predicted, notification is performed so that the operator can cope with it, such as displaying on the monitoring screen and alarm notification.