• Korean Journal of Human Ecology
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• v.1 no.2
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• pp.48-60
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• 1998

The purpose of this study was to investigate the relational between elderly women＇s sex-role attitude and their marital satisfaction. For this purpose, a questionnaire was surveyed 186 elderly women living in Kwangiu and Chonnam. The collected data were analyzed by using frequency, percentage, mean, standard deviation, ANOVA and stepwise regression analysis. The results of this research were as follows ; First The total points of the elderly women＇s sex-role attitude score was 16.4, which was represented mixed trends of traditional and modem. And the total points of the elderly women＇s marital satisfaction score was 31.6, which was higher than the median 30. Second, Elderly women＇s sex-role attitude score was meaningfully different according to education level, health state, economic state. And elderly women＇s marital satisfaction score was meaningfully different according to social activity, economic state, health state, education level, age. Third, As the results of regression analysis, it was shown that the highest influencing variable on their marital satisfaction was the social activity, economic state, education level. All of them explained 19% of their marital satisfaction. (Korean J Human Ecology 1(2):48-60, 1998)

• Journal of KIISE
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• v.42 no.6
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• pp.748-754
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• 2015

This paper proposes a sequential state estimation model consisting of continuous and discrete variables, as a way of generalizing all discrete-state factorial HMM, and gives a design of gait motion model based on the idea. The discrete state variable implements a Markov chain that models the gait dynamics, and for each state of the Markov chain, we created a Gaussian process over the space of the continuous variable. The Markov chain controls the switching among Gaussian processes, each of which models the rotation or various views of a gait state. Then a particle filter-based algorithm is presented to give an approximate filtering solution. Given an input vector sequence presented over time, this finds a trajectory that follows a Gaussian process and occasionally switches to another dynamically. Experimental results show that the proposed model can provide a very intuitive interpretation of video-based gait into a sequence of poses and a sequence of posture states.

• The Journal of the Acoustical Society of Korea
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• v.22 no.7
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• pp.528-538
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• 2003

A SCCRS (Speech and Character Combined Recognition System) is developed for working on mobile devices such as PDA (Personal Digital Assistants). In SCCRS, the feature extraction is separately carried out for speech and for hand-written character, but the recognition is performed in a common engine. The recognition engine employs essentially CHMM (Continuous Hidden Markov Model), which consists of variable parameter topology in order to minimize the number of model parameters and to reduce recognition time. For generating contort independent variable parameter model, we propose the SSMS(Successive State and Mixture Splitting), which gives appropriate numbers of mixture and of states through splitting in mixture domain and in time domain. The recognition results show that the proposed SSMS method can reduce the total number of GOPDD (Gaussian Output Probability Density Distribution) up to 40.0% compared to the conventional method with fixed parameter model, at the same recognition performance in speech recognition system.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.40 no.5
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• pp.410-416
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• 2003

In the affine projection algorithm(APA), the projection dimension depends on a number of projection basis and of elements of input vector used for updating of coefficients of the adaptive filter. The projection dimension is closely related to a convergence speed of the APA, and it determines computational complexity. As the adaptive filter approaches to steady state, convergence speed is decreased. Therefore it is possible to reduce projection dimension that determines convergence speed. In this paper, we proposed the variable dimension affine projection algorithm (VDAPA) that controls the projection dimension and uses the relation between variations of coefficients of the adaptive filter and convergence speed of the APA. The proposed method reduces computational complexity of the APA by modifying the number of projection basis on convergence state. For demonstrating the good performances of the proposed method, simulation results are compared with the APA and normalized LMS algorithm in convergence speed and computational quantity.

• Journal of Biosystems Engineering
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• v.31 no.3 s.116
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• pp.203-208
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• 2006

This study was conducted to improve the control performance of a current variable-rate controller for granular fertilizers. Simulation model was developed. Optimized proportional, integral and derivative gains were determined by simulation model using 2nd order PID gain learning algorithm, and these control gains were evaluated through the field tests. Important results of this study are as follows; 1. Principles of pre-existing variable-rate application of granular fertilizers were investigated. 2. Simulation model of a PID controller that could simulate the control system was developed by using Matlab/Simulink program. The program was to determine PID control coefficients through the simulation model and 2nd order PID gain learning algorithm. 3. PID control coefficients obtained from the simulation were applied to the developed model. When the step input was given, Maximum overshoot were 1.96%, rise time were 0.05 sec, settling time were 0.06 sec and steady state error were 0.21 % respectively. 4. The simulation model was verified through field tests. The errors of maximum overshoot were 10%, rise time were 0.11 sec, settling time were 0.40 sec and steady state error were 8% because of loads and noises. Rise time was decreased to one third of that of the pre-existing system. 5. If the speed of a fertilizing machine is $0.3{\sim}0.6\;m/s$ and the maximum rotation speed of a discharging roller is 64 rpm, rise time would be 0.26 sec and fertilizing machine would cover the distance of $0.07{\sim}0.15\;m$ with settling time of 0.4 sec, fertilizing machine would cover the distance of $0.12{\sim}0.24\;m$.

• THE INTERNATIONAL COMMERCE & LAW REVIEW
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• v.72
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• pp.139-160
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• 2016

Using the gravity model, this paper analyzes empirically how the world trade in goods is affected by regional trade agreements(RTAs) which have been spreading rapidly since the mid-1990s. This paper attempt to do the panel data analysis about 174 countries during the period of 1994-2008. These panel data include 157 RTAs. It is meaningful that this paper uses comprehensive data to analyze the net effect of regional trade agreements on the global trade volume. This provides a clue as to the answer to the stumbling block debate raised early in the regional trade agreement. Also, confirming how the participation of the WTO affected the trade volume among the member countries, the WTO-related dummy variables are additionally introduced to this gravity model. And as far as we know, the state system-related variables is first considered in this model. This variable reflects the social and cultural environments of countries as the proxy variable representing the sociocultural homogeneity. In all regressions, joining to the WTO and consistency of the state system have a positive effect on increasing the trade volumes between countries. According to the analysis of RTA trade effects, RTAs, on average, increase the volume of trade within the RTA region by 27%~37%, and decrease the volume of trade between the regional and the non-regional nation by 1.2%~3.4%.Therefore, the net effect of regional trade agreements on the promotion of global welfare is positive. For robustness check, we also introduce the interaction term of the dummy variable which reflects the RTA tightening and the continuous variable which reflects the distance effect. As a result, the RTAs alleviate the trade-decreasing effect which is caused by the distance between the countries.

• Transactions of the Korean Society of Automotive Engineers
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• v.21 no.2
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• pp.9-16
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• 2013

The comparisons of performance characteristics between the super-mirror face grinding machine using variable air pressure developed in this laboratory to grind precisely the sliding face of a surface hardened workpiece with thermal spray and the conventional one are investigated by measuring the surface roughness and hardness for a SCM440. To process variously workpiece according to shape, size and materials, the rotating and contacting forces of the developed grinding machine can be changed by air pressure. The surface roughness of processed workpiece can be also attained to state of mirror face by grinding precisely the sliding face with changing the rotating speed of diamond wheel. It is possible to be attached to the various machine tools because the super-mirror face grinding machine using variable air pressure is a small size. The grinding efficiency is elevated because it can be worked by two or more grinding machines attached to concurrently a machine tool for the large workpiece. In this study, results show that the cusp height of the super-mirror face grinding machine for the particle size of 100 and $1500No./mm^2$ is lower than that of the conventional one because the vibration is reduced by rotating very fast the diamond wheel with a pressed air and it can be processed by rotating the diamond wheel with a constantly varied air pressure perpendicular to workpiece surface, and that the workpiece in the super-mirror face grinding machine for the particle size of $3000No./mm^2$ can be processed to state of mirror face that is rarely seen by the cusp height. It is also found that the surface hardness of both the conventional and the super-mirror face grinding machines are increased as the particle size of diamond wheel is reduced, and the surface hardness of the super-mirror face grinding machine is HRC 1.1 ~ 1.8 higher than that of the conventional one.

• International Journal of Computer Science & Network Security
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• v.24 no.2
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• pp.31-42
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• 2024

Different industries mostly rely on quality certification for promoting their products or brands. Although getting quality certification, specifically by human experts is a tough job to do. But the field of machine learning play a vital role in every aspect of life, if we talk about quality certification, machine learning is having a lot of applications concerning, assigning and assessing quality certifications to different products on a macro level. Like other brands, wine is also having different brands. In order to ensure the quality of wine, machine learning plays an important role. In this research, we use two datasets that are publicly available on the "UC Irvine machine learning repository", for predicting the wine quality. Datasets that we have opted for our experimental research study were comprised of white wine and red wine datasets, there are 1599 records for red wine and 4898 records for white wine datasets. The research study was twofold. First, we have used a technique called backward elimination in order to find out the dependency of the dependent variable on the independent variable and predict the dependent variable, the technique is useful for predicting which independent variable has maximum probability for improving the wine quality. Second, we used a robust machine learning algorithm known as "XGBoost" for efficient prediction of wine quality. We evaluate our model on the basis of error measures, root mean square error, mean absolute error, R2 error and mean square error. We have compared the results generated by "XGBoost" with the other state-of-the-art machine learning techniques, experimental results have showed, "XGBoost" outperform as compared to other state of the art machine learning techniques.

• The Journal of the Acoustical Society of Korea
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• v.28 no.8
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• pp.815-821
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• 2009

Information processing in variable and noisy environments is usually accomplished by means of adaptive filters. Among various adaptive algorithms, Least Mean Square (LMS) has become the most popular for its robustness, good tracking capabilities and simplicity, both in terms of computational load and easiness of implementation. In practical application of the LMS algorithm, the most important key parameter is the Step Size. As is well known, if the Step Size is large, the convergence rate of the algorithm will be rapid, but the steady state mean square error (MSE) will increase. On the other hand, if the Step Size is small, the steady state MSE will be small, but the convergence rate will be slow. Many researches have been proposed to alleviate this drawback by using a variable Step Size. In this paper, a new variable Step Size LMS(VSSLMS) called Categorized VSSLMS (CVSSLMS) is proposed. CVSSLMS updates the Step Size by categorizing the current status of the gradient, hence significantly improves the convergence rate. The performance of the proposed algorithm was verified from the view point of convergence rate, Excessive Mean Square Error(EMSE), and complexity through experiments.

• Journal of Electrical Engineering and Technology
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• v.12 no.1
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• pp.225-229
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• 2017

This paper describes the loss analysis based on load conditions of the air conditioning compressor motors using variable dc-link voltage. The losses of PMSM (Permanent Magnet Synchronous Motor) should be analyzed by the PWM (Pulse Width Modulation) output of inverter. The harmonic loss by the PWM cannot consider that using the current source analysis of the inverter. In addition, when the voltage of dc-link is variable with the condition of variable speed and load conditions in motor, the losses of motor are also changeable, however it is hard to analyze those losses by only electromagnetic finite element method (FEM). Therefore, this paper proposes the analysis method considering the carrier frequency of the inverter and the varying state of the dc-link voltage through the FEM-control coupled analysis. Using proposed analysis method, additional core loss and eddy current loss of permanent magnet caused by PWM could be analyzed. Finally, the validity of the proposed analysis method is verified through the comparison the result of coupled analysis with experiment.