• The Korean Journal of Food And Nutrition
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• v.16 no.3
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• pp.192-196
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• 2003

The purpose of this study was to investigate the changes of body composition, serum lipids and several parameters of body fatness (percent body fat, waist-hip ratio) in abdominal women by exercise. For this study, 8-weeks intensive exercise(5km jogging/day, 50min/day) was continued by subjects and they limited only fat rich foods and controlled daily energy intake to 1,800kcal～2,100kcal per day. The subjects were 52 women and the distribution of ages was 36～54 years. The data were analyzed using SPSS/PC package program and the results were estimated by paired t-test, Pearson correlation. The results are summarized as follows : 1) After exercise-training for 8 weeks, percent body fat, body mass index, body weight, total cholesterol was decreased (p<0.05). 2) LDL cholesterol and triglyceride was decreased significantly(p=0.000). The changes in deep abdominal adipose tissue were related to changes in triglycerides. 3) After exercise training, the waist-hip ratio was significantly correlated to body weight and serum lipids. 4) According to the data of this study, Ⅰ recommended that obese women, especially, abdominal obese patients should exercise regularly and we should prolong many studies for obesity.

• PNF and Movement
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• v.5 no.2
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• pp.47-54
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• 2007

Purpose : The purpose of this study were to determine the effect of a Elastic Theraband Exercise Based of PNF L/E pattern on the gait of the chronic Hemiplegic Patients. Methods : We selected the 20 chronic Hemiplegic Patients not given treatment now and divided them into two groups of both 10 Elastic Theraband group and 10 Self Exercise. The first group went through a Elastic Theraband Exercise Based of PNF L/E pattern 30 minutes a day, 5 times a week, for 6 weeks. Exercise used to blue elastic band which 2 patterns of PNF by 1) hip extension - abduction - internal rotation with knee extension. 2) hip flexion - adduction - external rotation with knee flexion. The latter group experienced Self Exercise, 30 minutes a day, 5 times a week, for 6 weeks. Firstly, we measured the absolute improvement of gait velocity(m/s), cadence(steps/min) among walking characters. Secondly, we measured the functional walking ability such as Functional Ambulatory Category(FAC, score out of 5), Modified Motor Assesment Scale(MMAS, score out of 6). Data analysis was performed with using SPSS 12.0 win program. The descriptive analysis was used to obtain average and standard deviation. The independent t-test and the paired t-test were used to compare both the groups about pre and post training test. Treatment effects were established by pre and post assessment. Subjects tolerated the training well without side-effects. Therefore, the results of this study were as follows; Results : 1. There was a more significant improvement of Gait velocity(0.12m/s) Elastic Theraband group(p<.05). 2. There was a more significant improvement of cadence(9.40steps/min) Elastic Theraband group(p<.05). Conclusion : As we can see from above, the findings suggest that Elastic Theraband may be more effective than the Self Exercise for improving some gait parameters such as Gait velocity and Cadency. This conclusion also suggest that Elstic Theraband is more effective for the improvement of gait of chronic Hemiplegic Patients.

• Composites Research
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• v.33 no.3
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• pp.108-114
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• 2020

Piecewise Integrated Composite (PIC) beam is composed of different stacking against loading type depending upon location. The aim of current study is to assign robust stacking sequences against external loading to every corresponding part of the PIC beam based on the value of stress triaxiality at generated reference points using the k-NN (k-Nearest Neighbor) classification, which is one of representative machine learning techniques, in order to excellent superior bending characteristics. The stress triaxiality at reference points is obtained by three-point bending analysis of the Al beam with training data categorizing the type of external loading, i.e., tension, compression or shear. Loading types of each plane of the beam were classified by independent plane scheme as well as total beam scheme. Also, loading fidelities were calibrated for each case with the variation of hyper-parameters. Most effective stacking sequences were mapped into the PIC beam based on the k-NN classification model with the highest loading fidelity. FE analysis result shows the PIC beam has superior external loading resistance and energy absorption compared to conventional beam.

• Phonetics and Speech Sciences
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• v.12 no.3
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• pp.65-71
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• 2020

Feature normalization is a method to reduce the effect of environmental mismatch between the training and test conditions through the normalization of statistical characteristics of acoustic feature parameters. It demonstrates excellent performance improvement in the traditional Gaussian mixture model-hidden Markov model (GMM-HMM)-based speech recognition system. However, in a deep neural network (DNN)-based speech recognition system, minimizing the effects of environmental mismatch does not necessarily lead to the best performance improvement. In this paper, we attribute the cause of this phenomenon to information loss due to excessive feature normalization. We investigate whether there is a feature normalization method that maximizes the speech recognition performance by properly reducing the impact of environmental mismatch, while preserving useful information for training acoustic models. To this end, we introduce the mean and exponentiated variance normalization (MEVN), which is a compromise between the mean normalization (MN) and the mean and variance normalization (MVN), and compare the performance of DNN-based speech recognition system in noisy and reverberant environments according to the degree of variance normalization. Experimental results reveal that a slight performance improvement is obtained with the MEVN over the MN and the MVN, depending on the degree of variance normalization.

• Journal of the Korea Society of Computer and Information
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• v.20 no.5
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• pp.53-63
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• 2015

Singing voice synthesis is the generation of a song using a computer given its lyrics and musical notes. Hidden Markov models (HMM) have been proved to be the models of choice for text to speech synthesis. HMMs have also been used for singing voice synthesis research, however, a huge database is needed for the training of HMMs for singing voice synthesis. And commercially available singing voice synthesis systems which use the piano roll music notation, needs to adopt the easy to read standard music notation which make it suitable for singing learning applications. To overcome this problem, we use a speech database for training context dependent HMMs, to be used for singing voice synthesis. Pitch and duration control methods have been devised to modify the parameters of the HMMs trained on speech, to be used as the synthesis units for the singing voice. This work describes a singing voice synthesis system which uses a MusicXML based music score editor as the front-end interface for entry of the notes and lyrics to be synthesized and a hidden Markov model based text to speech synthesis system as the back-end synthesizer. A perceptual test shows the feasibility of our proposed system.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.3
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• pp.639-647
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• 2011

In this paper, we introduce a design methodology of data-centroid Radial Basis Function neural networks with extended polynomial function. The two underlying design mechanisms of such networks involve K-means clustering method and Particle Swarm Optimization(PSO). The proposed algorithm is based on K-means clustering method for efficient processing of data and the optimization of model was carried out using PSO. In this paper, as the connection weight of RBF neural networks, we are able to use four types of polynomials such as simplified, linear, quadratic, and modified quadratic. Using K-means clustering, the center values of Gaussian function as activation function are selected. And the PSO-based RBF neural networks results in a structurally optimized structure and comes with a higher level of flexibility than the one encountered in the conventional RBF neural networks. The PSO-based design procedure being applied at each node of RBF neural networks leads to the selection of preferred parameters with specific local characteristics (such as the number of input variables, a specific set of input variables, and the distribution constant value in activation function) available within the RBF neural networks. To evaluate the performance of the proposed data-centroid RBF neural network with extended polynomial function, the model is experimented with using the nonlinear process data(2-Dimensional synthetic data and Mackey-Glass time series process data) and the Machine Learning dataset(NOx emission process data in gas turbine plant, Automobile Miles per Gallon(MPG) data, and Boston housing data). For the characteristic analysis of the given entire dataset with non-linearity as well as the efficient construction and evaluation of the dynamic network model, the partition of the given entire dataset distinguishes between two cases of Division I(training dataset and testing dataset) and Division II(training dataset, validation dataset, and testing dataset). A comparative analysis shows that the proposed RBF neural networks produces model with higher accuracy as well as more superb predictive capability than other intelligent models presented previously.

• Journal of Korea Water Resources Association
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• v.43 no.8
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• pp.721-731
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• 2010

The rainfall-runoff relationship is very difficult to predict because it is complicate factor affected by many temporal and spatial parameters of the basin. In recent, models which is based on artificial intelligent such as neural network, genetic algorithm fuzzy etc., are frequently used to predict discharge while stochastic or deterministic or empirical models are used in the past. However, the discharge data which are generally used for prediction as training and validation set are often estimated from rating curve which has potential error in its estimation that makes a problem in reliability. Therefore, in this study, water stage is predicted from antecedent rainfall and water stage data for short term using three models of neural network which trained by error back propagation algorithm and optimized by genetic algorithm and training error back propagation after it is optimized by genetic algorithm respectively. As the result, the model optimized by Genetic Algorithm gives the best forecasting ability which is not much decreased as the forecasting time increase. Moreover, the models using stage data only as the input data give better results than the models using precipitation data with stage data.

• Journal of Korea Water Resources Association
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• v.51 no.1
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• pp.71-80
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• 2018

For the stream management, time of concentration is one of the important factors. In particular, as the requirement about various application of the stream increased, accuracy assessment of concentration time in the stream as waterfront area is extremely important for securing evacuation at the flood. the past studies for the assessment of concentration time, however, were only performed on the single hydrological event in the complex basin of natural streams. The development of a assessment methods for the concentration time on the complex hydrological event in a single watershed of urban streams is insufficient. Therefore, we estimated the concentration time using the rainfall- runoff data for the past 10 years (2006~2015) for the Oncheon stream, the representative stream of the Busan, where frequent flood were taken place by heavy rains, in addition, reviewed the reliability using artificial neural network method based on Matlab. We classified a total of 254 rainfalls events based on over unrained 12 hours. Based on the classification, we estimated 6 parameters (total precipitation, total runoff, peak precipitation/ total precipitation, lag time, time of concentration) to utilize for the training and validation of artificial neural network model. Consequently, correlation of the parameter, which was utilized for the training and the input parameter for the predict and verification were 0.807 and 0.728, respectively. Based on the results, we predict that it can be utilized to estimate concentration time and analyze reliability of urban stream.

• Information Systems Review
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• v.17 no.3
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• pp.39-64
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• 2015

As information and information technology become more important in competitive corporate environments, the risk of information security breaches has increased accordingly. Although organizations establish security measures to manage information security risks, members of organizations do not comply with them well, and their information security behavior intention is unclear. Therefore, to understand the information security risk management intention of the members of organizations, the present study developed a research model using Protection Motivation Theory, Supervisory Authority Pressure, and Background factors. This study presents empirical research findings based on the analysis of survey data from 201 members of financial institutions. Perceived Severity, Self-efficacy, and Supervisory Authority Pressure had a positive effect on intention; however, Perceived Vulnerability and Response Efficacy did not affect intention. Security Avoidance Habit, which was considered a background factor, had a negative effect on all parameters, and did not have an effect on intention. Security Awareness Training, another background factor, had a positive effect on information security risk management intention and perceived vulnerability, self-efficacy, response efficacy, and supervisory authority pressure, and had no effect on perceived severity. This study used supervisory authority pressure and background factors in the field of information security, and provided a basis to use supervisory authority pressure in future studies on behavior of organizations and members of an organization. In addition, the use of various background factors presented the groundwork for the expansion of protection motivation theory. Furthermore, practitioners can use the study findings as a foundation for organization's security activities, and to improve regulations.

• Journal of Navigation and Port Research
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• v.32 no.10
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• pp.743-750
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• 2008

Recently Bridges crossing waterway are constructed in navigable waterway, so marine accidents near bridges navigable waterway often occurred bemuse that has affect dangerous element for. This paper analysed the necessary environmental factors to navigate safely near bridges and how to set up the environmental factors. Marine accidents elements occurred near bridges relate to span of bridge, size of navigating ship, length of straight way and traffic volume except mistake of mariners. As results of marine accident parameter analysis, Span of bridge is necessary more than 300m at least based on marine accident's analysis, and in case of more than ship's Length 150m, span of bridge is necessary more than 500m, $3{\sim}4L$(L; Ship's Length). Length of straight way before bridge is necessary more than 8L to minimize the marine accident.