• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.6
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• pp.2751-2756
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• 2012

Selection of training set influences the classification accuracy in supervised classification using satellite imagery. Generally, if pure pixels which character of training set is clear were selected, whole accuracy is high while if mixed pixels were selected, accuracy is decreased because of low-resolution imagery or unclear distinguishment. However, it is too difficult to choose the pure pixels as training set actually. Accordingly, this study should be suggested the suitable classification method in case of mixed pixels choice. To achieve this, a few pure pixels were chosen as training set and classification accuracy was calculated which was compared with classification result using an equal number of mixed pixels. As a result, accuracy of SVM was the highest among the classification method using mixed pixels and it was a relatively small difference with the result of classification using pure pixels. Therefore, imagery classification using SVM is most suitable in the mixed area of construction and green because it is high possibility to choose mixed pixels as training set.

• PNF and Movement
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• v.8 no.1
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• pp.59-65
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• 2010

Purpose : The purposes of this study was to determine the effect of plyometric training and agility training on SEBT and dynamic balance of health young. Methods : Thirty healthy subjects in their 20s were randomly assigned to a plyometric exercise group, an agility training group, and a control group; each group had 10 subjects. The training starts first 2set after more 1set 2 weeks. SEBT is measured every two weeks. Results : The results of this research were as followings: (1) After treatment, there were significant SEBT scores differences in both plyometric and agility group compared with pre-treatment(p<0.05). (2) After treatment, there were significant SEBT scores differences in both agility and control group compared with pre-treatment (p<0.05). Conclusion : It was concluded that ployometric training was effective for improving balance than agility and control group. Therefore, further studies are required to investigate the effect of plyometric training for improving balance with sports injury patient.

• Current Research on Agriculture and Life Sciences
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• v.14
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• pp.61-65
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• 1996

This study was carried out to determine the effect of fruit-set position and number of fruit set per plant on yield and quality including netting in "Super VIP" melon cultivated in spring season. 1) In training system of one fruit per plant, index of netting was acceptable, regardless of the node position of fruit-set. The higher node order of fruit set was, the heavier, longer, bigger in diameter the fruit set was, and the thicker the flesh was. However, soluble suger content and storability was decreased with increased node order. 2) In training system of two fruit per plant, the higher the fruit-set position in node order was, the better the netting index, fruit quality including fruit weight, length, diameter, and thickness of flesh were, but there was no difference in storability. 3) Fruits produced by one-fruit-per-plant training were superior to those produced by two-fruit-per-plant training in maker quality including netting, sugar content, stcrability. The fruit set on 12th node in one-fruit-per-plant training were the best in market quality. 4) Yield was increased by two-fruit-per-plant training.

• Journal of Korean Academy of Nursing
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• v.16 no.1
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• pp.55-66
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• 1986

The effect on strength and endurance training (SET) (2 patients) were compared with those of strength training(ST) (2 patients) in patients with-chronic obstructive pulmonary disease. The result of training was assessed by 4 tests: maximal inspiratory pressure(PImax), sustainable inspiratory pressure (SIP), maximal voluntary ventiiation(MVV) and bronchitis-emphysema symptom checklist(BESC). Measurements were repeated before and after training per week for 6 weeks. The SET group performed inspiratory muscle training, using a inspiratory muscle trainer 30 minutes per day, 6 days per week and performing endurance training-12-minute walking-2 days per week for 6 weeks, whereas the ST-only group trained for 30 minutes daily, 6 days per week using inspiratory muscle trainer. SET was no significant increase in exercise performance, whereas ST produced an increase in SIP and a decrease in BESC. There was significant change in BESC betweet the two groups. A simple at home training program using inspiartory muscle trainer was more effective than that of SET program in improving exercise performance of some patients with COPD.h COPD.

• Journal of IKEEE
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• v.22 no.1
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• pp.14-20
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• 2018

The purpose of training a convolutional neural network (CNN) is to obtain weight factors that give high classification accuracies. The initial values of hyper-parameters affect the training results, and it is important to train a CNN with a suitable hyper-parameter set of a learning rate, a batch size, the initialization of weight factors, and an optimizer. We investigate the effects of a single hyper-parameter while others are fixed in order to obtain a hyper-parameter set that gives higher classification accuracies and requires shorter training time using a proposed VGG-like CNN for training since the VGG is widely used. The CNN is trained for four datasets of CIFAR10, CIFAR100, GTSRB and DSDL-DB. The effects of the normalization and the data transformation for datasets are also investigated, and a training scheme using merged datasets is proposed.

• The Journal of Information Technology and Database
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• v.2 no.2
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• pp.71-85
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• 1995

Many research results conducted by neural network researchers have claimed that the classification accuracy of neural networks is superior to, or at least equal to that of conventional methods. However, in series of neural network classifications, it was found that the classification accuracy strongly depends on the characteristics of training data set. Even though there are many research reports that the classification accuracy of neural networks can be different, depending on the composition and architecture of the networks, training algorithm, and test data set, very few research addressed the problem of classification accuracy when the basic assumption of data monotonicity is violated, In this research, development project of automated credit evaluation system is described. The finding was that arrangement of training data is critical to successful implementation of neural training to maintain monotonicity of the data set, for enhancing classification accuracy of neural networks.

• Near Infrared Analysis
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• v.1 no.2
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• pp.1-8
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• 2000

Using the net analyte signal, hybrid linear analysis was proposed to predict chemical concentration. In this paper, we select a sample from training set and apply orthogonal signal correction to obtain an improved pseudo unit spectrum for hybrid least analysis. using the mean spectrum of a calibration training set, we first show the calibration by hybrid least analysis is effective to the prediction of not only chemical concentrations but also physical property variables. Then, a pseudo unit spectrum from a training set is also tested with and without orthogonal signal correction. We use two data sets, one including five chemical concentrations and the other including ten physical property variables, to compare the performance of partial least squares and modified hybrid least analysis calibration methods. The results show that the hybrid least analysis with a selected training spectrum instead of well-measured pure spectrum still gives good performances, which is a little better than partial least squares.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.32B no.5
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• pp.747-755
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• 1995

When codebooks designed by a clustering algorithm using training sets, a time-averaged distortion, which is called the inside-training-set- distortion (ITSD), is usually calculated in each iteration of the algorithm, since the input probability function is unknown in general. The algorithm stops if the ITSD no more significantly decreases. Then, in order to test the trained codebook, the outside-training-set-distortion (OTSD) is to be calculated by a time-averaged approximation using the test set. Hence codebooks that yield small values of the OTSD are regarded as good codebooks. In other words, the calculation of the OTSD is a criterion to testing a trained codebook. But, such an argument is not always true if some conditions are not satisfied. Moreover, in order to obtain an approximation of the OTSD using the test set, it is known that a large test set is requared in general. But, large test set causes heavy calculation com0plexity. In this paper, from the analyses in [16], it has been revealed that the enough size of the test set is only the same as that of the codebook when codebook size is large. Then a simple method to testing trained codebooks is addressed. Experimental results on synthetic data and real images supporting the analysis are also provided and discussed.

• Journal of IKEEE
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• v.22 no.3
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• pp.816-821
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• 2018

Recently, machine learning algorithms based on artificial neural networks started to be used widely as classifiers in the field of EEG research for emotion analysis and disease diagnosis. When a machine learning model is used to classify EEG data, if training data is composed of only data having similar characteristics, classification performance may be deteriorated when applied to data of another group. In this paper, we propose a method to construct training data set by selecting several groups of data using semi-supervised learning algorithm to improve these problems. We then compared the performance of the two models by training the model with a training data set consisting of data with similar characteristics to the training data set constructed using the proposed method.

• Korean Journal of Remote Sensing
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• v.18 no.6
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• pp.369-376
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• 2002

This paper presents a method to enhance the performance of supervised classification by separating the spectral signature of the training data sets for each class. Using clustering technique, a training data set is divided into several subsets which show a pattern of the normal distribution with small value of spectral variances. Then a supervised classification is applied with the divided training data set as training data for the temporary subclasses of the original class. The proposed method is applied to a Landsat TM image of Busan area for the applicability test. The result shows that the proposed method produces better classified results than the conventional statistical classification methods. It is expected that the proposed method will reduce the effort and expense for selecting the training data set for each class in an area which has spectrally homogeneous signature.