• Journal of the Korean Society of Physical Medicine
• /
• v.4 no.1
• /
• pp.49-56
• /
• 2009

Purpose : The Purpose of this study was to investigate the effects of image training and vibration on performance of vertical jumping. Methods : Subjects was classified into two groups, which were image training group(n=20) and vibration application group(n=20). The standard methods of each intervention were image training with listening recorded indication for 5 minute and vibration with speed of $1200{\pm}200\;rpm$. Muscle strength was measured using vertical jump performance. Results : The vertical jump performance was significantly increased after image training and vibration application(p<.05), however, it was more significantly after image training(p<.05). Conclusion : This study showed that image training and vibration application were effective treatment strategy on increase of muscle strength. Therefore, it could be considered as a treatment method in the patients with musculoskeletal disease including fracture, chronic degenerative disease and disuse atrophy.

• The Journal of Korean Society for Neurotherapy
• /
• v.22 no.3
• /
• pp.7-10
• /
• 2018

Purpose The purpose of this study was to investigate the difference of brain activity during action observation training and image training throughout EEG. Methods This study was participated 1 healthy college student without mental illness or cognitive impairment. The subject was randomly selected from university students and was interested in participating in the experiment. The purpose of this study was to investigate the visual and auditory stimuli (action observation) and brain image training. Results The results of our study, EEG value measured o.1 during resting. But brain activity changed to 0.3 during action observation. Finally, it changed to .05 after brain image training. Conclusion EEG measurement results were showed that after watching the Ball squat video, Brain activity increased.

• International Commerce and Information Review
• /
• v.10 no.3
• /
• pp.41-57
• /
• 2008

This research studies the effects of pre-show promotion, at-show promotion, and booth staff training on the image-building and relationship improvement performance of exhibitors. To this purpose, we relate each performance dimension to tactical variables such as pre show promotion, at-show promotion, and booth staff training through related literature review and conduct empirical study on their relationship. The results of this study are as follows: (1) Pre-show promotion and booth staff training have positive influence on image-building and relationship improvement performance. (2) But, at-show promotion has no effect on image-building performance, and has a negative effect on relationship improvement performance. (3) Especially, pre-show promotion has the greatest effect on relationship improvement performance, and booth staff training has the greatest effect on image-building improvement.

• Journal of the Korean Applied Science and Technology
• /
• v.36 no.4
• /
• pp.1041-1049
• /
• 2019

This study gathered previous studies on the effects of image training in school physical education conducted in korea in order to investigate the average effect size as well as the factors that influence the effect sizes. This study connoted findings of individual studies related to image training in school physical education from 1995 to 2018. The results of this study were as follows: Firstly, the overall mean effect size of the image training in school physical education was large size(Cohen, 1988). Secondly, motor skills showed the large effect size than psychological variable. Thirdly, major factors that influence the effect of image training in school physical education appeared to be the type of motor learning, age, gender, training period, training frequency, training ime. Based on these findings, implications for future research and application of image training in school physical education were suggested.

• Journal of the Korea Convergence Society
• /
• v.8 no.11
• /
• pp.203-213
• /
• 2017

This study is to find the effectiveness of the invitational training in healthcare field of developing countries through grasping the change of the national image of Korea. To this end, we collected data on the satisfaction on the invitational training from 192 participants of short-term and long-term training programs conducted by the Korea Foundation for International Healthcare(KOFIH). T-test, ANOVA analysis and hierarchical regression analysis were conducted. The results of the analysis are as follows. First, the national image was enhanced after the invitational training. Second, satisfaction on contents of the training programs had a significant positive (+) effect on the national image. Third, English proficiency and nationality influenced national image significantly. According to this study, it was confirmed that the invitational training in healthcare was influential for enhancing the national image. To enhance the effectiveness of the invitational training, it is necessary to study the contents of the training program and the process of selecting the participants.

• International Journal of Contents
• /
• v.16 no.2
• /
• pp.8-18
• /
• 2020

Data is critical in deep learning but the scarcity of data often occurs in research, especially in the preparation of the paired training data. In this paper, document image binarization with unpaired data is studied by introducing adversarial learning, excluding the need for supervised or labeled datasets. However, the simple extension of the previous unpaired training to binarization inevitably leads to poor performance compared to paired data training. Thus, a new deep learning approach is proposed by introducing a multi-diversity of higher quality generated images. In this paper, a two-stage model is proposed that comprises the generative adversarial network (GAN) followed by the U-net network. In the first stage, the GAN uses the unpaired image data to create paired image data. With the second stage, the generated paired image data are passed through the U-net network for binarization. Thus, the trained U-net becomes the binarization model during the testing. The proposed model has been evaluated over the publicly available DIBCO dataset and it outperforms other techniques on unpaired training data. The paper shows the potential of using unpaired data for binarization, for the first time in the literature, which can be further improved to replace paired data training for binarization in the future.

• Journal of Korean Society for Geospatial Information Science
• /
• v.24 no.1
• /
• pp.121-128
• /
• 2016

Many studies are focused on image data and classifier for comparison or improvement of classification accuracy. Therefore studies are needed aspect of the training samples on supervised classification which depend on reference data or skill of analyst. This study tries to assess usability of field spectra as training samples on supervised classification. Classification accuracies of hyperspectral and multispectral images were assessed using training samples from image itself and field spectra, respectively. The results shown about 90% accuracy with training sample collected from image. Using field spectra as training sample, accuracy was decreased 10%p for hyperspectral image, and 20%p for multispectral image. Especially, some classes shown very low accuracies due to similar spectral characteristics on multispectral image. Therefore, field spectra might be used as training samples on classification of hyperspectral image, although it has limitation for multispectral image.

• Journal of Information Processing Systems
• /
• v.18 no.5
• /
• pp.701-710
• /
• 2022

With the development of artificial intelligence technology, various methods have been developed for recognizing objects in images using machine learning. Image segmentation is the most effective among these methods for recognizing objects within an image. Conventionally, image datasets of various classes are trained simultaneously. In situations where several classes require segmentation, all datasets have to be trained thoroughly. Such repeated training results in low training efficiency because most of the classes have already been trained. In addition, the number of classes that appear in the datasets affects training. Some classes appear in datasets in remarkably smaller numbers than others, and hence, the training errors will not be properly reflected when all the classes are trained simultaneously. Therefore, a new method that separates some classes from the dataset is proposed to improve efficiency during training. In addition, the accuracies of the conventional and proposed methods are compared.

• The Journal of the Acoustical Society of Korea
• /
• v.16 no.2
• /
• pp.58-65
• /
• 1997

A new automatic classification method with high and stable accuracy for time-series image data is presented in this paper. This method is based on prior condition that a classified map of the target area already exists, or at least one of the time-series image data had been classified. The classified map is used as a reference map to specify training areas of classification categories. The new automatic classification method consists of five steps, i.e., extraction of training data using reference map, detection of changed pixels based upon the homogeneity of training data, clustering of changed pixels, reconstruction of training data, and classification as like maximum likelihood classifier. In order to evaluate the performance of this method qualitatively, four time-series Landsat TM image data were classified by using this method and a conventional method which needs a skilled operator. As a results, we could get classified maps with high reliability and fast throughput, without a skilled operator.

• Journal of Multimedia Information System
• /
• v.9 no.2
• /
• pp.75-86
• /
• 2022

The existing image quality assessment (IQA) datasets have a small number of samples. Some methods based on transfer learning or data augmentation cannot make good use of image quality-related features. A No Reference (NR)-IQA method based on multi-task training and quality awareness is proposed. First, single or multiple distortion types and levels are imposed on the original image, and different strategies are used to augment different types of distortion datasets. With the idea of weak supervision, we use the Full Reference (FR)-IQA methods to obtain the pseudo-score label of the generated image. Then, we combine the classification information of the distortion type, level, and the information of the image quality score. The ResNet50 network is trained in the pre-train stage on the augmented dataset to obtain more quality-aware pre-training weights. Finally, the fine-tuning stage training is performed on the target IQA dataset using the quality-aware weights to predicate the final prediction score. Various experiments designed on the synthetic distortions and authentic distortions datasets (LIVE, CSIQ, TID2013, LIVEC, KonIQ-10K) prove that the proposed method can utilize the image quality-related features better than the method using only single-task training. The extracted quality-aware features improve the accuracy of the model.