• Physical Therapy Korea
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• v.28 no.2
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• pp.154-160
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• 2021

Background: Theoretically, balance is affected by the height of center of mass (COM) during quiet standing. However, no one examined this in humans with variables derived from the center of pressure (COP). Objects: We have conducted balance experiment to measure COP data during quiet standing, in order to examine how the COP measures were affected by the height of COM, vision, floor conditions, and gender. Methods: Twenty individuals stood still with feet together and arms at sides for 30 seconds on a force plate. Trials were acquired with three COM heights: 1% increased or decreased, and not changed, with two vision conditions: eyes closed (EC) and eyes open (EO), and with two floor conditions: unstable (foam pad) and stable (force plate) floor. Outcome variables included the mean distance, root mean square distance, total excursion, mean velocity, and 95% confidence circle area. Results: All outcome variables were associated with the COM height (p < 0.0005), vision (p < 0.0005), and floor condition (p < 0.003). The mean velocity and 95% confidence circle area were 5.7% and 21.8% greater, respectively, in raised COM than in lowered COM (24.6 versus 23.2 mm/s; 1,013.4 versus 832.3 mm²). However, there were no interactions between the COM height and vision condition (p > 0.096), and between the COM height and floor condition (p > 0.183) for all outcome variables. Furthermore, there was no gender difference in all outcome variables (p > 0.186). Conclusion: Balance was affected by the change of COM height induced by a weight belt in human. However, the effect was not affected by vision or floor condition. Our results should inform the design of balance exercise program to improve the outcome of the balance training.

• The Journal of Korean Institute for Practical Engineering Education
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• v.4 no.1
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• pp.146-155
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• 2012

This research was conducted as a part of research project entitled 'A Fact-finding Survey of Management of Vocational Training Institutes and the Development of Training Program Model for their CEO'. The purpose of this paper was to investigate developing strategies for vocational training institutes in three sector; public institute, private institute and private academy for life long education and to develop practical and professional programs based on competencies model of CEO. The major subjects of this paper were developing strategies of three type of vocational training institutes, exploration of competencies of CEO, and training program for CEO. Delphi method was applied two times. The panel consists of 30 experts who relate to vacational training. The panel of experts emphasized the different own mission and function among three type of vocational institutes. Public institutes support the government policy and private institutes and academy. Private institutes develop specialized training programs that reflect the regional demand. Private academies focus on short-term service training. To recognize changing vocational training policy, to develop vision of institute, ethical mind and sense of mission and so on are presented as competencies of CEO.

• Journal of Korea Multimedia Society
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• v.22 no.12
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• pp.1347-1355
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• 2019

Deep learning approach based on convolution neural network (CNN) has extensively studied in the field of computer vision. However, periocular feature extraction using CNN was not well studied because it is practically impossible to collect large volume of biometric data. This study uses the ResNet model which was trained with the ImageNet dataset. To overcome the problem of insufficient training data, we focused on the training of multi-layer perception (MLP) having simple structure rather than training the CNN having complex structure. It first extracts features using the pretrained ResNet model and reduces the feature dimension by principle component analysis (PCA), then trains a MLP classifier. Experimental results with the public periocular dataset UBIPr show that the proposed method is effective in person authentication using periocular region. Especially it has the advantage which can be directly applied for other biometric traits.

• Journal of Electrical Engineering and Technology
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• v.10 no.4
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• pp.1910-1914
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• 2015

Human posture recognition is an attractive and challenging topic in computer vision due to its promising applications in the areas of personal health care, environmental awareness, human-computer-interaction and surveillance systems. Human posture recognition in video sequences consists of two stages: the first stage is training and evaluation and the second is deployment. In the first stage, the system is trained and evaluated using datasets of human postures to ‘teach’ the system to classify human postures for any future inputs. When the training and evaluation process is deemed satisfactory as measured by recognition rates, the trained system is then deployed to recognize human postures in any input video sequence. Different classifiers were used in the training such as Multilayer Perceptron Feedforward Neural networks, Self-Organizing Maps, Fuzzy C Means and K Means. Results show that supervised learning classifiers tend to perform better than unsupervised classifiers for the case of human posture recognition.

• The Journal of Korea Robotics Society
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• v.14 no.2
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• pp.114-121
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• 2019

This paper presents a new benchmark system for visual odometry (VO) and monocular depth estimation (MDE). As deep learning has become a key technology in computer vision, many researchers are trying to apply deep learning to VO and MDE. Just a couple of years ago, they were independently studied in a supervised way, but now they are coupled and trained together in an unsupervised way. However, before designing fancy models and losses, we have to customize datasets to use them for training and testing. After training, the model has to be compared with the existing models, which is also a huge burden. The benchmark provides input dataset ready-to-use for VO and MDE research in 'tfrecords' format and output dataset that includes model checkpoints and inference results of the existing models. It also provides various tools for data formatting, training, and evaluation. In the experiments, the exsiting models were evaluated to verify their performances presented in the corresponding papers and we found that the evaluation result is inferior to the presented performances.

• Journal of Biosystems Engineering
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• v.17 no.1
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• pp.65-78
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• 1992

When using a computer vision system for a measurement, the geometrically distorted input image usually restricts the site and size of the measuring window. A geometrically distorted image caused by the image sensing and processing hardware degrades the accuracy of the visual measurement and prohibits the arbitrary selection of the measuring scope. Therefore, an image calibration is inevitable to improve the measuring accuracy. A calibration process is usually done via four steps such as measurement, modeling, parameter estimation, and compensation. In this paper, the efficient error calibration technique of a geometrically distorted input image was developed using a neural network. After calibrating a unit pixel, the distorted image was compensated by training CMLAN(Cerebellar Model Linear Associator Network) without modeling the behavior of any system element. The input/output training pairs for the network was obtained by processing the image of the devised sampled pattern. The generalization property of the network successfully compensates the distortion errors of the untrained arbitrary pixel points on the image space. The error convergence of the trained network with respect to the network control parameters were also presented. The compensated image through the network was then post processed using a simple DDA(Digital Differential Analyzer) to avoid the pixel disconnectivity. The compensation effect was verified using known sized geometric primitives. A way to extract directly a real scaled geometric quantity of the object from the 8-directional chain coding was also devised and coded. Since the developed calibration algorithm does not require any knowledge of modeling system elements and estimating parameters, it can be applied simply to any image processing system. Furthermore, it efficiently enhances the measurement accuracy and allows the arbitrary sizing and locating of the measuring window. The applied and developed algorithms were coded as a menu driven way using MS-C language Ver. 6.0, PC VISION PLUS library functions, and VGA graphic functions.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.6
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• pp.2190-2195
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• 2010

In order to examine whether the accommodative trainings with push-up or flipper methods induce the decrement of near addition on early presbyopia aged in 40s having less than 1.00 D addition, daily home vision training was performed for 12 weeks. We evaluated accommodative amplitude, accommodative facility, relative accommodation, and presbyopic addition at every one week. Two accommodative trainings significantly decreased the presbyopic addition as a degree from 0.125 D to 0.375 D, and push-up training was more effective than that of flipper training. Both push-up and flipper trainings are an useful methods to decrease the near addition of early presbyopia as an improvement of the accommodative amplitude.

• The Journal of Korean Academic Society of Nursing Education
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• v.14 no.1
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• pp.108-116
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• 2008

Purpose: The purpose of this study was to identify the experiences of Team-Spirit training in new nurses. Method: Data was collected through open-ended and self-reported questionnaires which were received from 47 new nurses who had finished team-spirit training for 2 days. The content analysis method was used to derive the core-category, categories and concepts of Team-Spirit training for new nurses. Result: The care category identified in new nurses trained in Team-Spirit was upgrade myself and our team. The following 4 categories also emerged; companion, interdependency, importance of community, and future growth of myself and our community. The derived 12 subcategories were intimacy, importance of companions, binding, partaking in difficulties, empowerment, observing rules, cooperation, consensus, self-pledge as a subordinator, motivating vision formation, developing professionalism, contribution and devotion. The 34 concepts were derived from the new nurses' statements. Conclusion: These results imply that Team-Spirit Training for nurses could contribute to companionship, interdependency, importance of community, and future growth of oneself and the hospital team.

• Journal of Korean Academy of Nursing Administration
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• v.11 no.1
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• pp.1-12
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• 2005

Purpose: The purpose was to analyze the effects of internal marketing activity factors on nurse's turnover intention, nursing task performance and nursing productivity. Methods: The subjects were 355 nurses who were working at the 3 universities hospital over 1 year. The instruments were used of internal marketing activity factors(Lee, 2001), turnover intension(Lee, 1995), nursing task performance(Park, 1988) and nursing productivity(McNeese-Smith, 1996). Results: The mean score of internal marketing activity factors was 2.79, education and training 2.97, individualization 2.93, communication 2.87, promotion 2.76, work environment 2.63, reward system 2.62, and management vision for employee 2.61. The turnover intention was 3.12, nursing task performance 3.49, and nursing productivity 3.38. The internal marketing activity factors were negatively correlated with turnover intention(r=-0.37, p<0.0001), and positively correlated with nursing task performance(r=0.29, p<0.0001) and nursing productivity(r=0.30, p<0.0001). The key predictor of turnover intension was reward system, education and training, communication, and salary. They explained 35.0% of the total variance. In nursing task performance, communication, management vision for employee, salary and unit explained 26.0% of the total variance. In nursing productivity, communication, reward, education and training, salary, and position explained 24.0%. Conclusions: To increase nurse's nursing task performance and nursing productivity and to decrease turnover intention, it is necessary to concentrate on improving communication and reward system in the internal marketing activity factors. Through these activities, the effectiveness of internal marketing strategies will be enhanced and finally, nursing organizational outcome will be increased.

• Journal of the Korea Society of Computer and Information
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• v.19 no.10
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• pp.35-42
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• 2014

It is possible to improve the obstacle avoidance capability by training and recognizing the obstacles which is in certain indoor environment. We propose the technique that use underlying intensity value along with intensity map from RGB-D image which is derived from stereo vision Kinect sensor and recognize an obstacle within constant distance. We test and experiment the accuracy and execution time of the pattern recognition algorithms like PCA, ICA, LDA, SVM to show the recognition possibility of it. From the comparison experiment between RGB-D data and intensity data, RGB-D data got 4.2% better accuracy rate than intensity data but intensity data got 29% and 31% faster than RGB-D in terms of training time and intensity data got 70% and 33% faster than RGB-D in terms of testing time for LDA and SVM, respectively. So, LDA, SVM have good accuracy and better training/testing time to use for obstacle avoidance based on intensity dataset of mobile robot.