• Journal of Animal Science and Technology
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• v.56 no.4
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• pp.15.1-15.10
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• 2014

Background: Morphologically classifying embryos is important for numerous laboratory techniques, which range from basic methods to methods for assisted reproduction. However, the standard method currently used for classification is subjective and depends on an embryologist's prior training. Thus, our work was aimed at developing software to classify morphological quality for blastocysts based on digital images. Methods: The developed methodology is suitable for the assistance of the embryologist on the task of analyzing blastocysts. The software uses artificial neural network techniques as a machine learning technique. These networks analyze both visual variables extracted from an image and biological features for an embryo. Results: After the training process the final accuracy of the system using this method was 95%. To aid the end-users in operating this system, we developed a graphical user interface that can be used to produce a quality assessment based on a previously trained artificial neural network. Conclusions: This process has a high potential for applicability because it can be adapted to additional species with greater economic appeal (human beings and cattle). Based on an objective assessment (without personal bias from the embryologist) and with high reproducibility between samples or different clinics and laboratories, this method will facilitate such classification in the future as an alternative practice for assessing embryo morphologies.

• PNF and Movement
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• v.16 no.1
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• pp.19-26
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• 2018

Purpose: The purpose of this study was to investigate changes in urinary frequency, residual urine volume, and quality of life following pelvic floor exercises using proprioceptive neuromuscular facilitation (PNF) exercise patterns and EMG biofeedback training in patients with stress urinary incontinence. Methods: The subjects were male patients diagnosed with stress urinary incontinence. This study used a single system design (A-B-C.) At baseline, the patients' symptoms prior to the treatment intervention were recorded (A section). Next, the patients performed the PNF exercise (B section). Thereafter, they performed the PNF exercise, with EMG biofeedback (C section). The subjects performed the exercises in each section for 1 week for a total of 3 weeks. Urinary frequency, residual urine volume, and quality of life of the subjects were measured. Results: The frequency of urination was 9 times in A, 8 times in A 'and B, and 7 times in C. The amount of residual urine decreased from 23.78ml in A to 21.85ml in A ', 14.85ml in B, and 14.63ml in C. The international prostate symptom score was 16 points in A, 14 points in A ', 11 points in B, and 7 points at A. The quality of life score was 4 points in A, 4 points in A ', 3 points in B, and 2 points in A. Conclusion: Both the PNF exercise and EMG biofeedback decreased urination frequency and residual urine volume and improved the quality of life of patients with stress urinary incontinence. EMG biofeedback training using the PNF technique was the most effective.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.10a
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• pp.575-577
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• 2013

The research was aimed at the hotel industry's employees in order to test the efficiency of e-Learning, which is emerging as the alternative training system to the conventional one. The independent variables are the quality of e-Learning, including the qualities of the system, contents, and service of e-Learning, and the learning characteristic factor, including the quality factor of e-Learning, the self-efficacy of the user, learning motivation, and the flow of learning. Furthermore, the intervening variables are its perceived usefulness and the satisfaction factor of the user known as the so-called utility of e-Learning, continuous intention to use in terms of efficaciousness, and the spread of education and training. The dependent variable is customer orientation, known as the ultimate efficaciousness of corporate e-Learning.

• Physical Therapy Rehabilitation Science
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• v.8 no.3
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• pp.134-140
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• 2019

Objective: The purpose of this study was to investigate the effect of exercise therapy and bedside ergometer exercise on muscle strength, function level, and quality of life of persons in intensive care. Design: Randomized Controlled Trial Methods: Sixteen patients in the ICU were randomly assigned to either the exercise group (n=8) or the bedside cycle ergometer group (n=8). Activities in the ICU exercise group (rolling, sitting at the edge of the bed, transfer from sitting to standing, standing balance training, ambulation) and bedside cycle ergometer group were performed 5 times a week for 30 minutes during the ICU admission period. Medical Research Council (MRC) and Functional Status Scale-Intensive Care Unit (FSS-ICU) parameters were assessed at the time of admission to the ICU, and reevaluation was assessed on the day of ICU discharge. The Short Form-36 (SF-36) was assessed at the time of discharge from the ICU. Results: MRC and FSS-ICU were significantly increased before and after intervention in both the experimental and control groups (p<0.05). There was a significant difference between MRC and FSS-ICU in the comparison of the changes before and after the intervention (p<0.05). SF-36 was compared between groups after intervention and there was a significant difference between the experimental and the control group (p<0.05). Conclusions: Muscle strength and functional levels improved after intervention in both the experimental and control groups. The ICU exercise group was more effective than the bedside cycle ergometer group to improve muscle strength, functional level, and quality of life performance of persons in the ICU.

• International conference on construction engineering and project management
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• 2024.07a
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• pp.311-318
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• 2024

Quality control in construction projects necessitates the detection of defects during construction. Currently, this task is performed manually by site supervisors. This manual process is inefficient, labor-intensive, and prone to human error, potentially leading to decreased productivity. To address this issue, research has been conducted to automate defect detection using computer vision-based object detection technologies. However, these studies often suffer from a lack of data for training deep learning models, resulting in inadequate accuracy. This study proposes a method to improve the accuracy of deep learning models through the use of virtual image data. The target building is created as a 3D model and finished with materials similar to actual components. Subsequently, a virtual defect texture is produced by layering three types of images: defect information, area information, and material information images, to fabricate materials with defects. Images are generated by rendering the 3D model and the defect, and annotations are created for segmentation. This approach creates a hybrid dataset by combining virtual data with actual site image data, which is then used to train the deep learning model. This research was conducted on the tile process of finishing construction projects, focusing on cracks and falls as the target defects. The training results of the deep learning model show that the F1-Score increased by 12.08% for falls and cracks when using the hybrid dataset compared to the real image dataset alone, validating the hybrid data approach. This study contributes not only to unmanned and automated smart construction management but also to enhancing safety on construction sites. To establish an integrated smart quality management system, it is necessary to detect various defects simultaneously with high accuracy. Utilizing this method for automatic defect detection in other types of construction can potentially expand the possibilities for implementing an integrated smart quality management system.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.9 no.1
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• pp.153-164
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• 2014

This study is to investigate the effects of intangible assets such as research & development, education & training and advertisement on firm values of high-technology firms and low-technology firms listed in the KOSDAQ market, and to analyze the value-relativeness between the audit quality of companies and the expenditure of intangible assets. The substitute measurement of firm values is Tobin's Q model. The sample period for positive analysis is from 2003 to 2008, and the samples, excepting for financial business, are manufacturing companies of closing accounts corporate on December, based on companies of KOSDAQ that are listed in security. Finally, data from about 305 companies are used in this analysis. Followings are the results of the analysis. First, research & development, education & training of high-technology firms have an effect on firm values, and education & training of low-technology have an effect on firm values. Second, we find that audit quality(BIG4) increases the value relevance of R&D expenditures of high-technology firms and audit quality(BIG4) increases the value relevance of education & training expenditures of low-technology firms. This paper is meaningful in that it verified the value-relativeness of cost of intangible assets compared with high-technology firms to low-technology firms.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.5
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• pp.1838-1844
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• 2010

In distance education, contents are to convey what to learn to learners, and the efficient quality assurance of contents is the very first step to the enhancement of distance education. Most studies of the quality assurance of contents have mostly centered around the development of evaluation tools, and few studies have ever focused on analysis of the quality of contents itself, since it's not easy to do that due to difficulties in the selection of evaluatees or of contents to be analyzed. The purpose of this study was to analyze the quality of 58 distance education contents of on-the-job training and another training for the acquisition of qualifications. As a result, the contents of the learning contents segment ranked first. Among the components of each segment, there was room for improvement in the level of learning and learning elements in the learning contents segment. In terms of instructional design, the quality of interaction components should be taken to another level to boost the quality of contents in this segment. The findings of the study are expected to give some suggestions about which parts of contents should be improved in quality from a perspective of contents developers or suppliers to enhance the overall quality of contents.

• International Journal of Quality Innovation
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• v.6 no.1
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• pp.43-57
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• 2005

Action learning motivates students to solve open-ended problems by 'developing skills through doing'. This paper reviews the concept of action learning and discusses the adoption of action learning approach to teach operations management at universities. It presents the design and delivery of an action-learning course at City University of Hong Kong. The course incorporates classroom lectures, tutorials and an action-learning workshop. The experience gained proves that action learning facilitates student participation and teamwork and provides a venue of accelerating learning where enables students to handle dynamic problem situations more effectively. The paper concludes that adopting action-learning approach can help lecturers to enhance quality teaching in operations management courses, and provide an alternate means of effective paradigm other than traditional classroom teaching and/or computer-based training at universities.

• Proceedings of the KSR Conference
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• 2007.11a
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• pp.172-178
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• 2007

The purpose of Railroad Vehicles License Management in accordance with the Railroad Safety Law is to improve the quality of railroad safety. And the train simulators come into use for training of train operation and testing to issue Railroad Vehicles License. So, It is very important that the simulators for the Railroad Vehicles License Test require to work correctly, to measure driver's operation quality in numerical system and to ensure the justice of license test. In fact, nobody can guarantee that the simulators are feasible performance to use in license test. The simulator for train driver license test can be used effectively for studying of needed performance and quality to test new driver's skill. This study hopefully provides the base for improving the quality of the simulator for train driver's license test and contributes furthermore to improve the simulator development technology and railroad safety.

• Journal of Environmental Science International
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• v.11 no.3
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• pp.201-207
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• 2002

This study was carried out to evaluate the artificial neural network algorithm for water quality forecasting in Chungju lake, north Chungcheong province. Multi-layer perceptron(MLP) was used to train artificial neural networks. MLP was composed of one input layer, two hidden layers and one output layer. Transfer functions of the hidden layer were sigmoid and linear function. The number of node in the hidden layer was decided by trial and error method. It showed that appropriate node number in the hidden layer is 10 for pH training, 15 for DO and BOD, respectively. Reliability index was used to verify for the forecasting power. Considering some outlying data, artificial neural network fitted well between actual water quality data and computed data by artificial neural networks.