• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.1
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• pp.247-256
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• 2013

This dissertation is based on the evaluation of service by the home help service users and suggested managing strategy through analyzing importance and performance. The subject is a person receiving the service from the three home long term institutions in S city, Gangwon province, and SPSS 12.0 is used to analyze data to conduct analysis of basic statistic, confidence level, and factors. The result came out that home help service's importance showed 4.55 out of 5, performance 4.26 out of 5. The most improvement needed factor was 'providing quick service' and 'offer service at ease'. Also it seemed that the four categories in tangibles and four categories in reliability and assurance, one category in responsiveness and empathy needs to be improved. The three in reliability and assurance and five properties in responsiveness and empathy need to remain its strength. In conclusion, in order to better the home help service first the progress result in focus improvement area needs to be achieved.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.5
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• pp.2587-2593
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• 2014

Ceramic Injection Molding (CIM) is one of wide used processes in industry field and the applications are gradually being expanded to parts of medical and electric devices. In this study, the CIM process were analyzed with FEM and process parameters were studied and analyzed the effect on product quality. The shape of simple flat plate was compared to the shapes with the hole, with the round corner portion or with the side wall portion. If there are holes then the hole around the uneven density distribution and the defects such as weld lines could be occurred. The Large radius of the corners of the product give good formability and fluidity. Not only the shape parameters of product but also the process parameters during CIM are studied. The simulation results showed that the process parameters of temperature, initial fractions and velocity are important design parameters to improve the quality of products.

• Journal of Korean Medical Science
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• v.33 no.43
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• pp.239.1-239.12
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• 2018

Background: We described a novel multi-step retinal fundus image reading system for providing high-quality large data for machine learning algorithms, and assessed the grader variability in the large-scale dataset generated with this system. Methods: A 5-step retinal fundus image reading tool was developed that rates image quality, presence of abnormality, findings with location information, diagnoses, and clinical significance. Each image was evaluated by 3 different graders. Agreements among graders for each decision were evaluated. Results: The 234,242 readings of 79,458 images were collected from 55 licensed ophthalmologists during 6 months. The 34,364 images were graded as abnormal by at-least one rater. Of these, all three raters agreed in 46.6% in abnormality, while 69.9% of the images were rated as abnormal by two or more raters. Agreement rate of at-least two raters on a certain finding was 26.7%-65.2%, and complete agreement rate of all-three raters was 5.7%-43.3%. As for diagnoses, agreement of at-least two raters was 35.6%-65.6%, and complete agreement rate was 11.0%-40.0%. Agreement of findings and diagnoses were higher when restricted to images with prior complete agreement on abnormality. Retinal/glaucoma specialists showed higher agreements on findings and diagnoses of their corresponding subspecialties. Conclusion: This novel reading tool for retinal fundus images generated a large-scale dataset with high level of information, which can be utilized in future development of machine learning-based algorithms for automated identification of abnormal conditions and clinical decision supporting system. These results emphasize the importance of addressing grader variability in algorithm developments.

• Asia-Pacific Journal of Business
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• v.11 no.3
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• pp.47-61
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• 2020

Purpose - This study investigates the relationship between firm-specific, general human capital and corporate innovation performance. Also, we examine when this relationship is more salient. Design/methodology/approach - We collected 1,195 survey data related to a sample of corporate innovation performance and human capital from 1) Korea Research for Vocational Education and Training and 2) NICE information service in Korea. In order to examine the corporate innovation performance, we use the ordered logit model. Findings - First, we find robust supports for our hypothesis that firm-specific and general human capital increase corporate innovation performance. Second, the effect of general human capital on corporate innovation performance is stronger when this relationship is combined with the firm international experience. Research implications or Originality - By integrating the human capital theory and corporate innovation literature, we propose that firm-specific and general human capital are the important determinant of innovation performance. The firm-specific human capital may increase innovation efficiencies. Also, retaining higher-quality general human capital is considered as an important innovation strategy since firms with higher-quality general human capital make greater innovation performance. Further, we show that the firm international experience is the crucial boundary condition. As a firm's experience in internationalization increases, firms can enhance the opportunities to develop new products by combining the skills and knowledge derived from general human capital with the experience gained through internationalization.

• Journal of the Korea Convergence Society
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• v.12 no.5
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• pp.1-7
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• 2021

In the latest trend of machine translation research, the model is pretrained through a large mono lingual corpus and then finetuned with a parallel corpus. Although many studies tend to increase the amount of data used in the pretraining stage, it is hard to say that the amount of data must be increased to improve machine translation performance. In this study, through an experiment based on the mBART model using parallel corpus filtering, we propose that high quality data can yield better machine translation performance, even utilizing smaller amount of data. We propose that it is important to consider the quality of data rather than the amount of data, and it can be used as a guideline for building a training corpus.

• Journal of Industrial Convergence
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• v.19 no.5
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• pp.91-102
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• 2021

The purpose of this study was to systematically review about randomized controlled trials the characteristics and effect of cognitive function intervention for patient with dementia. We searched studies published from January 2010 to June 2021 in 5 databases. A total 1,104 studies were found and included 27 studies in final analysis. Methodological quality was assessment with the Cochrane's RoB(risk of bias) tool. Mini-Mental State Examination(MMSE) was the most used as the assessment tool for identifying the cognitive function. Cognitive function intervention were exercise, art, cognitive stimulation, reminiscence, music, multimodal cognitive rehabilitation, virtual reality, horticultural, computerized cognitive training, intentional snoezelen, beauty, cooking, korean traditional familiarity program. Most of the intervention except exercise 2, virtual reality 1, beauty 1 were effective in improving cognitive function. This study provided a clinical evidence for planning and implementing intervention for cognitive function intervention. In the future, various intervention studies suitable for the characteristics of dementia should be conducted by improving the quality of research methods.

• Korean Journal of Agricultural Science
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• v.47 no.4
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• pp.1109-1122
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• 2020

The aim of this study was to develop a marbling classification and prediction model using small parts of sirloin images based on a deep learning algorithm, namely, a convolutional neural network (CNN). Samples were purchased from a commercial slaughterhouse in Korea, images for each grade were acquired, and the total images (n = 500) were assigned according to their grade number: 1++, 1+, 1, and both 2 & 3. The image acquisition system consists of a DSLR camera with a polarization filter to remove diffusive reflectance and two light sources (55 W). To correct the distorted original images, a radial correction algorithm was implemented. Color images of sirloins of Hanwoo (mixed with feeder cattle, steer, and calf) were divided and sub-images with image sizes of 161 × 161 were made to train the marbling prediction model. In this study, the convolutional neural network (CNN) has four convolution layers and yields prediction results in accordance with marbling grades (1++, 1+, 1, and 2&3). Every single layer uses a rectified linear unit (ReLU) function as an activation function and max-pooling is used for extracting the edge between fat and muscle and reducing the variance of the data. Prediction accuracy was measured using an accuracy and kappa coefficient from a confusion matrix. We summed the prediction of sub-images and determined the total average prediction accuracy. Training accuracy was 100% and the test accuracy was 86%, indicating comparably good performance using the CNN. This study provides classification potential for predicting the marbling grade using color images and a convolutional neural network algorithm.

• Journal of Korean Physical Therapy Science
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• v.28 no.1
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• pp.33-45
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• 2021

Background: Children with cerebral palsy have difficulty acquiring motor skills through motor learning due to lack of motor planning of the central nervous system and musculoskeletal dysfunction. Motor learning is the acquisition or modification of movements with the aim of developing skilled movements and behaviors. Cerebral palsy improve motor function through motor learning, and effective motor learning mainly depends on practice parameters such as learning feedback. Therefore, we investigate the effect of motor learning in children with cerebral palsy and try to present the possibility of clinical application. Design: A systemic review. Methods: Research papers were published from Jan, 2010 to Dec, 2020 and were searched using PubMed and Medline. The search terms are 'task specific training' OR 'motor learning' OR 'feedback(Mesh term)' OR 'goal activity' AND 'cerebral palsy(Mesh term)'. A total of eight papers were analyzed in this study. The paper presented the quality level based on the research evidence, and also presented PEDro (Physiotherapy Evidence Database) scores to evaluate the quality of design studies in randomized clinical trials. Results: The results showed that motor learning coaching in children with cerebral palsy improved motor function in post and follow up tests. Also, self-control feedback of motor learning is more effective than external control feedback. 100% external control feedback of motor learning is effective in the acquisition phase and 50% external feedback of motor learning is effective in the retain phase. Conclusion: These results suggest that it will be an important data for establishing evidence on the effect of motor learning arbitration methods in children with cerebral palsy to develop clinical applicability and protocols.

• International Journal of Computer Science & Network Security
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• v.21 no.2
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• pp.148-157
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• 2021

The effectiveness of recommendation systems depends on the performance of the algorithms with which these systems are designed. The quality of the algorithms themselves depends on the quality of the strategies with which they were designed. These strategies differ from author to author. Thus, designing a good recommendation system means implementing the good strategies. It's in this context that several research works have been proposed on various strategies applied to algorithms to meet the needs of recommendations. Researchers are trying indefinitely to address this objective of seeking the qualities of recommendation algorithms. In this paper, we propose a new algorithm for recommending learning items. Learner performance predictions and collaborative recommendation methods are used as strategies for this algorithm. The proposed performance prediction model is based on convolutional neural networks (CNN). The results of the performance predictions are used by the proposed recommendation algorithm. The results of the predictions obtained show the efficiency of Deep Learning compared to the k-nearest neighbor (k-NN) algorithm. The proposed recommendation algorithm improves the recommendations of the learners' learning items. This algorithm also has the particularity of dissuading learning items in the learner's profile that are deemed inadequate for his or her training.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.5
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• pp.1814-1828
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• 2021

Low-light image enhancement is a key technique to overcome the quality degradation of photos taken under scotopic vision illumination conditions. The degradation includes low brightness, low contrast, and outstanding noise, which would seriously affect the vision of the human eye recognition ability and subsequent image processing. In this paper, we propose an approach based on deep learning and Retinex theory to enhance the low-light image, which includes image decomposition, illumination prediction, image reconstruction, and image optimization. The first three parts can reconstruct the enhanced image that suffers from low-resolution. To reduce the noise of the enhanced image and improve the image quality, a super-resolution algorithm based on the Laplacian pyramid network is introduced to optimize the image. The Laplacian pyramid network can improve the resolution of the enhanced image through multiple feature extraction and deconvolution operations. Furthermore, a combination loss function is explored in the network training stage to improve the efficiency of the algorithm. Extensive experiments and comprehensive evaluations demonstrate the strength of the proposed method, the result is closer to the real-world scene in lightness, color, and details. Besides, experiments also demonstrate that the proposed method with the single low-light image can achieve the same effect as multi-exposure image fusion algorithm and no ghost is introduced.