• KIPS Transactions on Software and Data Engineering
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• v.10 no.11
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• pp.439-448
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• 2021

Driver negligence accounts for the largest proportion of the causes of traffic accidents, and research to detect them is continuously being conducted. This paper proposes a method to accurately detect a distracted driver and localize the most characteristic parts of the driver. The proposed method hierarchically constructs a CNN basic model that classifies 10 classes based on CAM in order to detect driver distration and 4 subclass models for detailed classification of classes having a confusing or common feature area in this model. The classification result output from each model can be considered as a new feature indicating the degree of matching with the CNN feature maps, and the accuracy of classification is improved by horizontally combining and learning them. In addition, by combining the heat map results reflecting the classification results of the basic and detailed classification models, the characteristic areas of attention in the image are found. The proposed method obtained an accuracy of 95.14% in an experiment using the State Farm data set, which is 2.94% higher than the 92.2%, which is the highest accuracy among the results using this data set. Also, it was confirmed by the experiment that more meaningful and accurate attention areas were found than the results of the attention area found when only the basic model was used.

• Korean Journal of Remote Sensing
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• v.38 no.5_1
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• pp.535-543
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• 2022

A drone is used to diagnose crop growth and to provide information through images in the agriculture field. In the case of using high spatial resolution drone images, growth information for each object can be produced. However, accurate object detection is required and adjacent objects should be efficiently classified. The purpose of this study is to develop a Chinese cabbage object detection algorithm using multispectral reflectance images observed from drone and computer vision techniques. Drone images were captured between 7 and 15 days after planting a Chinese cabbage from 2018 to 2020 years. The thresholds of object detection algorithm were set based on 2019 year, and the algorithm was evaluated based on images in 2018 and 2019 years. The vegetation area was classified using the characteristics of spectral reflectance. Then, morphology techniques such as dilatation, erosion, and image segmentation by considering the size of the object were applied to improve the object detection accuracy in the vegetation area. The precision of the developed object detection algorithm was over 95.19%, and the recall and accuracy were over 95.4% and 93.68%, respectively. The F1-Score of the algorithm was over 0.967 for 2 years. The location information about the center of the Chinese cabbage object extracted using the developed algorithm will be used as data to provide decision-making information during the growing season of crops.

• Journal of the Korean Society of Earth Science Education
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• v.14 no.1
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• pp.33-47
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• 2021

This study developed a big data-based STEAM (Science, Technology, Engineering, Art & Mathematics) program using WWT (World Wide Telescope), focusing on content elements of 'solar system', 'star and universe' in the 2015 revised science curriculum, and in order to find out the effectiveness of the STEAM program, analyzed creative problem solving, STEAM attitude, and STEAM satisfaction by applying it to one middle school 176 students simple random sampled. The results of this study are as follows. First, we developed a program to encourage students to actively and voluntarily participating, utilizing the astronomical data platform WWT. Second, in the paired t-test based on the difference between the pre- and post-scores of the creative problem solving measurement test, significant statistical test results were shown in 'idea adaptation', 'imaging', 'analogy', 'idea production' and 'elaboration' sub-factors except 'attention task' sub-factor (p < .05). Third, in the paired t-test based on the difference between the pre- and post-scores of the STEAM attitude test, significant statistical test results were shown in 'interest', 'communication', 'self-concept', 'self-efficacy' and 'science and engineering career choice' sub-factors except 'consideration' and 'usefulness / value recognition' sub-factors (p < .05). Fourth, in the STEAM satisfaction test conducted after class application, the average values of sub-factors were 3.16~3.90. The results indicated that students' understanding and interest in the science subject improved significantly through the big data-based STEAM program using the WWT.

• Korean Association of Arts Management
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• no.56
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• pp.119-144
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• 2020

Not only the government, but private corporations have contributed a lot to growth and development of culture. Corporations have mainly made charitable, dispensational Macenat activities which are separate from their business activities. Such an one-sided and charitable method of supporting culture cannot last long - Part of the reason why the number of corporations supporting culture is decreasing lies in it. In order to have sustainable partnership with culture, first, corporations should figure out needs of the other party. Second, the activities of corporations to support culture should be corporate citizenship activities which are linked to their business activities. In particular, the existing concepts of CSR and CSV have some limits. CSR separates business activities of corporations and their social contribution activities, and CSV mainly assumes corporate social activities helpful to their business activities. But, the concept of corporate citizenship suggested in this study assumes corporate activities where corporations do their best not only in their business activities, but in solution of social problems. Accordingly, searching for the ways to practice corporate citizenship, this study analyzed global agendas of UN, UNESCO, and UCLG which suggest sustainable development with culture and corporate citizenship activities related with culture among corporations in Korea and other countries. The findings and hints of the analysis are as follows. First, corporate citizenship activities can contribute to building of unique images of corporations and improvement of brand identities. Second, such activities can help corporations to be born again as life style companies by using local cultures and their attractiveness. Third, corporations should have partnership with cultural associations creating shared values and provide them with continuous and stable support. And, cultural associations should try to grow with corporations through efforts to develop attractive contents and programs harmonious with management purposes of corporations.

• Korean Association of Arts Management
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• no.54
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• pp.33-57
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• 2020

This study aimed to empirically analyze the effect of the appreciation of artwork in the workplace on creativity. To this end, two virtual workspace images with and without artwork were created, and an online survey was conducted with office workers. A regression analysis was performed on the results to investigate whether and how much the appreciation and recognition of artwork was effective for the creativity. As a result, among the factors of recognition according to the appreciation of artwork, "intellectual development" and "thinking" showed positive effects on the five sub-factors of creativity at work, such as original flexibility, alternative problem-solving skills, pursuit of adventure and freedom, individual independence, and exploratory immersion. Unlike most previous studies, however, "understanding" had a negative effect on original flexibility. In conclusion, it was found that some of the factors of the appreciation and recognition of artwork had a positive effect on creativity at work. This study provides implications that the appreciation of artwork in the workplace is effective for improving creativity at work and that it is important for each company to develop a streamlined approach based on its goal of pursuing a creative environment. In addition, it is expected that this study will contribute to the widespread use of artwork sharing services at workplaces as well as encouraging more empirical studies to be done on the effect of the services.

• Journal of Korean Home Economics Education Association
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• v.34 no.1
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• pp.1-16
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• 2022

This study was performed to develop educational materials in the form of card news that can be easily accessed on mobile phones or the Internet for milk intake education of the elderly based on the scientific evidence and their needs. The themes included in the card news were selected based on the literature and focus group interviews with 10 elderly individuals (78.10±6.66 years old). For the selected themes, information that elderly users most want to know was selected for the purpose of effective communication, while reflecting the eating habits, lifestyle, living environment, and nutrition and health status of the elderly in Korea. The draft of the card news was reviewed by the researcher, consulted by experts, and surveyed with 50 elderly individuals (70.44±5.16 years old). Based on the results of the review, consultations, and the survey, a final draft of the card news consisting of 12 pages was completed. The card news of the present study is expected to be an effective educational material considering the high level of satisfaction (higher than 4 on the 5-point scales) indicated by the survey respondents. Therefore this card news is expected to help increase milk intake through friendly milk education for the elderly.

• Therapeutic Science for Rehabilitation
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• v.11 no.4
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• pp.23-39
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• 2022

Objective : To summarize clinical and demographic variables and machine learning uses for predicting functional outcomes of patients with stroke. Methods : We searched PubMed, CINAHL and Web of Science to identify published articles from 2010 to 2021. The search terms were "machine learning OR data mining AND stroke AND function OR prediction OR/AND rehabilitation". Articles exclusively using brain imaging techniques, deep learning method and articles without available full text were excluded in this study. Results : Nine articles were selected for this study. Support vector machines (19.05%) and random forests (19.05%) were two most frequently used machine learning models. Five articles (55.56%) demonstrated that the impact of patient initial and/or discharge assessment scores such as modified ranking scale (mRS) or functional independence measure (FIM) on stroke patients' functional outcomes was higher than their clinical characteristics. Conclusions : This study showed that patient initial and/or discharge assessment scores such as mRS or FIM could influence their functional outcomes more than their clinical characteristics. Evaluating and reviewing initial and or discharge functional outcomes of patients with stroke might be required to develop the optimal therapeutic interventions to enhance functional outcomes of patients with stroke.

• Journal of the Korea Society of Computer and Information
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• v.27 no.12
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• pp.29-40
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• 2022

In this paper, we studied a system that detects and analyzes the pathological features of diabetic retinopathy using Mask R-CNN and a Random Forest classifier. Those are one of the deep learning techniques and automatically diagnoses diabetic retinopathy. Diabetic retinopathy can be diagnosed through fundus images taken with special equipment. Brightness, color tone, and contrast may vary depending on the device. Research and development of an automatic diagnosis system using artificial intelligence to help ophthalmologists make medical judgments possible. This system detects pathological features such as microvascular perfusion and retinal hemorrhage using the Mask R-CNN technique. It also diagnoses normal and abnormal conditions of the eye by using a Random Forest classifier after pre-processing. In order to improve the detection performance of the Mask R-CNN algorithm, image augmentation was performed and learning procedure was conducted. Dice similarity coefficients and mean accuracy were used as evaluation indicators to measure detection accuracy. The Faster R-CNN method was used as a control group, and the detection performance of the Mask R-CNN method through this study showed an average of 90% accuracy through Dice coefficients. In the case of mean accuracy it showed 91% accuracy. When diabetic retinopathy was diagnosed by learning a Random Forest classifier based on the detected pathological symptoms, the accuracy was 99%.

• Journal of Industrial Convergence
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• v.20 no.10
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• pp.157-167
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• 2022

The purpose of this study is to verify the educational effectiveness of interaction design in mobile APP by observing the impact of interaction design for elementary school education on enhancing learning immersion and continuous use intention, and propose an interaction design scheme based on elementary school education APP. The research methods are literature research and questionnaire survey. Specifically, through the literature research method, the concepts and prior studies on the concept, reviews the continuous use intention and previous research of interaction design. Then, conducts a questionnaire survey on elementary school students in South Korea and China to understand the interaction design, learning immersion, and continuous use intention, and analyzes the relationship between variables.The research result of this study is to observe the influence of interaction design elements within interaction on learning immersion and continuous use intention with elementary school students who are users of elementary school education application as the objects. The results show that interaction design within interaction has a positive impact on improving learning immersion and continuous use intention. It can be thought that this is because in mathematics/science education, it is easy to understand theoretical concepts or explanations, and stories and images will be continued at each stage to help students learn without being bored.In conclusion, this study can confirm that interactive inline design has a positive effect of enabling learners to engage in learning and continue to use.

• Journal of Korea Society of Industrial Information Systems
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• v.27 no.4
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• pp.19-27
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• 2022

In general, the performance of ML(Machine Learning) application is determined by various factors such as the type of ML model, the size of model (number of parameters), hyperparameters setting during the training, and training data. In particular, the recognition accuracy of ML may be deteriorated or experienced overfitting problem if the amount of dada used for training is insufficient. Existing studies focusing on image recognition have widely used open datasets for training and evaluating the proposed ML models. However, for specific applications where the sensor used, the target of recognition, and the recognition situation are different, it is necessary to build the dataset manually. In this case, the performance of ML largely depends on the quantity and quality of the data. In this paper, training data used for motion recognition application is augmented using the kernel density estimation algorithm which is a type of non-parametric estimation method. We then compare and analyze the recognition accuracy of a ML application by varying the number of original data, kernel types and augmentation rate used for data augmentation. Finally experimental results show that the recognition accuracy is improved by up to 14.31% when using the narrow bandwidth Tophat kernel.