• FLOWER RESEARCH JOURNAL
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• v.18 no.2
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• pp.136-141
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• 2010

In order to analyze the horticultural-therapy program, which was carried out targeting the mentally disabled, relevant 559 copies of 'Confirmation Note of horticultural activity' submitted for to be used the use in license examination to Korean Horticultural Therapy and Wellbeing Association were used as a tool. It contains 64 horticultural therapists for level 1 and 524 horticultural therapists for level 2 from May in 2000 to February in 2008. With the aim of examining difference depending on people covered by the program, license kind and horticultural therapy activity period in horticulture therapists, ${\chi}^2$ test was conducted on the basis of frequency in each. Data was analyzed by using SPSS (Statistical Package for the Social Science) Win 13.0 program, which was carried out targeting the mentally disabled, the 'art and craft activity' was the largest with 46.0%. In terms of 'growing activity', the 'normal growing' showed the highest ratio with 74.7%. In the 'art and craft activity', the 'flower decoration' showed the highest ratio with 37.5%. In the result of 'Cooking activity', the activity related to 'tea' was the largest ratio with 33.6%. As a result of 'learning activity', 'orientation' was the largest ratio with 47.6%. In the 'outdoor activity', 'excursion' was the largest ratio with 36.7%.

• Journal of agriculture & life science
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• v.45 no.6
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• pp.213-226
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• 2011

This study was performed to investigate the effect of essential oils and ethanolic extracts of approximately 650 plant species on acetylcholine esterase (AChE) enzyme activity using Ellman's colorimetric method in 96-well microplates. The results showed that the ethanolic extracts from twig of Sophora subprostrata, twig of Phellodendron amurense, seed of Corylopsis coreana, and essential oil (EO) from Citrus paradisi, Cupressus sempervirens, Ocimum basilicum, Pinus sylvestris and Rosmarinus officinalis inhibited more than 80% of AChE activity. Among these, EO from Pinus sylvestris, C. sempervirens and C paradisi exhibited higher values of AChE inhibitory activity, which were 75, 84 and 99% at a concentration of 50 ug/ml, respectively. Finally, EO from C paradisi (grapefruit, GEO) showed the highest inhibitory activity towards AChE, which showed 91% of inhibition at a concentration of 20 ug/ml. We also examined the anti-dementia effects of GEO in mouse by passive avoidance test and Morris water maze test. The model mouse (male, ICR) of dementia (negative control) was induced by administration of scopolamine (1 mg/kg body weight). The latency time of sample group administrated with GEO (100 mg/kg, p.o.) increased significantly as compared with negative control on passive avoidance test. There were significant recovery from the scopolamine-induced deficits on learning and memory in water maze test through daily administrations with GEO (100 mg/kg, p.o.). From these results, we conclude that GEO treatment might enhance the cognitive function, suggesting that the EO of C. paradis may be a potential candidate for improvement of perceptive ability and dementia.

• Journal of the Korea Convergence Society
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• v.12 no.10
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• pp.63-70
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• 2021

Generating a super-resolution meteological data by using a high-resolution deep neural network can provide precise research and useful real-life services. We propose a new technique of generating improved training data for super-resolution deep neural networks. To generate high-resolution meteorological data with domain specific knowledge, Lambert conformal conic projection and objective analysis were applied based on observation data and ERA5 reanalysis field data of specialized institutions. As a result, temperature and humidity analysis data based on domain specific knowledge showed improved RMSE by up to 42% and 46%, respectively. Next, a super-resolution generative adversarial network (SRGAN) which is one of the aritifial intelligence techniques was used to automate the manual data generation technique using damain specific techniques as described above. Experiments were conducted to generate high-resolution data with 1 km resolution from global model data with 10 km resolution. Finally, the results generated with SRGAN have a higher resoltuion than the global model input data, and showed a similar analysis pattern to the manually generated high-resolution analysis data, but also showed a smooth boundary.

• Journal of Digital Convergence
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• v.19 no.12
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• pp.29-42
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• 2021

As the low fertility intensifies in Korea, this study investigated fundamental differences between the government's low fertility policy and public perception of it. To this end, we selected four times 'Aging Society and Population Policy' documents and news comments for two weeks immediately after announcement of the third and fourth Policy as analysis targets. Then we conducted word frequency analysis, co-occurrence analysis and CONCOR analysis. As a result of analyses, first, direct childcare support during the first and second periods, and a social structural approach during third and fourth periods were noticeable. Second, it was revealed that both policies and comments aim for the work-family compatibility in 'parenting'. Lastly it was showed public interest in environment of raising children and the critical mind to effectiveness of the policy. This study is meaningful in that it confirmed the public perception using big data analysis, and it will help improve the direction for the future low fertility policy.

• Journal of the Korea Society of Computer and Information
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• v.26 no.11
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• pp.41-49
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• 2021

All application programs, including malware, call the Application Programming Interface (API) upon execution. Recently, using those characteristics, attempts to detect and classify malware based on API Call information have been actively studied. However, datasets containing API Call information require a large amount of computational cost and processing time. In addition, information that does not significantly affect the classification of malware may affect the classification accuracy of the learning model. Therefore, in this paper, we propose a method of extracting a essential feature set after reducing the dimensionality of API Call information by applying various feature selection methods. We used CICAndMal2020, a recently announced Android malware dataset, for the experiment. After extracting the essential feature set through various feature selection methods, Android malware classification was conducted using CNN (Convolutional Neural Network) and the results were analyzed. The results showed that the selected feature set or weight priority varies according to the feature selection methods. And, in the case of binary classification, malware was classified with 97% accuracy even if the feature set was reduced to 15% of the total size. In the case of multiclass classification, an average accuracy of 83% was achieved while reducing the feature set to 8% of the total size.

• Journal of Korean Library and Information Science Society
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• v.50 no.1
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• pp.85-103
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• 2019

This study aims to analyze the direction of the reading education programs based on the 2015 revised curriculum and to seek the plans for the school library and the teacher librarian to be able to contribute them. For this purpose, the types of school-based reading education programs in the report of 100 excellent school curriculum in 2016, which was first applied by the amended curriculum were analyzed through multiple intelligences. Upon the analysis results, the reading education programs in the schools showed to be operated with interpersonal Intelligence. Community-aligned reading was the most frequently operated in the primary schools while student reading club activities were the most common in the middle and high schools. In case of reading education program related to linguistic intelligence, the most commonly operated ways were reading books, writing with literatures, and writing book report, in primary, middle, and high schools, respectively. In case of reading education program related to spatial intelligence, media production type showed the most commonly operated in all types of schools. However, there was no reading program related to naturalist intelligence. Based on these analysis results, the plans to contribute the activation of reading education programs by school libraries under the 2015 amended curriculum were suggested in the aspects of development of connection programs with teachers, students and parents as the center of education community, installation and operation of maker spaces and enhancement of program management and inquiry-based learning competency of teacher librarians.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.4
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• pp.2060-2077
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• 2019

Recently, mobile healthcare services have attracted significant attention because of the emerging development and supply of diverse wearable devices. Smartwatches and health bands are the most common type of mobile-based wearable devices and their market size is increasing considerably. However, simple value comparisons based on accumulated data have revealed certain problems, such as the standardized nature of health management and the lack of personalized health management service models. The convergence of information technology (IT) and biotechnology (BT) has shifted the medical paradigm from continuous health management and disease prevention to the development of a system that can be used to provide ground-based medical services regardless of the user's location. Moreover, the IT-BT convergence has necessitated the development of lifestyle improvement models and services that utilize big data analysis and machine learning to provide mobile healthcare-based personal health management and disease prevention information. Users' health data, which are specific as they change over time, are collected by different means according to the users' lifestyle and surrounding circumstances. In this paper, we propose a prediction model of user physical activity that uses data characteristics-based long short-term memory (DC-LSTM) recurrent neural networks (RNNs). To provide personalized services, the characteristics and surrounding circumstances of data collectable from mobile host devices were considered in the selection of variables for the model. The data characteristics considered were ease of collection, which represents whether or not variables are collectable, and frequency of occurrence, which represents whether or not changes made to input values constitute significant variables in terms of activity. The variables selected for providing personalized services were activity, weather, temperature, mean daily temperature, humidity, UV, fine dust, asthma and lung disease probability index, skin disease probability index, cadence, travel distance, mean heart rate, and sleep hours. The selected variables were classified according to the data characteristics. To predict activity, an LSTM RNN was built that uses the classified variables as input data and learns the dynamic characteristics of time series data. LSTM RNNs resolve the vanishing gradient problem that occurs in existing RNNs. They are classified into three different types according to data characteristics and constructed through connections among the LSTMs. The constructed neural network learns training data and predicts user activity. To evaluate the proposed model, the root mean square error (RMSE) was used in the performance evaluation of the user physical activity prediction method for which an autoregressive integrated moving average (ARIMA) model, a convolutional neural network (CNN), and an RNN were used. The results show that the proposed DC-LSTM RNN method yields an excellent mean RMSE value of 0.616. The proposed method is used for predicting significant activity considering the surrounding circumstances and user status utilizing the existing standardized activity prediction services. It can also be used to predict user physical activity and provide personalized healthcare based on the data collectable from mobile host devices.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.2
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• pp.750-762
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• 2019

The importance of the call center as a contact point for the enterprise is growing. However, call centers have difficulty with their operating agents due to the agents' lack of knowledge and owing to frequent agent turnover due to downturns in the business, which causes deterioration in the quality of customer service. Therefore, through an N-bank call center case study, we developed a system to reduce the burden of keeping up business knowledge and to improve customer service quality. It is a "real-time agent advisor" system that provides agents with answers to customer questions in real time by combining AI technology for speech recognition, natural language processing, and questions & answers for existing call center information systems, such as a private branch exchange (PBX) and computer telephony integration (CTI). As a result of the case study, we confirmed that the speech recognition system for real-time call analysis and the corpus construction method improves the natural speech processing performance of the query response system. Especially with name entity recognition (NER), the accuracy of the corpus learning improved by 31%. Also, after applying the agent advisor system, the positive feedback rate of agents about the answers from the agent advisor was 93.1%, which proved the system is helpful to the agents.

• 재활복지
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• v.21 no.4
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• pp.177-193
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• 2017

The purpose of this study was to examine the factors that affect the awareness of disabled in undergraduate students. A sample of 1,998 respondents was surveyed. Of these, a total of 1,957 questionnaires were analysed excluding the 41 questionnaires that did not agree with the survey or had a missing data. The questionnaire consists of the general characteristics, experience related to disability and disability awareness and we used a 4-point Likert scale. Collected data was analyzed using the SPSS 24.0 program and descriptive statistics, t-test, variance analysis and multiple regression analysis were used. According to the results of the study, the disability awareness score of our respondents was average 40.52. Second, female students had more positive about disability awareness than male ones, and senior students had more positive awareness than other grade students. Third, non-rehabilitation department students' disability awareness was more positive than rehabilitation students. Fourth, the disability awareness of people who have a acquaintance member with disability or health worker were significantly positive. Finally, the awareness of people who experienced at the disability related institution were significantly positive. Therefore, to improve the awareness of undergraduate students about disabilities, it is required to activate positive contact experiences with people with disabilities rather than theoretical learning.

• Journal of Veterinary Clinics
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• v.36 no.1
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• pp.53-61
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• 2019

Total hip arthroplasty (THA) is a successful surgical treatment for both patients with chronical lameness and dogs who are nonresponsive to medical treatments, providing excellent joint function for returning dogs to the normal gait in 80% to 98% of hip dysplasia (HD) patients. The THA surgical implant system manufactured by BioMedtrix and Kyon are today widely accepted. When comparing the BioMedtrix biological fixation (BFX) system to the BioMedtrix cemented fixation (CFX) system, the many advantages of BFX, which include longer potential implant life, decreased risk of postoperative or later infection, and better implant stability, become evident. However, BFX implies a greater risk of femoral fracture during reaming and requires a more precise surgical technique to achieve good implant fit, given the press-fit nature of cementless THA. The purposes of this study are to both describe the mistakes and complications during stem implantation for beginner surgeons with both the BFX and the CFX systems and to document the initial result of 12 implantations in canine cadavers. Given the detailed evaluations of 3 specialists, who are Diplomate American College of Veterinary Surgeons (DACVS), only 3 of 11 stems were appropriately sized. Specifically, 6 stems were anteverted rather than being retroverted; further, although 7 stems were coaxial with the femoral long axis in the frontal plane, the other stems were in the varus at the frontal plane, with the proximal medial stem adjacent to the medial femoral cortex. Moderate angulation from the cranial to the caudal directions was found in 4 cases in the sagittal plane. Additionally, 1 case of femoral fissure and 1 case of perforated femoral cortex were reported. It is not easy for surgeons performing cementless THA for the first time to achieve a good result, even though they completed an educational course about it and given that catastrophic complications often occurred during early surgical clinical cases. Therefore, ex-vivo studies are sincerely required to get an expertise by rehearsing the preparation of the femoral envelop in isolated bones. Further studies should be conducted to achieve both highly accurate implant size and correct orientation during the preoperative planning. Additionally, surgeons' learning curve should be examined in future investigations.