• Journal of The Korean Association For Science Education
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• v.41 no.3
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• pp.237-250
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• 2021

Teachers play a key role in designing a students' learning experience. Teachers are asked to interpret the context in which they are located and to adjust their practice to fit circumstantial needs based on their teacher agency. In this study, we explore the emergence of teacher agency in distance learning caused by COVID-19 and we analyze factors shaping the teacher agency. For this purpose, we interviewed six secondary science teachers who practiced distance learning in 2020. Semi-constructed interviews and their artifacts were collected and analyzed. This study shows that teacher agency is captured when they respond to circumstantial change and modify their practice to achieve their professional purpose or adjust their practice in space for maneuvering or keep their practice consistent. This study also analyzes the factors that affect the emergence of teacher agency in two dimensions. One is individual and the other is contextual. In the individual dimension, educational values shaped by his/her experiences and short/long-term goals for the future support the emergence of teacher agency. In the contextual dimension, there are collaborative and flexible culture shared by the community, co-operation within the teacher community, and material support. On the other hand, in the individual dimension, the teachers' sense of their role, and no reflection for own practice constrain the emergence of teacher agency, and in the contextual dimension, performativity discourse and strong requirement without guidance constrain the emergence of teacher agency. We suggest an effective lens for establishing a strategy that support teachers' professional practice and the emergence of teacher agency.

• Journal of the Society of Disaster Information
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• v.17 no.1
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• pp.97-111
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• 2021

Purpose: It is expected that various infrastructures diagnosis equipment will be needed as infrastructures management is strengthened to implement the Framework Act on Sustainable Infrastructure Management. It is necessary for a certification system to supply certified products of a reasonable level in accordance with market requirements for various convergence equipment. This paper deals with the introduction of certification system of inspection and diagnosis equipment for infrastructure using advanced technologies. Method: The basic elements, systems and procedures of certification system were reviewed through analyzing and comparing the existing similar certification system in Korea. In addition, a survey was conducted on a catalog method and the minimum performance criterion (sampling survey and complete enumeration survey) to equipment developers (manufacturers), clients and equipment users. Result: This survey showed that clients preferred complete enumeration method on the basis of minimum performance, and equipment users also preferred complete enumeration survey and sample survey, for minimum performance, at a similar rate. On the other hand, equipment developers preferred the catalog method. Conclusion: Clients and users who are the users of the diagnostic equipment preferred the minimum performance criterion because their trust in quality is important. On the other hand, developers(manufacturers) preferred the catalog method which adopts self certification because it is regulated in developing various products. There is no specific plan for the minimum performance standards required for the introduction of the method which users demand, at present. In addition, it is not desirable to force to introduce a certification system because it requires a considerable period of study to prepare the specific standards. Therefore, it is appropriate to operate the system for a certain period of time centering around the catalog method for the stable and continuous development of the infrastructure diagnosis and test equipment market in Korea. Also, it is effective to expand and develop the certification system to the extent that it minimizes the impact on the market when specific plans for the standards are prepared in the future.

• Journal of the Korea Society of Computer and Information
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• v.26 no.4
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• pp.203-211
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• 2021

With the advent of the age of"untact" modern people are pursuing a healthy body and mind. In order to achieve well-being, LOHAS and Wellness,people prefer to use natural affinity alternative therapies, Aromatherapy. This study focuses on women in their 20s~50s in Zhejiang Province, with the aim of investigating their knowledge and use of essential oils.The questionnaire was divided into four parts: 3 questions for general question, 11 questions for knowledge, 13 questions for use and 9 questions for satisfaction. In addition, the study was conducted using the WeChat and the Wenjuanxing Program from July 5 to August 30, 2019. Finally, a total of 617 questionnaires were analyzed. In this study, SPSS WIN 21.0 program is used for frequency analysis. The level of knowledge and satisfaction is verified by Cronbach's α. And the following analysis results were obtained by frequency analysis, descriptive statistics, Chi-squared test(χ2), one-way ANOVA on the understanding level and usege of essential oils according to age. The results were as follows. The most common characteristics of subjects were the 20s, university students, essential oil recognition was high in having experience. There is no great difference in knowledge or satisfaction depending on age. knowledge and satisfaction was moderate. The results of experience in the use of essential oils were higher among all age groups, those who in their 30s did not think that the use of essential oils would be effective. However, people in their 20s and 40s and older have unclear answers, indicating that results showed a difference. The results of the survey on usage showed that there were significant differences in period of use, place of purchase, method of purchase, purpose of use, place of use, number of use, frequency of use, body parts of use. According to the study, awareness and knowledge of essential oils vary according to age, and those in their 20s use essential oils for facial skin, and those in their 30s and older use essential oils for stress relief and body management. This study provides basic information on marketing related to diversified essential oil products according to age.

• Korean Journal of Poultry Science
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• v.48 no.3
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• pp.133-142
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• 2021

The purpose of this study was to investigate whether enzyme-hydrolyzed poultry by-product meal (EHPBM) is more effective as a protein source than poultry by-product meal (PBM) and soybean meal (SBM) for broiler chickens. A group of 300 one-day-old broiler chicks was randomly allocated to three treatments with five replicates (20 birds/replicate) for five weeks. The treatments consisted of basal diets containing 1) SBM, 2) PBM, and 3) EHPBM. The EHPBM-fed group (1,853 g±125.60) showed the highest final body weight (P<0.05) when compared to the PBM-fed group (1,723 g±76.81) and SBM-fed group (1,545 g±62.31). The feed conversion ratio of the EHPBM treatment group (1.740±0.104) was significantly higher (P<0.05) than those of the SBM (1.653±0.056) and PBM groups (1.674±0.072). It can be speculated that the increased feed intake in the EHPBM group led to higher body weight gain and FCR. There was no significant effect of treatments on internal organ weight except for the bursa of Fabricius. Blood biochemical characteristic analysis showed that aspartate aminotransferase and alkaline phosphatase levels were higher in the EHPBM and PBM groups (P<0.05), probably due to the strained liver caused by the rapid growth of birds. In conclusion, EHPBM may partly replace conventional dietary protein sources such as soybean meal or poultry by-product meal and can be used to improve the productivity of broilers.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.175-197
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• 2021

Recently, with the development of deep learning technology, research on unstructured data analysis is being actively conducted, and it is showing remarkable results in various fields such as classification, summary, and generation. Among various text analysis fields, text classification is the most widely used technology in academia and industry. Text classification includes binary class classification with one label among two classes, multi-class classification with one label among several classes, and multi-label classification with multiple labels among several classes. In particular, multi-label classification requires a different training method from binary class classification and multi-class classification because of the characteristic of having multiple labels. In addition, since the number of labels to be predicted increases as the number of labels and classes increases, there is a limitation in that performance improvement is difficult due to an increase in prediction difficulty. To overcome these limitations, (i) compressing the initially given high-dimensional label space into a low-dimensional latent label space, (ii) after performing training to predict the compressed label, (iii) restoring the predicted label to the high-dimensional original label space, research on label embedding is being actively conducted. Typical label embedding techniques include Principal Label Space Transformation (PLST), Multi-Label Classification via Boolean Matrix Decomposition (MLC-BMaD), and Bayesian Multi-Label Compressed Sensing (BML-CS). However, since these techniques consider only the linear relationship between labels or compress the labels by random transformation, it is difficult to understand the non-linear relationship between labels, so there is a limitation in that it is not possible to create a latent label space sufficiently containing the information of the original label. Recently, there have been increasing attempts to improve performance by applying deep learning technology to label embedding. Label embedding using an autoencoder, a deep learning model that is effective for data compression and restoration, is representative. However, the traditional autoencoder-based label embedding has a limitation in that a large amount of information loss occurs when compressing a high-dimensional label space having a myriad of classes into a low-dimensional latent label space. This can be found in the gradient loss problem that occurs in the backpropagation process of learning. To solve this problem, skip connection was devised, and by adding the input of the layer to the output to prevent gradient loss during backpropagation, efficient learning is possible even when the layer is deep. Skip connection is mainly used for image feature extraction in convolutional neural networks, but studies using skip connection in autoencoder or label embedding process are still lacking. Therefore, in this study, we propose an autoencoder-based label embedding methodology in which skip connections are added to each of the encoder and decoder to form a low-dimensional latent label space that reflects the information of the high-dimensional label space well. In addition, the proposed methodology was applied to actual paper keywords to derive the high-dimensional keyword label space and the low-dimensional latent label space. Using this, we conducted an experiment to predict the compressed keyword vector existing in the latent label space from the paper abstract and to evaluate the multi-label classification by restoring the predicted keyword vector back to the original label space. As a result, the accuracy, precision, recall, and F1 score used as performance indicators showed far superior performance in multi-label classification based on the proposed methodology compared to traditional multi-label classification methods. This can be seen that the low-dimensional latent label space derived through the proposed methodology well reflected the information of the high-dimensional label space, which ultimately led to the improvement of the performance of the multi-label classification itself. In addition, the utility of the proposed methodology was identified by comparing the performance of the proposed methodology according to the domain characteristics and the number of dimensions of the latent label space.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.199-230
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• 2021

Innovative new products and services are being launched through the convergence between heterogeneous industries, and social interest and investment in convergence industries such as AI, big data-based future cars, and robots are continuously increasing. However, in the process of commercialization of convergence new products and services, there are many cases where they do not conform to the existing regulatory and legal system, which causes many difficulties in companies launching their products and services into the market. In response to these industrial changes, the current government is promoting the improvement of existing regulatory mechanisms applied to the relevant industry along with the expansion of investment in new industries. This study, in these convergence industry trends, aimed to analysis the existing regulatory system that is an obstacle to market entry of innovative new products and services in order to preemptively predict regulatory issues that will arise in emerging industries. In addition, it was intended to establish a regulatory impact analysis system to evaluate adequacy and prepare improvement measures. The flow of this study is divided into three parts. In the first part, previous studies on regulatory impact analysis and evaluation systems are investigated. This was used as basic data for the development direction of the regulatory impact framework, indicators and items. In the second regulatory impact analysis framework development part, indicators and items are developed based on the previously investigated data, and these are applied to each stage of the framework. In the last part, a case study was presented to solve the regulatory issues faced by actual companies by applying the developed regulatory impact analysis framework. The case study included the autonomous/electric vehicle industry and the Internet of Things (IoT) industry, because it is one of the emerging industries that the Korean government is most interested in recently, and is judged to be most relevant to the realization of an intelligent information society. Specifically, the regulatory impact analysis framework proposed in this study consists of a total of five steps. The first step is to identify the industrial size of the target products and services, related policies, and regulatory issues. In the second stage, regulatory issues are discovered through review of regulatory improvement items for each stage of commercialization (planning, production, commercialization). In the next step, factors related to regulatory compliance costs are derived and costs incurred for existing regulatory compliance are calculated. In the fourth stage, an alternative is prepared by gathering opinions of the relevant industry and experts in the field, and the necessity, validity, and adequacy of the alternative are reviewed. Finally, in the final stage, the adopted alternatives are formulated so that they can be applied to the legislation, and the alternatives are reviewed by legal experts. The implications of this study are summarized as follows. From a theoretical point of view, it is meaningful in that it clearly presents a series of procedures for regulatory impact analysis as a framework. Although previous studies mainly discussed the importance and necessity of regulatory impact analysis, this study presented a systematic framework in consideration of the various factors required for regulatory impact analysis suggested by prior studies. From a practical point of view, this study has significance in that it was applied to actual regulatory issues based on the regulatory impact analysis framework proposed above. The results of this study show that proposals related to regulatory issues were submitted to government departments and finally the current law was revised, suggesting that the framework proposed in this study can be an effective way to resolve regulatory issues. It is expected that the regulatory impact analysis framework proposed in this study will be a meaningful guideline for technology policy researchers and policy makers in the future.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.157-173
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• 2021

With the development of information technology, the amount of available information increases daily. However, having access to so much information makes it difficult for users to easily find the information they seek. Users want a visualized system that reduces information retrieval and learning time, saving them from personally reading and judging all available information. As a result, recommendation systems are an increasingly important technologies that are essential to the business. Collaborative filtering is used in various fields with excellent performance because recommendations are made based on similar user interests and preferences. However, limitations do exist. Sparsity occurs when user-item preference information is insufficient, and is the main limitation of collaborative filtering. The evaluation value of the user item matrix may be distorted by the data depending on the popularity of the product, or there may be new users who have not yet evaluated the value. The lack of historical data to identify consumer preferences is referred to as data sparsity, and various methods have been studied to address these problems. However, most attempts to solve the sparsity problem are not optimal because they can only be applied when additional data such as users' personal information, social networks, or characteristics of items are included. Another problem is that real-world score data are mostly biased to high scores, resulting in severe imbalances. One cause of this imbalance distribution is the purchasing bias, in which only users with high product ratings purchase products, so those with low ratings are less likely to purchase products and thus do not leave negative product reviews. Due to these characteristics, unlike most users' actual preferences, reviews by users who purchase products are more likely to be positive. Therefore, the actual rating data is over-learned in many classes with high incidence due to its biased characteristics, distorting the market. Applying collaborative filtering to these imbalanced data leads to poor recommendation performance due to excessive learning of biased classes. Traditional oversampling techniques to address this problem are likely to cause overfitting because they repeat the same data, which acts as noise in learning, reducing recommendation performance. In addition, pre-processing methods for most existing data imbalance problems are designed and used for binary classes. Binary class imbalance techniques are difficult to apply to multi-class problems because they cannot model multi-class problems, such as objects at cross-class boundaries or objects overlapping multiple classes. To solve this problem, research has been conducted to convert and apply multi-class problems to binary class problems. However, simplification of multi-class problems can cause potential classification errors when combined with the results of classifiers learned from other sub-problems, resulting in loss of important information about relationships beyond the selected items. Therefore, it is necessary to develop more effective methods to address multi-class imbalance problems. We propose a collaborative filtering model using CGAN to generate realistic virtual data to populate the empty user-item matrix. Conditional vector y identify distributions for minority classes and generate data reflecting their characteristics. Collaborative filtering then maximizes the performance of the recommendation system via hyperparameter tuning. This process should improve the accuracy of the model by addressing the sparsity problem of collaborative filtering implementations while mitigating data imbalances arising from real data. Our model has superior recommendation performance over existing oversampling techniques and existing real-world data with data sparsity. SMOTE, Borderline SMOTE, SVM-SMOTE, ADASYN, and GAN were used as comparative models and we demonstrate the highest prediction accuracy on the RMSE and MAE evaluation scales. Through this study, oversampling based on deep learning will be able to further refine the performance of recommendation systems using actual data and be used to build business recommendation systems.

• Journal of the Korean Institute of Landscape Architecture
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• v.49 no.4
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• pp.30-40
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• 2021

This study developed quantitative models to estimate the rainfall interception by seven native landscape tree species based on throughfall measurements. The tree species considered in this study were Abies holophylla, Acer palmatum, Ginkgo biloba, Pinus densiflora, Pinus koraiensis, Prunus yedoensis, and Zelkova serrata, which are frequently planted in the Korea. Among these species, 35.8% of the annual precipitation was intercepted by P. koraiensis, 34.1% by A. holophylla, 31.0% by Z. serrata, 27.6% by P. densiflora, 26.9% by G. biloba, 18.6% by A. palmatum, and 18.4% by P. yedoensis. All the quantitative models showed high fitness with r² values of 0.90-0.99. The annual rainfall interception from a tree with DBH of 20 cm were greatest with Z. serrata (5.1 m³/yr), followed by P. koraiensis (4.1 m³/yr), A. holophylla (3.1 m³/yr), G. biloba (2.8 m³/yr), P. densiflora (2.1 m³/yr), P. yedoensis (1.9 m³/yr), and A. palmatum (1.8 m³/yr) in order. Thus, evergreen tree species or those with a relatively high crown density were more effective in intercepting rainfall. In particular, the annual rainfall interception by Z. serrata was the greatest because its crown area, volume, and density were higher than those of the other species. This study pioneers in quantifying annual rainfall interception for landscape tree species in Korea. The study results can be useful for evaluating rainfall interception by landscape trees in urban greenspace design for governments and corporations.

• Journal of Life Science
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• v.29 no.11
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• pp.1208-1217
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• 2019

Saposhnikovia has been used as a traditional medicinal herb in Asia because of the reported anti-inflammatory, anti-allergic rhinitis, pro-whitening, anti-atopy, anti-allergy, and anti-dermatopathy effects of the phytochemical compounds it contains. In this study, we investigated the antioxidant effects of a Saposhnikovia extract after fermentation by Lactobacillus plantarum BHN-LAB 33. Saposhnikovia powder was inoculated with L. plantarum BHN-LAB 33 and fermented at $37^{\circ}C$ for 72 hr. After fermentation, the total polyphenol content of the Saposhnikovia extract increased by about 14%, and the total flavonoid content increased by about 9%. The superoxide dismutase-like activities, DPPH radical scavenging, ABTS radical scavenging, reducing power activity, and tyrosinase inhibition activity also increased after fermentation by approximately 70%, 80%, 45%, 39%, and 44%, respectively. The results confirmed that fermentation of a Saposhnikovia extract by L. plantarum BHN-LAB 33 is an effective way to increase the antioxidant effects of the extract. The bioconversion process investigated in this study may have the potential to produce phytochemical-enriched natural antioxidant agents with high added value from Saposhnikovia matrices. These results can also be applied to the development of improved foods and cosmetic materials.

• Korean Journal of Plant Resources
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• v.32 no.4
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• pp.312-317
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• 2019

This study was conducted with the purpose of examining the suitability of Euphorbia jolkinii Boiss. as cut flower, so that it may be introduced as a new ornamental crop. For this purpose, effect of various holding solutions on vase solution uptake rate, vase life, and relative fresh weight of cut flowering branches of E. jolkinii was examined. After harvest, cut branches were treated with 10, 50, and $100mg{\cdot}L^{-1}$ of 8-hydroxyquinoline sulfate (8-HQS), 0.1 and 0.2 mM of silver thiosulfate (STS), Chrysal, and Floralife. The cut branches of E. jolkinii were placed under the environmental conditions maintained at air temperature of $22.6^{\circ}C$, relative humidity of 45%, and 9/15h photoperiod that was controlled using fluorescent lamps (light intensity of $9.89{\mu}mol{\cdot}m^{-2}{\cdot}s^{-1}$). A holding solution containing $10mg{\cdot}L^{-1}$ 8-HQS was found to be significantly effective for vase solution uptake rate compared to control, $50mg{\cdot}L^{-1}$ 8-HQS, and $100mg{\cdot}L^{-1}$ 8-HQS treatments. However, no significant difference was found in vase life between the branches treated with $10mg{\cdot}L^{-1}$ 8-HQS holding solution and branches of the control group. Increasing holding solution concentrations of STS was found to have negative effect on the vase life of cut E. jolkinii branches. Relative fresh weight of cut E. jolkinii branches were significantly decreased by two commercial holding solutions, Chrysal and Floralife. It is expected that these results would aid further studies on utilization of E. jolkinii as cut flower crop.