• Environmental and Resource Economics Review
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• v.30 no.1
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• pp.129-167
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• 2021

The emission trading system is an essential policy for reducing greenhouse gas emissions and converting low-carbon society. EU ETS is a good benchmark that is ahead of Korea's emission trading system in terms of operating period and design know-how. Therefore, this study focused on the key design elements of EU ETS phase 4 such as total emission allowances issued (Cap), free allocation method, carbon leakage list, market stability reserve, and innovation supporting system. In addition, we analyzed the impact of key design elements and their changes during EU ETS Phase 1 to 4 on the design and operation of Korea emission trading system in the future. First of all, the expected impact on the design of Korea emission trading system is to increase three demands: preparing benchmark renewal plans, establishing criteria for selecting free allocation industries that reflect domestic industrial structure and characteristics and introducing two-stage evaluations for free allocation industries, and preparing specific plan to support innovation and industries using allowance auction revenues. The next three impacts on the operation of Korea emission trading system are the increased needs for objective and in-depth impact assessment of plan and amendments, provision of system stability and response opportunities by quickly confirming plan and amendments prior to the implementation, and coordination of the emission trading system governance and stakeholder participation encouragement.

• Journal of Environmental Impact Assessment
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• v.31 no.2
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• pp.106-116
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• 2022

Multilateral Development Banks (MDBs) have actively responded to global climate change, and developed and operated the Common Principles for Climate Finance Tracking. They estimate climate finance in a granular manner with a conservative view. In other words, the MDBs track their financing only for those elements or proportions of projects that directly contribute to or promote climate adaptation or mitigation. The MDBs have reported jointly on climate finance since the first edition in 2012, which reported for 2011 and up to the 10th edition in 2021, which reported for 2020. MDBs apply two difference methodologies for adaptation and mitigation. For adaptation, the methodology is based on a context and location specific approach and captures the amounts associated with activities directly linked to vulnerability to climate change. For mitigation, it is evaluated in accordance with a comprehensive list of activities thatreduce greenhouse gas emissions. The result of climate risk assessment is one of the major due diligence items for MDBs alongside with that of environmental and social impact assessment. Under the circumstance that many countries endeavor to deal with climate change at project level, it is meaningful to understand how MDBs have addressed climate change issues in their project approval process. This would be a good reference to establish a methodology for responding to climate change and to expand scope of environmental and social impact assessment.

• Journal of The Korean Association For Science Education
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• v.28 no.4
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• pp.269-281
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• 2008

In Korea, the criteria for the requirements of a secondary school science teacher's certificate are based entirely on the subjects and/or areas as prescribed in laws for the teacher's licensure examination. However, the criteria do not account for the specific competencies or qualities that a good science teacher should possess. The objective of the research was to explore and suggest the three lists of the image of an ideal science teacher, science teacher's role and science teacher's competency that might be used to establish the criteria for science teachers' certificate and the curricular content for science teacher education in Korea. In order to achieve this objective, the study used such research methods as literature analysis, status survey in combination with on-line workshop, in-depth interview, and professional consultation. The participants in the research comprised of a group of 258 students (186 middle school students and 72 high school students) and 13 in-service science teachers (8 middle school science teachers, 5 high school science teachers) for questionnaire survey and on-line workshop, and 4 science teachers for in-depth interview. The list of the image of ideal science teacher, science teacher's role, science teacher's competency contains 44, 32, and 75 statements, respectively. Based on the results of the research, this paper suggested that the criteria for the Korean secondary school science teacher's certificate requirements be selected and organized in consideration of the teachers' competencies rather than the courses and/or subject areas. It is also implied in the paper that further research over a period of time is necessary for using the competencies for curricular contents and/or science teacher's certificate standards.

• Journal of The Korean Association For Science Education
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• v.16 no.4
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• pp.376-388
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• 1996

The first and the second "Discussion Contest of Scientific Investigation" was evaluated in this study. This contest was a part of 'Korean Youth Science Festival' held in 1993 and 1994. The evaluation was based on the data collected from the middle school students of final teams, their teachers, a large number of middle school students and college students who were audience of the final competition. Questionnaires, interviews, reports of final teams, and video tape of final competition were used to collect data. The study focussed on three research questions. The first was about the preparation and the research process of students of final teams. The second was about the format and the proceeding of the Contest. The third was whether participating the Contest was useful experience for the students and the teachers of the final teams. The first area, the preparation and the research process of students, were investigated in three aspects. One was the level of cooperation, participation, support and the role of teachers. The second was the information search and experiment, and the third was the report writing. The students of the final teams from both years, had positive opinion about the cooperation, students' active involvement, and support from family and school. Students considered their teachers to be a guide or a counsellor, showing their level of active participation. On the other hand, the interview of 1993 participants showed that there were times that teachers took strong leading role. Therefore one can conclude that students took active roles most of the time while the room for improvement still exists. To search the information they need during the period of the preparation, student visited various places such as libraries, bookstores, universities, and research institutes. Their search was not limited to reading the books, although the books were primary source of information. Students also learned how to organize the information they found and considered leaning of organizing skill useful and fun. Variety of experiments was an important part of preparation and students had positive opinion about it. Understanding related theory was considered most difficult and important, while designing and building proper equipments was considered difficult but not important. This reflects the students' school experience where the equipments were all set in advance and students were asked to confirm the theories presented in the previous class hours. About the reports recording the research process, students recognize the importance and the necessity of the report but had difficulty in writing it. Their reports showed tendency to list everything they did without clear connection to the problem to be solved. Most of the reports did not record the references and some of them confused report writing with story telling. Therefore most of them need training in writing the reports. It is also desirable to describe the process of student learning when theory or mathematics that are beyond the level of middle school curriculum were used because it is part of their investigation. The second area of evaluation was about the format and the proceeding of the Contest, the problems given to students, and the process of student discussion. The format of the Contests, which consisted of four parts, presentation, refutation, debate and review, received good evaluation from students because it made students think more and gave more difficult time but was meaningful and helped to remember longer time according to students. On the other hand, students said the time given to each part of the contest was too short. The problems given to students were short and open ended to stimulate students' imagination and to offer various possible routes to the solution. This type of problem was very unfamiliar and gave a lot of difficulty to students. Student had positive opinion about the research process they experienced but did not recognize the fact that such a process was possible because of the oneness of the task. The level of the problems was rated as too difficult by teachers and college students but as appropriate by the middle school students in audience and participating students. This suggests that it is possible for student to convert the problems to be challengeable and intellectually satisfactory appropriate for their level of understanding even when the problems were difficult for middle school students. During the process of student discussion, a few problems were observed. Some problems were related to the technics of the discussion, such as inappropriate behavior for the role he/she was taking, mismatching answers to the questions. Some problems were related to thinking. For example, students thinking was off balanced toward deductive reasoning, and reasoning based on experimental data was weak. The last area of evaluation was the effect of the Contest. It was measured through the change of the attitude toward science and science classes, and willingness to attend the next Contest. According to the result of the questionnaire, no meaningful change in attitude was observed. However, through the interview several students were observed to have significant positive change in attitude while no student with negative change was observed. Most of the students participated in Contest said they would participate again or recommend their friend to participate. Most of the teachers agreed that the Contest should continue and they would recommend their colleagues or students to participate. As described above, the "Discussion Contest of Scientific Investigation", which was developed and tried as a new science contest, had positive response from participating students and teachers, and the audience. Two among the list of results especially demonstrated that the goal of the Contest, "active and cooperative science learning experience", was reached. One is the fact that students recognized the experience of cooperation, discussion, information search, variety of experiments to be fun and valuable. The other is the fact that the students recognized the format of the contest consisting of presentation, refutation, discussion and review, required more thinking and was challenging, but was more meaningful. Despite a few problems such as, unfamiliarity with the technics of discussion, weakness in inductive and/or experiment based reasoning, and difficulty in report writing, The Contest demonstrated the possibility of new science learning environment and science contest by offering the chance to challenge open tasks by utilizing student science knowledge and ability to inquire and to discuss rationally and critically with other students.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.27-42
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• 2020

Traditional companies with offline stores were unable to secure large display space due to the problems of cost. This limitation inevitably allowed limited kinds of products to be displayed on the shelves, which resulted in consumers being deprived of the opportunity to experience various items. Taking advantage of the virtual space called the Internet, online shopping goes beyond the limits of limitations in physical space of offline shopping and is now able to display numerous products on web pages that can satisfy consumers with a variety of needs. Paradoxically, however, this can also cause consumers to experience the difficulty of comparing and evaluating too many alternatives in their purchase decision-making process. As an effort to address this side effect, various kinds of consumer's purchase decision support systems have been studied, such as keyword-based item search service and recommender systems. These systems can reduce search time for items, prevent consumer from leaving while browsing, and contribute to the seller's increased sales. Among those systems, recommender systems based on association rule mining techniques can effectively detect interrelated products from transaction data such as orders. The association between products obtained by statistical analysis provides clues to predicting how interested consumers will be in another product. However, since its algorithm is based on the number of transactions, products not sold enough so far in the early days of launch may not be included in the list of recommendations even though they are highly likely to be sold. Such missing items may not have sufficient opportunities to be exposed to consumers to record sufficient sales, and then fall into a vicious cycle of a vicious cycle of declining sales and omission in the recommendation list. This situation is an inevitable outcome in situations in which recommendations are made based on past transaction histories, rather than on determining potential future sales possibilities. This study started with the idea that reflecting the means by which this potential possibility can be identified indirectly would help to select highly recommended products. In the light of the fact that the attributes of a product affect the consumer's purchasing decisions, this study was conducted to reflect them in the recommender systems. In other words, consumers who visit a product page have shown interest in the attributes of the product and would be also interested in other products with the same attributes. On such assumption, based on these attributes, the recommender system can select recommended products that can show a higher acceptance rate. Given that a category is one of the main attributes of a product, it can be a good indicator of not only direct associations between two items but also potential associations that have yet to be revealed. Based on this idea, the study devised a recommender system that reflects not only associations between products but also categories. Through regression analysis, two kinds of associations were combined to form a model that could predict the hit rate of recommendation. To evaluate the performance of the proposed model, another regression model was also developed based only on associations between products. Comparative experiments were designed to be similar to the environment in which products are actually recommended in online shopping malls. First, the association rules for all possible combinations of antecedent and consequent items were generated from the order data. Then, hit rates for each of the associated rules were predicted from the support and confidence that are calculated by each of the models. The comparative experiments using order data collected from an online shopping mall show that the recommendation accuracy can be improved by further reflecting not only the association between products but also categories in the recommendation of related products. The proposed model showed a 2 to 3 percent improvement in hit rates compared to the existing model. From a practical point of view, it is expected to have a positive effect on improving consumers' purchasing satisfaction and increasing sellers' sales.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.219-240
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• 2018

Sentiment analysis, which is one of the text mining techniques, is a method for extracting subjective content embedded in text documents. Recently, the sentiment analysis methods have been widely used in many fields. As good examples, data-driven surveys are based on analyzing the subjectivity of text data posted by users and market researches are conducted by analyzing users' review posts to quantify users' reputation on a target product. The basic method of sentiment analysis is to use sentiment dictionary (or lexicon), a list of sentiment vocabularies with positive, neutral, or negative semantics. In general, the meaning of many sentiment words is likely to be different across domains. For example, a sentiment word, 'sad' indicates negative meaning in many fields but a movie. In order to perform accurate sentiment analysis, we need to build the sentiment dictionary for a given domain. However, such a method of building the sentiment lexicon is time-consuming and various sentiment vocabularies are not included without the use of general-purpose sentiment lexicon. In order to address this problem, several studies have been carried out to construct the sentiment lexicon suitable for a specific domain based on 'OPEN HANGUL' and 'SentiWordNet', which are general-purpose sentiment lexicons. However, OPEN HANGUL is no longer being serviced and SentiWordNet does not work well because of language difference in the process of converting Korean word into English word. There are restrictions on the use of such general-purpose sentiment lexicons as seed data for building the sentiment lexicon for a specific domain. In this article, we construct 'KNU Korean Sentiment Lexicon (KNU-KSL)', a new general-purpose Korean sentiment dictionary that is more advanced than existing general-purpose lexicons. The proposed dictionary, which is a list of domain-independent sentiment words such as 'thank you', 'worthy', and 'impressed', is built to quickly construct the sentiment dictionary for a target domain. Especially, it constructs sentiment vocabularies by analyzing the glosses contained in Standard Korean Language Dictionary (SKLD) by the following procedures: First, we propose a sentiment classification model based on Bidirectional Long Short-Term Memory (Bi-LSTM). Second, the proposed deep learning model automatically classifies each of glosses to either positive or negative meaning. Third, positive words and phrases are extracted from the glosses classified as positive meaning, while negative words and phrases are extracted from the glosses classified as negative meaning. Our experimental results show that the average accuracy of the proposed sentiment classification model is up to 89.45%. In addition, the sentiment dictionary is more extended using various external sources including SentiWordNet, SenticNet, Emotional Verbs, and Sentiment Lexicon 0603. Furthermore, we add sentiment information about frequently used coined words and emoticons that are used mainly on the Web. The KNU-KSL contains a total of 14,843 sentiment vocabularies, each of which is one of 1-grams, 2-grams, phrases, and sentence patterns. Unlike existing sentiment dictionaries, it is composed of words that are not affected by particular domains. The recent trend on sentiment analysis is to use deep learning technique without sentiment dictionaries. The importance of developing sentiment dictionaries is declined gradually. However, one of recent studies shows that the words in the sentiment dictionary can be used as features of deep learning models, resulting in the sentiment analysis performed with higher accuracy (Teng, Z., 2016). This result indicates that the sentiment dictionary is used not only for sentiment analysis but also as features of deep learning models for improving accuracy. The proposed dictionary can be used as a basic data for constructing the sentiment lexicon of a particular domain and as features of deep learning models. It is also useful to automatically and quickly build large training sets for deep learning models.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.1-17
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• 2017

The deep learning framework is software designed to help develop deep learning models. Some of its important functions include "automatic differentiation" and "utilization of GPU". The list of popular deep learning framework includes Caffe (BVLC) and Theano (University of Montreal). And recently, Microsoft's deep learning framework, Microsoft Cognitive Toolkit, was released as open-source license, following Google's Tensorflow a year earlier. The early deep learning frameworks have been developed mainly for research at universities. Beginning with the inception of Tensorflow, however, it seems that companies such as Microsoft and Facebook have started to join the competition of framework development. Given the trend, Google and other companies are expected to continue investing in the deep learning framework to bring forward the initiative in the artificial intelligence business. From this point of view, we think it is a good time to compare some of deep learning frameworks. So we compare three deep learning frameworks which can be used as a Python library. Those are Google's Tensorflow, Microsoft's CNTK, and Theano which is sort of a predecessor of the preceding two. The most common and important function of deep learning frameworks is the ability to perform automatic differentiation. Basically all the mathematical expressions of deep learning models can be represented as computational graphs, which consist of nodes and edges. Partial derivatives on each edge of a computational graph can then be obtained. With the partial derivatives, we can let software compute differentiation of any node with respect to any variable by utilizing chain rule of Calculus. First of all, the convenience of coding is in the order of CNTK, Tensorflow, and Theano. The criterion is simply based on the lengths of the codes and the learning curve and the ease of coding are not the main concern. According to the criteria, Theano was the most difficult to implement with, and CNTK and Tensorflow were somewhat easier. With Tensorflow, we need to define weight variables and biases explicitly. The reason that CNTK and Tensorflow are easier to implement with is that those frameworks provide us with more abstraction than Theano. We, however, need to mention that low-level coding is not always bad. It gives us flexibility of coding. With the low-level coding such as in Theano, we can implement and test any new deep learning models or any new search methods that we can think of. The assessment of the execution speed of each framework is that there is not meaningful difference. According to the experiment, execution speeds of Theano and Tensorflow are very similar, although the experiment was limited to a CNN model. In the case of CNTK, the experimental environment was not maintained as the same. The code written in CNTK has to be run in PC environment without GPU where codes execute as much as 50 times slower than with GPU. But we concluded that the difference of execution speed was within the range of variation caused by the different hardware setup. In this study, we compared three types of deep learning framework: Theano, Tensorflow, and CNTK. According to Wikipedia, there are 12 available deep learning frameworks. And 15 different attributes differentiate each framework. Some of the important attributes would include interface language (Python, C ++, Java, etc.) and the availability of libraries on various deep learning models such as CNN, RNN, DBN, and etc. And if a user implements a large scale deep learning model, it will also be important to support multiple GPU or multiple servers. Also, if you are learning the deep learning model, it would also be important if there are enough examples and references.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.49 no.3
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• pp.167-171
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• 2004

The growth of soybean sprout was greatly influenced by watering systems： Fixed watering system (water tub was loaded at ceiling upper of culture box and water was showered by bottom holes) was estimated the better than that of reciprocating watering and tub immersing watering because it could cool down the temperature in culture box and wash the organic substances on the body of sprout. The fixed watering system showed good body color and preventing effect of partial rotting of sprout because it could discharge $\textrm{CO}_2$ gas effectively in culture box and keep the concentration below 5%. The concentration of gases at the bottom (about 30 cm height from basal plate) of culture box in fourth or fifth days was L6% for $\textrm{CO}_2$ and 13-16% for $\textrm{O}_2$, respectively. The optimum gas concentration in culture box was considered to be over 10% for $\textrm{O}_2$ and below 5% for $\textrm{CO}_2$.

• Korean Journal of Oriental Medicine
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• v.11 no.2
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• pp.1-22
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• 2005

Study on ${\ulcorner}$Bonchojeonghwa${\lrcorner}$, which is one of the most specialized medical books in Boncho(Herbal Medicines), has been done by comparing it with some other medical books published in the Chosun dynasty. Though there was not meaningful result on e names of Korean medicine by this study and more study should follow in the future, from medicines recorded in ${\ulcorner}$Inbu (a chapter of medical ingredients from human body)${\lrcorner}$, we can reach on some results as follows by comparing in names of Korean medicines, their medical components, relevant explanations and etc. 1. Though it is difficult to know the author and the published year due to absence of the preface and epilogue, the publication is presumed to date from mid-l7th century, from the facts that Muheeong's ${\ulcorner}$Shinnongbonchokyongso${\lrcorner}$ is in the ${\ulcorner}$Bonchojeonghwa${\lrcorner}$'s reference list, and that there is not Hangul expression in the names of medicines nor the Ching dynasty’s books as a reference. 2. As a result of studying on the names of medicines recorded in ${\ulcorner}$Inbu${\lrcorner}$ of the Chosun dynasty's famous medical books, before ${\ulcorner}$ Bonchojeonghwa${\lrcorner}$, 19 medicines in ${\ulcorner}$Hyangyakjipsungbang${\lrcorner}$, 25 in ${\ulcorner}$Donguibogam${\lrcorner}$, and after ${\ulcorner}$Bonchojeonghwa${\lrcorner}$, 6 in ${\ulcorner}$Uimumbogam${\lrcorner}$, 4 in ${\ulcorner}$Kwangjebikup${\lrcorner}$, 11 in ${\ulcorner}$Bangyakhappyon${\lrcorner}$. And there are 37 medicines which are unique, ${\ulcorner}$Bonchojeonghwa${\lrcorner}$ has 31, the biggest records among them. 3. As a result of studying on the names of medicines recorded in 「Inbu」 of the ${\ulcorner}$Bonchojeonghwa${\lrcorner}$ and ${\ulcorner}$ Donguibogam${\lrcorner}$, 22 medicines were recorded in the both books, 9 were only recorded in ${\ulcorner}$Bonchojeonghwa${\lrcorner}$ and 3 were only recorded in ${\ulcorner}$Donguibogam${\lrcorner}$. 3 out of the total 37 medicines recorded in ${\ulcorner}$Inbu${\lrcorner}$ are only recorded in ${\ulcorner}$Hangyakjipsungbang${\lrcorner}$, and more study on this is needed. 4. From the contents recorded in ${\ulcorner}$Bonchojeonghwa${\lrcorner}$ and ${\ulcorner}$Donguibogam${\lrcorner}$, Benchojeonghwa is more in detail than Donguibogam. Thus, it was specialized in Boncho (Herbal Medicines) enough to be compared with general medical books, and played a good role in leading medical science's specialization. 5. Late Chosun dynasty's medical study on Boncho (Herbal Medicines) just like ${\ulcorner}$Bonchojeonghwa${\lrcorner}$ didn't lead to an active development of knowledge communication due to Confucian ethics. This limitation created the trend relying on general medical books or Yaksungga (songs of memorizing Boncho information) for Boncho information, but Boncho information of late Chosun dynasty became more in detail. That is, while Bokhapbang, combination of various medicines, were developed in China, Danmibang, single medicine but different intensity, were developed in Chosun. And thus, even though the kinds of medicines became smaller, but its contents became rather rich. 5. The medicines recorded in ${\ulcorner}$Bonchojeonghwa${\lrcorner}$ and ${\ulcorner}$Donguibogam${\lrcorner}$ are, from the view point of today, unclean or rather uncomfortable to use. Out those medicines, Bunchung, Hwasijangsanginkol, Hongyon, Gonidoogun, Inkondang had been used for a very long time and which proves their medical efficacy, and it is a great pity that they can’t be tried today due to the limitation by modern ethics.

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.139-153
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• 2017

Corporate bankruptcy can cause great losses not only to stakeholders but also to many related sectors in society. Through the economic crises, bankruptcy have increased and bankruptcy prediction models have become more and more important. Therefore, corporate bankruptcy has been regarded as one of the major topics of research in business management. Also, many studies in the industry are in progress and important. Previous studies attempted to utilize various methodologies to improve the bankruptcy prediction accuracy and to resolve the overfitting problem, such as Multivariate Discriminant Analysis (MDA), Generalized Linear Model (GLM). These methods are based on statistics. Recently, researchers have used machine learning methodologies such as Support Vector Machine (SVM), Artificial Neural Network (ANN). Furthermore, fuzzy theory and genetic algorithms were used. Because of this change, many of bankruptcy models are developed. Also, performance has been improved. In general, the company's financial and accounting information will change over time. Likewise, the market situation also changes, so there are many difficulties in predicting bankruptcy only with information at a certain point in time. However, even though traditional research has problems that don't take into account the time effect, dynamic model has not been studied much. When we ignore the time effect, we get the biased results. So the static model may not be suitable for predicting bankruptcy. Thus, using the dynamic model, there is a possibility that bankruptcy prediction model is improved. In this paper, we propose RNN (Recurrent Neural Network) which is one of the deep learning methodologies. The RNN learns time series data and the performance is known to be good. Prior to experiment, we selected non-financial firms listed on the KOSPI, KOSDAQ and KONEX markets from 2010 to 2016 for the estimation of the bankruptcy prediction model and the comparison of forecasting performance. In order to prevent a mistake of predicting bankruptcy by using the financial information already reflected in the deterioration of the financial condition of the company, the financial information was collected with a lag of two years, and the default period was defined from January to December of the year. Then we defined the bankruptcy. The bankruptcy we defined is the abolition of the listing due to sluggish earnings. We confirmed abolition of the list at KIND that is corporate stock information website. Then we selected variables at previous papers. The first set of variables are Z-score variables. These variables have become traditional variables in predicting bankruptcy. The second set of variables are dynamic variable set. Finally we selected 240 normal companies and 226 bankrupt companies at the first variable set. Likewise, we selected 229 normal companies and 226 bankrupt companies at the second variable set. We created a model that reflects dynamic changes in time-series financial data and by comparing the suggested model with the analysis of existing bankruptcy predictive models, we found that the suggested model could help to improve the accuracy of bankruptcy predictions. We used financial data in KIS Value (Financial database) and selected Multivariate Discriminant Analysis (MDA), Generalized Linear Model called logistic regression (GLM), Support Vector Machine (SVM), Artificial Neural Network (ANN) model as benchmark. The result of the experiment proved that RNN's performance was better than comparative model. The accuracy of RNN was high in both sets of variables and the Area Under the Curve (AUC) value was also high. Also when we saw the hit-ratio table, the ratio of RNNs that predicted a poor company to be bankrupt was higher than that of other comparative models. However the limitation of this paper is that an overfitting problem occurs during RNN learning. But we expect to be able to solve the overfitting problem by selecting more learning data and appropriate variables. From these result, it is expected that this research will contribute to the development of a bankruptcy prediction by proposing a new dynamic model.