• The Mathematical Education
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• v.58 no.1
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• pp.101-120
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• 2019

The goal of this study was to apply an expectancy-value model(Wigfield & Eccles, 2000) to explain changes in six multi-ethnic students' achievement motivation in mathematics during sixth (2012) to eighth (2014) grades. In order to achieve this goal, we used narrative research methods. Although individual students' achievement motivation and mathematics related life experiences differed, there are some common factors influencing their motivation development, especially (a) roles played by parents and teachers; (b) assessment of peers' competencies; (c) past learning experiences related to mathematics curriculum; (d) perception of the relationship between mathematics competency and other subjects; (e) home backgrounds; and (f) perceived task values. In this study, we achieved some insight into why some multi-ethnic students are willing to study hard to get good scores while others are uninterested in mathematics, and why some multi-ethnic students are likely to pursue new mathematical tasks and persist despite challenges, while others easily give up studying mathematics in the face of adversity. We argue that in order to increase and sustain multi-ethnic students' achievement motivation, educators and parents should recognize that motivation is contextually formulated in the intersection of current people, time, and space, not a personal entity formed in an individual's mind. The findings of this study shed light on the development of achievement motivation and can inform efforts to develop multi-ethnic students' positive motivation, which might influence their mathematics achievement and success in school.

• The Journal of Korean Philosophical History
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• no.38
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• pp.303-328
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• 2013

The purpose of the present article is to understand moral instruction through Ethics written by Spinoza and enable the implications drawn from its understanding to give shape to lesson plans. In his representative book titled Ethics, Spinoza speculates ultimate substance from the metaphysical perspective and converges it into ethics. The ultimate substance, which is a cause of itself, refers to immanent cause of all things that have numerous attributes as essence. All things in nature develop the substance and exchange influence among individuals at the same time. A human in the influential relationship perceives things based on one's beneficialness and assigns moral words of good and evil. However, a human, who is a mode of substance, should escape from morals that are superficial, relative, and objective, in order to realize nature. Becoming a more complete human requires going through moral imagination in reality but going beyond the imagination ultimately. Moral instruction premises the moral imagination of a student who exists as a mode; meanwhile, it is a study to escape from the influence of moral imagination. Good and evil arise from the limitation that an existing human has, but if a life is to preserve the necessity of ultimate substance, moral instruction can be defined as the processes of alleviating the influence that hinders a human's nature from being realized. Giving shape to this processes with the basis on the Spinoza's epistemic argument, moral instructional texts can be composed of stages to form more adequate moral ideas about moral subjects gradually and cumulatively. The moral instruction like this expects moral awareness which is relatively perfect than the present moral imagination. Furthermore, with the teaching and learning like this sustained, it is expected that ultimately the limitation arising from sensible perception can be overcome to approach the realization of a human's nature.

• Journal of the Korean Association of Geographic Information Studies
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• v.26 no.3
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• pp.37-51
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• 2023

Spatial information is essential for interpreting urban phenomena. Methodologies for spatializing urban information, especially when it lacks location details, have been consistently developed. Typical methods include Geocoding using structured address information or place names, spatial integration with existing geospatial data, and manual tasks utilizing reference data. However, a vast number of documents produced by administrative agencies have not been deeply dealt with due to their unstructured nature, even when there's demand for spatialization. This research utilizes the natural language processing model BERT to spatialize public documents related to urban planning. It focuses on extracting sentence elements containing addresses from documents and converting them into structured data. The study used 18 years of urban planning public announcement documents as training data to train the BERT model and enhanced its performance by manually adjusting its hyperparameters. After training, the test results showed accuracy rates of 96.6% for classifying urban planning facilities, 98.5% for address recognition, and 93.1% for address cleaning. When mapping the result data on GIS, it was possible to effectively display the change history related to specific urban planning facilities. This research provides a deep understanding of the spatial context of urban planning documents, and it is hoped that through this, stakeholders can make more effective decisions.

• Journal of Korea Society of Industrial Information Systems
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• v.29 no.2
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• pp.55-68
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• 2024

Plant engineering companies create or refer to requirements documents for each related field, such as plant process/equipment/piping/instrumentation, in different engineering departments. The process-related requirements document includes not only a description of the process but also the requirements of the equipment or related facilities that will operate it. Since the authors and reviewers of the requirements documents are different, there is a possibility that inconsistencies may occur between equipment or parts design specifications described in different requirement documents. Ensuring consistency in these matters can increase the reliability of the overall plant design information. However, the amount of documents and the scattered nature of requirements for a same equipment and parts across different documents make it challenging for engineers to trace and manage requirements. This paper proposes a method to analyze requirement sentences and calculate the similarity of requirement sentences in order to identify semantically identical sentences. To calculate the similarity of requirement sentences, we propose a named entity recognition method to identify compound words for the parts and properties that are semantically central to the requirements. A method to calculate the similarity of the identified compound words for parts and properties is also proposed. The proposed method is explained using sentences in practical documents, and experimental results are described.

• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.25-38
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• 2019

Selecting high-quality information that meets the interests and needs of users among the overflowing contents is becoming more important as the generation continues. In the flood of information, efforts to reflect the intention of the user in the search result better are being tried, rather than recognizing the information request as a simple string. Also, large IT companies such as Google and Microsoft focus on developing knowledge-based technologies including search engines which provide users with satisfaction and convenience. Especially, the finance is one of the fields expected to have the usefulness and potential of text data analysis because it's constantly generating new information, and the earlier the information is, the more valuable it is. Automatic knowledge extraction can be effective in areas where information flow is vast, such as financial sector, and new information continues to emerge. However, there are several practical difficulties faced by automatic knowledge extraction. First, there are difficulties in making corpus from different fields with same algorithm, and it is difficult to extract good quality triple. Second, it becomes more difficult to produce labeled text data by people if the extent and scope of knowledge increases and patterns are constantly updated. Third, performance evaluation is difficult due to the characteristics of unsupervised learning. Finally, problem definition for automatic knowledge extraction is not easy because of ambiguous conceptual characteristics of knowledge. So, in order to overcome limits described above and improve the semantic performance of stock-related information searching, this study attempts to extract the knowledge entity by using neural tensor network and evaluate the performance of them. Different from other references, the purpose of this study is to extract knowledge entity which is related to individual stock items. Various but relatively simple data processing methods are applied in the presented model to solve the problems of previous researches and to enhance the effectiveness of the model. From these processes, this study has the following three significances. First, A practical and simple automatic knowledge extraction method that can be applied. Second, the possibility of performance evaluation is presented through simple problem definition. Finally, the expressiveness of the knowledge increased by generating input data on a sentence basis without complex morphological analysis. The results of the empirical analysis and objective performance evaluation method are also presented. The empirical study to confirm the usefulness of the presented model, experts' reports about individual 30 stocks which are top 30 items based on frequency of publication from May 30, 2017 to May 21, 2018 are used. the total number of reports are 5,600, and 3,074 reports, which accounts about 55% of the total, is designated as a training set, and other 45% of reports are designated as a testing set. Before constructing the model, all reports of a training set are classified by stocks, and their entities are extracted using named entity recognition tool which is the KKMA. for each stocks, top 100 entities based on appearance frequency are selected, and become vectorized using one-hot encoding. After that, by using neural tensor network, the same number of score functions as stocks are trained. Thus, if a new entity from a testing set appears, we can try to calculate the score by putting it into every single score function, and the stock of the function with the highest score is predicted as the related item with the entity. To evaluate presented models, we confirm prediction power and determining whether the score functions are well constructed by calculating hit ratio for all reports of testing set. As a result of the empirical study, the presented model shows 69.3% hit accuracy for testing set which consists of 2,526 reports. this hit ratio is meaningfully high despite of some constraints for conducting research. Looking at the prediction performance of the model for each stocks, only 3 stocks, which are LG ELECTRONICS, KiaMtr, and Mando, show extremely low performance than average. this result maybe due to the interference effect with other similar items and generation of new knowledge. In this paper, we propose a methodology to find out key entities or their combinations which are necessary to search related information in accordance with the user's investment intention. Graph data is generated by using only the named entity recognition tool and applied to the neural tensor network without learning corpus or word vectors for the field. From the empirical test, we confirm the effectiveness of the presented model as described above. However, there also exist some limits and things to complement. Representatively, the phenomenon that the model performance is especially bad for only some stocks shows the need for further researches. Finally, through the empirical study, we confirmed that the learning method presented in this study can be used for the purpose of matching the new text information semantically with the related stocks.

• Korean Journal of Environment and Ecology
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• v.34 no.5
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• pp.396-401
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• 2020

As the world becomes more globalized, the non-native species issue has emerged as a problem that is growing internationally. In particular, the number of non-native turtles found in the wild has been increasing sharply in South Korea. At least 15 species of non-native turtles, including the red-eared slider (Trachemys scripta elegans) first imported in the 1970s, have been found in Korea. On October 15, 2019, an alligator snapping turtle (AST, Macrochelys temminckii) was found in a stream located in Gwangju city, South Korea. The discovery of AST became a big issue in South Korea as the animal is known for its large body size and aggressiveness and was featured widely in the mass media. In this study, to learn the public's perception of non-native species, we examined comments (opinions) to the online news articles about the AST. We collected 1,100 comments from the Internet news articles on the AST. Out of the 1,100 comments, 342 (31.1%) comments were related to non-native species' issues. Most of the respondents (97.7%, n=334) stated that the non-native species are a problem. Forty two comments mentioned potential threats posed by non-native species: non-native species' aggressive nature (n=11, 26.2%) and ecological disturbance (n=31, 73.8%). Lack of responsibility (n=122, 51.7%) was the major causative factor for the introduction of non-native species, and followed by indiscriminate pet trade (n=99, 42.0%), absence of relevant legislation (n=13, 5.5%), and absence of treatment (n=2, 0.8%). Animal registration (n=59, 45.7%) was the most commonly mentioned as the way to deal with the issue of the non-native species' invasion. Our results show that the public is aware of the seriousness of the invasion of non-native species, including AST. This study highlights that researchers and government officials need to consider the public's perception and opinions. We believe that our study can serve as an essential reference for the policy direction and the management of non-native species.

• Korean Journal of Plant Taxonomy
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• v.49 no.3
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• pp.224-239
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• 2019

Species identification for the Korean tribe Desmodieae was conducted using the DNA barcoding genes rbcL, matK (from chloroplast DNA) and ITS (from nuclear ribosomal DNA). A total of 25 taxa (n = 75) in five genera were sequenced, and neighbor-joining trees were constructed using different combinations of DNA barcodes. When comparing these phylogenetic trees, a tree with all loci combined (rbcL + matK + ITS) showed the highest rate of identification success (72%). On this tree, two subtribes and five genera within the tribe were supported as monophyletic. In the Desmodiinae clade, Desmodium and Hylodesmum were more closely related to each other than to Ohwia. In the Hylodesmum clade, H. oldhamii was found to be a sister to H. podocarpum complex, and all taxa within the complex were identified successfully. Subsp. fallax, regarded as a variety of subsp. oxyphyllum, is closely clustered with subsp. podocarpum. Although var. mandshuricum has been regarded as a synonym of var. oxyphyllum, this taxon is supported as a distinct variety. For the Lespedezinae clade, all species of Kummerowia were monophyletic, while nine of 16 Lespedeza taxa were identified successfully. In particular, the resolution of Macrolespedeza (28.5%) was lower than that of Junceae (77.8%). Among the Lespedeza taxa, L. cuneata was distinguishable from L. lichiyuniae, despite morphological similarities. It has been suggested that both L. maritima and L. inschanica are hybrids. The former is thought to be an independent species. While it is difficult to determine whether the latter originated via hybridization, this study showed that it is closely related to L. juncea.

• Korean Journal of Plant Resources
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• v.21 no.1
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• pp.1-4
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• 2008

The clonal variation in female flowering was studied in Larix leptolepis clone bank, consisting of 116 clones, for three years. The between-year variation was large; i.e. the percentage of flowering grafts and average number of flowering per graft were $28.4{\sim}67.2$ and $9{\sim}176$, respectively. Differences in flowering abundance among clones were large and statistically significant in all the years studied. The variance of flowering abundance among clones was increased when flowering was poor. The average of broad-sense heritability of flowering abundance was 0.52. The genetic gain(%G) was estimated at 57.4% when the upper 30% clones were selected. The clonal stability of flowering abundance was compared using average number of flowering and coefficient of variance value of each clone. The clones such as Gyeonggi 9(29), Kangwon 37(137), Chungnam 6(46), Chungnam 14(414), R11, R8 showed abundant flowering and high stability.

• Proceedings of the Korea Water Resources Association Conference
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• 2004.05b
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• pp.1310-1315
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• 2004

용수수요 추정의 기본은 수도계획에 사용할 수 있는 실사용량에 대한 조사로 이에 대한 자료가 거의 없기 때문에 각 수도사업별로 제시되는 추정방법이 조금씩 상이하며, 추정방법의 진위를 가릴 수 없이 수요추정의 악순환을 초래하고 있는 실정이다. 기존 물사용량 예측은 급수량 기준의 도시 전체에 내한 평균 LPCD를 이용함에 따라 물사용 특성을 충분히 고려찬 수 없어 지역별 실제 물사용량과 큰 오차가 유발되었다. 그러므로 수도계획 및 설계에 사용할 수 있는 신뢰성 있는 설계인자를 도출하기가 불가능하여 물수요 관리정책 수립, 수도요금체계 조정 및 누수방지계획 수립 등 경제적인 수도시설의 건설에 애로를 겪고 있다. 본 연구에서는 생활용수 중 가정용수에 대하여 세탁기, 변기, 싱크대 등 수도전에 유량계를 설치 실제 가정에서 사용하고 있는 용도별 사용량을 실측, 파악하였으며, 이로부터 얻은 용도별 사용량에 대한 기간별 소비특성을 분석하였다. 이로부터 생활용수 사용량의 소비패턴 및 시간대별 부하율 산정이 가능하며, 각종 용도별 사용수량의 소비형태를 찾아낼 수 있었다. 또한 가정용수 중 용도별 물 사용비율은 세탁용수, 변기, 주방, 목욕용수의 순으로 나타났으며 주택유형별로는 아파트 연립주택, 다세대 주택, 단독주택의 순으로 단독주택에서의 물사용량이 가장 적게 나타났다. 생활용수 공급량에 내해서는 시간별, 주별, 월별 그리고 계절변동 총량을 파악하였으며 시간대별로는 오후 3시경이 최대 소비량을 보였고, 주별로는 월요일 그리고 월별로는 7월의 용수사용량이 가장 큰 것으로 조사 되었다. 본 연구로부터 도출된 용도별 실측 물사용량 자료 및 분석결과로부터, 지금까지 공급량 기준의 계획수립이 이루어져왔던 파종 수도시설 규모결정시 합리적인 용수수요예측 및 수요관리가 이루어질 수 있을 것으로 판단되며, 용수수요의 과다예측 오해 해소 등 경제적, 과학적 물관리 정책수립을 위한 기초자료를 제공할 수 있을 것으로 기대된다.는 경제적인 방법이 될 수 있다. 하천수 등의 상호 관계 분석을 통해 장기간의 유역 물순환체계 변화를 분석할 수 있었다.골풀과, 닭의장풀과가 각 1종씩으로, 조사지점( I )보다 좀 더 많은 종이 분포하는 것으로 조사되었다. 또한 어류는 조사지점( I )에서 3회에 걸쳐 총 396개체가 채집되어 3목 8과 21종이었다. 이 중 한국 고유종은 11종이었고, 외래 어종은 검정우럭과 2종이 조사되었으며, Zacco platypus(피라미), Zacco temmincki(갈겨니), Acheilongnathus koreanus(칼납자루), Odontobutis platycephala(동사리), Coreoleuciscus splendidus(쉬리) 순으로 분포하고 있었고, Acheilognathus signifer(묵납자루)는 댐 건설 전에는 많이 분포하였으나 현장조사에서 서식을 확인 할 수 언어 개체수의 큰 감소내지 멸종된 것으로 추정되었다.에서 동시에 시행하였다. 수술 후 1년 내 시행한 심초음파에서 모든 환아에서 단지 경등도 이하의 승모판 폐쇄 부전 소견을 보였다. 수술 후 조기 사망은 없었으며, 합병증으로는 유미흉이 한 명에서 있었다. 술 후 10개월째 허혈성 확장성 심근증이 호전되지 않아 Dor 술식을 시행한 후 사망한 예를 제외한 나머지 6명은 특이 증상 없이 정상 생활 중이다 결론: 좌관상동맥 페동맥이상 기시증은 드물기는 하나, 영유아기에 심근경색 및 허혈성 심근증 또는 선천성 승모판 폐쇄 부전등을 초래하는 심각한 선천성 심질환이다. 그러나 진단 즉시 직접 좌관상동맥-대동맥 이식술로 수술적 교정을 해줌으로써 좋은 성적을 기대할 수 있음을 보여주었다.특히 교사들이 중요하게 인식하는 해방적 행동에 대한 목표를 강조하여 적용할 필요가 있음을 시사하고 있다.교하여 유의한 차이가 관찰되지 않았다. 또한 HSP 환자군에서도 $IL1RN^{*}2$ allele 빈도와 carriage rat

• Journal of The Korean Society of Grassland and Forage Science
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• v.24 no.3
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• pp.245-252
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• 2004

Experiment was carried out to find the fertilizing effects of 8 different application rates of swine compost fermented with sawdust(SCS) including Chemical fertilizer(CF) on forage yield and soil chemical characteristics of mixed pastures sown in Sep. 1993 at National Livestock Research Institute, RDA., in Suwon during low years. It was arranged in a randomized complete block design with three replicates. Dry matter yield were shown at similar among treatments except Control and $50\%$ SCS of standard amount plot. In botanical composition, the legume and weeds percentages of each treatment were increased as advancing year. The final year's legume percentage were high in line with SCS fertilizing plots($39\%{\sim}43\%$), SCS + CF plots($30\%{\sim}41\%$) and CF plot($32\%$). In productions of TDN, NE and crude protein yield, SCS or SCS($75\%$) + CF($25\%$) were nearly same comparing those of CF, respectively. Phosphate, potassium, magnesium contents and K/(Ca + Mg) except calcium contents of those SCS fertilizing plots in plant were generally high with comparing CF. Those contents were proportional according to the fertilizing amount These result indicate the possibility to substitute chemical fertilizer for SCS($75\%$, 25ton/ha) + CF, $25\%$) as manure-N 210 kg/ha, but might be considered accumulation phosphate in the soil.