• The Bulletin of The Korean Astronomical Society
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• v.43 no.2
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• pp.30.1-30.1
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• 2018

일제 강점기를 지난 후 광복을 맞았지만, 전란의 폐허 속에 개설된 대학의 천문학과의 관측 시설들은 전문한 상태였다. 필자가 학부 재학 중이던 6-70년대까지도 시 시공(時空)의 흐름은 필요한 것을 직접 만들어 사용할 수밖에 없는 시대로 몰아가고 있었다. 당시를 회고 하며 지금까지 걸어 온 "천문기기 제작 연구의 삶"을 회고하고자 한다. 대학 재학 시절 교수님의 도움으로 막스토브 망원경을 제작하고, 40cm 카세그레인 망원경 등 광학계의 원리와 특성연구를 통해 부품 조립을 수행할 수 있었다. 태양 흑점관측을 위한 10cm 굴절 망원경의 투영시설을 고안하여 6개월 동안 관측하였지만. 석사 논문을 위해 광전측광 관측시스템을 제작하여 식쌍성 관측을 수행하였다. 그 결과 한국의 시설로 UBV 광도곡선 완성하여 1975년 가을 천문학회에서 발표하였다. 1976년 2월 국립천문대 천문계산연구실에 발령 받고 역서편찬 업무를 담당하면서 소백산 60cm 망원경 최종 설치를 끝내고, 천문대(현 역삼동 과총회관 빌딩) 옥상에 2m 규모의 목재 돔을 설계 제작하고 일반인들을 위한 대중천문 활동을 시작하였다. 재직 중에 항상 한국의 열악한 천문시설의 상황을 실감하고 20대를 마감하면서 퇴직하여 "한국천문기기 연구소"라는 명칭으로 천체 돔을 설계하고, 돔 제작기계를 개발하였다. 망원경만 보관 중인 국내 4개 대학에 돔을 납품 한 후 연세대학교 천문대의 직경 6m 스텐레스 돔을 제작하였다. 아울러 연세대 천문대 60cm망원경을 설치하면서 이 곳에 입사하여 관측 장비개발 연구와 관측에 전념하게 되었다. 재직 기간 중 대학의 배려로 카나다 국립천문대(DAO) 방문연구원으로 1.8m 망원경으로 식쌍성들의 분광관측을 수행하여 시선속도곡선을 완성하였고, 체류 중에 스텝들과 국내에서 사용할 60cm용 첨단 분광기를 설계하였으나 대학에 재원이 없어 제작을 못한 아쉬움이 남는다. 1989년 2월 충북대학교 천문우주학과에 부임하면서 열악한 상황이지만 교육과 연구 장비로 20cm와 35cm 소형 망원경의 디지털 광전측광시스템으로 간이 천문대를 설치하여 운영하였다. 학과 설립 10 주년(1998년)을 맞아 40cm 망원경과 6m 돔을 설치하여 교내천문대가 완공되었다. 2000년이 되면서 대중 천문활동 을 위해 이동 천문대를 제작하여 4륜 자동차에 견인하여 여러 지역을 찾아 관측과 강연 활동 등 학과의 대중천문 활동의 특성을 살리는 계기를 만들게 되었다. 학과 설립 20주년(2008년)을 맞으면서 충북 진천에 16개 자동분할 개폐식 스릿의 9m 돔 안에 1m 망원경을 원격관측 시설을 완비하여 대학 본부의 기관으로 충북대학교천문대를 개관하고 관측시설을 완비하였다. 우리의 전통적인 세종시대 천문시설은 당대 최대의 시설이지만 당시 유물들이 모두 소실되어 현존하는 것이 하나도 없음은 실로 아쉬움이 큰 것이었다. 누군가는 그 구조, 형태, 원리, 기능, 사용방법 등을 밝히고 복원을 시도해야 할 시급함이 있었다. 문헌을 통해 1991년부터 학부졸업 논문으로 "고천문 의기(儀器) 복원연구" 분야의 발표를 시작하였다. 그 결과를 통해 세종탄신일에 영릉에서 숭모제 행사 후 그 곳에서 수년간 세종시대 고천문의기 한가지씩 작동모델을 복원하여 제막식을 거행하였다, 유물복원 회사 (주)옛기술과 문화 와 함께 팀을 이루어 매년 제작할 종목을 준비하게 되었다. 간의(簡儀)를 복원한 후에는 일성정시의, 소간의, 앙부일구, 정남일구, 석각천문도, 혼천의, 혼상, 각종 해시계 등 매년 지속적으로 복원되어 큰 규모의 야외 전시장이 완성되었다. 작동모델 설계연구팀의 자문과 제작팀과의 팀웍으로 이룬 성과인 것이다. 한번 시작품이 발표된 모델들은 국내 과학관과 박물관, 천문관에서 후속 모델을 설치하였다. 한국천문연구원과 부산 동래읍성 내에 장영실 과학 동산은 간의와 혼상을 비롯한 각종 해시계들을 설치한 큰 규모의 야외 전시장이다. 조선의 명망 높은 유학자들이 인격적인 하늘을 살펴보았던 혼천의와 일만원권에 그려 있는 국보 230호 자명종 혼천시계(일만원권의 그림)의 작동 모델을 제작하였다. 이와 같은 연구 결과들은 석사과정 박사과정을 통하여 더 심층적인 연구들이 발표되었고, 각종 조선(한국)의 천문의기(天文儀器) 연구 자료들은 연구팀들을 통해 중국과 일본 등 해외에서도 발표되었다. 지금까지 복원된 유물들이 완성되기까지는 참여한 많은 연구원들과 제작팀들이 합심하여 각자의 역할을 수행하여 최종 작동모델들이 하나 둘 완성되는 것이었다. 이것은 참으로 보람된 일이었고, 은퇴 후 지금은 재능기부자로서 즐거운 삶을 이어 갈수 있게 되었다.

• Korean Journal of Remote Sensing
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• v.36 no.6_1
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• pp.1465-1483
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• 2020

Evapotranspiration is a concept that includes the evaporation from soil and the transpiration from the plant leaf. It is an essential factor for monitoring water balance, drought, crop growth, and climate change. Actual evapotranspiration (AET) corresponds to the consumption of water from the land surface and the necessary amount of water for the land surface. Because the AET is derived from multiplying the crop coefficient by the reference evapotranspiration (ET0), an accurate calculation of the ET0 is required for the AET. To date, many efforts have been made for gridded ET0 to provide multiple products now. This study presents a comparison between the ET0 products such as FAO56-PM, LDAPS, PKNU-NMSC, and MODIS to find out which one is more suitable for the local-scale hydrological and agricultural applications in Korea, where the heterogeneity of the land surface is critical. In the experiment for the period between 2016 and 2019, the daily and 8-day products were compared with the in-situ observations by KMA. The analyses according to the station, year, month, and time-series showed that the PKNU-NMSC product with a successful optimization for Korea was superior to the others, yielding stable accuracy irrespective of space and time. Also, this paper showed the intrinsic characteristics of the FAO56-PM, LDAPS, and MODIS ET0 products that could be informative for other researchers.

• The Journal of Korean Academy of Sensory Integration
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• v.19 no.1
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• pp.69-82
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• 2021

Objective : This recent work intended to provide basic information for researchers and practitioners related to occupational therapy about Developmental Coordination Disorder (DCD) in South Korea. The previous research of screening DCD and the effects of intervention programs were reviewed. Methods : Peer-reviewed papers relating to DCD and published in Korea from January 1990 to December 2020 were systematically reviewed. The search terms "developmental coordination disorder," "development coordination," and "developmental coordination" were used to identify previous Korean research in this area from three representation database, the Research Information Sharing Service, Korean Studies Information Service System, and Google Scholar. We found a total of 4,878 articles identified through the three search engines and selected seventeen articles for analysis after removing those that corresponded to the overlapping or exclusion criteria. We adopted "the conceptual model" to analyze the selected articles about DCD assessment and intervention. Results : We found that twelve of the 17 studies showed the qualitative level of Level 2 using non-randomized approach between the two groups. The Movement Assessment Battery for Children and its second edition were the most frequently used tools in assessing children for DCD. Among the intervention studies, the eight articles (47%) were adopted a dynamic systems approach; a normative functional skill framework and cognitive neuroscience were each used in 18% of the pieces; and 11% of the articles were applied neurodevelopmental theory. Only one article was used a combination approach of normative functional skill and general abilities. These papers were mainly focused on the movement characteristics of children with DCD and the intervention effect of exercise or sports programs. Conclusion : Most of the reviewed studies investigated the movement characteristics of DCD or explore the effectiveness of particular intervention programs. In the future, it would be useful to investigate the feasibility of different assessment tools and to establish the effectiveness of various interventions used in rehabilitation for better motor performance in children with DCD.

• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.191-206
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• 2022

Recently, it is a de-facto approach to utilize a pre-trained language model(PLM) to achieve the state-of-the-art performance for various natural language tasks(called downstream tasks) such as sentiment analysis and question answering. However, similar to any other machine learning method, PLM tends to depend on the data distribution seen during the training phase and shows worse performance on the unseen (Out-of-Distribution) domain. Due to the aforementioned reason, there have been many efforts to develop domain-specified PLM for various fields such as medical and legal industries. In this paper, we discuss the training of a finance domain-specified PLM for the Korean language and its applications. Our finance domain-specified PLM, KB-BERT, is trained on a carefully curated financial corpus that includes domain-specific documents such as financial reports. We provide extensive performance evaluation results on three natural language tasks, topic classification, sentiment analysis, and question answering. Compared to the state-of-the-art Korean PLM models such as KoELECTRA and KLUE-RoBERTa, KB-BERT shows comparable performance on general datasets based on common corpora like Wikipedia and news articles. Moreover, KB-BERT outperforms compared models on finance domain datasets that require finance-specific knowledge to solve given problems.

• Korean Journal of Mineralogy and Petrology
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• v.35 no.3
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• pp.313-331
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• 2022

Natural or native abiotic molecular hydrogen (H₂) is a major component in natural gas, however yet its importance in the global energy sector's usage as clean and renewable energy is underestimated. Here we review the occurrence and geological settings of native hydrogen to demonstrate the much widesprease H₂ occurrence in nature by comparison with previous estimations. Three main types of source rocks have been identified: (1) ultramafic rocks; (2) cratons comprising iron (Fe²⁺)-rich rocks; and (3) uranium-rich rocks. The rocks are closely associated with Precambrian crystalline basement and serpentinized ultramafic rocks from ophiolite and peridotite either at mid-ocean ridges or within continental margin(Zgonnik, 2020). Inorganic geological processes producing H₂ in the source rocks include (a) the reduction of water during the oxidation of Fe²⁺ in minerals (e.g., olivine), (b) water splitting due to radioactive decay, (c) degassing of magma at low pressure, and (d) the reaction of water with surface radicals during mechanical breaking (e.g., fault) of silicate rocks. Native hydrogen are found as a free gas (51%), fluid inclusions in various rock types (29%), and dissolved gas in underground water (20%) (Zgonnik, 2020). Although research on H₂ has not yet been carried out in Korea, the potential H₂ reservoirs in the Gyeongsang Basin are highly probable based on geological and geochemical characteristics including occurrence of ultramafic rocks, inter-bedded basaltic layers and iron-copper deposits within thick sedimentary basin and igneous activities at an active continental margin during the Permian-Paleogene. The native hydrogen is expected to be clean and renewable energy source in the near future. Therefore it is clear that the origin and exploration of the native hydrogen, not yet been revealed by an integrated studies of rock-fluid interaction studies, are a field of special interest, regardless of the presence of economic native hydrogen reservoirs in Korea.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.263-286
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• 2022

This study acquires NBA statistical information for a total of 32 years from 1990 to 2022 using web crawling, observes variables of interest through exploratory data analysis, and generates related derived variables. Unused variables were removed through a purification process on the input data, and correlation analysis, t-test, and ANOVA were performed on the remaining variables. For the variable of interest, the difference in the mean between the groups that advanced to the playoffs and did not advance to the playoffs was tested, and then to compensate for this, the average difference between the three groups (higher/middle/lower) based on ranking was reconfirmed. Of the input data, only this year's season data was used as a test set, and 5-fold cross-validation was performed by dividing the training set and the validation set for model training. The overfitting problem was solved by comparing the cross-validation result and the final analysis result using the test set to confirm that there was no difference in the performance matrix. Because the quality level of the raw data is high and the statistical assumptions are satisfied, most of the models showed good results despite the small data set. This study not only predicts NBA game results or classifies whether or not to advance to the playoffs using machine learning, but also examines whether the variables of interest are included in the major variables with high importance by understanding the importance of input attribute. Through the visualization of SHAP value, it was possible to overcome the limitation that could not be interpreted only with the result of feature importance, and to compensate for the lack of consistency in the importance calculation in the process of entering/removing variables. It was found that a number of variables related to three points and errors classified as subjects of interest in this study were included in the major variables affecting advancing to the playoffs in the NBA. Although this study is similar in that it includes topics such as match results, playoffs, and championship predictions, which have been dealt with in the existing sports data analysis field, and comparatively analyzed several machine learning models for analysis, there is a difference in that the interest features are set in advance and statistically verified, so that it is compared with the machine learning analysis result. Also, it was differentiated from existing studies by presenting explanatory visualization results using SHAP, one of the XAI models.

• Journal of Intelligence and Information Systems
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• v.26 no.3
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• pp.71-90
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• 2020

Recently, the global insurance industry is rapidly developing digital transformation through the use of artificial intelligence technologies such as machine learning, natural language processing, and deep learning. As a result, more and more foreign insurers have achieved the success of artificial intelligence technology-based InsurTech and platform business, and Ping An Insurance Group Ltd., China's largest private company, is leading China's global fourth industrial revolution with remarkable achievements in InsurTech and Digital Platform as a result of its constant innovation, using 'finance and technology' and 'finance and ecosystem' as keywords for companies. In response, this study analyzed the InsurTech and platform business activities of Ping An Insurance Group Ltd. through the ser-M analysis model to provide strategic implications for revitalizing AI technology-based businesses of domestic insurers. The ser-M analysis model has been studied so that the vision and leadership of the CEO, the historical environment of the enterprise, the utilization of various resources, and the unique mechanism relationships can be interpreted in an integrated manner as a frame that can be interpreted in terms of the subject, environment, resource and mechanism. As a result of the case analysis, Ping An Insurance Group Ltd. has achieved cost reduction and customer service development by digitally innovating its entire business area such as sales, underwriting, claims, and loan service by utilizing core artificial intelligence technologies such as facial, voice, and facial expression recognition. In addition, "online data in China" and "the vast offline data and insights accumulated by the company" were combined with new technologies such as artificial intelligence and big data analysis to build a digital platform that integrates financial services and digital service businesses. Ping An Insurance Group Ltd. challenged constant innovation, and as of 2019, sales reached $155 billion, ranking seventh among all companies in the Global 2000 rankings selected by Forbes Magazine. Analyzing the background of the success of Ping An Insurance Group Ltd. from the perspective of ser-M, founder Mammingz quickly captured the development of digital technology, market competition and changes in population structure in the era of the fourth industrial revolution, and established a new vision and displayed an agile leadership of digital technology-focused. Based on the strong leadership led by the founder in response to environmental changes, the company has successfully led InsurTech and Platform Business through innovation of internal resources such as investment in artificial intelligence technology, securing excellent professionals, and strengthening big data capabilities, combining external absorption capabilities, and strategic alliances among various industries. Through this success story analysis of Ping An Insurance Group Ltd., the following implications can be given to domestic insurance companies that are preparing for digital transformation. First, CEOs of domestic companies also need to recognize the paradigm shift in industry due to the change in digital technology and quickly arm themselves with digital technology-oriented leadership to spearhead the digital transformation of enterprises. Second, the Korean government should urgently overhaul related laws and systems to further promote the use of data between different industries and provide drastic support such as deregulation, tax benefits and platform provision to help the domestic insurance industry secure global competitiveness. Third, Korean companies also need to make bolder investments in the development of artificial intelligence technology so that systematic securing of internal and external data, training of technical personnel, and patent applications can be expanded, and digital platforms should be quickly established so that diverse customer experiences can be integrated through learned artificial intelligence technology. Finally, since there may be limitations to generalization through a single case of an overseas insurance company, I hope that in the future, more extensive research will be conducted on various management strategies related to artificial intelligence technology by analyzing cases of multiple industries or multiple companies or conducting empirical research.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.1-32
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• 2018

In addition to stakeholders including managers, employees, creditors, and investors of bankrupt companies, corporate defaults have a ripple effect on the local and national economy. Before the Asian financial crisis, the Korean government only analyzed SMEs and tried to improve the forecasting power of a default prediction model, rather than developing various corporate default models. As a result, even large corporations called 'chaebol enterprises' become bankrupt. Even after that, the analysis of past corporate defaults has been focused on specific variables, and when the government restructured immediately after the global financial crisis, they only focused on certain main variables such as 'debt ratio'. A multifaceted study of corporate default prediction models is essential to ensure diverse interests, to avoid situations like the 'Lehman Brothers Case' of the global financial crisis, to avoid total collapse in a single moment. The key variables used in corporate defaults vary over time. This is confirmed by Beaver (1967, 1968) and Altman's (1968) analysis that Deakins'(1972) study shows that the major factors affecting corporate failure have changed. In Grice's (2001) study, the importance of predictive variables was also found through Zmijewski's (1984) and Ohlson's (1980) models. However, the studies that have been carried out in the past use static models. Most of them do not consider the changes that occur in the course of time. Therefore, in order to construct consistent prediction models, it is necessary to compensate the time-dependent bias by means of a time series analysis algorithm reflecting dynamic change. Based on the global financial crisis, which has had a significant impact on Korea, this study is conducted using 10 years of annual corporate data from 2000 to 2009. Data are divided into training data, validation data, and test data respectively, and are divided into 7, 2, and 1 years respectively. In order to construct a consistent bankruptcy model in the flow of time change, we first train a time series deep learning algorithm model using the data before the financial crisis (2000~2006). The parameter tuning of the existing model and the deep learning time series algorithm is conducted with validation data including the financial crisis period (2007~2008). As a result, we construct a model that shows similar pattern to the results of the learning data and shows excellent prediction power. After that, each bankruptcy prediction model is restructured by integrating the learning data and validation data again (2000 ~ 2008), applying the optimal parameters as in the previous validation. Finally, each corporate default prediction model is evaluated and compared using test data (2009) based on the trained models over nine years. Then, the usefulness of the corporate default prediction model based on the deep learning time series algorithm is proved. In addition, by adding the Lasso regression analysis to the existing methods (multiple discriminant analysis, logit model) which select the variables, it is proved that the deep learning time series algorithm model based on the three bundles of variables is useful for robust corporate default prediction. The definition of bankruptcy used is the same as that of Lee (2015). Independent variables include financial information such as financial ratios used in previous studies. Multivariate discriminant analysis, logit model, and Lasso regression model are used to select the optimal variable group. The influence of the Multivariate discriminant analysis model proposed by Altman (1968), the Logit model proposed by Ohlson (1980), the non-time series machine learning algorithms, and the deep learning time series algorithms are compared. In the case of corporate data, there are limitations of 'nonlinear variables', 'multi-collinearity' of variables, and 'lack of data'. While the logit model is nonlinear, the Lasso regression model solves the multi-collinearity problem, and the deep learning time series algorithm using the variable data generation method complements the lack of data. Big Data Technology, a leading technology in the future, is moving from simple human analysis, to automated AI analysis, and finally towards future intertwined AI applications. Although the study of the corporate default prediction model using the time series algorithm is still in its early stages, deep learning algorithm is much faster than regression analysis at corporate default prediction modeling. Also, it is more effective on prediction power. Through the Fourth Industrial Revolution, the current government and other overseas governments are working hard to integrate the system in everyday life of their nation and society. Yet the field of deep learning time series research for the financial industry is still insufficient. This is an initial study on deep learning time series algorithm analysis of corporate defaults. Therefore it is hoped that it will be used as a comparative analysis data for non-specialists who start a study combining financial data and deep learning time series algorithm.

• Journal of the Korean Wood Science and Technology
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• v.13 no.1
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• pp.19-62
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• 1985

This study was performed to investigate: (i) the bending processing properties of silk worm oak (Quercus acutissima Carr.) and Korean red pine (Pinus densiflora S. et Z.) by boiling and steaming treatments; (ii) the effects of interrelated factors - sapwood and heartwood, annual ring placement, softening temperature and time, moisture content. and wood defects on bending processing properties; (iii) the changing rates of bending radii after release from a tension strap, and (iv) the improving methods of bending process by treatment with chemicals. The size of specimens tested was $15{\times}15{\times}350mm$ for boiling and steaming treatments and $5{\times}10{\times}200mm$ for treatments with chemicals. The specimens were green for boiling treatments and dried to 15 percent for steaming treatments. The specimens for treatments with chemicals were soaked in saturated urea solution, 35 percent formaldehyde solution, 25 percent polyethylene glycol -400 solution, and 25 percent ammonium hydroxide solution for 5 days and immediately followed the bending process, respectively. The results obtained were as follows: 1. The internal temperature of silk worm oak and Korean red pine by boiling and steaming time was raised slowly to $30^{\circ}C$ but rapidly from $30^{\circ}C$ to $80-90^{\circ}C$ and then slowly from $80-90^{\circ}C$ to $100^{\circ}C$. 2. The softening time required to the final temperature was directly proportional to the thickness of specimen. The time required from $25^{\circ}C$ to $100^{\circ}C$ for 15mm-squared specimen was 9.6-11.2 minutes in silk worm oak and 7.6-8.1 minutes in Korean red pine. 3. The moisture content (M.C.) of specimen by steaming time was increased rapidly first 4 minutes in the both species, and moderately from 4 to 20 minutes and then slowly and constantly in silk worm oak, and moderately from 4 to 15 minutes and then slowly and constantly in Korean red pine. The M.C. of 15mm-squared specimen in 50 minutes of steaming was increased to 18.0 percent in the oak and 22.4 percent in the pine from the initial conditioned M.C. of 15 percent The rate of moisture adsorption measured was therefore faster in the pine than in the oak. 4. The mechanical properties of the both species were decreased significantly with the increase of boiling rime. The decrement by the boiling treatment for 60 minutes was measured to 36.6-45.0 percent in compressive strength, 12.5-17.5 percent in tensile strength, 31.6-40.9 percent in modulus of rupture, and 23.3-34.6 percent in modulus of elasticity. 5. The minimum bending radius (M.B.R.) of sapwood and heartwood was 60-80 mm and 90 mm in silk worm oak, and 260 - 300 mm and 280 - 300 mm in Korean red pine, respectively. Therefore, the both species showed better bending processing properties in sapwood than in heartwood. 6. The M.B.R. of edge-grained and flat-grained specimen in suk worm oak was 60-80 mm, but the M.B.R. in Korean red pine was 240-280 mm and 260-360 mm, respectively. Comparing the M.B.R. of edge-grained with flat-grained specimen, in the pine the edge-grained showed better bending processing property than the flat-grained. 7. The bending processing properties of the both species were improved by the rising of softening temperature from $40^{\circ}C$ to $100^{\circ}C$. The minimum softening temperature for bending was $90^{\circ}C$ in silk worm oak and $80^{\circ}C$ in Korean red pine, and the dependency of softening temperature for bending was therefore higher in the oak than in the pine. 8. The bending processing properties of the both species were improved by the increase of softening time as well as temperature, but even after the internal temperature of specimen reaching to the final temperature, somewhat prolonged softening was required to obtain the best plastic conditions. The minimum softening time for bending of 15 mm-squared silk worm oak and Korean red pine specimen was 15 and 10 minutes in the boiling treatment, and 30 and 20 minutes in the steaming treatment, respectively. 9. The optimum M.C. for bending of silk worm oak was 20 percent, and the M.C. above fiber saturation point rather degraded the bending processing property, whereas the optimum M.C. of Korean red pine needed to be above 30 percent. 10. The bending works in the optimum conditions obtained as seen in Table 24 showed that the M.B.R. of silk worm oak and Korean red pine was 80 mm and 240 mm in the boiling treatment, and 50 mm and 280 mm in the steaming treatment, respectively. Therefore, the bending processing property of the oak was better in the steaming than in the boiling treatment, but that of the pine better in the boiling than in the steaming treatment. 11. In the bending without a tension strap, the radio r/t of the minimum bending radius t to the thickness t of silk worm oak and Korean red pine specimen amounted to 16.0 and 21.3 in the boiling treatment, and 17.3 and 24.0 in the steaming treatment, respectively. But in the bending with a tension strap, the r/t of the oak and the pine specimen decreased to 5.3 and 16.0 in t he boiling treatment, and 3.3 and 18.7 in the steaming treatment, respectively. Therefore, the bending processing properties of the both species were significantly improved by the strap. 12. The effect of pin knot on the degradation of bending processing property was very severe in silk worm oak by side, e.g. 90 percent of the oak specimens with pin knot on the concave side were ruptured when bent to a 100 mm radius but only 10 percent of the other specimens with pin knot on the convex side were ruptured. 13. The changing rate in the bending radius of specimen bent to a 300 mm radius after 30 days of exposure to room temperature conditions was measured to 4.0-10.3 percent in the boiling treatment and 13,0-15.0 percent in the steaming treatment. Therefore, the degree of spring back after release was higher in the steaming than in the boiling treatment. And the changing rate of moisture-proofing treated specimen by expoxy resin coating was only -1.0.0 percent. 14. Formaldehyde, 35 percent solution, and 25 percent polyethylene glycol-400 solution found no effect on the plasticization of the both species, but saturated urea solution and 25 percent ammonium hydroxide solution found significant effect in comparison to non-treated specimen. But the effect of the treatment with chemicals alone was inferior to that of the steaming treatment, and the steaming treatment after the treatment with chemicals improved 10-24 percent over the bending processing property of steam-bent specimen. 15. Three plasticity coefficients - load-strain coefficient, strain coefficient, and energy coefficient - were evaluated to be appropriate for the index of bending processing property because the coefficients had highly significant correlation with the bending radius. The fitness of the coefficients as the index was good at load-strain coefficient, energy coefficient, and strain coefficient, in order.