• Korean Journal of Remote Sensing
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• v.24 no.2
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• pp.189-194
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• 2008

Middle-infrared (MIR) spectral region between 3.0 and $5.0\;{\mu}m$ in wavelength is useful for observing high temperature events such as volcanic activities and forest fire. However, atmospheric effects and sun irradiance in day time has not been well studied for this MIR spectral band. The objectives of this basic study is to evaluate atmospheric effects and eventually to estimate surface temperature from a single channel MIR image, although a typical approach utilize split-window method using more than two channels. Several parameters are involved for the correction including various atmospheric data and sun-irradiance at the area of interest. To evaluate the effect of sun irradiance, MODIS MIR images acquired in day and night times were used for comparison. Atmospheric parameters were modeled by MODTRAN, and applied to a radiative transfer model for estimating the sea surface temperature. MODIS Sea Surface Temperature algorithm based upon multi-channel observation was performed in comparison with results from the radiative transfer model from a single channel. Temperature difference of the two methods was $0.89{\pm}0.54^{\circ}C$ and $1.25{\pm}0.41^{\circ}C$ from the day-time and night-time images, respectively. It is also shown that the emissivity effect has by more largely influenced on the estimated temperature than atmospheric effects. Although the test results encourage using a single channel MR observation, it must be noted that the results were obtained from water body not from land surface. Because emissivity greatly varies on land, it is very difficult to retrieval land surface temperature from a single channel MIR data.

• Journal of Korean Home Economics Education Association
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• v.33 no.3
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• pp.41-63
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• 2021

The purpose of this study was to investigate the nutritional knowledge and eating behavior of middle school students in Gwangju area in order to provide basic data for the development of home economics curriculum that can help the students form healthy eating habits. For this purpose, a self-administered questionnaire was distributed to middle school students in Gwangju area. A total of 330 questionnaires were collected by convenience sampling and analyzed using SPSS(Statistics Package for the Social Science) Version 20.0 for Windows. Results of this study are as follows. First, school curriculum session ranked top(31.82%) on the list of sources for middle school students to acquire nutrition knowledge. Second, the mean score of nutrition knowledge of all respondents was moderately high(14.33 points out of maximum 20 points). In terms of nutrition knowledge by gender, female students had a higher level of nutrition knowledge in the 'Food' domain than their male counterparts(p<0.05). With regard to nutrition knowledge by grade, Grade 9 showed a higher score than Grade 7 and 8(p<0.001). Third, the mean score of eating behavior of all respondents was at an average level(69.75 out of 100 points). In terms of eating behavior by gender, male students showed a higher score than their female counterparts(p<0.05). In particular, male students had higher scores than female students for the following items: "I exercise regularly after school"(p<0.001); "I regularly eat meal three times per day"(p<0.01); "I don't skip breakfast"(p<0.01); and "I don't eat sweet food often"(p<0.01). In terms of eating behavior by grade, Grade 9 showed higher scores than Grades 7 and 8 for the following items: "I eat meal out of thankfulness for those who have prepared food"(p<0.01) and "I eat grains for every meal"(p<0.01). Finally, with regard to eating behavior depending on the level of nutrition knowledge, the 'Upper' and 'Middle' groups had higher scores for eating behavior than the 'Lower' group, indicating that a lower level of nutrition knowledge resulted in a lower score in eating behavior. Based on the above results, home economics teachers responsible for dietary education should have a greater sense of mission and pride and make more efforts to improve nutrition knowledge and eating behavior of middle school students.

• The Journal of Korean Society for Radiation Therapy
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• v.24 no.2
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• pp.67-75
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• 2012

Purpose: The study is to check the specialty of radiotherapists working in the department of radiation oncology and find job satisfaction, organizational commitment and job commitment having an effect on professional parts. After making analysis of the mutual relation, it is to provide radiotechnologists with making progress in the future. Materials and Methods: From March 2 to March 30, we had carried out a survey with email. It is possible to have 272 questionnaires answered in the survey. We make use of SPSS 13.0 for Windows to analyze the data collected for study. Frequency and a percentage are meant to show general characteristics, and t-test and ANOVA to do the difference between general properties and professionalism. Pearson's correlation coefficient also is meant to do the correlation of professionalism, organizational job commitment and job satisfaction, and multiple regression analysis to do the factor for a relevant variable to affect professionalism. Results: There are subdivisions in the professionalism informing us of the self-regulation $17.74{\pm}2.32/3.55{\pm}.46$, a sense of calling $17.58{\pm}2.63/3.52{\pm}.53$, reference of the professional $17.14{\pm}2.39/3.43{\pm}.48$, service to the public $15.97{\pm}2.48/3.19{\pm}50$, and autonomy $15.68{\pm}2.28/3.14{\pm}46$. Grand mean turns out to be $83.89{\pm}7.63$(Summation of items)/$3.37{\pm}0.49$ (Numbers of items). When it comes to a statistical relation between general characteristics and professionalism, the statistics have it that these come within age (P<.001), period of employment (P<.001), education status (P<.05), a monthly income (P<.001), radiotherapists who get a special license (P<.001), the position (P<.001), and an opportunity for developing (P<.001). As a result of organizational commitment, job commitment, and job satisfaction, grand mean in organizational commitment proves to be $80.10{\pm}8.15/3.34{\pm}.34$. There are subvisions showing affective commitment $28.64{\pm}4.61$/3.58, continuance commitment $27.54{\pm}4.22/3.44{\pm}.53$, and normative commitment $23.95{\pm}2.94/2.99{\pm}.37$ in order of precedence. The average grade in job commitment is $32.47{\pm}5.77/3.30{\pm}.60$ and that in job satisfaction is $63.39{\pm}10.16/3.17{\pm}.51$, respectively. We find the positive relationship between professionalism and organizational commitment (r=.522, P<.05), between professionalism and job commitment (r=.444, P<.05), and between professionalism and job satisfaction (r=.507, P<.05). And we also get the positive relationship between organizational commitment and job commitment (r=.549, P<.05), between organizational commitment and job satisfaction (r=.433, P<.05), and between job commitment and job satisfaction (r=.462, P<.05). To catch the factors influencing the professionalism of radiotherapists, we used multiple regression analysis. According to the final model, it appears affective commitment (B=.755, P<.05), normative commitment (B=.305, P<.05), job satisfaction (B=.092, P<.05), an opportunity for developing (B=-1.505, P<.05), and the position (B=-1.155, P<.05) in order of precedence. It seems that explaining influece on $R^2$ is 0.504. Conclusion: The results of the factors that influence professionalism working as radiotherapists in the department of radiation oncology have it that the more affective commitment, normative commitment, and job satisfaction we feel, the more professionalism we recognize. We think that the focus of professionalism is increased if getting the chances for radiotherapists to have little to do with developing opportunities given.

• Progress in Medical Physics
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• v.20 no.4
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• pp.298-307
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• 2009

By using the MR T2 map technique, this study intends, first, to measure the change of T2 values of cartilage between healthy people and patients with osteoarthritis and, second, to assess the form and the damage of cartilage in the knee-joint, through which this study would consider the utility of the T2 map technique. Thirty healthy people were selected based on their clinical history and current status and another thirty patients with osteoarthritis of the knee who were screened by simple X-ray from November 2007 to December 2008 were selected. Their T2 Spin Echo (SE hereafter) images for the cartilage of the knee joint were collected by using the T2 SE sequence, one of the multi-echo methods (TR: 1,000 ms; TE values: 6.5, 13, 19.5, 26, 32.5. 40, 45.5, 52). Based on these images, the changes in the signal intensity (SI hereafter) for each section of the cartilage of the knee joint were measured, which yielded average values of T2 through the Origin 7.0 Professional (Northampton, MA 01060 USA). With these T2s, the independent samples T-test was performed by SPSS Window version 12.0 to run the quantitative analysis and to test the statistical significance between the healthy group and the patient group. Closely looking at T2 values for each anterior and lateral articular cartilage of the sagittal plane and the coronal plane, in the sagittal plane, the average T2 of the femoral cartilage in the patient group with arthritis of the knee ($42.22{\pm}2.91$) was higher than the average T2 of the healthy group ($36.26{\pm}5.01$). Also, the average T2 of the tibial cartilage in the patient group ($43.83{\pm}1.43$) was higher than the average T2 in the healthy group ($36.45{\pm}3.15$). In the case of the coronal plane, the average T2 of the medial femoral cartilage in the patient group ($45.65{\pm}7.10$) was higher than the healthy group ($36.49{\pm}8.41$) and so did the average T2 of the anterior tibial cartilage (i.e., $44.46{\pm}3.44$ for the patient group vs. $37.61{\pm}1.97$ for the healthy group). As for the lateral femoral cartilage in the coronal plane, the patient group displayed the higher T2 ($43.41{\pm}4.99$) than the healthy group did ($37.64{\pm}4.02$) and this tendency was similar in the lateral tibial cartilage (i.e., $43.78{\pm}8.08$ for the patient group vs. $36.62{\pm}7.81$ for the healthy group). Along with the morphological MR imaging technique previously used, the T2 map technique seems to help patients with cartilage problems, in particular, those with the arthritis of the knee for early diagnosis by quantitatively analyzing the structural and functional changes of the cartilage.

• The Journal of Korean Society for Radiation Therapy
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• v.25 no.2
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• pp.145-151
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• 2013

Purpose: In this study, we analyzed how the dose change by field size effects on atomic number of shielding materials while using 6 MeV election beam. Materials and Methods: The parallel plate chamber is mounted in $25{\times}25cm^2$ the phantom such that the entrance window of the detector is flush with the phantom surface. phantom was covered laterally with aluminum, copper and lead which thickness have 5% of allowable transmission and then the doses were measured in field size $6{\times}6$, $10{\times}10$ and $20{\times}20cm^2$ respectively. 100 cGy was irradiated using 6 MeV electron beam and SSD (Source Surface Distance) was 100 cm with $10{\times}10cm^2$ field size. To calculate the photon flux, electron flux and Energy deposition produced after pass materals respectively, MCNPX code was used. Results: The results according to the various shielding materials which have 5% of allowable transmission are as in the following. Thickness change rate with field size of $6{\times}6cm^2$ and $20{\times}20cm^2$ that compared to the field size of $10{\times}10cm^2$ found to be +0.06% and -0.06% with aluminum, +0.13% and -0.1% with copper, -1.53% and +1.92% with lead respectively. Compare to the field size $10{\times}10cm^2$, energy deposition for $6{\times}6cm^2$ and $20{\times}20cm^2$ had -4.3% and +4.85% respectively without shielding material. With aluminum it had -0.87% and +6.93% respectively and with lead it had -4.16% and +5.57% respectively. When it comes to photon flux with $6{\times}6cm^2$ and $20{\times}20cm^2$ of field sizes the chance -8.95% and +15.92% without shielding material respectively, with aluminum the number -15.56% and +16.06% respectively and with copper the chance -12.27% and +15.53% respectively, with lead the number +12.36% and -19.81% respectively. In case of electron flux in the same condition, the number -3.92% and +4.55% respectively without shielding material respectively, with aluminum the number +0.59% and +6.87% respectively, with copper the number -1.59% and +3.86% respectively, with lead the chance -5.15% and +4.00% respectively. Conclusion: In this study, we found that the required thickness of the shielding materials got thinner with low atomic number substance as the irradiation field is increasing. On the other hand, with high atomic number substance the required thickness had increased. In addition, bremsstrahlung radiation have an influence on low atomic number materials and high atomic number materials are effected by scattered electrons.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.1-21
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• 2020

With the rapid development of artificial intelligence technology, various techniques have been developed to extract meaningful information from unstructured text data which constitutes a large portion of big data. Over the past decades, text mining technologies have been utilized in various industries for practical applications. In the field of business intelligence, it has been employed to discover new market and/or technology opportunities and support rational decision making of business participants. The market information such as market size, market growth rate, and market share is essential for setting companies' business strategies. There has been a continuous demand in various fields for specific product level-market information. However, the information has been generally provided at industry level or broad categories based on classification standards, making it difficult to obtain specific and proper information. In this regard, we propose a new methodology that can estimate the market sizes of product groups at more detailed levels than that of previously offered. We applied Word2Vec algorithm, a neural network based semantic word embedding model, to enable automatic market size estimation from individual companies' product information in a bottom-up manner. The overall process is as follows: First, the data related to product information is collected, refined, and restructured into suitable form for applying Word2Vec model. Next, the preprocessed data is embedded into vector space by Word2Vec and then the product groups are derived by extracting similar products names based on cosine similarity calculation. Finally, the sales data on the extracted products is summated to estimate the market size of the product groups. As an experimental data, text data of product names from Statistics Korea's microdata (345,103 cases) were mapped in multidimensional vector space by Word2Vec training. We performed parameters optimization for training and then applied vector dimension of 300 and window size of 15 as optimized parameters for further experiments. We employed index words of Korean Standard Industry Classification (KSIC) as a product name dataset to more efficiently cluster product groups. The product names which are similar to KSIC indexes were extracted based on cosine similarity. The market size of extracted products as one product category was calculated from individual companies' sales data. The market sizes of 11,654 specific product lines were automatically estimated by the proposed model. For the performance verification, the results were compared with actual market size of some items. The Pearson's correlation coefficient was 0.513. Our approach has several advantages differing from the previous studies. First, text mining and machine learning techniques were applied for the first time on market size estimation, overcoming the limitations of traditional sampling based- or multiple assumption required-methods. In addition, the level of market category can be easily and efficiently adjusted according to the purpose of information use by changing cosine similarity threshold. Furthermore, it has a high potential of practical applications since it can resolve unmet needs for detailed market size information in public and private sectors. Specifically, it can be utilized in technology evaluation and technology commercialization support program conducted by governmental institutions, as well as business strategies consulting and market analysis report publishing by private firms. The limitation of our study is that the presented model needs to be improved in terms of accuracy and reliability. The semantic-based word embedding module can be advanced by giving a proper order in the preprocessed dataset or by combining another algorithm such as Jaccard similarity with Word2Vec. Also, the methods of product group clustering can be changed to other types of unsupervised machine learning algorithm. Our group is currently working on subsequent studies and we expect that it can further improve the performance of the conceptually proposed basic model in this study.