• Journal of Preventive Medicine and Public Health
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• v.32 no.1
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• pp.48-59
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• 1999

Objectives. This study was conducted to evaluate the association between upper extremity musculoskeletal symptoms and Rapid Upper Limb Assessment(RULA) in vehicle assembly line workers. The goal of this study is to show the feasibility of RULA as a checklist for work related musculoskeletal symptoms (WMSDs) in Korean workers. Methods. The total number of 199 people from the department of assembly and 115 people from the department of Quality Control(QC) in automotive plant were subjects for this cross sectional study. A standard symptom questionnaire survey has been used for the individual characteristics, work history, musculosketal symptoms and non-occupational covariates. The data were obtained by applying one-on-one interview for the all subjects. RULA has been applied for ergonomic work posture analysis and the primary ergonomic risk sure was computed by RULA method. Association between upper extremity musculoskeletal symptoms and RULA were assessed by multiple logistic regression analysis. Results. A total of 314 workers was examined. The prevalence of musculoskeletal symptoms by NIOSH case definition was 62.4%. The distribution of musculoskeletal symptoms by the part of the body turned out to be following; back:41.4%, neck: 32.8%, shoulder: 26.4%, arm: 10.5% and hand:29.3%. The relationship of the individual RULA scores were statistically significant for the prevalence of musculoskeletal symptoms. As the result of the multiple logistic regressioin analysis, grand final score (OR=2.250 95% CI: 1.402-3.612) was associated with musculoskeletal symptoms in any part of the body.; upper arm score(OR=1.786 95% CI: 1.036-3.079) and posture score A(OR=1.634 95% CI: 1.016-2.626) in neck; muscel use score(OR=3.076 95% CI:1.782-5.310) and posture score A(OR=1.798 95% CI: 1.072-3.017) in shoulder; upper arm score(OR=1.715 95% CI: 1.083-2.715) and muscel use score(OR=2.057 95% CI:1.303-3.248) in neck & shoulder; muscle use score(OR=10.662 95% CI: 3.180-35.742) in arm; writst/wist score(OR=2.068 95% CI: 1.130-3.786) and muscle use score(OR=2.215 95% CI: 1.284-3.819) in hand & wrist.; muscle use score of trunk (OR=2.601 95% CI: 1.147-5.901) in back. Conclusions. Musculoskeletal symptoms of the extremities were strongly associated with individual RULA body score. These results show that RULA can be used as a useful assessment tool for the evaluation of musculoskeletal loading which is known to contribute to work-related musculoskeletal disorders. RULA also can be used as a screening tool or incorporated into a wider ergonomic assessment of epidemiological, physical, mental, environmental and organizational factors. As shown in this study, complement of the analysis system for the other risk factors and characterizing between the upper limb and back part will be needed for future work.

• 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.

• Progress in Medical Physics
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• v.26 no.3
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• pp.168-177
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• 2015

For evaluating the treatment planning accurately, the quality assurance for treatment planning is recommended when patients were treated with IMRT which is complex and delicate. To realize this purpose, treatment plan quality assurance software can be used to verify the delivered dose accurately before and after of treatment. The purpose of this study is to evaluate the accuracy of treatment plan quality assurance software for each IMRT plan according to MLC DLG (dosimetric leaf gap). Novalis Tx with a built-in HD120 MLC was used in this study to acquire the MLC dynalog file be imported in MobiusFx. To establish IMRT plan, Eclipse RTP system was used and target and organ structures (multi-target, mock prostate, mock head/neck, C-shape case) were contoured in I'mRT phantom. To verify the difference of dose distribution according to DLG, MLC dynalog files were imported to MobiusFx software and changed the DLG (0.5, 0.7, 1.0, 1.3, 1.6 mm) values in MobiusFx. For evaluation dose, dose distribution was evaluated by using 3D gamma index for the gamma criteria 3% and distance to agreement 3 mm, and the point dose was acquired by using the CC13 ionization chamber in isocenter of I'mRT phantom. In the result for point dose, the mock head/neck and multi-target had difference about 4% and 3% in DLG 0.5 and 0.7 mm respectively, and the other DLGs had difference less than 3%. The gamma index passing-rate of mock head/neck were below 81% for PTV and cord, and multi-target were below 30% for center and superior target in DLGs 0.5, 0.7 mm, however, inferior target of multi-target case and parotid of mock head/neck case had 100.0% passing rate in all DLGs. The point dose of mock prostate showed difference below 3.0% in all DLGs, however, the passing rate of PTV were below 95% in 0.5, 0.7 mm DLGs, and the other DLGs were above 98%. The rectum and bladder had 100.0% passing rate in all DLGs. As the difference of point dose in C-shape were 3~9% except for 1.3 mm DLG, the passing rate of PTV in 1.0 1.3 mm were 96.7, 93.0% respectively. However, passing rate of the other DLGs were below 86% and core was 100.0% passing rate in all DLGs. In this study, we verified that the accuracy of treatment planning QA system can be affected by DLG values. For precise quality assurance for treatment technique using the MLC motion like IMRT and VMAT, we should use appropriate DLG value in linear accelerator and RTP system.

• Journal of The Korean Society of Grassland and Forage Science
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• v.40 no.1
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• pp.19-28
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• 2020

This study was conducted to compare the agronomic characteristics and productivity according to the species and varieties of winter forage crops in reclaimed land. Winter forage crops used in this study were developed in National Institute of Crop Science, RDA. Oats ('Samhan', 'Jopung', 'Taehan', 'Dakyung' and 'Hi-early'), forage barley ('Yeongyang', 'Yuyeon', 'Yujin', 'Dacheng' and 'Yeonho'), rye ('Gogu', 'Jogreen' and 'Daegokgreen') and triticale ('Shinyoung', 'Saeyoung', 'Choyoung', 'Sinseong', 'Minpung' and 'Gwangyoung') were planted in the reclaimed land of Sihwa district in Hwaseong, Gyeonggi-do in the autumn of 2018 and cultivated using each standard cultivation method, and harvested in May 2019(oat and rye: 8 May, barley and triticale: 20 May.) The emergency rate was the lowest in rye (84.4%), and forage barley, oat and triticale were in similar levels (92.8 to 98.8%). Triticale was the lowest (416 tiller/㎡) and oat was the highest (603 tiller/㎡) in tiller number. Rye was the earliest in the heading date (April 21), triticale was April 26, and oat and forage barley were in early May (May 2 and May 5). The plant height was the highest in rye (95.6 cm), and triticale and forage barley was similar (76.3 and 68.3cm) and oat was the lowest (54.2 cm). Dry matter(DM) content of rye was the highest in the average of 46.04% and the others were similar at 35.09~37.54%. Productivity was different among species and varieties, with the highest dry matter yield of forage barley (4,344 kg/ha), oat was similar to barley, and rye and triticale were lowest. 'Dakyoung' and 'Hi-early' were higher in DM yield (4,283 and 5,490 kg/ha), and forage barley were higher in 'Yeonho', 'Yujin' and 'Dacheng' varieties (4,888, 5,433 and 5,582 kg/ha). Crude protein content of oat (6.58%) tended to be the highest, and TDN(total digectible nutrient) content (63.61%) was higher than other varieties. In the RFV(relative feed value), oats averaged 119, while the other three species averaged 92~105. The weight of 1,000 grain was the highest in triticale (43.03 g) and the lowest in rye (31.61 g). In the evaluation of germination rate according to the salt concentration (salinity), the germination rate was maintained at about 80% from 0.2 to 0.4% salinity. The correlation coefficient between germination and salt concentration was high in oat and barley (-0.91 and -0.92) and lowest in rye (-0.66). In conclusion, forage barley and oats showed good productivity in reclaimed land. Adaptability is also different among varieties of forage crops. When growing forage crops in reclaimed land, the selection of highly adaptable species and varieties was recommended.

• Journal of dental hygiene science
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• v.17 no.6
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• pp.523-532
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• 2017

The purpose of this study was to provide basic data to standardize the clinical dental hygiene curriculum, based on analysis of current clinical dental hygiene curricula in Korea. We emailed questionnaires to 12 schools to investigate clinical dental hygiene curricula, from February to March, 2017. We analyzed the clinical dental hygiene curricula in 5 schools with a 3-year program and in 7 schools with a 4-year program. The questionnaire comprised nine items on topics relating to clinical dental hygiene, and four items relating to the dental hygiene process and oral prophylaxis. The questionnaire included details regarding the subject name, the grade/semester/credit system, course content and class hours, the number of senior professors, and the number of patients available for dental hygiene clinical training purposes. In total, there were 96 topics listed in the curricula relating to clinical dental hygiene training, and topics varied between the schools. There was an average of 20.4 topic credits, and more credits and hours were allocated to the 4-year program than to the 3-year program. On average, the ratio of students to professors was 21.4:1. Course content included infection control, concepts for dental hygiene processes, dental hygiene assessment, intervention and evaluation, case studies, and periodontal instrumentation. An average of 2 hours per patient was spent on dental hygiene practice, with an average of 1.9 visits. On average, student clinical training involved 19 patients and 26.6 patients in the 3-year and 4-year programs, respectively. The average participation time per student per topic was 38.0 hours and 53.1 hours, in the 3-year and 4-year programs, respectively. Standardizing the clinical dental hygiene curricula in Korea will require consensus guidelines on topics, the number of classes required to achieve core competencies as a dental hygienist, and theory and practice time.

• Journal of Intelligence and Information Systems
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• v.26 no.3
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• pp.127-147
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• 2020

The world is suffering from numerous human and economic losses due to the novel coronavirus infection (COVID-19). The Korean government established a strategy to overcome the national infectious disease crisis through research and development. It is difficult to find distinctive features and changes in a specific R&D field when using the existing technical classification or science and technology standard classification. Recently, a few studies have been conducted to establish a classification system to provide information about the investment research areas of infectious diseases in Korea through a comparative analysis of Korea government-funded research projects. However, these studies did not provide the necessary information for establishing cooperative research strategies among countries in the infectious diseases, which is required as an execution plan to achieve the goals of national health security and fostering new growth industries. Therefore, it is inevitable to study information services based on the classification system and classification model for establishing a national collaborative R&D strategy. Seven classification - Diagnosis_biomarker, Drug_discovery, Epidemiology, Evaluation_validation, Mechanism_signaling pathway, Prediction, and Vaccine_therapeutic antibody - systems were derived through reviewing infectious diseases-related national-funded research projects of South Korea. A classification system model was trained by combining Scopus data with a bidirectional RNN model. The classification performance of the final model secured robustness with an accuracy of over 90%. In order to conduct the empirical study, an infectious disease classification system was applied to the coronavirus-related research and development projects of major countries such as the STAR Metrics (National Institutes of Health) and NSF (National Science Foundation) of the United States(US), the CORDIS (Community Research & Development Information Service)of the European Union(EU), and the KAKEN (Database of Grants-in-Aid for Scientific Research) of Japan. It can be seen that the research and development trends of infectious diseases (coronavirus) in major countries are mostly concentrated in the prediction that deals with predicting success for clinical trials at the new drug development stage or predicting toxicity that causes side effects. The intriguing result is that for all of these nations, the portion of national investment in the vaccine_therapeutic antibody, which is recognized as an area of research and development aimed at the development of vaccines and treatments, was also very small (5.1%). It indirectly explained the reason of the poor development of vaccines and treatments. Based on the result of examining the investment status of coronavirus-related research projects through comparative analysis by country, it was found that the US and Japan are relatively evenly investing in all infectious diseases-related research areas, while Europe has relatively large investments in specific research areas such as diagnosis_biomarker. Moreover, the information on major coronavirus-related research organizations in major countries was provided by the classification system, thereby allowing establishing an international collaborative R&D projects.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.163-177
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• 2019

As smartphones are getting widely used, human activity recognition (HAR) tasks for recognizing personal activities of smartphone users with multimodal data have been actively studied recently. The research area is expanding from the recognition of the simple body movement of an individual user to the recognition of low-level behavior and high-level behavior. However, HAR tasks for recognizing interaction behavior with other people, such as whether the user is accompanying or communicating with someone else, have gotten less attention so far. And previous research for recognizing interaction behavior has usually depended on audio, Bluetooth, and Wi-Fi sensors, which are vulnerable to privacy issues and require much time to collect enough data. Whereas physical sensors including accelerometer, magnetic field and gyroscope sensors are less vulnerable to privacy issues and can collect a large amount of data within a short time. In this paper, a method for detecting accompanying status based on deep learning model by only using multimodal physical sensor data, such as an accelerometer, magnetic field and gyroscope, was proposed. The accompanying status was defined as a redefinition of a part of the user interaction behavior, including whether the user is accompanying with an acquaintance at a close distance and the user is actively communicating with the acquaintance. A framework based on convolutional neural networks (CNN) and long short-term memory (LSTM) recurrent networks for classifying accompanying and conversation was proposed. First, a data preprocessing method which consists of time synchronization of multimodal data from different physical sensors, data normalization and sequence data generation was introduced. We applied the nearest interpolation to synchronize the time of collected data from different sensors. Normalization was performed for each x, y, z axis value of the sensor data, and the sequence data was generated according to the sliding window method. Then, the sequence data became the input for CNN, where feature maps representing local dependencies of the original sequence are extracted. The CNN consisted of 3 convolutional layers and did not have a pooling layer to maintain the temporal information of the sequence data. Next, LSTM recurrent networks received the feature maps, learned long-term dependencies from them and extracted features. The LSTM recurrent networks consisted of two layers, each with 128 cells. Finally, the extracted features were used for classification by softmax classifier. The loss function of the model was cross entropy function and the weights of the model were randomly initialized on a normal distribution with an average of 0 and a standard deviation of 0.1. The model was trained using adaptive moment estimation (ADAM) optimization algorithm and the mini batch size was set to 128. We applied dropout to input values of the LSTM recurrent networks to prevent overfitting. The initial learning rate was set to 0.001, and it decreased exponentially by 0.99 at the end of each epoch training. An Android smartphone application was developed and released to collect data. We collected smartphone data for a total of 18 subjects. Using the data, the model classified accompanying and conversation by 98.74% and 98.83% accuracy each. Both the F1 score and accuracy of the model were higher than the F1 score and accuracy of the majority vote classifier, support vector machine, and deep recurrent neural network. In the future research, we will focus on more rigorous multimodal sensor data synchronization methods that minimize the time stamp differences. In addition, we will further study transfer learning method that enables transfer of trained models tailored to the training data to the evaluation data that follows a different distribution. It is expected that a model capable of exhibiting robust recognition performance against changes in data that is not considered in the model learning stage will be obtained.

• Journal of the Korean Wood Science and Technology
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• v.36 no.1
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• pp.110-123
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• 2008

The use of CCA as a wood preservative was recently inhibited due to its environmental pollution and human harmfulness. Instead of CCA, copper azole (CuAz) and alkaline copper quaternary (ACQ) have been used as alternative wood preservatives, but the price of the preservatives is much more expensive than that of CCA. As a substitute for high-priced CuAz and ACQ, environmentally friendly wood preservatives were formulated with okara, which is an organic waste from the production of tofu. Prior to formulating the preservatives, okara was hydrolyzed by three levels of sulfuric acid concentration (1, 2.5 and 5%) to easily penetrate the effective components of the preservatives into wood blocks. Final preservative solutions were formulated with the hydrolyzed okara and metal salts, such as copper sulfate, copper chloride and borax. The preservatives were treated into wood blocks by vacuum-pressure method to measure the treatability of the preservatives, and the treated wood blocks were placed in hot water for three days to measure the leachability of the preservatives. The effective components of the preservatives might be successfully penetrated into wood blocks through the uses of hydrolyzed okara and ammonia water. However, the leached amount of effective components was increased as the concentration of acid used for the hydrolysis of okara increased. The treatability and leachability of the preservatives were not affected by hydrolysis temperature but negatively affected by the addition of borax. Based on the results above, the optimal conditions for formulating okara-based wood preservatives cost-effectively and environmentally might be 1% acid hydrolysis of okara and the use of $CuCl_2$ as a metal salt. In addition, the treatability and leachability of okara-based wood preservatives were superior or no differences comparing with those of CuAz. Therefore, it is concluded that okara-based wood preservatives might have a potential to be used as an environmentally friendly wood preservative.

• Clinical and Experimental Pediatrics
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• v.52 no.11
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• pp.1249-1259
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• 2009

Purpose:From a societal perspective, we evaluated the cost-effectiveness of a novel sustained-release injection of recombinant human growth hormone (GH) administered on a weekly basis compared with that of the present daily GH injection for the treatment of children with GH deficiency. Methods:Health-related utility for GH therapy was measured based on the visual analogue scale. During July 2008, caregivers of 149 children receiving GH therapy form 2 study sites participated in a web-based questionnaire survey. The survey required the caregivers to rate their current subjective utility with daily GH injections or expected utility of weekly GH injections. Because there was no difference in the costs of the daily and weekly therapies, for the purposes of this study, only drug acquisition costs were considered. Results:Switching from daily to weekly injection of GH increased the utility from 0.584 to 0.784 and incurred an extra cost of 4,060,811 Korean won (KW) per year. The incremental cost-utility ratio (ICUR) for a base case was 20,305,055 KW per quality-adjusted life year (QALY) gained. Scenario analyses showed that the ICUR ranged from 15,751,198 to 25,489,929 KW per QALY. Conclusion:The ICUR for a base case and worst case scenario analyses ranged from 0.85 to 1.37-times per capita gross domestic product of Korea, which is considered to be within the generally accepted willingness-to-pay threshold. Thus, it is concluded that switching from daily to weekly injection of GH would be cost-effective.

• Food Science and Preservation
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• v.16 no.6
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• pp.977-984
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• 2009

Campbell Early grapes, the major grape variety in Korea, contain 13 - 15% (w/v) sugar, which is lower than that appropriate for fermentation of red wine. Therefore, chaptalization with sucrose is usually used to increase the sugar level to an extent adequate to produce a wine containing about 12% (v/v) alcohol. In the present study, fermentation of freeze-concentrated Campbell Early grape juice at $25^{\circ}Brix$ was investigated using several industrial wine yeasts including Saccharomyces cerevisiae OC2, S. cerevisiae Fermivin, and S. cerevisiae W-3. During fermentation, changes in the levels of soluble solids, alcohol, total acid, and yeast viable counts were investigated. Alcohol content reached maximal levels after 9 days of fermentation, and was 12.6% (v/v) when the Fermivin strain was used and 13% (v/v) when each of the OC2 and W-3 strains was used. No significant between-strain difference was found, except for slightly slower alcohol production and sugar consumption, and a higher total acid content when strain OC2 was used. Changes in the yeast viable counts were similar during fermentation. The physicochemical characteristics of Campbell Early wine prepared using freeze-concentrated juice were investigated by measuring the levels of total phenolic compounds, organic acids, acetaldehyde, and minor alcohols and assessment of color values. Similar levels of soluble solids and total phenolic contents were observed in wines fermented by the three different strains, but a higher level of total acid was noted in OC2-fermented wine and a lower level of alcohol in Fermivin-fermented wine. The highest level of malic acid and the lowest level of lactic acid were detected in Fermivin-fermented wine. Although the wines showed variable levels of acetaldehyde and minor alcohols, the concentrations of these materials were much lower than those mandated by legal regulations promulgated by the Korean National Tax Service. Red and violet colors in OC2-fermented wine were darker than those of W-3-fermented wine. In sensory evaluation, W-3-fermented wine obtained the highest scores for color and flavor. However, the best taste score was reported for the OC2-fermented wine, whereas Fermivin-fermented wine was awarded the highest score in overall acceptance.