• The Journal of Korean Society for Radiation Therapy
• /
• v.34
• /
• pp.31-42
• /
• 2022

Purpose: This study measures and compares the surface dose values in the virtual target volume using Tomotherapy, Halcyon, and TrueBeam equipment using 6MV-Flattening Filter-Free(FFF) energy. Materials and Methods: CT scan was performed under three conditions of without bolus, 0.5 cm bolus, and 1 cm bolus using an IMRT phantom (IBA, Germany). The Planning Target Volume (PTV) was set at the virtual target depth, and the treatment plan was established at 200 cGy at a time. For surface dosimetry, the Gafchromic EBT3 film was placed in the same section as the treatment planning system and repeated measurements were performed 10 times and then analyzed. Result: As a result of measuring the surface dose for each equipment, without, 0.5 cm, 1 cm bolus is in this order, and the result of Tomotherapy is 115.2±2.0 cGy, 194.4±3.3 cGy, 200.7±2.9 cGy, The result in Halcyon was 104.7±3.0 cGy, 180.1±10.8 cGy, 187.0±10.1 cGy, and the result in TrueBeam was 92.4±3.2 cGy, 148.6±5.7 cGy, 155.8±6.1 cGy, In all three conditions, the same as the treatment planning system, Tomotherapy, Halcyon, TreuBeam was measured highly in that order. Conclusion: Higher surface doses were measured in Tomotherapy and Halcyon compared to TrueBeam equipment. If the characteristics of each equipment are considered according to the treatment site and treatment purpose, it is expected that the treatment efficiency of the patient will increase as well as the treatment satisfaction of the patient.

• Korea Science and Art Forum
• /
• v.19
• /
• pp.281-292
• /
• 2015

For this study, I conducted a survey investigating current situation, user preference, and field experiment. Hospitals utilizing digital sign systems at least five years were selected, which are connected with visual elements (layout, typo, color) used in waiting areas and elements of the systems (time, video time line). The results obtained from the field survey showed that digital sign systems used the color of typo and background contrasted to one another to increase explicitness and to ensure easy understanding of contents. In addition, the Gothic typo with relatively high legibility was adopted. Time and video timeline, which characterize digital sign systems, showed the advertising screens of the hospitals and the guidance of medical treatment at regular intervals. Moreover, survey results on user satisfaction showed that a majority of respondents indicated they had difficulty in understanding digital information conveyed from digital sign systems due to time setting for rotational speed or the small size of typo although most of the users had previous experience with digital sign systems. The highest proportion of respondents (n=86, 86%) answered that information related to medical departments was what they sought most frequently and that this kind of information should be importantly considered in digital sign systems. For the experiment, new samples with restructured contents of current digital sign systems were created and tested while keeping its design unchanged as well as applying these new samples. Study participants were in their 20s through 50s. When the size of typo was larger under the same conditions for all age groups, study participants found the desired information approximately 3.5 seconds faster. In addition, those in their 20-30s and 40-50s showed the time difference of 4.7 seconds for small typo and 6 seconds for large typo, which suggested that there was a difference by age in the amount of time taken in the experiment to find the desired information from the rotating digital sign system regardless of age and the size of typo.

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.25 no.4
• /
• pp.427-435
• /
• 2023

Changes in flowering time due to weather fluctuations impact plant growth and ecosystem dynamics. Accurate prediction of flowering timing is crucial for effective forest ecosystem management. This study uses a process-based model to predict flowering timing in 2023 for five major tree species in Korean forests. Models are developed based on nine years (2009-2017) of flowering data for Abeliophyllum distichum, Robinia pseudoacacia, Rhododendron schlippenbachii, Rhododendron yedoense f. poukhanense, and Sorbus commixta, distributed across 28 regions in the country, including mountains. Weather data from the Automatic Mountain Meteorology Observation System (AMOS) and the Korea Meteorological Administration (KMA) are utilized as inputs for the models. The Single Triangle Degree Days (STDD) and Growing Degree Days (GDD) models, known for their superior performance, are employed to predict flowering dates. Daily temperature readings at a 1 km spatial resolution are obtained by merging AMOS and KMA data. To improve prediction accuracy nationwide, random forest machine learning is used to generate region-specific correction coefficients. Applying these coefficients results in minimal prediction errors, particularly for Abeliophyllum distichum, Robinia pseudoacacia, and Rhododendron schlippenbachii, with root mean square errors (RMSEs) of 1.2, 0.6, and 1.2 days, respectively. Model performance is evaluated using ten random sampling tests per species, selecting the model with the highest R². The models with applied correction coefficients achieve R² values ranging from 0.07 to 0.7, except for Sorbus commixta, and exhibit a final explanatory power of 0.75-0.9. This study provides valuable insights into seasonal changes in plant phenology, aiding in identifying honey harvesting seasons affected by abnormal weather conditions, such as those of Robinia pseudoacacia. Detailed information on flowering timing for various plant species and regions enhances understanding of the climate-plant phenology relationship.

• Journal of the Korean Society of Radiology
• /
• v.18 no.3
• /
• pp.293-300
• /
• 2024

On October 31, 2023, the revision of the Medical Technologist Act made it mandatory to complete field training courses in order to obtain a license as a radiologic technologist. Therefore, we would like to survey the actual situation of field training in medical institutions to inform the revised Medical Technologist Act and propose improvement measures to increase the effectiveness of field training. A survey was conducted from March to April, 2023, among radiologic technologists working in medical institutions. The questionnaire was sent through a form on a domestic portal site, Company N, and 120 respondents completed it. Eighty-two respondents, or 68.3 percent, had experience in educating on-the-job training students. 58% of the respondents were aware of the fact that the amendment to the Act on Medical Technologist etc. made field training mandatory to obtain a radiologic technologist license. In accordance with Article 9 of the Medical Technologist Act, which prohibits unlicensed persons from practicing, 50% of the respondents were aware that those who are in training to complete an education course equivalent to the license they are seeking to obtain at a university or other institution are allowed to practice as medical Technologists. When asked what is currently taught during fieldwork, 6% of respondents said that they are required to perform radiation-generating activities in addition to observing, guiding patients, and positioning and moving patients. When asked about the future direction of education as fieldwork becomes mandatory for licensure, 77% of respondents said that they will teach more than they currently do. When asked about the appropriate total length of fieldwork, 35% said 12 weeks and 480 hours, 33% said 8 weeks and 320 hours, and 27% said 16 weeks and 640 hours. It can be seen that the current on-the-job training is inadequate according to various regulations, and students' satisfaction is low. However, with the revision of the Act on Medical Technologists, field training has become mandatory to obtain a license as a radiologist, and it is necessary to improve the educational conditions of field training. Therefore, it is necessary to comply with the Nuclear Safety Act and the Rules on the Safety Management of Diagnostic Radiation Generating Devices, introduce standardized training objectives and evaluation systems, designate training hospitals and radiologists in charge of training, and introduce extended training periods and simulation exercises to internalize field training.

• Korean Journal of Occupational Health Nursing
• /
• v.13 no.1
• /
• pp.5-18
• /
• 2004

This study aims to investigate the use state of the health service computerization system in the occupational nursing field and the occupational nursers' satisfaction level, and provide basic data to promote the development of the health service computerization system for the nursing field. For this study, a questionnaire was provided to 118 occupational nurses who belong to Busan and Gyeongnam branches of KAOHN(Korean Association of Occupational Health Nurses) for 2 months (from Dec. 1, 2002 to Jan. 31, 2003). A tool of Choi Yong-Heui(2000) was used to investigate the satisfaction level of using the health service computerization system. The collected materials were analyzed in real number and percentage, average and standard deviation, t-test and ANOVA by using the SPSS WIN 10.0 program. This study is summarized as follows: 1. The average age was $31.99{\pm}5.58$ old in this study. The married were 54.2%. Participants who graduated from a junior college was 76.9%. The average service period was $4.48{\pm}4.68$ years. In service types, 79.7% of participants served in a health care center. The average service period was $3.22{\pm}2.89$ years. The service place which had 1000 workers or more was 35.6%. 2. Only 20.3% of participants in this study had a computer use education. 3. The field who participants used mostly was communication/internet, $3.29{\pm}.85$ hours in average. 4. 97.1% of occupational fields had computers and peripheral devices: 71.4% in pentium computer, 42.8% in the hard disk capacity of 20-29GB, 60.0% in 15 inch monitors, 86.2% in printers, 18.1% in digital cameras, 12.4% in LAN, and 9.5% in scanners. 80.1% of the occupational fields which were objects of study could use communication. 5. The occupational fields which did not introduced the health service computerization system were 62.8%. The main cause was attributable to entrepreneurs' insufficient recognition 66.6%. 51.5% of the entrepreneurs did not have an introduction plan. 37.2% of participating companies had the health service computerization system. 56.4% of them introduced it since the year 2000. 81.6% of the introduction motivation aimed to the efficiency of health service. The most issue upon introduction was insufficient understanding of a person in charge - 25.6%. The in-house development of the system covered 56.4%. 61.5% of the participants accepted their demands from the first stage of development. The direct effect of computerization showed the increase of 25.9% in the quickness and continuity of service treatment, and 25.9% in the serviceability of statistical treatment. 6. 22.0% of the participants had a computerization system use education. 69.2% of them had a in-house education. An educational method by nurses who used the computerization system was 76.9%. 92.3% of the education was helpful for practical duties. 7. An analysis of the computer use by health service fields showed that the medicine management in a health management field was 15.9%. the work environment measuring management in a work environment filed was 32.9%. the employment. general and special examination management in a heal th management field was 61.1 %. the various reports management in an administrative field was 64%. the health education data preparation management in an educational field was 58.0%. and the medicine and expendables management in an equipment management field was 51.6%. An analysis of the computerization system use showed that the various statistical data manage in a health management field was 13.0%. the work environment measuring management in a health management field was 34.8%. the personal disease management in a health management field was 51.9%. the heal education data preparation management in an educational field was 54.5%. and the equipment management of health care centers in an equipment management field was 52.6%. 8. 31.6% of the participants wanted that health service computerization system would include the generals of health services. 42.4% of the participants thought that first of all. the aggressive interest and investment of employers were required to build the health service computerization system. 9. The participants' satisfaction level on the computerization system use was $3.51{\pm}.57$ points. An analysis by each factor showed $3.62{\pm}.68$ points in a service change factor. $3.15{\pm}.63$ points in a computer program use factor, and $3.45{\pm}.71$ points in a continuous computerization use factor. 10. An analysis of the computerization system use by general characteristics of participants showed that the married (p = .022) had the satisfaction level higher than the unmarried. 11. The satisfaction level of the computerization system use by participants' computer use ability tended to be higher in proportion to the increase of computer use abilities in spreadsheet (F=2.606. p=.048). presentation (F=3.62. p=.012) and communication/internet(F=2.885. p=.0321. Based on the study results mentioned above. I will suggest as follows : The nationwide enlargement and repetition study is required for occupational nurses who serve in occupational nursing fields. The computerization system in a health service field is inferior comparing with other fields. The computerization system standard by business types and characteristics should be prepared through employers's aggressive participation and national support. Therefore various statistical data which occurs in occupational fields will be managed systematically and efficiently. A regular and systematic computer education plan for occupational nurses in charge of health services in the filed is urgently required to efficiently manage and improve the health of on-site workers.

• Journal of Intelligence and Information Systems
• /
• v.20 no.1
• /
• pp.163-176
• /
• 2014

Social media is becoming the platform for users to communicate their activities, status, emotions, and experiences to other people. In recent years, microblogs, such as Twitter, have gained in popularity because of its ease of use, speed, and reach. Compared to a conventional web blog, a microblog lowers users' efforts and investment for content generation by recommending shorter posts. There has been a lot research into capturing the social phenomena and analyzing the chatter of microblogs. However, measuring television ratings has been given little attention so far. Currently, the most common method to measure TV ratings uses an electronic metering device installed in a small number of sampled households. Microblogs allow users to post short messages, share daily updates, and conveniently keep in touch. In a similar way, microblog users are interacting with each other while watching television or movies, or visiting a new place. In order to measure TV ratings, some features are significant during certain hours of the day, or days of the week, whereas these same features are meaningless during other time periods. Thus, the importance of features can change during the day, and a model capturing the time sensitive relevance is required to estimate TV ratings. Therefore, modeling time-related characteristics of features should be a key when measuring the TV ratings through microblogs. We show that capturing time-dependency of features in measuring TV ratings is vitally necessary for improving their accuracy. To explore the relationship between the content of microblogs and TV ratings, we collected Twitter data using the Get Search component of the Twitter REST API from January 2013 to October 2013. There are about 300 thousand posts in our data set for the experiment. After excluding data such as adverting or promoted tweets, we selected 149 thousand tweets for analysis. The number of tweets reaches its maximum level on the broadcasting day and increases rapidly around the broadcasting time. This result is stems from the characteristics of the public channel, which broadcasts the program at the predetermined time. From our analysis, we find that count-based features such as the number of tweets or retweets have a low correlation with TV ratings. This result implies that a simple tweet rate does not reflect the satisfaction or response to the TV programs. Content-based features extracted from the content of tweets have a relatively high correlation with TV ratings. Further, some emoticons or newly coined words that are not tagged in the morpheme extraction process have a strong relationship with TV ratings. We find that there is a time-dependency in the correlation of features between the before and after broadcasting time. Since the TV program is broadcast at the predetermined time regularly, users post tweets expressing their expectation for the program or disappointment over not being able to watch the program. The highly correlated features before the broadcast are different from the features after broadcasting. This result explains that the relevance of words with TV programs can change according to the time of the tweets. Among the 336 words that fulfill the minimum requirements for candidate features, 145 words have the highest correlation before the broadcasting time, whereas 68 words reach the highest correlation after broadcasting. Interestingly, some words that express the impossibility of watching the program show a high relevance, despite containing a negative meaning. Understanding the time-dependency of features can be helpful in improving the accuracy of TV ratings measurement. This research contributes a basis to estimate the response to or satisfaction with the broadcasted programs using the time dependency of words in Twitter chatter. More research is needed to refine the methodology for predicting or measuring TV ratings.

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