• (The)Study of the Eastern Classic
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• no.69
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• pp.349-371
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• 2017

"Hajaeilgi(荷齋日記)" was written by Ji Gyu-sik, a gongin of Saongwon(司饔院)'s branch, almost everyday for 20 years and 7 months from January 1st, 1891 until the leap month of June 29th, 1911. It deals with many different areas including domestic and foreign circumstances, custom, rituals, all the affairs related to the branch, and also everyday life events he did see or hear about. Particularly, Ji Gyu-sik did not belong to the yangban class, and we can hardly find diaries written by such class' people. Here, what this author pays attention to among the things written in "Hajaeilgi" is the contents about folklore. Ji Gyu-sik wrote in his "Hajaeilgi" about things related to folklore, for example, seasonal customs, folk plays, rituals, or folk beliefs that were actually practiced then as a person not belonging to the yangban class. Such diaries are rare, and its value is highly appreciated as a material, and it is meaningful in the aspects of folklore as well. Therefore, as part of the work to look into folklore found in "Hajaeilgi", this author focuses on national rituals and folk beliefs in it. The things that have been discussed earlier can be summed up as follows: About national rituals, it is sorry that it only deals with the emperor's enthronement and emperor and crown prince's birthday in garye(嘉禮); however, it is meaningful to know that the people faithfully carried out national ceremonies and celebrations then. Particularly, it is noteworthy that during the national ceremonies or celebrations, students were asked to sing the national anthem. About hyungrye(凶禮), only it deals with the court funeral rite. Aside from Minbi's funeral rite, the court funeral rite was performed properly at the right time according to the procedure like before, and also, it seems that the people fulfilled it faithfully by order of the government. Also, it can be learned from it that Japanese killed Minbi, burned the body with oil, and left the ashes behind. About folk beliefs, the branch held a memorial service at the shrine of the town regularly. The town, too, performed Gocheongsinsa each year. The money needed for the memorial service was collected from the town people differently according to their financial situations, and they prepared for jesu for the ancestral rite altogether. The memorial service was carried out in Sansindang or Bugeundang, too, and it was common that they summoned a shaman to perform a gut. The diary is valuable as material. After being a Christian, Ji Gyu-sik once tried to abolish sinsa jesa held in hoesa and get rid of saesin(賽神), that is, a gut or pudakgeori, but he had no choice but to follow the precedent. Meanwhile, it is also noteworthy that when the town suffered from floor and infectious disease, Ji Gyu-sik installed an altar in front of his house for the town's wellbeing and health, prepared for jesu including offerings and drinks, and held a memorial service to Hwangcheonhuto(皇天后土; the gods of heaven and earth) accompanied with the town people. Also, when he had any hardships in his family, Ji Gyu-sik summoned a shaman for a gut or offered a devout prayer to the mountainous god. Such shamanism or the things like worshiping Gwanseongjegun(關聖帝君) mostly in the beginning of January and telling fortunes about the year were all folk beliefs. This was very popular among the people, and it seems that it was deeply rooted in his life as well. Also, Ji Gyu-sik supported Fengshui beliefs, and it seems that it is not different from the people's general tendencies, either. As described above, "Hajaeilgi" dealing with national rituals and folk beliefs in it is valuable as material and is meaningful for research on forklore, and moreover, it is also significant in the aspects of forklore as well.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.111-126
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• 2020

Most industries have recently become aware of the importance of customer lifetime value as they are exposed to a competitive environment. As a result, preventing customers from churn is becoming a more important business issue than securing new customers. This is because maintaining churn customers is far more economical than securing new customers, and in fact, the acquisition cost of new customers is known to be five to six times higher than the maintenance cost of churn customers. Also, Companies that effectively prevent customer churn and improve customer retention rates are known to have a positive effect on not only increasing the company's profitability but also improving its brand image by improving customer satisfaction. Predicting customer churn, which had been conducted as a sub-research area for CRM, has recently become more important as a big data-based performance marketing theme due to the development of business machine learning technology. Until now, research on customer churn prediction has been carried out actively in such sectors as the mobile telecommunication industry, the financial industry, the distribution industry, and the game industry, which are highly competitive and urgent to manage churn. In addition, These churn prediction studies were focused on improving the performance of the churn prediction model itself, such as simply comparing the performance of various models, exploring features that are effective in forecasting departures, or developing new ensemble techniques, and were limited in terms of practical utilization because most studies considered the entire customer group as a group and developed a predictive model. As such, the main purpose of the existing related research was to improve the performance of the predictive model itself, and there was a relatively lack of research to improve the overall customer churn prediction process. In fact, customers in the business have different behavior characteristics due to heterogeneous transaction patterns, and the resulting churn rate is different, so it is unreasonable to assume the entire customer as a single customer group. Therefore, it is desirable to segment customers according to customer classification criteria, such as loyalty, and to operate an appropriate churn prediction model individually, in order to carry out effective customer churn predictions in heterogeneous industries. Of course, in some studies, there are studies in which customers are subdivided using clustering techniques and applied a churn prediction model for individual customer groups. Although this process of predicting churn can produce better predictions than a single predict model for the entire customer population, there is still room for improvement in that clustering is a mechanical, exploratory grouping technique that calculates distances based on inputs and does not reflect the strategic intent of an entity such as loyalties. This study proposes a segment-based customer departure prediction process (CCP/2DL: Customer Churn Prediction based on Two-Dimensional Loyalty segmentation) based on two-dimensional customer loyalty, assuming that successful customer churn management can be better done through improvements in the overall process than through the performance of the model itself. CCP/2DL is a series of churn prediction processes that segment two-way, quantitative and qualitative loyalty-based customer, conduct secondary grouping of customer segments according to churn patterns, and then independently apply heterogeneous churn prediction models for each churn pattern group. Performance comparisons were performed with the most commonly applied the General churn prediction process and the Clustering-based churn prediction process to assess the relative excellence of the proposed churn prediction process. The General churn prediction process used in this study refers to the process of predicting a single group of customers simply intended to be predicted as a machine learning model, using the most commonly used churn predicting method. And the Clustering-based churn prediction process is a method of first using clustering techniques to segment customers and implement a churn prediction model for each individual group. In cooperation with a global NGO, the proposed CCP/2DL performance showed better performance than other methodologies for predicting churn. This churn prediction process is not only effective in predicting churn, but can also be a strategic basis for obtaining a variety of customer observations and carrying out other related performance marketing activities.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.149-171
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• 2020

The period at university is to make decision about getting an actual job. As our society develops rapidly and highly, jobs are diversified, subdivided, and specialized, and students' job preparation period is also getting longer and longer. This study analyzed the log data of college students to see how the various activities that college students experience inside and outside of school might have influences on employment. For this experiment, students' various activities were systematically classified, recorded as an activity data and were divided into six core competencies (Job reinforcement competency, Leadership & teamwork competency, Globalization competency, Organizational commitment competency, Job exploration competency, and Autonomous implementation competency). The effect of the six competency levels on the employment status (employed group, unemployed group) was analyzed. As a result of the analysis, it was confirmed that the difference in level between the employed group and the unemployed group was significant for all of the six competencies, so it was possible to infer that the activities at the school are significant for employment. Next, in order to analyze the impact of the six competencies on the qualitative performance of employment, we had ANOVA analysis after dividing the each competency level into 2 groups (low and high group), and creating 6 groups by the range of first annual salary. Students with high levels of globalization capability, job search capability, and autonomous implementation capability were also found to belong to a higher annual salary group. The theoretical contributions of this study are as follows. First, it connects the competencies that can be extracted from the school experience with the competencies in the Human Resource Management field and adds job search competencies and autonomous implementation competencies which are required for university students to have their own successful career & life. Second, we have conducted this analysis with the competency data measured form actual activity and result data collected from the interview and research. Third, it analyzed not only quantitative performance (employment rate) but also qualitative performance (annual salary level). The practical use of this study is as follows. First, it can be a guide when establishing career development plans for college students. It is necessary to prepare for a job that can express one's strengths based on an analysis of the world of work and job, rather than having a no-strategy, unbalanced, or accumulating excessive specifications competition. Second, the person in charge of experience design for college students, at an organizations such as schools, businesses, local governments, and governments, can refer to the six competencies suggested in this study to for the user-useful experiences design that may motivate more participation. By doing so, one event may bring mutual benefits for both event designers and students. Third, in the era of digital transformation, the government's policy manager who envisions the balanced development of the country can make a policy in the direction of achieving the curiosity and energy of college students together with the balanced development of the country. A lot of manpower is required to start up novel platform services that have not existed before or to digitize existing analog products, services and corporate culture. The activities of current digital-generation-college-students are not only catalysts in all industries, but also for very benefit and necessary for college students by themselves for their own successful career development.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.27-65
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• 2020

Many companies on information and communication technology make public their own developed AI technology, for example, Google's TensorFlow, Facebook's PyTorch, Microsoft's CNTK. By releasing deep learning open source software to the public, the relationship with the developer community and the artificial intelligence (AI) ecosystem can be strengthened, and users can perform experiment, implementation and improvement of it. Accordingly, the field of machine learning is growing rapidly, and developers are using and reproducing various learning algorithms in each field. Although various analysis of open source software has been made, there is a lack of studies to help develop or use deep learning open source software in the industry. This study thus attempts to derive a strategy for adopting the framework through case studies of a deep learning open source framework. Based on the technology-organization-environment (TOE) framework and literature review related to the adoption of open source software, we employed the case study framework that includes technological factors as perceived relative advantage, perceived compatibility, perceived complexity, and perceived trialability, organizational factors as management support and knowledge & expertise, and environmental factors as availability of technology skills and services, and platform long term viability. We conducted a case study analysis of three companies' adoption cases (two cases of success and one case of failure) and revealed that seven out of eight TOE factors and several factors regarding company, team and resource are significant for the adoption of deep learning open source framework. By organizing the case study analysis results, we provided five important success factors for adopting deep learning framework: the knowledge and expertise of developers in the team, hardware (GPU) environment, data enterprise cooperation system, deep learning framework platform, deep learning framework work tool service. In order for an organization to successfully adopt a deep learning open source framework, at the stage of using the framework, first, the hardware (GPU) environment for AI R&D group must support the knowledge and expertise of the developers in the team. Second, it is necessary to support the use of deep learning frameworks by research developers through collecting and managing data inside and outside the company with a data enterprise cooperation system. Third, deep learning research expertise must be supplemented through cooperation with researchers from academic institutions such as universities and research institutes. Satisfying three procedures in the stage of using the deep learning framework, companies will increase the number of deep learning research developers, the ability to use the deep learning framework, and the support of GPU resource. In the proliferation stage of the deep learning framework, fourth, a company makes the deep learning framework platform that improves the research efficiency and effectiveness of the developers, for example, the optimization of the hardware (GPU) environment automatically. Fifth, the deep learning framework tool service team complements the developers' expertise through sharing the information of the external deep learning open source framework community to the in-house community and activating developer retraining and seminars. To implement the identified five success factors, a step-by-step enterprise procedure for adoption of the deep learning framework was proposed: defining the project problem, confirming whether the deep learning methodology is the right method, confirming whether the deep learning framework is the right tool, using the deep learning framework by the enterprise, spreading the framework of the enterprise. The first three steps (i.e. defining the project problem, confirming whether the deep learning methodology is the right method, and confirming whether the deep learning framework is the right tool) are pre-considerations to adopt a deep learning open source framework. After the three pre-considerations steps are clear, next two steps (i.e. using the deep learning framework by the enterprise and spreading the framework of the enterprise) can be processed. In the fourth step, the knowledge and expertise of developers in the team are important in addition to hardware (GPU) environment and data enterprise cooperation system. In final step, five important factors are realized for a successful adoption of the deep learning open source framework. This study provides strategic implications for companies adopting or using deep learning framework according to the needs of each industry and business.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.23-46
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• 2021

Collaborative filtering, which is often used in personalization recommendations, is recognized as a very useful technique to find similar customers and recommend products to them based on their purchase history. However, the traditional collaborative filtering technique has raised the question of having difficulty calculating the similarity for new customers or products due to the method of calculating similaritiesbased on direct connections and common features among customers. For this reason, a hybrid technique was designed to use content-based filtering techniques together. On the one hand, efforts have been made to solve these problems by applying the structural characteristics of social networks. This applies a method of indirectly calculating similarities through their similar customers placed between them. This means creating a customer's network based on purchasing data and calculating the similarity between the two based on the features of the network that indirectly connects the two customers within this network. Such similarity can be used as a measure to predict whether the target customer accepts recommendations. The centrality metrics of networks can be utilized for the calculation of these similarities. Different centrality metrics have important implications in that they may have different effects on recommended performance. In this study, furthermore, the effect of these centrality metrics on the performance of recommendation may vary depending on recommender algorithms. In addition, recommendation techniques using network analysis can be expected to contribute to increasing recommendation performance even if they apply not only to new customers or products but also to entire customers or products. By considering a customer's purchase of an item as a link generated between the customer and the item on the network, the prediction of user acceptance of recommendation is solved as a prediction of whether a new link will be created between them. As the classification models fit the purpose of solving the binary problem of whether the link is engaged or not, decision tree, k-nearest neighbors (KNN), logistic regression, artificial neural network, and support vector machine (SVM) are selected in the research. The data for performance evaluation used order data collected from an online shopping mall over four years and two months. Among them, the previous three years and eight months constitute social networks composed of and the experiment was conducted by organizing the data collected into the social network. The next four months' records were used to train and evaluate recommender models. Experiments with the centrality metrics applied to each model show that the recommendation acceptance rates of the centrality metrics are different for each algorithm at a meaningful level. In this work, we analyzed only four commonly used centrality metrics: degree centrality, betweenness centrality, closeness centrality, and eigenvector centrality. Eigenvector centrality records the lowest performance in all models except support vector machines. Closeness centrality and betweenness centrality show similar performance across all models. Degree centrality ranking moderate across overall models while betweenness centrality always ranking higher than degree centrality. Finally, closeness centrality is characterized by distinct differences in performance according to the model. It ranks first in logistic regression, artificial neural network, and decision tree withnumerically high performance. However, it only records very low rankings in support vector machine and K-neighborhood with low-performance levels. As the experiment results reveal, in a classification model, network centrality metrics over a subnetwork that connects the two nodes can effectively predict the connectivity between two nodes in a social network. Furthermore, each metric has a different performance depending on the classification model type. This result implies that choosing appropriate metrics for each algorithm can lead to achieving higher recommendation performance. In general, betweenness centrality can guarantee a high level of performance in any model. It would be possible to consider the introduction of proximity centrality to obtain higher performance for certain models.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.191-207
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• 2021

Up to this day, mobile communications have evolved rapidly over the decades, mainly focusing on speed-up to meet the growing data demands of 2G to 5G. And with the start of the 5G era, efforts are being made to provide such various services to customers, as IoT, V2X, robots, artificial intelligence, augmented virtual reality, and smart cities, which are expected to change the environment of our lives and industries as a whole. In a bid to provide those services, on top of high speed data, reduced latency and reliability are critical for real-time services. Thus, 5G has paved the way for service delivery through maximum speed of 20Gbps, a delay of 1ms, and a connecting device of 106/㎢ In particular, in intelligent traffic control systems and services using various vehicle-based Vehicle to X (V2X), such as traffic control, in addition to high-speed data speed, reduction of delay and reliability for real-time services are very important. 5G communication uses high frequencies of 3.5Ghz and 28Ghz. These high-frequency waves can go with high-speed thanks to their straightness while their short wavelength and small diffraction angle limit their reach to distance and prevent them from penetrating walls, causing restrictions on their use indoors. Therefore, under existing networks it's difficult to overcome these constraints. The underlying centralized SDN also has a limited capability in offering delay-sensitive services because communication with many nodes creates overload in its processing. Basically, SDN, which means a structure that separates signals from the control plane from packets in the data plane, requires control of the delay-related tree structure available in the event of an emergency during autonomous driving. In these scenarios, the network architecture that handles in-vehicle information is a major variable of delay. Since SDNs in general centralized structures are difficult to meet the desired delay level, studies on the optimal size of SDNs for information processing should be conducted. Thus, SDNs need to be separated on a certain scale and construct a new type of network, which can efficiently respond to dynamically changing traffic and provide high-quality, flexible services. Moreover, the structure of these networks is closely related to ultra-low latency, high confidence, and hyper-connectivity and should be based on a new form of split SDN rather than an existing centralized SDN structure, even in the case of the worst condition. And in these SDN structural networks, where automobiles pass through small 5G cells very quickly, the information change cycle, round trip delay (RTD), and the data processing time of SDN are highly correlated with the delay. Of these, RDT is not a significant factor because it has sufficient speed and less than 1 ms of delay, but the information change cycle and data processing time of SDN are factors that greatly affect the delay. Especially, in an emergency of self-driving environment linked to an ITS(Intelligent Traffic System) that requires low latency and high reliability, information should be transmitted and processed very quickly. That is a case in point where delay plays a very sensitive role. In this paper, we study the SDN architecture in emergencies during autonomous driving and conduct analysis through simulation of the correlation with the cell layer in which the vehicle should request relevant information according to the information flow. For simulation: As the Data Rate of 5G is high enough, we can assume the information for neighbor vehicle support to the car without errors. Furthermore, we assumed 5G small cells within 50 ~ 250 m in cell radius, and the maximum speed of the vehicle was considered as a 30km ~ 200 km/hour in order to examine the network architecture to minimize the delay.

• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.39 no.4
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• pp.50-65
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• 2021

In this study, the water flow system by measuring the flow-way type and distance of flow path that composes the Gugok through literature survey, field survey, and map work on Gugok gardens in Korea whose existence has been confirmed, while investigating and analyzing watersheds, river orders, and river grades. It was intended to reveal the watershed distribution and stream morphological characteristics of the Gugok gardens and to use them as basic data for future enjoyment and conservation of the Gugok gardens. The conclusion of the study is as follows. First, Of the 93 Gugok gardens that have been confirmed to exist, it was found that 11 places(11.8%) were found to have a descending(top-down) type of Gugok that develops while descending along a stream. Second, As a result of analysis of the length of the flow path for each valley, Okryudonggugok(玉流洞九曲, Namsan-gugok) in Gimcheon, Gyeongsangbuk-do was found to have the shortest length of 0.44km among the surveyed valleys, while the flow distance of Muheulgugok(武屹九曲) located in Seongju-gun and Gimcheon-si, Gyeongsangbuk-do was 31.1km, showing the longest flowing distance. The average flow path length of the Gugok Garden in Korea was 6.24km, and the standard deviation was 4.63km, indicating that the deviation between the 'curved type'e and the 'valley type' was severe. In addition, 14(15.1%) Gugok gardens were found to be partially submerged due to dam construction. Third, As a result of analyzing the waters area where Gugok garden is located, the number of Nakdong river basins was much higher at 52 sites(55.9%), followed by the Hangang river basin at 27 sites(28.7%), the Geum river basin at 9 sites(9.7%), and the Yeongsan river and Seomjin river basins at 5(5.4%). Fourth, All Gugok gardens located in the Han river region were classified as the Han river system, and the Gugok garden located on the Nakdong river was classified as the main Nakdong river system, except for 7 places including 5 places in the Nakdong Gangnam Sea water system and 2 places in the Nakdong Gangdong sea water system. As a result of synthesizing the river order of the flow path where Gugok garden is located, Gugok, which uses the main stream as the base of Gugok, is 3 places in the Hangang water system, 5 places in the Nakdong river system, 2 places in the Geumgang water system, and 1 place in the Yeongsangam/Seomjin river system. A total of 11 locations(11.5%) were found, including 36 locations(38.2%) in the first branch, 29 locations(31.2%) in the second branch, and 16 locations(17.0%) in the third branch. And Gugok garden, located on the 4th tributary, was found to be Taehwa Five-gok(太華五曲) set in Yonghwacheon Stream in Cheorwon in the Han river system, and Hoenggyegok(橫溪九曲) in Yeongcheon Hoenggye Stream in the Nakdong river system. Fifth, As a result of the river grade analysis of the rivers located in the Gugok garden Forest, the grades of the rivers located in the Gugok garden were 13 national rivers(14.0%), 7 local first-class rivers(7.5%), and 74 local second-class rivers(78.5%) was shown.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.67 no.1
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• pp.1-8
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• 2022

In order to manufacture feed suitable for consumer use and provide feed value information, we analyzed the feed components of the four main forage rice varieties by plant parts harvested 30 days after heading. The contents of the six feed ingredients were significantly different (p<0.05) among harvested parts. In the panicle, the crude protein (CP) (6.97%) and lignin (3.11%) were the highest, while the crude ash (CA) and neutral detergent fiber (NDF) contents were significantly lower, resulting in a total digestible nutrient (TDN) content of 77.29%, which is higher than that of the stem (64.82%) and leaf blade and sheath (LBS) (63.57%) (p<0.05). In contrast, the content of crude fat (CF) did not differ significantly among parts (p<0.05). In panicles from 'Jonong', 'Nokyang' and 'Yeongwoo', the TDN content of each cultivar was 78.48-79.07%, with no significant difference among the varieties. In 'Mogwoo' (Mw), the CP content was 8.70%, which was much higher than that of other varieties (p<0.05). In particular, the Mw TDN content was slightly lower in the panicle (72.95%) but higher in the stem (75.37%) and LBS (66.49%) than in the other varieties. The CA, NDF, acid detergent fiber (ADF), and lignin contents were also very low compared to other varieties; therefore, the feed value of the stem and LBS was excellent. In addition, the total dry matter weight (DMW) was 123 g per hill, which was much higher than 82-105 g per hill for other varieties. The distribution of DMW by part was LBS (56.9 g), stem (36.8 g), and panicle (29.3 g), and because the parts, except the panicles, were much higher than the 43-57% of other varieties (grain straw ratio: 76%), rice straw is advantageous in terms of quantity and feed value when used as forage on farms. The relative feed value (RFV) of the four cultivars ranged from 86.79-403.74 across all parts, and hay of grade 3 or higher with an RFV of 100 or more increased with delayed heading in both stems and LBS. This is due to the accumulation of starch into grains during ripening, which supports the observation that the RFV of the early flowering 'Jonong' and 'Nokyang' panicles increased.

• The Journal of Korean Society for Radiation Therapy
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• v.33
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• pp.145-153
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• 2021

Purpose: The purpose of this study is to evaluate the ovarian dose during radiation therapy for breast cancer in women of childbearing age through an experiment. The ovarian dose is evaluated by comparing and analyzing between the calculated dose in the treatment planning system according to the treatment technique and the measured dose using a thermoluminescence dosimeter (TLD). The clinical usefulness of lead (Pb) apron is investigated through dose analysis according to whether or not it is used. Materials and Methods: Rando humanoid phantom was used for measurement, and wedge filter radiation therapy, 3D conformal radiation therapy, and intensity modulated radiation therapy were used as treatment techniques. A treatment plan was established so that 95% of the prescribed dose could be delivered to the right breast of the Rando humanoid phantom 3D image obtained using the CT simulator. TLD was inserted into the surface and depth of the virtual ovary of the Rando hunmanoid phantom and irradiated with radiation. The measurement location was the center of treatment and the point moved 2 cm to the opposite breast from the center of the Rando hunmanoid phantom, 5cm, 10cm, 12.5cm, 15cm, 17.5cm, 20cm from the boundary of the right breast to the center of treatment and downward, and the surface and depth of the right ovary. Measurements were made at a total of 9 central points. In the dose comparison of treatment planning systems, two wedge filter treatment techniques, three-dimensional conformal radiotherapy, and intensity-modulated radiation therapy were established and compared. Treatments were compared, and dose measurements according to the use of lead apron were compared and analyzed in intensity-modulated radiation therapy. The measured value was calculated by averaging three TLD values for each point and converting using the TLD calibration value, which was calculated as the point dose mean value. In order to compare the treatment plan value with the actual measured value, the absolute dose value was measured and compared at each point (%Diff). Results: At Point A, the center of treatment, a maximum of 201.7cGy was obtained in the treatment planning system, and a maximum of 200.6cGy was obtained in the TLD. In all treatment planning systems, 0cGy was calculated from Point G, which is a point 17.5cm downward from the breast interface. As a result of TLD, a maximum of 2.6cGy was obtained at Point G, and a maximum of 0.9cGy was obtained at Point J, which is the ovarian dose, and the absolute dose was 0.3%~1.3%. The difference in dose according to the use of lead aprons was from a maximum of 2.1cGy to a minimum of 0.1cGy, and the %Diff value was 0.1%~1.1%. Conclusion: In the treatment planning system, the difference in dose according to the three treatment plans did not show a significant difference from 0.85% to 2.45%. In the ovary, the difference between the Rando humanoid phantom's treatment planning system and the actual measured dose was within 0.9%, and the actual measured dose was slightly higher. This did not accurately reflect the effect of scattered radiation in the treatment planning system, and it is thought that the dose of scattered radiation and the dose taken by CBCT with TLD inserted were reflected in the actual measurement. In dosimetry according to the with or without a lead apron, when a lead apron was used, the closer the distance from the treatment range, the more effective the shielding was. Although it is not clinically appropriate for pregnancy or artificial insemination during radiotherapy, the dose irradiated to the ovaries during treatment is not expected to significantly affect the reproductive function of women of childbearing age after radiotherapy. However, since women of childbearing age have constant anxiety, it is thought that psychological stability can be promoted by presenting the data from this study.

• Trans-
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• v.12
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• pp.51-79
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• 2022

This study investigates the methods for deriving colors which can serve as a reference to users such as designers and or contents creators who search for online images from the web portal sites using specific words for color planning and more. Two experiments were conducted in order to accomplish this. Digital scenery photos within the geographic scope of Korea were downloaded from web portal sites, and those photos were studied to find out what colors were used to describe daytime and nighttime. Machine learning was used as the study methodology to classify colors in daytime and nighttime, and KSCA was used to derive the color frequency of daytime and nighttime photos and to compare and analyze the two results. The results of classifying the colors of daytime and nighttime photos using machine learning show that, when classifying the colors by 51~100%, the area of daytime colors was approximately 2.45 times greater than that of nighttime colors. The colors of the daytime class were distributed by brightness with white as its center, while that of the nighttime class was distributed with black as its center. Colors that accounted for over 70% of the daytime class were 647, those over 70% of the nighttime class were 252, and the rest (31-69%) were 101. The number of colors in the middle area was low, while other colors were classified relatively clearly into day and night. The resulting color distributions in the daytime and nighttime classes were able to provide the borderline color values of the two classes that are classified by brightness. As a result of analyzing the frequency of digital photos using KSCA, colors around yellow were expressed in generally bright daytime photos, while colors around blue value were expressed in dark night photos. For frequency of daytime photos, colors on the upper 40% had low chroma, almost being achromatic. Also, colors that are close to white and black showed the highest frequency, indicating a large difference in brightness. Meanwhile, for colors with frequency from top 5 to 10, yellow green was expressed darkly, and navy blue was expressed brightly, partially composing a complex harmony. When examining the color band, various colors, brightness, and chroma including light blue, achromatic colors, and warm colors were shown, failing to compose a generally harmonious arrangement of colors. For the frequency of nighttime photos, colors in approximately the upper 50% are dark colors with a brightness value of 2 (Munsell signal). In comparison, the brightness of middle frequency (50-80%) is relatively higher (brightness values of 3-4), and the brightness difference of various colors was large in the lower 20%. Colors that are not cool colors could be found intermittently in the lower 8% of frequency. When examining the color band, there was a general harmonious arrangement of colors centered on navy blue. As the results of conducting the experiment using two methods in this study, machine learning could classify colors into two or more classes, and could evaluate how close an image was with certain colors to a certain class. This method cannot be used if an image cannot be classified into a certain class. The result of such color distribution would serve as a reference when determining how close a certain color is to one of the two classes when the color is used as a dominant color in the base or background color of a certain design. Also, when dividing the analyzed images into several classes, even colors that have not been used in the analyzed image can be determined to find out how close they are to a certain class according to the color distribution properties of each class. Nevertheless, the results cannot be used to find out whether a specific color was used in the class and by how much it was used. To investigate such an issue, frequency analysis was conducted using KSCA. The color frequency could be measured within the range of images used in the experiment. The resulting values of color distribution and frequency from this study would serve as references for color planning of digital design regarding natural scenery in the geographic scope of Korea. Also, the two experiments are meaningful attempts for searching the methods for deriving colors that can be a useful reference among numerous images for content creator users of the relevant field.