• Journal of Korea Water Resources Association
• /
• v.55 no.8
• /
• pp.603-611
• /
• 2022

In urban areas, the impermeable area continues to increase due to urbanization, which interferes with the surface penetrating and infiltrating of rainwater, causing most rainwater runoff to the surface, deepening the distortion of water circulation. Distortion of water circulation affects not only flood disasters caused by rainfall and runoff, but also various aspects such as dry stream phenomenon, deterioration of water quality, and destruction of ecosystem balance, and the Ministry of Environment strongly recommends the use of Low Impact development (LID) techniques. In order to apply the LID technique, it is necessary to set a rainwater management target to handle the increase in outflow after the development of the target site, and the current standard sets the rainwater management target using the 10-year daily rainfall. In this study, the difference from the current standards was analyzed through statistical analysis and classification of independent rainfall ideas using inter-event time definition (IETD) in setting the target amount of rainwater management to improve water circulation. Using 30-year rainfall data from 1991 to 2020, methods such as autocorrelation coefficient (AC) analysis, variation coefficient (VC) analysis, and annual average number of rainfall event (NRE) analysis were applied, and IETD was selected according to the target rainfall period. The more samples the population had, the more IETD tended to increase. In addition, by analyzing the duration and time distribution of independent rainfall according to the IETD, a plan was proposed to calculate the standard design rainfall according to the rainwater management target amount. Therefore, it is expected that it will be possible to set an improved rainwater management target amount if sufficient samples of independent rainfall ideas are used through the selection of IETD as in this study.

• Korean Journal of Environment and Ecology
• /
• v.37 no.4
• /
• pp.273-288
• /
• 2023

Although many studies have been conducted on techniques and effects that can be applied to forest meditation in domestic forest healing meditation research, there has been little research on the space where forest meditation takes place. Nevertheless, a meditation space is not just a place concept but a forest environment element responsible for the healing function of a forest, i.e., a place containing healing factors, and can be an essential clue to the healing mechanism. Therefore, to determine whether a healing forest meditation space is suitable for meditation, this study selected the attribute items of the meditation space using the Delphi expert survey and then surveyed the user satisfaction of the healing forest meditation space using the IPA (Importance Performance Analysis) technique. The survey was conducted from August to November 2022, targeting 315 adults who used the forest meditation space at the National Center for Forest Therapy, the Saneum Healing Forest, and the Jathyanggi Pureunsup Arboretum in Gyeonggi Province. The result of the IPA analysis showed the average satisfaction with the forest meditation space was relatively high at 4.33 points on a 5-point Likert scale (4.33 points for the National Center for Forest Therapy, 4.34 points for the Saneum Healing Forest, and 4.37 points for the Jathyanggi Pureunsup Arboretum), indicating that the three healing forest meditation spaces were suitable for forest meditation. Satisfaction with the "Sounds of nature" was high in all three forests. On the other hand, all three forests showed a relatively low satisfaction with "Quietness," indicating it to be a priority problem to be addressed. Also, an open-ended questionnaire survey showed that the mediation space's natural elements, such as natural sounds, scenery, air, forest spaces, and scents, had a higher positive impact on meditation satisfaction than artificial elements, such as facilities. Therefore, it is essential to secure sound resources such as the sound of water and birds around the meditation space, and it is also necessary to consider ways to create a meditation forest in an independent area to avoid encounters with visitors and allow only participants in the forest healing meditation program to enter to increase satisfaction with forest meditation.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.30 no.4B
• /
• pp.337-346
• /
• 2010

Climate change has a huge impact on various parts of the world. This study quantified and analyzed the effects on hydrological behavior caused by climate, vegetation canopy and land use change of Soyanggang dam watershed (2,694.4 $km^2$) using the semi-distributed model SWAT (Soil Water Assessment Tool). For the 1997-2006 daily dam inflow data, the model was calibrated with the Nash-Sutcliffe model efficiencies between the range of 0.45 and 0.91. For the future climate change projection, three GCMs of MIROC3.2hires, ECHAM5-OM, and HadCM3 were used. The A2, A1B and B1 emission scenarios of IPCC (Intergovernmental Panel on Climate Change) were adopted. The data was corrected for each bias and downscaled by Change Factor (CF) method using 30 years (1977-2006, baseline period) weather data and 20C3M (20th Century Climate Coupled Model). Three periods of data; 2010-2039 (2020s), 2040-2069 (2050s), 2070-2099 (2080s) were prepared for future evaluation. The future annual temperature and precipitation were predicted to change from +2.0 to $+6.3^{\circ}C$ and from -20.4 to 32.3% respectively. Seasonal temperature change increased in all scenarios except for winter period of HadCM3. The precipitation of winter and spring increased while it decreased for summer and fall for all GCMs. Future land use and vegetation canopy condition were predicted by CA-Markov technique and MODIS LAI versus temperature regression respectively. The future hydrological evaluation showed that the annual evapotranspiration increases up to 30.1%, and the groundwater recharge and soil moisture decreases up to 55.4% and 32.4% respectively compared to 2000 condition. Dam inflow was predicted to change from -38.6 to 29.5%. For all scenarios, the fall dam inflow, soil moisture and groundwater recharge were predicted to decrease. The seasonal vapotranspiration was predicted to increase up to 64.2% for all seasons except for HadCM3 winter.

• Journal of the Korean Society of Clothing and Textiles
• /
• v.32 no.12
• /
• pp.1837-1847
• /
• 2008

The goal of this study is to express the image of chameleons-that change their colors by light, temperature and its mood-into the sexy styles of corresponding coquettish temperamental people in Wearable Art. The method used in this study was experimenting various production mediums, including creating the textured stretch fabric, in the process of expressing the conceptual characteristics of the chameleon in Wearable Art. The concept of the work was a concoction of 'tempting', 'splendid', 'brilliant', 'fascinating', etc. that highlighted the real disposition of the chameleon. The futuristic preference of the researcher was also implicated. "Comfortable" and "enjoyable" concepts via motions were improved with the its completeness. The point of the design and production is to express symbolically the chameleon in real life, analyzing its sleek body lines, conditional colors changing, outer skins and the cubic textures. The coquettish temperamental image, the conceptual image of the chameleon, was also expressed by implication into the whole work. The entire line of this work is body-conscious silhouette. It was symbolically selected to image the outline of the chameleon that has the slim and sleek body. The exposed back is intended to express symbolically the projected back bones of the chameleon. The hood of gentle triangle line expresses the smooth-lined head part. The irregular hemlines represent the elongated chameleon's tale. The chameleon with its colors of vivid tones is characterized the colors changing by its conditions. This point was importantly treated in the working process by trying the effects that the colors are seen slightly different according to the light and angles. The material was given the effect that its surface colors are seen different in lights and angles because of the wrinkles protruded lumpy-bumpy. The various stones of red and blue tones are very similar to the skin tones of the real chameleon, and their gradation makes the effect that the colors are visibly changed with each move. The textures of the chameleon were produced via the wrinkle effect of smoke-shape, which is the result of using the elastic threads on the basic mediums stitched with 50/50 chiffon and polyester along with velvet dot patterns. The stretching fabric by the impact of the elastic threads is as much suitable for making the body-conscious line. The stones are composed of acrylic cabochon and gemstone. They are symbolically expressed the lumpy and bumpy back skin of the chameleon and produced the effect of the colors visibly different. The primary technique used in this dress is the draping utilizing the biased grains. The front body piece is connected to the hood and joined to the back piece without any seam. For the irregular hemline flares, leaving the several rectangular pieces with bias grains, they were connected by interlocking. What defines the clothes is the person in action. Therefore, what decides the completeness of clothes might be its comfortable and enjoyable feeling by living and acting people. The chameleon dress could also reach its goal of comforting and pleasing Wearable Art in the process of studying the techniques and effects that visibly differentiate the colors. It is considered as a main point of the Wearable Art, which is a comfortable enjoyable clothing tempered with the artistic beauty.

• Journal of Intelligence and Information Systems
• /
• v.23 no.4
• /
• pp.169-196
• /
• 2017

The fourth industrial revolution represents a revolutionary change in the business environment and its ecosystem, which is a fusion of Information Technology (IT) and other industries. In line with these recent changes, the Ministry of Employment and Labor of South Korea announced 'the Fourth Industrial Revolution Leader Training Program,' which includes five key support areas such as (1) smart manufacturing, (2) Internet of Things (IoT), (3) big data including Artificial Intelligence (AI), (4) information security, and (5) bio innovation. Based on this program, we can get a glimpse of the South Korean government's efforts and willingness to emit leading human resource with advanced IT knowledge in various fusion technology-related and newly emerging industries. On the other hand, in order to nurture excellent IT manpower in preparation for the fourth industrial revolution, the role of educational institutions capable of providing high quality IT education services is most of importance. However, these days, most IT educational institutions have had difficulties in providing customized IT education services that meet the needs of consumers (i.e., learners), without breaking away from the traditional framework of providing supplier-oriented education services. From previous studies, it has been found that the provision of customized education services centered on learners leads to high satisfaction of learners, and that higher satisfaction increases not only task performance and the possibility of business application but also learners' recommendation intention. However, since research has not yet been conducted in a comprehensive way that consider both antecedent and consequent factors of the learner's satisfaction, more empirical research on this is highly desirable. With the advent of the fourth industrial revolution, a rising interest in various convergence technologies utilizing information technology (IT) has brought with the growing realization of the important role played by IT-related education services. However, research on the role of IT education service quality in the context of IT education is relatively scarce in spite of the fact that research on general education service quality and satisfaction has been actively conducted in various contexts. In this study, therefore, the five dimensions of IT education service quality (i.e., tangibles, reliability, responsiveness, assurance, and empathy) are derived from the context of IT education, based on the SERVPERF model and related previous studies. In addition, the effects of these detailed IT education service quality factors on learners' educational satisfaction and their work application/recommendation intentions are examined. Furthermore, the moderating roles of learner position (i.e., practitioner group vs. manager group) and participation motivation (i.e., voluntary participation vs. involuntary participation) in relationships between IT education service quality factors and learners' educational satisfaction, work application intention, and recommendation intention are also investigated. In an analysis using the structural equation model (SEM) technique based on a questionnaire given to 203 participants of IT education programs in an 'M' IT educational institution in Seoul, South Korea, tangibles, reliability, and assurance were found to have a significant effect on educational satisfaction. This educational satisfaction was found to have a significant effect on both work application intention and recommendation intention. Moreover, it was discovered that learner position and participation motivation have a partial moderating impact on the relationship between IT education service quality factors and educational satisfaction. This study holds academic implications in that it is one of the first studies to apply the SERVPERF model (rather than the SERVQUAL model, which has been widely adopted by prior studies) is to demonstrate the influence of IT education service quality on learners' educational satisfaction, work application intention, and recommendation intention in an IT education environment. The results of this study are expected to provide practical guidance for IT education service providers who wish to enhance learners' educational satisfaction and service management efficiency.

• Asia pacific journal of information systems
• /
• v.22 no.1
• /
• pp.29-52
• /
• 2012

Consumers differ in the way they make a purchase. An audio mania would willingly make a bold, yet serious, decision to buy a top-of-the-line home theater system, while he is not interested in replacing his two-decade-old shabby car. On the contrary, an automobile enthusiast wouldn't mind spending forty thousand dollars to buy a new Jaguar convertible, yet cares little about his junky component system. It is product involvement that helps us explain such differences among individuals in the purchase style. Product involvement refers to the extent to which a product is perceived to be important to a consumer (Zaichkowsky, 2001). Product involvement is an important factor that strongly influences consumer's purchase decision-making process, and thus has been of prime interest to consumer behavior researchers. Furthermore, researchers found that involvement is closely related to perceived risk (Dholakia, 2001). While abundant research exists addressing how product involvement relates to overall perceived risk, little attention has been paid to the relationship between involvement and different types of perceived risk in an electronic commerce setting. Given that perceived risk can be a substantial barrier to the online purchase (Jarvenpaa, 2000), research addressing such an issue will offer useful implications on what specific types of perceived risk an online firm should focus on mitigating if it is to increase sales to a fullest potential. Meanwhile, past research has focused on such consumer responses as information search and dissemination as a consequence of involvement, neglecting other behavioral responses like online merchant selection. For one example, will a consumer seriously considering the purchase of a pricey Guzzi bag perceive a great degree of risk associated with online buying and therefore choose to buy it from a digital storefront rather than from an online marketplace to mitigate risk? Will a consumer require greater trust on the part of the online merchant when the perceived risk of online buying is rather high? We intend to find answers to these research questions through an empirical study. This paper explores the impact of enduring product involvement and perceived risks on required trust level, and further on online merchant choice. For the purpose of the research, five types or components of perceived risk are taken into consideration, including financial, performance, delivery, psychological, and social risks. A research model has been built around the constructs under consideration, and 12 hypotheses have been developed based on the research model to examine the relationships between enduring involvement and five components of perceived risk, between five components of perceived risk and required trust level, between enduring involvement and required trust level, and finally between required trust level and preference toward an e-tailer. To attain our research objectives, we conducted an empirical analysis consisting of two phases of data collection: a pilot test and main survey. The pilot test was conducted using 25 college students to ensure that the questionnaire items are clear and straightforward. Then the main survey was conducted using 295 college students at a major university for nine days between December 13, 2010 and December 21, 2010. The measures employed to test the model included eight constructs: (1) enduring involvement, (2) financial risk, (3) performance risk, (4) delivery risk, (5) psychological risk, (6) social risk, (7) required trust level, (8) preference toward an e-tailer. The statistical package, SPSS 17.0, was used to test the internal consistency among the items within the individual measures. Based on the Cronbach's ${\alpha}$ coefficients of the individual measure, the reliability of all the variables is supported. Meanwhile, the Amos 18.0 package was employed to perform a confirmatory factor analysis designed to assess the unidimensionality of the measures. The goodness of fit for the measurement model was satisfied. Unidimensionality was tested using convergent, discriminant, and nomological validity. The statistical evidences proved that the three types of validity were all satisfied. Now the structured equation modeling technique was used to analyze the individual paths along the relationships among the research constructs. The results indicated that enduring involvement has significant positive relationships with all the five components of perceived risk, while only performance risk is significantly related to trust level required by consumers for purchase. It can be inferred from the findings that product performance problems are mostly likely to occur when a merchant behaves in an opportunistic manner. Positive relationships were also found between involvement and required trust level and between required trust level and online merchant choice. Enduring involvement is concerned with the pleasure a consumer derives from a product class and/or with the desire for knowledge for the product class, and thus is likely to motivate the consumer to look for ways of mitigating perceived risk by requiring a higher level of trust on the part of the online merchant. Likewise, a consumer requiring a high level of trust on the merchant will choose a digital storefront rather than an e-marketplace, since a digital storefront is believed to be trustworthier than an e-marketplace, as it fulfills orders by itself rather than acting as an intermediary. The findings of the present research provide both academic and practical implications. The first academic implication is that enduring product involvement is a strong motivator of consumer responses, especially the selection of a merchant, in the context of electronic shopping. Secondly, academicians are advised to pay attention to the finding that an individual component or type of perceived risk can be used as an important research construct, since it would allow one to pinpoint the specific types of risk that are influenced by antecedents or that influence consequents. Meanwhile, our research provides implications useful for online merchants (both online storefronts and e-marketplaces). Merchants may develop strategies to attract consumers by managing perceived performance risk involved in purchase decisions, since it was found to have significant positive relationship with the level of trust required by a consumer on the part of the merchant. One way to manage performance risk would be to thoroughly examine the product before shipping to ensure that it has no deficiencies or flaws. Secondly, digital storefronts are advised to focus on symbolic goods (e.g., cars, cell phones, fashion outfits, and handbags) in which consumers are relatively more involved than others, whereas e- marketplaces should put their emphasis on non-symbolic goods (e.g., drinks, books, MP3 players, and bike accessories).

• Journal of Intelligence and Information Systems
• /
• v.19 no.3
• /
• pp.141-156
• /
• 2013

Social media is a representative form of the Web 2.0 that shapes the change of a user's information behavior by allowing users to produce their own contents without any expert skills. In particular, as a new communication medium, it has a profound impact on the social change by enabling users to communicate with the masses and acquaintances their opinions and thoughts. Social media data plays a significant role in an emerging Big Data arena. A variety of research areas such as social network analysis, opinion mining, and so on, therefore, have paid attention to discover meaningful information from vast amounts of data buried in social media. Social media has recently become main foci to the field of Information Retrieval and Text Mining because not only it produces massive unstructured textual data in real-time but also it serves as an influential channel for opinion leading. But most of the previous studies have adopted broad-brush and limited approaches. These approaches have made it difficult to find and analyze new information. To overcome these limitations, we developed a real-time Twitter trend mining system to capture the trend in real-time processing big stream datasets of Twitter. The system offers the functions of term co-occurrence retrieval, visualization of Twitter users by query, similarity calculation between two users, topic modeling to keep track of changes of topical trend, and mention-based user network analysis. In addition, we conducted a case study on the 2012 Korean presidential election. We collected 1,737,969 tweets which contain candidates' name and election on Twitter in Korea (http://www.twitter.com/) for one month in 2012 (October 1 to October 31). The case study shows that the system provides useful information and detects the trend of society effectively. The system also retrieves the list of terms co-occurred by given query terms. We compare the results of term co-occurrence retrieval by giving influential candidates' name, 'Geun Hae Park', 'Jae In Moon', and 'Chul Su Ahn' as query terms. General terms which are related to presidential election such as 'Presidential Election', 'Proclamation in Support', Public opinion poll' appear frequently. Also the results show specific terms that differentiate each candidate's feature such as 'Park Jung Hee' and 'Yuk Young Su' from the query 'Guen Hae Park', 'a single candidacy agreement' and 'Time of voting extension' from the query 'Jae In Moon' and 'a single candidacy agreement' and 'down contract' from the query 'Chul Su Ahn'. Our system not only extracts 10 topics along with related terms but also shows topics' dynamic changes over time by employing the multinomial Latent Dirichlet Allocation technique. Each topic can show one of two types of patterns-Rising tendency and Falling tendencydepending on the change of the probability distribution. To determine the relationship between topic trends in Twitter and social issues in the real world, we compare topic trends with related news articles. We are able to identify that Twitter can track the issue faster than the other media, newspapers. The user network in Twitter is different from those of other social media because of distinctive characteristics of making relationships in Twitter. Twitter users can make their relationships by exchanging mentions. We visualize and analyze mention based networks of 136,754 users. We put three candidates' name as query terms-Geun Hae Park', 'Jae In Moon', and 'Chul Su Ahn'. The results show that Twitter users mention all candidates' name regardless of their political tendencies. This case study discloses that Twitter could be an effective tool to detect and predict dynamic changes of social issues, and mention-based user networks could show different aspects of user behavior as a unique network that is uniquely found in Twitter.

• Journal of Intelligence and Information Systems
• /
• v.20 no.1
• /
• pp.35-48
• /
• 2014

According to the 2013 construction market outlook report, the liquidation of construction companies is expected to continue due to the ongoing residential construction recession. Bankruptcies of construction companies have a greater social impact compared to other industries. However, due to the different nature of the capital structure and debt-to-equity ratio, it is more difficult to forecast construction companies' bankruptcies than that of companies in other industries. The construction industry operates on greater leverage, with high debt-to-equity ratios, and project cash flow focused on the second half. The economic cycle greatly influences construction companies. Therefore, downturns tend to rapidly increase the bankruptcy rates of construction companies. High leverage, coupled with increased bankruptcy rates, could lead to greater burdens on banks providing loans to construction companies. Nevertheless, the bankruptcy prediction model concentrated mainly on financial institutions, with rare construction-specific studies. The bankruptcy prediction model based on corporate finance data has been studied for some time in various ways. However, the model is intended for all companies in general, and it may not be appropriate for forecasting bankruptcies of construction companies, who typically have high liquidity risks. The construction industry is capital-intensive, operates on long timelines with large-scale investment projects, and has comparatively longer payback periods than in other industries. With its unique capital structure, it can be difficult to apply a model used to judge the financial risk of companies in general to those in the construction industry. Diverse studies of bankruptcy forecasting models based on a company's financial statements have been conducted for many years. The subjects of the model, however, were general firms, and the models may not be proper for accurately forecasting companies with disproportionately large liquidity risks, such as construction companies. The construction industry is capital-intensive, requiring significant investments in long-term projects, therefore to realize returns from the investment. The unique capital structure means that the same criteria used for other industries cannot be applied to effectively evaluate financial risk for construction firms. Altman Z-score was first published in 1968, and is commonly used as a bankruptcy forecasting model. It forecasts the likelihood of a company going bankrupt by using a simple formula, classifying the results into three categories, and evaluating the corporate status as dangerous, moderate, or safe. When a company falls into the "dangerous" category, it has a high likelihood of bankruptcy within two years, while those in the "safe" category have a low likelihood of bankruptcy. For companies in the "moderate" category, it is difficult to forecast the risk. Many of the construction firm cases in this study fell in the "moderate" category, which made it difficult to forecast their risk. Along with the development of machine learning using computers, recent studies of corporate bankruptcy forecasting have used this technology. Pattern recognition, a representative application area in machine learning, is applied to forecasting corporate bankruptcy, with patterns analyzed based on a company's financial information, and then judged as to whether the pattern belongs to the bankruptcy risk group or the safe group. The representative machine learning models previously used in bankruptcy forecasting are Artificial Neural Networks, Adaptive Boosting (AdaBoost) and, the Support Vector Machine (SVM). There are also many hybrid studies combining these models. Existing studies using the traditional Z-Score technique or bankruptcy prediction using machine learning focus on companies in non-specific industries. Therefore, the industry-specific characteristics of companies are not considered. In this paper, we confirm that adaptive boosting (AdaBoost) is the most appropriate forecasting model for construction companies by based on company size. We classified construction companies into three groups - large, medium, and small based on the company's capital. We analyzed the predictive ability of AdaBoost for each group of companies. The experimental results showed that AdaBoost has more predictive ability than the other models, especially for the group of large companies with capital of more than 50 billion won.

• Journal of Intelligence and Information Systems
• /
• v.25 no.4
• /
• pp.35-52
• /
• 2019

As public services are provided in various forms, including e-government, the level of public demand for public service quality is increasing. Although continuous measurement and improvement of the quality of public services is needed to improve the quality of public services, traditional surveys are costly and time-consuming and have limitations. Therefore, there is a need for an analytical technique that can measure the quality of public services quickly and accurately at any time based on the data generated from public services. In this study, we analyzed the quality of public services based on data using process mining techniques for civil licensing services in N city. It is because the N city's building license complaint service can secure data necessary for analysis and can be spread to other institutions through public service quality management. This study conducted process mining on a total of 3678 building license complaint services in N city for two years from January 2014, and identified process maps and departments with high frequency and long processing time. According to the analysis results, there was a case where a department was crowded or relatively few at a certain point in time. In addition, there was a reasonable doubt that the increase in the number of complaints would increase the time required to complete the complaints. According to the analysis results, the time required to complete the complaint was varied from the same day to a year and 146 days. The cumulative frequency of the top four departments of the Sewage Treatment Division, the Waterworks Division, the Urban Design Division, and the Green Growth Division exceeded 50% and the cumulative frequency of the top nine departments exceeded 70%. Higher departments were limited and there was a great deal of unbalanced load among departments. Most complaint services have a variety of different patterns of processes. Research shows that the number of 'complementary' decisions has the greatest impact on the length of a complaint. This is interpreted as a lengthy period until the completion of the entire complaint is required because the 'complement' decision requires a physical period in which the complainant supplements and submits the documents again. In order to solve these problems, it is possible to drastically reduce the overall processing time of the complaints by preparing thoroughly before the filing of the complaints or in the preparation of the complaints, or the 'complementary' decision of other complaints. By clarifying and disclosing the cause and solution of one of the important data in the system, it helps the complainant to prepare in advance and convinces that the documents prepared by the public information will be passed. The transparency of complaints can be sufficiently predictable. Documents prepared by pre-disclosed information are likely to be processed without problems, which not only shortens the processing period but also improves work efficiency by eliminating the need for renegotiation or multiple tasks from the point of view of the processor. The results of this study can be used to find departments with high burdens of civil complaints at certain points of time and to flexibly manage the workforce allocation between departments. In addition, as a result of analyzing the pattern of the departments participating in the consultation by the characteristics of the complaints, it is possible to use it for automation or recommendation when requesting the consultation department. In addition, by using various data generated during the complaint process and using machine learning techniques, the pattern of the complaint process can be found. It can be used for automation / intelligence of civil complaint processing by making this algorithm and applying it to the system. This study is expected to be used to suggest future public service quality improvement through process mining analysis on civil service.

• The Journal of Korean Society for Radiation Therapy
• /
• v.32
• /
• pp.17-29
• /
• 2020

Purpose: The purpose of this work was to investigate the dosimetric impact of mixed energy partial arc technique on prostate cancer VMAT. Materials and Methods: This study involved prostate only patients planned with 70Gy in 30 fractions to the planning target volume (PTV). Femoral heads, Bladder and Rectum were considered as oragan at risk (OARs). For this study, mixed energy partial arcs (MEPA) were generated with gantry angle set to 180°~230°, 310°~50° for 6MV arc and 130°~50°, 310°~230° for 15MV arc. Each arc set the avoidance sector which is gantry angle 230°~310°, 50°~130° at first arc and 50°~310° at second arc. After that, two plans were summed and were analyzed the dosimetry parameter of each structure such as Maximum dose, Mean dose, D2%, Homogeneity index (HI) and Conformity Index (CI) for PTV and Maximum dose, Mean dose, V70Gy, V50Gy, V30Gy, and V20Gy for OARs and Monitor Unit (MU) with 6MV 1 ARC, 6MV, 10MV, 15MV 2 ARC plan. Results: In MEPA, the maximum dose, mean dose and D2% were lower than 6MV 1 ARC plan(p<0.0005). However, the average difference of maximum dose was 0.24%, 0.39%, 0.60% (p<0.450, 0.321, 0.139) higher than 6MV, 10MV, 15MV 2 ARC plan, respectively and D2% was 0.42%, 0.49%, 0.59% (p<0.073, 0.087, 0.033) higher than compared plans. The average difference of mean dose was 0.09% lower than 10MV 2 ARC plan, but it is 0.27%, 0.12% (p<0.184, 0.521) higher than 6MV 2 ARC, 15MV 2 ARC plan, respectively. HI was 0.064±0.006 which is the lowest value (p<0.005, 0.357, 0.273, 0.801) among the all plans. For CI, there was no significant differences which were 1.12±0.038 in MEPA, 1.12±0.036, 1.11±0.024, 1.11±0.030, 1.12±0.027 in 6MV 1 ARC, 6MV, 10MV, 15MV 2 ARC, respectively. MEPA produced significantly lower rectum dose. Especially, V70Gy, V50Gy, V30Gy, V20Gy were 3.40, 16.79, 37.86, 48.09 that were lower than other plans. For bladder dose, V30Gy, V20Gy were lower than other plans. However, the mean dose of both femoral head were 9.69±2.93, 9.88±2.5 which were 2.8Gy~3.28Gy higher than other plans. The mean MU of MEPA were 19.53% lower than 6MV 1 ARC, 5.7% lower than 10MV 2 ARC respectively. Conclusion: This study for prostate radiotherapy demonstrated that a choice of MEPA VMAT has the potential to minimize doses to OARs and improve homogeneity to PTV at the expense of a moderate increase in maximum and mean dose to the femoral heads.