• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.109-125
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• 2019

As interest on intelligent search engines increases, various studies have been conducted to extract and utilize the features related to products intelligencely. In particular, when users search for goods in e-commerce search engines, the 'color' of a product is an important feature that describes the product. Therefore, it is necessary to deal with the synonyms of color terms in order to produce accurate results to user's color-related queries. Previous studies have suggested dictionary-based approach to process synonyms for color features. However, the dictionary-based approach has a limitation that it cannot handle unregistered color-related terms in user queries. In order to overcome the limitation of the conventional methods, this research proposes a model which extracts RGB values from an internet search engine in real time, and outputs similar color names based on designated color information. At first, a color term dictionary was constructed which includes color names and R, G, B values of each color from Korean color standard digital palette program and the Wikipedia color list for the basic color search. The dictionary has been made more robust by adding 138 color names converted from English color names to foreign words in Korean, and with corresponding RGB values. Therefore, the fininal color dictionary includes a total of 671 color names and corresponding RGB values. The method proposed in this research starts by searching for a specific color which a user searched for. Then, the presence of the searched color in the built-in color dictionary is checked. If there exists the color in the dictionary, the RGB values of the color in the dictioanry are used as reference values of the retrieved color. If the searched color does not exist in the dictionary, the top-5 Google image search results of the searched color are crawled and average RGB values are extracted in certain middle area of each image. To extract the RGB values in images, a variety of different ways was attempted since there are limits to simply obtain the average of the RGB values of the center area of images. As a result, clustering RGB values in image's certain area and making average value of the cluster with the highest density as the reference values showed the best performance. Based on the reference RGB values of the searched color, the RGB values of all the colors in the color dictionary constructed aforetime are compared. Then a color list is created with colors within the range of ${\pm}50$ for each R value, G value, and B value. Finally, using the Euclidean distance between the above results and the reference RGB values of the searched color, the color with the highest similarity from up to five colors becomes the final outcome. In order to evaluate the usefulness of the proposed method, we performed an experiment. In the experiment, 300 color names and corresponding color RGB values by the questionnaires were obtained. They are used to compare the RGB values obtained from four different methods including the proposed method. The average euclidean distance of CIE-Lab using our method was about 13.85, which showed a relatively low distance compared to 3088 for the case using synonym dictionary only and 30.38 for the case using the dictionary with Korean synonym website WordNet. The case which didn't use clustering method of the proposed method showed 13.88 of average euclidean distance, which implies the DBSCAN clustering of the proposed method can reduce the Euclidean distance. This research suggests a new color synonym processing method based on RGB values that combines the dictionary method with the real time synonym processing method for new color names. This method enables to get rid of the limit of the dictionary-based approach which is a conventional synonym processing method. This research can contribute to improve the intelligence of e-commerce search systems especially on the color searching feature.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.57-73
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• 2021

Maintenance and prevention of failure through anomaly detection of ICT infrastructure is becoming important. System monitoring data is multidimensional time series data. When we deal with multidimensional time series data, we have difficulty in considering both characteristics of multidimensional data and characteristics of time series data. When dealing with multidimensional data, correlation between variables should be considered. Existing methods such as probability and linear base, distance base, etc. are degraded due to limitations called the curse of dimensions. In addition, time series data is preprocessed by applying sliding window technique and time series decomposition for self-correlation analysis. These techniques are the cause of increasing the dimension of data, so it is necessary to supplement them. The anomaly detection field is an old research field, and statistical methods and regression analysis were used in the early days. Currently, there are active studies to apply machine learning and artificial neural network technology to this field. Statistically based methods are difficult to apply when data is non-homogeneous, and do not detect local outliers well. The regression analysis method compares the predictive value and the actual value after learning the regression formula based on the parametric statistics and it detects abnormality. Anomaly detection using regression analysis has the disadvantage that the performance is lowered when the model is not solid and the noise or outliers of the data are included. There is a restriction that learning data with noise or outliers should be used. The autoencoder using artificial neural networks is learned to output as similar as possible to input data. It has many advantages compared to existing probability and linear model, cluster analysis, and map learning. It can be applied to data that does not satisfy probability distribution or linear assumption. In addition, it is possible to learn non-mapping without label data for teaching. However, there is a limitation of local outlier identification of multidimensional data in anomaly detection, and there is a problem that the dimension of data is greatly increased due to the characteristics of time series data. In this study, we propose a CMAE (Conditional Multimodal Autoencoder) that enhances the performance of anomaly detection by considering local outliers and time series characteristics. First, we applied Multimodal Autoencoder (MAE) to improve the limitations of local outlier identification of multidimensional data. Multimodals are commonly used to learn different types of inputs, such as voice and image. The different modal shares the bottleneck effect of Autoencoder and it learns correlation. In addition, CAE (Conditional Autoencoder) was used to learn the characteristics of time series data effectively without increasing the dimension of data. In general, conditional input mainly uses category variables, but in this study, time was used as a condition to learn periodicity. The CMAE model proposed in this paper was verified by comparing with the Unimodal Autoencoder (UAE) and Multi-modal Autoencoder (MAE). The restoration performance of Autoencoder for 41 variables was confirmed in the proposed model and the comparison model. The restoration performance is different by variables, and the restoration is normally well operated because the loss value is small for Memory, Disk, and Network modals in all three Autoencoder models. The process modal did not show a significant difference in all three models, and the CPU modal showed excellent performance in CMAE. ROC curve was prepared for the evaluation of anomaly detection performance in the proposed model and the comparison model, and AUC, accuracy, precision, recall, and F1-score were compared. In all indicators, the performance was shown in the order of CMAE, MAE, and AE. Especially, the reproduction rate was 0.9828 for CMAE, which can be confirmed to detect almost most of the abnormalities. The accuracy of the model was also improved and 87.12%, and the F1-score was 0.8883, which is considered to be suitable for anomaly detection. In practical aspect, the proposed model has an additional advantage in addition to performance improvement. The use of techniques such as time series decomposition and sliding windows has the disadvantage of managing unnecessary procedures; and their dimensional increase can cause a decrease in the computational speed in inference.The proposed model has characteristics that are easy to apply to practical tasks such as inference speed and model management.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.231-252
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• 2021

Artificial intelligence (AI) is a key technology that will change the future the most. It affects the industry as a whole and daily life in various ways. As data availability increases, artificial intelligence finds an optimal solution and infers/predicts through self-learning. Research and investment related to automation that discovers and solves problems on its own are ongoing continuously. Automation of artificial intelligence has benefits such as cost reduction, minimization of human intervention and the difference of human capability. However, there are side effects, such as limiting the artificial intelligence's autonomy and erroneous results due to algorithmic bias. In the labor market, it raises the fear of job replacement. Prior studies on the utilization of artificial intelligence have shown that individuals do not necessarily use the information (or advice) it provides. Algorithm error is more sensitive than human error; so, people avoid algorithms after seeing errors, which is called "algorithm aversion." Recently, artificial intelligence has begun to be understood from the perspective of the augmentation of human intelligence. We have started to be interested in Human-AI collaboration rather than AI alone without human. A study of 1500 companies in various industries found that human-AI collaboration outperformed AI alone. In the medicine area, pathologist-deep learning collaboration dropped the pathologist cancer diagnosis error rate by 85%. Leading AI companies, such as IBM and Microsoft, are starting to adopt the direction of AI as augmented intelligence. Human-AI collaboration is emphasized in the decision-making process, because artificial intelligence is superior in analysis ability based on information. Intuition is a unique human capability so that human-AI collaboration can make optimal decisions. In an environment where change is getting faster and uncertainty increases, the need for artificial intelligence in decision-making will increase. In addition, active discussions are expected on approaches that utilize artificial intelligence for rational decision-making. This study investigates the impact of artificial intelligence on decision-making focuses on human-AI collaboration and the interaction between the decision maker personal traits and advisor type. The advisors were classified into three types: human, artificial intelligence, and human-AI collaboration. We investigated perceived usefulness of advice and the utilization of advice in decision making and whether the decision-maker's personal traits are influencing factors. Three hundred and eleven adult male and female experimenters conducted a task that predicts the age of faces in photos and the results showed that the advisor type does not directly affect the utilization of advice. The decision-maker utilizes it only when they believed advice can improve prediction performance. In the case of human-AI collaboration, decision-makers higher evaluated the perceived usefulness of advice, regardless of the decision maker's personal traits and the advice was more actively utilized. If the type of advisor was artificial intelligence alone, decision-makers who scored high in conscientiousness, high in extroversion, or low in neuroticism, high evaluated the perceived usefulness of the advice so they utilized advice actively. This study has academic significance in that it focuses on human-AI collaboration that the recent growing interest in artificial intelligence roles. It has expanded the relevant research area by considering the role of artificial intelligence as an advisor of decision-making and judgment research, and in aspects of practical significance, suggested views that companies should consider in order to enhance AI capability. To improve the effectiveness of AI-based systems, companies not only must introduce high-performance systems, but also need employees who properly understand digital information presented by AI, and can add non-digital information to make decisions. Moreover, to increase utilization in AI-based systems, task-oriented competencies, such as analytical skills and information technology capabilities, are important. in addition, it is expected that greater performance will be achieved if employee's personal traits are considered.

• The Journal of Korean Society for Radiation Therapy
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• v.30 no.1_2
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• pp.27-34
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• 2018

Purpose : To evaluate the effect of carbon couch side rail and vacuum immobilization device in case of lung RPO irradiation. Materials and Methods : The 10, 20, 30 mm thickness of vac-lok's right side were obtained. To measure of doses, glass dosimeters were used and measured reference point is left lung center at the phantom. A, B, C, and D points are left, right, down, and up directions based on the center point. In the state of Side-Rail-Out, place the without vac-lok, with the thickness of 10, 20, and 30 mm vac-lok. After the glass dosimeters was inserted in center, A, B, C, and D points, 100 MU of 6 MV X-ray were irradiated to the referenced center point in the condition of $10{\times}10cm^2$ field size, SAD 100 cm, gantry angle 225, 300 MU/min dose rate. Five measurements were made for each point. In the state of Side-Rail-In, five measurement were made for each point under the same conditions. The average is measured on each of the five Side-Rail-Out and Side-Rail-In measurements. Results : In the presence of side rail, the dose reduction ratio was -11.8 %, -12.3 %, -4.1 %, -12.3 %, -7.3 % for each A, B, C, and D points. In the state of Side-Rail-Out, the dose reduction ratio for the using 10 mm thickness of vac-lok was -0.9 % than without vac-lok. The dose reduction ratio for the using 20 mm thickness of vac-lok was -2.0 %, for the using 30 mm thickness of the vac-lok was -3.0 % than without vac-lok. In the state of Side-Rail-In, the dose reduction ratio for the using 10 mm thickness of vac-lok was -1.0 % than without vac-lok. The dose reduction ratio for the using 20 mm vac-lok was -2.1 %, for the using 30 mm vac-lok was -3.0 % than without vac-lok. Based on the value of no vac-lok dose in the Side-Rail-In state, The dose reduction ratios for the using 10 mm, 20 mm and 30 mm thickness of vac-loks In the Side-Rail-Out that the center point were -12.7 %, -13.7 %, -14.2 % and -12.8 %, -13.8 %, -14.5 % respectively at point A. The dose reduction ratios for the same conditions to the B point were -4.9 %, -6.1 %, -7.1 % and -13.4 %, -14.4 %, -15.5 % respectively at point C. The dose reduction ratios for the same conditions to the D point were -8.4 %, -9.0 %, -10.4 % respectively. Conclusion : The attenuation was caused by presence of side rails and thickness of vac-lok. Pay attention to these attenuation factors, making it a more effective radiation therapy.

• (The) Research of the performance art and culture
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• no.18
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• pp.257-282
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• 2009

Le $th{\acute{e}}{\hat{t}}re$ musical en $Cor{\acute{e}}e$ est devenu aujourd'hui une mode. Beaucoup parlent de musical, mais beaucoup de musicals sont $mont{\acute{e}}s$ sur la $sc{\grave{e}}ne$ sans des recherches $n{\acute{e}}cessaires$ suffisantes. C'est pour cette raison que la plupart des spectacles musicaux qui sont $cr{\acute{e}}es$ en $Cor{\acute{e}}e$ sont $destin{\acute{e}}s$ en faillite $d{\grave{e}}s$ la naissance. D'ailleurs les musicals $liscenci{\acute{e}}s$ par $l^{\prime}Am{\acute{e}}rique$, la France qui sont $mont{\acute{e}}s$ sur la $sc{\grave{e}}ne$ de $Cor{\acute{e}}e$ aujourd'hui sont assez pour nuire le $march{\acute{e}}$ $cor{\acute{e}}en$ $constitu{\acute{e}}$ autour de musical. Quels sont donc les $probl{\grave{e}}mes$ que posent les musicals $liscenci{\acute{e}}s$? $Premi{\grave{e}}rement$ ils encouragent la mime, pas la $cr{\acute{e}}ation$. En d'autre terme, les musicals $liscenci{\acute{e}}s$ que les $Cor{\acute{e}}ens$ montent sur la $sc{\grave{e}}ne$ ne sont pas les $cr{\acute{e}}ations$ propres chez les $Cor{\acute{e}}ens$, par contre, ces derniers sont $demand{\acute{e}}s$ de suivre ${\grave{a}}$ la mot les indications $dict{\acute{e}}es$ par le droit d'auteur. Les $cr{\acute{e}}ateurs$ $cor{\acute{e}}ens$ ne sont pas libres de $cr{\acute{e}}ation$. $Deuxi{\grave{e}}mement$ les musicals ${\grave{a}}$ la mode ont pouvoir de $d{\acute{e}}truire$ la $diversit{\acute{e}}$ de l'homme. L'homme se $caract{\grave{e}}rise$ par la $diversit{\acute{e}}$ et se $diff{\grave{e}}re$ l'un par rapport ${\grave{a}}$ l'autre. C'est l'essence de l'homme. Tous sont $diff{\acute{e}}rents$. Pourtant le musical qui $r{\grave{e}}gne$ sur tous genres de spectacles d'aujourd'hui de $Cor{\acute{e}}e$ ne laisse pas vivre d'autres genres de spectacles, la danse, le $th{\acute{e}}{\hat{a}}tre$, etc. Seul le musical est $compt{\acute{e}}$. $Troisi{\grave{e}}mement$ le musical ne peut pas nier $compl{\grave{e}}tement$ son origine commerciale. En fait le musical est devenu une chose important depuis des investigations immenses par le gouvernement $d^{\prime}Am{\acute{e}}rique$ pour donner des travails aux gens. Donc, $d{\grave{e}}s$ le $d{\acute{e}}but$, il n'y avait pas de $consid{\acute{e}}rations$ artistiques dans et sur le musical. Comme c'est par le commerce que le musical est $r{\acute{e}}pandu$, s'il y aura un $probl{\grave{e}}me$ quelconque, il est $tr{\grave{e}}s$ possible qu'on cherche ${\grave{a}}$ $r{\acute{e}}soudre$ le $probl{\grave{e}}me$ par la vue de commerce. En comprenant les $probl{\grave{e}}mes$ $mentionn{\acute{e}}s$ $l{\grave{a}}$-dessus, il faut $pr{\acute{e}}parer$ le changement de $march{\acute{e}}$ $cor{\acute{e}}enne$ de musical. Le moyen le plus $su{\hat{r}}$ de se $pr{\acute{e}}parer$ est de trouver la $r{\acute{e}}solution$ dans la culture. Pourtant cette $derni{\grave{e}}re$ ne signifie pas la tradition comme $c^{\prime}{\acute{e}}tait$ le cas $jusqu^{\prime}{\grave{a}}$ aujourd'hui, car les $Cor{\acute{e}}ens$ ne sont pas ${\acute{e}}duqu{\acute{e}}s$ par cette $derni{\grave{e}}re$. La $modernit{\acute{e}}$, disons occidentale, traverse la $Cor{\acute{e}}e$. Donc, la signification du mot 'culture' doit ${\hat{e}}tre$ $bas{\acute{e}}e$ sur la tradition et la $modernit{\acute{e}}$ en $m{\hat{e}}me$ temps.

• (The) Research of the performance art and culture
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• no.34
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• pp.383-421
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• 2017

Because the Jitsori and the Hotsori of the Beompae(the Korean Budhhist chant) has no meter and no Jangdan(a Rhythmic cycle of the Korean Music), so it is hard to analyze the melody of the Beompae. Also the melody of the Beompae is different from that of the other Korean traditional music, so studying of the Beompae has been out of the limelight of many scholars, studying the Korean music. But the melody of Beompae had been handed down for thousands of years in Korea, it and other Korean trditional music, had exchanged the impacts each other for a longtime. So I thinks that the Korean Beomapae have shared the similarity of the musical features with the other Korean traditional music. Because the Beompae of the Bulmosan Yeongsanjae on the Geongsangnamdo province has also no meters and no Jangdan, it is difficult to understand, too. But because the Onghoge of Bulmosan Yeongsanjae have a well-regulated melodic structure in comparison with the Beompae of the Seoul province, so called Geongjae Beompae, it seem to be easy to analyze its melody. So I will analyze the melody of Bulmosan Yeongsanjae Onghoge. This analyze should be contribute to investigate the rule of the melodic progress method on the convoluted Beompae melody. Onghoge has been sung on the procedure for Siryeon, Samsiniun(Goebuliun), Jojeonjeoman, Sinjungjakbeop. And the monk for the ritual has sung the chant first to purify the ritual place and to protect the soul. They has called the song, Onghoge a Jitsori at the Bulmosan Yeongsanjae preservation society of the Gyeongnam province. Commonly, there were Jitsori and Hotsori in the Beompae melody, and the melody of Jitsori is longer than that of the Hotsori. So, the melody of Onghoge is lengthened. In other word, the melody of the Onghoge show the lengthened and curved melodic feture of the Beompae very well. Hahn Manyeong, who had studied on the Beompae, Budhhist chant, said that the Hotsori has five letters in a phrase, and there were 4 phrases in a song. And he had insisted that the form of the song, Hotsori, is ABAB. I analyze the melody of the Onghoge by the Hahn's method. I will extract the Wonjeom(a primary tone of a skeletal melodic structure) from the melody of Onghoge, and in the progress of the Wonjeom of Onghoge melodies, I will arrange the repeat of the Wonjeom melody. It is a structural melody of Onghoge. The first phrase of Bulmosan Yeongsanjae Onghoge, 'Pal bu geum gang ho do ryang(八部金剛護道場)' have 4 structural melodies, the second phrase 'Gong sin sog bu bo cheon wang(空神速赴報天王)', the third phrase 'Sam gye je cheon ham le jip(三界諸天咸來集)', the firth phrase 'Yeo geum bul chal bo jeong sang(如今佛刹補禎祥)' have 2 structural melodies each. The structural melodies of Onghoge are 10 in total. And the structural melody of the Onghoge is formed the shape of 'Mi - La - do - La - Mi'. All of the Onghoge melodies is repeated 10 times by the melodic shape. The form of the Onghoge is not ABAB by Hahn, but is 10 times repeat of the shape.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.1-25
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• 2020

Sentiment Analysis (SA) is a Natural Language Processing (NLP) task that analyzes the sentiments consumers or the public feel about an arbitrary object from written texts. Furthermore, Aspect-Based Sentiment Analysis (ABSA) is a fine-grained analysis of the sentiments towards each aspect of an object. Since having a more practical value in terms of business, ABSA is drawing attention from both academic and industrial organizations. When there is a review that says "The restaurant is expensive but the food is really fantastic", for example, the general SA evaluates the overall sentiment towards the 'restaurant' as 'positive', while ABSA identifies the restaurant's aspect 'price' as 'negative' and 'food' aspect as 'positive'. Thus, ABSA enables a more specific and effective marketing strategy. In order to perform ABSA, it is necessary to identify what are the aspect terms or aspect categories included in the text, and judge the sentiments towards them. Accordingly, there exist four main areas in ABSA; aspect term extraction, aspect category detection, Aspect Term Sentiment Classification (ATSC), and Aspect Category Sentiment Classification (ACSC). It is usually conducted by extracting aspect terms and then performing ATSC to analyze sentiments for the given aspect terms, or by extracting aspect categories and then performing ACSC to analyze sentiments for the given aspect category. Here, an aspect category is expressed in one or more aspect terms, or indirectly inferred by other words. In the preceding example sentence, 'price' and 'food' are both aspect categories, and the aspect category 'food' is expressed by the aspect term 'food' included in the review. If the review sentence includes 'pasta', 'steak', or 'grilled chicken special', these can all be aspect terms for the aspect category 'food'. As such, an aspect category referred to by one or more specific aspect terms is called an explicit aspect. On the other hand, the aspect category like 'price', which does not have any specific aspect terms but can be indirectly guessed with an emotional word 'expensive,' is called an implicit aspect. So far, the 'aspect category' has been used to avoid confusion about 'aspect term'. From now on, we will consider 'aspect category' and 'aspect' as the same concept and use the word 'aspect' more for convenience. And one thing to note is that ATSC analyzes the sentiment towards given aspect terms, so it deals only with explicit aspects, and ACSC treats not only explicit aspects but also implicit aspects. This study seeks to find answers to the following issues ignored in the previous studies when applying the BERT pre-trained language model to ACSC and derives superior ACSC models. First, is it more effective to reflect the output vector of tokens for aspect categories than to use only the final output vector of [CLS] token as a classification vector? Second, is there any performance difference between QA (Question Answering) and NLI (Natural Language Inference) types in the sentence-pair configuration of input data? Third, is there any performance difference according to the order of sentence including aspect category in the QA or NLI type sentence-pair configuration of input data? To achieve these research objectives, we implemented 12 ACSC models and conducted experiments on 4 English benchmark datasets. As a result, ACSC models that provide performance beyond the existing studies without expanding the training dataset were derived. In addition, it was found that it is more effective to reflect the output vector of the aspect category token than to use only the output vector for the [CLS] token as a classification vector. It was also found that QA type input generally provides better performance than NLI, and the order of the sentence with the aspect category in QA type is irrelevant with performance. There may be some differences depending on the characteristics of the dataset, but when using NLI type sentence-pair input, placing the sentence containing the aspect category second seems to provide better performance. The new methodology for designing the ACSC model used in this study could be similarly applied to other studies such as ATSC.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.16 no.3
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• pp.193-211
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• 2021

Recently, interest in Korean Wave craze and K-beauty, led by K-pop, is increasing. In addition, the popularity and influence of the domestic beauty service industry has increased, and the economic and cultural ripple effects have been continuously expanding. The need to professional manpower training in response to the demand for manpower due to the growing development of domestic beauty services is emphasized, and the number of trainees who are actual consumers of beauty academy is increasing. Therefore, the purpose of our study is to examine the importance of quality factors of educational services to achieve educational purposes in the educational services provided by the Beauty Academy and the relationship between relationship commitment and educational service performance. Furthermore, it is to draw the importance of administrative support services, educational programs as well as educational service provision activities. However, the research for professional manpower training according to the provision of beauty services is insufficient compared to the development speed of the beauty industry. Therefore, at the present time when beauty service education is emphasized, our study will examine the relationship between relationship commitment and educational service performance based on the quality of education service by the students of domestic beauty academy. The measurement variables set for our study are program, instructor quality, tuition, external service, service fairness, relationship commitment, trust level, and educational service performance. The variables were analyzed and derived through the survey, and the following contents were derived from the empirical analysis. First, the quality of education service provided by the beauty academy, such as program, external service, service fairness, relationship commitment and trust level, had a significant effect on relationship commitment. Educational services provided by the institute, such as the systematicity and diversity of educational programs, enabled students to have a uniform relationship commitment. The quality of education service itself is to learn the expertise necessary for providing beauty service from the standpoint of the students and play an organic role in the relationship with the institute. Second, the moderating effect of trust level between academies and students was significant in the quality of education service and the relationship commitment. This means that students will feel higher level of service quality through the practical trust relationship of the students about the educational services provided by the institute. Based on the results of the empirical analysis, the implications of our study are to find ways to improve the students' ability and satisfaction represented by the results of educational services. This is because the quality of education services provided by the institute called Beauty Academy will have a great impact on the career choice of educational facilities and students. The characteristics of consistency, convenience, and knowledge orientation of education itself should be considered comprehensively, and a strong market position should be established through image formation through external service factors, which are external environments of academies.Furthermore, in terms of presenting differentiated strategies with competitors, the educational service quality factors play a significant role in the commitment to the relationship with the students, so the role of relationship marketing will be important for the psychological stability experienced by the students by grasping the demand accompanying the behavior of the students in advance.

• The Journal of Korean Society for Radiation Therapy
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• v.32
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• pp.7-15
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• 2020

Purpose: In modern radiotherapy technology, several methods of image guided radiation therapy (IGRT) are used to deliver accurate doses to tumor target locations and normal organs, including CBCT (Cone Beam Computed Tomography) and other devices, ExacTrac System, other than CBCT equipped with linear accelerators. In previous studies comparing the two systems, positional errors were analysed rearwards using Offline-view or evaluated only with a Yaw rotation with the X, Y, and Z axes. In this study, when using CBCT and ExacTrac to perform 6 Degree of the Freedom(DoF) Online IGRT in a treatment center with two equipment, the difference between the set-up calibration values seen in each system, the time taken for patient set-up, and the radiation usefulness of the imaging device is evaluated. Materials and Methods: In order to evaluate the difference between mobile calibrations and exposure radiation dose, the glass dosimetry and Rando Phantom were used for 11 cancer patients with head circumference from March to October 2017 in order to assess the difference between mobile calibrations and the time taken from Set-up to shortly before IGRT. CBCT and ExacTrac System were used for IGRT of all patients. An average of 10 CBCT and ExacTrac images were obtained per patient during the total treatment period, and the difference in 6D Online Automation values between the two systems was calculated within the ROI setting. In this case, the area of interest designation in the image obtained from CBCT was fixed to the same anatomical structure as the image obtained through ExacTrac. The difference in positional values for the six axes (SI, AP, LR; Rotation group: Pitch, Roll, Rtn) between the two systems, the total time taken from patient set-up to just before IGRT, and exposure dose were measured and compared respectively with the RandoPhantom. Results: the set-up error in the phantom and patient was less than 1mm in the translation group and less than 1.5° in the rotation group, and the RMS values of all axes except the Rtn value were less than 1mm and 1°. The time taken to correct the set-up error in each system was an average of 256±47.6sec for IGRT using CBCT and 84±3.5sec for ExacTrac, respectively. Radiation exposure dose by IGRT per treatment was measured at 37 times higher than ExacTrac in CBCT and ExacTrac at 2.468mGy and 0.066mGy at Oral Mucosa among the 7 measurement locations in the head and neck area. Conclusion: Through 6D online automatic positioning between the CBCT and ExacTrac systems, the set-up error was found to be less than 1mm, 1.02°, including the patient's movement (random error), as well as the systematic error of the two systems. This error range is considered to be reasonable when considering that the PTV Margin is 3mm during the head and neck IMRT treatment in the present study. However, considering the changes in target and risk organs due to changes in patient weight during the treatment period, it is considered to be appropriately used in combination with CBCT.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.1-21
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• 2021

As the global business environment changes, uncertainties in technology development and market needs increase, and competition among companies intensifies, interests and demands for R&D activities of individual companies are increasing. In order to cope with these environmental changes, R&D companies are strengthening R&D investment as one of the means to enhance the qualitative competitiveness of R&D while paying more attention to facility investment. As a result, facilities or R&D investment elements are inevitably a burden for R&D companies to bear future uncertainties. It is true that the management strategy of increasing investment in R&D as a means of enhancing R&D capability is highly uncertain in terms of corporate performance. In this study, the structural factors that influence the R&D capabilities of companies are explored in terms of technology management capabilities, R&D capabilities, and corporate classification attributes by utilizing data mining techniques, and the characteristics these individual factors present according to the level of R&D capabilities are analyzed. This study also showed cluster analysis and experimental results based on evidence data for all domestic R&D companies, and is expected to provide important implications for corporate management strategies to enhance R&D capabilities of individual companies. For each of the three viewpoints, detailed evaluation indexes were composed of 7, 2, and 4, respectively, to quantitatively measure individual levels in the corresponding area. In the case of technology management capability and R&D capability, the sub-item evaluation indexes that are being used by current domestic technology evaluation agencies were referenced, and the final detailed evaluation index was newly constructed in consideration of whether data could be obtained quantitatively. In the case of corporate classification attributes, the most basic corporate classification profile information is considered. In particular, in order to grasp the homogeneity of the R&D competency level, a comprehensive score for each company was given using detailed evaluation indicators of technology management capability and R&D capability, and the competency level was classified into five grades and compared with the cluster analysis results. In order to give the meaning according to the comparative evaluation between the analyzed cluster and the competency level grade, the clusters with high and low trends in R&D competency level were searched for each cluster. Afterwards, characteristics according to detailed evaluation indicators were analyzed in the cluster. Through this method of conducting research, two groups with high R&D competency and one with low level of R&D competency were analyzed, and the remaining two clusters were similar with almost high incidence. As a result, in this study, individual characteristics according to detailed evaluation indexes were analyzed for two clusters with high competency level and one cluster with low competency level. The implications of the results of this study are that the faster the replacement cycle of professional managers who can effectively respond to changes in technology and market demand, the more likely they will contribute to enhancing R&D capabilities. In the case of a private company, it is necessary to increase the intensity of input of R&D capabilities by enhancing the sense of belonging of R&D personnel to the company through conversion to a corporate company, and to provide the accuracy of responsibility and authority through the organization of the team unit. Since the number of technical commercialization achievements and technology certifications are occurring both in the case of contributing to capacity improvement and in case of not, it was confirmed that there is a limit in reviewing it as an important factor for enhancing R&D capacity from the perspective of management. Lastly, the experience of utility model filing was identified as a factor that has an important influence on R&D capability, and it was confirmed the need to provide motivation to encourage utility model filings in order to enhance R&D capability. As such, the results of this study are expected to provide important implications for corporate management strategies to enhance individual companies' R&D capabilities.