• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.43-62
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• 2019

At one time, the anomaly detection sector dominated the method of determining whether there was an abnormality based on the statistics derived from specific data. This methodology was possible because the dimension of the data was simple in the past, so the classical statistical method could work effectively. However, as the characteristics of data have changed complexly in the era of big data, it has become more difficult to accurately analyze and predict the data that occurs throughout the industry in the conventional way. Therefore, SVM and Decision Tree based supervised learning algorithms were used. However, there is peculiarity that supervised learning based model can only accurately predict the test data, when the number of classes is equal to the number of normal classes and most of the data generated in the industry has unbalanced data class. Therefore, the predicted results are not always valid when supervised learning model is applied. In order to overcome these drawbacks, many studies now use the unsupervised learning-based model that is not influenced by class distribution, such as autoencoder or generative adversarial networks. In this paper, we propose a method to detect anomalies using generative adversarial networks. AnoGAN, introduced in the study of Thomas et al (2017), is a classification model that performs abnormal detection of medical images. It was composed of a Convolution Neural Net and was used in the field of detection. On the other hand, sequencing data abnormality detection using generative adversarial network is a lack of research papers compared to image data. Of course, in Li et al (2018), a study by Li et al (LSTM), a type of recurrent neural network, has proposed a model to classify the abnormities of numerical sequence data, but it has not been used for categorical sequence data, as well as feature matching method applied by salans et al.(2016). So it suggests that there are a number of studies to be tried on in the ideal classification of sequence data through a generative adversarial Network. In order to learn the sequence data, the structure of the generative adversarial networks is composed of LSTM, and the 2 stacked-LSTM of the generator is composed of 32-dim hidden unit layers and 64-dim hidden unit layers. The LSTM of the discriminator consists of 64-dim hidden unit layer were used. In the process of deriving abnormal scores from existing paper of Anomaly Detection for Sequence data, entropy values of probability of actual data are used in the process of deriving abnormal scores. but in this paper, as mentioned earlier, abnormal scores have been derived by using feature matching techniques. In addition, the process of optimizing latent variables was designed with LSTM to improve model performance. The modified form of generative adversarial model was more accurate in all experiments than the autoencoder in terms of precision and was approximately 7% higher in accuracy. In terms of Robustness, Generative adversarial networks also performed better than autoencoder. Because generative adversarial networks can learn data distribution from real categorical sequence data, Unaffected by a single normal data. But autoencoder is not. Result of Robustness test showed that he accuracy of the autocoder was 92%, the accuracy of the hostile neural network was 96%, and in terms of sensitivity, the autocoder was 40% and the hostile neural network was 51%. In this paper, experiments have also been conducted to show how much performance changes due to differences in the optimization structure of potential variables. As a result, the level of 1% was improved in terms of sensitivity. These results suggest that it presented a new perspective on optimizing latent variable that were relatively insignificant.

• Korean Journal of Environment and Ecology
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• v.34 no.6
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• pp.558-572
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• 2020

The purpose of this study was to classify the types of degraded areas of Mt. Jirisan section in Baekdudaegan and survey the actual condition of each damage type to use it as basic data for the direction of the restoration of damaged areas according to damage type based on the vegetation information of reference ecosystem. The analysis of the Mt. Jirisan section's actual degraded conditions showed that the total number of patches of degraded areas was 57, and the number of patches and size of degraded areas was higher at the low average altitude and gentle slope. Grasslands (deserted lands) and cultivated areas accounted for a high portion of the damage types, indicating that agricultural land use was a major damage factor. The survey on the conditions of 14 degraded areas showed that the types of damage were classified into the grassland, cultivated area, restoration area, logged-off land, and bare ground. The analysis of the degree of disturbance (the ratio of annual and biennial herb, urbanized index, and disturbance index) by each type showed that the simple single-layer vegetation structure mostly composed of the herbaceous and the degree of disturbance were high in the grassland and cultivated land. The double-layer vegetation structure appeared in the restoration area where the pine seedlings were planted, and the inflow of naturalized plants was especially high compared to other degraded areas due to disturbances caused by the restoration project and the nearby hiking trails. Although the inflow of naturalized plants was low because of high altitude in bare ground, the proportion of annual and biennial herb was high, indicating that all surveyed degraded areas were in early succession stages. The stand ordination by type of damage showed the restoration area on the I-axis, cultivated area, grassland, logged-off land, and bare ground in that order, indicating the arrangement by the damage type. Moreover, the stand ordination of the degraded areas and reference ecosystem based on floristic variation showed a clear difference in species composition. This study diagnosed the status of each damage type based on the reference ecosystem information according to the ecological restoration procedure and confirmed the difference in species composition between the diagnosis result and the reference ecosystem. These findings can be useful basic data for establishing the restoration goal and direction in the future.

• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.167-183
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• 2023

In the recent field of recommendation systems, various studies have been conducted to model sparse data effectively. Among these, GLocal-K(Global and Local Kernels for Recommender Systems) is a research endeavor combining global and local kernels to provide personalized recommendations by considering global data patterns and individual user characteristics. However, due to its utilization of kernel tricks, GLocal-K exhibits diminished performance on highly sparse data and struggles to offer recommendations for new users or items due to the absence of side information. In this paper, to address these limitations of GLocal-K, we propose the GEase-K (Global and EASE kernels for Recommender Systems) model, incorporating the EASE(Embarrassingly Shallow Autoencoders for Sparse Data) model and leveraging side information. Initially, we substitute EASE for the local kernel in GLocal-K to enhance recommendation performance on highly sparse data. EASE, functioning as a simple linear operational structure, is an autoencoder that performs highly on extremely sparse data through regularization and learning item similarity. Additionally, we utilize side information to alleviate the cold-start problem. We enhance the understanding of user-item similarities by employing a conditional autoencoder structure during the training process to incorporate side information. In conclusion, GEase-K demonstrates resilience in highly sparse data and cold-start situations by combining linear and nonlinear structures and utilizing side information. Experimental results show that GEase-K outperforms GLocal-K based on the RMSE and MAE metrics on the highly sparse GoodReads and ModCloth datasets. Furthermore, in cold-start experiments divided into four groups using the GoodReads and ModCloth datasets, GEase-K denotes superior performance compared to GLocal-K.

• (The) Research of the performance art and culture
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• no.42
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• pp.147-181
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• 2021

Dalgubal drum dance is inherited in a recreated form by incorporating regional symbolism and the dance philosophy and artisticity of Young Hwangbo, the creator, based on the traditional drum dance of the Yeongnam region. This dance having popularity with the transformation of traditional Korean culture has been invited not only to Yeongnam region including Daegu but also to international various venues. This study explores what the movement characteristics of this Dalgubal drum dance are and the unique charm and symbolic meaning of this dance. Specific analysis was conducted through analyzing Dalgubal drum dance video film of the 89th Korean Myeongmujeon's by using Laban Movement Analysis as a research method. The special features of this dance resulted from the LMA analysis in terms of the four categories-Body, Effort, Shape, and Space-reveal simple yet cheerful personalities and strong yet patient characteristics of the people in Daegu. The harmony of drum sounds(music) and movements(dance) creates various characteristics of dances and reveals the beauty and excitement of unique Korean dance. In particular, drum play and its related dance movements create curved linear spatial pattern of arm movements, Spiral Shape in body posture, and diverse floor patterns occupying whole stage space. These movements show the three-dimensional spatial beauty and the artistic ideas for recreation of traditional drum dance, which considered with the spatial structure of the proscenium stage. In addition, the well-organized structure and harmonious movements of this dance show the traditional Korean philosophy, implying heaven, earth, and human being and the wholeness, and the harmony of yin and yang. The dance aims at communication between the audiences and dancers through sharing excitement and the aesthetic beauty of dance. This can be interpreted as a meaningful expression of traditional Korean philosophy developed with the unique value and characteristics of Korean dance.

• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.32 no.2
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• pp.27-41
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• 2014

This study aims to investigate the history, cultural values prototype through literature analysis, characteristics of construction, location, space structure and landscape characteristics by Arc-GIS on the Munam pavilion(聞巖亭) in Changnyeong. The results were as follows. First, Shin-cho((辛礎, 1549~1618) is the builder of the Munam pavilion and builder's view of nature is to go back to nature. The period of formation of Munam pavilion is between 1608-1618 as referred from document of retire from politics and build a pavilion. Secondly, Munam pavilion is surrounded by mountains and located at the top of steep slope. Pavilion was known as scenic site of the area. But damaged in a past landscape is caused by near the bridge, agricultural facilities, town, the Kye-sung stream of masonry and beams. Thirdly, Munam pavilion is divided into the main space, which is located on the pavilion, space in located on the pavilion east and west and the orient space, which is located on the Youngjeonggak. Of these, original form of Munam pavilion is a simple structure composed of pavilion and Munam rock, thus at the time of the composition seems to be a direct entry is possible, unlike the current entrance. Fourth, Spatial composition of Munam pavilion is divided into vegetation such as Lagerstroemia indica trees in Sa-ri in Changnyeong, ornament such as letters carved on the rocks and pavilion containing structure. The vegetation around the building is classified as precincts and outside of the premises. Planting of precincts was limited. Outside of area consists of front on the pavilion, which is covered with Lagerstroemia Indica forest and Pinus densiflora forest at the back of the pavilion. Ofthese,LargeLagerstroemiaIndicaforestcorrespondstothenaturalheritageasHistoricalrecordsofrarespeciesresourcesthatareassociated withbuilder. Letterscarvedontherocksrepresenttheboundaryof space, which is close to the location of the Munam pavilion and those associated with the builder as ornaments. Letters carved on the rocks front on the pavilion are rare cases that are made sequentially with a constant direction and rules as act of record for families to honor the achievements. Fifth, 'The eight famous spots of Munam' is divided into landscape elements that have nothing to do with bearing 4 places and landscape elements that have to do with bearing 4 places. Unrelated bearings of landscape elements are Lagerstroemia indica trees in Sa-ri in Changnyeong, Pinus densiflora forest at the back of the pavilion, Okcheon valley, Gwanryongsa temple and Daeheungsa temple. Bearing that related element of absolute orientation, which is corresponding to the elements are Daeheungsa temple, Hwawangsan mountain, Kye-sung stream and Yeongchwisan mountain. Relative bearing is Gwanryongsa temple, Yeongchwisan mountain and Kye-sung stream Gongjigi hill. At Lagerstroemia indica trees in Sa-ri in Changnyeong, Pinus densiflora forest at the back of the pavilion, Kye-sung stream and Okcheon valley, elements are exsting. Currently, it is difficult to confirm the rest of the landscape elements. Because, it is a generic element that reliable estimate of the target and locations are impossible for element. Munam pavilion is made for turn to nature by Shin-cho(辛礎). That was remained a record such as Munamzip(聞巖集) and Munamchungueirok(聞巖忠義錄) that is relating to construction of pavilion. Munam pavilion located in a unique form, archival culture through the letters carved on the rocks and Large Lagerstroemia indica forest and through eight famous spots, cultural landscape elements can be assumed that those elements are remained.

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

• The Journal of Korean Society for Radiation Therapy
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• v.22 no.2
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• pp.97-103
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• 2010

Purpose: Treating same region with different modalities there is a limit to evaluate the total absorbed dose of normal tissues. The reason is that it does not support to communication each modalities yet. In this article, it evaluates absorbed dose of the patients who had been treated same region by a tomotherapy and a linear accelerator. Materials and Methods: After reconstructing anatomic structure with a anthropomorphic phantom, administrate 45 Gy to a tumor in linac plan system as well as prescribe 15 Gy in tomotherapy plan system for make an ideal treatment plan. After the plan which made by tomoplan system transfers to the oncentra plan system for reproduce plan under the same condition and realize total treatment plan with summation 45 Gy linac treatment plan. To evaluate the absorbed dose of two different modalities, do a comparative study both a simple summation dose values and integration dose values. Then compare and analyze absorbed dose of normal tissues and a tumor with the patients who had been exposured radiation by above two differents modalities. Results: The result of compared data, in case of minimum dose, there are big different dose values in spleen (12.4%). On the other hand, in case of the maximum dose, it reports big different in a small bowel (10.2%) and a cord (5.8%) in head & neck cancer patients, there presents that oral (20.3%), right lens (7.7%) in minimum dose value. About maximum dose, it represents that spinal (22.5), brain stem (12%), optic chiasm (8.9%), Rt lens (11.5%), mandible (8.1%), pituitary gland (6.2%). In case of Rt abdominal cancer patients, there represents big different minimum dose as Lt kidney (20.3%), stomach (8.1%) about pelvic cancer patients, it reports there are big different in minimum dose as a bladder (15.2%) as well as big different value in maximum dose as a small bowel (5.6%), a bladder (5.5%) in addition, making treatment plan it is able us to get. Conclusion: In case of comparing both simple summation absorbed dose and integration absorbed dose, the minimum dose are represented higher as well as the maximum dose come out lower and the average dose are revealed similar with our expected values data. It is able to evaluate tumor & normal tissue absorbed dose which could had been not realized by treatment plan system. The DVH of interesting region are prescribed lower dose than expected. From now on, it needs to develop the new modality which are able to realize exact dose distribution as well as integration absorbed dose evaluation in same treatment region with different modalities.

• Journal of Korean Medical classics
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• v.20 no.4
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• pp.91-117
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• 2007

Through a simple study of the medical classics in the '$\bar{A}yurveda$', we have summarized them as follows. 1) Traditional Indian medicine started in the Ganges river area at about 1500 B. C. E. and traces of medical science can be found in the "Rigveda" and "Atharvaveda". 2) The "Charaka" and "$Su\acute{s}hruta$(妙聞集)", ancient texts from India, are not the work of one person, but the result of the work and errors of different doctors and philosophers. Due to the lack of historical records, the time of Charaka or $Su\acute{s}hruta$(妙聞)s' lives are not exactly known. So the completion of the "Charaka" is estimated at 1st${\sim}$2nd century C. E. in northwestern India, and the "$Su\acute{s}hruta$" is estimated to have been completed in 3rd${\sim}$4th century C. E. in central India. Also, the "Charaka" contains details on internal medicine, while the "$Su\acute{s}hruta$" contains more details on surgery by comparison. 3) '$V\bar{a}gbhata$', one of the revered Vriddha Trayi(triad of the ancients, 三醫聖) of the '$\bar{A}yurveda$', lived and worked in about the 7th century and wrote the "$A\d{s}\d{t}\bar{a}nga$ $A\d{s}\d{t}\bar{a}nga$ $h\d{r}daya$ $sa\d{m}hit\bar{a}$ $samhit\bar{a}$(八支集)" and "$A\d{s}\d{t}\bar{a}nga$ Sangraha $samhit\bar{a}$(八心集)", where he tried to compromise and unify the "Charaka" and "$Su\acute{s}hruta$". The "$A\d{s}\d{t}\bar{a}nga$ Sangraha $samhit\bar{a}$" was translated into Tibetan and Arabic at about the 8th${\sim}$9th century, and if we generalize the medicinal plants recorded in each the "Charaka", "$Su\acute{s}hruta$" and the "$A\d{s}\d{t}\bar{a}nga$ Sangraha $samhit\bar{a}$", there are 240, 370, 240 types each. 4) The 'Madhava' focused on one of the subjects of Indian medicine, '$Nid\bar{a}na$' ie meaning "the cause of diseases(病因論)", and in one of the copies found by Bower in 4th century C. E. we can see that it uses prescriptions from the "BuHaLaJi(布哈拉集)", "Charaka", "$Su\acute{s}hruta$". 5) According to the "Charaka", there were 8 branches of ancient medicine in India : treatment of the body(kayacikitsa), special surgery(salakya), removal of alien substances(salyapahartka), treatment of poison or mis-combined medicines(visagaravairodhikaprasamana), the study of ghosts(bhutavidya), pediatrics(kaumarabhrtya), perennial youth and long life(rasayana), and the strengthening of the essence of the body(vajikarana). 6) The '$\bar{A}yurveda$', which originated from ancient experience, was recorded in Sanskrit, which was a theorization of knowledge, and also was written in verses to make memorizing easy, and made medicine the exclusive possession of the Brahmin. The first annotations were 1060 for the "Charaka", 1200 for the "$Su\acute{s}hruta$", 1150 for the "$A\d{s}\d{t}\bar{a}nga$ Sangraha $samhit\bar{a}$", and 1100 for the "$Nid\bar{a}na$", The use of various mineral medicines in the "Charaka" or the use of mercury as internal medicine in the "$A\d{s}\d{t}\bar{a}nga$ Sangraha $samhit\bar{a}$", and the palpation of the pulse for diagnosing in the '$\bar{A}yurveda$' and 'XiZhang(西藏)' medicine are similar to TCM's pulse diagnostics. The coexistence with Arabian 'Unani' medicine, compromise with western medicine and the reactionism trend restored the '$\bar{A}yurveda$' today. 7) The "Charaka" is a book inclined to internal medicine that investigates the origin of human disease which used the dualism of the 'Samkhya', the natural philosophy of the 'Vaisesika' and the logic of the 'Nyaya' in medical theories, and its structure has 16 syllables per line, 2 lines per poem and is recorded in poetry and prose. Also, the "Charaka" can be summarized into the introduction, cause, judgement, body, sensory organs, treatment, pharmaceuticals, and end, and can be seen as a work that strongly reflects the moral code of Brahmin and Aryans. 8) In extracting bloody pus, the "Charaka" introduces a 'sharp tool' bloodletting treatment, while the "$Su\scute{s}hruta$" introduces many surgical methods such as the use of gourd dippers, horns, sucking the blood with leeches. Also the "$Su\acute{s}hruta$" has 19 chapters specializing in ophthalmology, and shows 76 types of eye diseases and their treatments. 9) Since anatomy did not develop in Indian medicine, the inner structure of the human body was not well known. The only exception is 'GuXiangXue(骨相學)' which developed from 'Atharvaveda' times and the "$A\d{s}\d{t}\bar{a}nga$ Sangraha $samhit\bar{a}$". In the "$A\d{s}\d{t}\bar{a}nga$ Sangraha $samhit\bar{a}$"'s 'ShenTiLun(身體論)' there is a thorough listing of the development of a child from pregnancy to birth. The '$\bar{A}yurveda$' is not just an ancient traditional medical system but is being called alternative medicine in the west because of its ability to supplement western medicine and, as its effects are being proved scientifically it is gaining attention worldwide. We would like to say that what we have researched is just a small fragment and a limited view, and would like to correct and supplement any insufficient parts through more research of new records.

• The Journal of Dong Guk Oriental Medicine
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• v.10
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• pp.119-145
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• 2008

Through a simple study of the medical classics in the '$\bar{A}yurveda$', we have summarized them as follows. 1) Traditional Indian medicine started in the Ganges river area at about 1500 B. C. E. and traces of medical science can be found in the "Rigveda" and "Atharvaveda". 2) The "Charaka(閣羅迦集)" and "$Su\acute{s}hruta$(妙聞集)", ancient texts from India, are not the work of one person, but the result of the work and errors of different doctors and philosophers. Due to the lack of historical records, the time of Charaka(閣羅迦) or $Su\acute{s}hruta$(妙聞)s' lives are not exactly known. So the completion of the "Charaka" is estimated at 1st$\sim$2nd century C. E. in northwestern India, and the "$Su\acute{s}hruta$" is estimated to have been completed in 3rd$\sim$4th century C. E. in central India. Also, the "Charaka" contains details on internal medicine, while the "$Su\acute{s}hruta$" contains more details on surgery by comparison. 3) '$V\bar{a}gbhata$', one of the revered Vriddha Trayi(triad of the ancients, 三醫聖) of the '$\bar{A}yurveda$', lived and worked in about the 7th century and wrote the "$Ast\bar{a}nga$ $Ast\bar{a}nga$ hrdaya $samhit\bar{a}$ $samhit\bar{a}$(八支集) and "$Ast\bar{a}nga$ Sangraha $samhit\bar{a}$(八心集)", where he tried to compromise and unify the "Charaka" and "$Su\acute{s}hruta$". The "$Ast\bar{a}nga$ Sangraha $samhit\bar{a}$" was translated into Tibetan and Arabic at about the 8th$\sim$9th century, and if we generalize the medicinal plants recorded in each the "Charaka", "$Su\acute{s}hruta$" and the "$Ast\bar{a}nga$ Sangraha $samhit\bar{a}$", there are 240, 370, 240 types each. 4) The 'Madhava' focused on one of the subjects of Indian medicine, '$Nid\bar{a}na$' ie meaning "the cause of diseases(病因論)", and in one of the copies found by Bower in 4th century C. E. we can see that it uses prescriptions from the "BuHaLaJi(布唅拉集)", "Charaka", "$Su\acute{s}hruta$". 5) According to the "Charaka", there were 8 branches of ancient medicine in India : treatment of the body(kayacikitsa), special surgery(salakya), removal of alien substances(salyapahartka), treatment of poison or mis-combined medicines(visagaravairodhikaprasamana), the study of ghosts(bhutavidya), pediatrics(kaumarabhrtya), perennial youth and long life(rasayana), and the strengthening of the essence of the body(vajikarana). 6) The '$\bar{A}yurveda$', which originated from ancient experience, was recorded in Sanskrit, which was a theorization of knowledge, and also was written in verses to make memorizing easy, and made medicine the exclusive possession of the Brahmin. The first annotations were 1060 for the "Charaka", 1200 for the "$Su\acute{s}hruta$", 1150 for the "$Ast\bar{a}nga$ Sangraha $samhit\bar{a}$", and 1100 for the "$Nid\bar{a}na$". The use of various mineral medicines in the "Charaka" or the use of mercury as internal medicine in the "$Ast\bar{a}nga$ Sangraha $samhit\bar{a}$", and the palpation of the pulse for diagnosing in the '$\bar{A}yurveda$' and 'XiZhang(西藏)' medicine are similar to TCM's pulse diagnostics. The coexistence with Arabian 'Unani' medicine, compromise with western medicine and the reactionism trend restored the '$\bar{A}yurveda$' today. 7) The "Charaka" is a book inclined to internal medicine that investigates the origin of human disease which used the dualism of the 'Samkhya', the natural philosophy of the 'Vaisesika' and the logic of the 'Nyaya' in medical theories, and its structure has 16 syllables per line, 2 lines per poem and is recorded in poetry and prose. Also, the "Charaka" can be summarized into the introduction, cause, judgement, body, sensory organs, treatment, pharmaceuticals, and end, and can be seen as a work that strongly reflects the moral code of Brahmin and Aryans. 8) In extracting bloody pus, the "Charaka" introduces a 'sharp tool' bloodletting treatment, while the "$Su\acute{s}hruta$" introduces many surgical methods such as the use of gourd dippers, horns, sucking the blood with leeches. Also the "$Su\acute{s}hruta$" has 19 chapters specializing in ophthalmology, and shows 76 types of eye diseases and their treatments. 9) Since anatomy did not develop in Indian medicine, the inner structure of the human body was not well known. The only exception is 'GuXiangXue(骨相學)' which developed from 'Atharvaveda' times and the "$Ast\bar{a}nga$ Sangraha $samhit\bar{a}$". In the "$Ast\bar{a}nga$ Sangraha $samhit\bar{a}$"'s 'ShenTiLun(身體論)' there is a thorough listing of the development of a child from pregnancy to birth. The '$\bar{A}yurveda$' is not just an ancient traditional medical system but is being called alternative medicine in the west because of its ability to supplement western medicine and, as its effects are being proved scientifically it is gaining attention worldwide. We would like to say that what we have researched is just a small fragment and a limited view, and would like to correct and supplement any insufficient parts through more research of new records.

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.95-118
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• 2017

Recent centrally the downtown area, the transaction between the row housing and multiplex housing is activated and platform services such as Zigbang and Dabang are growing. The row housing and multiplex housing is a blind spot for real estate information. Because there is a social problem, due to the change in market size and information asymmetry due to changes in demand. Also, the 5 or 25 districts used by the Seoul Metropolitan Government or the Korean Appraisal Board(hereafter, KAB) were established within the administrative boundaries and used in existing real estate studies. This is not a district classification for real estate researches because it is zoned urban planning. Based on the existing study, this study found that the city needs to reset the Seoul Metropolitan Government's spatial structure in estimating future housing prices. So, This study attempted to classify the area without spatial heterogeneity by the reflected the property price characteristics of row housing and Multiplex housing. In other words, There has been a problem that an inefficient side has arisen due to the simple division by the existing administrative district. Therefore, this study aims to cluster Seoul as a new area for more efficient real estate analysis. This study was applied to the hedonic model based on the real transactions price data of row housing and multiplex housing. And the K-Means Clustering algorithm was used to cluster the spatial structure of Seoul. In this study, data onto real transactions price of the Seoul Row housing and Multiplex Housing from January 2014 to December 2016, and the official land value of 2016 was used and it provided by Ministry of Land, Infrastructure and Transport(hereafter, MOLIT). Data preprocessing was followed by the following processing procedures: Removal of underground transaction, Price standardization per area, Removal of Real transaction case(above 5 and below -5). In this study, we analyzed data from 132,707 cases to 126,759 data through data preprocessing. The data analysis tool used the R program. After data preprocessing, data model was constructed. Priority, the K-means Clustering was performed. In addition, a regression analysis was conducted using Hedonic model and it was conducted a cosine similarity analysis. Based on the constructed data model, we clustered on the basis of the longitude and latitude of Seoul and conducted comparative analysis of existing area. The results of this study indicated that the goodness of fit of the model was above 75 % and the variables used for the Hedonic model were significant. In other words, 5 or 25 districts that is the area of the existing administrative area are divided into 16 districts. So, this study derived a clustering method of row housing and multiplex housing in Seoul using K-Means Clustering algorithm and hedonic model by the reflected the property price characteristics. Moreover, they presented academic and practical implications and presented the limitations of this study and the direction of future research. Academic implication has clustered by reflecting the property price characteristics in order to improve the problems of the areas used in the Seoul Metropolitan Government, KAB, and Existing Real Estate Research. Another academic implications are that apartments were the main study of existing real estate research, and has proposed a method of classifying area in Seoul using public information(i.e., real-data of MOLIT) of government 3.0. Practical implication is that it can be used as a basic data for real estate related research on row housing and multiplex housing. Another practical implications are that is expected the activation of row housing and multiplex housing research and, that is expected to increase the accuracy of the model of the actual transaction. The future research direction of this study involves conducting various analyses to overcome the limitations of the threshold and indicates the need for deeper research.