• Tuberculosis and Respiratory Diseases
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• v.45 no.2
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• pp.322-332
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• 1998

Background: Accurate staging is important to determine treatment modalities and to predict prognosis for the patients with lung cancer. The simple two-stage system of the Veteran's Administration Lung Cancer study Group has been used for staging of small cell lung cancer(SCLC) because treatment usually consists of chemotherapy with or without radiotherapy. However, this system does not accurately reflect segregation of patients into homogenous prognostic groups. Therefore, a variety of new staging system have been proposed as more intensive treatments including either intensive radiotherapy or surgery enter clinical trials. We evaluate the prognostic importance of TNM staging, which has the advantage of providing a uniform detailed classification of tumor spread, in patients with SCLC. Methods: The medical records of 166 patients diagnosed with SCLC between January 1989 and December 1996 were reviewed retrospectively. The influence of TNM stage on survival was analyzed in 147 patients, among 166 patients, who had complete TNM staging data. Results: Three patients were classified in stage I / II, 15 in stage III a, 78 in stage IIIb and 48 in stage IV. Survival rate at 1 and 2 years for these patients were as follows: stage I / II, 75% and 37.5% ; stage IIIa, 46.7% and 25.0% ; stage III b, 34.3% and 11.3% ; and stage IV, 2.6% and 0%. The 2-year survival rates for 84 patients who received chemotherapy(more than 2 cycles) with or without radiotherapy were as follows: stage I / II, 37.5% ; stage rna, 31.3% ; stage IIIb 13.5% ; and stage IV 0%. Overall outcome according to TNM staging was significantly different whether or not received treatment. However, there was no significant difference between stage IIIa and stage IIIb though median survival and 2-year survival rate were higher in stage IIIa than stage IIIb. Conclusion: These results suggest that the TNM staging system may be helpful for predicting the prognosis of patients with SCLC.

• Journal of the Korean Institute of Landscape Architecture
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• v.42 no.4
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• pp.10-20
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• 2014

This study was carried out for the purpose of analysis correlations between street furniture design elements and preference of street furniture. This research analyzed the various street furniture set on Ilsan new town street, which are rest, hygiene, light, information, sale, traffic, and landscape. This study has processed by analysing street furniture literature investigation and consideration of theory. First, for analysed effect of street furniture element, has been appear as element 'relaxation facilities', 'information facilities', landscape facilities' mainly effect on street. Specially, 'rest', 'landscape', 'information' in order had a major influence on scenery. Each kind of 'chair elements' in rest facilities, 'sign board' in information facilities, 'sculpture', 'fountain' in 'landscape facilities' has analyzed as the main elements in the landscape affinity property affecting factor. Second, the results of analyzed landscape elements (shape, colour, texture, scale) affect to the affinity of street furniture. chair which are included in rest facilities affect "texture", "form", "scale", "color" in the order of preference of the molding design elements that influence landscape. Particularly, showed statistically significant on 'colour' element affecting the landscape preference than the other three elements. It means as the chair element which is rest facilities mainly affect on preferences, rather than texture, form, scale, colour. Monument in the landscape associated with a preference 'colour', 'shape', 'texture' 'scale' and appears to be in order of impact so we could get the consequence like chair and rest facilities show different aspects of the respectively. It means, visual element which are colour and shape significantly impact on landscape preferences. Third, information facilities such as signboard formative elements of landscape design preferences and correlation with negative showed that the correlation. That mean if the sign board is very negative influence on landscape preferences and the correlations of the design formative elements appear in order of 'scale', 'colour', 'texture'. It also means that the 'scale' namely the size of advertising material and colour are adversely affected in terms of landscape. As these results, when design street furniture as the street scenery, facilities according to the kind of the shape element and need to focus on relative shape element according to the kind of facilities difference. Finally, so far as to clarify the street furniture, mainly 'function' and 'system' classification shows undesirable in outdoor scape. Thus, performed studies in relationship with landscape, classify 'kind of facilities' is more desirable than 'system'.

• Journal of Distribution Science
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• v.9 no.2
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• pp.83-95
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• 2011

Conventional markets in Korea have played a pivotal role in the vitalization of local communities and economies along with the distribution of products. Although many people believe the markets to be disorderly, they are lively and provide local people with things to enjoy, watch and buy. However, superstores have undergone a mushrooming proliferation since Korea opened its gates to multinational superstores in 1996. This phenomenon has caused a crisis for Korea's conventional markets. They have lost their competitiveness because of this environmental change, inefficient management, and their outmoded facilities. Government efforts to revitalize the markets have centered on redevelopment of the facilities, a perspective that has caused not only the fall of the old business districts but also the decline of the distribution function. Under these conditions, the traditional market has re-entered into competition. The Korean government enacted a special law to revitalize the conventional markets and has been implementing many policies to support them since 2003. In 2009, the government amended the law and adopted the Business Improvement District System. The government also changed the official term from 'old markets' to 'Conventional markets'. Despite this legal amendment, though, we still need to re-establish the concept of the Conventional market. Historically, markets grew up spontaneously to dispose of surplus products. Some manmade markets were established through urban planning or as public facilities. Their businesses transactions have always been based on mutual trust between consumers and trades people, the traditional way of commercial dealing. Conventional markets can be defined, then, as creatures of societal necessity where transactions for services and products are based on mutual trust. Problematically, unlisted markets are left out of government support. Although unlisted markets have performed almost the same functions as listed markets, they exist only as a statistic as far as the special law is concerned. In some areas, there are more unlisted markets than unlisted ones. Therefore, it is necessary to establish systematic management methods for the unlisted markets. Some unlisted markets received support in the form of facility improvement from local governments' budgets in the early stage of the special law's enforcement. The current government also assists with safety issues involving unlisted markets; however, the current special law provides no legal framework for unlisted markets. Moreover, consumers cannot tell the difference between unlisted markets and listed ones. Finding a solution to this problemrequires new standards and a wider scope of support by which the efficiency of the market improvement support system might be enhanced.

• Korean Journal of Heritage: History & Science
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• v.49 no.2
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• pp.20-55
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• 2016

Koguryo's tomb mural paintings in North Korea are our precious cultural heritage which have been designated as UNESCO World Heritage property receiving high praise in the following criterion, i) exceptional creativeness of human being, ii) representative value showing the stage of development in construction history of East-Asia, iii) aesthetic superiority iv) uniqueness of building construction including tombs' ceiling. Mural paintings have been found from almost 100 tombs of the Koguryo dynasty out of 130 which are scattered across Huanren County, Lianoning Province, Ji'an, Jilin Province in China and Pyongyang in North Korea. Especially, most of them are gathered in Pyongyang from 4th and 5th century. Peculiarly, some of them have been constructed before King Jangsu's transfer of the capital to Pyongyang(AD 427). It can be regarded that Pyongyang territory had been under control of Koguryo and to become a new capital in the near future. And dense emergence of such tombs since the capital transfer from Gungnae City to Pyongyang during the reign of Jangsu is linked closely to the construction of tombs for rulers under strengthen royal authority of Jangsu and centralized system of authoritarian rule. Tomb mural paintings describe the owner's figure pictorially based on the truth just as in his living years. General lifestyles of ruling powers and sovereigns can be seen from the wall paintings portraying several buildings with various styles, figures, manners of living, which are considered that the tomb owner had led politically and sociologically in his life. In spite of not enough proofs to approve figure of architectures or "Gong-Po" in wall paintings on the tombs as those of Koguryo, it is persuasive with consideration for painting and decoration inside the tomb like wooden building in real life for the purpose of reenacting and continuing the tomb owner's luxurious life after death. "Du-Gong-Po-Zak" had appeared in company with Koguryo tomb murals and it can be found in most of the murals. And the emergence of substantial "Gong-Po-Do" can be counted more than a century ahead of the figure in murals. It could be a reasonable assumption as regards Koguryo tomb murals time of appearance match up with production period of Gahyungmyunggi(家形明器) and Hwasangseok(畵像石) Hwasangjeon(畵像塼) Design in the Mural Painting of the East-Han(東漢) Ancient Tombs in China. On this study, architectural "Gong-Po"s described in Koguryo tomb murals are categorized largely in "Bi(non)-Po-Zak-kye", "Jun(semi)-Po-Zak-kye", and "Po-Zak-kye" based on presence of "Ju-Du", "Cheom-Cha", and "Cheom-Cha-Sal-Mi" with developmental aspect, and, "Po-zak" is subdivided as "Bi(non)-Cheul-Mok" and "Cheul-Mok" types.

• Journal of the Korean Institute of Landscape Architecture
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• v.47 no.2
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• pp.117-132
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• 2019

The study examines the significance and limits of modern park planning by analyzing major planning documents of Gyeongseong in the Japanese colonial era. Among seven selected documents from 1925 to 1940, which show the contents related with park planning, documents of 1930 and 1940 presented the official park plan of Gyeongseong. By the 1920s, the park plan was not a major concern in urban planning of Gyeongseong; however, as the planning law as enacted in 1934, the park plan legally became a part of the official master planning process in the 1930s. In 1940, the most comprehensive park plan for Gyeongseong was published. In the beginning of modern urban planning, a park was mainly perceived as a sanitation utility. From the 1920s to the 1930s, the park planning system was significantly improved including systemic classification of parks, guideline development considering spatial planning, and introduction of a concept of infra-structural green space. Despite of the improvement in the park planning, the actual quantity of the overall green spaces barely changed and there was a huge discrepancy between the planning ideal and the reality. The Gyeongseong stadium was the only facility newly built in the 1920s, and only two parks were constructed in the 1930s. The plan to build 38 new parks in the 1930, and 140 in the 1940 was barely realized. However, there were efforts to improve parks and green spaces of Gyeongseong: Such as appropriating natural forest as parks, designating royal palaces as parks, and focusing on constructing smaller scale children's parks. Even though the ideal plan could not be fully implemented due to the war time situation and tight budget, the park system of Gyeongseong provided the framework of park planning of Seoul after the independence.

• Journal of the Korean Institute of Landscape Architecture
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• v.47 no.2
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• pp.59-75
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• 2019

This study was carried out to explore the 4th Industrial Revolution technology from the perspective of the landscape industry to provide the basic data necessary to increase the virtuous circle value. The 4th Industrial Revolution, the characteristics of the landscape industry and urban regeneration were considered and the methodology was established and studied including the technical classification system suitable for systematic research, which was selected as a framework. First, the 4th Industrial Revolution technology based on digital data was selected, which could be utilized to increase the value of the virtuous circle for the landscape industry. From 'Element Technology Level', and 'Core Technology' such as the Internet of Things, Cloud Computing, Big Data, Artificial Intelligence, Robot, 'Peripheral Technology', Virtual or Augmented Reality, Drones, 3D 4D Printing, and 3D Scanning were highlighted as the 4th Industrial Revolution technology. It has been shown that it is possible to increase the value of the virtuous circle when applied at the 'Trend Level', in particular to the landscape industry. The 'System Level' was analyzed as a general-purpose technology, and based on the platform, the level of element technology(computers, and smart devices) was systematically interconnected, and illuminated with the 4th Industrial Revolution technology based on digital data. The application of the 'Trend Level' specific to the landscape industry has been shown to be an effective technology for increasing the virtuous circle values. It is possible to realize all synergistic effects and implementation of the proposed method at the trend level applying the element technology level. Smart gardens, smart parks, etc. have been analyzed to the level they should pursue. It was judged that Smart City, Smart Home, Smart Farm, and Precision Agriculture, Smart Tourism, and Smart Health Care could be highly linked through the collaboration among technologies in adjacent areas at the Trend Level. Additionally, various utilization measures of related technology applied at the Trend Level were highlighted in the process of urban regeneration, public service space creation, maintenance, and public service. In other words, with the realization of ubiquitous computing, Hyper-Connectivity, Hyper-Reality, Hyper-Intelligence, and Hyper-Convergence were proposed, reflecting the basic characteristics of digital technology in the landscape industry can be achieved. It was analyzed that the landscaping industry was effectively accommodating and coordinating with the needs of new characters, education and consulting, as well as existing tasks, even when participating in urban regeneration projects. In particular, it has been shown that the overall landscapig area is effective in increasing the virtuous circle value when it systems the related technology at the trend level by linking maintenance with strategic bridgehead. This is because the industrial structure is effective in distributing data and information produced from various channels. Subsequent research, such as demonstrating the fusion of the 4th Industrial Revolution technology based on the use of digital data in creation, maintenance, and service of actual landscape space is necessary.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.103-128
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• 2021

Recently, investors' interest and the influence of stock-related information dissemination are being considered as significant factors that explain stock returns and volume. Besides, companies that develop, distribute, or utilize innovative new technologies such as artificial intelligence have a problem that it is difficult to accurately predict a company's future stock returns and volatility due to macro-environment and market uncertainty. Market uncertainty is recognized as an obstacle to the activation and spread of artificial intelligence technology, so research is needed to mitigate this. Hence, the purpose of this study is to propose a machine learning model that predicts the volatility of a company's stock price by using the internet search volume of artificial intelligence-related technology keywords as a measure of the interest of investors. To this end, for predicting the stock market, we using the VAR(Vector Auto Regression) and deep neural network LSTM (Long Short-Term Memory). And the stock price prediction performance using keyword search volume is compared according to the technology's social acceptance stage. In addition, we also conduct the analysis of sub-technology of artificial intelligence technology to examine the change in the search volume of detailed technology keywords according to the technology acceptance stage and the effect of interest in specific technology on the stock market forecast. To this end, in this study, the words artificial intelligence, deep learning, machine learning were selected as keywords. Next, we investigated how many keywords each week appeared in online documents for five years from January 1, 2015, to December 31, 2019. The stock price and transaction volume data of KOSDAQ listed companies were also collected and used for analysis. As a result, we found that the keyword search volume for artificial intelligence technology increased as the social acceptance of artificial intelligence technology increased. In particular, starting from AlphaGo Shock, the keyword search volume for artificial intelligence itself and detailed technologies such as machine learning and deep learning appeared to increase. Also, the keyword search volume for artificial intelligence technology increases as the social acceptance stage progresses. It showed high accuracy, and it was confirmed that the acceptance stages showing the best prediction performance were different for each keyword. As a result of stock price prediction based on keyword search volume for each social acceptance stage of artificial intelligence technologies classified in this study, the awareness stage's prediction accuracy was found to be the highest. The prediction accuracy was different according to the keywords used in the stock price prediction model for each social acceptance stage. Therefore, when constructing a stock price prediction model using technology keywords, it is necessary to consider social acceptance of the technology and sub-technology classification. The results of this study provide the following implications. First, to predict the return on investment for companies based on innovative technology, it is most important to capture the recognition stage in which public interest rapidly increases in social acceptance of the technology. Second, the change in keyword search volume and the accuracy of the prediction model varies according to the social acceptance of technology should be considered in developing a Decision Support System for investment such as the big data-based Robo-advisor recently introduced by the financial sector.

• 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.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.219-240
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• 2018

Sentiment analysis, which is one of the text mining techniques, is a method for extracting subjective content embedded in text documents. Recently, the sentiment analysis methods have been widely used in many fields. As good examples, data-driven surveys are based on analyzing the subjectivity of text data posted by users and market researches are conducted by analyzing users' review posts to quantify users' reputation on a target product. The basic method of sentiment analysis is to use sentiment dictionary (or lexicon), a list of sentiment vocabularies with positive, neutral, or negative semantics. In general, the meaning of many sentiment words is likely to be different across domains. For example, a sentiment word, 'sad' indicates negative meaning in many fields but a movie. In order to perform accurate sentiment analysis, we need to build the sentiment dictionary for a given domain. However, such a method of building the sentiment lexicon is time-consuming and various sentiment vocabularies are not included without the use of general-purpose sentiment lexicon. In order to address this problem, several studies have been carried out to construct the sentiment lexicon suitable for a specific domain based on 'OPEN HANGUL' and 'SentiWordNet', which are general-purpose sentiment lexicons. However, OPEN HANGUL is no longer being serviced and SentiWordNet does not work well because of language difference in the process of converting Korean word into English word. There are restrictions on the use of such general-purpose sentiment lexicons as seed data for building the sentiment lexicon for a specific domain. In this article, we construct 'KNU Korean Sentiment Lexicon (KNU-KSL)', a new general-purpose Korean sentiment dictionary that is more advanced than existing general-purpose lexicons. The proposed dictionary, which is a list of domain-independent sentiment words such as 'thank you', 'worthy', and 'impressed', is built to quickly construct the sentiment dictionary for a target domain. Especially, it constructs sentiment vocabularies by analyzing the glosses contained in Standard Korean Language Dictionary (SKLD) by the following procedures: First, we propose a sentiment classification model based on Bidirectional Long Short-Term Memory (Bi-LSTM). Second, the proposed deep learning model automatically classifies each of glosses to either positive or negative meaning. Third, positive words and phrases are extracted from the glosses classified as positive meaning, while negative words and phrases are extracted from the glosses classified as negative meaning. Our experimental results show that the average accuracy of the proposed sentiment classification model is up to 89.45%. In addition, the sentiment dictionary is more extended using various external sources including SentiWordNet, SenticNet, Emotional Verbs, and Sentiment Lexicon 0603. Furthermore, we add sentiment information about frequently used coined words and emoticons that are used mainly on the Web. The KNU-KSL contains a total of 14,843 sentiment vocabularies, each of which is one of 1-grams, 2-grams, phrases, and sentence patterns. Unlike existing sentiment dictionaries, it is composed of words that are not affected by particular domains. The recent trend on sentiment analysis is to use deep learning technique without sentiment dictionaries. The importance of developing sentiment dictionaries is declined gradually. However, one of recent studies shows that the words in the sentiment dictionary can be used as features of deep learning models, resulting in the sentiment analysis performed with higher accuracy (Teng, Z., 2016). This result indicates that the sentiment dictionary is used not only for sentiment analysis but also as features of deep learning models for improving accuracy. The proposed dictionary can be used as a basic data for constructing the sentiment lexicon of a particular domain and as features of deep learning models. It is also useful to automatically and quickly build large training sets for deep learning models.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.131-145
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• 2020

In line with the trend of industrial innovation, IoT technology utilized in a variety of fields is emerging as a key element in creation of new business models and the provision of user-friendly services through the combination of big data. The accumulated data from devices with the Internet-of-Things (IoT) is being used in many ways to build a convenience-based smart system as it can provide customized intelligent systems through user environment and pattern analysis. Recently, it has been applied to innovation in the public domain and has been using it for smart city and smart transportation, such as solving traffic and crime problems using CCTV. In particular, it is necessary to comprehensively consider the easiness of securing real-time service data and the stability of security when planning underground services or establishing movement amount control information system to enhance citizens' or commuters' convenience in circumstances with the congestion of public transportation such as subways, urban railways, etc. However, previous studies that utilize image data have limitations in reducing the performance of object detection under private issue and abnormal conditions. The IoT device-based sensor data used in this study is free from private issue because it does not require identification for individuals, and can be effectively utilized to build intelligent public services for unspecified people. Especially, sensor data stored by the IoT device need not be identified to an individual, and can be effectively utilized for constructing intelligent public services for many and unspecified people as data free form private issue. We utilize the IoT-based infrared sensor devices for an intelligent pedestrian tracking system in metro service which many people use on a daily basis and temperature data measured by sensors are therein transmitted in real time. The experimental environment for collecting data detected in real time from sensors was established for the equally-spaced midpoints of 4×4 upper parts in the ceiling of subway entrances where the actual movement amount of passengers is high, and it measured the temperature change for objects entering and leaving the detection spots. The measured data have gone through a preprocessing in which the reference values for 16 different areas are set and the difference values between the temperatures in 16 distinct areas and their reference values per unit of time are calculated. This corresponds to the methodology that maximizes movement within the detection area. In addition, the size of the data was increased by 10 times in order to more sensitively reflect the difference in temperature by area. For example, if the temperature data collected from the sensor at a given time were 28.5℃, the data analysis was conducted by changing the value to 285. As above, the data collected from sensors have the characteristics of time series data and image data with 4×4 resolution. Reflecting the characteristics of the measured, preprocessed data, we finally propose a hybrid algorithm that combines CNN in superior performance for image classification and LSTM, especially suitable for analyzing time series data, as referred to CNN-LSTM (Convolutional Neural Network-Long Short Term Memory). In the study, the CNN-LSTM algorithm is used to predict the number of passing persons in one of 4×4 detection areas. We verified the validation of the proposed model by taking performance comparison with other artificial intelligence algorithms such as Multi-Layer Perceptron (MLP), Long Short Term Memory (LSTM) and RNN-LSTM (Recurrent Neural Network-Long Short Term Memory). As a result of the experiment, proposed CNN-LSTM hybrid model compared to MLP, LSTM and RNN-LSTM has the best predictive performance. By utilizing the proposed devices and models, it is expected various metro services will be provided with no illegal issue about the personal information such as real-time monitoring of public transport facilities and emergency situation response services on the basis of congestion. However, the data have been collected by selecting one side of the entrances as the subject of analysis, and the data collected for a short period of time have been applied to the prediction. There exists the limitation that the verification of application in other environments needs to be carried out. In the future, it is expected that more reliability will be provided for the proposed model if experimental data is sufficiently collected in various environments or if learning data is further configured by measuring data in other sensors.