• Journal of Preventive Medicine and Public Health
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• v.25 no.3 s.39
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• pp.247-268
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• 1992

This study was conducted to evaluate the effect of VDT work on eyes and vision among workers in a TV manufacturing plant. The study subjects consisted of 264 screen workers and 74 non-screen workers who were less than 40 years old male and had no history of opthalmic diseases such as corneal opacities, trauma, keratitis, etc and whose visual acuity on pre-employment health examination by Han's test chart was 1.0 or above. The screen workers were divided into two groups by actual time for screen work in a day : Group I, 60 workers, lesser than 4 hours a day and group II, 204 workers, more than 4 hours a day. From July to October 1992 a questionnaire was administered to all the study subjects for the general charateristics and subjective eye symptoms after which the opthalmologic tests such as visual acuity, spherical equivalent, lacrimal function, ocular pressure, slit lamp test, fundoscopy were conducted by one opthalmologist. The proportion of workers whose present visual acuity was decreased more than 0.15 in comparison with that on the pre-employment health examination by Han's test chart was 20.6% in Group II. 15.0% in Group I and 14.9% in non-screen workers. However, the differences in proportion were not statistically significant. The proportion of workers with decreased visual acuity was not associated with the age, working duration, use of magnifying glass and type of shift work (independent variables) in all of the three groups. However, screen workers working under poor illumination had a higher proportion of persons with decreased visual acuity than those working under adequate illumination (P<0.05) . The proportion of workers whose near vision was decreased was 27.5% in Group II, 18.3% in Group I, and 28.4% in non-screen workers and these differences in proportion were not statistically significant. Changes of near vision were not associated with 4 independent variables in all of the three groups. Six out of seven subjective eye symptoms except tearing were more common in Group I than in non-screen workers and more common in Group II than in Group I (P<0.01). Mean of the total scores for seven subjective symptoms of each worker(2 points for always, 1 point for sometimes, 0 point for never) was not significantly different between workers with decreased visual acuity and workers with no vision change. However, mean of the total scores for Group II was higher than those for the Group I and non-screen workers (P<0.01). Total eye symptom scores were significantly correlated with the grade of screen work, use of magnifying glass, and type of shift work. There was no independent variable which was correlated with the difference in visual acuity between the pre-employment health examination and the present state, the difference between far and near visions, lacrimal function, ocular pressure, and spherical equivalent. Multiple linear regression analysis for the subjective eye symptom scores revealed a positive linear relationship with actual time for screen work and shift work(P<0.01). In this study it was not observed that the VDT work decreased visual acuity but it induces subjective eye symptoms such as eye fatigue, blurred vision, ocular discomfort, etc. Maintenance of adequate illumination in the work place and control of excessive VDT work are recommended to prevent such eye symptoms.

• Journal of the Korean Society of Food Science and Nutrition
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• v.42 no.9
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• pp.1499-1512
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• 2013

The purpose of this study was to compare the coffee shop visits of Koreans and Mongolians, and to determine the quality attributes that should be managed by Importance-Performance Analysis (IPA). The survey was conducted in Seoul and the Gyeonggi Province of Korea, and at Ulaanbaatar in Mongolia from April to May 2012. The questionnaire was distributed to 380 Koreans and 380 Mongolians, with 253 and 250 responses from the Koreans and Mongolians, respectively, used for statistical analyses. From the results, Koreans visited coffee shops more frequently than Mongolians, with both groups mainly visiting a coffee shop with friends. Koreans also spent more time in a coffee shop than Mongolians. In addition, they generally used a coffee shop, regardless of time. In terms of coffee preference, Koreans preferred Americano and Mongolians preferred Espresso. The most frequently stated purpose of Koreans for visiting a coffee shop was to rest, while Mongolians typically visited to drink coffee. The general price range respondents spent on coffee was less than 4~8 thousand won for the Koreans and 2~4 thousand won for the Mongolians. Both Koreans and Mongolians obtained information about coffee shops from recommendations. According to the IPA results of 20 quality attributes of coffee shops, the selection attributes with high importance but low satisfaction were quality, price, and kindness for Koreans, but none of the attributes was found for Mongolians.

• Journal of the Korean Society for Library and Information Science
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• v.17
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• pp.11-47
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• 1989

This study intends to examine the background and the procedure of the carving of the tablets of the second edition of Dae-Jang-Mock­Lock(재조대장목록). the time and the route of the moving of the tablets. into Haein-sa, and the contents and the system of it. This study is mainly based on the second edition of Dae-Jang-Mock-Lock. But the other closely related materials such as restored first. edition of the Dae- Jang-Mock-Lock, Koryo Sin-Jo-Dae-Jang-Byeol-Lock (고려신조대장교정별록). Kae-Won-Seok-Kyo-Lock (개원석교록). Sok-Kae­Won-Seok-Kyo-Lock (속개원석교록). Jeong-Won-Sin-Jeong-Seok-Kyo­Lock(정원신정석교록), Sok-Jeong-Won-Seok-Kyo-Lock(속정원석교록), Dea-Jung-Sang-Bu-Beob-Bo-Lock(대중상부법보록), and Kyeong-Woo-Sin-Su-Beob-Bo-Lock(경우신수법보록), are also analysed and closely examined. The results of this study can be summarized as follows: 1. The second edition of Tripitaka Koreana(고려대장경) was carved for the purpose of defending the country from Mongolia with the power of Buddhism, after the tablets of the first edition in Buin-sa(부이사) was destroyed by fire. 2. In 1236. Dae-Jang-Do-Gam(대장도감) was established, and the preparation for the recarving of the tablets such as comparison between the content, of the first edition of Tripitalk Koreana, Gal-Bo-Chik-Pan-Dae­Jang-Kyeong and Kitan Dae- Jang-Kyeong, transcription of the original copy and the preparation of the wood, etc. was started. 3. In 1237 after the announcement of Dae-Jang-Gyeong-Gak-Pan-Gun­Sin-Gi-Go-Mun(대장경핵판군신석고문), the carving was started on a full scale. And seven years later (1243), Bun-Sa-Dae-Jang-Do-Gam(분사대장도감) was established in the area of the South to expand and hasten the work. And a large number of the tablets were carved in there. 4. It took 16 years to carve the main text and the supplements of the second edition of Tripitaka Koreana, the main text being carved from 1237 to 1248 and the supplement from 1244 to 1251. 5. It can be supposed that the tablets of the second edition of Tripitaka Koreana, stored in Seon-Won-Sa(선원사), Kang-Wha(강화), for about 140 years, was moved to Ji-Cheon-Sa(지천사), Yong-San(용산), and to Hae-In-Sa(해인사) again, through the west and the south sea and Jang-Gyeong-Po(장경포), Go-Ryeong(고령), in the autumn of the same year. 6. The second edition of Tripitaka Koreana was carved mainly based on the first edition, comparing with Gae-Bo-Chik-Pan-Dae-Jang-Kyeong(개보판대장경) and Kitan Dae-Jang-Kyeong(계단대장경). And the second edition of Dae-Jang-Mock-Lock also compiled mainly based on the first edition with the reference to Kae-Won-Seok-Kyo-Lock and Sok-Jeong-Won-Seok-Kyo-Lock. 7. Comparing with the first edition of Dae-Jang-Mock-Lock, in the second edition 7 items of 9 volumes of Kitan text such as Weol-Deung­Sam-Mae-Gyeong-Ron(월증삼매경론) are added and 3 items of 60 volumes such as Dae-Jong-Ji-Hyeon-Mun-Ron(대종지현문논) are substituted into others from Cheon chest(천함) to Kaeng chest(경함), and 92 items of 601 volumes such as Beob-Won-Ju-Rim-Jeon(법원주임전) are added after Kaeng chest. And 4 items of 50 volumes such as Yuk-Ja-Sin-Ju-Wang-Kyeong(육자신주왕경) are ommitted in the second edition. 8. Comparing with Kae-Won-Seok-Kyo-Lock, Cheon chest to Young chest (영함) of the second edition is compiled according to Ib-Jang-Lock(입장록) of Kae-Won-Seok-Kyo-Lock. But 15 items of 43 vol­umes such as Bul-Seol-Ban-Ju-Sam-Mae-Kyeong(불설반주삼매경) are ;added and 7 items of 35 volumes such as Dae-Bang-Deung-Dae-Jib-Il­Jang-Kyeong(대방등대집일장경) are ommitted. 9. Comparing with Sok-Jeong-Won-Seok-Kyo-Lock, 3 items of the 47 volumes (or 49 volumes) are ommitted and 4 items of 96 volumes are ;added in Caek chest(책함) to Mil chest(밀함) of the second edition. But the items are arranged in the same order. 10. Comparing with Dae- Jung-Sang-Bo-Beob-Bo-Lock, the arrangement of the second edition is entirely different from it. But 170 items of 329 volumes are also included in Doo chest(두함) to Kyeong chest(경함) of the second edition, and 53 items of 125 volumes in Jun chest(존함) to Jeong chest(정함). And 10 items of 108 volumes in the last part of Dae-Jung-Sang-Bo-Beob-Bo-Lock are ommitted and 3 items of 131 volumes such as Beob-Won-Ju-Rim-Jeon(법원주임전) are added in the second edition. 11. Comparing with Kyeong-Woo-Sin-Su-Beob-Bo-Lock, all of the items (21 items of 161 volumes) are included in the second edition without ;any classificatory system. And 22 items of 172 volumes in the Seong­Hyeon-Jib-Jeon(성현집전) part such as Myo-Gak-Bi-Cheon(묘각비전) are ommitted. 12. The last part of the second edition, Joo chest(주함) to Dong chest (동함), includes 14 items of 237 volumes. But these items cannot be found in any other former Buddhist catalog. So it might be supposed as the Kitan texts. 13. Besides including almost all items in Kae-Won-Seok-Kyo-Lock and all items in Sok-Jeong-Won-Seok-Kyo-Lock, Dae-Jung-Sang-Bo­Beob-Bo-Lock, and Kyeong-Woo-Sin-Su-Beob-Bo-Lock, the second edition of Dae-Jang-Mock-Lock includes more items, at least 20 items of about 300 volumes of Kitan Tripitaka and 15 items of 43 volumes of traditional Korean Tripitake that cannot be found any others. Therefore, Tripitaka Koreana can be said as a comprehensive Tripitaka covering all items of Tripitakas translated in Chinese character.

• Journal of Intelligence and Information Systems
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• v.26 no.3
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• pp.149-169
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• 2020

"The Urban Renewal New Deal project", one of the government's major national projects, is about developing underdeveloped areas by investing 50 trillion won in 100 locations on the first year and 500 over the next four years. This project is drawing keen attention from the media and local governments. However, the project model which fails to reflect the original characteristics of the area as it divides project area into five categories: "Our Neighborhood Restoration, Housing Maintenance Support Type, General Neighborhood Type, Central Urban Type, and Economic Base Type," According to keywords for successful urban regeneration in Korea, "resident participation," "regional specialization," "ministerial cooperation" and "public-private cooperation", when local governments propose urban regeneration projects to the government, they can see that it is most important to accurately understand the characteristics of the city and push ahead with the projects in a way that suits the characteristics of the city with the help of local residents and private companies. In addition, considering the gentrification problem, which is one of the side effects of urban regeneration projects, it is important to select and implement urban regeneration types suitable for the characteristics of the area. In order to supplement the limitations of the 'Urban Regeneration New Deal Project' methodology, this study aims to propose a system that recommends urban regeneration types suitable for urban regeneration sites by utilizing various machine learning algorithms, referring to the urban regeneration types of the '2025 Seoul Metropolitan Government Urban Regeneration Strategy Plan' promoted based on regional characteristics. There are four types of urban regeneration in Seoul: "Low-use Low-Level Development, Abandonment, Deteriorated Housing, and Specialization of Historical and Cultural Resources" (Shon and Park, 2017). In order to identify regional characteristics, approximately 100,000 text data were collected for 22 regions where the project was carried out for a total of four types of urban regeneration. Using the collected data, we drew key keywords for each region according to the type of urban regeneration and conducted topic modeling to explore whether there were differences between types. As a result, it was confirmed that a number of topics related to real estate and economy appeared in old residential areas, and in the case of declining and underdeveloped areas, topics reflecting the characteristics of areas where industrial activities were active in the past appeared. In the case of the historical and cultural resource area, since it is an area that contains traces of the past, many keywords related to the government appeared. Therefore, it was possible to confirm political topics and cultural topics resulting from various events. Finally, in the case of low-use and under-developed areas, many topics on real estate and accessibility are emerging, so accessibility is good. It mainly had the characteristics of a region where development is planned or is likely to be developed. Furthermore, a model was implemented that proposes urban regeneration types tailored to regional characteristics for regions other than Seoul. Machine learning technology was used to implement the model, and training data and test data were randomly extracted at an 8:2 ratio and used. In order to compare the performance between various models, the input variables are set in two ways: Count Vector and TF-IDF Vector, and as Classifier, there are 5 types of SVM (Support Vector Machine), Decision Tree, Random Forest, Logistic Regression, and Gradient Boosting. By applying it, performance comparison for a total of 10 models was conducted. The model with the highest performance was the Gradient Boosting method using TF-IDF Vector input data, and the accuracy was 97%. Therefore, the recommendation system proposed in this study is expected to recommend urban regeneration types based on the regional characteristics of new business sites in the process of carrying out urban regeneration projects."

• The Journal of Korean Society for Radiation Therapy
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• v.16 no.2
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• pp.9-17
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• 2004

Purpose : Although Improve of CT, MRI Radio-diagnosis and Radiation Therapy Planing, but we still use ICRU38 Planning system(2D film-based) broadly. 3-Dimensional ICR plan(CT image based) is not only offer tumor and normal tissue dose but also support DVH information. On this study, we plan irradiation-goal dose on CTV(CTV plan) and irradiation-goal dose on ICRU 38 point(ICRU38 plan) by use CT image. And compare with tumor-dose, rectal-dose, bladder-dose on both planning, and analysis DVH Method and Material : Sample 11 patients who treated by Ir-192 HDR. After 40Gy external radiation therapy, ICR plan established. All the patients carry out CT-image scanned by CT-simulator. And we use PLATO(Nucletron) v.14.2 planing system. We draw CTV, rectum, bladder on the CT image. And establish plan irradiation-$100\%$ dose on CTV(CTV plan) and irradiation-$100\%$ dose on A-point(ICRU38 plan) Result : CTV volume($average{\pm}SD$) is $21.8{\pm}26.6cm^3$, rectum volume($average{\pm}SD$) is $60.9{\pm}25.0cm^3$, bladder volume($average{\pm}SD$) is $116.1{\pm}40.1cm^3$ sampled 11 patients. The volume including $100\%$ dose is $126.7{\pm}18.9cm^3$ on ICRU plan and $98.2{\pm}74.5cm^3$ on CTV plan. On ICRU planning, the other one's $22.0cm^3$ CTV volume who residual tumor size excess 4cm is not including $100\%$ isodose. 8 patient's $12.9{\pm}5.9cm^3$ tumor volume who residual tumor size belows 4cm irradiated $100\%$ dose. Bladder dose(recommended by ICRU 38) is $90.1{\pm}21.3\%$ on ICRU plan, $68.7{\pm}26.6\%$ on CTV plan, and rectal dose is $86.4{\pm}18.3\%,\;76.9{\pm}15.6\%$. Bladder and Rectum maximum dose is $137.2{\pm}50.1\%,\;101.1{\pm}41.8\%$ on ICRU plan, $107.6{\pm}47.9\%,\;86.9{\pm}30.8\%$ on CTV plan. Therefore CTV plan more less normal issue-irradiated dose than ICRU plan. But one patient case who residual tumor size excess 4cm, Normal tissue dose more higher than critical dose remarkably on CTV plan. $80\%$over-Irradiated rectal dose(V80rec) is $1.8{\pm}2.4cm^3$ on ICRU plan, $0.7{\pm}1.0cm^3$ on CTV plan. $80\%$over-Irradiated bladder dose(V80bla) is $12.2{\pm}8.9cm^3$ on ICRU plan, $3.5{\pm}4.1cm^3$ on CTV plan. Likewise, CTV plan more less irradiated normal tissue than ICRU38 plan. Conclusion : Although, prove effect and stability about previous ICRU plan, if we use CTV plan by CT image, we will reduce normal tissue dose and irradiated goal-dose at residual tumor on small residual tumor case. But bigger residual tumor case, we need more research about effective 3D-planning.

• Journal of Intelligence and Information Systems
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• v.18 no.2
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• pp.143-156
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• 2012

People easily believe that news and stock index are closely related. They think that securing news before anyone else can help them forecast the stock prices and enjoy great profit, or perhaps capture the investment opportunity. However, it is no easy feat to determine to what extent the two are related, come up with the investment decision based on news, or find out such investment information is valid. If the significance of news and its impact on the stock market are analyzed, it will be possible to extract the information that can assist the investment decisions. The reality however is that the world is inundated with a massive wave of news in real time. And news is not patterned text. This study suggests the stock-index invest model based on "News Big Data" opinion mining that systematically collects, categorizes and analyzes the news and creates investment information. To verify the validity of the model, the relationship between the result of news opinion mining and stock-index was empirically analyzed by using statistics. Steps in the mining that converts news into information for investment decision making, are as follows. First, it is indexing information of news after getting a supply of news from news provider that collects news on real-time basis. Not only contents of news but also various information such as media, time, and news type and so on are collected and classified, and then are reworked as variable from which investment decision making can be inferred. Next step is to derive word that can judge polarity by separating text of news contents into morpheme, and to tag positive/negative polarity of each word by comparing this with sentimental dictionary. Third, positive/negative polarity of news is judged by using indexed classification information and scoring rule, and then final investment decision making information is derived according to daily scoring criteria. For this study, KOSPI index and its fluctuation range has been collected for 63 days that stock market was open during 3 months from July 2011 to September in Korea Exchange, and news data was collected by parsing 766 articles of economic news media M company on web page among article carried on stock information>news>main news of portal site Naver.com. In change of the price index of stocks during 3 months, it rose on 33 days and fell on 30 days, and news contents included 197 news articles before opening of stock market, 385 news articles during the session, 184 news articles after closing of market. Results of mining of collected news contents and of comparison with stock price showed that positive/negative opinion of news contents had significant relation with stock price, and change of the price index of stocks could be better explained in case of applying news opinion by deriving in positive/negative ratio instead of judging between simplified positive and negative opinion. And in order to check whether news had an effect on fluctuation of stock price, or at least went ahead of fluctuation of stock price, in the results that change of stock price was compared only with news happening before opening of stock market, it was verified to be statistically significant as well. In addition, because news contained various type and information such as social, economic, and overseas news, and corporate earnings, the present condition of type of industry, market outlook, the present condition of market and so on, it was expected that influence on stock market or significance of the relation would be different according to the type of news, and therefore each type of news was compared with fluctuation of stock price, and the results showed that market condition, outlook, and overseas news was the most useful to explain fluctuation of news. On the contrary, news about individual company was not statistically significant, but opinion mining value showed tendency opposite to stock price, and the reason can be thought to be the appearance of promotional and planned news for preventing stock price from falling. Finally, multiple regression analysis and logistic regression analysis was carried out in order to derive function of investment decision making on the basis of relation between positive/negative opinion of news and stock price, and the results showed that regression equation using variable of market conditions, outlook, and overseas news before opening of stock market was statistically significant, and classification accuracy of logistic regression accuracy results was shown to be 70.0% in rise of stock price, 78.8% in fall of stock price, and 74.6% on average. This study first analyzed relation between news and stock price through analyzing and quantifying sensitivity of atypical news contents by using opinion mining among big data analysis techniques, and furthermore, proposed and verified smart investment decision making model that could systematically carry out opinion mining and derive and support investment information. This shows that news can be used as variable to predict the price index of stocks for investment, and it is expected the model can be used as real investment support system if it is implemented as system and verified in the future.

• Journal of Intelligence and Information Systems
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• v.16 no.3
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• pp.77-97
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• 2010

Market timing is an investment strategy which is used for obtaining excessive return from financial market. In general, detection of market timing means determining when to buy and sell to get excess return from trading. In many market timing systems, trading rules have been used as an engine to generate signals for trade. On the other hand, some researchers proposed the rough set analysis as a proper tool for market timing because it does not generate a signal for trade when the pattern of the market is uncertain by using the control function. The data for the rough set analysis should be discretized of numeric value because the rough set only accepts categorical data for analysis. Discretization searches for proper "cuts" for numeric data that determine intervals. All values that lie within each interval are transformed into same value. In general, there are four methods for data discretization in rough set analysis including equal frequency scaling, expert's knowledge-based discretization, minimum entropy scaling, and na$\ddot{i}$ve and Boolean reasoning-based discretization. Equal frequency scaling fixes a number of intervals and examines the histogram of each variable, then determines cuts so that approximately the same number of samples fall into each of the intervals. Expert's knowledge-based discretization determines cuts according to knowledge of domain experts through literature review or interview with experts. Minimum entropy scaling implements the algorithm based on recursively partitioning the value set of each variable so that a local measure of entropy is optimized. Na$\ddot{i}$ve and Booleanreasoning-based discretization searches categorical values by using Na$\ddot{i}$ve scaling the data, then finds the optimized dicretization thresholds through Boolean reasoning. Although the rough set analysis is promising for market timing, there is little research on the impact of the various data discretization methods on performance from trading using the rough set analysis. In this study, we compare stock market timing models using rough set analysis with various data discretization methods. The research data used in this study are the KOSPI 200 from May 1996 to October 1998. KOSPI 200 is the underlying index of the KOSPI 200 futures which is the first derivative instrument in the Korean stock market. The KOSPI 200 is a market value weighted index which consists of 200 stocks selected by criteria on liquidity and their status in corresponding industry including manufacturing, construction, communication, electricity and gas, distribution and services, and financing. The total number of samples is 660 trading days. In addition, this study uses popular technical indicators as independent variables. The experimental results show that the most profitable method for the training sample is the na$\ddot{i}$ve and Boolean reasoning but the expert's knowledge-based discretization is the most profitable method for the validation sample. In addition, the expert's knowledge-based discretization produced robust performance for both of training and validation sample. We also compared rough set analysis and decision tree. This study experimented C4.5 for the comparison purpose. The results show that rough set analysis with expert's knowledge-based discretization produced more profitable rules than C4.5.

• Journal of Intelligence and Information Systems
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• v.25 no.4
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• pp.141-154
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• 2019

Rapid growth of internet technology and social media is progressing. Data mining technology has evolved to enable unstructured document representations in a variety of applications. Sentiment analysis is an important technology that can distinguish poor or high-quality content through text data of products, and it has proliferated during text mining. Sentiment analysis mainly analyzes people's opinions in text data by assigning predefined data categories as positive and negative. This has been studied in various directions in terms of accuracy from simple rule-based to dictionary-based approaches using predefined labels. In fact, sentiment analysis is one of the most active researches in natural language processing and is widely studied in text mining. When real online reviews aren't available for others, it's not only easy to openly collect information, but it also affects your business. In marketing, real-world information from customers is gathered on websites, not surveys. Depending on whether the website's posts are positive or negative, the customer response is reflected in the sales and tries to identify the information. However, many reviews on a website are not always good, and difficult to identify. The earlier studies in this research area used the reviews data of the Amazon.com shopping mal, but the research data used in the recent studies uses the data for stock market trends, blogs, news articles, weather forecasts, IMDB, and facebook etc. However, the lack of accuracy is recognized because sentiment calculations are changed according to the subject, paragraph, sentiment lexicon direction, and sentence strength. This study aims to classify the polarity analysis of sentiment analysis into positive and negative categories and increase the prediction accuracy of the polarity analysis using the pretrained IMDB review data set. First, the text classification algorithm related to sentiment analysis adopts the popular machine learning algorithms such as NB (naive bayes), SVM (support vector machines), XGboost, RF (random forests), and Gradient Boost as comparative models. Second, deep learning has demonstrated discriminative features that can extract complex features of data. Representative algorithms are CNN (convolution neural networks), RNN (recurrent neural networks), LSTM (long-short term memory). CNN can be used similarly to BoW when processing a sentence in vector format, but does not consider sequential data attributes. RNN can handle well in order because it takes into account the time information of the data, but there is a long-term dependency on memory. To solve the problem of long-term dependence, LSTM is used. For the comparison, CNN and LSTM were chosen as simple deep learning models. In addition to classical machine learning algorithms, CNN, LSTM, and the integrated models were analyzed. Although there are many parameters for the algorithms, we examined the relationship between numerical value and precision to find the optimal combination. And, we tried to figure out how the models work well for sentiment analysis and how these models work. This study proposes integrated CNN and LSTM algorithms to extract the positive and negative features of text analysis. The reasons for mixing these two algorithms are as follows. CNN can extract features for the classification automatically by applying convolution layer and massively parallel processing. LSTM is not capable of highly parallel processing. Like faucets, the LSTM has input, output, and forget gates that can be moved and controlled at a desired time. These gates have the advantage of placing memory blocks on hidden nodes. The memory block of the LSTM may not store all the data, but it can solve the CNN's long-term dependency problem. Furthermore, when LSTM is used in CNN's pooling layer, it has an end-to-end structure, so that spatial and temporal features can be designed simultaneously. In combination with CNN-LSTM, 90.33% accuracy was measured. This is slower than CNN, but faster than LSTM. The presented model was more accurate than other models. In addition, each word embedding layer can be improved when training the kernel step by step. CNN-LSTM can improve the weakness of each model, and there is an advantage of improving the learning by layer using the end-to-end structure of LSTM. Based on these reasons, this study tries to enhance the classification accuracy of movie reviews using the integrated CNN-LSTM model.

• 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.

• Journal of Chest Surgery
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• v.39 no.3 s.260
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• pp.184-193
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• 2006

Background: Off-pump coronary artery bypass grafting (OPCAB) has been proven to result in less morbidity. The patients who have left ventricular dysfunction may have benefits by avoiding the adverse effects of the cardiopulmonary bypass. The present study compared early and midterm outcomes of off-pump versus on-pump coronary artery bypass grafting (On pump CABG) in patients with severe left ventricular dysfunction. Material and Method: Ninety hundred forth six patients underwent isolated coronary artery bypass grafting by one surgeon between January 2001 and Febrary 2005.. Data were collected in 100 patients who had left ventricular ejection fraction (L VEF) less than $35\%$ (68 OPCAB; 32 On pump CABG). Mean age of patients were 62.9$\pm$9.0 years in OPCAS group and 63.8$\pm$8.0 years in On pump CABG group. We compared the preoperative risk factors and evaluated early and midterm outcomes. Result: In OPCAB and On pump CABG group, mean number of used grafts per patient were 2.75$\pm$0.72, 2.78$\pm$0.55 and mean number of distal anastomoses were 3.00$\pm$0.79, 3.16$\pm$0.72 respectively. There was one perioperative death in OPCAB group ($1.5\%$). The operation time, ventilation time, ICU stay time, CK-MB on the first postoperative day, and occurrence rate of complications were significantly low in OPCAB group. Mean follow-up time was 26.6$\pm$12.8 months (4${\~}$54 months). Mean LVEF of OPCAB and On pump CABG group improved significantly from $27.1\pm4.5\%$ to $40.7\pm13.0\%$ and $26.9\pm5.4\%$ to $33.3\pm13.7\%$. The 4-year actuarial survival rate of OPCAB and On pump CABG group were $92.2\%,\;88.3\%$ and the 4-year freedom rates from cardiac death were $97.7\%,\;96.4\%$ respectively. There were no significant differences between two groups in 4 year freedom rate from cardiac event and angina. Conclusion: OPCAS improves myocardial function and favors early and mid-term outcomes in patients with severe left ventricular dysfunction compared to On pump CABG group. Therefore, OPCAB is a preferable operative strategy even in patients with severe left ventricular dysfunction.