• Journal of Intelligence and Information Systems
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• v.21 no.4
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• pp.37-51
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• 2015

Due to the development of internet technology and the rapid increase of internet data, various studies are actively conducted on how to use and analyze internet data for various purposes. In particular, in recent years, a number of studies have been performed on the applications of text mining techniques in order to overcome the limitations of the current application of structured data. Especially, there are various studies on sentimental analysis to score opinions based on the distribution of polarity such as positivity or negativity of vocabularies or sentences of the texts in documents. As a part of such studies, this study tries to predict ups and downs of stock prices of companies by performing sentimental analysis on news contexts of the particular companies in the Internet. A variety of news on companies is produced online by different economic agents, and it is diffused quickly and accessed easily in the Internet. So, based on inefficient market hypothesis, we can expect that news information of an individual company can be used to predict the fluctuations of stock prices of the company if we apply proper data analysis techniques. However, as the areas of corporate management activity are different, an analysis considering characteristics of each company is required in the analysis of text data based on machine-learning. In addition, since the news including positive or negative information on certain companies have various impacts on other companies or industry fields, an analysis for the prediction of the stock price of each company is necessary. Therefore, this study attempted to predict changes in the stock prices of the individual companies that applied a sentimental analysis of the online news data. Accordingly, this study chose top company in KOSPI 200 as the subjects of the analysis, and collected and analyzed online news data by each company produced for two years on a representative domestic search portal service, Naver. In addition, considering the differences in the meanings of vocabularies for each of the certain economic subjects, it aims to improve performance by building up a lexicon for each individual company and applying that to an analysis. As a result of the analysis, the accuracy of the prediction by each company are different, and the prediction accurate rate turned out to be 56% on average. Comparing the accuracy of the prediction of stock prices on industry sectors, 'energy/chemical', 'consumer goods for living' and 'consumer discretionary' showed a relatively higher accuracy of the prediction of stock prices than other industries, while it was found that the sectors such as 'information technology' and 'shipbuilding/transportation' industry had lower accuracy of prediction. The number of the representative companies in each industry collected was five each, so it is somewhat difficult to generalize, but it could be confirmed that there was a difference in the accuracy of the prediction of stock prices depending on industry sectors. In addition, at the individual company level, the companies such as 'Kangwon Land', 'KT & G' and 'SK Innovation' showed a relatively higher prediction accuracy as compared to other companies, while it showed that the companies such as 'Young Poong', 'LG', 'Samsung Life Insurance', and 'Doosan' had a low prediction accuracy of less than 50%. In this paper, we performed an analysis of the share price performance relative to the prediction of individual companies through the vocabulary of pre-built company to take advantage of the online news information. In this paper, we aim to improve performance of the stock prices prediction, applying online news information, through the stock price prediction of individual companies. Based on this, in the future, it will be possible to find ways to increase the stock price prediction accuracy by complementing the problem of unnecessary words that are added to the sentiment dictionary.

• Journal of Korean Society of Disaster and Security
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• v.9 no.2
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• pp.53-62
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• 2016

This paper is shown the new method using LED Light, which the light environment is upgraded the natural LED light in the area of Disaster Prevention and Safety Management (PDSD), which the events of deaths is reduced on the Suicide, the Infectious diseases, the safety accidents, the Traffic Accident, the crime, the fire, the Nature Disaster, and which the health and the environment and the safety is implemented using the value of the color LED Light. Research findings include,during 3 weeks in the November 2016, in the ten residents (average living 28.7 years, age 67.5 years) with depressive symptoms in the northern part of Seoul, according to the request of the user, the PDSD natural light LED lighting was installed in the home bedroom or the living room, expectations for the ability to restore physical and mental stability were high (88%), in the same way, after 1 week and 3 weeks, the physical and mental changes were compared and the results,84% in the first week and 90% in the third week and thereafter, the effect of relieving depression was high. We conclude that patients with depression have a good sleep, an uneasy feeling, a sense of security, a good night's sleep, and a good feeling. The PDSD LED Light is expected to contribute in the various areas, which reduced the suicides, which give increased immunity from infectious diseases, which give a crash to reduce accidents caused by negligence, which improve the safe operation of heavy vehicles in which a traffics accident incidence installed on the highest point, which improve the safety function on the 'safety way home' for the safety of the community, which due to fire gives alleviate the emotional anxiety of firefighters, which improve the environment for long-term control room working during decision making caused by natural disasters.

• Journal of the Korean Society for Library and Information Science
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• v.15
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• pp.225-266
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• 1988

Various theories and techniques for the subject catalog have been developed since Charles Ammi Cutter first tried to formulate rules for the construction of subject headings in 1876. However, they do not seem to be appropriate to Korean language because the syntax and semantics of Korean language are different from those of English and other European languages. This study therefore attempts to develop a new Korean subject indexing system, namely Korean String Indexing System(KOSIS), in order to increase the use of subject catalogs. For this purpose, advantages and disadvantages between the classed subject catalog nd the alphabetical subject catalog, which are typical subject ca-alogs in libraries, are investigated, and most of remarkable subject indexing systems, in particular the PRECIS developed by the British National Bibliography, are reviewed and analysed. KOSIS is a string indexing based on purely the syntax and semantics of Korean language, even though considerable principles of PRECIS are applied to it. The outlines of KOSIS are as follows: 1) KOSIS is based on the fundamentals of natural language and an ingenious conjunction of human indexing skills and computer capabilities. 2) KOSIS is. 3 string indexing based on the 'principle of context-dependency.' A string of terms organized accoding to his principle shows remarkable affinity with certain patterns of words in ordinary discourse. From that point onward, natural language rather than classificatory terms become the basic model for indexing schemes. 3) KOSIS uses 24 role operators. One or more operators should be allocated to the index string, which is organized manually by the indexer's intellectual work, in order to establish the most explicit syntactic relationship of index terms. 4) Traditionally, a single -line entry format is used in which a subject heading or index entry is presented as a single sequence of words, consisting of the entry terms, plus, in some cases, an extra qualifying term or phrase. But KOSIS employs a two-line entry format which contains three basic positions for the production of index entries. The 'lead' serves as the user's access point, the 'display' contains those terms which are themselves context dependent on the lead, 'qualifier' sets the lead term into its wider context. 5) Each of the KOSIS entries is co-extensive with the initial subject statement prepared by the indexer, since it displays all the subject specificities. Compound terms are always presented in their natural language order. Inverted headings are not produced in KOSIS. Consequently, the precision ratio of information retrieval can be increased. 6) KOSIS uses 5 relational codes for the system of references among semantically related terms. Semantically related terms are handled by a different set of routines, leading to the production of 'See' and 'See also' references. 7) KOSIS was riginally developed for a classified catalog system which requires a subject index, that is an index -which 'trans-lates' subject index, that is, an index which 'translates' subjects expressed in natural language into the appropriate classification numbers. However, KOSIS can also be us d for a dictionary catalog system. Accordingly, KOSIS strings can be manipulated to produce either appropriate subject indexes for a classified catalog system, or acceptable subject headings for a dictionary catalog system. 8) KOSIS is able to maintain a constistency of index entries and cross references by means of a routine identification of the established index strings and reference system. For this purpose, an individual Subject Indicator Number and Reference Indicator Number is allocated to each new index strings and new index terms, respectively. can produce all the index entries, cross references, and authority cards by means of either manual or mechanical methods. Thus, detailed algorithms for the machine-production of various outputs are provided for the institutions which can use computer facilities.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.1-17
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• 2019

Because stock price forecasting is an important issue both academically and practically, research in stock price prediction has been actively conducted. The stock price forecasting research is classified into using structured data and using unstructured data. With structured data such as historical stock price and financial statements, past studies usually used technical analysis approach and fundamental analysis. In the big data era, the amount of information has rapidly increased, and the artificial intelligence methodology that can find meaning by quantifying string information, which is an unstructured data that takes up a large amount of information, has developed rapidly. With these developments, many attempts with unstructured data are being made to predict stock prices through online news by applying text mining to stock price forecasts. The stock price prediction methodology adopted in many papers is to forecast stock prices with the news of the target companies to be forecasted. However, according to previous research, not only news of a target company affects its stock price, but news of companies that are related to the company can also affect the stock price. However, finding a highly relevant company is not easy because of the market-wide impact and random signs. Thus, existing studies have found highly relevant companies based primarily on pre-determined international industry classification standards. However, according to recent research, global industry classification standard has different homogeneity within the sectors, and it leads to a limitation that forecasting stock prices by taking them all together without considering only relevant companies can adversely affect predictive performance. To overcome the limitation, we first used random matrix theory with text mining for stock prediction. Wherever the dimension of data is large, the classical limit theorems are no longer suitable, because the statistical efficiency will be reduced. Therefore, a simple correlation analysis in the financial market does not mean the true correlation. To solve the issue, we adopt random matrix theory, which is mainly used in econophysics, to remove market-wide effects and random signals and find a true correlation between companies. With the true correlation, we perform cluster analysis to find relevant companies. Also, based on the clustering analysis, we used multiple kernel learning algorithm, which is an ensemble of support vector machine to incorporate the effects of the target firm and its relevant firms simultaneously. Each kernel was assigned to predict stock prices with features of financial news of the target firm and its relevant firms. The results of this study are as follows. The results of this paper are as follows. (1) Following the existing research flow, we confirmed that it is an effective way to forecast stock prices using news from relevant companies. (2) When looking for a relevant company, looking for it in the wrong way can lower AI prediction performance. (3) The proposed approach with random matrix theory shows better performance than previous studies if cluster analysis is performed based on the true correlation by removing market-wide effects and random signals. The contribution of this study is as follows. First, this study shows that random matrix theory, which is used mainly in economic physics, can be combined with artificial intelligence to produce good methodologies. This suggests that it is important not only to develop AI algorithms but also to adopt physics theory. This extends the existing research that presented the methodology by integrating artificial intelligence with complex system theory through transfer entropy. Second, this study stressed that finding the right companies in the stock market is an important issue. This suggests that it is not only important to study artificial intelligence algorithms, but how to theoretically adjust the input values. Third, we confirmed that firms classified as Global Industrial Classification Standard (GICS) might have low relevance and suggested it is necessary to theoretically define the relevance rather than simply finding it in the GICS.

• Korean Journal of Remote Sensing
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• v.35 no.6_4
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• pp.1319-1326
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• 2019

The KARI(Korea Aerospace Research Institute) operates the government satellite information application consultation to cope with ever-increasing demand for satellite images in the public sector, and carries out various support projects including the generation and provision of mosaic images on the Korean Peninsula every year to enhance user convenience and promote the use of satellite images. In particular, the government has wanted to increase the utilization of mosaic images on the Korean Peninsula and seek to classify and update mosaic images so that users can use them in their businesses easily. However, it is necessary to test and verify whether the classification results of the mosaic images can be utilized in the field since the original spectral information is distorted during pan-sharpening and color balancing, and there is a limitation that only R, G, and B bands are provided. Therefore, in this study, the reliability of the classification result of the mosaic image was compared to the result of KOMPSAT-3 image. The study found that the accuracy of the classification result of KOMPSAT-3 image was between 81~86% (overall accuracy is about 85%), while the accuracy of the classification result of mosaic image was between 69~72% (overall accuracy is about 72%). This phenomenon is interpreted not only because of the distortion of the original spectral information through pan-sharpening and mosaic processes, but also because NDVI and NDWI information were extracted from KOMPSAT-3 image rather than from the mosaic image, as only three color bands(R, G, B) were provided. Although it is deemed inadequate to distribute classification results extracted from mosaic images at present, it is believed that it will be necessary to explore ways to minimize the distortion of spectral information when making mosaic images and to develop classification techniques suitable for mosaic images as well as the provision of NIR band information. In addition, it is expected that the utilization of images with limited spectral information could be increased in the future if related research continues, such as the comparative analysis of classification results by geomorphological characteristics and the development of machine learning methods for image classification by objects of interest.

• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.7
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• pp.1216-1221
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• 2022

Ships tend to be larger to increase the efficiency of cargo transportation. Larger ships lead to increased travel time for ship workers, increased work intensity, and reduced work efficiency. Problems such as increased work intensity are reducing the influx of young people into labor, along with the phenomenon of avoidance of high intensity labor by the younger generation. In addition, the rapid aging of the population and decrease in the young labor force aggravate the labor shortage problem in the maritime industry. To overcome this, the maritime industry has recently introduced technologies such as an intelligent production design platform and a smart production operation management system, and a smart autonomous logistics system in one of these technologies. The smart autonomous logistics system is a technology that delivers various goods using intelligent mobile robots, and enables the robot to drive itself by using sensors such as lidar and camera. Therefore, in this paper, it was checked whether the mobile robot could autonomously drive to the stop sign by detecting the passage way of the ship deck. The autonomous driving was performed by detecting the passage way of the ship deck through the camera mounted on the mobile robot based on the data learned through Nvidia's End-to-end learning. The mobile robot was stopped by checking the stop sign using SSD MobileNetV2. The experiment was repeated five times in which the mobile robot autonomously drives to the stop sign without deviation from the ship deck passage way at a distance of about 70m. As a result of the experiment, it was confirmed that the mobile robot was driven without deviation from passage way. If the smart autonomous logistics system to which this result is applied is used in the marine industry, it is thought that the stability, reduction of labor force, and work efficiency will be improved when workers work.

• The Journal of Korean Society for Radiation Therapy
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• v.12 no.1
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• pp.91-104
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• 2000

Recently linear accelerator in radiation therapy in asymmetric field has been easily used since the improvement and capability of asymmetrical field adjustment attached to the machine. It has been thought there have been some significant errors in dose calculation when asymmetrical radiation fields have been utilized in practice of radiation treatments if the fundamental data for dose calculation have been measured in symmetrical standard fields. This study investigated how much the measured data of dose distributions and their isodose curves are different between in asymmetrical and symmetrical standard fields, and how much there difference affect the error in dose calculation in conventional method measured in symmetrical standard field. The distributions of radiation dose were measured by photon diode detector in the water phantom (RFA-300P, Scanditronix, Sweden) as tissue equivalent material on utilization of 6 MV linear accelerator with source surface distance (SSD) 1000 mm. The photon diode detector has the velocity of 1 mm per second from water surface to 250 mm depth in the field size of $40mm{\times}40mm\;to\;250mm{\times}250mm\;symmetric\;field\;and\;40mm{\times}20mm\;to\;250mm{\times}125mm$ asymmetrical fields. The measurements of percent depth dose (PDD) and subsequent plotting of their isodose curves were performed from water surface to 250mm dmm from Y-center axis in $100mm{\times}50mm$ field in order to absence the variability of depth dose according to increasing field sizes and their affects to plotted isodose curves. The difference of PDD between symmetric and asymmetric field was maximum $4.1\%\;decrease\;in\;40mm{\times}20mm\;field,\;maximum\;6.6\%\;decrease\;in\;100mm{\times}50mm\;and\;maximum\;10.2\%\;decrease\;200mm{\times}100mm$, the larger decrease difference of PDD as the greater field size and as greater the depth, The difference of PDD between asymmetrical field and equivalent square field showed maximum $2.4\%\;decrease\;in\;60mm{\times}30mm\;field,\;maximum\;4.8\%\;decrease\;in\;150mm{\times}75mm\;and\;maximum\;6.1\%\;decrease\;in\;250mm{\times}125mm$, and the larger decreased differenced PDD as the greater field size and as greater the depth, these differences of PDD were out of $5\%$ of dose calculation as defined by international Commission on radiation unit and Measurements(ICRU). In the dose distribution of asymmetrical field (half beam) the plotted isodose curves were observed to have deviations by decreased PDD as greater as the blocking of the beam moved closer to the central axis, and as the asymmetrical field increased by moving the block 10 mm keeping away from the central axis, the PDD increased and plotted isodose curves were gradually more flattened, due to reduced amount of the primary beam and the fraction of low energy soft radiations by passing thougepth in asymmetrical field by moving independent jaw each 10 h beam flattening filter. As asymmetrical radiation field as half beam radiation technique is used, the radiation dosimetry calculated in utilizing the fundamental data which measured in standard symmetrical field should be converted on bases of nearly measured data in asymmetrical field, measured beam data flies of various asymmetrical field in various energy and be necessary in each institution.

• The Journal of Korean Academy of Prosthodontics
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• v.38 no.5
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• pp.583-594
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• 2000

Dislodgement of a crown or extension bridge and the loosening of a retainer of a bridge is a serious clinical problem in fixed restoration. Generally these problems are considered to be associated with deformation of the restoration. During biting, the restoration is subjected to complex forces and deforms considerably within the limit of its elasticity. Deformation of the restoration under the occlusal force induces excessive stress in the cement film, which then leads to the cement fracture. Such a fracture may eventually cause loss of the restoration. Because most of the past retention tests for full veneer crown were done without fatigue loading, they were not exactly simulating intraoral environment. And the purpose of this study was to evaluate the effect of cyclic cantilever loading on the retentive strength of full veneer crowns depending on different type of cements and taper of prepared abutment. Steel dies with $8^{\circ}\;or\;16^{\circ}$ convergence angle were fabricated through milling and crowns with the same method. These dies and crowns were divided into 8 groups. Group 1 : $16^{\circ}$ taper die, cementation with zinc phosphate cement, without loading Group 2 : $16^{\circ}$ taper die, cementation with zinc phosphate cement, with loading Group 3 : $8^{\circ}$ taper die, cementation with zinc phosphate cement, without loading Group 4 : $8^{\circ}$ taper die, cementation with zinc phosphate cement, with loading Group 5 : $16^{\circ}$ taper die, cementation with Panavia 21, without loading Group 6 : $16^{\circ}$ taper die, cementation with Panavia 21, with loading Group 7 : $8^{\circ}$ taper die, cementation with Panavia 21 without loading Group 8 : $8^{\circ}$ taper die, cementation with Panavia 21, with loading After checking the fit of die and crown, the luting surface of dies and inner surface of crowns were air-abraded for 10 seconds. The crowns were cemented to the dies, with cements mixed according to the manufacturer's recommendations. A static load of 5kg was then applied for 10 minutes with static loading device. Twenty-four hours later, group 1, 3, 5, 7 were only thermocycled, group 2, 4, 6, 8 were subjected to cyclic loading after thermocycling. Retentive tests were performed on the Instron machine. From the finding of this study, the following conclusions were obtained 1. Panavia 21 showed significantly higher retentive strength than zinc phosphate cement for all groups (p<0.05). 2. There was a significant difference in the retentive strength between $8^{\circ}\;and\;16^{\circ}$ taper for zinc phosphate cement(p<0.05), but no significant difference for Panavia 21 (p>0.05). 3. Cyclic loading significantly decreased the retentive strength for all groups(p<0.05). 4. For zinc phosphate cement, there was 35% reduction of the retentive strength after loading in the $16^{\circ}$ taper die, 25% in the $8^{\circ}$ taper die, and for Panavia 21, 21% in the $16^{\circ}$ taper die, 18% in the $8^{\circ}$ taper die.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.6-7
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• 2021

Vegetation development in rivers is one of the important issues not only in academic fields such as geomorphology, ecology, hydraulics, etc., but also in river management practices. The problem of river vegetation is directly connected to the harmony of conflicting values of flood management and ecosystem conservation. In Korea, since the 2000s, the issue of river vegetation and land formation has been continuously raised under various conditions, such as the regulating rivers downstream of the dams, the small eutrophicated tributary rivers, and the floodplain sites for the four major river projects. In this background, this study proposes a method for classifying the distribution of vegetation in rivers based on remote sensing data, and presents the results of applying this to the Naeseong Stream. The Naeseong Stream is a representative example of the river landscape that has changed due to vegetation development from 2014 to the latest. The remote sensing data used in the study are images of Sentinel 1 and 2 satellites, which is operated by the European Aerospace Administration (ESA), and provided by Google Earth Engine. For the ground truth, manually classified dataset on the surface of the Naeseong Stream in 2016 were used, where the area is divided into eight types including water, sand and herbaceous and woody vegetation. The classification method used a random forest classification technique, one of the machine learning algorithms. 1,000 samples were extracted from 10 pre-selected polygon regions, each half of them were used as training and verification data. The accuracy based on the verification data was found to be 82~85%. The model established through training was also applied to images from 2016 to 2020, and the process of changes in vegetation zones according to the year was presented. The technical limitations and improvement measures of this paper were considered. By providing quantitative information of the vegetation distribution, this technique is expected to be useful in practical management of vegetation such as thinning and rejuvenation of river vegetation as well as technical fields such as flood level calculation and flow-vegetation coupled modeling in rivers.

• Journal of Intelligence and Information Systems
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• v.22 no.3
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• pp.71-89
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• 2016

Sentiment analysis is used for identifying emotions or sentiments embedded in the user generated data such as customer reviews from blogs, social network services, and so on. Various research fields such as computer science and business management can take advantage of this feature to analyze customer-generated opinions. In previous studies, the star rating of a review is regarded as the same as sentiment embedded in the text. However, it does not always correspond to the sentiment polarity. Due to this supposition, previous studies have some limitations in their accuracy. To solve this issue, the present study uses a supervised sentiment classification model to measure a more accurate sentiment polarity. This study aims to propose an advanced sentiment classifier and to discover the correlation between movie reviews and box-office success. The advanced sentiment classifier is based on two supervised machine learning techniques, the Support Vector Machines (SVM) and Feedforward Neural Network (FNN). The sentiment scores of the movie reviews are measured by the sentiment classifier and are analyzed by statistical correlations between movie reviews and box-office success. Movie reviews are collected along with a star-rate. The dataset used in this study consists of 1,258,538 reviews from 175 films gathered from Naver Movie website (movie.naver.com). The results show that the proposed sentiment classifier outperforms Naive Bayes (NB) classifier as its accuracy is about 6% higher than NB. Furthermore, the results indicate that there are positive correlations between the star-rate and the number of audiences, which can be regarded as the box-office success of a movie. The study also shows that there is the mild, positive correlation between the sentiment scores estimated by the classifier and the number of audiences. To verify the applicability of the sentiment scores, an independent sample t-test was conducted. For this, the movies were divided into two groups using the average of sentiment scores. The two groups are significantly different in terms of the star-rated scores.