• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.1-27
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• 2017

It is known that the economic sentiment index and macroeconomic indicators are closely related because economic agent's judgment and forecast of the business conditions affect economic fluctuations. For this reason, consumer sentiment or confidence provides steady fodder for business and is treated as an important piece of economic information. In Korea, private consumption accounts and consumer sentiment index highly relevant for both, which is a very important economic indicator for evaluating and forecasting the domestic economic situation. However, despite offering relevant insights into private consumption and GDP, the traditional approach to measuring the consumer confidence based on the survey has several limits. One possible weakness is that it takes considerable time to research, collect, and aggregate the data. If certain urgent issues arise, timely information will not be announced until the end of each month. In addition, the survey only contains information derived from questionnaire items, which means it can be difficult to catch up to the direct effects of newly arising issues. The survey also faces potential declines in response rates and erroneous responses. Therefore, it is necessary to find a way to complement it. For this purpose, we construct and assess an index designed to measure consumer economic sentiment index using sentiment analysis. Unlike the survey-based measures, our index relies on textual analysis to extract sentiment from economic and financial news articles. In particular, text data such as news articles and SNS are timely and cover a wide range of issues; because such sources can quickly capture the economic impact of specific economic issues, they have great potential as economic indicators. There exist two main approaches to the automatic extraction of sentiment from a text, we apply the lexicon-based approach, using sentiment lexicon dictionaries of words annotated with the semantic orientations. In creating the sentiment lexicon dictionaries, we enter the semantic orientation of individual words manually, though we do not attempt a full linguistic analysis (one that involves analysis of word senses or argument structure); this is the limitation of our research and further work in that direction remains possible. In this study, we generate a time series index of economic sentiment in the news. The construction of the index consists of three broad steps: (1) Collecting a large corpus of economic news articles on the web, (2) Applying lexicon-based methods for sentiment analysis of each article to score the article in terms of sentiment orientation (positive, negative and neutral), and (3) Constructing an economic sentiment index of consumers by aggregating monthly time series for each sentiment word. In line with existing scholarly assessments of the relationship between the consumer confidence index and macroeconomic indicators, any new index should be assessed for its usefulness. We examine the new index's usefulness by comparing other economic indicators to the CSI. To check the usefulness of the newly index based on sentiment analysis, trend and cross - correlation analysis are carried out to analyze the relations and lagged structure. Finally, we analyze the forecasting power using the one step ahead of out of sample prediction. As a result, the news sentiment index correlates strongly with related contemporaneous key indicators in almost all experiments. We also find that news sentiment shocks predict future economic activity in most cases. In almost all experiments, the news sentiment index strongly correlates with related contemporaneous key indicators. Furthermore, in most cases, news sentiment shocks predict future economic activity; in head-to-head comparisons, the news sentiment measures outperform survey-based sentiment index as CSI. Policy makers want to understand consumer or public opinions about existing or proposed policies. Such opinions enable relevant government decision-makers to respond quickly to monitor various web media, SNS, or news articles. Textual data, such as news articles and social networks (Twitter, Facebook and blogs) are generated at high-speeds and cover a wide range of issues; because such sources can quickly capture the economic impact of specific economic issues, they have great potential as economic indicators. Although research using unstructured data in economic analysis is in its early stages, but the utilization of data is expected to greatly increase once its usefulness is confirmed.

• Journal of Intelligence and Information Systems
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• v.26 no.3
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• pp.109-125
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• 2020

The Ministry of National Defense is pushing for the Defense Acquisition Program to build strong defense capabilities, and it spends more than 10 trillion won annually on defense improvement. As the Defense Acquisition Program is directly related to the security of the nation as well as the lives and property of the people, it must be carried out very transparently and efficiently by experts. However, the excessive diversification of laws and regulations related to the Defense Acquisition Program has made it challenging for many working-level officials to carry out the Defense Acquisition Program smoothly. It is even known that many people realize that there are related regulations that they were unaware of until they push ahead with their work. In addition, the statutory statements related to the Defense Acquisition Program have the tendency to cause serious issues even if only a single expression is wrong within the sentence. Despite this, efforts to establish a sentence comparison system to correct this issue in real time have been minimal. Therefore, this paper tries to propose a "Comparison System between the Statement of Military Reports and Related Laws" implementation plan that uses the Siamese Network-based artificial neural network, a model in the field of natural language processing (NLP), to observe the similarity between sentences that are likely to appear in the Defense Acquisition Program related documents and those from related statutory provisions to determine and classify the risk of illegality and to make users aware of the consequences. Various artificial neural network models (Bi-LSTM, Self-Attention, D_Bi-LSTM) were studied using 3,442 pairs of "Original Sentence"(described in actual statutes) and "Edited Sentence"(edited sentences derived from "Original Sentence"). Among many Defense Acquisition Program related statutes, DEFENSE ACQUISITION PROGRAM ACT, ENFORCEMENT RULE OF THE DEFENSE ACQUISITION PROGRAM ACT, and ENFORCEMENT DECREE OF THE DEFENSE ACQUISITION PROGRAM ACT were selected. Furthermore, "Original Sentence" has the 83 provisions that actually appear in the Act. "Original Sentence" has the main 83 clauses most accessible to working-level officials in their work. "Edited Sentence" is comprised of 30 to 50 similar sentences that are likely to appear modified in the county report for each clause("Original Sentence"). During the creation of the edited sentences, the original sentences were modified using 12 certain rules, and these sentences were produced in proportion to the number of such rules, as it was the case for the original sentences. After conducting 1 : 1 sentence similarity performance evaluation experiments, it was possible to classify each "Edited Sentence" as legal or illegal with considerable accuracy. In addition, the "Edited Sentence" dataset used to train the neural network models contains a variety of actual statutory statements("Original Sentence"), which are characterized by the 12 rules. On the other hand, the models are not able to effectively classify other sentences, which appear in actual military reports, when only the "Original Sentence" and "Edited Sentence" dataset have been fed to them. The dataset is not ample enough for the model to recognize other incoming new sentences. Hence, the performance of the model was reassessed by writing an additional 120 new sentences that have better resemblance to those in the actual military report and still have association with the original sentences. Thereafter, we were able to check that the models' performances surpassed a certain level even when they were trained merely with "Original Sentence" and "Edited Sentence" data. If sufficient model learning is achieved through the improvement and expansion of the full set of learning data with the addition of the actual report appearance sentences, the models will be able to better classify other sentences coming from military reports as legal or illegal. Based on the experimental results, this study confirms the possibility and value of building "Real-Time Automated Comparison System Between Military Documents and Related Laws". The research conducted in this experiment can verify which specific clause, of several that appear in related law clause is most similar to the sentence that appears in the Defense Acquisition Program-related military reports. This helps determine whether the contents in the military report sentences are at the risk of illegality when they are compared with those in the law clauses.

• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.36 no.3
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• pp.115-127
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• 2018

This study was attempted to find out the value of cultural assets through the clear diagnosis and prescription of the dead and weakness factors of the Population of Retusa Fringe Trees in Pyeongji-ri, Jinan(Natural Monument No. 214), The results are as follows. First, Since the designation of 13 natural monuments in 1968, since 1973, many years have passed since then. In particular, despite the removal of some of the buried soil during the maintenance process, such as retreating from the fence of the primary school after 2010, Second, The first and third surviving tree of the designated trees also have many branches that are dead, the leaves are dull, and the amount of leaves is small. vitality of tree is 'extremely bad', and the first branch has already been faded by a large number of branches, and the amount of leaves is considerably low this year, so that only two flowers are bloomed. The second is also in a 'bad'state, with small leaves, low leaf density, and deformed water. The largest number 1 in the world is added to the concern that the s coverd oil is assumed to be paddy soils. Third, It is found that the composition ratio of silt is high because it is known as '[silty loam(SiL)]'. In addition, the pH of the northern soil at pH 1 was 6.6, which was significantly different from that of the other soil. In addition, the organic matter content was higher than the appropriate range, which is considered to reflect the result of continuous application for protection management. Fourth, It is considered that the root cause of failure and growth of Jinan pyeongji-ri Population of Retusa Fringe Trees group is chronic syndrome of serious menstrual deterioration due to covered soil. This can also be attributed to the newly planted succession and to some of the deaths. Fifthly, It is urgent to gradually remove the subsoil part, which is estimated to be the cause of the initial damage. Above all, it is almost impossible to remove the coverd soil after grasping the details of the soil, such as clayey soil, which is buried in the rootstock. After removal of the coverd soil, a pestle is installed to improve the respiration of the roots and the ground with Masato. And the dead 4th dead wood and the 5th and 6th dead wood are the best, and the lower layer vegetation is mown. The viable neck should be removed from the upper surface, and the bark defect should undergo surgery and induce the development of blindness by vestibule below the growth point. Sixth, The underground roots should be identified to prepare a method to improve the decompression of the root and the respiration of the soil. It is induced by the shortening of rotten roots by tracing the first half of the rootstock to induce the generation of new roots. Seventh, We try mulching to suppress weed occurrence, trampling pressure, and soil moisturizing effect. In addition, consideration should be given to the fertilization of the foliar fertilizer, the injection of the nutrients, and the soil management of the inorganic fertilizer for the continuous nutrition supply. Future monitoring and forecasting plans should be developed to check for changes continuously.

• Journal of Korean Institute of Fire Investigation
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• v.9 no.1
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• pp.103-121
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• 2006

We are living in more comfortable circumstances with the social developments and the improvement of the standard of living, but, on the other hand, we are exposed to an increase of the occurrences of tires on account of large-sized, higher stories, deeper underground building and the use of various energy resources. The materials of the floor in a residence modern society have been going through various alterations in accordance with the uses of a residence and are now used as final goods in interioring the bottom of apartments, houses and shops. There are so many kinds of materials you usually come in contact with, but in the first place, we need to make an experiment on the spread of the fire with the hypocaust used as the floors of apartments, etc. and the floor covers you usually can get easily. We, scientific investigators, can get in contact with the accidents caused by incendiarism or an accidental fire closely connected with petroleum stuffs on the floor materials that give rise to lots of problems. on this account, I'd like to propose that we conduct an experiment on fire shapes by each petroleum stuff and that discriminate an accidental tire from incendiarism. In an investigation, it seems that finding a live coal could be an essential part of clearing up the cause of a tire but it could not be the cause of a fire itself. And besides, all sorts of tire cases or fire accidents have some kind of legislation and standard to minimize and at an early stage cope with the damage by tires. That is to say, we are supposed to install each kind of electric apparatus, automatic alarm equipment, automatic fire extinguisher in order to protect ourselves from the danger of fires and check them at any time and also escape urgently in case of fire-outbreaking or build a tire-proof construction to prevent flames from proliferating to the neighboring areas. Namely, you should take several factors into consideration to investigate a cause of a case or an accident related to fire. That means it's not in reason for one investigator or one investigative team to make clear of the starting part and the cause of a tire. accordingly, in this thesis, explanations would be given set limits to the judgement and verification on the cause of a fire and the concrete tire-spreading part through investigation on the very spot that a fire broke out. The fire-discernment would also be focused on the early stage fire-spreading part fire-outbreaking resources, and I think the realities of police tire investigations and the problems are still a matter of debate. The cause of a fire must be examined into by logical judgement on the basis of abundant scientific knowledge and experience covering the whole of fire phenomena. The judgement of the cause should be made with fire-spreading situation at the spot as the central figure and in case of verifying, you are supposed to prove by the situational proof from the traces of the tire-spreading to the fire-outbreaking sources. The causal relation on a fire-outbreak should not be proved by arbitrary opinion far from concrete facts, and also there is much chance of making mistakes if you draw deduction from a coincidence. It is absolutely necessary you observe in an objective attitude and grasp the situation of a tire in the investigation of the cause. Having a look at the spot with a prejudice is not allowed. The source of tire-outbreak itself is likely to be considered as the cause of a tire and that makes us doubt about the results according to interests of the independent investigators. So to speak, they set about investigations, the police investigation in the hope of it not being incendiarism, the fire department in the hope of it not being problems in installments or equipments, insurance companies in the hope of it being any incendiarism, electric fields in the hope of it not being electric defects, the gas-related in the hope of it not being gas problems. You could not look forward to more fair investigation and break off their misgivings. It is because the firing source itself is known as the cause of a fire and civil or criminal responsibilities are respected to the firing source itself. On this occasion, investigating the cause of a fire should be conducted with research, investigation, emotion independent, and finally you should clear up the cause with the results put together.

• 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.26 no.2
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• pp.105-129
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• 2020

This study uses corporate data from 2012 to 2018 when K-IFRS was applied in earnest to predict default risks. The data used in the analysis totaled 10,545 rows, consisting of 160 columns including 38 in the statement of financial position, 26 in the statement of comprehensive income, 11 in the statement of cash flows, and 76 in the index of financial ratios. Unlike most previous prior studies used the default event as the basis for learning about default risk, this study calculated default risk using the market capitalization and stock price volatility of each company based on the Merton model. Through this, it was able to solve the problem of data imbalance due to the scarcity of default events, which had been pointed out as the limitation of the existing methodology, and the problem of reflecting the difference in default risk that exists within ordinary companies. Because learning was conducted only by using corporate information available to unlisted companies, default risks of unlisted companies without stock price information can be appropriately derived. Through this, it can provide stable default risk assessment services to unlisted companies that are difficult to determine proper default risk with traditional credit rating models such as small and medium-sized companies and startups. Although there has been an active study of predicting corporate default risks using machine learning recently, model bias issues exist because most studies are making predictions based on a single model. Stable and reliable valuation methodology is required for the calculation of default risk, given that the entity's default risk information is very widely utilized in the market and the sensitivity to the difference in default risk is high. Also, Strict standards are also required for methods of calculation. The credit rating method stipulated by the Financial Services Commission in the Financial Investment Regulations calls for the preparation of evaluation methods, including verification of the adequacy of evaluation methods, in consideration of past statistical data and experiences on credit ratings and changes in future market conditions. This study allowed the reduction of individual models' bias by utilizing stacking ensemble techniques that synthesize various machine learning models. This allows us to capture complex nonlinear relationships between default risk and various corporate information and maximize the advantages of machine learning-based default risk prediction models that take less time to calculate. To calculate forecasts by sub model to be used as input data for the Stacking Ensemble model, training data were divided into seven pieces, and sub-models were trained in a divided set to produce forecasts. To compare the predictive power of the Stacking Ensemble model, Random Forest, MLP, and CNN models were trained with full training data, then the predictive power of each model was verified on the test set. The analysis showed that the Stacking Ensemble model exceeded the predictive power of the Random Forest model, which had the best performance on a single model. Next, to check for statistically significant differences between the Stacking Ensemble model and the forecasts for each individual model, the Pair between the Stacking Ensemble model and each individual model was constructed. Because the results of the Shapiro-wilk normality test also showed that all Pair did not follow normality, Using the nonparametric method wilcoxon rank sum test, we checked whether the two model forecasts that make up the Pair showed statistically significant differences. The analysis showed that the forecasts of the Staging Ensemble model showed statistically significant differences from those of the MLP model and CNN model. In addition, this study can provide a methodology that allows existing credit rating agencies to apply machine learning-based bankruptcy risk prediction methodologies, given that traditional credit rating models can also be reflected as sub-models to calculate the final default probability. Also, the Stacking Ensemble techniques proposed in this study can help design to meet the requirements of the Financial Investment Business Regulations through the combination of various sub-models. We hope that this research will be used as a resource to increase practical use by overcoming and improving the limitations of existing machine learning-based models.

• Korean Journal of Weed Science
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• v.7 no.2
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• pp.186-199
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• 1987

This study was carried out to investigate the growth of Korean lawn grass (Zoysia japonica Steud.), penncross bentgrass (Agrostis palustris Huda) and seaside bentgrass (Agrostis spp.) under application of 21 pre- and post-emergence herbicides and the weeding effect of 14 annual and 4 perennial weeds with them for the purpose of the systematic chemical weed control in turf. The results obtained were as follows; 1. Napropamide, napropamide + triclopyr and benefin were safe for Korean lawn grass and two kinds of bentgrasses when they were treated at 4 and 25 days after transplanting of turfgrasses. Simazine, lenacil and bentazon inhibited the growth of bentgrasses, but not Korean lawn grass. 2. The preemergence application of simazine, benefin and napropamide + simazine showed excellent control for Digitaria sanguinalis, Cyperus amuricus, Chenopodium album, Portulaca oleracea and Centipeda minima. Lenacil was excellent for control of all the tested weeds except Chenopodium album, napropamide excellent for them except Cyperus amuricus and Portulaca oleraces, and bentazon good for them except Digitaria sanguinalis. When simazine was treated with either napropamide or triclopyr at preemergence of weeds, weeding effect increased without inhibition of lawn growth. 3. The postemergence application of mecoprop, bentazon, benefin + dicamba and benefin + mecoprop was safe to bentgrasses. All the tested postemergence herbicides except simazine + atrazine did not inhibit the growth of Korean lawn grass. 4. Other postemergence herbicides mecoprop and triclopyr were excellent for the control of Echinochloa crusgalli and those except benefin and mecoprop excellent for Kummerovia striata. Digitaria sanguinalis was controlled by treating with all the tested post emergence herbicides and Cyperus amuricus controlled only by bentazon. 5. The growth rates of bentgrasses treated with simazine, lenacil and napropamide + simazine were lower than that of hand-weeded check, and those of benefin, bentazon, napropamide, napropamide + triclopyr, stomp, bensulide and triclopyr were higher than that one when applied at spring season. Korean lawn grass growth appeared to be good under application of all the tested preemergence herbicides at spring. Lanacil and bentazone showed poor control of Echinochloa crusgalli, and bensulide showed poor control of Erigeron canadensis. Also, napropamide and bentazon were not good for Kummerovia striata control. However, at the respective rates of all the tested herbicides, these three weeds were greatly controlled by 85-100% of weeding effect. 6. At the application of autumn season, bentazon, napropamide, pendimethalin, benefin, napropamide + triclopyr, bensulide and triclopyr seemed to be safe against three kinds of turfgrasses. But simazine, napropamide + simazine inhibited the growth of bentgrasses except Korean lawn grass. In terms of weed control performance, triclopyr was poor for controlling Echinochloa crusgalli and bentazon and stomp for Poa annua, napropamide, benefin and bensulide for Stellaria medico. Stellaria uliginosa and Cerastium caespitosum were well controlled by all the tested preemergence herbicides. 7. Korean lawn grass was safe when paraquat and glyphosate were treated at the dormanant season of turfgrass. These herbicides showed excellent controll of Poa annua but poor control of perennials in order of Trifolium repens < Miscanthus sinensis < Calystegia japonica < Artemisia asiatica. 8. In field test, all of 19 herbicides seemed to be safe when treated at Korean lawn grass. All of 10 preemergence herbicides were excellent for controlling annual weeds, but poor for perennial ones. All of 9 postemergence herbicides showed a excellent control for broad-leaf weeds.

• Radiation Oncology Journal
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• v.19 no.3
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• pp.275-286
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• 2001

Purpose : To setup procedures of quality assurance (OA) for implementing intensity modulated radiation therapy (IMRT) clinically, report OA procedures peformed for one patient with prostate cancer. Materials and methods : $P^3IMRT$ (ADAC) and linear accelerator (Siemens) with multileaf collimator are used to implement IMRT. At first, the positional accuracy, reproducibility of MLC, and leaf transmission factor were evaluated. RTP commissioning was peformed again to consider small field effect. After RTP recommissioning, a test plan of a C-shaped PTV was made using 9 intensity modulated beams, and the calculated isocenter dose was compared with the measured one in solid water phantom. As a patient-specific IMRT QA, one patient with prostate cancer was planned using 6 beams of total 74 segmented fields. The same beams were used to recalculate dose in a solid water phantom. Dose of these beams were measured with a 0.015 cc micro-ionization chamber, a diode detector, films, and an array detector and compared with calculated one. Results : The positioning accuracy of MLC was about 1 mm, and the reproducibility was around 0.5 mm. For leaf transmission factor for 10 MV photon beams, interleaf leakage was measured $1.9\%$ and midleaf leakage $0.9\%$ relative to $10\times\;cm^2$ open filed. Penumbra measured with film, diode detector, microionization chamber, and conventional 0.125 cc chamber showed that $80\~20\%$ penumbra width measured with a 0.125 cc chamber was 2 mm larger than that of film, which means a 0.125 cc ionization chamber was unacceptable for measuring small field such like 0.5 cm beamlet. After RTP recommissioning, the discrepancy between the measured and calculated dose profile for a small field of $1\times1\;cm^2$ size was less than $2\%$. The isocenter dose of the test plan of C-shaped PTV was measured two times with micro-ionization chamber in solid phantom showed that the errors upto $12\%$ for individual beam, but total dose delivered were agreed with the calculated within $2\%$. The transverse dose distribution measured with EC-L film was agreed with the calculated one in general. The isocenter dose for the patient measured in solid phantom was agreed within $1.5\%$. On-axis dose profiles of each individual beam at the position of the central leaf measured with film and array detector were found that at out-of-the-field region, the calculated dose underestimates about $2\%$, at inside-the-field the measured one was agreed within $3\%$, except some position. Conclusion : It is necessary more tight quality control of MLC for IMRT relative to conventional large field treatment and to develop QA procedures to check intensity pattern more efficiently. At the conclusion, we did setup an appropriate QA procedures for IMRT by a series of verifications including the measurement of absolute dose at the isocenter with a micro-ionization chamber, film dosimetry for verifying intensity pattern, and another measurement with an array detector for comparing off-axis dose profile.

• The Journal of Korean Medicine Ophthalmology and Otolaryngology and Dermatology
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• v.15 no.2
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• pp.271-288
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• 2002

Authors investigated the pathogenesis and treatment of dennatosurgical diseases in the ImJeungJiNamUiAn(臨證指南醫案). 1. The symptoms and diseases of dermatosurgery were as follows; 1) BanSaJinRa(반사진라) : eczema, atopic dermatitis, seborrheic dermatitis, psoriasis, lichen planus, pityriasis rosea, hives, dermographism, angioedema, cholinergic urticaria, urticaria pigmentosa, acne, milium, syringoma, keratosis pilaris, discoid lupus erythematosus, hypersensitivity vasculitis, drug eruption, polymorphic light eruption, rheumatic fever, juvenile rheumatoid arthritis(Still's disease), acute febrile neutrophilic dermatosis(Sweet's syndrome), Paget's disease, folliculitis, viral exanthems, molluscum contagiosum, tinea, tinea versicolor, lymphoma, lymphadenitis, lymphangitis, granuloma annulare, cherry angioma 2) ChangYang(瘡瘍) : acute stage eczema, seborrheic dermatitis, stasis ulcer, intertrigo, xerosis, psoriasis, lichen planus, ichthyosis, pityriasis rosea, rosacea, acne, keratosis pilaris, dyshidrosis, dermatitis herpetiformis, herpes gestationis, bullae in diabetics, pemphigus, lupus erythematosus, fixed drug eruption, erythema multiforme, toxic epidermal necrolysis, toxic shock syndrome, staphylococcal scaled skin syndrome, scarlet fever, folliculitis, impetigo, pyoderma gangrenosum, tinea, candidiasis, scabies, herpes simplex, herpes zoster, chicken pox, Kawasaki syndrome, lipoma, goiter, thyroid nodule, thyroiditis, hyperthyroidism, thyroid cancer, benign breast disorder, breast carcinoma, hepatic abscess, appendicitis, hemorrhoid 3) Yeok(疫) : scarlet fever, chicken pox, measles, rubella, exanthem subitum, erythema infectiosum, Epstein-Barr virus infection, cytomegalovirus infection, hand-foot-mouth disease, Kawasaki disease 4) Han(汗) : hyperhidrosis 2. The pathogenesis and treatment of dermatosurgery were as follows; 1) When the pathogenesis of BalSa(발사), BalJin(發疹), BalLa(발라) and HangJong(項腫) are wind-warm(風溫), exogenous cold with endogenous heat(外寒內熱), wind-damp(風濕), the treatment of evaporation(解表) with Menthae Herba(薄荷), Arctii Fructus(牛蒡子), Forsythiae Fructus(連翹) Mori Cortex(桑白皮), Fritillariae Cirrhosae Bulbus(貝母), Armeniaoae Amarum Semen(杏仁), Ephedrae Herba(麻黃), Cinnamomi Ramulus(桂枝), Curcumae Longae Rhizoma(薑黃), etc can be applied. 2) When the pathogenesis of BuYang(부양), ChangI(瘡痍) and ChangJilGaeSeon(瘡疾疥癬) are wind-heat(風熱), blood fever with wind transformation(血熱風動), wind-damp(風濕), the treatment of wind-dispelling(疏風) with Arctii Fructus(牛蒡子), Schizonepetae Herba(荊芥), Ledebouriellae Radix(防風), Dictamni Radicis Cortex(白鮮皮), Bombyx Batrytioatus(白？?), etc can be applied. 3) When the pathogenesis of SaHuHaeSu(사후해수), SaJin(사진), BalJin(發疹), EunJin(은진) and BuYang(부양) are wind-heat(風熱), exogenous cold with endogenous heat(外寒內熱), exogenous warm pathogen with endogenous damp-heat(溫邪外感 濕熱內蘊), warm pathogen's penetration(溫邪內陷), insidious heat's penetration of pericardium(伏熱入包絡), the treatment of Ki-cooling(淸氣) with TongSeongHwan(通聖丸), Praeparatum(豆？), Phyllostachys Folium(竹葉), Mori Cortex(桑白皮), Tetrapanacis Medulla(通草), etc can be applied. 4) When the pathogenesis of JeokBan(적반), BalLa(발라), GuChang(久瘡), GyeolHaek(結核), DamHaek(痰核), Yeong(？), YuJu(流注), Breast Diseases(乳房疾患) and DoHan(盜汗) are stagnancy's injury of Ki and blood(鬱傷氣血), gallbladder fire with stomach damp(膽火胃濕), deficiency of Yin in stomach with Kwolum's check (胃陰虛 厥陰乘), heat's penetration of blood collaterals with disharmony of liver and stomach(熱入血絡 肝胃不和), insidious pathogen in Kwolum(邪伏厥陰), the treatment of mediation(和解) with Prunellae Spica(夏枯草), Chrysanthemi Flos(菊花), Mori Folium (桑葉), Bupleuri Radix(柴胡), Coptidis Rhizoma(黃連), Scutellariae Radix(黃芩), Gardeniae Fructus(梔子), Cyperi Rhizoma(香附子), Toosendan Fructus(川？子), Curcumae Radix(鬱金), Moutan Cortex(牧丹皮), Paeoniae Radix Rubra(赤芍藥), Unoariae Ramulus Et Uncus(釣鉤藤), Cinnamorni Ramulus(桂枝), Paeoniae Radix Alba(白芍藥), Polygoni Multiflori Radix (何首烏), Cannabis Fructus (胡麻子), Ostreae Concha(牡蠣), Zizyphi Spinosae Semen(酸棗仁), Pinelliae Rhizoma(半夏), Poria(백복령). etc can be applied. 5) When the pathogenesis of BanJin(반진), BalLa(발라), ChangI(瘡痍), NamgChang(膿瘡). ChangJilGaeSeon(瘡疾疥癬), ChangYang(瘡瘍), SeoYang(署瘍), NongYang(膿瘍) and GweYang(潰瘍) are wind-damp(風濕), summer heat-damp(暑濕), damp-warm(濕溫), downward flow of damp-heat(濕熱下垂), damp-heat with phlegm transformation(濕熱化痰), gallbladder fire with stomach damp(膽火胃濕), overdose of cold herbs(寒凉之樂 過服), the treatment of damp-resolving(化濕) with Pinelliae Rhizoma(半夏), armeniacae Amarum Semen(杏仁), Arecae Pericarpium(大腹皮), Poria(백복령), Coicis Semen(薏苡仁), Talcum(滑石), Glauberitum(寒水石), Dioscoreae Tokoro Rhizoma(？？), Alismatis Rhizoma(澤瀉), Phellodendri Cortex(黃柏), Phaseoli Radiati Semen(？豆皮), Bombycis Excrementum(?沙), Bombyx Batryticatus(白？?), Stephaniae Tetrandrae Radix(防己), etc can be applied. 6) When the pathogenesis of ChangPo(瘡泡), hepatic abscess(肝癰) and appendicitis(腸癰) are food poisoning(食物中毒), Ki obstruction & blood stasis in the interior(기비혈어재과), damp-heat stagnation with six Bu organs suspension(濕熱結聚 六腑不通), the treatment of purgation(通下) with DaeHwangMokDanPiTang(大黃牧丹皮湯), Manitis Squama(穿山甲), Curcumae Radix(鬱金), Curcumae Longae Rhizoma(薑黃), Tetrapanacis Medulla(通草), etc can be applied. 7) When the pathogenesis of JeokBan(적반), BanJin(반진), EunJin(은진). BuYang(부양), ChangI(瘡痍), ChangPo(瘡泡), GuChang(久瘡), NongYang(膿瘍), GweYang(潰瘍), Jeong(정), Jeol(癤), YeokRyeo(疫？) and YeokRyeolpDan(疫？入？) are wind-heat stagnation(風熱久未解), blood fever in Yangmyong(陽明血熱), blood fever with transformation(血熱風動), heat's penetration of blood collaterals(熱入血絡). fever in blood(血分有熱), insidious heat in triple energizer(三焦伏熱), pathogen's penetration of pericardium(心包受邪), deficiency of Yong(營虛), epidemic pathogen(感受穢濁), the treatment of Yong & blood-cooling(淸營凉血) with SeoGakJiHwangTang(犀角地黃湯), Scrophulariae Radix(玄參), Salviae Miltiorrhizae Radix(丹參), Angelicae Gigantis Radix(當歸), Polygoni Multiflori Radix(何首烏), Cannabis Fructus(胡麻子), Biotae Semen(柏子仁), Liriopis Tuber(麥門冬), Phaseoli Semen(赤豆皮), Forsythiae Fructus(連翹), SaJin(사진), YangDok(瘍毒) and YeokRyeoIpDan(역려입단) are insidious heat's penetration of pericardium(伏熱入包絡), damp-warm's penetration of blood collaterals(濕溫入血絡), epidemic pathogen's penetration of pericardium(심포감수역려), the treatment of resuscitation(開竅) with JiBoDan(至寶丹), UHwangHwan(牛黃丸), Forsythiae Fructus(連翹), Curcumae Radix(鬱金), Tetrapanacis Medulla(通草), Acori Graminei Rhizoma(石菖蒲), etc can be applied. 9) When the pathogenesis of SaHuSinTong(사후신통), SaHuYeolBuJi(사후열부지), ChangI(瘡痍), YangSon(瘍損) and DoHan(盜汗) are deficiency of Yin in Yangmyong stomach(陽明胃陰虛), deficiency of Yin(陰虛), the treatment of Yin-replenishing(滋陰) with MaekMunDongTang(麥門冬湯), GyeongOkGo(瓊玉膏), Schizandrae Fructus(五味子), Adenophorae Radix(沙參), Lycii Radicis Cortex (地骨皮), Polygonati Odorati Rhizoma(玉竹), Dindrobii Herba(石斛), Paeoniae Radix Alba(白芍藥), Ligustri Lucidi Fructus (女貞子), etc can be applied. 10) When the pathogenesis of RuYang(漏瘍) is endogenous wind in Yang collaterals(陽絡內風), the treatment of endogenous wind-calming(息風) with Mume Fructus(烏梅), Paeoniae Radix Alba (白芍藥), etc be applied. 11) When the pathogenesis of GuChang(久瘡), GweYang(潰瘍), RuYang(漏瘍), ChiChang(痔瘡), JaHan(自汗) and OSimHan(五心汗) are consumption of stomach(胃損), consumption of Ki & blood(氣血耗盡), overexertion of heart vitality(勞傷心神), deficiency of Yong(營虛), deficiency of Wi(衛虛), deficiency of Yang(陽虛), the treatment of Yang-restoring & exhaustion-arresting(回陽固脫) with RijungTang(理中湯), jinMuTang(眞武湯), SaengMaekSaGunjaTang(生脈四君子湯), Astragali Radix (황기), Ledebouriellae Radix(防風), Cinnamomi Ramulus(桂枝), Angelicae Gigantis Radix(當歸), Ostreae Concha(牡蠣), Zanthoxyli Fructus(川椒), Cuscutae Semen(兎絲子), etc can be applied.

• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.141-166
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• 2019

Recently, channels like social media and SNS create enormous amount of data. In all kinds of data, portions of unstructured data which represented as text data has increased geometrically. But there are some difficulties to check all text data, so it is important to access those data rapidly and grasp key points of text. Due to needs of efficient understanding, many studies about text summarization for handling and using tremendous amounts of text data have been proposed. Especially, a lot of summarization methods using machine learning and artificial intelligence algorithms have been proposed lately to generate summary objectively and effectively which called "automatic summarization". However almost text summarization methods proposed up to date construct summary focused on frequency of contents in original documents. Those summaries have a limitation for contain small-weight subjects that mentioned less in original text. If summaries include contents with only major subject, bias occurs and it causes loss of information so that it is hard to ascertain every subject documents have. To avoid those bias, it is possible to summarize in point of balance between topics document have so all subject in document can be ascertained, but still unbalance of distribution between those subjects remains. To retain balance of subjects in summary, it is necessary to consider proportion of every subject documents originally have and also allocate the portion of subjects equally so that even sentences of minor subjects can be included in summary sufficiently. In this study, we propose "subject-balanced" text summarization method that procure balance between all subjects and minimize omission of low-frequency subjects. For subject-balanced summary, we use two concept of summary evaluation metrics "completeness" and "succinctness". Completeness is the feature that summary should include contents of original documents fully and succinctness means summary has minimum duplication with contents in itself. Proposed method has 3-phases for summarization. First phase is constructing subject term dictionaries. Topic modeling is used for calculating topic-term weight which indicates degrees that each terms are related to each topic. From derived weight, it is possible to figure out highly related terms for every topic and subjects of documents can be found from various topic composed similar meaning terms. And then, few terms are selected which represent subject well. In this method, it is called "seed terms". However, those terms are too small to explain each subject enough, so sufficient similar terms with seed terms are needed for well-constructed subject dictionary. Word2Vec is used for word expansion, finds similar terms with seed terms. Word vectors are created after Word2Vec modeling, and from those vectors, similarity between all terms can be derived by using cosine-similarity. Higher cosine similarity between two terms calculated, higher relationship between two terms defined. So terms that have high similarity values with seed terms for each subjects are selected and filtering those expanded terms subject dictionary is finally constructed. Next phase is allocating subjects to every sentences which original documents have. To grasp contents of all sentences first, frequency analysis is conducted with specific terms that subject dictionaries compose. TF-IDF weight of each subjects are calculated after frequency analysis, and it is possible to figure out how much sentences are explaining about each subjects. However, TF-IDF weight has limitation that the weight can be increased infinitely, so by normalizing TF-IDF weights for every subject sentences have, all values are changed to 0 to 1 values. Then allocating subject for every sentences with maximum TF-IDF weight between all subjects, sentence group are constructed for each subjects finally. Last phase is summary generation parts. Sen2Vec is used to figure out similarity between subject-sentences, and similarity matrix can be formed. By repetitive sentences selecting, it is possible to generate summary that include contents of original documents fully and minimize duplication in summary itself. For evaluation of proposed method, 50,000 reviews of TripAdvisor are used for constructing subject dictionaries and 23,087 reviews are used for generating summary. Also comparison between proposed method summary and frequency-based summary is performed and as a result, it is verified that summary from proposed method can retain balance of all subject more which documents originally have.