• Journal of Intelligence and Information Systems
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• v.19 no.1
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• pp.95-110
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• 2013

Recently, the amount of unstructured data being generated through a variety of social media has been increasing rapidly, resulting in the increasing need to collect, store, search for, analyze, and visualize this data. This kind of data cannot be handled appropriately by using the traditional methodologies usually used for analyzing structured data because of its vast volume and unstructured nature. In this situation, many attempts are being made to analyze unstructured data such as text files and log files through various commercial or noncommercial analytical tools. Among the various contemporary issues dealt with in the literature of unstructured text data analysis, the concepts and techniques of opinion mining have been attracting much attention from pioneer researchers and business practitioners. Opinion mining or sentiment analysis refers to a series of processes that analyze participants' opinions, sentiments, evaluations, attitudes, and emotions about selected products, services, organizations, social issues, and so on. In other words, many attempts based on various opinion mining techniques are being made to resolve complicated issues that could not have otherwise been solved by existing traditional approaches. One of the most representative attempts using the opinion mining technique may be the recent research that proposed an intelligent model for predicting the direction of the stock index. This model works mainly on the basis of opinions extracted from an overwhelming number of economic news repots. News content published on various media is obviously a traditional example of unstructured text data. Every day, a large volume of new content is created, digitalized, and subsequently distributed to us via online or offline channels. Many studies have revealed that we make better decisions on political, economic, and social issues by analyzing news and other related information. In this sense, we expect to predict the fluctuation of stock markets partly by analyzing the relationship between economic news reports and the pattern of stock prices. So far, in the literature on opinion mining, most studies including ours have utilized a sentiment dictionary to elicit sentiment polarity or sentiment value from a large number of documents. A sentiment dictionary consists of pairs of selected words and their sentiment values. Sentiment classifiers refer to the dictionary to formulate the sentiment polarity of words, sentences in a document, and the whole document. However, most traditional approaches have common limitations in that they do not consider the flexibility of sentiment polarity, that is, the sentiment polarity or sentiment value of a word is fixed and cannot be changed in a traditional sentiment dictionary. In the real world, however, the sentiment polarity of a word can vary depending on the time, situation, and purpose of the analysis. It can also be contradictory in nature. The flexibility of sentiment polarity motivated us to conduct this study. In this paper, we have stated that sentiment polarity should be assigned, not merely on the basis of the inherent meaning of a word but on the basis of its ad hoc meaning within a particular context. To implement our idea, we presented an intelligent investment decision-support model based on opinion mining that performs the scrapping and parsing of massive volumes of economic news on the web, tags sentiment words, classifies sentiment polarity of the news, and finally predicts the direction of the next day's stock index. In addition, we applied a domain-specific sentiment dictionary instead of a general purpose one to classify each piece of news as either positive or negative. For the purpose of performance evaluation, we performed intensive experiments and investigated the prediction accuracy of our model. For the experiments to predict the direction of the stock index, we gathered and analyzed 1,072 articles about stock markets published by "M" and "E" media between July 2011 and September 2011.

• Proceedings of the KOR-KST Conference
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• 1995.02a
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• pp.101-113
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• 1995

The objective of this research is to test the feasibility of developing a statewide truck traffic forecasting methodology for Wisconsin by using Origin-Destination surveys, traffic counts, classification counts, and other data that are routinely collected by the Wisconsin Department of Transportation (WisDOT). Development of a feasible model will permit estimation of future truck traffic for every major link in the network. This will provide the basis for improved estimation of future pavement deterioration. Pavement damage rises exponentially as axle weight increases, and trucks are responsible for most of the traffic-induced damage to pavement. Consequently, forecasts of truck traffic are critical to pavement management systems. The pavement Management Decision Supporting System (PMDSS) prepared by WisDOT in May 1990 combines pavement inventory and performance data with a knowledge base consisting of rules for evaluation, problem identification and rehabilitation recommendation. Without a r.easonable truck traffic forecasting methodology, PMDSS is not able to project pavement performance trends in order to make assessment and recommendations in the future years. However, none of WisDOT's existing forecasting methodologies has been designed specifically for predicting truck movements on a statewide highway network. For this research, the Origin-Destination survey data avaiiable from WisDOT, including two stateline areas, one county, and five cities, are analyzed and the zone-to'||'&'||'not;zone truck trip tables are developed. The resulting Origin-Destination Trip Length Frequency (00 TLF) distributions by trip type are applied to the Gravity Model (GM) for comparison with comparable TLFs from the GM. The gravity model is calibrated to obtain friction factor curves for the three trip types, Internal-Internal (I-I), Internal-External (I-E), and External-External (E-E). ~oth "macro-scale" calibration and "micro-scale" calibration are performed. The comparison of the statewide GM TLF with the 00 TLF for the macro-scale calibration does not provide suitable results because the available 00 survey data do not represent an unbiased sample of statewide truck trips. For the "micro-scale" calibration, "partial" GM trip tables that correspond to the 00 survey trip tables are extracted from the full statewide GM trip table. These "partial" GM trip tables are then merged and a partial GM TLF is created. The GM friction factor curves are adjusted until the partial GM TLF matches the 00 TLF. Three friction factor curves, one for each trip type, resulting from the micro-scale calibration produce a reasonable GM truck trip model. A key methodological issue for GM. calibration involves the use of multiple friction factor curves versus a single friction factor curve for each trip type in order to estimate truck trips with reasonable accuracy. A single friction factor curve for each of the three trip types was found to reproduce the 00 TLFs from the calibration data base. Given the very limited trip generation data available for this research, additional refinement of the gravity model using multiple mction factor curves for each trip type was not warranted. In the traditional urban transportation planning studies, the zonal trip productions and attractions and region-wide OD TLFs are available. However, for this research, the information available for the development .of the GM model is limited to Ground Counts (GC) and a limited set ofOD TLFs. The GM is calibrated using the limited OD data, but the OD data are not adequate to obtain good estimates of truck trip productions and attractions .. Consequently, zonal productions and attractions are estimated using zonal population as a first approximation. Then, Selected Link based (SELINK) analyses are used to adjust the productions and attractions and possibly recalibrate the GM. The SELINK adjustment process involves identifying the origins and destinations of all truck trips that are assigned to a specified "selected link" as the result of a standard traffic assignment. A link adjustment factor is computed as the ratio of the actual volume for the link (ground count) to the total assigned volume. This link adjustment factor is then applied to all of the origin and destination zones of the trips using that "selected link". Selected link based analyses are conducted by using both 16 selected links and 32 selected links. The result of SELINK analysis by u~ing 32 selected links provides the least %RMSE in the screenline volume analysis. In addition, the stability of the GM truck estimating model is preserved by using 32 selected links with three SELINK adjustments, that is, the GM remains calibrated despite substantial changes in the input productions and attractions. The coverage of zones provided by 32 selected links is satisfactory. Increasing the number of repetitions beyond four is not reasonable because the stability of GM model in reproducing the OD TLF reaches its limits. The total volume of truck traffic captured by 32 selected links is 107% of total trip productions. But more importantly, ~ELINK adjustment factors for all of the zones can be computed. Evaluation of the travel demand model resulting from the SELINK adjustments is conducted by using screenline volume analysis, functional class and route specific volume analysis, area specific volume analysis, production and attraction analysis, and Vehicle Miles of Travel (VMT) analysis. Screenline volume analysis by using four screenlines with 28 check points are used for evaluation of the adequacy of the overall model. The total trucks crossing the screenlines are compared to the ground count totals. L V/GC ratios of 0.958 by using 32 selected links and 1.001 by using 16 selected links are obtained. The %RM:SE for the four screenlines is inversely proportional to the average ground count totals by screenline .. The magnitude of %RM:SE for the four screenlines resulting from the fourth and last GM run by using 32 and 16 selected links is 22% and 31 % respectively. These results are similar to the overall %RMSE achieved for the 32 and 16 selected links themselves of 19% and 33% respectively. This implies that the SELINICanalysis results are reasonable for all sections of the state.Functional class and route specific volume analysis is possible by using the available 154 classification count check points. The truck traffic crossing the Interstate highways (ISH) with 37 check points, the US highways (USH) with 50 check points, and the State highways (STH) with 67 check points is compared to the actual ground count totals. The magnitude of the overall link volume to ground count ratio by route does not provide any specific pattern of over or underestimate. However, the %R11SE for the ISH shows the least value while that for the STH shows the largest value. This pattern is consistent with the screenline analysis and the overall relationship between %RMSE and ground count volume groups. Area specific volume analysis provides another broad statewide measure of the performance of the overall model. The truck traffic in the North area with 26 check points, the West area with 36 check points, the East area with 29 check points, and the South area with 64 check points are compared to the actual ground count totals. The four areas show similar results. No specific patterns in the L V/GC ratio by area are found. In addition, the %RMSE is computed for each of the four areas. The %RMSEs for the North, West, East, and South areas are 92%, 49%, 27%, and 35% respectively, whereas, the average ground counts are 481, 1383, 1532, and 3154 respectively. As for the screenline and volume range analyses, the %RMSE is inversely related to average link volume. 'The SELINK adjustments of productions and attractions resulted in a very substantial reduction in the total in-state zonal productions and attractions. The initial in-state zonal trip generation model can now be revised with a new trip production's trip rate (total adjusted productions/total population) and a new trip attraction's trip rate. Revised zonal production and attraction adjustment factors can then be developed that only reflect the impact of the SELINK adjustments that cause mcreases or , decreases from the revised zonal estimate of productions and attractions. Analysis of the revised production adjustment factors is conducted by plotting the factors on the state map. The east area of the state including the counties of Brown, Outagamie, Shawano, Wmnebago, Fond du Lac, Marathon shows comparatively large values of the revised adjustment factors. Overall, both small and large values of the revised adjustment factors are scattered around Wisconsin. This suggests that more independent variables beyond just 226; population are needed for the development of the heavy truck trip generation model. More independent variables including zonal employment data (office employees and manufacturing employees) by industry type, zonal private trucks 226; owned and zonal income data which are not available currently should be considered. A plot of frequency distribution of the in-state zones as a function of the revised production and attraction adjustment factors shows the overall " adjustment resulting from the SELINK analysis process. Overall, the revised SELINK adjustments show that the productions for many zones are reduced by, a factor of 0.5 to 0.8 while the productions for ~ relatively few zones are increased by factors from 1.1 to 4 with most of the factors in the 3.0 range. No obvious explanation for the frequency distribution could be found. The revised SELINK adjustments overall appear to be reasonable. The heavy truck VMT analysis is conducted by comparing the 1990 heavy truck VMT that is forecasted by the GM truck forecasting model, 2.975 billions, with the WisDOT computed data. This gives an estimate that is 18.3% less than the WisDOT computation of 3.642 billions of VMT. The WisDOT estimates are based on the sampling the link volumes for USH, 8TH, and CTH. This implies potential error in sampling the average link volume. The WisDOT estimate of heavy truck VMT cannot be tabulated by the three trip types, I-I, I-E ('||'&'||'pound;-I), and E-E. In contrast, the GM forecasting model shows that the proportion ofE-E VMT out of total VMT is 21.24%. In addition, tabulation of heavy truck VMT by route functional class shows that the proportion of truck traffic traversing the freeways and expressways is 76.5%. Only 14.1% of total freeway truck traffic is I-I trips, while 80% of total collector truck traffic is I-I trips. This implies that freeways are traversed mainly by I-E and E-E truck traffic while collectors are used mainly by I-I truck traffic. Other tabulations such as average heavy truck speed by trip type, average travel distance by trip type and the VMT distribution by trip type, route functional class and travel speed are useful information for highway planners to understand the characteristics of statewide heavy truck trip patternS. Heavy truck volumes for the target year 2010 are forecasted by using the GM truck forecasting model. Four scenarios are used. Fo~ better forecasting, ground count- based segment adjustment factors are developed and applied. ISH 90 '||'&'||' 94 and USH 41 are used as example routes. The forecasting results by using the ground count-based segment adjustment factors are satisfactory for long range planning purposes, but additional ground counts would be useful for USH 41. Sensitivity analysis provides estimates of the impacts of the alternative growth rates including information about changes in the trip types using key routes. The network'||'&'||'not;based GMcan easily model scenarios with different rates of growth in rural versus . . urban areas, small versus large cities, and in-state zones versus external stations. cities, and in-state zones versus external stations.

• Clinical and Experimental Pediatrics
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• v.51 no.1
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• pp.33-41
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• 2008

Purpose : It is now understood that blood pressure (BP) measurement in the routine pediatric examination is very important because of the relevance of childhood BP to pediatric health care and the development of adult essential hypertension. There hasn't been a reference table of BP for Korean children and adolescents up to now. This study was to make normative BP references and to provide criteria of hypertension for Korean children and adolescents. Methods : BP measurements were done on 57,433 Koean children and adolescents (male: 29,443, female: 27,990), aged 7 to 20 years, in 2005. Heights and weights were measured simultaneously. Oscillometric devices, Dinamap Procare 200 (GE Inc., Milwaukee, Wi, USA), were used for the measurements. BPs were measured 2 times and mean levels were gathered for the analysis. Outliers of 2,373 subjects with overweight per height, over ＋3SD, were excluded for the analysis. For the BP centiles adjusted by sex, age and height, fixed modified LMS method which was adopted from the mixed effect model of 2004 Task Force in NHLBI (USA) was used. Results : Normative BP tables for Korean children and adolescents adjusted for height percentiles (5th, 10th, 25th, 50th, 75th, 90th, 95th), gender (male, female) and age(7 to 18 years) were completed. Height centiles of Korean children and adolescents are available from Korean Center for Disease Control and Prevention homepage, http://www.cdc.go.kr/webcdc/. Criteria of hypertension (95th, 99th percentile) and normal range of BP (50th, 90th) adjusted for height percentiles, age and gender were made. Conclusion : This is the first study to make normative BP tables and define hypertension for the Korean children and adolescents. Reliability and accuracy of Dinamap Procare 200 oscillometer for BP measurements remains debatable.

• Journal of Korean Society of Transportation
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• v.30 no.1
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• pp.7-18
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• 2012

This study suggested a method of forecasting market-share of each mode after introducing new urban rail transit lines. The study reflected the observed market share of presently operating urban rail transit into forecasting process in order to improve accuracy in predicting market share of each modes. For more realistic representation of the forecasting model, we categorized O/D pairs according to attributes of trip distance, access time and number of transfers. The analysis results of traveler's mode choice behavior with observed data showed that the trip distances are longer, the share of urban rail tends to be higher, and that the number of transfers is fewer and the access times are lesser, the share of urban rail also tends to be higher. Then, incremental logit model was used in estimating mode choice probabilities for O/D pairs along with rail transit lines while utilizing observed market shares of each modes and differences in transit service level. As the next step, the market share of rail transit after introducing new rail transit lines was forecasted by using incremental logit model with the intial share values calculated the previous analysis step. It also reflected changes in level of service for automobile in highway due to changes in highway systems and changes in mode shares after introducing new lines of rail transit. It can be expected that the proposed method would more realistically duplicates phenomena of mode choice behavior for rail transit and that it would be more theoretically logical than the typical existing methods using SP data and incremental logit model or using addictive logit model in this country.

• Journal of Korean Society of Forest Science
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• v.85 no.3
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• pp.453-461
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• 1996

Three Phyllostachys stands of P. pubescens, P. bambusoides and P. nigra var, henonis in Sunchon were studied to investigate biomass, net production and nutrient distribution. Five $10m{\times}10m$ quadrats were set up and 20 sample culms of 2 years and over were harvested for dimension analysis in each stand. One year old culms and subterranean parts were estimated by the harvested quadrat method. The largest mean DBH, height and basal area were shown in P. pubescens stand, and followed by P. nigra var. henonis stand and P. bambusoides stand. There was little difference in accuracy among three allometric biomass regression models of logWt=A+B1ogD, $logWt=A+BlogD^2H$ and logWt=A+BlogD+ClogH, where Wt, D and H were dry weight, DBH and height, respectively. Analysis of covariance showed that there were significant differences in intercept among the linear allometric biomass regressons of three Phyllostachys species. Biomass included subterranean parts was the largest in P. pubescens stand(103.621t/ha), and followed by P. nigra var. henonis stand(86.447t/ha) and P. bambusoides stand(36.767t/ha). Leaf biomass was 6.3% to 7.8% of total biomass in each stands. The ratio of aboveground biomass and subterranean biomass in each stand was 1.87 to 2.26. Net production included subterranean parts was the greatest in P. pubescens stand(6.115t/ha/yr), and followed by P. nigra var. henonis stand(5.609t/ha/yr) and P, bambusoides stand(3.252t/ha/yr). The highest net assimilation ratio was estimated in P. pubescens stand(2.979), and followed by P. nigra var. henonis stand(2.752) and P. bambusoides stand(2.187). Biomass accumulation ratio of each stand was 2.679 to 5.358. Concentrations of N, P and Mg were the highest in leaves, and followed by subterranean parts, and culms+branches in all three species. Concentration of Ca was the highest in leaves, and followed by culms+branches, and subterranean parts in all three species. The difference in biomass among three species stands was caused by their culm size, leaf biomass, net assimilation ratio, and efficiency of leaves to produce culms.

• Analytical Science and Technology
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• v.33 no.3
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• pp.151-158
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• 2020

Alcohol, which can easily be obtained in the same way as ordinary beverages, is harmful enough to cause death due to excessive drinking and chronic alcohol intake, so it is important to maintain a proper amount of drinking and healthy drinking habits. In addition, the incidence of behavioral disturbances and impaired judgments that can be caused by chronic alcohol drinking of more than adequate amounts of alcohol is also significant. Accordingly it is very useful for forensic science to check whether the person involved is drunken or is alcoholism state in various accidents. Currently, in Korea, alcohol consumption is determined by detecting the level of alcohol or alcohol metabolism 'ethyl glucuronide (EtG)' in blood or urine samples. However, analysis of alcohol or EtG in blood or urine can only provide information about the current state of alcohol consumption because of a narrow window of detection time. Therefore, it is important to analyze the EtG as a long-term direct alcohol metabolite bio-marker in human hair and to investigate relationship between alcohol consumption and EtG concentration for the evaluation of chronic ethanol consumption. In this study, we established an analytical method for the detection of EtG in Korean hair efficiently and validated selectivity, linearity, limits of detection (LOD), limits of quantification (LOQ), matrix effect, recovery, process efficiency, accuracy and precision using liquid chromatography tandem mass spectrometry (LC-MS/MS). In addition, the assay performance was evaluated in Korean social drinker's hair and the postmortem hair of a chronic alcoholism. The results of this study can be useful in monitoring the alcohol abuse of Korean in clinical cases and legal procedures related to custody and provide a useful tool to evaluate postmortem diagnosis of alcoholic ketoacidosis in forensics.

• Journal of the Korean Society of Food Science and Nutrition
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• v.46 no.3
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• pp.358-367
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• 2017

Preservatives, as food additives, are occasionally intrinsic to natural raw materials or sometimes generated during the fermentation process as reported in many research articles. Preservative compounds in raw food materials may persist in the final food product, which is not supposed to include such preservative compounds. In this study, we validated an analytical method for preservative compounds in raw materials of functional foods. Quantification of benzoic acid and sorbic acid was determined using HPLC-PDA analysis after distillation, whereas propionic acid was quantified with GC-FID. A significant set of validation data (accuracy, precision, linearity, recovery, etc) was acquired. A total of 212 samples were collected for analysis of naturally occurred preservatives, and preservatives were detected in 85 samples. Most of the detected samples showed less 10 mg/kg of preservatives. The results of this study provide fundamental data on naturally occurring preservatives in raw materials of functional foods. Moreover, building up a database of naturally occurring preservatives could solve problems in the current scientific data.

• Korean Journal of Environmental Agriculture
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• v.37 no.2
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• pp.104-116
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• 2018

BACKGROUND: This experiment was conducted to establish a simultaneous analysis method for 7 kinds of herbicides in 3 different classes having similar physicochemical property as diphenyl ether(bifenox and oxyfluorfen), dinitroaniline (ethalfluralin and trifluralin), and chloroacetamide (metolachlor, pretilachlor, and thenylchlor) in crops using GC-ECD/MS. METHODS AND RESULTS: All the 7 pesticide residues were extracted with acetone from representative samples of five raw products which comprised apple, green pepper, Kimchi cabbage, hulled rice and soybean. The extract was diluted with saline water and directly partitioned into n-hexane/dichloromethane(80/20, v/v) to remove polar co-extractives in the aqueous phase. For the hulled rice and soybean samples, n-hexane/acetonitrile partition was additionally employed to remove non-polar lipids. The extract was finally purified by optimized Florisil column chromatography. The analytes were separated and quantitated by GLC with ECD using a DB-1 capillary column. Accuracy and precision of the proposed method was validated by the recovery experiment on every crop samples fortified with bifenox, ethalfluralin, metolachlor, oxyfluorfen, pretilachlor, thenylchlor, and trifluralin at 3 concentration levels per crop in each triplication. CONCLUSION: Mean recoveries of the 7 pesticide residues ranged from 75.7 to 114.8% in five representative agricultural commodities. The coefficients of variation were all less than 10%, irrespective of sample types and fortification levels. Limit of quantitation (LOQ) of the analytes were 0.004 (etahlfluralin and trifluralin), 0.008 (metolachlor and pretilachlor), 0.006 (thenylchlor), 0.002 (oxyfluorfen), and 0.02 (bifenox) mg/kg as verified by the recovery experiment. A confirmatory technique using GC/MS with selected-ion monitoring was also provided to clearly identify the suspected residues. Therefore, this analytical method was reproducible and sensitive enough to determine the residues of bifenox, ethalfluralin, metolachlor, oxyfluorfen, pretilachlor, thenylchlor, and trifluralin in agricultural commodities.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.11 no.3
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• pp.1-15
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• 2016

This study has analyzed predication capabilities leveraging multi-variate model, logistic regression model, and artificial neural network model based on financial information of medium-small sized companies list in KOSDAQ. 83 delisted companies from 2009 to 2012 and 83 normal companies, i.e. 166 firms in total were sampled for the analysis. Modelling with training data was mobilized for 100 companies inlcuding 50 delisted ones and 50 normal ones at random out of the 166 companies. The rest of samples, 66 companies, were used to verify accuracies of the models. Each model was designed by carrying out T-test with 79 financial ratios for the last 5 years and identifying 9 significant variables. T-test has shown that financial profitability variables were major variables to predict a financial risk at an early stage, and financial stability variables and financial cashflow variables were identified as additional significant variables at a later stage of insolvency. When predication capabilities of the models were compared, for training data, a logistic regression model exhibited the highest accuracy while for test data, the artificial neural networks model provided the most accurate results. There are differences between the previous researches and this study as follows. Firstly, this study considered a time-series aspect in light of the fact that failure proceeds gradually. Secondly, while previous studies constructed a multivariate discriminant model ignoring normality, this study has reviewed the regularity of the independent variables, and performed comparisons with the other models. Policy implications of this study is that the reliability for the disclosure documents is important because the simptoms of firm's fail woule be shown on financial statements according to this paper. Therefore institutional arragements for restraing moral laxity from accounting firms or its workers should be strengthened.