• Journal of Korean Society of Forest Science
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• v.89 no.1
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• pp.49-54
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• 2000

Stand level survival prediction model was developed that incorporated the incidence of fusiform rust(Cronartium quercuum [Berk.] Miyabe ex Shirai f. sp. fusiforme) and allowed the transition of trees from an uninfected stage to an infected stage. The influence of hardwood interspecific competition on the survival of unthinned planted stands of loblolly pine (Pinus taeda L.) was analyzed by using of information from twelve years of tracking a set of permanent plots representing a broad range of plantation parameters. Significant interaction effects between site index and hardwood basal area per acre were revealed in the survival model. Survival of the planted pines decreased with increasing density of hardwood trees per acre and site index as the productivity rating of the forest land. The effects of hardwood trees interspecific competition on loblolly pine tended to show a negative effect on predicted future number of planted pine trees.

• Journal of KIISE:Software and Applications
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• v.29 no.9
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• pp.669-675
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• 2002

In recent years most of personalized recommender systems in electronic commerce utilize collaborative filtering algorithm in order to recommend more appropriate items. User-based collaborative filtering is based on the ratings of other users who have similar preferences to a user in order to predict the rating of an item that the user hasn't seen yet. This nay decrease the accuracy of prediction because the similarity between two users is computed with respect to the two users and only when an item has been rated by the users. In item-based collaborative filtering, the preference of an item is predicted based on the similarity between the item and each of other items that have rated by users. This method, however, uses the ratings of users who are not the neighbors of a user for computing the similarity between a pair of items. Hence item-based collaborative filtering may degrade the accuracy of a recommender system. In this paper, we present a new approach that a user's neighborhood is used when we compute the similarity between the items in traditional item-based collaborative filtering in order to compensate the weak points of the current item-based collaborative filtering and to improve the prediction accuracy. We empirically evaluate the accuracy of our approach to compare with several different collaborative filtering approaches using the EachMovie collaborative filtering data set. The experimental results show that our approach provides better quality in prediction and recommendation list than other collaborative filtering approaches.

• Korean Journal of Environmental Agriculture
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• v.30 no.3
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• pp.268-274
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• 2011

BACKGROUND: A large scale of sediment load delivered from watershed causes substantial waterway damages and water quality degradation. Controlling sediment loading requires the knowledge of the soil erosion and sedimentation. The various factors such as watershed size, slope, climate, land use may affect sediment delivery processes. Traditionally sediment delivery ratio prediction equations have been developed by relating watershed characteristics to measured sediment yield divided by predicted gross erosion. However, sediment prediction equations have been developed for only a few regions because of limited sediment data. Besides, little research has been done on the prediction of sediment delivery ratio for asia monsoon period in mountainous watershed. METHODS AND RESULTS: In this study Tank model was expanded and applied for estimating sediment yield to Oship River of east coast. The rainfall-runoff in 2006 was verified using the Tank model and we derived good result between observed and calculated discharge in 2009 at the same conditions. In relation to sediment yield, the sediment delivery rate of 2006 was very high than 2009 regardless of methods for estimating sediment load. It was thought to be affected by heavy rainfall due to the typhoon. CONCLUSION(s): For estimating sediment volume from watershed, long-term monitoring data on discharge and sediment is needed. This model will be able to apply to predict discharge and sediment yield simultaneously in ungauged area. This approach is more effective and less expensive method than the traditional method which needs a lot of data collection.

• Journal of Korea Water Resources Association
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• v.43 no.8
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• pp.721-731
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• 2010

The rainfall-runoff relationship is very difficult to predict because it is complicate factor affected by many temporal and spatial parameters of the basin. In recent, models which is based on artificial intelligent such as neural network, genetic algorithm fuzzy etc., are frequently used to predict discharge while stochastic or deterministic or empirical models are used in the past. However, the discharge data which are generally used for prediction as training and validation set are often estimated from rating curve which has potential error in its estimation that makes a problem in reliability. Therefore, in this study, water stage is predicted from antecedent rainfall and water stage data for short term using three models of neural network which trained by error back propagation algorithm and optimized by genetic algorithm and training error back propagation after it is optimized by genetic algorithm respectively. As the result, the model optimized by Genetic Algorithm gives the best forecasting ability which is not much decreased as the forecasting time increase. Moreover, the models using stage data only as the input data give better results than the models using precipitation data with stage data.

• Journal of the Korea Convergence Society
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• v.9 no.11
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• pp.227-235
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• 2018

The aim of this study is to predict the sensitivity of motion sickness (MS) using pattern of cardinal gaze position (CGP) before experiencing the virtual reality (VR) content. Twenty volunteers of both genders (8 females, mean age $28.42{\pm}3.17$) participated in this experiment. They was required to measure the pattern of CGP for 5 minute, and then watched VR content for 15 minute. After watching VR content, subjective experience for MS reported from participants using by 'Simulator Sickness Questionnaire (SSQ)'. Statistical significance between CGP and SSQ score were confirmed using Pearson correlation analysis and independent t-test, and prediction model was extracted from multiple regression model. PCPA & PCPR indicators from CGP revealed significantly difference and strong or moderate positive correlation with SSQ score. Extracted prediction model was tested using correlation coefficient and mean error, SSQ score between subjective rating and prediction model showed strong positive correlation and low difference.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.219-239
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• 2019

As the importance of providing customized services to individuals becomes important, researches on personalized recommendation systems are constantly being carried out. Collaborative filtering is one of the most popular systems in academia and industry. However, there exists limitation in a sense that recommendations were mostly based on quantitative information such as users' ratings, which made the accuracy be lowered. To solve these problems, many studies have been actively attempted to improve the performance of the recommendation system by using other information besides the quantitative information. Good examples are the usages of the sentiment analysis on customer review text data. Nevertheless, the existing research has not directly combined the results of the sentiment analysis and quantitative rating scores in the recommendation system. Therefore, this study aims to reflect the sentiments shown in the reviews into the rating scores. In other words, we propose a new algorithm that can directly convert the user 's own review into the empirically quantitative information and reflect it directly to the recommendation system. To do this, we needed to quantify users' reviews, which were originally qualitative information. In this study, sentiment score was calculated through sentiment analysis technique of text mining. The data was targeted for movie review. Based on the data, a domain specific sentiment dictionary is constructed for the movie reviews. Regression analysis was used as a method to construct sentiment dictionary. Each positive / negative dictionary was constructed using Lasso regression, Ridge regression, and ElasticNet methods. Based on this constructed sentiment dictionary, the accuracy was verified through confusion matrix. The accuracy of the Lasso based dictionary was 70%, the accuracy of the Ridge based dictionary was 79%, and that of the ElasticNet (${\alpha}=0.3$) was 83%. Therefore, in this study, the sentiment score of the review is calculated based on the dictionary of the ElasticNet method. It was combined with a rating to create a new rating. In this paper, we show that the collaborative filtering that reflects sentiment scores of user review is superior to the traditional method that only considers the existing rating. In order to show that the proposed algorithm is based on memory-based user collaboration filtering, item-based collaborative filtering and model based matrix factorization SVD, and SVD ++. Based on the above algorithm, the mean absolute error (MAE) and the root mean square error (RMSE) are calculated to evaluate the recommendation system with a score that combines sentiment scores with a system that only considers scores. When the evaluation index was MAE, it was improved by 0.059 for UBCF, 0.0862 for IBCF, 0.1012 for SVD and 0.188 for SVD ++. When the evaluation index is RMSE, UBCF is 0.0431, IBCF is 0.0882, SVD is 0.1103, and SVD ++ is 0.1756. As a result, it can be seen that the prediction performance of the evaluation point reflecting the sentiment score proposed in this paper is superior to that of the conventional evaluation method. In other words, in this paper, it is confirmed that the collaborative filtering that reflects the sentiment score of the user review shows superior accuracy as compared with the conventional type of collaborative filtering that only considers the quantitative score. We then attempted paired t-test validation to ensure that the proposed model was a better approach and concluded that the proposed model is better. In this study, to overcome limitations of previous researches that judge user's sentiment only by quantitative rating score, the review was numerically calculated and a user's opinion was more refined and considered into the recommendation system to improve the accuracy. The findings of this study have managerial implications to recommendation system developers who need to consider both quantitative information and qualitative information it is expect. The way of constructing the combined system in this paper might be directly used by the developers.

• Asia Marketing Journal
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• v.15 no.4
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• pp.61-101
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• 2014

This study investigates when and how disagreements in online customer ratings prompt more favorable product evaluations. Among the three metrics of volume, valence, and variance that feature in the research on online customer ratings, volume and valence have exhibited consistently positive patterns in their effects on product sales or evaluations (e.g., Dellarocas, Zhang, and Awad 2007; Liu 2006). Ratings variance, or the degree of disagreement among reviewers, however, has shown rather mixed results, with some studies reporting positive effects on product sales (e.g., Clement, Proppe, and Rott 2007) while others finding negative effects on product evaluations (e.g., Zhu and Zhang 2010). This study aims to resolve these contradictory findings by introducing preference heterogeneity as a possible moderator and causal attribution as a mediator to account for the moderating effect. The main proposition of this study is that when preference heterogeneity is perceived as high, a disagreement in ratings is attributed more to reviewers' different preferences than to unreliable product quality, which in turn prompts better quality evaluations of a product. Because disagreements mostly result from differences in reviewers' tastes or the low reliability of a product's quality (Mizerski 1982; Sen and Lerman 2007), a greater level of attribution to reviewer tastes can mitigate the negative effect of disagreement on product evaluations. Specifically, if consumers infer that reviewers' heterogeneous preferences result in subjectively different experiences and thereby highly diverse ratings, they would not disregard the overall quality of a product. However, if consumers infer that reviewers' preferences are quite homogeneous and thus the low reliability of the product quality contributes to such disagreements, they would discount the overall product quality. Therefore, consumers would respond more favorably to disagreements in ratings when preference heterogeneity is perceived as high rather than low. This study furthermore extends this prediction to the various levels of average ratings. The heuristicsystematic processing model so far indicates that the engagement in effortful systematic processing occurs only when sufficient motivation is present (Hann et al. 2007; Maheswaran and Chaiken 1991; Martin and Davies 1998). One of the key factors affecting this motivation is the aspiration level of the decision maker. Only under conditions that meet or exceed his aspiration level does he tend to engage in systematic processing (Patzelt and Shepherd 2008; Stephanous and Sage 1987). Therefore, systematic causal attribution processing regarding ratings variance is likely more activated when the average rating is high enough to meet the aspiration level than when it is too low to meet it. Considering that the interaction between ratings variance and preference heterogeneity occurs through the mediation of causal attribution, this greater activation of causal attribution in high versus low average ratings would lead to more pronounced interaction between ratings variance and preference heterogeneity in high versus low average ratings. Overall, this study proposes that the interaction between ratings variance and preference heterogeneity is more pronounced when the average rating is high as compared to when it is low. Two laboratory studies lend support to these predictions. Study 1 reveals that participants exposed to a high-preference heterogeneity book title (i.e., a novel) attributed disagreement in ratings more to reviewers' tastes, and thereby more favorably evaluated books with such ratings, compared to those exposed to a low-preference heterogeneity title (i.e., an English listening practice book). Study 2 then extended these findings to the various levels of average ratings and found that this greater preference for disagreement options under high preference heterogeneity is more pronounced when the average rating is high compared to when it is low. This study makes an important theoretical contribution to the online customer ratings literature by showing that preference heterogeneity serves as a key moderator of the effect of ratings variance on product evaluations and that causal attribution acts as a mediator of this moderation effect. A more comprehensive picture of the interplay among ratings variance, preference heterogeneity, and average ratings is also provided by revealing that the interaction between ratings variance and preference heterogeneity varies as a function of the average rating. In addition, this work provides some significant managerial implications for marketers in terms of how they manage word of mouth. Because a lack of consensus creates some uncertainty and anxiety over the given information, consumers experience a psychological burden regarding their choice of a product when ratings show disagreement. The results of this study offer a way to address this problem. By explicitly clarifying that there are many more differences in tastes among reviewers than expected, marketers can allow consumers to speculate that differing tastes of reviewers rather than an uncertain or poor product quality contribute to such conflicts in ratings. Thus, when fierce disagreements are observed in the WOM arena, marketers are advised to communicate to consumers that diverse, rather than uniform, tastes govern reviews and evaluations of products.

• Physical Therapy Korea
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• v.12 no.3
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• pp.74-83
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• 2005

The purpose of this study was to produce the regression equation from non-exercise $VO_{2max}$ of healthy young adults and to develop a maximal oxygen consumption ($VO_{2max}$) regression model. This model was based on heart rate non-exercise predictor variables (rest heart rate, maximal heart rate/rest heart rate), as an extra addition to the general regression which can reflect an individual's inherent or acquired cardiorespiratory fitness. The subjects were 101 healthy young adults aged 19 to 35 years. Exercise testing was measured by using a Balke protocol for treadmill and indirect calorimetry. The prediction equation was analyzed by using stepwise multiple regression procedures. The mean of $VO_{2max}$ was $39.02{\pm}6.72\;m{\ell}/kg/min$ (mean${\pm}$SD). The greatest variable correlated to $VO_{2max}$ was %fat. The predictor variable used in the non-exercise $VO_{2max}$ included %fat, gender, habitual physical activity and $HR_{max}/HR_{rest}$. The non-exercise $VO_{2max}$ estimation was as follows: $VO_{2max}$($m{\ell}/kg/min$)=55.58-.41(%fat)+.59(physical activity rating)-2.69($HR_{max}/HR_{rest}$)-5.36 (male=0, female=1); (R=.85, SEE=3.64, R2=.72: including heart rate variable); $VO_{2max}$($m{\ell}/kg/min$)=48.47-.41(%fat)+.45(physical activity rating)-5.12 (male=0, female=1); (R=.84, SEE=3.74, R2=.70: with the exception of heart rate variable). As an added heart rate variable, there was only a 2% coefficient of determination improved. Therefore, these results demonstrated that heart rate variable correlation with a non-exercise regression model was very low. In conclusion, for healthy young korean adults, those variables that can affect non-exercise $VO_{2max}$ estimation turned out to be only % fat, gender, and physical activity. We suggest that further research of predictor variables for non-exercise $VO_{2max}$ is necessary for different patient groups who cannot perform maximal exercise or submaximal exercise.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.11
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• pp.5578-5586
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• 2012

The accurate estimation of discharge is very essential as the important factor of river design for the utilization and flood control, hydraulic construction design. The present discharge production is using the stage-discharge relationship curve in the river. The rating curve uses the method by predicting the discharge based on regression analysis using the measured stage and discharge data in a flood season. The method is comparatively convenient and has especially advantages in that it can predict the discharge having the difficulty of observation in a flood season. However, this method has basically room for improvement because the method only uses the relationship between stage and discharge, and doesn't reflect the hydraulic parameters such as hydraulic radius, energy slope, roughness, topography, etc.. Therefore, in this study, discharge was predicted using the convenient calculation method with empirical parameters of the Manning and Chezy equations, which were proposed by Choo et at (2011) in KSCE as a new methodology for estimating discharge in open channel. The proposed method can conveniently estimate empirical parameters in both of Manning and Chezy equations and the discharge is estimated in the open channels. There are proved by using data measured in meandering lab. channel and India canal and the accuracies show about determination coefficient 0.8. Accordingly, this method will be used in actual field if this study is continuously conducted.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.16 no.6
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• pp.295-303
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• 2016

The purposes of this study are to describe the process for the instrument construction and the development of scales on rating college students' adaptability and to analyze the technical qualities of the test. The primary goal of this study is to inform students and institutions what is needed to college student's adjustment process into university and college life. The scales are tested by specialty group and statistical methods, and finally composed of 142 items, which measures 8 scales, the academic integration, the social integration into college, career identity, emotional stability, learning condition's stability, relationship with professors, satisfaction degree of educational service, satisfaction degree of college education. This study analyzed 1,959 students' responses from 4 colleges and universities. This study confirms that the scales which this study developed show high concurrent evidence with the college student's adaptability inventory for Korean university and college students based on various development process, specially rapid great change of college. The result of factor analysis shows the evidence based on internal structures of the scales. The Cronbach's ${\alpha}$ of the subscales is .965, from 742 to .937. The prediction model to determine the possibility of dropout by 7 scales is statistically significant in .05, except learning condition's stability. According to CFA Model, RMSEA= .08~.09. dependence factor variance are explained by this study's CFA model. In conclusion, this study confirms that the scales which this study developed are valid and reliable instrument for Korean university and college students to predict their adaptability to college.