• Journal of Distribution Science
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• v.16 no.11
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• pp.21-27
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• 2018

Purpose - This study investigates the financial ratio of savings banks and the effect of the ratio having influence upon bankruptcy by quantitative empirical analysis of forecast model to give material of better management and objective evidence of management strategy and way of advancement and risk control. Research design, data, and methodology - The author added two growth indexes, three fluidity indexes, five profitability indexes, and four activity indexes CAMEL rating to not only the balance sheets but also the income statement of thirty savings banks that suspended business from 2011 to 2015 and collected fourteen financial ratio indexes. IBMSPSS VER. 21.0 was used. Results - Variables having influence upon bankruptcy forecast models included total asset increase ratio and operating income increase ratio of growth index and sales to account receivable ratio, and tangible equity ratio and liquidity ratio of liquidity ratio. The study selected total asset operating ratio, and earning and expenditure ratio from profitability index, and receivable turnover ratio of activity index. Conclusions - Financial supervising system should be improved and financial consumers should be protected to develop saving bank and to control risk, and information on financial companies should be strengthened.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.25 no.3
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• pp.141-149
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• 2015

In this study, there is an investigation about the sound quality of automotive horn that attached to luxury sedans. In order to define a questionnaire of horn sound quality the factor analysis is conducted. Ten automotive horns are selected for this research and ten passenger cars(nine is luxury sedan and one economy class car). Luxury is used for the questionnaire as an attribute for the sound quality of car horn. The interior noises for ten test cars are recorded and used for the subjective analysis of car horn sound. In the paper, new sound metric for the car horn sound is presented. The new sound metric is used for the objective sound index for the prediction of subjective sound quality of car horn.

• Asian-Australasian Journal of Animal Sciences
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• v.28 no.8
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• pp.1090-1094
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• 2015

The aim of the study was to select traits that may constitute a prospective criterion for breeding value prediction of young horses. The results of 1,232 starts of 894 four-, five-, six-, and seven-year-old horses, obtained during jumping championships for young horses which had not been evaluated in, alternative to championships, training centres were analyed. Nine traits were chosen of those recorded: ranking in the championship, elimination (y/n), conformation, rating of style on day one, two, and three, and penalty points on day one, two, and three of a championship. (Co)variance components were estimated via the Gibbs sampling procedure and adequate (co)variance component ratios were calculated. Statistical classifications were trait dependent but all fitted random additive genetic and permanent environment effects. It was found that such characteristics as penalty points and jumping style are potential indicators of jumping ability, and the genetic variability of the traits was within the range of 14% to 27%. Given the low genetic correlations between the conformation and other results achieved on the parkour, the relevance of assessment of conformation in four-years-old horses has been questioned.

• Journal of the Ergonomics Society of Korea
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• v.22 no.3
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• pp.57-73
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• 2003

Although body pressure distribution is sensitive to movements and is relatively simple to measure even in small space, there are few researches involving applications to driver's posture and its change. The main objective in this study is the application of body pressure distribution measurements for the prediction of the driver's posture and its change. This requires quantitative analyses of the dynamic body pressure distribution, which is the change of body pressure distribution with time. The experiment involved 16 male subjects who drove for 45 minutes in a seating buck. Measurement time, stature group, and lumbar support prominence were selected as independent variables, with subjective ratings of driver's discomfort, body posture data of hip, torso. knee angle, and body pressure data variables as dependent variables. The body pressure change variables and subjective ratings were found to increase as the measurement time increased and body pressure ratio variables reflected the torso angle. From the results and analysis of the body posture data and subjective rating results, it was predicted that the seats and the design of the lumbar supports used in the experiment was not fit for tall subjects, which could also be confirmed through the body pressure distribution data.

• Journal of Korean Academy of Nursing
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• v.14 no.2
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• pp.84-92
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• 1984

This article examined relationships between selected variables, such as demographic background, care, treatment variables, environmental characteristics, and patient's daily behavior and mood change. Relationship were determined between independent variabltherapeutic-rapeutie approach, demographic data, environmental management approach-,and dependent variable-patient's daily behavioral and mood change. 35 patients selected within some criteria in a psychiatric ward, were obserbed during 5 weeks by use of Wyatt's Behavior & Mood Rating Scale ac-cording to the object of the study. At the same time, the frequence of the care and treatment were collected. Criteria for sample selection and independent variables as an influential factor to the patient behavioral change, based on a literature revienw and clinical experiences. Pearson's correlation and multiple regression analysis were used to determine the influfntial factors to the patient behavioral change. Systematic reading (r=.8324), Psychiatrist's individual interview (r=.5764), tranquilizer (r=.3441) and hospitalization processing date (r=.4143) were related with patient's behavioral change. That is these 4 variables can be said to influence to the patient's behavior and mood. A stepwise multiple regression analysis of the effect of the independent varibles of systematic reading, psychintrists individual interview, tranquilizer and hospitalization processing date on the dependent variable, patient's behavioral change was carried out. Systematic reading with on R²of. 69 revealed to be the main influential factor to the patient's behavior and mood change, as the next factor psychiatrist individual interview. A total inclusion of these factors revealed a 73% prediction for the patient's behavior and mood change. But the most influential factor was the interaction of the systematic reading and psychiatrist's individual interview.

• Journal of Information Technology Applications and Management
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• v.24 no.1
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• pp.169-185
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• 2017

This study proposes a novel recommendation algorithm that reflects the results from trust/distrust network analysis as a solution to enhance prediction accuracy of recommender systems. The recommendation algorithm of our study is based on memory-based collaborative filtering (CF), which is the most popular recommendation algorithm. But, unlike conventional CF, our proposed algorithm considers not only the correlation of the rating patterns between users, but also the results from trust/distrust relationship network analysis (e.g. who are the most trusted/distrusted users?, whom are the target user trust or distrust?) when calculating the similarity between users. To validate the performance of the proposed algorithm, we applied it to a real-world dataset that contained the trust/distrust relationships among users as well as their numeric ratings on movies. As a result, we found that the proposed algorithm outperformed the conventional CF with statistical significance. Also, we found that distrust relationship was more important than trust relationship in measuring similarities between users. This implies that we need to be more careful about negative relationship rather than positive one when tracking and managing social relationships among users.

• Journal of ILASS-Korea
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• v.21 no.3
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• pp.144-154
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• 2016

This review will be concentrated on the spray characteristics of biobutanol and its blends fuels in internal combustion engines including compression ignition, spark ignition and gas turbine engines. Butanol can be produced by fermentation from sucrose-containing feedstocks, starchy materials and lignocellulosic biomass. Among four isomers of butanol, n-butanol and iso-butanol has been used in CI and SI engines. This is due to higher octane rating and lower water solubility of both butanol compared with other isomers. The researches on the spray characteristics of neat butanol can be classified into the application to CI and SI engines, particularly GDI engine. Two empirical correlations for the prediction of spray angle for butanol as a function of Reynolds number was newly suggested. However, the applicability for the suggested empirical correlation is not yet proved. The butanol blended fuels used for the investigation of spray characteristics includes butanol-biodiesel blend, butanol-gasoline blend, butano-jet A blend and butanol-other fuel blends. Three blends such as butanol/ethanol, butanol/heptane and butanol/heavy fuel oil blends are included in butanol-other fuel blends. Even though combustion and emission characteristics of butanol/diesel fuel blend in CI engines were broadly investigated, study on spray characteristics of butanol/diesel fuel blend could not be found in the literature. In addition, the more study on the spray characteristics of butanol /gasoline blend is required.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.5
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• pp.2381-2399
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• 2019

Traditional recommendation algorithms on Collaborative Filtering (CF) mainly focus on the rating prediction with explicit ratings, and cannot be applied to the top-N recommendation with implicit feedbacks. To tackle this problem, we propose a new collaborative filtering approach namely Maximize MAP with Matrix Factorization (MFMAP). In addition, in order to solve the problem of non-smoothing loss function in learning to rank (LTR) algorithm based on pairwise, we also propose a smooth MAP measure which can be easily implemented by standard optimization approaches. We perform experiments on three different datasets, and the experimental results show that the performance of MFMAP is significantly better than other recommendation approaches.

• ETRI Journal
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• v.43 no.1
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• pp.95-108
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• 2021

Application (app) ratings are feedback provided voluntarily by users and serve as important evaluation criteria for apps. However, these ratings can often be biased owing to insufficient or missing votes. Additionally, significant differences have been observed between numeric ratings and user reviews. This study aims to predict the numeric ratings of Google apps using machine learning classifiers. It exploits numeric app ratings provided by users as training data and returns authentic mobile app ratings by analyzing user reviews. An ensemble learning model is proposed for this purpose that considers term frequency/inverse document frequency (TF/IDF) features. Three TF/IDF features, including unigrams, bigrams, and trigrams, were used. The dataset was scraped from the Google Play store, extracting data from 14 different app categories. Biased and unbiased user ratings were discriminated using TextBlob analysis to formulate the ground truth, from which the classifier prediction accuracy was then evaluated. The results demonstrate the high potential for machine learning-based classifiers to predict authentic numeric ratings based on actual user reviews.

• Journal of Information Technology Services
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• v.21 no.4
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• pp.75-90
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• 2022

From the past to the present, reviews have had much influence on consumers' purchasing decisions. Companies are making various efforts, such as introducing a review incentive system to increase the number of reviews. Recently, as various types of reviews can be left, reviews have begun to be recognized as interesting new content. This way, reviews have become essential in creating loyal customers. Therefore, research and utilization of reviews are being actively conducted. Some studies analyze reviews to discover customers' needs, studies that upgrade recommendation systems using reviews, and studies that analyze consumers' emotions and attitudes through reviews. However, research that predicts the future using reviews is insufficient. This study used a dataset consisting of two reviews written in pairs with differences in usage periods. In this study, the direction of consumer product evaluation is predicted using KoBERT, which shows excellent performance in Text Deep Learning. We used 7,233 reviews collected to demonstrate the excellence of the proposed model. As a result, the proposed model using the review text and the star rating showed excellent performance compared to the baseline that follows the majority voting.