• Journal of the Institute of Electronics Engineers of Korea CI
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• v.42 no.6
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• pp.63-74
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• 2005

In Electronic Commerce, the latest most of the personalized recommender systems have applied to the collaborative filtering technique. This method calculates the weight of similarity among users who have a similar preference degree in order to predict and recommend the item which hits to propensity of users. In this case, we commonly use Pearson Correlation Coefficient. However, this method is feasible to calculate a correlation if only there are the items that two users evaluated a preference degree in common. Accordingly, the accuracy of prediction falls. The weight of similarity can affect not only the case which predicts the item which hits to propensity of users, but also the performance of the personalized recommender system. In this study, we verify the improvement of the prediction accuracy through an experiment after observing the rule of the weight of similarity applying Vector similarity, Entropy, Inverse user frequency, and Default voting of Information Retrieval field. The result shows that the method combining the weight of similarity using the Entropy with Default voting got the most efficient performance.

• Nutrition Research and Practice
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• v.6 no.1
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• pp.51-60
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• 2012

Weight-controlling can be supported by a proper prescription of energy intake. The individual energy requirement is usually determined through resting energy expenditure (REE) and physical activity. Because REE contributes to 60-70% of daily energy expenditure, the assessment of REE is very important. REE is often predicted using various equations, which are usually based on the body weight, height, age, gender, and so on. The aim of this study is to validate the published predictive equations for resting energy expenditure in 76 normal weight and 52 obese Korean children and adolescents in the 7-18 years old age group. The open-circuit indirect calorimetry using a ventilated hood system was used to measure REE. Sixteen REE predictive equations were included, which were based on weight and/or height of children and adolescents, or which were commonly used in clinical settings despite its use based on adults. The accuracy of the equations was evaluated on bias, RMSPE, and percentage of accurate prediction. The means of age and height were not significantly different among the groups. Weight and BMI were significantly higher in obese group (64.0 kg, $25.9kg/m^2$) than in the non-obese group (44.8 kg, $19.0kg/m^2$). For the obese group, the Molnar, Mifflin, Liu, and Harris-Benedict equations provided the accurate predictions of > 70% (87%, 79% 77%, and 73%, respectively). On the other hand, for non-obese group, only the Molnar equation had a high level of accuracy (bias of 0.6%, RMSPE of 90.4 kcal/d, and accurate prediction of 72%). The accurate prediction of the Schofield (W/WH), WHO (W/WH), and Henry (W/WH) equations was less than 60% for all groups. Our results showed that the Molnar equation appears to be the most accurate and precise for both the non-obese and the obese groups. This equation might be useful for clinical professionals when calculating energy needs in Korean children and adolescents.

• The Journal of the Acoustical Society of Korea
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• v.38 no.6
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• pp.716-723
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• 2019

Fingerprint matching accuracy is essential in deploying a music search service. This paper deals with a method to improve fingerprint matching accuracy by utilizing an auxiliary information which is called power weight. Power weight is an expected robustness of each hash bit. While the previous power mask binarizes the expected robustness into strong and weak bits, the proposed method utilizes a real-valued function of the expected robustness as weights for fingerprint matching. As a countermeasure to the increased storage cost, we propose a compression method for the power weight which has strong temporal correlation. Experiments on the publicly-available music datasets confirmed that the proposed power weight is effective in improving fingerprint matching performance.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.12 no.2
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• pp.143-148
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• 2012

This paper proposes a new method to improve performance of AdaBoost by using a distance weight function to increase the accuracy of its machine learning processes. The proposed distance weight algorithm improves classification in areas where the original binary classifier is weak. This paper derives the new algorithm's optimal solution, and it demonstrates how classifier accuracy can be improved using the proposed Distance Sensitive AdaBoost in a simulation experiment of pedestrian detection.

• Journal of Information Processing Systems
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• v.13 no.2
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• pp.256-267
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• 2017

An active research area in computer vision, stereo matching is aimed at obtaining three-dimensional (3D) information from a stereo image pair captured by a stereo camera. To extract accurate 3D information, a number of studies have examined stereo matching algorithms that employ adaptive support weight. Among them, the adaptive census transform (ACT) algorithm has yielded a relatively strong matching capability. The drawbacks of the ACT, however, are that it produces low matching accuracy at the border of an object and is vulnerable to noise. To mitigate these drawbacks, this paper proposes and analyzes the features of an improved stereo matching algorithm that not only enhances matching accuracy but also is also robust to noise. The proposed algorithm, based on the ACT, adopts the truncated absolute difference and the multiple sparse windows method. The experimental results show that compared to the ACT, the proposed algorithm reduces the average error rate of depth maps on Middlebury dataset images by as much as 2% and that is has a strong robustness to noise.

• International Journal of Highway Engineering
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• v.12 no.4
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• pp.187-196
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• 2010

The purpose of this study is to develop the calibration method for temperature effects to improve the accuracy of the Weigh-In-Motion(WIM) system for collecting long-term truck weight data. WIM system was installed at a location where the truck traffic volume is high and weight data has been collected from January 2010. In this study, as a calibration measure, the first axle weight of Truck Type 10, the semi tractor-trailer is used based on the fact that the first axle weight is relatively constant, independent of the cargo weight. From this fact, calibration equations are developed from the relationship between the axle weight and the temperature(daily mean, maximum and minimum). Analysis on calibrated weight data shows adequacy of the proposed calibration method. Results of this study can be used to improve the accuracy of the WIM system and to carry out more rational design of pavement and bridge structures.

• Journal of Advanced Navigation Technology
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• v.27 no.5
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• pp.658-665
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• 2023

Take-off weight is a key factor for improving accuracy when estimating an aircraft's carbon emissions and fuel consumption. However, the takeoff weight contains sensitive payload information that can infer the airline's management strategy, making it impossible to leak it outside. Although several models for estimating takeoff weight have been presented in previous studies, the researcher points out that there are limitations of the study caused by variables at the pilot's discretion. In this paper, several variables related to takeoff weight are identified to suggest a way to control these limits. Among them, variables that can improve the accuracy of takeoff weight are selected and an estimation equation is presented by applying them to ADS-B information. The proposed estimation does not estimate the average takeoff weight but has the advantage of being able to estimate all ranges of the takeoff weight.

• Korean Journal of Agricultural Science
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• v.46 no.2
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• pp.293-301
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• 2019

This study was done to estimate the effect of progeny numbers and pedigree depth on the accuracy of the estimated breeding value (EBV) using best linear unbiased prediction (BLUP) method in Hanwoo. The experiment groups (sire = 100, 200, and 300; progeny = 4 and 8) were made by random sampling and by genetic evaluation of the following traits: Body weight (BW), carcass weight (CW), eye muscle area (EMA), back fat thickness (BFT) and marbling score (MS9). As a result of the genetic evaluation, the accuracy of the EBV was roughly 30 - 60% with 4 progenies, and the accuracy of the EBV increased by about 50 - 75% with 8 progenies. In the other words, when the number of progenies increased from 4 to 8, the accuracy of the EBV simultaneously increased by about 15 - 20%. Moreover, when the number of sires was higher, variations in the accuracy of the EBV within the groups for each trait decreased. Therefore, this result indicates that not only the number of progeny but also the number of sires can affect the accuracy of the EBV. Consequently, collecting information on the progeny and careful management of that information are very important things in the Hanwoo breeding system. Therefore, the EBV can show more precise results when conducting genetic evaluations.

• The Journal of Korean Medicine
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• v.41 no.2
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• pp.58-79
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• 2020

Objectives: The purpose of this study is to predict the weight loss by applying machine learning using real-world clinical data from overweight and obese adults on weight loss program in 4 Korean Medicine obesity clinics. Methods: From January, 2017 to May, 2019, we collected data from overweight and obese adults (BMI≥23 kg/m2) who registered for a 3-month Gamitaeeumjowi-tang prescription program. Predictive analysis was conducted at the time of three prescriptions, and the expected reduced rate and reduced weight at the next order of prescription were predicted as binary classification (classification benchmark: highest quartile, median, lowest quartile). For the median, further analysis was conducted after using the variable selection method. The data set for each analysis was 25,988 in the first, 6,304 in the second, and 833 in the third. 5-fold cross validation was used to prevent overfitting. Results: Prediction accuracy was increased from 1^st to 2^nd and 3^rd analysis. After selecting the variables based on the median, artificial neural network showed the highest accuracy in 1^st (54.69%), 2^nd (73.52%), and 3^rd (81.88%) prediction analysis based on reduced rate. The prediction performance was additionally confirmed through AUC, Random Forest showed the highest in 1^st (0.640), 2^nd (0.816), and 3^rd (0.939) prediction analysis based on reduced weight. Conclusions: The prediction of weight loss by applying machine learning showed that the accuracy was improved by using the initial weight loss information. There is a possibility that it can be used to screen patients who need intensive intervention when expected weight loss is low.

• Journal of the Korea Society of Computer and Information
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• v.18 no.1
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• pp.177-183
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• 2013

In this paper, Cohen's kappa and weighted kappa are applied to measuring classification accuracy when performing classification in data minig. Cohen's kappa compensates for classifications that may be due to chance and is used for the data with nominal or ordinal scales. Especially, for the ordinal data, weighted kappa which measures the classification accuracy by quantifying the classification errors as weights is used. We used two weights (linear weight, quadratic weight) for calculations of weighted kappa. Also for the calculation and comparison of kappa and weighted kappa we used a real data set, fat-liver data.