• Journal of Veterinary Clinics
• /
• 제36권1호
• /
• pp.62-67
• /
• 2019

This study was performed to apply the measurement of heart rate and velocity in training horses for assessing race performance. Additionally, we aimed to identify parameters that can be used to evaluate the training level and exercise capacity. Eleven healthy 2- to 6-year-old Thoroughbreds were trained by the standard training program and heart rate and velocity were measured by using heart monitoring system and GPS. Regression analysis in heart rate and velocity data was performed to calculate velocity parameters. The mean maximal heart rate in gallop was $214{\pm}11bpm$. The mean $V_{140}$, $V_{180}$, $V_{200}$ and $VHR_{max}$ were $13.8{\pm}4.3km/h$, $37.5{\pm}3.8km/h$, $49.3{\pm}4.3km/h$ and $57.4{\pm}7.1km/h$ respectively. The mean $V_{140}$ of high performance racehorses was significantly higher than that of low performance racehorses (P < 0.05). Moreover, analyzing the correlation between velocity parameters and racing ability-related categories showed that $V_{140}$ was positively correlated with rating (P < 0.05), $V_{180}$ and $VHR_{max}$ were positively correlated with prize money per race (P < 0.05). Also, $V_{140}$ was significantly correlated with G1F (P < 0.05). The results of this study have shown that the measurement of heart rate and velocity during training could be useful methods to assess fitness for races or performance potential. Especially, $V_{140}$ is a good parameter to evaluate a performance of racehorses in Korea.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 한국정보통신학회 2014년도 춘계학술대회
• /
• pp.108-111
• /
• 2014

In this paper, we tried to identify implications of selecting gifted of information science & followed educational system via analyzing each of student's characteristics in each subjects they study within Science Education Institute for the Gifted. A study of the existing institutions do not have experience of the gifted students based on assessment through observation of the 1-year science, mathematics and information science education in the List of attribute analysis. Learners of Information Science became with analysis that Attitude Category was superior in mathematics to the subject of science and Problem Solving Category regardless of the subjects showed similar. As to, Attitude Category, Problem Solving Category and Mathematics Cognition Category was analyzed to be closed and we could confirm through the qualitative observation record. On this, the researcher concluded that the mathematics could know the effect fitness by a learner rather than the subject of science as to an attitude and problem resolution area.

• Animal Bioscience
• /
• 제35권11호
• /
• pp.1760-1770
• /
• 2022

Objective: It is crucial to accurately determine the net energy (NE) values of feed ingredients because the NE system is expected to be applied to the formulation of broilers feed. The NE values of 5 wheat and 5 wheat brans were determined in 12-to 14-day old Arbor Acres (AA) broilers with substitution method and indirect calorimetry method. Methods: A total of 12 diets, including 2 reference diets (REF) and 10 test diets (5 wheat diets and 5 wheat bran diets) containing 30% of test ingredients, were randomly fed to 864 male AA birds with 6 replicates of 12 birds per treatment. These birds were used to determine metabolizable energy (ME) (8 birds per replicate) in the chicken house and NE (4 birds per replicate) in the chamber respectively at the same time. After a 4-d dietary and environment adaptation period, growth performance, energy values, energy balance and energy utilization were measured during the following 3 d. Multiple linear regression analyses were further performed to generate prediction equations for NE values based on the chemical components and ME values. The NE prediction equation were also validated on another wheat diet and another wheat bran diet with high correlation (r = 0.98, r = 0.75). Results: The NE values of 5 wheat and 5 wheat bran samples are 9.34, 10.02, 10.27, 11.33, and 10.49 MJ/kg, and 5.37, 5.17, 4.87, 5.06, and 4.88 MJ/kg DM, respectively. The equation with the best fit were NE = 1.968AME-0.411×ADF-14.227 (for wheat) and NE = -0.382×CF-0.362×CP-0.244×ADF+20.870 (for wheat bran). Conclusion: The mean NE values of wheat and wheat bran are 10.29 and 5.07 MJ/kg DM in AA broilers. The NE values of ingredients could be predicted by their chemical composition and energy value with good fitness.

• Journal of Navigation and Port Research
• /
• 제46권1호
• /
• pp.42-56
• /
• 2022

Due to the 4th Industrial Revolution announced at the Davos Forum, the World Economic Forum, in 2016, industrial trends around the world are changing rapidly and intelligently. Among them, the digital twin is drawing attention from all industries as a groundbreaking technology that reduces unnecessary costs and trial and error by implementing real objects, systems, and environments in the same way in the real virtual world and using them to perform simulation analysis. In particular, there is a lot of interest in the application of digital twin technology in solving ports safety and efficiency challenges at once. However, there is a lack of in-depth research for the application of digital twin technology in the port, and in particular, there is a lack of research on measurable data for the implementation of the digital twin in ports. The purpose of this study was to increase granularity and connectivity through measurable data investigation for the application of digital twin technology at container ports. Based on the study results, data factors for container port application were classified into crane data, operational data, physical data, and transportation data, and factor composition, correlation with factors, and fitness were confirmed through confirmatory factor analysis.

• Journal of the Korea Computer Graphics Society
• /
• 제30권3호
• /
• pp.171-178
• /
• 2024

Exercises using a Balance Board (BB) are effective in developing balance, strengthening core muscles, and improving physical fitness and concentration. In particular, the Smart Balance Board (SBB), which integrates with various digital content, provides appropriate feedback compared to traditional balance boards, maximizing the effectiveness of the exercise. However, most systems only offer visual and auditory feedback, failing to evaluate the impact on user engagement, interest, and the accuracy of exercise postures. This study proposes an Immersive Smart Balance Board (I-SBB) that utilizes multiple sensors to enable training with various feedback mechanisms and precise postures. The proposed system, based on Arduino, consists of a gyro sensor for measuring the board's posture, a communication module for wired/wireless communication, an infrared sensor to guide the user's foot placement, and a vibration motor for tactile feedback. The board's posture measurements are smoothly corrected using a Kalman Filter, and the multi-sensor data is processed in real-time using FreeRTOS. The proposed I-SBB is shown to be effective in enhancing user concentration and engagement, as well as generating interest, by integrating with diverse content.

• Journal of Gifted/Talented Education
• /
• 제11권1호
• /
• pp.81-98
• /
• 2001

The purpose of this study was to indicate the ways to identify the gifted child in the sports, and the research direction in this area in the future. The most of researches related to the early development of the children who showed the gifted talents in the certain sports skills were published from the European area such as the old East Germany or Soviet Union. Those research papers or articles were main focus of this study to be analyzed. The analysis in this study will be assistant to provide the direction of the research in the future about the method to identify appropriately on the children with the gifted sports skills. -Related to the period of selection of the children with sports skill gifted, the divided selection procedure will be good such as the 1st selection and 2nd selection. In the 1st selection, the goal will be to test the potentials of the children with the sports talents in the public school with creating the special group, and the 2nd selection will be to examine the qualification of the admission to the special schools for the children with the sports talents. -Related to the method of selection of the children with sports talents, the special item on the developmental procedure should be considered on the each field as well as the potentials focused. And, the physical factors, the condition of growth and development, psychological factor, and environmental factor should be comprehensively considered. -The system of promotion in the period of the primary school will be appropriate in the special group for the children with the sports talents, and the special school will be good from the middle school. The system outlooks will be adequate if shaped as the hierarchical structure. -The curriculum for the children with the sports talents will be proper that the basic fitness for the earlier ages and the professional skills will be recommended if getting older. -The continuous research is strongly required for the judgement and promotion of the children with sports talents, therefore, the multiple intelligences approach will be helpful for the development of the bodily-kinesthetic intelligence, and in other words, the new era of the genetic method will be another prospective area such as the DNA analysis in the identification of the talented children for the sports filed.

• Journal of Intelligence and Information Systems
• /
• 제16권4호
• /
• pp.99-112
• /
• 2010

Ensemble learning is a method for improving the performance of classification and prediction algorithms. It is a method for finding a highly accurateclassifier on the training set by constructing and combining an ensemble of weak classifiers, each of which needs only to be moderately accurate on the training set. Ensemble learning has received considerable attention from machine learning and artificial intelligence fields because of its remarkable performance improvement and flexible integration with the traditional learning algorithms such as decision tree (DT), neural networks (NN), and SVM, etc. In those researches, all of DT ensemble studies have demonstrated impressive improvements in the generalization behavior of DT, while NN and SVM ensemble studies have not shown remarkable performance as shown in DT ensembles. Recently, several works have reported that the performance of ensemble can be degraded where multiple classifiers of an ensemble are highly correlated with, and thereby result in multicollinearity problem, which leads to performance degradation of the ensemble. They have also proposed the differentiated learning strategies to cope with performance degradation problem. Hansen and Salamon (1990) insisted that it is necessary and sufficient for the performance enhancement of an ensemble that the ensemble should contain diverse classifiers. Breiman (1996) explored that ensemble learning can increase the performance of unstable learning algorithms, but does not show remarkable performance improvement on stable learning algorithms. Unstable learning algorithms such as decision tree learners are sensitive to the change of the training data, and thus small changes in the training data can yield large changes in the generated classifiers. Therefore, ensemble with unstable learning algorithms can guarantee some diversity among the classifiers. To the contrary, stable learning algorithms such as NN and SVM generate similar classifiers in spite of small changes of the training data, and thus the correlation among the resulting classifiers is very high. This high correlation results in multicollinearity problem, which leads to performance degradation of the ensemble. Kim,s work (2009) showedthe performance comparison in bankruptcy prediction on Korea firms using tradition prediction algorithms such as NN, DT, and SVM. It reports that stable learning algorithms such as NN and SVM have higher predictability than the unstable DT. Meanwhile, with respect to their ensemble learning, DT ensemble shows the more improved performance than NN and SVM ensemble. Further analysis with variance inflation factor (VIF) analysis empirically proves that performance degradation of ensemble is due to multicollinearity problem. It also proposes that optimization of ensemble is needed to cope with such a problem. This paper proposes a hybrid system for coverage optimization of NN ensemble (CO-NN) in order to improve the performance of NN ensemble. Coverage optimization is a technique of choosing a sub-ensemble from an original ensemble to guarantee the diversity of classifiers in coverage optimization process. CO-NN uses GA which has been widely used for various optimization problems to deal with the coverage optimization problem. The GA chromosomes for the coverage optimization are encoded into binary strings, each bit of which indicates individual classifier. The fitness function is defined as maximization of error reduction and a constraint of variance inflation factor (VIF), which is one of the generally used methods to measure multicollinearity, is added to insure the diversity of classifiers by removing high correlation among the classifiers. We use Microsoft Excel and the GAs software package called Evolver. Experiments on company failure prediction have shown that CO-NN is effectively applied in the stable performance enhancement of NNensembles through the choice of classifiers by considering the correlations of the ensemble. The classifiers which have the potential multicollinearity problem are removed by the coverage optimization process of CO-NN and thereby CO-NN has shown higher performance than a single NN classifier and NN ensemble at 1% significance level, and DT ensemble at 5% significance level. However, there remain further research issues. First, decision optimization process to find optimal combination function should be considered in further research. Secondly, various learning strategies to deal with data noise should be introduced in more advanced further researches in the future.

• Journal of Intelligence and Information Systems
• /
• 제22권1호
• /
• pp.139-157
• /
• 2016

Bankruptcy involves considerable costs, so it can have significant effects on a country's economy. Thus, bankruptcy prediction is an important issue. Over the past several decades, many researchers have addressed topics associated with bankruptcy prediction. Early research on bankruptcy prediction employed conventional statistical methods such as univariate analysis, discriminant analysis, multiple regression, and logistic regression. Later on, many studies began utilizing artificial intelligence techniques such as inductive learning, neural networks, and case-based reasoning. Currently, ensemble models are being utilized to enhance the accuracy of bankruptcy prediction. Ensemble classification involves combining multiple classifiers to obtain more accurate predictions than those obtained using individual models. Ensemble learning techniques are known to be very useful for improving the generalization ability of the classifier. Base classifiers in the ensemble must be as accurate and diverse as possible in order to enhance the generalization ability of an ensemble model. Commonly used methods for constructing ensemble classifiers include bagging, boosting, and random subspace. The random subspace method selects a random feature subset for each classifier from the original feature space to diversify the base classifiers of an ensemble. Each ensemble member is trained by a randomly chosen feature subspace from the original feature set, and predictions from each ensemble member are combined by an aggregation method. The k-nearest neighbors (KNN) classifier is robust with respect to variations in the dataset but is very sensitive to changes in the feature space. For this reason, KNN is a good classifier for the random subspace method. The KNN random subspace ensemble model has been shown to be very effective for improving an individual KNN model. The k parameter of KNN base classifiers and selected feature subsets for base classifiers play an important role in determining the performance of the KNN ensemble model. However, few studies have focused on optimizing the k parameter and feature subsets of base classifiers in the ensemble. This study proposed a new ensemble method that improves upon the performance KNN ensemble model by optimizing both k parameters and feature subsets of base classifiers. A genetic algorithm was used to optimize the KNN ensemble model and improve the prediction accuracy of the ensemble model. The proposed model was applied to a bankruptcy prediction problem by using a real dataset from Korean companies. The research data included 1800 externally non-audited firms that filed for bankruptcy (900 cases) or non-bankruptcy (900 cases). Initially, the dataset consisted of 134 financial ratios. Prior to the experiments, 75 financial ratios were selected based on an independent sample t-test of each financial ratio as an input variable and bankruptcy or non-bankruptcy as an output variable. Of these, 24 financial ratios were selected by using a logistic regression backward feature selection method. The complete dataset was separated into two parts: training and validation. The training dataset was further divided into two portions: one for the training model and the other to avoid overfitting. The prediction accuracy against this dataset was used to determine the fitness value in order to avoid overfitting. The validation dataset was used to evaluate the effectiveness of the final model. A 10-fold cross-validation was implemented to compare the performances of the proposed model and other models. To evaluate the effectiveness of the proposed model, the classification accuracy of the proposed model was compared with that of other models. The Q-statistic values and average classification accuracies of base classifiers were investigated. The experimental results showed that the proposed model outperformed other models, such as the single model and random subspace ensemble model.

• The Journal of Korean Academy of Prosthodontics
• /
• 제51권4호
• /
• pp.276-283
• /
• 2013

Purpose: The purpose of this study was to investigate the fit and screw joint stability between Ready-made abutment and CAD-CAM custom-made abutment. Materials and methods: Osstem implant system was used. Ready-made abutment (Transfer abutment, Osstem Implant Co. Ltd, Busan, Korea), CAD-CAM custom-made abutment (CustomFit abutment, Osstem Implant Co. Ltd, Busan, Korea) and domestically manufactured CAD-CAM custom-made abutment (Myplant, Raphabio Co., Seoul, Korea) were fabricated five each and screws were provided by each company. Fixture and abutments were tightening with 30Ncm according to the manufacturer's instruction and then preloding reverse torque values were measured 3 times repeatedly. Kruskal-Wallis test was used for statistical analysis of the preloading reverse torque values (${\alpha}=.05$). After specimens were embedded into epoxy resin, wet cutting and polishing was performed and FE-SEM imaging was performed, on the contact interface. Results: The pre-loading reverse torque values were $26.0{\pm}0.30Ncm$ (ready-made abutment; Transfer abutment) and $26.3{\pm}0.32Ncm$ (CAD-CAM custom-made abutment; CustomFit abutment) and $24.7{\pm}0.67Ncm$ (CAD-CAM custom-made abutment; Myplant). The domestically manufactured CAD-CAM custom-made abutment (Myplant abutment) presented lower pre-loading reverse torque value with statistically significant difference than that of the ready-made abutment (Transfer abutment) and CAD-CAM custom-made abutment (CustomFit abutment) manufactured from the same company (P=.027) and showed marginal gap in the fixture-abutment interface. Conclusion: Within the limitation of the present in-vitro study, in domestically manufactured CAD-CAM custom-made abutment (Myplant abutment) showed lower screw joint stability and fitness between fixture and abutment.

• Korean Chemical Engineering Research
• /
• 제55권3호
• /
• pp.322-331
• /
• 2017

District heating was first introduced in Korea in 1985. As the service life of the underground thermal piping network has increased for more than 30 years, the maintenance of the underground thermal pipe has become an important issue. A variety of complex technologies are required for periodic inspection and operation management for the maintenance of the aged thermal piping network. Especially, it is required to develop a model that can be used for decision making in order to derive optimal maintenance and replacement point from the economic viewpoint in the field. In this study, the analysis was carried out based on the repair history and accident data at the operation of the thermal pipe network of five districts in the Korea District Heating Corporation. A failure probability model was developed by introducing statistical techniques of qualitative analysis and binomial logistic regression analysis. As a result of qualitative analysis of maintenance history and accident data, the most important cause of pipeline damage was construction erosion, corrosion of pipe and bad material accounted for about 82%. In the statistical model analysis, by setting the separation point of the classification to 0.25, the accuracy of the thermal pipe breakage and non-breakage classification improved to 73.5%. In order to establish the failure probability model, the fitness of the model was verified through the Hosmer and Lemeshow test, the independent test of the independent variables, and the Chi-Square test of the model. According to the results of analysis of the risk of thermal pipe network damage, the highest probability of failure was analyzed as the thermal pipeline constructed by the F construction company in the reducer pipe of less than 250mm, which is more than 10 years on the Seoul area motorway in winter. The results of this study can be used to prioritize maintenance, preventive inspection, and replacement of thermal piping systems. In addition, it will be possible to reduce the frequency of thermal pipeline damage and to use it more aggressively to manage thermal piping network by establishing and coping with accident prevention plan in advance such as inspection and maintenance.