• Journal of Animal Environmental Science
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• v.7 no.1
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• pp.1-12
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• 2001

This study was conducted to develope an automatic liquid feeder for early weaning piglets and to test the efficacy of the system. The liquid feeder consists of water heating and discharge unit, dry diet storing and discharge unit, mixing and discharge unit, mixed liquid feed-delivering unit, and the central control part which control each unit, feeding frequency and the amount of feeding. For investigating the possibility of practical use, a feeding trial was carried out using eighteen three way crossbred piglets weaned on 19 days of age for the experimental period of six weeks. Experimental diet was provided in liquid form using the automatic liquid feeder for the first three weeks and in dry form for the later three weeks. The water heating and discharge unit exactly supplied warm water by 27 $m\ell$/s, into the mixing unit. The dry diet storing and discharge unit supplied dry feed by 3.7g/s, into the mixing unit. Being compared with the standard growth rate suggested by NRC, average daily gain of the piglets during the first three weeks of liquid feeding was lower by 10%, while it was higher during three weeks of dry feeding and over the whole experimental period by 24 and 17%, respectively. Feed/gain was 1.09, 2.14 and 1.89 for the first 3 weeks, later 3 weeks, and whole period, respectively. Diarrhea was observed for three days from day 3 to day 7 after feeding liquid diet, but no pig died of it. In conclusion, a preliminary test for the newly developed an automatic liquid feeder using 19 days of age weaning piglets showed that the unit was successfully operated without any major problems. Piglets raised on a liquid diet through the unit developed grew less during the first three weeks, but their growth and feed intake were greatly improved thereafter, indicating the developed automatic liquid feeder may be practically used in swine industry.

• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.57-78
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• 2023

COVID-19, which started in Wuhan, China in November 2019, spread beyond China in 2020 and spread worldwide in March 2020. It is important to prevent a highly contagious virus like COVID-19 in advance and to actively treat it when confirmed, but it is more important to identify the confirmed fact quickly and prevent its spread since it is a virus that spreads quickly. However, PCR test to check for infection is costly and time consuming, and self-kit test is also easy to access, but the cost of the kit is not easy to receive every time. Therefore, if it is possible to determine whether or not a person is positive for COVID-19 based on the sound of a cough so that anyone can use it easily, anyone can easily check whether or not they are confirmed at anytime, anywhere, and it can have great economic advantages. In this study, an experiment was conducted on a method to identify whether or not COVID-19 was confirmed based on a cough sound. Cough sound features were extracted through MFCC, Mel-Spectrogram, and spectral contrast. For the quality of cough sound, noisy data was deleted through SNR, and only the cough sound was extracted from the voice file through chunk. Since the objective is COVID-19 positive and negative classification, learning was performed through XGBoost, LightGBM, and FCNN algorithms, which are often used for classification, and the results were compared. Additionally, we conducted a comparative experiment on the performance of the model using multidimensional vectors obtained by converting cough sounds into both images and vectors. The experimental results showed that the LightGBM model utilizing features obtained by converting basic information about health status and cough sounds into multidimensional vectors through MFCC, Mel-Spectogram, Spectral contrast, and Spectrogram achieved the highest accuracy of 0.74.

• Journal of Practical Agriculture & Fisheries Research
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• v.4 no.1
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• pp.119-127
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• 2002

This study was conducted to develop the heat exchanger by utilizing the heat energy of underground water(15℃), which might be used for cooling and heating system of the agricultural facilities. We developed the heat exchanger, parallel type plat fin tube made of Aluminum(Al 6063), which was named Aloo-Heat(No. of The registration design : 0247164, by Korean Intellectual property Office). The fin of exchanger was design of the granulated surface for minimizing fouling factor and dew forms, and also placed parallel to the tube in order to minimized the resistance of flows. 1. Aloo-heat was designed to have 0.03m for inside diameter, 0.036m for outside diameter of tube, 0.0012m for thickness of fin and 0.032m for length of plat fin. 2. t was also designed to have 1.5248m²/m for outside area of heat transfer, 0.0942m²/m for inside area contacting hot liquid, and the ratio (Ra) was 16.1869. 3. Efficiency of the fin was 93 percentage when fin length was 0.032m, and the fin thickness satisfied equation $\frac{h{\rho}}{k}$< 0.2 when it was 0.0012m. 4. According to the performance test of Aloo-heat, as the temperature and rate increased, the heating value also increased, heating value was 504kJ/h·m and 6,048kJ/h·m when it was 60℃, 10 𝑙/min and 80℃, 40 𝑙/min respectively. 5. The test of heating value was confident, because correlation value(R²) was 0.9898 for the temperature and 0.9721 for flow rate of hot liquid, respectively.

• Geophysics and Geophysical Exploration
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• v.25 no.4
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• pp.167-176
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• 2022

We designed a borehole deviation survey tool applicable for steel-cased holes, K-DEV, and developed a prototype for a depth of 500 m aiming to development of own equipment required to secure deep subsurface characterization technologies. K-DEV is equipped with sensors that provide digital output with verified high performance; moreover, it is also compatible with logging winch systems used in Korea. The K-DEV prototype has a nonmagnetic stainless steel housing with an outer diameter of 48.3 mm, which has been tested in the laboratory for water resistance up to 20 MPa and for durability by running into a 1-km deep borehole. We confirmed the operational stability and data repeatability of the prototype by constantly logging up and down to the depth of 600 m. A high-precision micro-electro-mechanical system (MEMS) gyroscope was used for the K-DEV prototype as the gyro sensor, which is crucial for azimuth determination in cased holes. Additionally, we devised an accurate trajectory survey algorithm by employing Unscented Kalman filtering and data fusion for optimization. The borehole test with K-DEV and a commercial logging tool produced sufficiently similar results. Furthermore, the issue of error accumulation due to drift over time of the MEMS gyro was successfully overcome by compensating with stationary measurements for the same attitude at the wellhead before and after logging, as demonstrated by the nearly identical result to the open hole. We believe that the methodology of K-DEV development and operational stability, as well as the data reliability of the prototype, were confirmed through these test applications.

• Journal of Korea Entertainment Industry Association
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• v.13 no.2
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• pp.197-205
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• 2019

This study was to compare arterial stiffness and hemodynamic responses between male smokers and non-smokers. This study also investigates the influences of smoking before exercise on arterial stiffness and hemodynamic responses. 24 male subjects of age 20-29 without history of cardiorespiratory disease were divided into smokers and non-smokers. Smokers had more than 5 years of smoking experience. In order to evaluate the effects of pre-exercise smoking, smokers were tested twice, once with a cigarette before the exercise and the other once without one. Data was collected from bio-impedance analysis, SphygmoCor XCEL, graded exercise test, and fitness test. Main results of this study are as follows: First, there are differences between smokers and non-smokers in cardiorespiratory and hemodynamic response functions, as shown by maximal oxygen consumption, exercise duration, and heart rate. Second, the although the arterial stiffness between smokers and non-smokers showed statistically significant differences in the speed of the pulse wave velocity and augmentation index, smoker had a faster rate. It shows that smoking behavior has a negative impact on the cardiovascular system. Third, pre-exercise smoking behavior does have an impact on cardiorespiratory and hemodynamic response functions, as shown by exercise duration and heart rate. Lastly, arterial stiffness between smokers and non-smokers showed statistically not significant in the speed of the pulse wave velocity and augmentation index. However, the difference was not statistically significant. Brachial systolic pressure, a component of pulse wave analysis, on the other hand, was significantly dependent on pre-exercise smoking behavior. Subjects who participated in this study are college students in early 20s. Given their relatively short history of smoking, it is possible that their smoking habits are not severe enough to develop into cardiorespiratory or cardiovascular diseases. But Smokers showed lower levels of cardiopulmonary functions, as maximal oxygen consumption and exercise duration than nonsmokers.

• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.139-157
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• 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.

• Journal of the Korea Concrete Institute
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• v.26 no.2
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• pp.151-157
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• 2014

Since recently developed Ultra-High-Performance-Concrete (UHPC) provides very high strength, stiffness, and durability, many studies have been made on the application of the UHPC to bridge decks. Due to high strength and stiffness of UHPC bridge deck, the structural contribution of top flange of steel girder composite to UHPC deck would be much lower than that of conventional concrete deck. At this point of view, this study proposes a inverted-T shaped steel girder composite to UHPC deck. This girder requires a new type of shear connector because conventional shear connectors are welded on top flange. This study also proposes three different types of shear connectors, and evaluate their ultimate strength via push-out static test. The first one is a stud shear connector welded directly to the web of the girder in the transverse direction. The second one is a puzzle-strip type shear connector developed by the European Commission, and the last one is the combination of the stud and the puzzle-strip shear connectors. Experimental results showed that the ultimate strength of the transverse stud was 26% larger than that given in the AASHTO LRFD Bridge Design Specifications, but a splitting crack observed in the UHPC deck was so severe that another measure needs to be developed to prevent the splitting crack. The ultimate strength of the puzzle-strip specimen was 40% larger than that evaluated by the equation of European Commission. The specimens combined with stud and puzzle-strip shear connectors provided less strength than arithmetical sum of those. Based on the experimental observations, there appears to be no advantage of combining transverse stud and puzzle-strip shear connectors.

• Journal of Animal Science and Technology
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• v.50 no.1
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• pp.33-44
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• 2008

The purpose of this study was to estimate the genetic parameters and trend of Landrace and Yorkshire pigs, which were raised on private farms from 1999 to 2005 and tested for their reproductive performance by the Korea Animal Improvement Association. Prior to analysis, records without pedigree or having value with larger than±3×standard deviation for the Total number of born were excluded. The effects of breed and environmental factors were estimated with least square method(Harvey, 1979), and estimation of breeding values and genetic parameters were performed on the data of 1’st litter only with GIBBSF90(Misztal, 2001) which was programmed according to Gibbs Sampling method based on Bayesian Inference by Gianola and Fernando(1986), Jensen(1994) and others. Gibbs sampling was performed 50,000 times for each parameter, and the first 5000 samples were regarded as those in burn-in period and thus, excluded for post hoc analysis. Total number of born and total number of accident were statistically significant(p<0.01) for the breed, farrowing year, farrowing season and parity effects, and the number born alive at birth was statistically significantp<(0.01) for the breed, farrowing year, farrowing season and parity effects. No particular trend was observed in the genetic and phenotypic improvement of the total number of born and number born alive at birth before 2001, when the piglet registration system started, but the tendencies of increasing for the total number of born and number born alive and decreasing for the total number of accident were observed since 2001. Somewhat higher heritability estimates of our study seems to be attributed to the situations that first parity records with poor farrowing performances were used in the analyses and it was impossible to obtain accurate reproductive performance due to the absence of criteria for record keeping at the level of individual farms.

• Journal of The Korean Association For Science Education
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• v.36 no.4
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• pp.563-579
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• 2016

This study seeks educational implication by analyzing research papers dealing with science assessment in the most recent 30 years in Korea. The main purpose of the study is to analyze the trends in published papers on science assessment, their purpose, methodology, and key words, especially concentrating on the cognitive and affective domains. We selected 273 research articles and categorized them by research object, subject, methodology, and contents. To examine the factors that affect the research trend, we also tried to contextualize papers' theme in terms of changes in national curriculum and assessment system during the contemporary period. As a result, an overall research trend reflects changes in science curriculum and assessment events such as implementation of college scholastic ability test or performance assessment. There is an unequal distribution in various aspects of the researches, showing a superiority in cognitive domains than the affective ones. By using standardized data obtained through the national and international assessment of educational achievement in science, quantitative researches were superior to qualitative ones. Studies on cognitive domain use variously written- and performance-based tests, whereas most studies of the affective ones prefer written tests. Applied research and evaluation research are predominant comparing to basic ones, which most of the research methodology is based on statistics. Lastly, we found out that key words and subjects tend to be subdivided and detailed rather than general and comprehensive, as time goes on. Such trend will be helpful to elaborate and refine assessment tools that have been regarded as a problem.

• Journal of Intelligence and Information Systems
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• v.16 no.4
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• pp.67-84
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• 2010

In terms of business, forecasting is a work of what is expected to happen in the future to make managerial decisions and plans. Therefore, the accurate forecasting is very important for major managerial decision making and is the basis for making various strategies of business. But it is very difficult to make an unbiased and consistent estimate because of uncertainty and complexity in the future business environment. That is why we should use scientific forecasting model to support business decision making, and make an effort to minimize the model's forecasting error which is difference between observation and estimator. Nevertheless, minimizing the error is not an easy task. Case-based reasoning is a problem solving method that utilizes the past similar case to solve the current problem. To build the successful case-based reasoning models, retrieving the case not only the most similar case but also the most relevant case is very important. To retrieve the similar and relevant case from past cases, the measurement of similarities between cases is an important key factor. Especially, if the cases contain symbolic data, it is more difficult to measure the distances. The purpose of this study is to improve the forecasting accuracy of case-based reasoning approach using fuzzy relation and composition. Especially, two methods are adopted to measure the similarity between cases containing symbolic data. One is to deduct the similarity matrix following binary logic(the judgment of sameness between two symbolic data), the other is to deduct the similarity matrix following fuzzy relation and composition. This study is conducted in the following order; data gathering and preprocessing, model building and analysis, validation analysis, conclusion. First, in the progress of data gathering and preprocessing we collect data set including categorical dependent variables. Also, the data set gathered is cross-section data and independent variables of the data set include several qualitative variables expressed symbolic data. The research data consists of many financial ratios and the corresponding bond ratings of Korean companies. The ratings we employ in this study cover all bonds rated by one of the bond rating agencies in Korea. Our total sample includes 1,816 companies whose commercial papers have been rated in the period 1997~2000. Credit grades are defined as outputs and classified into 5 rating categories(A1, A2, A3, B, C) according to credit levels. Second, in the progress of model building and analysis we deduct the similarity matrix following binary logic and fuzzy composition to measure the similarity between cases containing symbolic data. In this process, the used types of fuzzy composition are max-min, max-product, max-average. And then, the analysis is carried out by case-based reasoning approach with the deducted similarity matrix. Third, in the progress of validation analysis we verify the validation of model through McNemar test based on hit ratio. Finally, we draw a conclusion from the study. As a result, the similarity measuring method using fuzzy relation and composition shows good forecasting performance compared to the similarity measuring method using binary logic for similarity measurement between two symbolic data. But the results of the analysis are not statistically significant in forecasting performance among the types of fuzzy composition. The contributions of this study are as follows. We propose another methodology that fuzzy relation and fuzzy composition could be applied for the similarity measurement between two symbolic data. That is the most important factor to build case-based reasoning model.