• Animal Bioscience
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• v.34 no.9
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• pp.1451-1459
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• 2021

Objective: The objectives of the study were to evaluate reproductive performance and selection response through genetic trend of community-based breeding programs (CBBPs) of Bonga sheep. Methods: Reproduction traits data were collected between 2012 and 2018 from Bonga sheep CBBPs. Phenotypic performance was analyzed using the general linear model procedures of Statistical Analysis System. Genetic parameters were estimated by univariate animal model for age at first lambing (AFL) and repeatability models for lambing interval (LI), litter size (LS), and annual reproductive rate (ARR) traits using restricted maximum likelihood method of WOMBAT. For correlations bivariate animal model was used. Best model was chosen based on likelihood ratio test. The genetic trends were estimated by the weighted regression of the average breeding value of the animals on the year of birth/lambing. Results: The overall least squares mean±standard error of AFL, LI, LS, and ARR were 375±12.5, 284±9.9, 1.45±0.010, and 2.31±0.050, respectively. Direct heritability estimates for AFL, LI, LS, and ARR were 0.07±0.190, 0.06±0.120, 0.18±0.070, and 0.25±0.203, respectively. The low heritability for both AFL and LI showed that these traits respond little to selection programs but rather highly depend on animal management options. The annual genetic gains were -0.0281 days, -0.016 days, -0.0002 lambs and 0.0003 lambs for AFL, LI, LS, and ARR, respectively. Conclusion: Implications of the result to future improvement programs were improving management of animals, conservation of prolific flocks and out scaling the CBBP to get better results.

• Journal of the Korea Safety Management & Science
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• v.24 no.4
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• pp.1-8
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• 2022

Elevators are the main means of transport in buildings. A malfunction of an elevator in operation may cause in convenience to users. Furthermore, fatal accidents, such as injuries and death, may occur to the passengers also. Therefore, it is important to prevent failure before accidents happen. In related studies, preventive measures are proposed through analyzing failures, and the lifespan of elevator components. However, these methods are limited to existing an elevator model and its surroundings, including operating conditions and installed environments. Vibration occurs when the elevator is operated. Experts have classified types of faults, which are symptoms for malfunctions (failures), via analyzing vibration. This study proposes an artificial intelligent model for classifying faults automatically with deep learning algorithms through elevator vibration data, hereby preventing failures before they occur. In this study, the vibration data of six elevators are collected. The proposed methodology in this paper removes "the measurement error data" with incorrect measurements and extracts operating sections from the input datasets for proceeding deep learning models. As a result of comparing the performance of training five deep learning models, the maximum performance indicates Accuracy 97% and F1 Score 97%, respectively. This paper presents an artificial intelligent model for detecting elevator fault automatically. The users' safety and convenience may increase by detecting fault prior to the fatal malfunctions. In addition, it is possible to reduce manpower and time by assisting experts who have previously classified faults.

• Atmosphere
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• v.31 no.5
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• pp.511-523
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• 2021

This study explores the optimal ensemble size to improve the prediction performance of the Korea Meteorological Administration's operational climate prediction system, global seasonal forecast system version 5 (GloSea5). The GloSea5 produces an ensemble of hindcast data using the stochastic kinetic energy backscattering version2 (SKEB2) and timelagged ensemble. An experiment to increase the hindcast ensemble from 3 to 14 members for four initial dates was performed and the improvement and effect of the prediction performance considering Root Mean Square Error (RMSE), Anomaly Correlation Coefficient (ACC), ensemble spread, and Ratio of Predictable Components (RPC) were evaluated. As the ensemble size increased, the RMSE and ACC prediction performance improved and more significantly in the high variability area. In spread and RPC analysis, the prediction accuracy of the system improved as the ensemble size increased. The closer the initial date, the better the predictive performance. Results show that increasing the ensemble to an appropriate number considering the combination of initial times is efficient.

• Journal of Information Processing Systems
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• v.18 no.4
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• pp.549-561
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• 2022

In the Khmer writing system, the Khmer script is the official letter of Cambodia, written from left to right without a space separator; it is complicated and requires more analysis studies. Without clear standard guidelines, a space separator in the Khmer language is used inconsistently and informally to separate words in sentences. Therefore, a segmented method should be discussed with the combination of the future Khmer natural language processing (NLP) to define the appropriate rule for Khmer sentences. The critical process in NLP with the capability of extensive data language analysis necessitates applying in this scenario. One of the essential components in Khmer language processing is how to split the word into a series of sentences and count the words used in the sentences. Currently, Microsoft Word cannot count Khmer words correctly. So, this study presents a systematic library to segment Khmer phrases using the bi-directional maximal matching (BiMM) method to address these problematic constraints. In the BiMM algorithm, the paper focuses on the Bidirectional implementation of forward maximal matching (FMM) and backward maximal matching (BMM) to improve word segmentation accuracy. A digital or prefix tree of data structure algorithm, also known as a trie, enhances the segmentation accuracy procedure by finding the children of each word parent node. The accuracy of BiMM is higher than using FMM or BMM independently; moreover, the proposed approach improves dictionary structures and reduces the number of errors. The result of this study can reduce the error by 8.57% compared to FMM and BFF algorithms with 94,807 Khmer words.

• Journal of Physiology & Pathology in Korean Medicine
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• v.36 no.5
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• pp.193-208
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• 2022

In this study, we proposed a mathematical model that can explain the source of the observed variability of repeated measurement data collected in Korean medicine clinical practice, and conducted a pilot analysis to infer the source of these variability based on our model. Mathematical model was constructed by dividing the observed variations into three components: common time-dependent variations, signal shift, and measurement error. To show the applicability of our model in real data, we analyzed 20 repeated measurement data of Korean clinical indicators in graduate students of Pusan National University Graduate School of Korean Medicine. We showed how to infer each source of variations based on our model and also showed the limitation of inference given the acquired the dataset. On the basis of objective recognition of these source of the variability, we hope that quantitative investigations on these sources for each Korean medicine clinical indicator are made in the future, so that they can be used in the clinical and educational areas of Korean medicine.

• The Journal of the Acoustical Society of Korea
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• v.42 no.5
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• pp.377-387
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• 2023

This paper presents accuracy of a method that directly estimates equivalent properties of a 1-3 piezocomposite for modeling it into the single phase homogeneous piezomaterial. This direct estimation method finds individual components of a material property matrix based on the piezoelectric constitutive equations, which represent mechanical and electrical behaviors and their couplings. Equivalent properties on a single 1-3 piezocomposite hydrophone are derived, and their accuracy depending on pairing of the constitutive equations is investigated by comparing them with finite element analysis for the whole domain. The accuracy is related to elastic characteristics of a matrix polymer, and the error is analyzed so that some guidelines for correct estimation are suggested. Fidelity of estimated properties and equivalent modeling is shown in a stave scale including hydrophones and surrounding acoustic structures as well, and reduced computational cost is verified.

• International conference on construction engineering and project management
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• 2009.05a
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• pp.700-706
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• 2009

Bridges are vital components of any road network which demand crucial and timely decision-making for Maintenance, Repair and Rehabilitation (MR&R) activities. Bridge Management Systems (BMSs) as a decision support system (DSS), have been developed since the early 1990's to assist in the management of a large bridge network. Historical condition ratings obtained from biennial bridge inspections are major resources for predicting future bridge deteriorations via BMSs. Available historical condition ratings in most bridge agencies, however, are very limited, and thus posing a major barrier for obtaining reliable future structural performances. To alleviate this problem, the verified Backward Prediction Model (BPM) technique has been developed to help generate missing historical condition ratings. This is achieved through establishing the correlation between known condition ratings and such non-bridge factors as climate and environmental conditions, traffic volumes and population growth. Such correlations can then be used to obtain the bridge condition ratings of the missing years. With the help of these generated datasets, the currently available bridge deterioration model can be utilized to more reliably forecast future bridge conditions. In this paper, the prediction accuracy based on 4 and 9 BPM-generated historical condition ratings as input data are compared, using deterministic and stochastic bridge deterioration models. The comparison outcomes indicate that the prediction error decreases as more historical condition ratings obtained. This implies that the BPM can be utilised to generate unavailable historical data, which is crucial for bridge deterioration models to achieve more accurate prediction results. Nevertheless, there are considerable limitations in the existing bridge deterioration models. Thus, further research is essential to improve the prediction accuracy of bridge deterioration models.

• Journal of the Korea Society for Simulation
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• v.19 no.3
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• pp.127-135
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• 2010

Modern marine navigation requires officers on the bridge to monitor a torrent of data on both the insides and outsides of the ship from numerous useful devices. But despite these tools, navigators can still find it difficult to make a safe decision for two reasons: one is that too much data if provided too quickly tends to cause fatigue and overwhelm the officer, and the other is that any inconsistency across data from several different types of devices can lead to confusion. Indeed, the fact remains that the many marine accidents can be attributed to human error, and hence there is a strong need for decision-support tools for marine navigation. One technique of providing decision support is through the use of simulation to evaluate or predict system dynamics over time using an accurate model. This paper, as a simulation method for risk prediction for a navigation safety information system of ship, suggests a navigation prediction simulation system using various knowledge bases and discrete event simulation methodology, and supports the validity of the system through the examples of components in a restricted navigation situation scenario.

• Korean Journal of Social Welfare Studies
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• v.41 no.4
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• pp.345-368
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• 2010

The purpose of this study is to develop the School Multicultural Climate(SMC) scale for students and to evaluate its reliability and validity. This study comprises of both qualitative and quantitative research. Preliminary items were developed based on the theoretical literature and interviews with students. The scale was evaluated with students in grades 4 through 6 in the seven elementary schools. Exploratory factor analysis was determined that the scale was composed of four components: Equal Status, Mutual Cooperation, Friendly Relations, Supportive Norms. The scale demonstrated that Cronbach's alpha=.943 for the internal consistency of total items. And the standard error of the measurement, another way of evaluating reliability, was 3.33. Criteria-related validity was evaluated by showing that the differences of the students' recognition of the school multicultural climate level, which depend on the availability of the multiculture-related policy, was statistically significant. The correlation analysis for the convergent validity was performed with the theoretically related variables such as self esteem and school adjustment. It was found that the SMC scale was a reliable and valid measure for evaluating the multicultural climate level of elementary school.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.3B
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• pp.263-269
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• 2006

An optimization model was developed for groundwater development and management in coastal areas. The optimization model consists of coastal groundwater flow model and optimization techniques. The objective of this work is to validate sharp-interface model which is one of major components of the optimization model. A laboratory experimental model is built to simulate freshwater lens, i.e., layer of freshwater floating on top of saltwater, phenomena. Experimental results for the position of fresh-saltwater sharp-interface and the salinity in well are compared with numerical results. Average ratio of relative error is estimated approximately between 2.91% and 4.39%. And the numerical results are in good agreement with the laboratory results of water quality in well in addition to the position of sharp-interface. Accordingly the evaluation of coastal groundwater flow using sharp-interface model can produce reasonable results.