• Journal of Korean Society on Water Environment
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• v.25 no.2
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• pp.311-321
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• 2009

Long-Term Hydrologic Impact Assessment (L-THIA) was modified to improve runoff and pollutant load prediction for Korean watersheds with changes in land use classification and event mean concentration produced from observed data in Korea. The L-THIA model was linked with SCE-UA, which is one of the global optimization techniques, to automatically calibrate direct runoff. Modified L-THIA model was applied to Gumho River Basins to analyze spatial distribution of nonpoint source pollution. The results of model calibration during 1991~2000 and validation during 1981~1990 for direct runoff represented high model efficiency of 0.76 for calibration and 0.86 for validation. As a results of spatial analysis of nonpoint source pollution, the BOD was mainly loaded from urban area but SS, TN, and TP from agricultural area which is mainly located along the stream. Modified L-THIA model improve its accuracy with minimum imput data and application efforts. From this study, we can find out the L-THIA model is very useful tool to predict direct runoff and pollutant loads from the watershed and spatial analysis of nonpoint source pollution.

• Journal of Digital Convergence
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• v.10 no.8
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• pp.173-178
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• 2012

In the generated model for the recognition vocabulary, tri-phones which is not make preparations are produced. Therefore this model does not generate an initial estimate of parameter words, and the system can not configure the model appear as disadvantages. As a result, the sophistication of the Gaussian model is fall will degrade recognition. In this system, we propose the error correction system using out-of vocabulary rejection algorithm. When the systems are creating a vocabulary recognition model, recognition rates are improved to refuse the vocabulary which is not registered. In addition, this system is seized the lexical analysis and meaning using probability distributions, and this system deactivates the string before phoneme change was applied. System analysis determine the rate of error correction using phoneme similarity rate and reliability, system performance comparison as a result of error correction rate improve represent 2.8% by method using error patterns, fault patterns, meaning patterns.

• Journal of Ocean Engineering and Technology
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• v.29 no.1
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• pp.56-61
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• 2015

To improve the accuracy of a machine, research needs to be conducted on the relationship between the output variables and design variables of a spindle-shaped part from the thermal and static viewpoints. Therefore, research was carried out by examining the correlation of each variable to find the optimum conditions. Moreover, DOE (design of experiments) was extensively used. The model used in this study was a grinding spindle to which a hydrostatic bearing was applied. This model was used in a preliminary analysis based on the experimental results of the previous studies. The influences of the output variables and design variables were compared through a main effect analysis. Generated response surfaces were applied to the Kriging model. To optimize the model, a screening method was selected. In comparison with the initial model, the deformation of the optimized model designed by DOE decreased by 4.1 μm, while the thermal deformation decreased by 1.2 μm. Therefore, it was efficient to design a spindle-shaped part through DOE to improve the accuracy of the machine.

• Journal of The Korean Association of Information Education
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• v.21 no.4
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• pp.393-401
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• 2017

The purpose of this study is to develop a deep learning based learning system for improving learner's data analytical thinking ability. The contents of the study are as follows. First, deep learning was applied to the discovery learning model to improve data analytical thinking ability. This is a learning method that can generate a model showing the relationship of given data by using the deep learning method, then apply the model to new data to obtain the result. Second, we developed a deep learning based system for DBD learning model. Specifically, we developed a system to generate a model of data using the deep learning method and to apply this model. The research of deep learning based learning system will be a new approach to improve learner's data analytical thinking ability in future society where data becomes more important.

• Journal of Advanced Navigation Technology
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• v.20 no.5
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• pp.387-393
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• 2016

To perform realistic air traffic control (ATC) simulation in various air traffic situations, an aircraft dynamic model that is accurate and efficient is required. In this research, an improved five degree of freedom (5-DOF) dynamic model with feedback control and guidance law is developed, which utilizes selected performance data and operational specifications from the base of aircraft data (BADA) and estimations using aircraft design techniques to improve the simulation fidelity. In addition, takeoff weight is estimated based on the aircraft type and flight plan to improve simulation accuracy. The dynamic model is validated by comparing the simulation results with recorded flight trajectories. An ATC simulation system using this 5-DOF model can be used for various ATC related research.

• The Journal of Korean Association of Computer Education
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• v.21 no.4
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• pp.39-52
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• 2018

There has been a lot of interest in flipped learning, but lacked practical research to support the faculty developing flipped learning courses in higher education institutions. The purpose of this study is to develop a consulting model for developing a flipped learning course that can be practically used by institutions such as center for teaching and learning that support faculty teaching to improve the quality of instruction. A 'Coaching based on flip learning course development consulting model' was derived through literature review and expert feedback. The 8-step model is as follows: 1) survey of class development, 2) course orientation, 3) flipped learning, 4) expert class analysis, 5) flipped learning coaching, 6) flipped learning operation, 7) instructor reflection, and 8) feedback and revision. Based on the results of this study, it is possible to provide professional and practical guide systematically, and also help to improve quality of lesson.

• Journal of Korean Academy of Nursing
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• v.50 no.4
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• pp.554-570
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• 2020

Purpose: This study aimed to develop and test a structural equation model of health-related quality of life among older women following bilateral total knee replacement based on a literature review and Wilson and Cleary's model of health-related quality of life. Methods: One hundred ninety three women who were diagnosed with osteoarthritis, were older than 65 years, and were between 13 weeks and 12 months of having a bilateral total knee replacement were recruited from an outpatient clinic. Data were collected from July 2017 to April 2018 using a structured questionnaire and medical records. Data were analyzed using SPSS/WIN 22.0, AMOS 22.0, and Smart PLS 3.2.4. Results: The fitness of the hypothetical model was good, with coefficients of determination (R²) ranging between .28 and .75 and predictive relevance (Q²) between .26 and .73. The standardized root mean square residual of the model fit indices for the hypothetical model was .04; which explained 64.2% of physical and 62.5% of mental health-related quality of life. Self-efficacy, symptom status, functional status, and general health perceptions had a significant direct effect on physical health-related quality of life, while social support, symptom status, and general health perceptions had a significant direct effect on participants' mental-health-related quality of life. Conclusion: To improve the physical and mental quality of life of older women who receive bilateral knee replacement, nursing-based intervention strategies that reduce symptoms, improve functional status, and increase health perceptions, self-efficacy, and social support are needed. The most important factor is the symptom status.

• Journal of Digital Convergence
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• v.11 no.7
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• pp.209-214
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• 2013

Improve the recognition performance of speech recognition systems as a method for recognizing human listening skills were incorporated into the system. In noisy environments by separating the speech signal and noise, select the desired speech signal. but In terms of practical performance of speech recognition systems are factors. According to recognized environmental changes due to noise speech detection is not accurate and learning model does not match. In this paper, to improve the speech recognition feature extraction using gamma tone and learning model using acoustic model was proposed. The proposed method the feature extraction using auditory scene analysis for human auditory perception was reflected In the process of learning models for recognition. For performance evaluation in noisy environments, -10dB, -5dB noise in the signal was performed to remove 3.12dB, 2.04dB SNR improvement in performance was confirmed.

• Journal of the Korea Society of Computer and Information
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• v.13 no.7
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• pp.199-205
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• 2008

The centralized conference model has advantage of conference management and control. however it's scalability has been limited as performance degrades largely with increasing number of conference users. So new distributed conference models which improve scalability of centralized conference model have been suggested recently. In the distributed conference model. as conference users exceed a predefined maximum number, a new conference server is added to the conference dynamically. In this paper, We have proposed a new method which increases efficiency of conference event package processing that primary conference server should charge in the distributed conference environment. The primary conference server exchanges informations with each secondary conference servers and conference users by using conference event package. And from the conference information database it selects SIP(Session Initiation Protocol) UA(User Agent) which will share notification to the conference users, and transfers lists to each conference servers. The conference servers make the selected UAs share processing of conference event package, so loads of SIP signal processing decrease, and improve scalability of distributed conference model. The performance of our proposed model is evaluated by experiments.

• The Journal of Information Systems
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• v.18 no.4
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• pp.41-57
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• 2009

Although financial information is a great influence upon determining of the group which use them, detection of management fraud and earning manipulation is a difficult task using normal audit procedures and corporate credit evaluation processes, due to the shortage of knowledge concerning the characteristics of management fraud, and the limitation of time and cost. These limitations suggest the need of systemic process for !he effective risk of earning manipulation for credit evaluators, external auditors, financial analysts, and regulators. Moot researches on management fraud have examined how various characteristics of the company's management features affect the occurrence of corporate fraud. This study examines financial characteristics of companies engaged in fraudulent financial reporting and suggests a model and system for detecting GAAP violations to improve reliability of accounting information and transparency of their management. Since the detection of management fraud has limited proven theory, this study used the detecting method of outlier(upper, and lower bound) financial ratio, as a real-field application. The strength of outlier detecting method is its use of easiness and understandability. In the suggested model, 14 variables of the 7 useful variable categories among the 76 financial ratio variables are examined through the distribution analysis as possible indicators of fraudulent financial statements accounts. The developed model from these variables show a 80.82% of hit ratio for the holdout sample. This model was developed as a financial outlier detecting system for a financial institution. External auditors, financial analysts, regulators, and other users of financial statements might use this model to pre-screen potential earnings manipulators in the credit evaluation system. Especially, this model will be helpful for the loan evaluators of financial institutes to decide more objective and effective credit ratings and to improve the quality of financial statements.