• Journal of Engineering Education Research
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• v.22 no.5
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• pp.3-12
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• 2019

Due to the expansion of rolling admissions and diversification of college admissions, the widening gap between the level of scholastic ability and academic performance is the reality of college education. Thus, based on the incoming class of College A, this study analyzes the correlation between incoming students who enrolled in a college mathematics course during their first semester. Through this analysis, this study searches for a way to efficiently instruct students from various learning backgrounds when enrolled in the same course. Also, this study searches for a solution to lower the deviation of college mathematics' academic performance among engineering majors by examining the efficiency of a joint managed college mathematics curriculum.

• Archives of design research
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• v.17 no.2
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• pp.463-470
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• 2004

This paper studied tangible interaction design of VR platform and its applications that are economic In development process and cost, flexible by contents and installation conditions, and that has business potential for consumer market. The design solution uses video based virtual world and tangible interaction by motion tracking. Our platform enables a user to monitor their action and to collaborate with other users of remote place within attractive interaction feedback. We developed two design applications, Glass Xylophone 2003 and VR Class, in our platform. Glass Xylophone 2003 provides interactive music performance and helps self practice of glass xylophone. VR Class gives more serious distance learning experience with tutoring and group collaboration. They are presented in public exhibitions and tested by exhibition visitors. They showed application potential of this design solution in interactive game, distance learning, and entertainment field.

• Communications for Statistical Applications and Methods
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• v.23 no.2
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• pp.131-146
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• 2016

In research on behavioral studies, significant attention has been paid to the stage-sequential process for longitudinal data. Latent class profile analysis (LCPA) is an useful method to study sequential patterns of the behavioral development by the two-step identification process: identifying a small number of latent classes at each measurement occasion and two or more homogeneous subgroups in which individuals exhibit a similar sequence of latent class membership over time. Maximum likelihood (ML) estimates for LCPA are easily obtained by expectation-maximization (EM) algorithm, and Bayesian inference can be implemented via Markov chain Monte Carlo (MCMC). However, unusual properties in the likelihood of LCPA can cause difficulties in ML and Bayesian inference as well as estimation in small samples. This article describes and addresses erratic problems that involve conventional ML and Bayesian estimates for LCPA with small samples. We argue that these problems can be alleviated with a small amount of prior input. This study evaluates the performance of likelihood and MCMC-based estimates with the proposed prior in drawing inference over repeated sampling. Our simulation shows that estimates from the proposed methods perform better than those from the conventional ML and Bayesian method.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.1
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• pp.144-150
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• 2017

Recently, deep learning is used for intelligent processing and accuracy improvement of data. It is formed calculation model composed of multi data processing layer that train the data representation through an abstraction of the various levels. A category of deep learning, convolution neural network is utilized in various research fields, which are human pose estimation, face recognition, image classification, speech recognition. When using the deep layer and lots of class, CNN that show a good performance on image classification obtain higher classification rate but occur the overfitting problem, when using a few data. So, we design the training network based on convolution neural network and trained our image data set for object classification in few class problem. The experiment show the higher classification rate of 7.06% in average than the previous networks designed to classify the object in 1000 class problem.

• Journal of the Society of Naval Architects of Korea
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• v.54 no.1
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• pp.43-48
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• 2017

In recent years, the demand for ships and offshore platforms that can navigate and operate through the Arctic Ocean has been rapidly increasing due to global warming and large reservoirs of oil and natural gas in the area. Winterization design is one of the key issues to consider in the robust structural safety design and building of ships that operate in the Arctic and Sub-Arctic regions. However, international regulations for winterization design in Arctic condition regulated that only those ships and offshore platforms with a Polar Class designation and/or an alternative standard. In order to cope with the rising demand for operating in the Arctic region, existing and new Arctic vessels with a Polar Class designation are lacking to cover for adequate winterization design with HSE philosophy. Existing ships and offshore platform was not designed based on reliable data based on numerical and experiment studies. There are only designed as a performance and functional purposes. It is very important to obtain of reliable data and provide of design guidance of the anti-icing structures by taking the effects of low temperature into consideration. Therefore, the main objective of this paper reconsiders anti-icing design of aluminum helideck using the heating cable. To evaluate of reliable data and recommend of anti-icing design method, various types of analysis and methods can be applied in general. In the present study, finite element method carried out the thermal analysis with cold chamber testing for performance and capacity of heating cables.

• Journal of Korean Biological Nursing Science
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• v.10 no.2
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• pp.162-169
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• 2008

Purpose: This study was conducted to reorganize the course of basic nursing science (BNS). Methods: The curriculums of 10 leading nursing colleges (domestic and abroad) were analyzed. And a survey was performed on 178 nursing students to identify the perceived level of knowledge, clinical application, the adequacy of class hours and further improvements for the course of BNS. Results: The various levels of credits and percentage were found in the curriculums of other nursing colleges (12-18 credits and 8.6, 13.6%, respectively). The perceived levels of knowledge, clinical application were directly proportional to the adequacy of class hours, and students suggested the increment of class hours and in-depth study. Based on these results, the course of BNS was reorganized as follows: 1) The course of BNS was divided into 2 courses (BNS 1, 2) and total credits were increased to 5 credits. 2) The BNS 1 course was focused on basic concepts to understand human anatomy and physiology. And BNS 2 consisted of detailed structures and functions of human body system. 3) 12 Quizzes were added. Conclusion: The efforts to reorganize the curriculum of BNS might strengthen nursing students' ability to understand nursing phenomena, help student with academic performance and clinical training.

• The Korean Journal of Ecology
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• v.26 no.2
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• pp.75-81
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• 2003

This study was performed to prove if the high resolution satellite imagery of IKONOS is suitable for preparing digital vegetation map which is becoming increasingly important in ecological science. Seven classes for forest area and five classes for non-forest area were taken for classification. Three methods, such as the pixel based classification, the segment based classification with majority principle, and the segment based classification with maximum likelihood, were applied to classify IKONOS imagery taken in April 2000. As a whole, the segment based classification shows better performance in classifying the high resolution satellite imagery of IKONOS. Through the comparison of accuracies and kappa values of the above 3 classification methods, the segment based classification with maximum likelihood was proved to be the best suitable for preparing the vegetation map with the help of IKONOS imagery. This is true not only from the viewpoint of accuracy, but also for the purpose of preparing a polygon based vegetation map. On the basis of the segment based classification with the maximum likelihood, a digital vegetation map in which each vegetation class is delimitated in the form of a polygon could be prepared.

• New & Renewable Energy
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• v.17 no.3
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• pp.16-23
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• 2021

Recently, the Korean government announced a plan to activate renewable energies, with focus on clean energy sources such as solar and wind power as the core and the goal of achieving carbon neutrality by 2050. Unlike other photovoltaic (PV) systems, offshore PV installations are advantageous for large-scale expansion because of the ease of securing sites; they also enable lowering the power generation costs based on construction of large-scale power facilities of megawatt class or higher owing to low noise and landscape damage. However, any power generation should proceed with consideration of the special environmental conditions of the ocean. Above all, when installing large-scale facilities, it is important to reduce fluctuations of the structure and secure stability to actively respond to waves. This study is concerned with the development of a floating body technology that actively responds to waves so as to enable commercialization of offshore solar power. A unit platform for research and development on offshore PV generation was installed in the Saemangeum sea, and the structural fluctuations and stability were analyzed to ensure conformity with the major performance indicators.

• Journal of Convergence for Information Technology
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• v.12 no.4
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• pp.260-269
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• 2022

In this paper, We propose an anomaly detection model using deep neural network to automate the identification of outliers of the national air pollution measurement network data that is previously performed by experts. We generated training data by analyzing missing values and outliers of weather data provided by the Institute of Environmental Research and based on the BeatGAN model of the unsupervised learning method, we propose a new model by changing the kernel structure, adding the convolutional filter layer and the transposed convolutional filter layer to improve anomaly detection performance. In addition, by utilizing the generative features of the proposed model to implement and apply a retraining algorithm that generates new data and uses it for training, it was confirmed that the proposed model had the highest performance compared to the original BeatGAN models and other unsupervised learning model like Iforest and One Class SVM. Through this study, it was possible to suggest a method to improve the anomaly detection performance of proposed model while avoiding overfitting without additional cost in situations where training data are insufficient due to various factors such as sensor abnormalities and inspections in actual industrial sites.

• Journal of the Society of Naval Architects of Korea
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• v.48 no.6
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• pp.575-580
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• 2011

Icing problem of ice-strengthened vessels is an important issue when operating in low temperature environment and it can cause damage to deck machineries and emergency equipments. Many ice-strengthened vessels have since been constructed and operated in accordance with the ice class rules such as Det Norske Veritas (DNV), Russian Maritime Register of Shipping (RS), American Bureau of Shipping (ABS) and so on. Therefore winterization is defined as the preparation of a ship for safe operation. In this research, anti-icing performance tests of weather-tight door have been carried out at various temperature conditions($5^{\circ}C$, $-10^{\circ}C$, $-20^{\circ}C$, $-30^{\circ}C$, $-40^{\circ}C$) in the low temperature cold room facility and then, ambient temperature, specimen temperature, electric current and temperature of heating cable were measured during the test operations. This research describes the construction guidelines of weather-tight door based on anti-icing test results to apply to the full-scale vessels.