• Korean Journal of Computational Design and Engineering
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• v.13 no.2
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• pp.139-152
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• 2008

In order to survive worldwide competition, today's industries are experiencing strong pressure to introduce higher quality products with lower cost and shorter lead-time. Therefore, the role of design in the process of product development is increasing in significance. In this research, two methods for improving the design capability are proposed: construction of design environment and design automation using 3D CAD system. The designers and design process are the core of product design using 3D CAD system. In order to maximize the design performance, construction of the design environment including selection of a suitable system, designer training for best use of the system, establishment of an efficient design process, and stabilization of the environment are required. A method is suggested to construct design environment by systematizing the contents of the projects and consulting experiences carried out for various categories of business such as electronic devices, motorcycles, electricity parts, sanitary wares, injection molds, and die casing molds. Design automation helps reduce tedious and time-consuming jobs, simplify complicated and error-prone modeling and drawing works to shorten the lead time and improve the product quality. To develop a design automation system, understanding the process and the related knowledge on design are very important before implementing the system using API provided by 3D CAD system. In this research, an eight-step procedure is proposed for the development of a design automation system. These eight steps are analysis of needs, determination of specification, verification of specification using 3D CAD system, inspection of related API functions, programming, field test, application in practice, and maintenance. A case study in which five design automation systems in the design of turbine generators using the proposed method is introduced in detail. These systems play important roles in the generation of various output items including 3D models, drafts, material information, and NC data. The case study shows how effectively the design time is reduced and the quality improved using those systems.

• Proceedings of the Korea Association of Information Systems Conference
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• 2000.11a
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• pp.1.2-24
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• 2000

The field of neural networks has been a recent surge in activity as a result of progress in developments of efficient training algorithms. For this reason, and coupled with the widespread availability of powerful personal computer hardware for running simulations of networks, there is increasing focus on the potential benefits this field can offer. The neural network may be viewed as an advanced pattern recognition technique and can be applied in many areas such as financial time series forecasting, medical diagnostic expert system and etc.. The intention of this study is to build and implement one simple artificial neural networks hereinafter called ANN. For this purpose, some literature survey was undertaken to understand the structures and algorithms of ANN theoretically. Based on the review of theories about ANN, the system adopted 3-layer back propagation algorithms as its learning algorithm to simulate one case of medical diagnostic model. The adopted ANN algorithm was performed in PC by using turbo PASCAL and many input parameters such as the numbers of layers, the numbers of nodes, the number of cycles for learning, learning rate and momentum term. The system output more or less successful results which nearly agree with goals we assumed. However, the system has some limitations such as the simplicity of the programming structure and the range of parameters it can dealing with. But, this study is useful for understanding general algorithms and applications of ANN system and can be expanded for further refinement for more complex ANN algorithms.

• Journal of the Korea Society of Computer and Information
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• v.10 no.4 s.36
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• pp.385-396
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• 2005

In this paper, a new structural approach to on-line signature verification is presented. A primitive pattern is defined as a part segmented by a local minimal position of speed. And a structural description of signature is composed of subpatterns which are defined as such forms as rotation shape, cusp shape and bell shape, acquired by composition of the primitives regarding the directional changes. As the matching method to find identical parts between two signatures, a modified DP(dynamic programming) matching algorithm is presented. And also, variation and complexity of local parts are computed from the training samples, and reference model and decision boundary are derived from these. Error rate, execution time and memory usage are compared among the functional approach, the parametric approach and the proposed structural approach. It is found that the average error rate can be reduced from 14.2% to 4.05% when the local parts of a signature are weighted and the complexity is used as a factor of decision threshold. Though the error rate is similar to that of functional approaches. time consumption and memory usage of the proposed structural approach are shown to be very effective.

• Journal of the Ergonomics Society of Korea
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• v.30 no.1
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• pp.251-257
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• 2011

When designing a ship, ergonomic considerations are crucial when minimizing a navigator's fatigue due to the burden of work, and to appropriately operate the navigational equipment for each given situation by helping the operator to understand the surroundings as well as the physical functions of the ship. However, insufficient consideration of ergonomic elements in the actual design of ship Bridges is lowering the performance of safe navigation and allows for the possibility of operation or readout errors. Consequently, these errors lead to an increase in maritime accidents. Therefore, this study conducted a usability evaluation on the importance of and the usage frequency of navigational equipment, their influence on actual navigation, and the possibility of error upon operation or readout between training ship officers, to derive an optimized layout that includes the consideration of ergonomic factors for on-Bridge navigational equipment, which are currently arranged differently according to their type or size. The optimized layout of on-Bridge navigational equipment was carried out based on the evaluation results, using the Lingo program. Through the process of optimization, revised layouts of on-Bridge navigational equipments(control and display device) were suggested, considering emergency situations(ship collision, stranding, fire and explosion, sinking, etc.) during navigation.

• Journal of The Korean Association of Information Education
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• v.23 no.1
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• pp.39-53
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• 2019

In the era of the Fourth Industrial Revolution, improving computational thinking to solve problems by smoothly operating and utilizing software. Therefore, the ministry of education has introduced systematic SW education into public education as a revised 2015 curriculum with the aim of raising computational thinking. In order for SW education to be systematically stabilized at the school site, the teaching ability of the teacher must be supported above all. For this purpose, the government is promoting 'SWEET' for improvement of the elementary pre-service teachers' SW education program. However, even if the policy is pursued, there is a tendency to take measures to improve the teaching and learning methods in the field later, and since the subject of change in education is the teacher, it is necessary to grasp the reco-gnition, experience of teachers. This study analyzed qualitatively perception and experience of elementary pre-service teachers about SW education and suggested design guidelines for pre-service teacher training such as providing various learning examples.

• Journal of Practical Engineering Education
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• v.9 no.1
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• pp.23-30
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• 2017

This is a study on a case applying flipped learning to coding classes that is a college liberal arts course. A required coding class for the students who do not major in computers needs a teaching method differentiated from a coding education for training experts. The present study presented a flipped learning teaching model for the coding education of non-major students, and observed its effect and possibility. Flipped learning enables learners to learn with on-line contents anywhere and anytime they want and concentrate on practice education based on what they learned during class. Accordingly, the study sought for the solution to maximize the efficiency of teaching and learning through flipped learning. A pre and post surveys after applying a flipped learning to a practical class confirmed that the students taught using flipped learning were more positively assessed in learning satisfaction than those taught using a traditional method, and that in academic achievement as well, flipped learning was more effective.

• The Bulletin of The Korean Astronomical Society
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• v.44 no.1
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• pp.54.2-54.2
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• 2019

Removing noise which occurs inevitably when taking image data has been a big concern. There is a way to raise signal-to-noise ratio and it is regarded as the only way, image stacking. Image stacking is averaging or just adding all pixel values of multiple pictures taken of a specific area. Its performance and reliability are unquestioned, but its weaknesses are also evident. Object with fast proper motion can be vanished, and most of all, it takes too long time. So if we can handle single shot image well and achieve similar performance, we can overcome those weaknesses. Recent developments in deep learning have enabled things that were not possible with former algorithm-based programming. One of the things is generating data with more information from data with less information. As a part of that, we reproduced stacked image from single shot image using a kind of deep learning, conditional generative adversarial network (cGAN). r-band camcol2 south data were used from SDSS Stripe 82 data. From all fields, image data which is stacked with only 22 individual images and, as a pair of stacked image, single pass data which were included in all stacked image were used. All used fields are cut in $128{\times}128$ pixel size, so total number of image is 17930. 14234 pairs of all images were used for training cGAN and 3696 pairs were used for verify the result. As a result, RMS error of pixel values between generated data from the best condition and target data were $7.67{\times}10^{-4}$ compared to original input data, $1.24{\times}10^{-3}$. We also applied to a few test galaxy images and generated images were similar to stacked images qualitatively compared to other de-noising methods. In addition, with photometry, The number count of stacked-cGAN matched sources is larger than that of single pass-stacked one, especially for fainter objects. Also, magnitude completeness became better in fainter objects. With this work, it is possible to observe reliably 1 magnitude fainter object.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.7
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• pp.2496-2512
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• 2021

The requirements for powerful computing capability, high capacity, low latency and low energy consumption of emerging services, pose severe challenges to the fifth-generation (5G) network. As a promising paradigm, mobile edge networks can provide services in proximity to users by deploying computing components and cache at the edge, which can effectively decrease service delay. However, the coexistence of heterogeneous services and the sharing of limited resources lead to the competition between various services for multiple resources. This paper considers two typical heterogeneous services: computing services and content delivery services, in order to properly configure resources, it is crucial to develop an effective offloading and caching strategies. Considering the high energy consumption of 5G base stations, this paper considers the hybrid energy supply model of traditional power grid and green energy. Therefore, it is necessary to design a reasonable association mechanism which can allocate more service load to base stations rich in green energy to improve the utilization of green energy. This paper formed the joint optimization problem of computing offloading, caching and resource allocation for heterogeneous services with the objective of minimizing the on-grid power consumption under the constraints of limited resources and QoS guarantee. Since the joint optimization problem is a mixed integer nonlinear programming problem that is impossible to solve, this paper uses deep reinforcement learning method to learn the optimal strategy through a lot of training. Extensive simulation experiments show that compared with other schemes, the proposed scheme can allocate resources to heterogeneous service according to the green energy distribution which can effectively reduce the traditional energy consumption.

• Journal of The Korean Association of Information Education
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• v.25 no.2
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• pp.317-325
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• 2021

In this paper, the degree of reflection of SW·AI elements and CT elements was investigated and analyzed for a total of 44 textbooks of Korean, social, moral, mathematics and science textbooks based on the 2015 revised curriculum. As a result of the analysis, most of the activities of data collection, data analysis, and data presentation, which are ICT elements, were not reflected, and algorithm and programming elements were not reflected among SW·AI content elements, and there were no abstraction, automation, and generalization elements among CT elements. Therefore, in order to effectively implement SW·AI convergence education in elementary school subjects, we will expand ICT utilization activities to SW·AI utilization activities. Training on the understanding of SW·AI convergence education and improvement of teaching and learning methods using SW·AI is needed for teachers. In addition, it is necessary to establish an information curriculum and secure separate class hours for substantial SW·AI education.

• Journal of the Korea Society of Computer and Information
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• v.25 no.12
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• pp.165-172
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• 2020

Recently, due to exponential growth of access information on the web, the importance of predicting a user's next web page use has been increasing. One of the methods that can be used for predicting user's next web page is deep learning. To predict next web page, web logs are analyzed by data preprocessing and then a user's next web page is predicted on the output of the analyzed web logs using a deep learning algorithm. In this paper, we propose a framework for web page prediction that includes methods for web log preprocessing followed by deep learning techniques for web prediction. To increase the speed of preprocessing of large web log, a Hadoop based MapReduce programming model is used. In addition, we present a web prediction system that uses an efficient deep learning technique on the output of web log preprocessing for training and prediction. Through experiment, we show the performance improvement of our proposed method over traditional methods. We also show the accuracy of our prediction.