• International Journal of Fuzzy Logic and Intelligent Systems
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• 제5권4호
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• pp.281-285
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• 2005

Learning and evolving are two basics for data mining. As compared with classical learning theory based on objective function with minimizing training errors, the recently evolutionary computing has had an efficient approach for constructing optimal model without the minimizing training errors. The global search of evolutionary computing in solution space can settle the local optima problems of learning models. In this research, combining co-evolving algorithm into statistical learning theory, we propose an co-evolutionary computing for statistical learning theory for overcoming local optima problems of statistical learning theory. We apply proposed model to classification and prediction problems of the learning. In the experimental results, we verify the improved performance of our model using the data sets from UCI machine learning repository and KDD Cup 2000.

• KIPS Transactions on Software and Data Engineering
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• 제9권6호
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• pp.177-186
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• 2020

Recent advances in deep learning technology have improved image recognition performance in the field of computer vision, and serverless computing is emerging as the next generation cloud computing technology for event-based cloud application development and services. Attempts to use deep learning and serverless computing technology to increase the number of real-world image recognition services are increasing. Therefore, this paper describes how to develop an efficient deep learning based image recognition service system using serverless computing technology. The proposed system suggests a method that can serve large neural network model to users at low cost by using AWS Lambda Server based on serverless computing. We also show that we can effectively build a serverless computing system that uses a large neural network model by addressing the shortcomings of AWS Lambda Server, cold start time and capacity limitation. Through experiments, we confirmed that the proposed system, using AWS Lambda Serverless Computing technology, is efficient for servicing large neural network models by solving processing time and capacity limitations as well as cost reduction.

• International Journal of Advanced Culture Technology
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• 제9권4호
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• pp.110-117
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• 2021

The purpose of the study is to develop effective teaching methods to strengthen the major learning capabilities of electronic engineering learners through inquiry learning using computing thinking ability. To this end, first, in the electronic engineering curriculum, we performed teaching-learning through an inquiry and learning model related to mathematics, probability, and statistics under the theme of various majors in electronic engineering, focusing on understanding computing thinking skills. Second, an efficient electronic engineering subject inquiry class operation using computing thinking ability was conducted, and electronic engineering-linked education contents based on the components of computer thinking were presented. Third, by conducting a case study on inquiry-style teaching using computing thinking skills in the electronic engineering curriculum, we identified the validity of the teaching method to strengthen major competency. In order to prepare for the 4th Industrial Revolution, by implementing mathematics, probability, statistics-related linkage, and convergence education to foster convergent talent, we tried to present effective electronic engineering major competency enhancement measures and cope with innovative technological changes.

• Educational Technology International
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• 제18권2호
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• pp.131-157
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• 2017

The purposes of the study were to investigate the participation of artists in paper circuit computing learning and to conduct an in-depth study on the formation and development of practical knowledge. To do this, we selected as research participants six artists who participated in the learning program of an art museum, and used various methods such as pre-open questionnaires, participation observation, and individual interviews to collect data. The collected data were analyzed based on community of practice theory. Results showed that the artists participated in the learning based on a desire to use new technology or find a new work production method for interacting with their audiences. In addition, the artists actively formed practical knowledge in the curriculum and tried to apply paper circuit computing to their works. To continuously develop the research, participants formed a study group or set up a practical goal through planned exhibitions. The results of this study can provide implications for practical approaches to, and utilization of, paper circuit computing.

• Journal of Information Technology Services
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• 제6권2호
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• pp.99-112
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• 2007

In this study we designed the killer services for the scene of labor learning in ubiquitous computing. To achieve this study, we have explored the unmet needs of teachers in the scene of labor learning and examined whether the unmet needs could be served by the resources and capabilities of ubiquitous computing. Then, we have crafted a detail killer services that includes value propositions and resource maps by using statistical methodology. Finally, the killer services for the scene of labor learning proposed to serve educational users with the service architecture. The result of this study will be applied to develop new business model in ubiquitous computing as the basic research.

• The Journal of Korean Institute for Practical Engineering Education
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• 제3권1호
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• pp.69-75
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• 2011

This paper presents a model of e-learning based on ubiquitous computing configuration. First of all, we survey the advanced e-learning systems for foreign and domestic universities. Next we propose the optimal e-learning model based on ubiquitous computing configuration. The proposed e-learning model as following. we propose the e-learning system's hardware and software configurations, that are server and networking systems. Also, we construct the proposed e-learning systems's services. There are attendance and absence service, class management service, common knowledge service, score processing service, facilities management service, personal management service, personal authorization issue management service, campus guide service, lecture-hall management service. Then we propose the laboratory equipment management service, experimental materials management service etc. The proposed model of e-learning based on ubiquitous computing configuration will be able to contribute to the next generation university educational paradigm.

• 한국디지털정책학회:학술대회논문집
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• 한국디지털정책학회 2005년도 춘계학술대회
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• pp.605-615
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• 2005

This paper represent a method of U-Learning based on advanced e-Learning. Ubiquitous computing configuration and advanced Information technology. As we know well, the 21th century is called knowledge based informational society. Many scholar stress that the improved 21th century's educational paradigm be able to success based on advanced educational paradigm. Therefore, we discuss the material for e-Learning fields including with necessity, vision, law, quality authorization etc. Also, we discuss the relational technologies including with meta data, standardization, identification etc. Finally, we propose a method for constructing the U-Learning based on advanced e-Learning and Ubiquitous computing configuration.

• Journal of The Korean Association of Information Education
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• 제22권3호
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• pp.297-305
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• 2018

Physical computing is the concept of expanding computing to humans, environments, and objects. It draws attention as a programming learning medium based on physical outputs in integration of hardware and software. This study developed a programming learning model based on interactive prototyping using the characteristics of physical computing with a high degree of technical freedom and analyzed its learning effect in an experiment. To examine the effect of the experimental treatment, this researcher divided fifty nine 5th-grade elementary students into an experimental group and into a control group. the interactive prototyping programming learning model was applied to the experimental group, and a linear sequential programming learning model was applied to the control group. Information Science Creative Personality Test was conducted before and after the experimental treatment. Analysis of Covariance was conducted with the pre-test scores of the two groups. As a result, it was proved that there was the effect of learning at the significance level of .05. It indicates that the physical computing based interactive prototyping programming learning model is applicable to the programming learning for 5th-grade elementary students.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권9호
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• pp.3663-3679
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• 2020

Scheduling plays a dynamic role in cloud computing in generating as well as in efficient distribution of the resources of each task. The principle goal of scheduling is to limit resource starvation and to guarantee fairness among the parties using the resources. The demand for resources fluctuates dynamically hence the prearranging of resources is a challenging task. Many task-scheduling approaches have been used in the cloud-computing environment. Security in cloud computing environment is one of the core issue in distributed computing. We have designed a deep learning-based security model for scheduling tasks in cloud computing and it has been implemented using CloudSim 3.0 simulator written in Java and verification of the results from different perspectives, such as response time with and without security factors, makespan, cost, CPU utilization, I/O utilization, Memory utilization, and execution time is compared with Round Robin (RR) and Waited Round Robin (WRR) algorithms.

• IEMEK Journal of Embedded Systems and Applications
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• 제17권2호
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• pp.77-83
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• 2022

In this paper, the two-stage object detection approach is proposed to implement a deep learning-based label inspection system on edge computing environments. Since the label printed on the products during the production process contains important information related to the product, it is significantly to check the label information is correct. The proposed system uses the lightweight deep learning model that able to employ in the low-performance edge computing devices, and the two-stage object detection approach is applied to compensate for the low accuracy relatively. The proposed Two-Stage object detection approach consists of two object detection networks, Label Area Detection Network and Character Detection Network. Label Area Detection Network finds the label area in the product image, and Character Detection Network detects the words in the label area. Using this approach, we can detect characters precise even with a lightweight deep learning models. The SF-YOLO model applied in the proposed system is the YOLO-based lightweight object detection network designed for edge computing devices. This model showed up to 2 times faster processing time and a considerable improvement in accuracy, compared to other YOLO-based lightweight models such as YOLOv3-tiny and YOLOv4-tiny. Also since the amount of computation is low, it can be easily applied in edge computing environments.