• Smart Media Journal
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• v.11 no.11
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• pp.25-31
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• 2022

Deep learning-based image processing is essential for autonomous vehicles. To process road images in real-time in a System-on-Chip (SoC) environment, we need to execute deep learning models on a NPU (Neural Procesing Units) specialized for deep learning operations. In this study, we imported seven open-source image processing deep learning models, that were developed on GPU servers, to Texas Instrument Deep Learning (TIDL) NPU environment. We confirmed that the models imported in this study operate normally in the SoC virtual environment through performance evaluation and visualization. This paper introduces the problems that occurred during the migration process due to the limitations of NPU environment and how to solve them, and thereby, presents a reference case worth referring to for developers and researchers who want to port deep learning models to SoC environments.

• Journal of the Korea Society of Computer and Information
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• v.29 no.4
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• pp.31-38
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• 2024

This study proposes hyper-parameter settings for developing a generative AI-based learning support tool to facilitate programming education in online distance learning. We implemented an experimental tool that can set research hyper-parameters according to three different learning contexts, and evaluated the quality of responses from the generative AI using the tool. The experiment with the default hyper-parameter settings of the generative AI was used as the control group, and the experiment with the research hyper-parameters was used as the experimental group. The experiment results showed no significant difference between the two groups in the "Learning Support" context. However, in other two contexts ("Code Generation" and "Comment Generation"), it showed the average evaluation scores of the experimental group were found to be 11.6% points and 23% points higher than those of the control group respectively. Lastly, this study also observed that when the expected influence of response on learning motivation was presented in the 'system content', responses containing emotional support considering learning emotions were generated.

• Journal of the Korea Computer Industry Society
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• v.3 no.8
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• pp.1007-1018
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• 2002

A systematic and multi-dimensional analysis in evaluation results may play a role of providing both learners and instructors with essential information. Conventional types of evaluation research have a tendency of partiality for developing evaluation tools, and analyzing the evaluation results has mostly been fragmentary and a one-dimensional analysis. In this study, through analyzing evaluation results in a multi-dimensional way, a multi-dimensional evaluation result analyzing system was developed for the purpose of providing various information for both learners and instructors to accomplish quality learning teaching. Dimensions are classified into four dimensions including period, student, difficulty degree, and evaluation domain. Analysis results are presented in various types of Dcube, graphs, and spreadsheets.

• KIISE Transactions on Computing Practices
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• v.24 no.3
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• pp.122-128
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• 2018

In recent years, virtual training has attracted a lot of attention because it is as effective as traditional in-person training and provides more cost-effective and safer learning environment. However, the existing virtual training systems rely on the evaluator's qualitative judgement when evaluating and analyzing trainees' performance. Additionally, evaluation and analysis functions are only available on certain systems so those functions need to be developed for each system. In this paper, we propose an extensible evaluation and analysis system for virtual training using experiential knowledge collected from experts for providing effective evaluation and analysis. Specifically, we provide a method of applying Open API so that the proposed system works with different types of virtual training system. In addition, the experiential knowledge is constructed in advance for the evaluation and analysis so that the efficiency of the evaluation with the comparison target is increased. This experiential knowledge can be quantitatively compared to trainees' performance according to the proposed evaluation and analysis procedures.

• KEPCO Journal on Electric Power and Energy
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• v.7 no.1
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• pp.115-123
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• 2021

Through machine learning-based load prediction, it is possible to prevent excessive power generation or unnecessary economic investment by estimating the appropriate amount of facility investment in consideration of the load that will increase in the future or providing basic data for policy establishment to distribute the maximum load. However, in order to secure the reliability of the developed load prediction model in the field, the performance comparison verification between the distribution line load prediction models must be preceded, but a comparative performance verification system between the distribution line load prediction models has not yet been established. As a result, it is not possible to accurately determine the performance excellence of the load prediction model because it is not possible to easily determine the likelihood between the load prediction models. In this paper, we developed a reliability verification system for load prediction models including a method of comparing and verifying the performance reliability between machine learning-based load prediction models that were not previously considered, verification process, and verification result visualization methods. Through the developed load prediction model reliability verification system, the objectivity of the load prediction model performance verification can be improved, and the field application utilization of an excellent load prediction model can be increased.

• Journal of the Korean Operations Research and Management Science Society
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• v.25 no.3
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• pp.137-154
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• 2000

This paper suggests a balanced scorecard (BSC) framework for measuring and evaluating the performance of knowledge workers in professional service firms(PSFs) which are typical knowldege-based organizations. As a strategic learning system, the balanced scorecard allows business leaders to drive and modify their business strategies based on the balanced measurement of key performance indicators(KPIs), which are basically divided into four domains such as financial achievement, customer orientation, internal business process, and innovation and learning. Conducting a focused case study on performance evaluation of knowledge workers from a balanced viewpoint, we could evaluate their competency and potential in more comprehensive manner. We also employ the analytic hierarchy process (AHP) approach for derive relative weights of key performance indicators and link it to a spreadsheet model for rating the individual performance of knowledge workers in a systematic way.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.2
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• pp.313-318
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• 2021

The advent of the Deep Learning has applied to many industrial and general applications having an impact on our lives these days. Certain type of machine learning model is needed to be designed for a specific problem of field. Recently, there are many instances to solve the various COVID-19 related problems using deep learning model. Therefore, in this paper, a deep learning model for predicting number of outpatients of a hospital in advance is suggested. The suggested deep learning model is designed by using the Keras in Jupyter Notebook. The prediction result is being analyzed with the real data in graph, as well as the loss rate with some validation data to verify either for the underfitting or the overfitting.

• Korean Journal of Artificial Intelligence
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• v.11 no.2
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• pp.19-27
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• 2023

In recent times, an exponential increase in Internet traffic has been observed as a result of advancing development of the Internet of Things, mobile networks with sensors, and communication functions within various devices. Further, the COVID-19 pandemic has inevitably led to an explosion of social network traffic. Within this context, considerable attention has been drawn to research on network traffic analysis based on machine learning. In this paper, we design and develop a new machine learning framework for network traffic analysis whereby normal and abnormal traffic is distinguished from one another. To achieve this, we combine together well-known machine learning algorithms and network traffic analysis techniques. Using one of the most widely used datasets KDD CUP'99 in the Weka and Apache Spark environments, we compare and investigate results obtained from time series type analysis of various aspects including malicious codes, feature extraction, data formalization, network traffic measurement tool implementation. Experimental analysis showed that while both the logistic regression and the support vector machine algorithm were excellent for performance evaluation, among these, the logistic regression algorithm performs better. The quantitative analysis results of our proposed machine learning framework show that this approach is reliable and practical, and the performance of the proposed system and another paper is compared and analyzed. In addition, we determined that the framework developed in the Apache Spark environment exhibits a much faster processing speed in the Spark environment than in Weka as there are more datasets used to create and classify machine learning models.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.35 no.5
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• pp.259-266
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• 2022

In this study, we developed a deep learning and vision sensor-based assembly performance evaluation method isfor prefabricated steel structures. The assembly parts were segmented using a modified version of the receptive field block convolution module inspired by the eccentric function of the human visual system. The quality of the assembly was evaluated by detecting the bolt holes in the segmented assembly part and calculating the bolt hole positions. To validate the performance of the evaluation, models of standard and defective assembly parts were produced using a 3D printer. The assembly part segmentation network was trained based on the 3D model images captured from a vision sensor. The sbolt hole positions in the segmented assembly image were calculated using image processing techniques, and the assembly performance evaluation using the k-nearest neighbor algorithm was verified. The experimental results show that the assembly parts were segmented with high precision, and the assembly performance based on the positions of the bolt holes in the detected assembly part was evaluated with a classification error of less than 5%.

• Journal of the Korea Convergence Society
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• v.10 no.7
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• pp.411-419
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• 2019

The purpose of this study is to examine nursing students' satisfaction and to analyze satisfaction difference according to the general characteristics of the research subjects in blended learning. A survey was conducted for 4th grade nursing students who took 'Introduction to Nursing I (1)' course and agreed to participate in the study. 122 answers out of total 142 were analyzed. The findings are as follows; first, students' satisfaction was topped by off-line learning, followed by on-line learning, and blended learning; second, there was a difference in on-line learning satisfaction depending on the gender among the general characteristics of the research subjects, and also there was a significant difference in off-line learning satisfaction depending on the self-evaluation on grade; third, students pointed out that what needs to be improved most is technical support and system. This study would provide the measures to improve utilization and effectiveness of blended learning, and the basic data to establish university policies for the blended learning.