• Journal of Advanced Navigation Technology
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• v.14 no.6
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• pp.874-881
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• 2010

A automatic flight control system(AFCS) of UAS needs to control its flight path along target path exactly as adjusts flight coefficient itself depending on static or dynamic changes of airplane's features such as type, size or weight. In this paper, we propose system which tunes control gain autonomously depending on change of airplane's feature in flight as adding MLM(Machine Learning Module) on AFCS. MLM is designed with Linear Regression algorithm and Reinforcement Learning and it includes EvM(Evaluation Module) which evaluates learned control gain from MLM and verified system. This system is tested on beaver FDC simulator and we present its analysed result.

• Journal of the Korea Institute of Military Science and Technology
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• v.22 no.2
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• pp.278-286
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• 2019

Conventional methods for selecting jamming techniques in electronic warfare are based on libraries in which a list of jamming techniques for radar signals is recorded. However, the choice of jamming techniques by the library is limited when modified signals are received. In this paper, we propose a method to predict the jamming technique for radar signals by using deep learning methods. Long short-term memory(LSTM) is a deep running method which is effective for learning the time dependent relationship in sequential data. In order to determine the optimal LSTM model structure for jamming technique prediction, we test the learning parameter values that should be selected, such as the number of LSTM layers, the number of fully-connected layers, optimization methods, the size of the mini batch, and dropout ratio. Experimental results demonstrate the competent performance of the LSTM model in predicting the jamming technique for radar signals.

• Nuclear Engineering and Technology
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• v.53 no.12
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• pp.3944-3951
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• 2021

Several reasons such as no free lunch theorem indicate that there is not a universal Feature selection (FS) technique that outperforms other ones. Moreover, some approaches such as using synthetic dataset, in presence of large number of FS techniques, are very tedious and time consuming task. In this study to tackle the issue of dependency of estimation accuracy on the selected FS technique, a methodology based on the heterogeneous ensemble is proposed. The performance of the major learning algorithms of neural network (i.e. the FFNN-BR, the FFNN-LM) in combination with the diverse FS techniques (i.e. the NCA, the F-test, the Kendall's tau, the Pearson, the Spearman, and the Relief) and different combination techniques of the heterogeneous ensemble (i.e. the Min, the Median, the Arithmetic mean, and the Geometric mean) are considered. The target parameters/transients of Bushehr nuclear power plant (BNPP) are examined as the case study. The results show that the Min combination technique gives the more accurate estimation. Therefore, if the number of FS techniques is m and the number of learning algorithms is n, by the heterogeneous ensemble, the search space for acceptable estimation of the target parameters may be reduced from n × m to n × 1. The proposed methodology gives a simple and practical approach for more reliable and more accurate estimation of the target parameters compared to the methods such as the use of synthetic dataset or trial and error methods.

• International Journal of Internet, Broadcasting and Communication
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• v.14 no.3
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• pp.285-291
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• 2022

The number of people enrolling in universities is rising due to the simplicity of applying and the benefit of earning a bachelor's degree. However, the on-time graduation rate has declined since plenty of students fail to complete their courses and take longer to get their diplomas. Even though there are various reasons leading to the aforementioned problem, it is crucial to emphasize the cause originating from the management and care of learners. In fact, understanding students' difficult situations and offering timely Number of Test data and advice would help prevent college dropouts or graduate delays. In this study, we present a machine learning-based method for early detection at-risk students, using data obtained from graduates of the Faculty of Information Technology, Dainam University, Vietnam. We experiment with several fundamental machine learning methods before implementing the parameter optimization techniques. In comparison to the other strategies, Random Forest and Grid Search (RF&GS) and Random Forest and Random Search (RF&RS) provided more accurate predictions for identifying at-risk students.

• Journal of the Korea Computer Graphics Society
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• v.27 no.3
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• pp.13-23
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• 2021

In this paper, we proposed a framework that generates the trajectory of a single rigid body based on its COM configuration and contact pose. Because we use a smaller input dimension than when we use a full body state, we can improve the learning time for reinforcement learning. Even with a 68% reduction in learning time (approximately two hours), the character trained by our network is more robust to external perturbations tolerating an external force of 1500 N which is about 7.5 times larger than the maximum magnitude from a previous approach. For this framework, we use centroidal dynamics to calculate the next configuration of the COM, and use reinforcement learning for obtaining a policy that gives us parameters for controlling the contact positions and forces.

• Journal of Korea Society of Digital Industry and Information Management
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• v.18 no.1
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• pp.79-91
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• 2022

In this study, the effects of self-efficacy, perceived usefulness, perceived ease of use, and depression on college students' academic persistence in the COVID-19 epidemic and the resulting non-face-to-face education situation were identified as mediating effects on learning satisfaction. In the second semester of 2020, a survey was conducted on students enrolled in a four-year university in Daegu and the data were statistically analyzed. The path coefficient was estimated by the Smart PLS bootstrap method and the significance of the path coefficient was verified. The Sobel Test was conducted to verify the mediating effect of academic continuity intention as a parameter. The research results can be summarized as follows. First, it was found that self-efficacy and perceived usefulness had a significant influence in the relationship with learning satisfaction. Second, the relationship between learning satisfaction and academic continuity intention was found to have a significant influence. Third, depression and ease of use did not show any significant influence in the relationship between learning satisfaction. Finally, a Sobel Test was conducted to verify the mediating effect of academic continuity intention with self-efficacy, usefulness, ease of use, and depression as independent variables and learning satisfaction as parameters. As a result of both regression analyses, it was found that β values decreased, and learning satisfaction had a mediating effect. As a result of this study, it is suggested that research to increase learner satisfaction and develop various contents to increase the effectiveness of education that can increase self-efficacy and perceived usefulness should be conducted in parallel. I think this study can be used as basic data in establishing measures to continue studying for college students in natural disaster situations or psychological crisis situations called COVID-19.

• The Journal of the Convergence on Culture Technology
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• v.9 no.2
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• pp.475-481
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• 2023

Softwares, especially like machine learning applications, affect human's life style tremendously. Accordingly, the importance of the cost model for softwares increases rapidly. As cost models, LOC(Line of Code) and M/M(Man-Month) estimates the quantitative aspects of the software. Differently from them, FP(Function Point) focuses on estimating the functional characteristics of software. FP is efficient in the aspect that it estimates qualitative characteristics. FP, however, has a limit for evaluating machine learning softwares because FP does not evaluate the critical factors of machine learning software. In this paper, we propose an extended function point(ExFP) that extends FP to adopt hyper parameter and the complexity of its optimization as the characteristics of the machine learning applications. In the evaluation reflecting the characteristics of machine learning applications. we reveals the effectiveness of the proposed ExFP.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.6
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• pp.1-7
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• 2023

This paper developed a training model to classify normal roads and flooded roads using artificial intelligence technology. We expanded the diversity of learning data using various data augmentation techniques and implemented a model that shows good performance in various environments. Transfer learning was performed using the CNN-based Resnet152v2 model as a pre-learning model. During the model learning process, the performance of the final model was improved through various parameter tuning and optimization processes. Learning was implemented in Python using Google Colab NVIDIA Tesla T4 GPU, and the test results showed that flooding situations were detected with very high accuracy in the test dataset.

• Journal of Distribution Science
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• v.13 no.12
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• pp.113-131
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• 2015

Purpose - In today's era of globalization, the competitive power of enterprises is growing fiercer, calling for organizations to be able to respond flexibly to survive and maintain predominance in competition. In turn, keen competition exists among enterprises for the systematic management of members' knowledge to secure predominance in such competition. Under such circumstances, SMEs must find and utilize positive causes for change that affect organizational performance. The objective of this study is to analyze the structural relationship between four factors known from prior research-a CEO's positive leadership, members' positive life positions, learning organization activities, and job engagement-and organizational performance. Research design, data, and methodology - To achieve this objective, this study established the following four research problems. First, do CEOs' positive leadership, members' positive life positions, learning organization activities, and job engagement affect organizational performance? Second, do CEOs' positive leadership, members' positive life positions, and learning organization activities affect job engagement? Third, do CEOs' positive leadership and members' positive life positions affect learning organization activities? Fourth, does CEOs' positive leadership affect members' positive life positions. Additionally, to achieve the objective of this study, the research model was selected on the basis of a documentary survey of 787 full-time employees at 100 SMEs, which was used to collect related data. Results - The following conclusions were drawn. First, a CEO's positive leadership directly affects members' positive life positions, learning organization activities, and job engagement. Second, positive leadership only indirectly affects organizational performance. That is, positive leadership has an indirect effect on organizational performance given the parameters of members' positive life positions, learning organization activities, and job engagement. Third, members' positive life positions directly affect learning organization activities and job engagement, but indirectly affect organizational performance with learning organization activities and job engagement as parameters. Fourth, learning organization activities directly affect job engagement and organizational performance. Additionally, learning organization activities indirectly affect organizational performance with job engagement as a parameter. Fifth, job engagement directly affects organizational performance. Conclusions - A CEO's positive leadership and members' positive life positions do not directly affect organizational performance but have a positive effect through learning organization activities and job engagement. In particular, CEOs' positive leadership was proven to be the major factor to affect members' positive life positions, learning organization attitudes, and job engagement, and learning organization activities and job engagement were found to be major factors that directly affect organizational performance. Considering these conclusions, the direct effect of a CEO's positive leadership on organizational performance is not statistically significant but seems to affect members' positive life positions, learning organization activities, and job engagement, which ultimately affects organizational performance. In addition, CEOs' positive leadership is an important factor that enhances the factors with the strongest effect on organizational performance-activities of learning organizations and job engagement.

• Journal of Korea Multimedia Society
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• v.6 no.1
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• pp.136-147
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• 2003

To achieve an optimal performance for a given problem, we need an integrative process of the parameter optimization via learning and the structure optimization via model selection. In this paper, we propose an efficient optimization method for improving generalization performance by considering the property of each sub-method and by combining them with common theoretical properties. First, weight parameters are optimized by natural gradient teaming with adaptive regularization, which uses a diverse error function. Second, the network structure is optimized by eliminating unnecessary parameters with natural pruning. Through iterating these processes, candidate models are constructed and evaluated based on the Bayesian Information Criterion so that an optimal one is finally selected. Through computational experiments on benchmark problems, we confirm the weight parameter and structure optimization performance of the proposed method.