• Journal of the Institute of Electronics Engineers of Korea SC
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• v.38 no.4
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• pp.11-19
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• 2001

In this paper, we study the convergence property of iterative learning control (ILC). First, we present a new method to prove the convergence of ILC using sup-norm. Then, we propose a new type of ILC algorithm adopting intervalized learning scheme and show that the monotone convergence of the output error can be obtained for a given time interval when the proposed ILC algorithm is applied to a class of linear dynamic systems. We also show that the divided time interval is affected from the learning gain and that convergence speed of the proposed learning scheme can be increased by choosing the appropriate learning gain. To show the effectiveness of the proposed algorithm, two numerical examples are given.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.45 no.1
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• pp.131-138
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• 1996

In this paper, we consider an iterative learning control system(ILCS) consisting of an iterative learning controller, a feedback controller and a controlled plant in the frequency domain. At first, we review the convergence of ILCS. And we give some design guidelines of the ILCS using a nominal model of the plant. Then we present the structured and the unstructured uncertainty bound which guarantees the convergence of the designed iterative learning controller. In particular, we analyze the relationship between the convergence and the magnitude and phase uncertainties. In order to show the usefulness of the proposed analysis and design guidelines, we present some simulation examples. (author). 13 refs., 5 figs.

• ETRI Journal
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• v.43 no.6
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• pp.1004-1012
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• 2021

The collaboration productively interacting between multi-agents has become an emerging issue in real-world applications. In reinforcement learning, multi-agent environments present challenges beyond tractable issues in single-agent settings. This collaborative environment has the following highly complex attributes: sparse rewards for task completion, limited communications between each other, and only partial observations. In particular, adjustments in an agent's action policy result in a nonstationary environment from the other agent's perspective, which causes high variance in the learned policies and prevents the direct use of reinforcement learning approaches. Unexpected social loafing caused by high dispersion makes it difficult for all agents to succeed in collaborative tasks. Therefore, we address a paradox caused by the social loafing to significantly reduce total returns after a certain timestep of multi-agent reinforcement learning. We further demonstrate that the collaborative paradox in multi-agent environments can be avoided by our proposed effective early stop method leveraging a metric for social loafing.

• International journal of advanced smart convergence
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• v.6 no.4
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• pp.67-72
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• 2017

Deep learning neural network becomes very popular nowadays due to the reason that it can learn a very complex dataset such as the image dataset. Although deep learning neural network can produce high accuracy on the image dataset, it needs a lot of time to reach the convergence stage. To solve the issue, we have proposed a scaling method to improve the neural network to achieve the convergence stage in a shorter time than the original method. From the result, we can observe that our algorithm has higher performance than the other previous work.

• Convergence Security Journal
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• v.16 no.5
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• pp.81-86
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• 2016

Recently, increasing security threats are not only interfering with business continuity of companies but they are al so causing serious problems on social and national levels. As violation of intellectual property rights increases due to growing competition between different companies and countries, companies are now required to follow various IT compliance regulations, under relevant legal obligations. This study proposed a model of convergence security compliance management by using machine learning, in order to help companies actively utilize IT compliance.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.5
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• pp.629-635
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• 1999

A second-order iterative learning control algorithm with feedback is proposed in this paper, in which a feedback term is added in the learning control scheme for the enhancement of convergence speed and robustness to disturbances or system parameter variations. The convergence proof of the proposed algorithm is givenl, and the sufficient condition for the convergence of the algorithm is provided. And it also includes the discussions about the convergence performance of the algorithm when the initial condition at the beginning of each iteration differs from the previous value of the initial. Simulation results show the validity and efficiency of the proposed algorithm.

• Journal of information and communication convergence engineering
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• v.2 no.1
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• pp.36-39
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• 2004

In this paper, a method of improving the learning speed and convergence rate is proposed to exploit the advantages of artificial neural networks and neuro-fuzzy systems. This method is applied to the XOR problem, n bit parity problem, which is used as the benchmark in the field of pattern recognition. The method is also applied to the recognition of digital image for practical image application. As a result of experiment, it does not always guarantee convergence. However, the network showed considerable improvement in learning time and has a high convergence rate. The proposed network can be extended to any number of layers. When we consider only the case of the single layer, the networks had the capability of high speed during the learning process and rapid processing on huge images.

• Proceedings of the Korea Information Processing Society Conference
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• 2018.05a
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• pp.350-351
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• 2018

최근 의료 현장에 인공지능 기술의 도입이 가속화 되고 있다. 특히, 의료영상 분석 분야의 관련된 기 시스템 및 소프트웨어의 패러다임을 변화시키고 있다. 본 연구는 인공지능 기술을 적용하기 위한 학습의료영상 구성을 제안하고 이를 기반으로 X-ray 영상 중 손부위에 적용하여 오른손과 왼손을 판별하는 응용에 적용하였다. 그리고 Deep Learning Algorithm의 CNN을 개선하여 개발한 Advanced GoogLeNet를 적용하여 97%이상의 정확도를 보였다. 본 연구를 통해 얻어진 인공지능에 적용하기 위한 학습데이터 셋 구성과 개선된 알고리즘은 다양한 의료영상분석에 적용하고자 한다.

• Journal of Korea Multimedia Society
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• v.21 no.12
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• pp.1459-1466
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• 2018

Robots which detect human and recognize action are important factors for human interaction, and many researches have been conducted. Recently, deep learning technology has developed and learning based robot's technology is a major research area. These studies require a database to learn and evaluate for intelligent human perception. In this paper, we propose a multi-modal sensor-based image database condition considering the security task by analyzing the image database to detect the person in the outdoor environment and to recognize the behavior during the running of the robot.

• International Journal of Internet, Broadcasting and Communication
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• v.13 no.4
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• pp.129-134
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• 2021

Since the first semester of 2020, domestic and overseas universities mostly provided untact online classes and limitedly provided face-to-face classes due to COVID-19 in operating courses. The convergence subjects provided in undergraduate courses attach importance to contents-centered, design-based, hands-on education, and field experience. In the situation where online education was not revitalized, instructors in charge of convergence subjects had difficulty in developing online class materials, and students' satisfaction with the classes was not high. Especially, a problem was raised that students taking the convergence subjects that included practice had difficulty in communicating with the instructors. We would investigate the present condition of distance learning in domestic universities, which came suddenly due to the global pandemic of infectious disease and make suggestions for effective distance learning in the coming era of Metaverse by emphasizing the interaction and communication between instructors and learners through an analysis of distance learning of a convergence subject.