• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권9호
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• pp.2942-2960
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• 2022

The core of cognitive radio is the problem concerning intelligent decision-making for communication parameters, the objective of which is to find the most appropriate parameter configuration to optimize transmission performance. The current algorithms have the disadvantages of high dependence on prior knowledge, large amount of calculation, and high complexity. We propose a new decision-making model by making full use of the interactivity of reinforcement learning (RL) and applying the Q-learning algorithm. By simplifying the decision-making process, we avoid large-scale RL, reduce complexity and improve timeliness. The proposed model is able to find the optimal waveform parameter configuration for the communication system in complex channels without prior knowledge. Moreover, this model is more flexible than previous decision-making models. The simulation results demonstrate the effectiveness of our model. The model not only exhibits better decision-making performance in the AWGN channels than the traditional method, but also make reasonable decisions in the fading channels.

• 지식경영연구
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• 제13권3호
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• pp.105-125
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• 2012

Previous studies have focused on individual and organizational learning. Amid an increasingly complex business environment, a team system designed to improve flexibility and adaptability constitutes the most basic part of an organization. Still, team learning has rarely been discussed. In addition, team learning behavior, despite being an important part of a team process, is often mentioned as a team-level outcome variable. Given that team learning behavior involves constant changes in thinking and behavior, a shared belief among team members is needed in order to positively influence innovative performance of a team. In spite of that, there has been only limited discussion of it. Besides, few domestic studies have dealt with R&D teams that can clearly demonstrate team learning behavior and team innovative performance. This study is an empirical analysis of the impact of team efficacy on team innovative performance and the mediating role of team learning behavior based on materials collected from team leaders and their immediate subordinates in 268 R&D teams. The analysis showed that team learning behavior actually has a positive effect on team innovative performance. Team efficacy also turned out to have a positive influence on team learning behavior. Lastly, the study found that team learning behavior played a mediating role in the relationship between team efficacy and team innovative performance. Based on those results, the study has identified implications and suggested directions for future research.

• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 2001년도 The Pacific Aisan Confrence On Intelligent Systems 2001
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• pp.59-64
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• 2001

The robot soccer simulation game is a dynamic multi-agent environment. In this paper we suggest a new reinforcement learning approach to each agent's dynamic positioning in such dynamic environment. Reinforcement learning is the machine learning in which an agent learns from indirect, delayed reward an optimal policy to chose sequences of actions that produce the greatest cumulative reward. Therefore the reinforcement learning is different from supervised learning in the sense that there is no presentation of input pairs as training examples. Furthermore, model-free reinforcement learning algorithms like Q-learning do not require defining or learning any models of the surrounding environment. Nevertheless it can learn the optimal policy if the agent can visit every state- action pair infinitely. However, the biggest problem of monolithic reinforcement learning is that its straightforward applications do not successfully scale up to more complex environments due to the intractable large space of states. In order to address this problem. we suggest Adaptive Mediation-based Modular Q-Learning (AMMQL)as an improvement of the existing Modular Q-Learning (MQL). While simple modular Q-learning combines the results from each learning module in a fixed way, AMMQL combines them in a more flexible way by assigning different weight to each module according to its contribution to rewards. Therefore in addition to resolving the problem of large state effectively, AMMQL can show higher adaptability to environmental changes than pure MQL. This paper introduces the concept of AMMQL and presents details of its application into dynamic positioning of robot soccer agents.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2003년도 추계학술대회
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• pp.1584-1589
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• 2003

Simple and effectively developed learning control logic is used to control vibration of U type Tuned Liquid Damper system. The purpose of this paper is design optimal control system to deal with unknown errors from nonlinearity and variation that cost modeling difficulty in complex structure and is followed with the desired behavior. Finally this hybrid control method applied to U type Tuned Liquid Damper structure gives the benefit from better performance of precision and stability of the structure by reducing vibration effect. This research leads to safety design in various structure to robust unspecified foreign disturbances such as earthquake.

• E2M - 전기 전자와 첨단 소재
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• 제11권11호
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• pp.1-8
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• 1998

This paper presents an arificial neuro-fuzzy technique based prtial discharge (PD) pattern classifier to power system application. This may require a complicated analysis method employ -ing an experts system due to very complex progressing discharge form under exter-nal stress. After referring briefly to the developments of artificical neural network based PD measurements, the paper outlines how the introduction of new emerging technology has resulted in the design of a number of PD diagnostic systems for practical applicaton of residual lifetime prediction. The appropriate PD data base structure and selection of learning data size of PD pattern based on fractal dimentsional and 3-D PD-normalization, extraction of relevant characteristic fea-ture of PD recognition are discussed. Some practical aspects encountered with unknown stress in the neuro-fuzzy techniques based real time PD recognition are also addressed.

• 전자공학회논문지CI
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• 제45권3호
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• pp.31-39
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• 2008

지금까지 이러닝 시스템을 통해서 학습 능력을 향상시키는 기술이 많이 나와 있다. 대부분의 이러닝 시스템에서 학습자들은 강의 자료와 학습문제를 통해서 학습을 한다. 그러나, 때로는 학습자간의 자료공유나 토론을 통해서 학습능력과 학습 의욕을 향상시킬 수 있다. 이 경우에 일반적으로 게시판을 통해서 학습 자료를 공유하거나 MSN과 같은 메신저를 사용하여 학습자들끼리 토론 및 자료를 공유한다. 하지만, 이와 같은 형태의 학습 공유 유형은 학습 자료가 주제별로 분류되어 있지 않기 때문에 학습자가 관련 자료를 검색하는 일이 쉽지 않다. 그 결과 학습에 크게 도움이 되지 않는다. 대부분의 텍스트 마이닝 기술은 문서데이터의 집합으로부터 요약 데이터를 추출하거나 유사한 문서의 집합을 분류하는 기술이다. 따라서, 본 논문에서 학습자가 학습능력을 향상시킬 수 있도록 이러닝 시스템에 텍스트 마이닝 기술을 적용하여 효과적으로 이러닝 자료를 분류하여 학습자에게 도움이 되는 시스템을 구현하고 평가하였다.

• 문화기술의 융합
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• 제9권3호
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• pp.647-654
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• 2023

딥러닝은 이미지, 텍스트와 같이 복잡한 데이터를 분류 및 인식하는데 유용한 방법으로 딥러닝 기법의 정확도는 딥러닝이 인터넷상의 AI 기반의 서비스를 유용하게 하는데 기초가 되었다. 그러나 딥러닝에서 훈련에 사용되는 방대한 양의 사용자 데이터는 사생활 침해 문제를 야기하였고 사진이나 보이스와 같이 사용자이 개인적이고 민감한 데이터를 수집한 기업들이 데이터들을 무기한으로 소유한다. 사용자들은 자신의 데이터를 삭제할 수 없고 사용되는 목적도 제한할 수 없다. 예를 들면, 환자 진료기록에 대한 딥러닝 기술을 적용하기 원하는 의료기관들과 같은 데이터소유자들은 사생활과 기밀유지 문제로 환자의 데이터를 공유할 수 없고 딥러닝 기술의 혜택을 받기 어렵다. 우리는 멀티 파티 시스템에서 다수의 작업자들이 입력 데이터집합을 공유하지 않고 신경망 모델을 공동으로 사용할 수 있는 프라이버시 보존 기술을 적용한 딥러닝 방법을 설계한다. 변형된 확률적 경사 하강에 기초한 최적화 알고리즘을 이용하여 하위 집합을 선택적으로 공유할 수 있는 방법을 이용하였고 결과적으로 개인정보를 보호하면서 학습 정확도를 증가시킨 학습을 할 수 있도록 하였다.

• Journal of Multimedia Information System
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• 제7권1호
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• pp.11-16
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• 2020

Deep Learning has become the most important technology in the field of artificial intelligence machine learning, with its high performance overwhelming existing methods in various applications. In this paper, an interactive window service based on object recognition technology is proposed. The main goal is to implement an object recognition technology using this deep learning technology to remove the existing eye tracking technology, which requires users to wear eye tracking devices themselves, and to implement an eye tracking technology that uses only usual cameras to track users' eye. We design an interactive system based on efficient eye detection and pupil tracking method that can verify the user's eye movement. To estimate the view-direction of user's eye, we initialize to make the reference (origin) coordinate. Then the view direction is estimated from the extracted eye pupils from the origin coordinate. Also, we propose a blink detection technique based on the eye apply ratio (EAR). With the extracted view direction and eye action, we provide some augmented information of interest without the existing complex and expensive eye-tracking systems with various service topics and situations. For verification, the user guiding service is implemented as a proto-type model with the school map to inform the location information of the desired location or building.

• 한국소음진동공학회논문집
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• 제23권5호
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• pp.438-444
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• 2013

This paper presents the replication of a desired vibration response by iterative learning control (ILC) system for a vibration motion replication actuator. The vibration motion replication actuator has parameter uncertainties including system nonlinearity and joint nonlinearity. Vehicle manufacturers worldwide are increasingly relying on road simulation facilities that put simulated loads and stresses on vehicles and subassemblies in order to reduce development time. Road simulation algorithm is the key point of developing road simulation system. With the rapid progress of digital signal processing technology, more complex control algorithms including iterative learning control can be utilized. In this paper, ILC algorithm was utilized to produce simultaneously the six channels of desired responses using the Stewart platform composed of six linear electro-magnetic actuators with Halbach magnet array. The convergence rate and accuracy showed reasonable results to meet the requirement. It shows that the algorithm is acceptable to replicate multi-channel vibration responses.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2000년도 하계학술대회 논문집 D
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• pp.2305-2307
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• 2000

In this paper we propose a Neuro-Fuzzy modeling method using mGA for complex nonlinear system. mGA has more effective and adaptive structure than sGA with respect to using the changeable-length string. This paper suggest a new coding method for applying the model's input and output data to the number of optimul rules of fuzzy models and the structure and parameter identifications of membership function simultaneously. The proposed method realize optimal fuzzy inference system using the learning ability of Neural network. For fine-tune of the identified parameter by mGA, back-propagation algorithm used for optimulize the parameter of fuzzy set. The proposed fuzzy modeling method is applied to a nonlinear system to prove the superiority of the proposed approach through compare with ANFIS.