• 제어로봇시스템학회:학술대회논문집
• /
• 2003.10a
• /
• pp.142-145
• /
• 2003

Many control applications using Neural Network need a priori information about the objective system. But it is impossible to get exact information about the objective system in real world. To solve this problem, several control methods were proposed. Reinforcement learning control using neural network is one of them. Basically reinforcement learning control doesn't need a priori information of objective system. This method uses reinforcement signal from interaction of objective system and environment and observable states of objective system as input data. But many methods take too much time to apply to real-world. So we focus on faster learning to apply reinforcement learning control to real-world. Two data types are used for reinforcement learning. One is reinforcement signal data. It has only two fixed scalar values that are assigned for each success and fail state. The other is observable state data. There are infinitive states in real-world system. So the number of observable state data is also infinitive. This requires too much learning time for applying to real-world. So we try to reduce the number of observable states by classification of states with Self-Organizing Map. We also use neural dynamic programming for controller design. An inverted pendulum on the cart system is simulated. Failure signal is used for reinforcement signal. The failure signal occurs when the pendulum angle or cart position deviate from the defined control range. The control objective is to maintain the balanced pole and centered cart. And four states that is, position and velocity of cart, angle and angular velocity of pole are used for state signal. Learning controller is composed of serial connection of Self-Organizing Map and two Multi-layer Feed-Forward Neural Networks.

• Journal of Institute of Control, Robotics and Systems
• /
• v.3 no.6
• /
• pp.594-601
• /
• 1997

A new algorithm is proposed for training the multilayer perceptrion(MLP) in pattern classification problems to accelerate the learning speed. It is shown that the sigmoid activation function of the output node can have deterimental effect on the performance of learning. To overcome this detrimental effect and to use the information fully in supervised learning, an objective function for binary modes is proposed. This objective function is composed with two new output activation functions which are selectively used depending on desired values of training patterns. The effect of the objective function is analyzed and a training algorithm is proposed based on this. Its performance is tested in several examples. Simulation results show that the performance of the proposed method is better than that of the conventional error back propagation (EBP) method.

• Korean Medical Education Review
• /
• v.10 no.2
• /
• pp.19-24
• /
• 2008

Purpose: The 'learning cycle' proposed by Guilbert in 1981 has been accredited as an effective and useful model for curriculum design. Three components of learning cycle, learning objective, instructional method, and assessment are connected organically and form basic structure of curriculum. In this study, we intend to analyze how the learning cycle and its three components are applied to present medical curriculum and examine the points at issue of the learning cycle in medical education. Also, we try to identify the educational significance of the leaning cycle in medical education. Results: First, concerning the learning objective, it was identified that impractical and abstract expressions are major controversial points. Also, there is a need to make learning objectives covering entire medical curriculum. Second, because of various structural problems, it is hard to practice new and various instructional methods. Third, even though there is a growing need for medical curriculum to develop and utilize more various and detailed assessment and evaluation, it was revealed that only are standardized and traditional assessments mainly used. Conclusion: Synthetically, we have some suggestions as follows. First, it is necessary to specify and actualize the learning objectives. Also, instructional methods and assessments should be diversified. And finally, there is a need to build organic and delicate medical curriculum by applying the learning cycle to medical education more actively.

• IEMEK Journal of Embedded Systems and Applications
• /
• v.19 no.2
• /
• pp.107-114
• /
• 2024

Mobile ad hoc networks represent self-configuring networks of mobile devices that communicate without relying on a fixed infrastructure. However, traditional routing protocols in such networks encounter challenges in selecting efficient and reliable routes due to dynamic nature of these networks caused by unpredictable mobility of nodes. This often results in a failure to meet the low-delay and low-energy consumption requirements crucial for such networks. In order to overcome such challenges, our paper introduces a novel multi-objective and adaptive routing scheme based on the Q-learning reinforcement learning algorithm. The proposed routing scheme dynamically adjusts itself based on measured network states, such as traffic congestion and mobility. The proposed approach utilizes Q-learning to select routes in a decentralized manner, considering factors like energy consumption, load balancing, and the selection of stable links. We present a formulation of the multi-objective optimization problem and discuss adaptive adjustments of the Q-learning parameters to handle the dynamic nature of the network. To speed up the learning process, our scheme incorporates informative shaped rewards, providing additional guidance to the learning agents for better solutions. Implemented on the widely-used AODV routing protocol, our proposed approaches demonstrate better performance in terms of energy efficiency and improved message delivery delay, even in highly dynamic network environments, when compared to the traditional AODV. These findings show the potential of leveraging reinforcement learning for efficient routing in ad hoc networks, making the way for future advancements in the field of mobile ad hoc networking.

• Journal of the Korean Institute of Educational Facilities
• /
• v.18 no.2
• /
• pp.25-33
• /
• 2011

This study performs the objective evaluation on simple comparison and calculation works by students under LED illumination conditions according to three types of color temperatures (7000K, 5000K, 3000K) in the classroom, Since the objective of this study is to develop an electric lighting conditions suitable for students in the classroom, the learning performance under three types of LED illumination conditions were analyzed. The 4 kinds of simple tests concerning with learning performance were developed and test results under natural light and LED illumination by 3 types of color temperatures were analyzed. There were differences by t-test in most cases among simple experiment results of different illumination conditions ($p{\leq}.05$). It was confirmed that illumination condition plays an important role when students simply compare words or perform arithmetic calculations. The experimental results of this study might be applied to designing better luminous environment.

• Journal of Korean Academy of Nursing
• /
• v.40 no.2
• /
• pp.182-190
• /
• 2010

Purpose: This study was done to identify self-directed learning readiness, achievement goal orientations, learning satisfaction and learning achievement, and to evaluate the factors affecting learning achievement for nursing students using a web-based Health Assessment e-Book. Methods: The research design was a cross-sectional study with a structured questionnaire and data were collected before using the web-based Health Assessment e-Book and 1 week after finishing. The participants were 80 nursing students who were taking the Health Assessment class from March to June 2009. Results: Mean score for subjective learning achievement was 31.26 and for objective learning achievement, 69.25. Subjective and objective learning achievement were positively correlated with self-directed learning readiness, mastery goal, attitude toward distance education, and learning satisfaction. In subjective learning achievement, learning satisfaction and mastery goal were significant predictive factors and explained 64% of the variance. Objective learning achievement was significantly predicted by learning satisfaction and self-directed learning readiness, which explained 24% of the variance. Conclusion: Learning satisfaction, mastery goal and self-directed learning readiness were found to be very important factors associated with learning achievement for nursing students using a web-based Health Assessment e-Book. To provide high quality and effective web-based courses and to improve nursing students' learning achievement and learning satisfaction, educators should consider the learner's characteristics from the initial stages of lecture planning.

• Journal of Korean Elementary Science Education
• /
• v.35 no.1
• /
• pp.39-53
• /
• 2016

This study aimed to understand students' learning in elementary science classes in terms of participatory perspective. Participatory perspective is based on the participationist views on learning. Based on the participatory perspective, this study used two concepts of participationism: 'the changes of learning on commognition' of Sfard (2007) and 'the identity' of Wenger (1998/2007). Based on these concepts, four episodes of an elementary science class were analyzed. The results showed that students carried out their learning from objective-level learning to meta-level learning. And students defined who they are by identifying and negotiating scientific meaning during the learning. These results showed students become members of science community through their participations in science class.

• International Journal of Computer Science & Network Security
• /
• v.22 no.6
• /
• pp.203-211
• /
• 2022

Multi-objective optimization problems (MOPs) arise in many real-world applications. MOPs involve two or more objectives with the aim to be optimized. With these problems improvement of one objective may led to deterioration of another. The primary goal of most multi-objective evolutionary algorithms (MOEA) is to generate a set of solutions for approximating the whole or part of the Pareto optimal front, which could provide decision makers a good insight to the problem. Over the last decades or so, several different and remarkable multi-objective evolutionary algorithms, have been developed with successful applications. However, MOEAs are still in their infancy. The objective of this research is to study how to use and apply machine learning (ML) to improve evolutionary multi-objective optimization (EMO). The EMO method is the multi-objective evolutionary algorithm based on decomposition (MOEA/D). The MOEA/D has become one of the most widely used algorithmic frameworks in the area of multi-objective evolutionary computation and won has won an international algorithm contest.

• Journal of information and communication convergence engineering
• /
• v.20 no.1
• /
• pp.22-30
• /
• 2022

The reinforcement learning algorithm has proven its potential in solving sequential decision-making problems under uncertainties, such as finding paths to route data packets in wireless sensor networks. With reinforcement learning, the computation of the optimum path requires careful definition of the so-called reward function, which is defined as a linear function that aggregates multiple objective functions into a single objective to compute a numerical value (reward) to be maximized. In a typical defined linear reward function, the multiple objectives to be optimized are integrated in the form of a weighted sum with fixed weighting factors for all learning agents. This study proposes a reinforcement learning -based routing protocol for wireless sensor network, where different learning agents prioritize different objective goals by assigning weighting factors to the aggregated objectives of the reward function. We assign appropriate weighting factors to the objectives in the reward function of a sensor node according to its hop-count distance to the sink node. We expect this approach to enhance the effectiveness of multi-objective reinforcement learning for wireless sensor networks with a balanced trade-off among competing parameters. Furthermore, we propose SDN (Software Defined Networks) architecture with multiple controllers for constant network monitoring to allow learning agents to adapt according to the dynamics of the network conditions. Simulation results show that our proposed scheme enhances the performance of wireless sensor network under varied conditions, such as the node density and traffic intensity, with a good trade-off among competing performance metrics.

• International Journal of Contents
• /
• v.15 no.2
• /
• pp.59-74
• /
• 2019

The objective of this study was to analyze the influence of physical education classes based on Flipped Learning on self-directed learning abilities and learning attitude towards these classes, for middle school students. The study selected 90 students as an experimental group (3 classes) and 97 students as a control group (3 classes), among 240 students of the first-year students attending a middle school located at Jeonju City of South Korea, applying convenience sampling, one of the non-probability sampling methods. For the experimental group, 36 sessions of physical education classes were held for 14 weeks, while the control group received teacher-centered classes. Comparing the results with the control group, the experimental group showed significant differences in terms of all sub factors of self-directed learning abilities, namely; desire for learning, learning objective establishment, basic self-management abilities, selection of learning strategy and self-reflection. Moreover, the experimental group manifested significant differences in terms of all sub factors of attitude towards the physical education subjects, namely; positive emotions, negative emotions, health & physical strength, interpersonal relations, physical activities & movements, and active participation & positive performance. From the findings, it can be considered that physical education classes based on Flipped Learning contributed to improving self-directed learning abilities and attitude towards physical education classes. This result can serve as a significant basic material for designing and performing classes in raising the understanding of Flipped Learning and effectively applying Flipped Learning in physical education classes.