• Spatial Information Research
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• v.2 no.2
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• pp.175-188
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• 1994

A competitive learning network was proposed as unsupervised training method of remote sensing data, Its performance and computational re¬quirements were compared with conventional clustering techniques such as Se¬quential and K - Means. An airborne remote sensing data set was used to study the performance of these classifiers. The proposed algorithm required a little more computational time than the conventional techniques. However, the perform¬ance of competitive learning network algorithm was found to be slightly more than those of Sequential and K - Means clustering techniques.

• 제어로봇시스템학회:학술대회논문집
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• 1989.10a
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• pp.653-662
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• 1989

Cerebellar Model Arithmetic Controller (CMAC) has been introduced as an adaptive control function generator. CMAC computes control functions referring to a distributed memory table storing functional values rather than by solving equations analytically or numerically. CMAC has a unique mapping structure as a coarse coding and supervisory delta-rule learning property. In this paper, learning aspects and a convergence of the CMAC were investigated. The efficient training algorithms were developed to overcome the limitations caused by the conventional maximum error correction training and to eliminate the accumulated learning error caused by a sequential node training. A nonlinear function generator and a motion generator for a two d.o.f. manipulator were simulated. The efficiency of the various learning algorithms was demonstrated through the cpu time used and the convergence of the rms and maximum errors accumulated during a learning process. A generalization property and a learning effect due to the various gains were simulated. A uniform quantizing method was applied to cope with various ranges of input variables efficiently.

• 한국기계연구소 소보
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• s.19
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• pp.93-115
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• 1989

Cerebellar Model Arithmetic Controller(CMAC) has been introduced as an adaptive control function generator. CMAC computes control functions referring to a distributed memory table storing functional values rather than by solving equations analytically or numerically. CMAC has a unique mapping structure as a coarse coding and supervisory delta-rule learning property. In this paper, learning aspects and a convergence of the CMAC were investigated. The efficient training algorithms were developed to overcome the limitations caused by the conventional maximum error correction training and to eliminate the accumulated learning error caused by a sequential node training. A nonlinear function generator and a motion generator for a two d. o. f. manipulator were simulated. The efficiency of the various learning algorithms was demonstrated through the cpu time used and the convergence of the rms and maximum errors accumulated during a learning process; A generalization property and a learning effect due to the various gains were simulated. A uniform quantizing method was applied to cope with various ranges of input variables efficiently.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.458-459
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• 2021

Recently, researches using deep learning technology based on Wi-Fi fingerprints have been conducted for accurate services in indoor location-based services. Among the deep learning models, an RNN model that can store information from the past can store continuous movements in indoor positioning, thereby reducing positioning errors. At this time, continuous sequential data is required as training data. However, since Wi-Fi fingerprint data is generally managed only with signals for a specific location, it is inappropriate to use it as training data for an RNN model. This paper proposes a path generation method through prediction of a moving path based on Wi-Fi fingerprint data extended to region data through clustering to generate sequential input data of the RNN model.

• The Journal of Korea Robotics Society
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• v.16 no.1
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• pp.56-63
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• 2021

As the use of robots in service area increases, research has been conducted to replace human tasks in daily life with robots. Among them, this study focuses on the tidy-up task on a desk using a robot arm. The order in which tidy-up motions are carried out has a great impact on the success rate of the task. Therefore, in this study, a neural network-based method for determining the priority of the tidy-up motions from the input image is proposed. Reinforcement learning, which shows good performance in the sequential decision-making process, is used to train such a task planner. The training process is conducted in a virtual tidy-up environment that is configured the same as the actual tidy-up environment. To transfer the learning results in the virtual environment to the actual environment, the input image is preprocessed into a segmented image. In addition, the use of a neural network that excludes unnecessary tidy-up motions from the priority during the tidy-up operation increases the success rate of the task planner. Experiments were conducted in the real world to verify the proposed task planning method.

• Journal of the Korean earth science society
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• v.24 no.6
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• pp.495-507
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• 2003

The purpose of this study was three-folded to suggest the Simplifying Conditions Method (SCM) as a means of task analysis and sequencing of instructional content, to develop teaching-learning materials by analyzing part of the geological time scale of the earth science and finally to analyze the effectiveness of this method. SCM began by simplifying a complex task into the basic components by eliminating various complexities, which produced a simple representative of the entire task. The next step was to relax conditions on the basic version one by one, thereby gradually introducing progressively more complex tasks to the students. This sequential strategy enabled students to understand the task holistically and to acquire authentic skills from very onset of the course. Moreover, Early mastery of skills enhances the effectiveness and efficiency of instruction. The result of this study revealed that instruction through SCM was more effective in developing students' self-directed learning characteristics and academic achievement than instruction through sequential task analysis methodology.

• Journal of Korea Multimedia Society
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• v.6 no.5
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• pp.788-796
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• 2003

This paper proposes using dynamic momentum for squential learning method. Using The dynamic momentum improves convergence speed and performance by the variable momentum, also can identify it in the RMSE(root mean squared error). The proposed method is reflected using variable momentum according to current state. While static momentum is equally influenced on the whole, dynamic momentum algorithm can control the convergence rate and performance. According to the variable change of momentum by training. Unlike former classification and regression problems, this paper confirms both performance and regression rate of the dynamic momentum. Using RMSE(root mean square error ), which is one of the regression methods. The proposed dynamic momentum has been applied to the kernel adatron and kernel relaxation as the new sequential learning method of support vector machine presented recently. In order to show the efficiency of the proposed algorithm, SONAR data, the neural network classifier standard evaluation data, are used. The simulation result using the dynamic momentum has a better convergence rate, performance and RMSE than those using the static moment, respectively.

• Journal of IKEEE
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• v.24 no.4
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• pp.991-997
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• 2020

Reinforcement learning has been applied mainly in sequential decision-making problems. Especially in recent years, reinforcement learning combined with neural networks has brought successful results in previously unsolved fields. However, reinforcement learning using deep neural networks has the disadvantage that it is too complex for immediate use in the field. In this paper, we implemented path planning algorithm for mobile robots using Q-learning, one of the easy-to-learn reinforcement learning algorithms. We used real-time Q-learning to update the Q-table in real-time since the Q-learning method of generating Q-tables in advance has obvious limitations. By adjusting the exploration strategy, we were able to obtain the learning speed required for real-time Q-learning. Finally, we compared the performance of real-time Q-learning and DQN.

• Perspectives in Nursing Science
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• v.7 no.1
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• pp.36-42
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• 2010

Purpose: This study was done to identify the preferences for learning style and the degree of self-directed learning and influencing factors on it among nursing students working on a Bachelor of Science in a nursing program at Suwon. Methods: The study sample included 156 nursing students. A self-report questionnaire was used to assess the data. The data was analyzed using the SPSS/WIN program for descriptive and inferential statistics. Results: Most of the students preferred lectures rather than discussion or team projects as a teaching method. Students preferred deliberating, sensing, and the use of visuals for their learning style. In addition, they favored sequential learning over comprehensive learning. Self directed learning had better outcomes in 3rd and 4th year students than 1st or 2nd year students. Additionally, active learners and high achievers who had a good GPA showed higher self directed learning than the others. Conclusion: In order to maximize students' self-directed learning, study guidance will be necessary for freshmen and for some who experience difficulties in studying nursing courses. Nursing faculty members should pay close attention to facilitate student's self directed learning, and encourage more discussions in the classes.

• Journal of Fisheries and Marine Sciences Education
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• v.29 no.3
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• pp.909-919
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• 2017

The purpose of this study was to apply the learning clinic program to the maladjusted children to help the cognitive processing style, sense type and learning strategy. The results were as follows. First, the cognitive processing style of low-grade elementary school children is divided into the concept of sequential low-order style, which analyzes information sequentially and consecutively, concrete thinking style that processes real and direct information coming in from outside, and invisible principle or information. The abstract cognitive thinking style improved after the process before the program proceeded. However, There was no meaningful result in the simultaneous processing cognitive style which had excellent intuition and emotion and likes change. Second, the temporal lobe in which the linguistic activity is viewed, heard and spoken in the sensory type, the function of the occipital lobe in which the character or the language is processed is improved, but the function of the parietal lobe in moving and manipulating the body is not significant. Finally, factors that contribute to learning such as sincerity, learning initiative, study method, study habits, and concentration are helpful in learning and school life.