• International Journal of Fuzzy Logic and Intelligent Systems
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• v.8 no.3
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• pp.220-224
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• 2008

This paper presents the polygon-based Q-leaning and Parallel SVM algorithm for object search with multiple robots. We organized an experimental environment with one hundred mobile robots, two hundred obstacles, and ten objects. Then we sent the robots to a hallway, where some obstacles were lying about, to search for a hidden object. In experiment, we used four different control methods: a random search, a fusion model with Distance-based action making (DBAM) and Area-based action making (ABAM) process to determine the next action of the robots, and hexagon-based Q-learning, and dodecagon-based Q-learning and parallel SVM algorithm to enhance the fusion model with Distance-based action making (DBAM) and Area-based action making (ABAM) process. In this paper, the result show that dodecagon-based Q-learning and parallel SVM algorithm is better than the other algorithm to tracking for object.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.3
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• pp.169-175
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• 1996

In this paper, a new parallel neural network system that performs dynamic competitive learning is proposed. Conventional learning mehtods utilize the full dimension of the original input patterns. However, a particular attribute or dimension of the input patterns does not necessarily contribute to classification. The proposed system consists of parallel neural networks with the reduced input dimension in order to take advantage of the information in each dimension of the input patterns. Consensus schemes were developed to decide the netowrks performs a competitive learning that dynamically generates output neurons as learning proceeds. Each output neuron has it sown class threshold in the proposed dynamic competitive learning. Because the class threshold in the proposed dynamic learning phase, the proposed neural netowrk adapts properly to the input patterns distribution. Experimental results with remote sensing and speech data indicate the improved performance of the proposed method compared to the conventional learning methods.

• Journal of Practical Engineering Education
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• v.15 no.1
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• pp.183-192
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• 2023

This study analyzes the performance of flipped learning, an offline class method conducted in a degree-linked work-learning parallel system. Training in the work-study parallel system, which is conducted as part of job competency improvement, has thoroughly adhered to the offline method, but in line with COVID-19, unlike before, it is changing in the direction of using the online method more actively. However, educational methods such as flipped learning are not new because the degree-linked operation is applied to the academic system and education method of universities and is practically the same form as general university education. Therefore, it is necessary to analyze the educational performance and complementary points of flipped learning, which has recently been expanded in the degree-linked work-study parallel system, considering the characteristics of this system, in which classes are held only on weekends. As a result of statistical analysis based on the survey, some of the outcomes of flipped learning have been confirmed, and in order to increase the performances, it is necessary to continuously seek out specific measures to encourage learning and expand communication between instructors and students.

• The Journal of Korea Robotics Society
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• v.16 no.4
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• pp.306-312
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• 2021

This paper describes localization of autonomous underwater vehicles(AUV), which can be used when some navigation sensor data are an outlier. In that situation, localization through existing navigation algorithms causes problems in long-range localization. Even if an outlier sensor data occurs once, problems of localization will continue. Also, if outlier sensor data is related to azimuth (direction of AUV), it causes bigger problems. Therefore, a parallel localization module, in which different algorithms are performed in a normal and abnormal situation should be designed. Before designing a parallel localization module, it is necessary to study an effective method in the abnormal situation. So, we propose a localization method through machine learning. For this method, a learning model consists of only Fully-Connected and trains through randomly contaminated real sea data. The ground truth of training is displacement between subsequent GPS data. As a result, average error in localization through the learning model is 0.4 times smaller than the average error in localization through the existing navigation algorithm. Through this result, we conclude that it is suitable for a component of the parallel localization module.

• Journal of Practical Engineering Education
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• v.15 no.1
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• pp.223-232
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• 2023

The Work-Study parallel program has been promoted as a key policy to resolve the mismatch between industrial sites and school education and realize a competency-centered society, and as of December 2022, 16,664 companies participated in the training. Learning companies play a very important role as education and training supply organizations that conduct field training. In this study, for the evaluation of learning companies participating in work-study program, the authors derive important factors that determine the quality of on-site education and training by analyzing the cognitive structure of experts in charge of the company and present evaluation indicators for learning enterprises. Therefore, it is intended to lay the foundation for promoting the quality of work-study parallel program.

• Journal of Institute of Control, Robotics and Systems
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• v.5 no.8
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• pp.914-922
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• 1999

A study on the improvement of tracking performance of a 3 DOF planar parallel manipulator is performed. A class of adaptive tracking control sheme is designed using self tuning adaptive fuzzy logic control theory. This control sheme is composed of three classical PD controller and a multi learning type self tuning adaptive fuzzy logic controller set. PD controller is tuned roughly by manual setting a priori and fuzzy logic controller is tuned precisely by the gradient descent method for a global solution during run-time, so the proposed control scheme is tuned more rapidly and precisely than the single learning type self tuning adaptive fuzzy logic control sheme for a local solution. The control performance of the proposed algorithm is verified through experiments.

• Industry Promotion Research
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• v.5 no.1
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• pp.47-52
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• 2020

In this study, parallel work-learning training, which was started in 2013 as a pre-employment promotion policy, is an important factor that determines the success or failure of training. As a time when various institutional supplementation is needed to encourage company participation, this study is to identify the factors of participation of companies participating in work-learning parallel. To this end, a questionnaire survey was conducted of companies participating in parallel work-learning in Chungnam, and the results were analyzed using the structural equation model. As a result of the study, the reason for the company's participation in parallel work-learning was firstly, 84% of government subsidy received education and training expenses. Second, 66% of workers were able to pay less than regular workers, and thirdly, it was easy to hire new employees in the field. 26%, 17% of them were invited by acquaintances for no particular reason. Therefore, the study suggests that participation in the work-learning parallel training contributes to the management costs, management of employee turnover, and human resource development. In future research, it is necessary to subdivide tests and estimates by conducting studies on regions, occupations, gender, wages, and years of service in Korea.

• Journal of Korea Society of Industrial Information Systems
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• v.26 no.4
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• pp.11-18
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• 2021

We propose a method that combines learning in a virtual environment and a real environment for indoor autonomous driving through reinforcement learning. In case of learning only in the real environment, it takes about 80 hours, but in case of learning in both the real and virtual environments, it takes 40 hours. There is an advantage in that it is possible to obtain optimized parameters through various experiments through fast learning while learning in a virtual environment and a real environment in parallel. After configuring a virtual environment using indoor hallway images, prior learning was carried out on the desktop, and learning in the real environment was conducted by connecting various sensors based on Jetson Xavier. In addition, in order to solve the accuracy problem according to the repeated texture of the indoor corridor environment, it was possible to determine the corridor wall object and increase the accuracy by learning the feature point detection that emphasizes the lower line of the corridor wall. As the learning progresses, the experimental vehicle drives based on the center of the corridor in an indoor corridor environment and moves through an average of 70 steering commands.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.1
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• pp.13-19
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• 2019

The accuracy of machine learning is greatly affected by amount of learning data and quality of data. Collecting existing Web-based learning data has danger that data unrelated to actual learning can be collected, and it is impossible to secure data transparency. In this paper, we propose a method for collecting data directly in parallel by blocks in a block - chain structure, and comparing the data collected by each block with data in other blocks to select only good data. In the proposed system, each block shares data with each other through a chain of blocks, utilizes the All-reduce structure of Parallel-SGD to select only good quality data through comparison with other block data to construct a learning data set. Also, in order to verify the performance of the proposed architecture, we verify that the original image is only good data among the modulated images using the existing benchmark data set.

• International Journal of Control, Automation, and Systems
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• v.1 no.1
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• pp.142-148
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• 2003

Reinforcement learning is considered as an important tool for robotic learning in unknown/uncertain environments. In this paper, we propose an evaluation function expressed in a vector form to realize multi-dimensional reinforcement learning. The novel feature of the proposed method is that learning one behavior induces parallel learning of other behaviors though the objectives of each behavior are different. In brief, all behaviors watch other behaviors from a critical point of view. Therefore, in the proposed method, there is cross-criticism and parallel learning that make the multi-dimensional learning process more efficient. By ap-plying the proposed learning method, we carried out multi-dimensional evaluation (reward) and multi-dimensional learning simultaneously in one trial. A special neural network (Q-net), in which the weights and the output are represented by vectors, is proposed to realize a critic net-work for Q-learning. The proposed learning method is applied for behavior planning of mobile robots.