• The Transactions of the Korean Institute of Electrical Engineers P
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• v.67 no.3
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• pp.143-148
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• 2018

In this paper, the optimal fuzzy logic controller(FLC) for a hybrid renewable energy system(HRES) is proposed. Generally, hybrid renewable energy systems can consist of wind power, solar power, fuel cells and storage devices. The proposed FLC can effectively control the entire HRES by determining the output power of the fuel cell or the absorption power of the electrolyzer. In general, fuzzy logic controllers can be optimized by classical optimization algorithms such as genetic algorithms(GA) or particle swarm optimization(PSO). However, these FLC have a disadvantage in that their performance varies greatly depending on the control parameters of the optimization algorithms. Therefore, we propose a method to optimize the fuzzy logic controller using the teaching-learning based optimization(TLBO) algorithm which does not have the control parameters of the algorithm. The TLBO algorithm is an optimization algorithm that mimics the knowledge transfer mechanism in a class. To verify the performance of the proposed algorithm, we modeled the hybrid system using Matlab Tool and compare and analyze the performance with other classical optimization algorithms. The simulation results show that the proposed method shows better performance than the other methods.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.12
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• pp.1782-1791
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• 2017

In this paper, an optimal design of hybrid magnetic levitation(Maglev) system using intelligent optimization algorithms is proposed. The proposed maglev system adopts hybrid suspension system with permanent-magnet(PM) and electro magnet(EM) to reduce the suspension power loss and the teaching-learning based optimization(TLBO) that can overcome the drawbacks of conventional intelligent optimization algorithm is used. To obtain the mathematical model of hybrid suspension system, the magnetic equivalent circuit including leakage fluxes are used. Also, design restrictions such as cross section areas of PM and EM, the maximum length of PM, magnetic force are considered to choose the optimal parameters by intelligent optimization algorithm. To meet desired suspension power and lower power loss, the multi object function is proposed. To verify the proposed object function and intelligent optimization algorithms, we analyze the performance using the mean value and standard error of 10 simulation results. The simulation results show that the proposed method is more effective than conventional optimization methods.

• Journal of the Korea Society of Computer and Information
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• v.19 no.9
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• pp.33-38
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• 2014

The FCM based hybrid RBF network is a heterogeneous learning network model that applies FCM algorithm between input and middle layer and applies Max_Min algorithm between middle layer and output. The Max-Min neural network uses winner nodes of the middle layer as input but shows inefficient learning in performance when the input vector consists of too many patterns. To overcome this problem, we propose a dynamic learning rate control based on fuzzy logic. The proposed method first classifies accurate/inaccurate class with respect to the difference between target value and output value with threshold and then fuzzy membership function and fuzzy decision logic is designed to control the learning rate dynamically. We apply this proposed RBF network to the character recognition problem and the efficacy of the proposed method is verified in the experiment.

• Journal of KIISE
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• v.43 no.3
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• pp.362-369
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• 2016

This study is directed toward the design of a hybrid algorithm for syllable-based Korean POS tagging. Previous syllable-based works on Korean POS tagging have relied on a sequence labeling method and mostly used only a machine learning method. We present a new algorithm integrating a machine learning method and a pre-analyzed dictionary. We used a Sejong tagged corpus for training and evaluation. While the machine learning engine achieved eojeol precision of 0.964, the proposed hybrid engine achieved eojeol precision of 0.990. In a Quiz domain test, the machine learning engine and the proposed hybrid engine obtained 0.961 and 0.972, respectively. This result indicates our method to be effective for Korean POS tagging.

• Journal of IKEEE
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• v.26 no.4
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• pp.742-745
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• 2022

In this paper, we propose a hybrid image processing and deep learning-based method for detecting the presence of power lines in infrared images. Deep learning-based methods can learn feature vectors from a large number of data without much effort, resulting in outstanding performances in various fields. However, it is difficult to apply human intuition to the deep learning-based methods while image processing techniques can be used to apply human intuition. Based on these, we propose a method that exploits both advantages to detect the existence of power lines in infrared images. To this end, five methods have been applied and compared to find the most effective image processing technique for detecting the presence of power lines. As a result, the proposed method achieves 99.48% of accuracy which is higher than those of methods based on either image processing or deep learning.

• The Journal of the Convergence on Culture Technology
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• v.8 no.1
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• pp.121-129
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• 2022

This study explored effective teaching-learning activities under the hybrid teaching model combining flipped learning, direct instruction, cooperative learning and distance learning. Subjects were 99 college students enrolled in 2 teacher preparation courses. Students evaluated effectiveness of 22 teaching-learning activities according to Likert scale at the end of the semester. As a result, all 22 teaching learning activities were considered effective more or less. They facilitated motivation, comprehension, participation, thinking, and led to mastery of the task. ② Activities scored low were 'questioning and answering at the beginning of online class', 'open book test'. ③ Hybrid teaching model was favored over traditional instruction. Open ended responses were consistent with the ones in the questionnaire. This research supports the notion that key teaching models to be combined for successful flipped learning in college setting are direct instruction and cooperative learning. Furthermore, effective and ineffective teaching strategies for flipped learning were found.

• International Journal of Control, Automation, and Systems
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• v.5 no.6
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• pp.674-680
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• 2007

The probabilistic roadmap (PRM) method, which is a popular path planning scheme, for a manipulator, can find a collision-free path by connecting the start and goal poses through a roadmap constructed by drawing random nodes in the free configuration space. PRM exhibits robust performance for static environments, but its performance is poor for dynamic environments. On the other hand, reinforcement learning, a behavior-based control technique, can deal with uncertainties in the environment. The reinforcement learning agent can establish a policy that maximizes the sum of rewards by selecting the optimal actions in any state through iterative interactions with the environment. In this paper, we propose efficient real-time path planning by combining PRM and reinforcement learning to deal with uncertain dynamic environments and similar environments. A series of experiments demonstrate that the proposed hybrid path planner can generate a collision-free path even for dynamic environments in which objects block the pre-planned global path. It is also shown that the hybrid path planner can adapt to the similar, previously learned environments without significant additional learning.

• Journal of the Korean Geosynthetics Society
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• v.22 no.1
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• pp.67-74
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• 2023

In order to monitor the stability of slopes, research on data-based slope failure prediction and early warning is increasing. However, most papers overlook the quality of data. Poor data quality can cause problems such as false alarms. Therefore, this paper proposes a two-step hybrid approach consisting of rules and machine learning models for quality control of data collected from slopes. The rule-based has the advantage of high accuracy and intuitive interpretation, and the machine learning model has the advantage of being able to derive patterns that cannot be explicitly expressed. The hybrid approach was able to take both of these advantages. Through a case study, the performance of using the two methods alone and the case of using the hybrid approach was compared, and the hybrid method was judged to have high performance. Therefore, it is judged that using a hybrid method is more appropriate than using the two methods alone for data quality control.

• Smart Media Journal
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• v.5 no.4
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• pp.75-82
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• 2016

Although Korea's economic and domestic automobile market through the change of road environment are growth, the traffic accident rate has also increased, and the casualties is at a serious level. For this reason, the government is establishing and promoting policies to open traffic accident data and solve problems. In this paper, describe the method of predicting traffic accidents by eliminating the class imbalance using the traffic accident data and constructing the Hybrid Model. Using the original traffic accident data and the sampled data as learning data which use FP-Growth algorithm it learn patterns associated with traffic accident injury severity. Accordingly, In this paper purpose a method for predicting the severity of a victim of a traffic accident by analyzing the association patterns of two learning data, we can extract the same related patterns, when a decision tree and multinomial logistic regression analysis are performed, a hybrid model is constructed by assigning weights to related attributes.

• Journal of the Korean Data and Information Science Society
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• v.17 no.2
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• pp.269-278
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• 2006

This paper presents the new hybrid data mining technique using error pattern, modeling of improving classification accuracy. The proposed method improves classification accuracy by combining two different supervised learning methods. The main algorithm generates error pattern modeling between the two supervised learning methods(ex: Neural Networks, Decision Tree, Logistic Regression and so on.) The Proposed modeling method has been applied to the simulation of 10,000 data sets generated by Normal and exponential random distribution. The simulation results show that the performance of proposed method is superior to the existing methods like Logistic regression and Discriminant analysis.