• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.4
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• pp.203-210
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• 2017

Data clustering is one of the most difficult and challenging problems and can be formally considered as a particular kind of NP-hard grouping problems. The K-means algorithm is one of the most popular and widely used clustering method because it is easy to implement and very efficient. However, it has high possibility to trap in local optimum and high variation of solutions with different initials for the large data set. Therefore, we need study efficient computational intelligence method to find the global optimal solution in data clustering problem within limited computational time. The objective of this paper is to propose a combined artificial bee colony (CABC) with K-means for initialization and finalization to find optimal solution that is effective on data clustering optimization problem. The artificial bee colony (ABC) is an algorithm motivated by the intelligent behavior exhibited by honeybees when searching for food. The performance of ABC is better than or similar to other population-based algorithms with the added advantage of employing fewer control parameters. Our proposed CABC method is able to provide near optimal solution within reasonable time to balance the converged and diversified searches. In this paper, the experiment and analysis of clustering problems demonstrate that CABC is a competitive approach comparing to previous partitioning approaches in satisfactory results with respect to solution quality. We validate the performance of CABC using Iris, Wine, Glass, Vowel, and Cloud UCI machine learning repository datasets comparing to previous studies by experiment and analysis. Our proposed KABCK (K-means+ABC+K-means) is better than ABCK (ABC+K-means), KABC (K-means+ABC), ABC, and K-means in our simulations.

• Structural Engineering and Mechanics
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• v.83 no.3
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• pp.327-340
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• 2022

The Collapse Margin Ratio (CMR) is a notable index used for seismic assessment of the structures. As proposed by FEMA P695, a set of analyses including the Nonlinear Static Analysis (NSA), Incremental Dynamic Analysis (IDA), together with Fragility Analysis, which are typically time-taking and computationally unaffordable, need to be conducted, so that the CMR could be obtained. To address this issue and to achieve a quick and efficient method to estimate the CMR, the Artificial Neural Network (ANN), Response Surface Method (RSM), and Adaptive Neuro-Fuzzy Inference System (ANFIS) will be introduced in the current research. Accordingly, using the NSA results, an attempt was made to find a fast and efficient approach to derive the CMR. To this end, 5016 IDA analyses based on FEMA P695 methodology on 114 various Reinforced Concrete (RC) frames with 1 to 12 stories have been carried out. In this respect, five parameters have been used as the independent and desired inputs of the systems. On the other hand, the CMR is regarded as the output of the systems. Accordingly, a double hidden layer neural network with Levenberg-Marquardt training and learning algorithm was taken into account. Moreover, in the RSM approach, the quadratic system incorporating 20 parameters was implemented. Correspondingly, the Analysis of Variance (ANOVA) has been employed to discuss the results taken from the developed model. Additionally, the essential parameters and interactions are extracted, and input parameters are sorted according to their importance. Moreover, the ANFIS using Takagi-Sugeno fuzzy system was employed. Finally, all methods were compared, and the effective parameters and associated relationships were extracted. In contrast to the other approaches, the ANFIS provided the best efficiency and high accuracy with the minimum desired errors. Comparatively, it was obtained that the ANN method is more effective than the RSM and has a higher regression coefficient and lower statistical errors.

• The Mathematical Education
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• v.63 no.2
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• pp.139-163
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• 2024

The purpose of this study is to examine the capabilities of ChatGPT as a tool for supporting students in generating mathematical arguments that can be considered proofs. To examine this, we engaged students enrolled in a mathematics pathways course in evaluating and revising their original arguments using ChatGPT feedback. Students attempted to find and prove a method for the area of a triangle given its side lengths. Instead of directly asking students to prove a formula, we asked them to explore a method to find the area of a triangle given the lengths of its sides and justify why their methods work. Students completed these ChatGPT-embedded proving activities as class homework. To investigate the capabilities of ChatGPT as a proof tutor, we used these student homework responses as data for this study. We analyzed and compared original and revised arguments students constructed with and without ChatGPT assistance. We also analyzed student-written responses about their perspectives on mathematical proof and proving and their thoughts on using ChatGPT as a proof assistant. Our analysis shows that our participants' approaches to constructing, evaluating, and revising their arguments aligned with their perspectives on proof and proving. They saw ChatGPT's evaluations of their arguments as similar to how they usually evaluate arguments of themselves and others. Mostly, they agreed with ChatGPT's suggestions to make their original arguments more proof-like. They, therefore, revised their original arguments following ChatGPT's suggestions, focusing on improving clarity, providing additional justifications, and showing the generality of their arguments. Further investigation is needed to explore how ChatGPT can be effectively used as a tool in teaching and learning mathematical proof and proof-writing.

• Journal of Christian Education in Korea
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• v.63
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• pp.407-433
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• 2020

South Korea is currently facing multiculturalism, appropriately demanding broadened view and access to the study of Unification, especially in terms of who should be the subject. In congruence, recent Unification Education studies are proposing values stressed in Multiculturalism to function as the core ground, upon which other traits and contents are to follow. Similarly, Christian Education field is also reflecting the recent trend, welcoming 'everyone' to participate in preparing reunification of Korean Peninsula. However, most studies consist of suggesting and introducing directions for new approaches, not so much of dealing with systems or guidelines for practical practices. Information provided in fragments can rather cause confusion in understanding the concept, and lack of specific guidelines can soon lead to poor performances. In this study, Universal Design for Learning (UDL) is introduced as a methodology to be examined, whether it could effectively embody and systemize the newly emerged Multicultural Christian Unification Education. Therefore, the purpose of this study is 1) to generalize the concept of Christian unification education introduced in the multicultural era and 2) examine whether UDL is suitable for uniformizing and spreading the Multicultural approach of Christian Unification Education. Specifically, this study highly focused on examining whether UDL is a suitable methodology to achieve the goal of Multicultural Christian unification education, while uniformizing the diversities within multicultural Christian Unification Education. Through a thorough research, it was proved that the purpose and characteristics of multicultural Christian unification education are similar to those of UDL in that both focus on accepting heterogeneity as something "different" rather than something "wrong." Also, it was found that the application of the theory and guidelines of UDL can effectively unify while respecting the diverse methodologies used in Multicultural Christian Education.

• Journal of The Korean Association For Science Education
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• v.43 no.4
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• pp.403-414
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• 2023

In this study, we examined the adaptive practices of science teachers in their classrooms and their perspectives on the distinguishing features of these practices within science subjects. Our analysis comprised 339 cases from 128 middle and high school science teachers nationwide, and 199 cases on the characteristics of adaptive practices in science disciplines. The primary findings were as follows: First, the most significant characteristic of adaptive practice in science disciplines pertained to experimental procedures. Within the 'suggestion of additional materials/activities' category, the most frequently cited adaptive practice, teachers incorporated demonstrations to either facilitate student comprehension or enhance motivation. Additionally, 'experimental equipment manipulation or presentation of inquiry skills' emerged as the second most common adaptive practice related to experiments. Notably, over 50% of teacher responses regarding the characteristics of adaptive practices in science pertained to experiment guidance. Second, many adaptive practices involving difficulties experienced by students in learning situations were presented, particularly in areas such as numeracy and literacy. Many cases were related to the basic ability of mathematics used as a tool in science learning and understanding scientific terms in Chinese characters. Third, beyond 'experiment guidance', the characteristic adaptive practices of science subjects were related to 'connections between scientific theory and the real world', 'misconception guidance in science', 'cultivation of scientific thinking', and 'convergence approaches'. Fourth, the cases of adaptive practice presented by the science teachers differed by school level and major; therefore, it is necessary to consider school level or major in future research related to adaptive practice. Fifth, most of the adaptive action items with a small number of cases were adaptive actions executed from a macroscopic perspective, so it is necessary to pay attention to related professionalism. Finally, based on the results of this study, the implications for science education were discussed.

• Journal of The Korean Association For Science Education
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• v.29 no.5
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• pp.477-491
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• 2009

The purpose of this study is to analyze activities of mentally retarded students studying science within inclusive classes from the aspect of activity sharing to investigate ways of improving their involvement in the tasks. For this study, three mentally retarded students and their peer group in inclusive classes were observed and videotaped for 12 science class sessions about forces and waves. There were many cases in which task involvement of mentally retarded students changed according to three degrees of their activity sharing: well-synchronized, delayed and estranged. When degrees of activity sharing were estranged or delayed, task involvement of the mentally retarded students faded from activeness to passiveness. When the degree of activity sharing was well-synchronized, the mentally retarded students showed interest in learning and were able to participate in science class more actively. Different patterns of activity sharing of mentally retarded students between teacher-centered activities and student-centered activities were observed. In most cases of teacher-centered activities, the monotonous pace could deprive the mentally retarded students of their chance to catch up. As a result, their delayed degrees of activity sharing were faded into estranged degrees. In many cases of student-centered activities, various pacing according to the groups or students could provide mentally retarded students with a chance to catch up, so they could be well-synchronized. In one case of teacher-centered activity, the mentally retarded students were well-synchronized with the teacher's repeated explanations and well-matched illustrations on the blackboard and textbooks. In some cases of student-centered activities, students were well-synchronized with positive relations with and appropriate intervention by other students. In conclusion, various approaches to encourage activity sharing of mentally retarded students with normal ones should be pursued to improve task involvement and academic achievement of mentally retarded students.

• Journal of Korean Home Economics Education Association
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• v.35 no.3
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• pp.135-149
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• 2023

The purpose of this study is to develop and implement an integrated course model that centers around the subject of Home Economics Education Curriculum and Teaching Methods and its pedagogical approaches, as well as the subject of Chinese Language and Literature Curriculum and Teaching Methods and its pedagogical methods. This study aims to provide a framework to prepare pre-service teachers to effectively address a variety of educational issues in future educational settings. To achieve these objectives, the study utilizes Fogarty's connected model as a guiding framework to explore the impact of the integrated curriculum on fostering collaborative and divergent thinking among students. The findings of this research confirm that this model not only cultivates interdisciplinary competencies among course participants but also goes beyond the mere transmission of knowledge to build the capacities needed for forming an educational community, thereby increasing course satisfaction. Additionally, the study substantiates the importance of learner-centered strategies, cooperative learning, and diverse evaluation mechanisms. Such an integrated course model has the potential to revolutionize not only pre-service teacher education but also to be applicable in in-service teacher training, thus contributing to solving a broader range of educational issues.

• The Journal of the Acoustical Society of Korea
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• v.39 no.5
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• pp.414-423
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• 2020

In this paper, we propose a Dilated Convolution Gate Linear Unit (DCGLU) to mitigate the lack of sparsity and small receptive field problems caused by the segmentation map extraction process in sound event detection with weak labels. In the advent of deep learning framework, segmentation map extraction approaches have shown improved performance in noisy environments. However, these methods are forced to maintain the size of the feature map to extract the segmentation map as the model would be constructed without a pooling operation. As a result, the performance of these methods is deteriorated with a lack of sparsity and a small receptive field. To mitigate these problems, we utilize GLU to control the flow of information and Dilated Convolutional Neural Networks (DCNNs) to increase the receptive field without additional learning parameters. For the performance evaluation, we employ a URBAN-SED and self-organized bird sound dataset. The relevant experiments show that our proposed DCGLU model outperforms over other baselines. In particular, our method is shown to exhibit robustness against nature sound noises with three Signal to Noise Ratio (SNR) levels (20 dB, 10 dB and 0 dB).

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.17 no.2
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• pp.15-23
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• 2017

The person Re-identification is the most challenging part of computer vision due to the significant changes in human pose and background clutter with occlusions. The picture from non-overlapping cameras enhance the difficulty to distinguish some person from the other. To reach a better performance match, most methods use feature selection and distance metrics separately to get discriminative representations and proper distance to describe the similarity between person and kind of ignoring some significant features. This situation has encouraged us to consider a novel method to deal with this problem. In this paper, we proposed an enhanced recurrent neural network with three-tier hierarchical network for person re-identification. Specifically, the proposed recurrent neural network (RNN) model contain an iterative expectation maximum (EM) algorithm and three-tier Hierarchical network to jointly learn both the discriminative features and metrics distance. The iterative EM algorithm can fully use of the feature extraction ability of convolutional neural network (CNN) which is in series before the RNN. By unsupervised learning, the EM framework can change the labels of the patches and train larger datasets. Through the three-tier hierarchical network, the convolutional neural network, recurrent network and pooling layer can jointly be a feature extractor to better train the network. The experimental result shows that comparing with other researchers' approaches in this field, this method also can get a competitive accuracy. The influence of different component of this method will be analyzed and evaluated in the future research.

• Proceedings of the Korean System Dynamics Society
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• 2002.02a
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• pp.19-40
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• 2002

The intent of this study is to develop system dynamics model for assessment of organizational and human factors in nuclear power plant which can contribute to secure the nuclear safety. Previous studies are classified into two major approaches. One is engineering approach such as ergonomics and probability safety assessment(PSA). The other is social science approach such like sociology, organization theory and psychology. Both have contributed to find organization and human factors and to present guideline to lessen human error in NPP. But, since these methodologies assume that relationship among factors is independent they don't explain the interactions among factors or variables in NPP. To overcome these limits, we have developed system dynamics model which can show cause and effect among factors and quantify organizational and human factors. The model we developed is composed of 16 functions of job process in nuclear power, and shows interactions among various factors which affects employees' productivity and job quality. Handling variables such like degree of leadership, adjustment of number of employee, and workload in each department, users can simulate various situations in nuclear power plant in the organization side. Through simulation, user can get insight to improve safety in plants and to find managerial tools in the organization and human side. Analyzing pattern of variables, users can get knowledge of their organization structure, and understand stands of other departments or employees. Ultimately they can build learning organization to secure optimal safety in nuclear power plant.