• Journal of Information Processing Systems
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• v.20 no.1
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• pp.105-115
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• 2024

The hybrid teaching approach of "MOOC + SPOC + Flipped Classroom" overcomes the constraints of time and space that are typically associated with traditional teaching methods, thus compensating for the shortcomings of traditional approaches. These changes in education are driven by the "Internet+" wave and the growing popularity of online teaching. The "MOOC + SPOC + Flipped Classroom" hybrid teaching mode can successfully compensate for the drawbacks of traditional teaching methods, thereby overcoming their restrictions. By defining relevant concepts, one can distill the key characteristics of the "MOOC + SPOC + Flipped Classroom" hybrid teaching mode. Formative assessment was employed to thoroughly evaluate the effectiveness of this teaching approach. By leveraging the advantages of massive open online course (MOOC), small private online course (SPOC), and flipped classroom, the "MOOC +SPOC + Flipped Classroom" teaching mode incorporates real-time student assessment through peer evaluation, computer-aided evaluation, and teacher evaluation. This mode promotes the simultaneous development of theoretical knowledge and practical skills, helping students to establish strong foundations while fostering their practical abilities. While the traditional teaching method remains fruitful, the convenience of today's network allows the teaching profession to continually evolve. The traditional teaching mode heavily relies on teachers, making it impossible to conduct lessons without them. However, the development of MOOC enables students to seek knowledge online from their preferred teachers, rather than solely relying on their assigned instructors.

• 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.

• Steel and Composite Structures
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• v.28 no.4
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• pp.509-516
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• 2018

The differential quadrature (DQ) and teaching-learning based optimization (TLBO) methods are coupled to introduce a hybrid numerical method for maximizing fundamental natural frequency of laminated composite skew plates. The fiber(s) orientations are selected as design variable(s). The first-order shear deformation theory (FSDT) is used to obtain the governing equations of the plate. The equations of motion and the related boundary conditions are discretized in space domain by employing the DQ method. The discretized equations are transferred from the time domain into the frequency domain to obtain the fundamental natural frequency. Then, the DQ solution is coupled with the TLBO method to find the maximum frequency of the plate and its related optimum stacking sequences of the laminate. Convergence and applicability of the proposed method are shown and the optimum fundamental frequency parameter of the plates with different skew angle, boundary conditions, number of layers and aspect ratio are obtained. The obtained results can be used as a benchmark for further studies.

• Structural Engineering and Mechanics
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• v.62 no.5
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• pp.537-550
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• 2017

Structural sizing is a rewarding task due to its non-convex constrained nature in the design space. In order to provide both global exploration and proper search refinement, a hybrid method is developed here based on outstanding features of Evolutionary Computing and Teaching-Learning-Based Optimization. The new method introduces an observer phase for memory exploitation in addition to vector-sum movements in the original teacher and learner phases. Proper integer coding is suited and applied for structural size optimization together with a fly-to-boundary technique and an elitism strategy. Performance of the proposed method is further evaluated treating a number of truss examples compared with teaching-learning-based optimization. The results show enhanced capability of the method in efficient and stable convergence toward the optimum and effective capturing of high quality solutions in discrete structural sizing problems.

• 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.22 no.8
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• pp.93-100
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• 2017

After the fourth industrial revolution came along, SW education to improve creativity and problem-solving ability began in elementary, middle, and high schools first and then in universities as well positively; however, research on the curriculum or what it has to pursue is not yet enough. Here, this study will investigate the current status of SW education provided in software-oriented schools operated in universities and also given as cultural studies in general universities and examines the curriculum or the standard plan for education. In most schools, it is operated as similar subject names, and diverse methods are tried on- and off-line to cultivate computing thinking skills. Also, to study SW education programs that can be operated in the general cultural courses of universities and find out how to utilize them, this author suggests the goal setting, educational contents, and teaching methods for SW education. As follow-up tasks, it will be needed to apply the suggested programs to the field and find out new evaluation methods in order to cultivate creative and convergent persons of ability.

• The Journal of the Korea Contents Association
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• v.14 no.6
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• pp.453-465
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• 2014

Although fairly descent amount of research outcomes has been generated on problem-based learning(PBL) with regard to science and medical subjects, relatively less outcome has been generated on social science and management subjects. Therefore this study attempts to exam effect of problem-based learning in food service entrepreneurship subject on improving problem solving skills. After literature review, for constructing for this research frame, each class schedules were formed by hybrid-PEL and traditional lecture style respectively then problems, solution, and evaluation process were generated for hybrid-PBL. Through the result, there was hardly any differences occurred before and after traditional lecture approach on students' problem solving skills. There was, however, certain differences appeared on students' skills after hybrid-PBL approach. Through the study, hybrid-PBL learning can be an effective teaching method in social science and management subject for improving students' problem solving skill.

• The Journal of the Korea Contents Association
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• v.13 no.7
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• pp.497-510
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• 2013

The researcher's objective is to analyze a case study from the teaching profession course entitled "Instructional Methods and Educational Technology: IMET," in which the instructional method 'Music Technology-Based Learning: MTBL' has been implemented. In addition to the analysis and the completion of the associated 16-week coursework, the researcher conducted an open-ended survey, and conducted and analyzed in-depth interviews, with participants in the case study. Using MTBL in IMET has elicited mostly favorable responses from participants such as "interesting" and "fun." They also mentioned "active learning participation." There were also negative responses to MTBL, citing "the lack of interconnection between MTBL and other teaching profession courses," "the difficulty of hybrid and convergence classes," and "the need for additional time and attention in an individual study." The analysis of this case study indicates that the use of MTBL in teaching profession courses somewhat enhances the understanding of the general concept of integrating technology into education, although some difficulties remain. The analysis of more case studies is required in order to establish more effective training courses, in which learning is not limited to the theoretical aspects of education. Using MTBL as an integrated part of the educational method would help to foster more creative and professional teachers.

• Structural Engineering and Mechanics
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• v.87 no.6
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• pp.555-574
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• 2023

An efficient optimization algorithm and damage-sensitive objective function are two main components in optimization-based Finite Element Model Updating (FEMU). A suitable combination of these components can considerably affect damage detection accuracy. In this study, a new hybrid damage-sensitive objective function is proposed based on combining two different objection functions to detect the location and extent of damage in structures. The first one is based on Generalized Pseudo Modal Strain Energy (GPMSE), and the second is based on the element's Generalized Flexibility Matrix (GFM). Four well-known population-based metaheuristic algorithms are used to solve the problem and report the optimal solution as damage detection results. These algorithms consist of Cuckoo Search (CS), Teaching-Learning-Based Optimization (TLBO), Moth Flame Optimization (MFO), and Jaya. Three numerical examples and one experimental study are studied to illustrate the capability of the proposed method. The performance of the considered metaheuristics is also compared with each other to choose the most suitable optimizer in structural damage detection. The numerical examinations on truss and frame structures with considering the effects of measurement noise and availability of only the first few vibrating modes reveal the good performance of the proposed technique in identifying damage locations and their severities. Experimental examinations on a six-story shear building structure tested on a shake table also indicate that this method can be considered as a suitable technique for damage assessment of shear building structures.

• Proceedings of the Optical Society of Korea Conference
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• 2003.07a
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• pp.60-61
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• 2003

A new approach to reduce the computation time of genetic algorithm (GA) for making binary phase holograms is described. Synthesized holograms having diffraction efficiency of 75.8% and uniformity of 5.8% are proven in computer simulation and experimentally demonstrated. Recently, computer-generated holograms (CGHs) having high diffraction efficiency and flexibility of design have been widely developed in many applications such as optical information processing, optical computing, optical interconnection, etc. Among proposed optimization methods, GA has become popular due to its capability of reaching nearly global. However, there exits a drawback to consider when we use the genetic algorithm. It is the large amount of computation time to construct desired holograms. One of the major reasons that the GA' s operation may be time intensive results from the expense of computing the cost function that must Fourier transform the parameters encoded on the hologram into the fitness value. In trying to remedy this drawback, Artificial Neural Network (ANN) has been put forward, allowing CGHs to be created easily and quickly (1), but the quality of reconstructed images is not high enough to use in applications of high preciseness. For that, we are in attempt to find a new approach of combiningthe good properties and performance of both the GA and ANN to make CGHs of high diffraction efficiency in a short time. The optimization of CGH using the genetic algorithm is merely a process of iteration, including selection, crossover, and mutation operators [2]. It is worth noting that the evaluation of the cost function with the aim of selecting better holograms plays an important role in the implementation of the GA. However, this evaluation process wastes much time for Fourier transforming the encoded parameters on the hologram into the value to be solved. Depending on the speed of computer, this process can even last up to ten minutes. It will be more effective if instead of merely generating random holograms in the initial process, a set of approximately desired holograms is employed. By doing so, the initial population will contain less trial holograms equivalent to the reduction of the computation time of GA's. Accordingly, a hybrid algorithm that utilizes a trained neural network to initiate the GA's procedure is proposed. Consequently, the initial population contains less random holograms and is compensated by approximately desired holograms. Figure 1 is the flowchart of the hybrid algorithm in comparison with the classical GA. The procedure of synthesizing a hologram on computer is divided into two steps. First the simulation of holograms based on ANN method [1] to acquire approximately desired holograms is carried. With a teaching data set of 9 characters obtained from the classical GA, the number of layer is 3, the number of hidden node is 100, learning rate is 0.3, and momentum is 0.5, the artificial neural network trained enables us to attain the approximately desired holograms, which are fairly good agreement with what we suggested in the theory. The second step, effect of several parameters on the operation of the hybrid algorithm is investigated. In principle, the operation of the hybrid algorithm and GA are the same except the modification of the initial step. Hence, the verified results in Ref [2] of the parameters such as the probability of crossover and mutation, the tournament size, and the crossover block size are remained unchanged, beside of the reduced population size. The reconstructed image of 76.4% diffraction efficiency and 5.4% uniformity is achieved when the population size is 30, the iteration number is 2000, the probability of crossover is 0.75, and the probability of mutation is 0.001. A comparison between the hybrid algorithm and GA in term of diffraction efficiency and computation time is also evaluated as shown in Fig. 2. With a 66.7% reduction in computation time and a 2% increase in diffraction efficiency compared to the GA method, the hybrid algorithm demonstrates its efficient performance. In the optical experiment, the phase holograms were displayed on a programmable phase modulator (model XGA). Figures 3 are pictures of diffracted patterns of the letter "0" from the holograms generated using the hybrid algorithm. Diffraction efficiency of 75.8% and uniformity of 5.8% are measured. We see that the simulation and experiment results are fairly good agreement with each other. In this paper, Genetic Algorithm and Neural Network have been successfully combined in designing CGHs. This method gives a significant reduction in computation time compared to the GA method while still allowing holograms of high diffraction efficiency and uniformity to be achieved. This work was supported by No.mOl-2001-000-00324-0 (2002)) from the Korea Science & Engineering Foundation.