• Fashion & Textile Research Journal
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• v.18 no.6
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• pp.766-775
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• 2016

In the era of globalization and unlimited competition, Korean universities need a breakthrough in their education system according to the changing education landscape, such as lower graduation requirements to cultivate more multi-talented convergence leaders. While each student has different learning capabilities, which results in different performance and achievements in the same class, the uniform education that most universities are currently offering fails to accommodate such differences. Blended learning, synergically combining offline and online classes, enlarges learning space and enriches learning experiences through diversified tools and materials, including multimedia. Recently, universities are increasingly adopting video contents and on-offline convergence learning strategy. Thus, this study suggests a teaching method based on blended learning to more effectively teach existing pattern CAD and virtual CAD in the Apparel Pattern CAD class. To this end, this researcher developed a teaching-learning method and curriculum according to the blended learning phase and video-based contents. The curriculum consisted of 2D CAD (SuperAlpha: Plus) and 3D CAD (CLO) software learning for 15 weeks. Then, it was loaded to the Learning Management System (LMS) and operated for 15 weeks both online and offline. The performance analysis of LMS usage found that class materials, among online postings, were viewed the most. The discussion menu most accurately depicted students' participation, and students who did not participate in discussions were estimated to check postings less than participating students. A survey on the blended learning found that students prefer digital or more digitized classes, while preferring face to face for Q&As.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.17 no.5
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• pp.187-203
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• 2022

As the economic, social, and environmental problems of the local community reach a serious level, our society is realizing the need to foster young talents who discover opportunities in local issues through entrepreneurship education and create social values through creative challenges. However, entrepreneurship education programs are generally focused on commerciality, so customized education programs to solve regional problems are insufficient. Therefore, this study aimed to develop a community problem-solving entrepreneurship curriculum. In this study, a competency based curriculum model was applied to develop the curriculum, and regional problem-solving entrepreneurship competencies were derived through expert advice from a total of 10 people. In the process, the Delphi methodology was additionally used to reduce the possibility of errors in the competency model. As a result of the study, a total of 23 regional problem-solving entrepreneurship competencies were confirmed, and knowledge(K) - skill(S) - attitude(A) by competency consisted of 5, 9, and 9, respectively. By applying this to Dunham's problem-solving six-step model, modular learning support measures were developed in the order of phase 1(problem discovery), phase 2(problem analysis), phase 3(plan), phase 4(measure), and phase 5(evaluation). This study is meaningful in that it integrated theory and practice by developing specific entrepreneurship curriculum and learning support measures based on the theoretical model devised in social welfare. In addition, it has implications in that it developed a regional problem-solving entrepreneurship competency model based on expert advice and proposed a specific curriculum based on this.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.7
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• pp.179-189
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• 1994

A two-phase neural network finds exact feasible solutions for a constrained optimization programming problem. The time-varying programming neural network is a modified steepest-gradient algorithm which solves time-varying optimization problems. In this paper, we propose a time-varying two-phase optimization neural network which incorporates the merits of the two-phase neural network and the time-varying neural network. The proposed algorithm is applied to system identification and function approximation using a multi-layer perceptron. Particularly training of a multi-layer perceptrion is regarded as a time-varying optimization problem. Our algorithm can also be applied to the case where the weights are constrained. Simulation results prove the proposed algorithm is efficient for solving various optimization problems.

• Physical Therapy Korea
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• v.8 no.1
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• pp.9-19
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• 2001

During therapy sessions, feedback is often provided concurrently by the physical therapist as the patient attempts to perform a movement and after the movement attempt. This feedback is provided to enhance the patient's balance abilities. However, recent studies in nondisabled populations have suggested that frequent feedback may be detrimental to retention or learning of motor skills. This study compared the effects of 100% relative frequency of knowledge of performance (KP) with 66% relative frequency of KP for motor learning on balance retraining in patients with hemiplegia. Twenty patients with hemiplegic were randomly assigned to one of two experimental groups. The acquisition phase consisted of 16 blocks of 5 trials for 2 days (80 total practice trials). The retention phase consisted of 2 blocks of a short-term retention test, one day after the end of the acquisition phase and a long-term retention test, one week after the end of the short-term retention test. In the 100% feedback condition, participants received feedback after every practice trial. A faded KP schedule was used in the 66% condition. No significant differences were found between the two groups during all experimental phases (acquisition and retention phases), (p>.05). However, there were significant decreases in balance index for both groups of acquisition phase (p<.05). These results suggest that 66% relative frequency of KP is not more effective than 100% relative frequency of KP with respect to retention over time when hemiparetic patients attempt to learn balance.

• Geomechanics and Engineering
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• v.31 no.2
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• pp.129-147
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• 2022

The properties of soil are naturally highly variable and thus, to ensure proper safety and reliability, we need to test a large number of samples across the length and depth. In pile foundations, conducting field tests are highly expensive and the traditional empirical relations too have been proven to be poor in performance. The study proposes a state-of-art Particle Swarm Optimization (PSO) hybridized Artificial Neural Network (ANN), Extreme Learning Machine (ELM) and Adaptive Neuro Fuzzy Inference System (ANFIS); and comparative analysis of metaheuristic models (ANN-PSO, ELM-PSO, ANFIS-PSO) for prediction of bearing capacity of pile foundation trained and tested on dataset of nearly 300 dynamic pile tests from the literature. A novel ensemble model of three hybrid models is constructed to combine and enhance the predictions of the individual models effectively. The authenticity of the dataset is confirmed using descriptive statistics, correlation matrix and sensitivity analysis. Ram weight and diameter of pile are found to be most influential input parameter. The comparative analysis reveals that ANFIS-PSO is the best performing model in testing phase (R² = 0.85, RMSE = 0.01) while ELM-PSO performs best in training phase (R² = 0.88, RMSE = 0.08); while the ensemble provided overall best performance based on the rank score. The performance of ANN-PSO is least satisfactory compared to the other two models. The findings were confirmed using Taylor diagram, error matrix and uncertainty analysis. Based on the results ELM-PSO and ANFIS-PSO is proposed to be used for the prediction of bearing capacity of piles and ensemble learning method of joining the outputs of individual models should be encouraged. The study possesses the potential to assist geotechnical engineers in the design phase of civil engineering projects.

• The Bulletin of The Korean Astronomical Society
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• v.44 no.1
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• pp.46.2-46.2
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• 2019

We propose a deep learning model that can generate a waveform of coalescing binary black holes in merging and ring-down phases in less than one second with a graphics processing unit (GPU) as an approximant of gravitational waveforms. Up to date, numerical relativity has been accepted as the most adequate tool for the accurate prediction of merger phase of waveform, but it is known that it typically requires huge amount of computational costs. We present our method can generate the waveform with ~98% matching to that of the status-of-the-art waveform approximant, effective-one-body model calibrated to numerical relativity simulation and the time for the generation of ~1500 waveforms takes O(1) seconds. The validity of our model is also tested through the recovery of signal-to-noise ratio and the recovery of waveform parameters by injecting the generated waveforms into a public open noise data produced by LIGO. Our model is readily extendable to incorporate additional physics such as higher harmonics modes of the ring-down phase and eccentric encounters, since it only requires sufficient number of training data from numerical relativity simulations.

• Communications for Statistical Applications and Methods
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• v.29 no.2
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• pp.251-261
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• 2022

As AI has a wide range of influence on human social life, issues of transparency and ethics of AI are emerging. In particular, it is widely known that due to the existence of historical bias in data against ethics or regulatory frameworks for fairness, trained AI models based on such biased data could also impose bias or unfairness against a certain sensitive group (e.g., non-white, women). Demographic disparities due to AI, which refer to socially unacceptable bias that an AI model favors certain groups (e.g., white, men) over other groups (e.g., black, women), have been observed frequently in many applications of AI and many studies have been done recently to develop AI algorithms which remove or alleviate such demographic disparities in trained AI models. In this paper, we consider a problem of using the information in the sensitive variable for fair prediction when using the sensitive variable as a part of input variables is prohibitive by laws or regulations to avoid unfairness. As a way of reflecting the information in the sensitive variable to prediction, we consider a two-stage procedure. First, the sensitive variable is fully included in the learning phase to have a prediction model depending on the sensitive variable, and then an imputed sensitive variable is used in the prediction phase. The aim of this paper is to evaluate this procedure by analyzing several benchmark datasets. We illustrate that using an imputed sensitive variable is helpful to improve prediction accuracies without hampering the degree of fairness much.

• Journal of IKEEE
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• v.28 no.2
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• pp.180-186
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• 2024

This paper proposes an efficient fault diagnosis method for ITSC(Inter-Turn Short Circuit) in three-phase induction motors using CNN. By utilizing only the D-axis component of the D-Q synchronous coordinate system, it compares SWM(Slide Window Method) and GAF(Gramian Angular Field) methods for image encoding. Results show GAF achieving ~74% accuracy, while SWM achieves ~65%, indicating GAF's superiority by 9%. Learning time (~14.74s) remains consistent, particularly with epochs ≤ 100, showcasing faster learning.

• Advances in nano research
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• v.12 no.2
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• pp.185-195
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• 2022

Deep learning is another field of artificial intelligence (AI) utilized for computer aided diagnosis (CAD) and image processing in scientific research. Considering numerous mechanical repetitive tasks, reading image slices need time and improper with geographical limits, so the counting of image information is hard due to its strong subjectivity that raise the error ratio in misdiagnosis. Regarding the highest mortality rate of Lung cancer, there is a need for biopsy for determining its class for additional treatment. Deep learning has recently given strong tools in diagnose of lung cancer and making therapeutic regimen. However, identifying the pathological lung cancer's class by CT images in beginning phase because of the absence of powerful AI models and public training data set is difficult. Convolutional Neural Network (CNN) was proposed with its essential function in recognizing the pathological CT images. 472 patients subjected to staging FDG-PET/CT were selected in 2 months prior to surgery or biopsy. CNN was developed and showed the accuracy of 87%, 69%, and 69% in training, validation, and test sets, respectively, for T1-T2 and T3-T4 lung cancer classification. Subsequently, CNN (or deep learning) could improve the CT images' data set, indicating that the application of classifiers is adequate to accomplish better exactness in distinguishing pathological CT images that performs better than few deep learning models, such as ResNet-34, Alex Net, and Dense Net with or without Soft max weights.

• Korean Institute of Interior Design Journal
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• v.14 no.2 s.49
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• pp.206-216
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• 2005

The purpose of this study is a development of $teaching{\cdot}learning$ program based on interior design WBI. WBI's meaning is Web Based Instruction. There can be several procedures that should be designed at the first step: Analyzing, Designing, Building, Testing, Recycling. These stages are constructed based on the theory of procedure about the computer and the $teaching{\cdot}learning$. Each stage is presented the contents of development, the contents of main, the method advantages and problem solving. These procedures and contents of development are the research method simultaneously with the result. Upon the completion of the system, it should undergo the same process continuously so that it can improve over a period of time, contribute to the interior design of students. In addition continuously repetitive requirements pop up in the phase. And it is necessary for this program try to map out a strategy developing and a policy support from university. It's very important that develop the contents of interior design from professor, students and field professor.