• Archives of Plastic Surgery
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• v.47 no.3
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• pp.242-249
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• 2020

Background The Exoscope is a novel high-definition digital camera system. There is limited evidence signifying the use of exoscopic devices in microsurgery. This trial objectively assesses the effects of the use of the Exoscope as an alternative to the standard operating microscope (OM) on the performance of experts in a simulated microvascular anastomosis. Methods Modus V Exoscope and OM were used by expert microsurgeons to perform standardized tasks. Hand-motion analyzer measured the total pathlength (TP), total movements (TM), total time (TT), and quality of end-product anastomosis. A clinical margin of TT was performed to prove non-inferiority. An expert performed consecutive microvascular anastomoses to provide the exoscopic learning curve until reached plateau in TT. Results Ten micro sutures and 10 anastomoses were performed. Analysis demonstrated statistically significant differences in performing micro sutures for TP, TM, and TT. There was statistical significance in TM and TT, however, marginal non-significant difference in TP regarding microvascular anastomoses performance. The intimal suture line analysis demonstrated no statistically significant differences. Non-inferiority results based on clinical inferiority margin (Δ) of TT=10 minutes demonstrated an absolute difference of 0.07 minutes between OM and Exoscope cohorts. A 51%, 58%, and 46% improvement or reduction was achieved in TT, TM, TP, respectively, during the exoscopic microvascular anastomosis learning curve. Conclusions This study demonstrated that experts' Exoscope anastomoses appear non-inferior to the OM anastomoses. Exoscopic microvascular anastomosis was more time consuming but end-product (patency) in not clinically inferior. Experts' "warm-up" learning curve is steep but swift and may prove to reach clinical equality.

• Journal of The Korean Association of Information Education
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• v.5 no.2
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• pp.240-257
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• 2001

Although many attempts have been made to use the Internet for educational purposes, not many attempts have achieved their goals. Such failure is mainly due to the lack of understanding on the way to use the Internet. The goal of this research is to investigate the potentiality of the Project-Based Learning approach using the Internet(NetPBL) and the ways to utilize the NetPBL. The NetPBL can be utilized through various activities such as keypals, mentoring, use of resources, cooperative learning, publishing, survey and data analysis, cooperative problem solving, simulation, and social action. Such diversity of the NetPBL can create a problem-based, context-based and learner-centered environment, which takes various types of the Internet use. In spite of such potentiality, little is known on how to implement the NetPBL. On this point, this research attempts to synthesize instructional strategies to implement the NetPBL at the macro and the micro level. At the macro level, instructional process is divided into four steps such as plan, preparation, implementation and closure, and some instructional suggestions are made for each step. At the micro level, detailed instructional strategies are suggested for the facilitation of self-directed learning and cooperative learning.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.6 no.1
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• pp.7-17
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• 1997

During the past decade, there were many well-established theories for the adaptive control of linear systems, but there exists relatively little general theory for the adaptive control of nonlinear systems. Adaptive control technique is essential for providing a stable and robust performance for application of industrial robot control. Neural network computing methods provide one approach to the development of adaptive and learning behavior in robotic system for manufacturing. Computational neural networks have been demonstrated which exhibit capabilities for supervised learning, matching, and generalization for problems on an experimental scale. Supervised learning could improve the efficiency of training and development of robotic systems. In this paper, a new scheme of adaptive-neuro control system to implement real-time control of robot manipulator using digital signal processors is proposed. Digital signal processors, DSPs, are micro-processors that are developed particularly for fast numerical computations involving sums and products of variables. The proposed neuro control algorithm is one of learning a model based error back-propagation scheme using Lyapunov stability analysis method. The proposed adaptive-neuro control scheme is illustrated to be an efficient control scheme for implementation of real-time control for SCARA robot with four-axes by experiment.

• Korean Journal of Adult Nursing
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• v.20 no.4
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• pp.548-560
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• 2008

Purpose: This study was conducted to develop a simulation-based education course and to evaluate the results after the application for second year nursing students. Methods: This study was a non-equivalent control pre-post design. Based on the clinical situation scenarios about patients with COPD and MI, a total of two simulation-based learning modules was developed. Pretest and posttest was conducted to evaluate the difference in critical thinking disposition, problem solving, and clinical competence between two groups of 102 students for the experimental group, 2007 and 90 students for the control group, 2006. The experimental group conducted a clinical performance evaluation in the final test, on December 10, 2007. Results: In the experimental group, knowledge related to learning objectives was significantly increased and core intervention was performed almost exactly, but the same result was not observed in domains of analysis of laboratory test, and nursing education for patients. Self-evaluated clinical competence and problem solving level were significantly more improved in the experimental group than control group, but critical thinking disposition level wasn't. Conclusion: In conclusion, a simulation-based education course needs to utilize self-directed learning module like computer-based learning through web contents and MicroSim and video productions for improving nursing students' critical thinking.

• Journal of Korea Multimedia Society
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• v.20 no.5
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• pp.748-757
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• 2017

In order to recognize the facial expressions, good features that can express the facial expressions are essential. It is also essential to find the characteristic areas where facial expressions appear discriminatively. In this study, we propose a directional LBP feature for facial expression recognition and a method of finding directional LBP operation and feature region for facial expression classification. The proposed directional LBP features to characterize facial fine micro-patterns are defined by LBP operation factors (direction and size of operation mask) and feature regions through AdaBoost learning. The facial expression classifier is implemented as a SVM classifier based on learned discriminant region and directional LBP operation factors. In order to verify the validity of the proposed method, facial expression recognition performance was measured in terms of accuracy, sensitivity, and specificity. Experimental results show that the proposed directional LBP and its learning method are useful for facial expression recognition.

• The Journal of the Korea Contents Association
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• v.13 no.9
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• pp.467-475
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• 2013

Micro-teaching is one of instructional methods to improve teaching skills whereby teachers present short sessions and receive feedbacks on their performance. In this research, we developed the web based micro-teaching system for more efficient and effective teacher training by placing videotaped session and peer feedbacks near rather than far from each other on the screen. We analyzed previous studies related to the micro-teaching system, and interviewed pre-service teachers to find some suggestions. Based on this analysis, we drew design principles and developed web based micro-teaching system, which helps teachers to plan instructional strategies, to reflect teaching skills, and to participate in peer assessment. We hope that the system will be useful to not only teacher training but also other fields such as presentation or interview skills.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.37 no.2
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• pp.143-149
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• 2024

Segmentation of material phases through image analysis is essential for analyzing the microstructure of materials. Micro-CT images exhibit variations in grayscale values depending on the phases constituting the material. Phase segmentation is generally achieved by comparing the grayscale values in the images. In the case of waste concrete used as a recycled aggregate, it is challenging to distinguish between hydrated cement paste and natural aggregates, as these components exhibit similar grayscale values in micro-CT images. In this study, we propose a method for automatically separating the aggregates in concrete, in micro-CT images. Utilizing the Unet-VGG16 deep-learning network, we introduce a technique for segmenting the 2D aggregate images and stacking them to obtain 3D aggregate images. Image filtering is employed to separate aggregate particles from the selected 3D aggregate images. The performance of aggregate segmentation is validated through accuracy, precision, recall, and F1-score assessments.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1997.04a
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• pp.62-67
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• 1997

This paper reprdsents a new scheme of neural network control system analysis the robustues of robot manipulator using digital signal processors. Digtal signal processors, DSPs, are micro-processors that are particularly developed for fast numerical computations involving sums and products of variables. Digital version of most advanced control algorithms can be defined as sums and products of measured variables, thus it can be programmed and executed through DSPs. In additions, DSPs are a s fast in computation as most 32-bit micro-processors and yet at a fraction of their prices. These features make DSPs a viable computational tool in digital implementation of sophisticated controllers. Durng past decade it was proposed the well-established theorys for the adaptive control of linear systems, but there exists relatively little general theory for the adaptive control of nonlinear systems. The proposed neuro network control algorithm is one of learning a model based error back-propagation scheme using Lyapunov stability analysis method.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.853-858
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• 1993

The error back-propagation(BP) algorithm is widely used for finding optimum weights of multi-layer neural networks. However, the critical drawback of the BP algorithm is its slow convergence of error. The major reason for this slow convergence is the premature saturation which is a phenomenon that the error of a neural network stays almost constant for some period time during learning. An inappropriate selections of initial weights cause each neuron to be trapped in the premature saturation state, which brings in slow convergence speed of the multi-layer neural network. In this paper, to overcome the above problem, Micro-Genetic algorithms(.mu.-GAs) which can allow to find the near-optimal values, are used to select the proper weights and slopes of activation function of neurons. The effectiveness of the proposed algorithms will be demonstrated by some computer simulations of two d.o.f planar robot manipulator.

• English Language & Literature Teaching
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• v.10 no.1
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• pp.77-106
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• 2004

This study aims to investigate how the four college students interact with one another to discuss and construct meaning in a small reading group. Additionally, this study attempts to examine how the participants played their roles in the group. Data sources consisted of transcripts of the students' interactions, questionnaires and informal interviews, and the researchers' observation notes. The data revealed that the participants contributed fairly steadily to the interactions by checking with own grammatical knowledge, providing lexical information, understanding the micro level context, and presenting the macro level context. Several pedagogical implications are presented for the practical classroom. Findings of the study suggest effective ways to implement group activities in reading classes and a teacher's role for optimum group learning.