• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1999.10a
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• pp.162-169
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• 1999

In this paper, it is presented a new scheme of adaptive-neuro control system to implement real-time control of robot manipulator using digital Signal Processors. Digital signal processors DSPs. are micro-processors that are particularly developed for 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 addition, DSPs are as fast in computation as most 32-bit micro-processors and yet at a fraction of their prices. These features make DSPs a biable computatinal tool in digital implementation of sophisticated controllers. Unlike the well-established theory for the adaptive control of linear systems, 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 robot control. 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 a efficient control scheme for implementation of real-time control of robot system by the simulation and experiment.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.10
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• pp.4369-4377
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• 2011

The goal of this research is to development a coordination supporting script in CSCL and tried to measure of coordination supporting script's effectiveness. Therefor, in this research, after derive macro-script design principle and activity principle from former study and CSCL tool, after derive micro-script from role sign several reference, and then based on those principle developed coordination supporting script. As providing coordination supporting script or not, there will be the sense making difference of effectiveness of coordination supporting script in learning activity(shared mental model). The result of this study may have implication for the research on macro-script and micro-script and educational practice.

• Proceedings of the Korea Information Processing Society Conference
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• 2018.10a
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• pp.1001-1004
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• 2018

As the industry generates a lot of data, it is increasingly dependent on the IoT platform. For this reason, the performance and anomaly detection of IoT platform is becoming an important factor. In this paper, we propose a system model of IoT platform that detects device anomaly without performance issue. The proposed system uses Micro Batch which calculates the data transmission cycle to provide Soft Real-time service. In the industry, it was difficult to collect abnormal data, so the Hotelling's $T^2$ model was applied to the data analysis experiment. And the Hotelling's $T^2$ model successfully detected anomalies.

• International Journal of Computer Science & Network Security
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• v.21 no.5
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• pp.256-266
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• 2021

The Convolutional Neural Network (CNN) has recently made potential improvements in face verification applications. In fact, different models based on the CNN have attained commendable progress in the classification rate using a massive amount of data in an uncontrolled environment. However, the enormous computation costs and the considerable use of storage causes a noticeable problem during training. To address these challenges, we focus on relevant data trained within the CNN model by integrating a lifting method for a better tradeoff between the data size and the computational efficiency. Our approach is characterized by the advantage that it does not need any additional space to store the features. Indeed, it makes the model much faster during the training and classification steps. The experimental results on Labeled Faces in the Wild and YouTube Faces datasets confirm that the proposed CNN framework improves performance in terms of precision. Obviously, our model deliberately designs to achieve significant speedup and reduce computational complexity in deep CNNs without any accuracy loss. Compared to the existing architectures, the proposed model achieves competitive results in face recognition tasks

• Educational Technology International
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• v.17 no.1
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• pp.87-116
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• 2016

In this research, we designed a teacher professional development (PD) program where a small group of mathematics teachers could share, reflect on, and discuss their pedagogical knowledge and practices of ICT-integrated lessons, using a video annotation tool called DIVER. The main purposes of this paper are both micro and macro: to examine how the teachers were engaged in the meaning-making process in a video-based PD (micro); and to derive implications about how to design effective video-based teacher PD programs toward a teacher community of practices (macro). To examine teachers' meaning-making in the PD sessions, discourse data from a series of 10 meetings was segmented into idea units and coded to identify discourse patterns, focusing on (a) participation levels, (b) conversation topics, and (c) conversation depth. Regarding the affordance of DIVER, discourse patterns of two meetings, before and after individual annotation with DIVER were compared through qualitative vignette analysis. Overall, we found that the teacher discourse shifted the focus from surface features to deeper pedagogical issues as the PD sessions progressed. In particular, the annotation function in DIVER afforded the teachers to exercise descriptive analyses of video clips in a flexible manner, thereby helping them cognitively prepared to take interpretative and evaluative stances in face-to-face discussions with colleagues. In conclusion, deriving from our research experiences, we discuss the possibilities and challenges of designing video-based teacher PD in a school context.

• Journal of the Korea Society for Simulation
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• v.32 no.1
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• pp.35-44
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• 2023

In recent, there are many research cases that analyze trends or research trends with text analysis. When collecting documents by searching for keywords in abbreviations for data analysis, it is necessary to disambiguate abbreviations. In many studies, documents are classified by hand-work reading the data one by one to find the data necessary for the study. Most of the studies to disambiguate abbreviations are studies that clarify the meaning of words and use supervised learning. The previous method to disambiguate abbreviation is not suitable for classification studies of documents looking for research data from abbreviation search documents, and related studies are also insufficient. This paper proposes a method of semi-automatically classifying documents collected by abbreviations by going topic modeling with Non-Negative Matrix Factorization, an unsupervised learning method, in the data pre-processing step. To verify the proposed method, papers were collected from academic DB with the abbreviation 'MSA'. The proposed method found 316 papers related to Micro Services Architecture in 1,401 papers. The document classification accuracy of the proposed method was measured at 92.36%. It is expected that the proposed method can reduce the researcher's time and cost due to hand work.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.36 no.5
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• pp.323-330
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• 2023

The development of an analysis model that reflects the microstructure characteristics of polyvinyl alcohol (PVA) fiber-reinforced cementitious composites, which have a highly complex microstructure, enables synergy between efficient material design and real experiments. PVA fiber orientations are an important factor that influences the mechanical behavior of PVA fiber-reinforced cementitious composites. Owing to the difficulty in distinguishing the gray level value obtained from micro-CT images of PVA fibers from adjacent phases, fiber segmentation is time-consuming work. In this study, a micro-CT test with a voxel size of 0.65 ㎛³ was performed to investigate the three-dimensional distribution of fibers. To segment the fibers and generate training data, histogram, morphology, and gradient-based phase-segmentation methods were used. A U-net model was proposed to segment fibers from micro-CT images of PVA fiber-reinforced cementitious composites. Data augmentation was applied to increase the accuracy of the training, using a total of 1024 images as training data. The performance of the model was evaluated using accuracy, precision, recall, and F1 score. The trained model achieved a high fiber segmentation performance and efficiency, and the approach can be applied to other specimens as well.

• Journal of Digital Convergence
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• v.20 no.1
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• pp.309-316
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• 2022

This study is a study on the YouTube e-learning advanced service plan in the non-face-to-face era. The trends in education change were examined through literature research and prior research, and improvement measures were suggested through online surveys and in-depth interviews. As for the research method, the first online survey was conducted based on the Honeycomb model and the Likert 5-point scale targeting 90 MZ generation who have experience learning on YouTube for a total of 14 days from October 15 to 28, 2021. A second in-depth interview was conducted with 6 people who answered that the frequency of learning through YouTube is high. As a result of the experiment, users thought that there was an improvement point according to the purpose of learning, and they were able to derive elements that felt a problem in common. In addition, I proposed a new YouTube learning platform through additional questions. Through this study, it is expected that YouTube e-learning service reference materials can be used to respond to the post-non-face-to-face era.

• KIEAE Journal
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• v.15 no.2
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• pp.27-35
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• 2015

Purpose: Healthcare is on the whole a personal and critical service that consumer's use, whereas hospitalization is as a rule painful, because nature nurtures and Sun Light Luminosity for healthcare settings is considered healing. The performance and design of climate responsive buildings such as AKU requires a detailed study of attributes of climate both at micro as well as macro level. The therapeutic value of contact with nature through window view, greenery and landscape is calculated there. Method: A two prong strategy is been devised for this article, at micro level three typical morphologies are analysed by creating same environment of neighboring building on sun shading chart, radiation and temperature range. Since the analysis of local climate helps to determine the design strategies for hospital Healing Environment which is suitable for Karachi climate; in order to track the macro climatic behaviour, a considerable analysis of psychometrics chart for AKU Karachi are designed on Climate Consultant (CC) and analysed by Machine Learning. Climate Consultant proposes different design strategies suitable for Karachi. And on the other hand time wise illumination sources for clinical area which are then measured on psychrometric chart- according to singular space: multi patient admission, secondly: acute ambulatory ward, and tertiary: multi windowed space according to the mushrabiyah and sky light pattern. Result: Our findings support the hypothesis that windowed wall is 75-80% more healing wall; an accelerated evidence was found for healing at macro level if the form of the hospital is designed according to the climatologically preferences, whereas at micro level: the light resource becomes the staff attentiveness determinant. In Conclusion evidence was provided that the actual form of luminosity results consequently in satisfaction while light entering from several set of windows and other sources might be valued if design according to the healing environment. The data added on the sun shading chart to calculate rays entraining into space in patient room equal to 124416.21 Watts/ meter $m^2$ is calculated as precise healing rate-and is confirmed by questionnaire from patients belonging from each clinical stage having different illnesses.

• Journal of Digital Convergence
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• v.12 no.2
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• pp.531-537
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• 2014

In this paper, we introduce a model of the teaching and learning method for multidisciplinary convergence capstone design at Kongju National University's Engineering Department. At Kongju national University, various capstone design works are designed and proceeded by multidisciplinary students at the summer session. The multidisciplinary approach described in this paper includes the involvement of five department's student who have not collaborated in capstone design experience. This study focuses on multidisciplinary capstone design education by using the micro controller board called Arduino Uno that consists of an assortment of sensors and actuators. The result of self-satisfaction survey was shown the meaningful teaching process for the engineering department students who could have more creative and industrial experiences. As a result, we are able to get the result of the possible directions for future technology education in the area of convergence multidisciplinary capstone design.