• Journal of Digital Convergence
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• v.18 no.3
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• pp.35-42
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• 2020

Digital transformation is about revolutionizing the interaction between virtual and reality. The complex problems that arise in this process must be solved, and one of the methods is computing thinking. Therefore, this study aims to observe whether software education that uses unplugged as liberal education is effective in enhancing computing thinking. For this, 5 elements of computing thinking were extracted and unplugged was applied to liberal software classes, and classes were conducted. During one semester, 16 sessions of classes were conducted and computing thinking enhancement was measured through surveys. As a result, the computing thinking of the students increased overall after classes. Observation surveys showed that, among computing thinking elements, students of all academic fields felt difficulties conceptualizing abstraction elements, those of arts and physical education felt more difficulties with algorithm elements, and those of the humanities felt more difficulties with pattern recognition elements. In the future, various contents for each element should be developed by academic field to aid learner understanding.

• YAKHAK HOEJI
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• v.51 no.6
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• pp.482-489
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• 2007

Kaempferitrin, isolated from Kenaf (Hibiscus cannabinus), was examined to evaluate its modulatory effects on antigen-presenting cell functions of macrophages/monocytes such as phagocytosis of foreign materials, up-regulation of costimulatory molecules (CD40, CD80 and CD86), adhesion molecule activation, and antigen processing and presentation. Kaempferitrin strongly blocked up-regulation of CD40, CD80 and CD86, but not pattern recognition receptor (PRR) (e.g., TLR2). It also suppressed functional activation of CD29 (${\beta}1$-integrins), as assessed by cell-cell adhesion assay, required for T cell-antigen-presenting cell (APC) interaction. Furthermore, this compound did not block a simple activation of CD29, as assessed by cell-fibronectin adhesion assay. However, the compound did not diminish phagocytic uptake, an initial step for antigen processing, and ROS generation in RAW264.7 cells. In particular, to understand molecular mechanism of kaempferitrin-mediated inhibition, the regulatory role of LPS-induced signaling events was examined using immunoblotting analysis. Interestingly, this compound dose dependently suppressed the phosphorylation of $I{\kappa}B{\alpha}$, Src, Akt and Syk, demonstrating that it can negatively modulate the activation of these signaling enzymes. Therefore, our data suggested that kaempferitrin may be involved in regulating APC function-relevant immune responses of macrophages and monocytes by regulating intracellular signaling.

• Journal of the Korea Society of Computer and Information
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• v.12 no.1 s.45
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• pp.51-56
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• 2007

Feature functions were used for the classification of music. The spectral roll-off, variance, average peak level, and class were chosen to make up a feature function vector. Among these, it is the spectral roll-off function that has a low-frequency to high-frequency ratio. To find the optimal roll-off point, the roll-off points from 0.05 to 0.95 were swept. The classification success rate was monitored as the roll-off point was being changed. The data that were used for the experiments were taken from the sounds made by a modern violin and a baroque one. Their shapes and sounds are similar, but they differ slightly in sound texture. As such, the data obtained from the sounds of these two kinds of violin can be useful in finding an adequate roll-off point. The optimal roll-off point, as determined through the experiment, was 0.85. At this point, the classification success rate was 85%, which was the highest.

• Journal of the Korean Geotechnical Society
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• v.18 no.5
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• pp.123-132
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• 2002

The purpose of this study is to predict the shear behavior of the weathered mudstone soil using dynamic neural network which mimics the biological system of human brain. SNN and RNN, which are kinds of the dynamic neural network realizing continuously a pattern recognition as time goes by, are used to predict a nonlinear behavior of soil. After analysis, parameters which have an effect on learning and predicting of neural network, the teaming rate, momentum constant and the optimum neural network model are decided to be 0.5, 0.7, 8$\times$18$\times$2 in SU model and 0.3, 0.9, 8$\times$24$\times$2 in R model. The results of appling both networks showed that both networks predicted the shear behavior of soil in normally consolidated state well, but RNN model which is effective fir input data of irregular patterns predicted more efficiently than SNN model in case of the prediction in overconsolidated state.

• Journal of The Korean Association of Information Education
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• v.23 no.5
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• pp.427-440
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• 2019

Software courses required for all students regardless of their major in many universities. SW educational effectiveness model needs to be developed to enable effective communication among students, professors, and SW educators, and to identify the responsibilities of SW educators. SW educational effectiveness model based on literature review is composed of computational thinking, SW literacy, SW awareness, and SW attitude. Computational thinking, focused on analysis and design of problem solving processes, consists of decomposition, pattern recognition, abstraction, and algorithms. SW literacy involves viewing social development based on SW beyond information literacy in the digital age. The SW awareness and attitude were organized by considering the collegiate contexts. The SW educational effectiveness model will be used as the basis for diagnosis tools as further studies.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.29 no.1
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• pp.127-137
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• 2019

The growth in smartphone service has given rise to an increase in frequency and importance of authentication. Existing smartphone authentication mechanisms such as passwords, pattern lock and fingerprint recognition require a high level of awareness and authenticate users temporarily with a point-of-entry techniques. To overcome these disadvantages, there have been active researches in behavior-based authentication. However, previous studies focused on enhancing the accuracy of the authentication. Since authentication is directly used by people, it is necessary to reflect actual users' perception. This paper proposes user perception on behavior-based authentication with feature analysis. We conduct user survey to empirically understand user perception regarding behavioral authentication with selected authentication features. Then, we analyze acceptance of the behavioral authentication to provide continuous authentication with minimal awareness while using the device.

• Journal of IKEEE
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• v.23 no.1
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• pp.120-126
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• 2019

Recently, accurate prediction of power consumption based on machine learning techniques in Internet of Energy (IoE) has been actively studied using the large amount of electricity data acquired from advanced metering infrastructure (AMI). In this paper, we propose a deep learning model based on Gated Recurrent Unit (GRU) as an artificial intelligence (AI) network that can effectively perform pattern recognition of time series data such as the power consumption, and analyze performance of the prediction based on real household power usage data. In the performance analysis, performance comparison between the proposed GRU-based learning model and the conventional learning model of Long Short Term Memory (LSTM) is described. In the simulation results, mean squared error (MSE), mean absolute error (MAE), forecast skill score, normalized root mean square error (RMSE), and normalized mean bias error (NMBE) are used as performance evaluation indexes, and we confirm that the performance of the prediction of the proposed GRU-based learning model is greatly improved.

• BMB Reports
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• v.52 no.6
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• pp.373-378
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• 2019

The nucleotide-binding and oligomerization domain (NOD) is an innate pattern recognition receptor that recognizes pathogen- and damage-associated molecular patterns. The 29-kDa amino-terminal fibronectin fragment (29-kDa FN-f) is a matrix degradation product found in the synovial fluids of patients with osteoarthritis (OA). We investigated whether NOD2 was involved in 29-kDa FN-f-induced pro-catabolic gene expression in human chondrocytes. The expression of mRNA and protein was measured using quantitative real-time polymerase chain reaction (qrt-PCR) and Western blot analysis. Small interfering RNAs were used for knockdown of NOD2 and toll-like receptor 2 (TLR-2). An immunoprecipitation assay was performed to examine protein interactions. The NOD2 levels in human OA cartilage were much higher than in normal cartilage. NOD1 and NOD2 expression, as well as pro-inflammatory cytokines, including interleukin-1beta (IL-$1{\beta}$) and tumor necrosis factor-alpha (TNF-${\alpha}$), were upregulated by 29-kDa FN-f in human chondrocytes. NOD2 silencing showed that NOD2 was involved in the 29-kDa FN-f-induced expression of TLR-2. Expressions of IL-6, IL-8, matrix metalloproteinase (MMP)-1, -3, and -13 were also suppressed by TLR-2 knockdown. Furthermore, NOD2 and TLR-2 knockdown data demonstrated that both NOD2 and TLR-2 modulated the expressions of their adaptors, receptorinteracting protein 2 (RIP2) and myeloid differentiation 88, in 29-kDa FN-f-treated chondrocytes. 29-kDa FN-f enhanced the interaction of NOD2, RIP2 and transforming growth factor beta-activated kinase 1 (TAK1), an indispensable signaling intermediate in the TLR-2 signaling pathway, and activated nuclear factor-${\kappa}B$ (NF-${\kappa}B$), subsequently leading to increased expressions of pro-inflammatory cytokines and cartilage-degrading enzymes. These results demonstrate that 29-kDa FN-f modulated pro-catabolic responses via cross-regulation of NOD2 and TLR-2 signaling pathways.

• Journal of the Korea Society of Computer and Information
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• v.27 no.5
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• pp.55-67
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• 2022

Sensor data can provide fault diagnosis for equipment. However, the cause analysis for fault results of equipment is not often provided. In this study, we propose an explainable convolutional neural network framework for the sensor-based time series classification model. We used sensor-based time series dataset, acquired from vehicles equipped with sensors, and the Wafer dataset, acquired from manufacturing process. Moreover, we used Cycle Signal dataset, acquired from real world mechanical equipment, and for Data augmentation methods, scaling and jittering were used to train our deep learning models. In addition, our proposed classification models are convolutional neural network based models, FCN, 1D-CNN, and ResNet, to compare evaluations for each model. Our experimental results show that the ResNet provides promising results in the context of time series classification with accuracy and F1 Score reaching 95%, improved by 3% compared to the previous study. Furthermore, we propose XAI methods, Class Activation Map and Layer Visualization, to interpret the experiment result. XAI methods can visualize the time series interval that shows important factors for sensor data classification.

• Archives of design research
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• v.16 no.2
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• pp.375-384
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• 2003

This study is on the strategy for establishing successful corporate brand image, by understanding the need for increasing brand value based on the level of brand recognition. In order to carry this out, the PICS (Product Image Concentration System) is suggested, which includes Brand Image Analysis on a high-level, Product Image Programming based on the result of the image analysis, and Product Image Coherency Assessment and Management, resulting in setting up a guideline for gaining competitive advantage and brand management. Brand Image Analysis is a method that utilizes image association to understand brand disposition by analyzing the association pattern among available visual materials to measure the corporate and brand image inclinations. As the next step, Product Image Programming establishes design philosophy and principles based on the analysis of brand image, and the Visual Programming is a process for visualizing the intended product image direction. Lastly, Product Image Coherency Assessment examines whether to incorporate design philosophy and principles or not to arrive at an agreed evaluation criteria for developing designs coherent with the brand image. The PICS (Product Image Concentration System) is a practical method for increasing a company' competitive advantage and managing brand. The expectation on this system is to provide a guideline for applying brand image in design process more objectively. For further study, diversification of image spectrum based on expressive keywords and comparative analysis on images as well as a product image interpretation program to understand the order of visual materials will be necessary.