• Journal of Practical Engineering Education
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• v.11 no.2
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• pp.167-174
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• 2019

In the case of Java programming education for non-major undergraduates, there are no examples of applying the physical computing education method. The advantage of physical computing education is that you can directly check the SW processing output result according to the input value of digital and analog sensor, so that you can quickly correct programming errors and improve learner's learning interest and satisfaction. In this paper, we use the microbits to combine physical computing education with basic Java programming education. In addition, according to the computational thinking process, we proposed an educational method for creating Java programs using microbits. Through block programming to control the microbits, we designed an algorithm and applied a training method to convert it into a Java program. In addition, the results of students' evaluations were analyzed in the course applying the education method, and the effectiveness of the education method using the microbit was analyzed.

• Korean Journal of Biological Psychiatry
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• v.11 no.2
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• pp.155-164
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• 2004

Objective:The present study examined the association between basic neurocognitive functions and emotional recognition in chronic schizophrenia. Furthermore, to Investigate cognitive variable related to emotion recognition in Schizophrenia. Methods:Forty eight patients from the Yongin Psychiatric Rehabilitation Center were evaluated for neurocognitive function, and Emotional Recognition Test which has four subscales finding emotional clue, discriminating emotions, understanding emotional context and emotional capacity. Measures of neurocognitive functioning were selected based on hypothesized relationships to perception of emotion. These measures included:1) Letter Number Sequencing Test, a measure of working memory;2) Word Fluency and Block Design, a measure of executive function;3) Hopkins Verbal Learning Test-Korean version, a measure of verbal memory;4) Digit Span, a measure of immediate memory;5) Span of Apprehension Task, a measure of early visual processing, visual scanning;6) Continuous Performance Test, a measure of sustained attention functioning. Correlation analyses between specific neurocognitive measures and emotional recognition test were made. To examine the degree to which neurocognitive performance predicting emotional recognition, hierarchical regression analyses were also made. Results:Working memory, and verbal memory were closely related with emotional discrimination. Working memory, Span of Apprehension and Digit Span were closely related with contextual recognition. Among cognitive measures, Span of Apprehension, Working memory, Digit Span were most important variables in predicting emotional capacity. Conclusion:These results are relevant considering that emotional information processing depends, in part, on the abilities to scan the context and to use immediate working memory. These results indicated that mul- tifaceted cognitive training program added with Emotional Recognition Task(Cognitive Behavioral Rehabilitation Therapy added with Emotional Management Program) are promising.

• Journal of Educational Research in Mathematics
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• v.24 no.2
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• pp.269-283
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• 2014

Recently, 3S Mathematics Education (Storytelling mathematics education, SMART mathematics education, and STEAM mathematics education) is emphasized. Based on recently published report on Storytelling mathematics textbook, we propose executable expression based SMART storytelling mathematics related to the elementary mathematic curriculum on 3D building blocks. We designed letters and expressions to represent three dimensional shape of 3D building blocks, and we compare its characteristics with that of LEGO blocks. We assert that text-based executable expressions not only construct what students want to make but also teachers can read students thinking process and can support educational help based on students needs. We also present linear function, quadratic function, and function variable concepts using executable expressions based on 3D building block as an example of SMART storytelling mathematics. This research was supported by the collaborated creativity mentoring project between Siheung City and college of education at Seoul National University. We hope designed executable expressions can be used for the development of SMART storytelling mathematics education.

• Proceedings of the IEEK Conference
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• summer
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• pp.410-415
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• 2004

Employing knowledge of adaptive possibilities of agents in multi-agents system, we have explored new aspects of distributed modular systems for building ubiquitous heterogeneous robotic systems using intelligent building blocks (I-BLOCKS) [1] as reconfigurable modules. This paper describes early technological approaches related to technical design, experimental developments and evaluation of adaptive processing and information interaction among I-BLOCKS allowing users to easily develop modular robotic systems. The processing technology presented in this paper is embedded inside each $DUPLO^1$ brick by microprocessor as well as selected sensors and actuators in addition. Behaviors of an I-BLOCKS modular structure are defined by the internal processing functionality of each I-Block in such structure and communication capacities between I-BLOCKS. Users of the I-BLOCKS system can easily do 'programming by building' and thereby create specific functionalities of a modular robotic structure of intelligent artefacts without the need to learn and use traditional programming language. From investigating different effects of modern artificial intelligence, I-BLOCKS we have developed might possibly contain potential possibilities for developing modular robotic system with different types of morphology, functionality and behavior. To assess these potential I-BLOCKS possibilities, the paper presents a limited range of different experimental scenarios in which I-BLOCKS have been used to set-up reconfigurable modular robots. The paper also reports briefly about earlier experiments of I-BLOCKS created on users' natural inspiration by a just defined concept of modular artefacts.

• Journal of The Korean Association of Information Education
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• v.19 no.4
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• pp.533-544
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• 2015

In this study, the direction for hierarchical curriculum organization about software education in Korea was suggested in terms of overall execution of software education. The international case studies especially was conducted in order to suggest the propel educational programming language for level of students in the programming activity. In terms of the international case studies, the type of programming language was examined, which is suggested to each school level as a part of required regular curriculum. Then, the direction was supposed to suggest the instructional system organization of software education for Korea through the result of case studies. The results of case studies indicated that elementary school use the block based programming language, and text based programming languages are used from middle school.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.46 no.5
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• pp.48-55
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• 2009

Prior research works in indoor-outdoor classification have been conducted based on a simple combination of low-level features. However, since there are many challenging problems due to the extreme variability of the scene contents, most methods proposed recently tend to combine the low-level features with high-level information such as the presence of trees and sky. To extract these regions from videos, we need to conduct additional tasks, which may yield the increasing number of feature dimensions or computational burden. Therefore, an efficient indoor-outdoor scene classification method is proposed in this paper. First, the video is divided into the five same-sized blocks. Then we define and use the edge and color orientation histogram (ECOH) descriptors to represent each sub-block efficiently. Finally, all ECOH values are simply concatenated to generated the feature vector. To justify the efficiency and robustness of the proposed method, a diverse database of over 1200 videos is evaluated. Moreover, we improve the classification performance by using different weight values determined through the learning process.

• Journal of Ocean Engineering and Technology
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• v.18 no.6 s.61
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• pp.79-83
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• 2004

The curved blocks which compose the bow and stem of a ship contain many skewed joints that are inclined horizontally and vertically. Most of these joints have a large fitness error and are continuously changing their form and are not easily accessible. The welding position and parameter values should be appropriately set in correspondence to the shape and the inclination of the joints. The welding parameters such as current, voltage, travel speed, and melting rate, are related to each other and their values must be in a specific limited range for the sound welding. These correlations and the ranges are dependent up on the kind and size of wire, shielding gas, joint shape and fitness. To determine these relationships, extensive welding experiments were performed. The experimental data were processed using several information processing technologies. The regression method was used to determine the relationship between current voltage, and deposition rate. When a joint is inclined, the weld bead should be confined to a the limited size, inorder to avoid undercut as well as overlap due to flowing down of molten metal by gravity. The dependency of the limited weld size which is defined as the critical deposited area on various factors such as the horizontally and vertically inclined angle of the joint, skewed angle of the joint, up or down welding direction and weaving was investigated through a number of welding experiments. On the basis of this result, an ANN system was developed to estimate the critical deposited area. The ANN system consists of a 4 layer structure and uses an error back propagation learning algorithm. The estimated values of the ANN were validated using experimental values.

• The KIPS Transactions:PartB
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• v.10B no.3
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• pp.311-320
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• 2003

The Optimal filter yielding optimal texture feature separation is a most effective technique for extracting the texture objects from multiple textures images. But, most optimal filter design approaches are restricted to the issue of supervised problems. No full-unsupervised method is based on the recognition of texture objects in image. We propose a novel approach that uses unsupervised learning schemes for efficient texture image analysis, and the band-pass feature of Gabor-filter is used for the optimal filter design. In our approach, the self-organizing neural network for multiple texture image identification is based on block-based clustering. The optimal frequency of Gabor-filter is turned to the optimal frequency of the distinct texture in frequency domain by analyzing the spatial frequency. In order to show the performance of the designed filters, after we have attempted to build a various texture images. The texture objects extraction is achieved by using the designed Gabor-filter. Our experimental results show that the performance of the system is very successful.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.5
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• pp.3132-3138
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• 2014

In many communication systems, intersymbol interference, DC and impulsive noise are hard-to-solve problems. For the purpose of cancelling such interferences, the concept of lagged cross-correlation of probability has been used for blind equalization. However, this algorithm has a large burden of computation. In this paper, a recursive method of the algorithm based on the lagged probability correlation is proposed. The summation operation in the calculation of gradient of the cost is transformed into a recursive gradient calculation. The recursive method shows to reduce the high computational complexity of the algorithm from O(NM) to O(M) for M symbols and N block data having advantages in implementation while keeping the robustness against those interferences. From the results of the simulation, the proposed method yields the same learning performance with reduced computation complexity.

• Convergence Security Journal
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• v.21 no.1
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• pp.93-100
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• 2021

Damage caused by intelligent cyber attacks not only disrupts system operations and leaks information, but also entails massive economic damage. Recently, cyber attacks have a distinct goal and use advanced attack tools and techniques to accurately infiltrate the target. In order to minimize the damage caused by such an intelligent cyber attack, it is necessary to block the cyber attack at the beginning or during the attack to prevent it from invading the target's core system. Recently, technologies for predicting cyber attack paths and analyzing risk level of cyber attack using big data or artificial intelligence technologies are being studied. In this paper, a cyber attack path analysis method using attack tree and RFI is proposed as a basic algorithm for the development of an automated cyber attack path prediction system. The attack path is visualized using the attack tree, and the priority of the path that can move to the next step is determined using the RFI technique in each attack step. Based on the proposed mechanism, it can contribute to the development of an automated cyber attack path prediction system using big data and deep learning technology.