• Plant Journal
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• v.17 no.3
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• pp.37-41
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• 2021

P&ID((Piping and Instrument Diagram) is a key drawing in the engineering industry because it contains information about the units and instrumentation of the plant. Until now, simple repetitive tasks like listing symbols in P&ID drawings have been done manually, consuming lots of time and manpower. Currently, a deep learning model based on CNN(Convolutional Neural Network) is studied for drawing object detection, but the detection time is about 30 minutes and the accuracy is about 90%, indicating performance that is not sufficient to be implemented in the real word. In this study, the detection of symbols in a drawing is performed using 1-stage object detection algorithms that process both region proposal and detection. Specifically, build the training data using the image labeling tool, and show the results of recognizing the symbol in the drawing which are trained in the deep learning model.

• Journal of Convergence for Information Technology
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• v.11 no.5
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• pp.138-149
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• 2021

Our society is developing exponentially, but schools are not keeping up with the pace of society's development, and they are not providing digital literacy education suitable for the growth and development of students. Thus, this study identified the actual conditions and problems of digital literacy education at school sites and sought the direction of development of digital literacy education. By identifying the current state of schools in which the 2015 curriculum is operated, we sought the direction of the development of digital literacy education for our school. First, old digital devices should be replaced, laptops or smart devices should be provided for each student, and internet access should be available throughout the school. Second, digital literacy education should be provided to teachers by providing various training opportunities.Third, coding education where you can express what you think as logical thinking, Software training should increase the level of the algorithmic domain that shows the computational thinking process of discovering problems and automating a given problem into a computer programming language, there is enough robot that can be seen operating the program, digital parish will need to be delivered.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.2
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• pp.244-251
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• 2020

This paper presents a design of the Meta-Class Repository (MCR) which maintain syntactically analyzed and structured meta-class information from UML diagrams, and then proposes 'meta-class,' also known as super-class, to construct structured information analyzed syntactically. The MCR is a collection of these meta-classes which contains the information extracted from diagrams. This paper also presents a design of the Code Generation Engine (CGE) which roles generating codes corresponding classes from UML diagrams based on the MCR maintaining a collection of meta-classes which is syntactically-analyzed and constructed in previous process. The logics of CGE are designed to generate codes collaborated with MCR and CGE with integration. The logics of CGE mechanism is presented with the form of finite state machine to present the algorithms of code generation formally and have the advantages of simplicity and easiness in development.

• Journal of The Korean Association of Information Education
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• v.25 no.2
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• pp.317-325
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• 2021

In this paper, the degree of reflection of SW·AI elements and CT elements was investigated and analyzed for a total of 44 textbooks of Korean, social, moral, mathematics and science textbooks based on the 2015 revised curriculum. As a result of the analysis, most of the activities of data collection, data analysis, and data presentation, which are ICT elements, were not reflected, and algorithm and programming elements were not reflected among SW·AI content elements, and there were no abstraction, automation, and generalization elements among CT elements. Therefore, in order to effectively implement SW·AI convergence education in elementary school subjects, we will expand ICT utilization activities to SW·AI utilization activities. Training on the understanding of SW·AI convergence education and improvement of teaching and learning methods using SW·AI is needed for teachers. In addition, it is necessary to establish an information curriculum and secure separate class hours for substantial SW·AI education.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.13 no.3
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• pp.135-144
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• 2003

An intrusion detection system shows different result over overall detection area according to its detection characteristics of inner detection algorithms or techniques. To expand detection areas, we requires an integrated detection which can be archived both by deploying a few detection systems which detect different detection areas and by combining their results. In addition to expand detection areas, we need to decrease the workload of security managers by false alarms and improve the correctness by minimizing false alerts which happen during the process of integration. In this paper, a method for aggregation detection use fuzzy inference to integrate a vague detection results which imply the characteristics of detection systems. Their analyzed detection characteristics are expressed as fuzzy membership functions and fuzzy rule bases which are applied through the process of fuzzy control. And, it integrate a vague decision results and minimize the number of false alerts by reflecting the characteristics of detection systems. Also it does minimize inference objects by applying thresholds decided through several experiments.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.16 no.4
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• pp.105-118
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• 2006

The purpose of this research is twofold. The first is to develop the measures for evaluating performance of fingerprint sensor modules quantitatively and objectively. The second is to present the methodology for evaluating compatibilities among disparate fingerprint sensors. This paper focuses on the performance evaluation not of fingerprint authentication algorithm but of fingerprint sensors. Presented in this paper are several indicators and their measuring schemes such as the actual resolution of fingerprint images, the level of distortion by horizontal and vertical resolutions of fingerprint image, the intensity distribution for various illuminating conditions. Nine commercial sensor modules have been tested and the test results are expressed by using 95% confidence interval based on 50 acquired fingerprint images. The experimental results are compared with the manufacturer's sensor specification.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.21 no.2
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• pp.3-11
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• 2011

Evaluation of cube roots in characteristic three finite fields is required for Tate (or modified Tate) pairing computation. The Hamming weights (the number of nonzero coefficients) in the polynomial representations of $x^{1/3}$ and $x^{2/3}$ determine the efficiency of cube roots computation, where $F_{3^m}$is represented as $F_3[x]/(f)$ and $f(x)=x^m+ax^k+b{\in}F_3[x]$ (a, $b{\in}F_3$) is an irreducible trinomial. O. Ahmadi et al. determined the Hamming weights of $x^{1/3}$ and $x^{2/3}$ for all irreducible trinomials. In this paper, we present formulas for cube roots in $F_{3^m}$ using the shifted polynomial basis(SPB). Moreover, we provide the suitable shifted polynomial basis bring no further modular reduction process.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.6
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• pp.706-713
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• 2020

To control the LED lighting for broadcasting, LED current control using tri-stimulus values is used for RGB LEDs. For the convenience of control, this control is approximated as a linear function or used as an appropriate value through trial and error. Also, it is not suitable for broadcast lighting because it does not use a diffuser plate applied for mixing sufficient light and color required for actual it. In this study, a neural network with excellent nonlinear function approximation is used as a control method for LED panels for broadcast lighting. We intend to implement an LED panels controller suitable for the desired chromaticity coordinates and dimming values of intensity. As a result of the performance evaluation, the errors of the xy chromaticity coordinates are mostly ±0.02 and the acceptable range of ANSI C78.377A was satisfied. The average errors of the xy chromaticity coordinate are xerror=0.0044 and yerror=0.0030, respectively, and we confirmed the superiority and stable performance of the proposed algorithm.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.4
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• pp.625-632
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• 2021

This paper studies the effect of the number of filters in the CNN (Convolutional Neural Network) layer on the performance of a noise attenuator. Speech is estimated from a noised speech signal using a 64-neuron, 16-kernel CNN filter and an error back-propagation algorithm. In this study, in order to verify the performance of the noise attenuator with respect to the number of filters, a program using Keras library was written and simulation was performed. As a result of simulation, it can be seen that this system has the smallest MSE (Mean Squared Error) and MAE (Mean Absolute Error) values when the number of filters is 16, and the performance is the lowest when there are 4 filters. And when there are more than 8 filters, it was shown that the MSE and MAE values do not differ significantly depending on the number of filters. From these results, it can be seen that about 8 or more filters must be used to express the characteristics of the speech signal.

• Journal of the Korea institute for structural maintenance and inspection
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• v.25 no.5
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• pp.135-140
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• 2021

In this study, the concrete aggregate shape features were extracted from the cross-section of a normal concrete strength cylinder, and the compressive strength of the cylinder was predicted using artificial neural networks and image processing technology. The distance-angle features of aggregates, along with general aggregate shape features such as area, perimeter, major/minor axis lengths, etc., were numerically expressed and utilized for the compressive strength prediction. The results showed that compressive strength can be predicted using only the aggregate shape features of the cross-section without using major variables. The artificial neural network algorithm was able to predict concrete compressive strength within a range of 4.43% relative error between the predicted strength and test results. This experimental study indicates that various material properties such as rheology, and tensile strength of concrete can be predicted by utilizing aggregate shape features.