• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.10
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• pp.1359-1368
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• 2021

Steganography is a science of embedding secret data into innocent data and its goal is to conceal the existence of a carrier data. Many research on Steganography has been proposed by various hiding and detection techniques that are based on different algorithms. On the other hand, very few studies have been conducted to analyze the performance of each Steganography software. This paper describes five different Steganography software, each having its own algorithms, and analyzes the difference of each inherent feature. Image quality metrics of Peak Signal to Noise Ratio (PSNR) and Structural SIMilarity (SSIM) are used to define its performance of each Steganography software. We extracted PSNR and SSIM results of a quantitative amount of embedded output images for those five Steganography software. The results will show the optimal steganography software based on the evaluation metrics and ultimately contribute to forensics.

• Journal of the Korea Society of Computer and Information
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• v.25 no.12
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• pp.25-34
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• 2020

In this paper, we empirically compare the effectiveness of training models to recognize beauty-related skin disease using supervised deep learning algorithms. Recently, deep learning algorithms are being actively applied for various fields such as industry, education, and medical. For instance, in the medical field, the ability to diagnose cutaneous cancer using deep learning based artificial intelligence has improved to the experts level. However, there are still insufficient cases applied to disease related to skin beauty. This study experimentally compares the effectiveness of identifying beauty-related skin disease by applying deep learning algorithms, considering CNN, ResNet, and SE-ResNet. The experimental results using these training models show that the accuracy of CNN is 71.5% on average, ResNet is 90.6% on average, and SE-ResNet is 95.3% on average. In particular, the SE-ResNet-50 model, which is a SE-ResNet algorithm with 50 hierarchical structures, showed the most effective result for identifying beauty-related skin diseases with an average accuracy of 96.2%. The purpose of this paper is to study effective training and methods of deep learning algorithms in consideration of the identification for beauty-related skin disease. Thus, it will be able to contribute to the development of services used to treat and easy the skin disease.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.8
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• pp.4057-4071
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• 2018

The software centered world following the fourth industrial revolution is rapidly approaching us. Countries around the world attach importance to software's ability as one of the key elements for training future human resources. In order to train software centered human resources, each university has designated Software Education as an essential curriculum for not only major but also non-majors. In the past Software Education was an education for a major, but recent Software Education was changed to the essential education that is necessary for all living in the software centered world. In the past the curriculum was focused on software development and implementation-oriented education, but recent curriculum emphasizes sequential arranging and thinking of problem solving. In order to reflect trends in recent Software Education in detail, we integrate Software Education with major concept of Computational Thinking. In this paper, we analyzed the effect of the main concept of Computational Thinking on Software Education for non-majored learners who received Software Education based on Computational Thinking (here refers to learners who major in humanities, social sciences and arts). In addition, research models of satisfaction, self-efficacy, and occupational change was established as the elements of Software Education, and it was found that there was a relation between Computational Thinking and Software Education.

• Electronics and Telecommunications Trends
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• v.36 no.6
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• pp.67-77
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• 2021

Since Richard Feynman presented the concept of quantum computers, quantum computing have been identified today overcoming the limits of supercomputing in various applications. Quantum hardware has steadily developed into 50 to hundreds of qubits of various quantum hardware technologies based on superconductors, semiconductors, and trapped ions over 40 years. However, it is possible to use a NISQ (Noisy Intermediate Scale Quantum) level quantum device that currently has hardware constraints. In addition, the software environment in which quantum algorithms for problem solving in various applications can be executed is pursuing research with quantum computing software such as programming language, compiler, control, testing and verification. The development of quantum software is essential amid intensifying technological competition for the commercialization of quantum computers. Therefore, this paper introduces the trends of the latest technology, focusing on quantum computing software platforms, and examines important software component technologies.

• The Journal of Korean Association of Computer Education
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• v.18 no.6
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• pp.23-31
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• 2015

The main aim in software education is to improve problem-solving ability based on computational thinking with the healthy information ethics. For this purpose, many institutions have attempted various educational programs such as Educational Programming Language, Physical Computing, and Robot education. However, it is possible to obscure the essence of computer education for computational thinking if the computer education focuses on using certain special education programming language and products. Therefore, this paper suggests a method of algorithm education using RAPTOR which is a visual programming development environment and is based on flowcharts. In order to verify the effectiveness of the algorithms education using the RAPTOR, 16 high-school students were applied to an educational program for twelve hours on five steps and then we obtained positive results.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.11
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• pp.912-917
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• 2001

In this study, we define the measuring method of crank angle precisely using an event and perform a study on the hardware structure and software algorithms which is applicable for the commercial engine. Also we developed a Computer-ECU(Personal computer based electronic control unit) using a computer and a microprocessor, for performing the ignition at a desire position(angle) and for controlling a duty ratio a pulse for ISC(Idle speed control). We applied these algorithms to the modeling which is induced a concept of event and got a better result than a conventional ECU in the state of transient as a result of performing air fuel ratio control in a commercial engine. This technique can be used for the back to improve ECU performance. It the present type of Hybrid I. C voltage regulator is altered to the new type of regulator, we will be surely able to reduce the production cost as well as simplify the design of alternator\`s rear bracket and rectifier part because of the removal of trio diode. Experiment is taken by MS-R004.

• The KIPS Transactions:PartD
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• v.8D no.1
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• pp.81-86
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• 2001

one key goal of software testing is to generate a 'good' test data set, which is consideres as the most difficult and time-consuming task. This paper discusses how genetic algorithns can be used for automatic generation of test data set for software testing. We employ mutation testing to show the effectiveness of genetic algorithms (GAs) in automatic test data generation. The approach presented in this paper is different from other in that test generation process requireas no lnowledge of implementation details of a program under test. In addition, we have conducted some experiments and compared our approach with random testing which is also regarded as a black-box test generation technique to show its effectiveness.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.152-154
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• 2005

In this paper, we propose a new architecture of Fuzzy Polynomial Neural Networks(FPNN) by means of genetically optimized Fuzzy Polynomial Neuron(FPN) and discuss its comprehensive design methodology involving mechanisms of genetic optimization, especially Genetic Algorithms(GAs). The design of the network exploits the extended Group Method of Data Handling(GMDH) with some essential parameters of the network being provided by the designer and kept fixed throughout the overall development process. This restriction may hamper a possibility of producing an optimal architecture of the model. The proposed FPNN gives rise to a structurally optimized network and comes with a substantial level of flexibility in comparison to the one we encounter in conventional FPNNs. It is shown that the proposed genetic algorithms-based Fuzzy Polynomial Neural Networks is more useful and effective than the existing models for nonlinear process. We experimented with Medical Imaging System(MIS) dataset to evaluate the performance of the proposed model.

• ETRI Journal
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• v.31 no.6
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• pp.732-740
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• 2009

This paper describes the implementation of a digital audio effect system-on-a-chip (SoC), which integrates an embedded digital signal processor (DSP) core, audio codec intellectual property, a number of peripheral blocks, and various audio effect algorithms. The audio effect SoC is developed using a software and hardware co-design method. In the design of the SoC, the embedded DSP and some dedicated hardware blocks are developed as a hardware design, while the audio effect algorithms are realized using a software centric method. Most of the audio effect algorithms are implemented using a C code with primitive functions that run on the embedded DSP, while the equalization effect, which requires a large amount of computation, is implemented using a dedicated hardware block with high flexibility. For the optimized implementation of audio effects, we exploit the primitive functions of the embedded DSP compiler, which is a very efficient way to reduce the code size and computation. The audio effect SoC was fabricated using a 0.18 ${\mu}m$ CMOS process and evaluated successfully on a real-time test board.

• Journal of Information Processing Systems
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• v.13 no.5
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• pp.1089-1102
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• 2017

In software systems, it has been observed that a fault is often caused by an interaction between a small number of input parameters. Even for moderately sized software systems, exhaustive testing is practically impossible to achieve. This is either due to time or cost constraints. Combinatorial (t-way) testing provides a technique to select a subset of exhaustive test cases covering all of the t-way interactions, without much of a loss to the fault detection capability. In this paper, an approach is proposed to generate 2-way (pairwise) test sets using genetic algorithms. The performance of the algorithm is improved by creating an initial solution using the overlap coefficient (a similarity matrix). Two mutation strategies have also been modified to improve their efficiency. Furthermore, the mutation operator is improved by using a combination of three mutation strategies. A comparative survey of the techniques to generate t-way test sets using genetic algorithms was also conducted. It has been shown experimentally that the proposed approach generates faster results by achieving higher percentage coverage in a fewer number of generations. Additionally, the size of the mixed covering arrays was reduced in one of the six benchmark problems examined.