• Journal of KIISE:Software and Applications
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• v.33 no.3
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• pp.301-315
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• 2006

In component-based system, the qualities of components as reusable units are the most important to success the component-based development. Therefore, before software implementation phase, the designed components should be measurable to improve the qualities of the components and the measured results should be reflected in the component-based development phase. In addition, the qualities of the components should be measured accurately. Accordingly, this paper proposes cohesion and coupling metrics applying static and dynamic dependency characteristics by the interdependence between classes. We prove the theoretical soundness of the proposed metrics by the axiom of briand et al. A case study and a comparison with the conventional metrics verify the practicality of the proposed metrics. The development times and endeavors to design the components is reduced, because the proposed metrics measure the qualities of components accurately.

• Annual Conference of KIPS
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• 2019.10a
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• pp.1019-1022
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• 2019

최근의 영상 처리 분야는 딥러닝 기법들의 성능이 입증됨에 따라 다양한 분야에서 이와 같은 기법들을 활용해 영상에 대한 분류, 분석, 검출 등을 수행하려는 시도가 활발하다. 그중에서도 의료 진단 보조 역할을 할 수 있는 의료 영상 분석 소프트웨어에 대한 기대가 증가하고 있는데, 본 연구에서는 캡슐내시경 영상에 주목하였다. 캡슐내시경은 주로 소장 촬영을 목표로 하며 식도부터 대장까지 약 8~10시간 동안 촬영된다. 이로 인해 CT, MR, X-ray와 같은 다른 의료 영상과 다르게 하나의 데이터 셋이 10~15만 장의 이미지를 갖는다. 일반적으로 캡슐내시경 영상을 판독하는 순서는 위장관 교차점(Z-Line, 유문판, 회맹판)을 기준으로 위장관 랜드마크(식도, 위, 소장, 대장)를 구분한 뒤, 각 랜드마크 별로 병변 정보를 찾아내는 방식이다. 그러나 워낙 방대한 영상 데이터를 가지기 때문에 의사 혹은 의료 전문가가 영상을 판독하는데 많은 시간과 노력이 소모되고 있다. 본 논문의 목적은 캡슐내시경 영상의 판독에서 모든 환자에 대해 공통으로 수행되고, 판독하는 데 많은 시간을 차지하는 위장관 랜드마크를 찾는 것에 있다. 이를 위해, 위장관 랜드마크를 식별할 수 있는 CNN 학습 모델을 설계하였으며, 더욱 효과적인 학습을 위해 전처리 과정으로 학습에 방해가 되는 학습 노이즈 영상들을 제거하고 위장관 랜드마크 별 특징 분석을 진행하였다. 총 8명의 환자 데이터를 가지고 학습된 모델에 대해 평가 및 검증을 진행하였는데, 무작위로 환자 데이터를 샘플링하여 학습한 모델을 평가한 결과, 평균 정확도가 95% 가 확인되었으며 개별 환자별로 교차 검증 방식을 진행한 결과 평균 정확도 67% 가 확인되었다.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.10
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• pp.679-690
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• 2013

Smartphones have features that users feel free to install/delete the program they want. Their emergence makes many developers rush into the Smartphone application development market. Thus, developing good applications quickly is becoming even more intense competition in the market. Because, however, the application development and deployment procedures are simple in the Android environments and anyone can participate in the development easily, applications not validated thoroughly are likely to be deployed. Therefore, a systematic approach that can verify Android-based applications with fewer burdens is required. In this paper, we propose a method that generates automatically GUI-based testing scenarios for the Android applications. The automated test scenario generation can reduce the time which the developer spends on testing, thus it can improve the productivity of the development in the testing phase.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.12
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• pp.477-484
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• 2018

Due to the advancement of computer and information technologies, numerous papers have been published. As new research fields continue to be created, users have a lot of trouble finding and categorizing their interesting papers. In order to alleviate users' this difficulty, this paper presents a method of grouping similar papers and clustering them. The presented method extracts primary keywords from the abstracts of each paper by using TF-IDF. Based on TF-IDF values extracted using K-means clustering algorithm, our method clusters papers to the ones that have similar contents. To demonstrate the practicality of the proposed method, we use paper data in FGCS journal as actual data. Based on these data, we derive the number of clusters using Elbow scheme and show clustering performance using Silhouette scheme.

• The Journal of the Korea Contents Association
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• v.19 no.7
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• pp.91-98
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• 2019

The purpose of this study was to develop and test an eye tracker focusing on early childhood participants, based on the characteristics of early childhood eye tracking studies. Eye tracking collects eye movement data of the subject, which provides scientific evidence of human cognition and thinking. The researcher built a Do It Yourself eye tracker camera module from general electronic components, and used Viewpoint analysis software from Arrington Research. The researcher compared the eye tracking data between the DIY eye tracker group and Tobii Pro eye tracker group, which provides a professional eye tracking system. Eye tracking data was collected from 52 five-year old children. The average proportion of valid trials between the two groups was compared with t test, and no significant difference was found. This result indicates that the DIY eye tracker can be used to collect valid eye tracking data from young children under certain research environment.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.251-253
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• 2019

This paper describes an implementation of a security SoC (System-on-Chip) prototype that interfaces a microprocessor with a block cipher crypto-core. The Cortex-M0 was used as a microprocessor, and a crypto-core implemented by integrating ARIA and AES into a single hardware was used as an intellectual property (IP). The integrated ARIA-AES crypto-core supports five modes of operation including ECB, CBC, CFB, CTR and OFB, and two master key sizes of 128-bit and 256-bit. The integrated ARIA-AES crypto-core was interfaced to work with the AHB-light bus protocol of Cortex-M0, and the crypto-core IP was expected to operate at clock frequencies up to 50 MHz. The security SoC prototype was verified by BFM simulation, and then hardware-software co-verification was carried out with FPGA implementation.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.31 no.3
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• pp.411-422
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• 2021

The autonomous driving system mounted on the unmanned vehicle recognizes the external environment through several sensors and derives the optimum control value through it. Recently, studies on physical level attacks that maliciously manipulate sensor data by performing signal-injection attacks have been published. signal-injection attacks are performed at the physical level and are difficult to detect at the software level because the sensor measures erroneous data by applying physical manipulations to the surrounding environment. In order to detect a signal-injection attack, it is necessary to verify the dependability of the data measured by the sensor. As so far, various methods have been proposed to attempt physical level attacks against sensors mounted on autonomous driving systems. However, it is still insufficient that methods for defending and detecting the physical level attacks. In this paper, we demonstrate signal-injection attacks targeting MEMS sensors that are widely used in unmanned vehicles, and propose a method to detect the attack. We present a signal-injection detection model to analyze the accuracy of the proposed method, and verify its effectiveness in a laboratory environment.

• Journal of the Korea Society for Simulation
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• v.29 no.4
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• pp.85-94
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• 2020

In determining the feasibility of planning and launching railway transportation projects, various decision-making processes are essentially required. LCC(Life Cycle Cost) value including total construction cost and operation cost is estimated in approximation Model with rough guideline. In this study, modeling and simulation-based analysis method is proposed to support the decision making process of railroad transportation and derivation of LCC. Firstly, cost analysis model was constructed by collecting various existing rail transportation business data to enable analyze based on numerical data, and the result were analyzed by DOE(Design Of Experiments) and RSM (Response Surface Method) simulation. Professional commercial software tools were used for effective model construction and simulation. In order to verify the research results, the actual railroad transportation projects were selected, and the results of the analysis were compared.

• Journal of The Korean Association of Information Education
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• v.25 no.2
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• pp.337-346
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• 2021

Despite the increasing rate of use of data science in various fields of society, research on data science education programs is relatively inadequate. In this study, a data science education program for elementary school students was developed and its effectiveness was verified. We created a program that collects data using microbit, one of the physical computing tools, and developed an education program that performs the data science stage of analyzing the collected data to derive results. A study was conducted on 10 students enrolled in the Information Gifted Program at 00 University, and pre- and post-tests of computing thinking skills were conducted to verify the effectiveness. As a result, it was found that the data science education program developed through this study has a significant effect on improving the computational thinking of elementary school students.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.7
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• pp.307-314
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• 2022

Recently, researches using deep learning-based models are being actively conducted to replace statistical-based time series forecast techniques to predict electric power demand. The result of analyzing the researches shows that the performance of the LSTM-based prediction model is acceptable, but it is not sufficient for long-term regional-wide power demand prediction. In this paper, we propose a WaveNet deep learning model to predict electric power demand 24-hour-ahead with temperature data in order to achieve the prediction accuracy better than MAPE value of 2% which statistical-based time series forecast techniques can present. First of all, we illustrate a delated causal one-dimensional convolutional neural network architecture of WaveNet and the preprocessing mechanism of the input data of electric power demand and temperature. Second, we present the training process and walk forward validation with the modified WaveNet. The performance comparison results show that the prediction model with temperature data achieves MAPE value of 1.33%, which is better than MAPE Value (2.33%) of the same model without temperature data.