• Journal of International Society for Simulation Surgery
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• v.3 no.1
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• pp.1-8
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• 2016

In the field of Radiology, the Computer Aided Diagnosis is the technology which gives valuable information for surgical purpose. For its importance, several computer vison methods are processed to obtain useful information of images acquired from the imaging devices such as X-ray, Magnetic Resonance Imaging (MRI) and Computed Tomography (CT). These methods, called pattern recognition, extract features from images and feed them to some machine learning algorithm to find out meaningful patterns. Then the learned machine is then used for exploring patterns from unseen images. The radiologist can therefore easily find the information used for surgical planning or diagnosis of a patient through the Computer Aided Diagnosis. In this paper, we present a review on three widely-used methods applied to Computer Aided Diagnosis. The first one is the image processing methods which enhance meaningful information such as edge and remove the noise. Based on the improved image quality, we explain the second method called segmentation which separates the image into a set of regions. The separated regions such as bone, tissue, organs are then delivered to machine learning algorithms to extract representative information. We expect that this paper gives readers basic knowledges of the Computer Aided Diagnosis and intuition about computer vision methods applied in this area.

• Journal of the Korean Operations Research and Management Science Society
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• v.9 no.2
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• pp.46-55
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• 1984

The purpose of this parer is to provide a survey of the state of the art of routing methods in store-and-forward computer networks. The survey is carried out in line with a new taxonomy： heuristic methods, user-optimization methods, and system-optimization methods. This taxonomy on routing algorithms is based on two viewpoints： the level of optimization and the relative difficulty for the implementation in real computer networks. Some actual methods implemented in real computer networks are surveyed as well as the theoretical studies in the literature. This paper concludes with some points in need of further researches.

• Proceedings of the Korean Information Science Society Conference
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• 2005.07b
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• pp.334-336
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• 2005

본 논문은 정형 명세 언어인 Esterel이 가지는 취약점을 보안하기 위해 Safety-Critical Aided Development Environment를 추가로 활용하여 신뢰성 있고 안전적인 임베디드 제어 소프트웨어 개발을 위한 기법을 제시하고 있다. 그 뿐만 아니라 제시한 기법을 이용하여 개발된 간단한 자동 강속 제어 소프트웨어를 인터페이스와 기능 부분에 대해서 각각 확인 및 검증을 수행한 후 임베디드 시스템인 레고 마인드 스톰으로 제작된 차량 로봇에 탑재한 후 실험을 하고, 실험을 통해 기존 개발 기법과의 차이점을 분석한다.

• Proceedings of the Korean Information Science Society Conference
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• 2005.07a
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• pp.772-774
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• 2005

안전성이 강조되는 실시간 시스템에서 시스템이 시간의 제약을 만족해야만 한다. 실시간 시스템에서 오류는 잘못된 응답 뿐 만이 아니라 시간적으로 늦은 응답에 대해서도 오류로 분류를 할 수가 있다. 이런 오류들을 모니터하기 위해서 본 논문에서는 커널에 Timed Conformance Monitor를 모듈로 추가하였다. Timed Conformance Monitor를 통해서 실시간 태스크가 시간의 제약을 만족하는지를 분석하고 또한 분석 결과에 따라 오류를 처리할 수 있는 Fault Handler를 추가하여 실시간 시스템에 대한 Fault Tolerance를 보장해 줄 수 있다.

• International Journal of Computer Science & Network Security
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• v.22 no.10
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• pp.230-236
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• 2022

Objectives: The primary goal of this article is to compare the multiple algorithms used for deadlock handling methods and then outline the common method in deadlock handling methods. Methods: The article methodology begins with introducing a literature review studying different algorithms used in deadlock detection and many algorithms for deadlocks prevented, recovered, and avoided. Discussion and analysis of the literature review were done to classify and compare the studied algorithms. Findings: The results showed that the deadlock detection method solves the deadlock. As soon as the real-time deadlock detection algorithm is identified and indicated, it performs better than the non-real-time deadlock detection algorithm. Our novelty the statistics that we get from the percentages of reviewing outcomes that show the most effective rate of 47% is in deadlock prevention. Then deadlock detection and recovery with 28% finally, a rate of 25% for deadlock avoidance.

• Journal of The Korean Association of Information Education
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• v.18 no.1
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• pp.195-202
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• 2014

Computer education in Korea needs to be modified from IT utilization to principle-oriented form of computer science education. In this study the achievement, teaching-learning methods, and evaluation methods in the computer system education area of computer science education are suggested on the elementary and middle schools in Korea. For these purpose the curriculums of computer science in United Kingdom and United States are analyzed.

• IEIE Transactions on Smart Processing and Computing
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• v.4 no.1
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• pp.28-34
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• 2015

Several parallelization methods that can be applied to High Efficiency Video Coding (HEVC) decoders are evaluated. The market requirements of high-resolution videos, such as Full HD and UHD, have been increasing. To satisfy the market requirements, several parallelization methods for HEVC decoders have been studied. Understanding these parallelization methods and objective comparisons of these methods are crucial to the real-time decoding of high-resolution videos. This paper introduces the parallelization methods that can be used in HEVC decoders and evaluates the parallelization methods comparatively. The experimental results show that the average speed-up factors of tile-level parallelism, wavefront parallel processing (WPP), frame-level parallelism, and 2D-wavefront parallelism are observed up to 4.59, 4.00, 2.20, and 3.16, respectively.

• Proceedings of the Korean Information Science Society Conference
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• 2005.11b
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• pp.337-339
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• 2005

본 논문에서는 임베디드 시스템의 제어를 위해 동기화 언어인 에스테렐로 개발된 소프트웨어가 소프트웨어 요구사항을 만족하는지 확인하기 위한 Model-based Test(MBT) 프레임 워크를 제안한다. 제안된 프레임 워크를 기반으로 작성한 프로토 타입의 테스트 도구는 요구사항 모델을 창조하여 On-the-fly 방법을 통해 테스트 케이스를 랜덤하게 자동으로 생성하여 소프트웨어에 대하 테스트를 자동으로 수행한다. 간단한 case study로 레고 마인드스톰 로봇 제어 소프트웨어에 MBT를 수행하고 그 결과를 확인한다.

• International Journal of Computer Science & Network Security
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• v.21 no.6
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• pp.200-206
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• 2021

Fraud in e-commerce transaction increased in the last decade especially with the increasing number of online stores and the lockdown that forced more people to pay for services and groceries online using their credit card. Several machine learning methods were proposed to detect fraudulent transaction. Neural networks showed promising results, but it has some few drawbacks that can be overcome using optimization methods. There are two categories of learning optimization methods, first-order methods which utilizes gradient information to construct the next training iteration whereas, and second-order methods which derivatives use Hessian to calculate the iteration based on the optimization trajectory. There also some training refinements procedures that aims to potentially enhance the original accuracy while possibly reduce the model size. This paper investigate the performance of several NN models in detecting fraud in e-commerce transaction. The backpropagation model which is classified as first learning algorithm achieved the best accuracy 96% among all the models.

• International Journal of Computer Science & Network Security
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• v.22 no.5
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• pp.127-134
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• 2022

Generally developed for medical testing, electrocardiogram (ECG) recordings seizure the cardiac electrical signals from the surface of the body. ECG study can consequently be a vital first step to support analyze, comprehend, and expect cardiac ailments accountable for 31% of deaths globally. Different tools are used to analyze ECG signals based on computational methods, and explicitly machine learning method. In all abovementioned computational simulations are prevailing tools for cataloging and clustering. This review demonstrates the different effective methods for heart disease based on computational methods for ECG analysis. The accuracy in machine learning and three-dimensional computer simulations, among medical inferences and contributions to medical developments. In the first part the classification and the methods developed to get data and cataloging between standard and abnormal cardiac activity. The second part emphases on patient analysis from entire ECG recordings due to different kind of diseases present. The last part represents the application of wearable devices and interpretation of computer simulated results. Conclusively, the discussion part plans the challenges of ECG investigation and offers a serious valuation of the approaches offered. Different approaches described in this review are a sturdy asset for medicinal encounters and their transformation to the medical world can lead to auspicious developments.