• 한국생물정보학회:학술대회논문집
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• 한국생물정보시스템생물학회 2005년도 BIOINFO 2005
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• pp.339-343
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• 2005

Boolean networks(BN) construction is one of the commonly used methods for building gene networks from time series microarray data. However, BN has two major drawbacks. First, it requires heavy computing times. Second, the binary transformation of the microarray data may cause a loss of information. This paper propose two methods using liner regression to construct gene regulatory networks. The first proposed method uses regression based BN variable selection method, which reduces the computing time significantly in the BN construction. The second method is the regression based network method that can flexibly incorporate the interaction of the genes using continuous gene expression data. We construct the network structure from the simulated data to compare the computing times between Boolean networks and the proposed method. The regression based network method is evaluated using a microarray data of cell cycle in Caulobacter crescentus.

• 한국사이버테러정보전학회:학술대회논문집
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• 한국사이버테러정보전학회 2004년도 제1회 춘계학술발표대회
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• pp.35-42
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• 2004

유비쿼터스 컴퓨팅(Ubiquitous Computing)은 많은 분야의 실생활에서 적용이 되고 있다 이미 각 선진국에서는 좀 더 사용자들에게 편리한 유비쿼터스 환경을 제공하기 위해 유비쿼터스에 대한 다양한 연구를 추진 중 이다. 언제 어디서나 사용자가 원하는 정보와 서비스를 제공받을 수 있다는 이점이 있지만 다른 한편으로는 유비쿼터스 네트워크의 취약점을 이용한 여러 가지 공격 즉 Rogue AP, If spoofing, DoS 등의 공격에 사회적으로 큰 혼란을 가져올 수 도 있다. 이에 본 논문에서는 유비쿼터스 컴퓨팅 네트워크 환경에서의 보안요구 사항등을 분석해보고 유비쿼터스 컴퓨팅 환경의 네트워크 인프라 구축을 위한 핵심기술인 무선 'Ad hoc' 와 RFID에 대해 연구하고자 한다. 연구하고자 한다.

• IEIE Transactions on Smart Processing and Computing
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• 제5권4호
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• pp.289-293
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• 2016

As more mobile devices are equipped with multi-core CPUs and are required to execute many compute-intensive multimedia applications, it is important to optimize the systems, considering the underlying parallel hardware architecture. In this paper, we implement and optimize ray-tracing application tailored to a given mobile computing platform with multiple heterogeneous processing elements. In this paper, a lightweight ray-tracing application is specified and implemented in Kahn process network (KPN) model-of-computation, which is known to be suitable for the description of real-time applications. We take an open-source C/C++ implementation of ray-tracing and adapt it to KPN description in the Distributed Application Layer framework. Then, several possible configurations are evaluated in the target mobile computing platform (Exynos 5422), where eight heterogeneous ARM cores are integrated. We derive the optimal degree of parallelism and a suitable distribution of the replicated tasks tailored to the target architecture.

• 한국컴퓨터산업학회논문지
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• 제3권2호
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• pp.199-208
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• 2002

컴퓨팅 환경의 변화 때문에 최근 에이젼트 기술이 주목을 받고 있다. 네트워크에 에이젼트를 분산 배치시킴으로서 서비스의 실현이 용이한 위치에서 필요한 서비스를 신속하게 제공하게 함으로서 네트워크의 효율성을 증대시킬 필요성이 점점 커지고 있다. 본 논문에서는 자바로 프로그래밍한 에이젼트를 실제 서비스의 실현이 용이한 위치에 있는 네트워크에 배치하고, 매니져에서는 에이젼트에 접속하여 서비스코드의 URL을 에이젼트에 알려주었다. 에이젼트는 해당 URL에서 서비스코드를 다운로드받아서 실행시켜 그 결과를 매니져에게 돌려주는 분산컴퓨팅 환경을 자바로 구현해본다.

• 융합보안논문지
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• 제5권4호
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• pp.59-65
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• 2005

유비쿼터스 컴퓨팅(Ubiquitous Computing)은 많은 분야의 실생활에서 적용이 되고 있다. 이미 각 선진국에서는 좀 더 사용자들에게 편리한 유비쿼터스 환경을 제공하기 위해 유비쿼터스에 대한 다양한 연구를 추진 중이다. 언제 어디서나 사용자가 원하는 정보와 서비스를 제공받을 수 있다는 이점이 있지만 다른 한편으로는 유비쿼터스 네트워크의 취약점을 이용한 여러 가지 공격 즉 Rogue AP, IP spoofing, DoS 등의 공격에 사회적으로 큰 혼란을 가져올 수 도 있다. 이에 본 논문에서는 유비쿼터스 컴퓨팅 네트워크 환경에서의 보안요구 사항등을 분석해보고 유비쿼터스 컴퓨팅 환경의 네트워크 인프라 구축을 위한 핵심기술인 무선 'Ad hoc'와 RFID에 대해 연구하고자 한다.

• Journal of Communications and Networks
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• 제15권2호
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• pp.207-216
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• 2013

Vehicular ad-hoc networks (VANETs) are self-organizing, self-healing networks which provide wireless communication among vehicular and roadside devices. Applications in such networks can take advantage of the use of simultaneous connections, thereby maximizing the throughput and lowering latency. In order to take advantage of all radio interfaces of the vehicle and to provide good quality of service for vehicular applications, we developed a seamless flow mobility management architecture based on vehicular network application classes with network-based mobility management. Our goal is to minimize the time of flow connection exchange in order to comply with the minimum requirements of vehicular application classes, as well as to maximize their throughput. Network simulator (NS-3) simulations were performed to analyse the behaviour of our architecture by comparing it with other three scenarios. As a result of this work, we observed that the proposed architecture presented a low handover time, with lower packet loss and lower delay.

• 한국항행학회논문지
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• 제14권6호
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• pp.984-989
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• 2010

본 논문에서는 초소형 정밀 마이크로 흐름센서를 설계하기 위해 Java 멀티스레드를 이용한 병렬 프로그래밍 기법을 도입하여 센서 모듈의 성능 분석과 개선이 가능한 병렬처리형 설계 툴을 개발하였다. 연산에 따른 기본 성능을 측정하기 위하여 열운송 방정식에 지배되는 포텐셜 문제를 두 개의 실험모델로 나누어 실험을 수행하였다. 시뮬레이션 결과 네트워크 PC의 수를 증가시키면 이와 비례하는 속도향상 특성이 나타났다. 따라서 본 연구에서 제안하는 병렬화 방안은 대규모 연산모델에도 적용 가능함을 확인하였다.

• 전자통신동향분석
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• 제34권1호
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• pp.23-35
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• 2019

In the upcoming post-app era, real-time, intelligent and immersive services such as autonomous vehicles, virtual secretaries, virtual reality, and augmented reality are expected to dominate. However, there is a growing demand for new networking and computing infrastructure capabilities because existing physical connection-oriented networks and centralized cloud-based service environments have inherent limitations to effectively accommodate these services. To this end, research on intelligent edge network computing technology is underway to analyze the contextual situation of human and things and to configure the service environment on the network edge so that the application services can be performed optimally. In this article, we describe the technology issues for edge network intelligence and introduce related research trends.

• ETRI Journal
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• 제44권2호
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• pp.194-207
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• 2022

In the last decade, substantial progress has been achieved in intelligent traffic control technologies to overcome consistent difficulties of traffic congestion and its adverse effect on smart cities. Edge computing is one such advanced progress facilitating real-time data transmission among vehicles and roadside units to mitigate congestion. An edge computing-based deep reinforcement learning system is demonstrated in this study that appropriately designs a multiobjective reward function for optimizing different objectives. The system seeks to overcome the challenge of evaluating actions with a simple numerical reward. The selection of reward functions has a significant impact on agents' ability to acquire the ideal behavior for managing multiple traffic signals in a large-scale road network. To ascertain effective reward functions, the agent is trained withusing the proximal policy optimization method in several deep neural network models, including the state-of-the-art transformer network. The system is verified using both hypothetical scenarios and real-world traffic maps. The comprehensive simulation outcomes demonstrate the potency of the suggested reward functions.

• Journal of Computing Science and Engineering
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• 제9권4호
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• pp.204-213
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• 2015

Under the conditional independence assumption among local features, the Naive Bayes Nearest Neighbor (NBNN) classifier has been recently proposed and performs classification without any training or quantization phases. While the original NBNN shows high classification accuracy without adopting an explicit training phase, the conditional independence among local features is against the compositionality of objects indicating that different, but related parts of an object appear together. As a result, the assumption of the conditional independence weakens the accuracy of classification techniques based on NBNN. In this work, we look into this issue, and propose a novel Bayesian network for an NBNN based classification to consider the conditional dependence among features. To achieve our goal, we extract a high-level feature and its corresponding, multiple low-level features for each image patch. We then represent them based on a simple, two-level layered Bayesian network, and design its classification function considering our Bayesian network. To achieve low memory requirement and fast query-time performance, we further optimize our representation and classification function, named relation-based Bayesian network, by considering and representing the relationship between a high-level feature and its low-level features into a compact relation vector, whose dimensionality is the same as the number of low-level features, e.g., four elements in our tests. We have demonstrated the benefits of our method over the original NBNN and its recent improvement, and local NBNN in two different benchmarks. Our method shows improved accuracy, up to 27% against the tested methods. This high accuracy is mainly due to consideration of the conditional dependences between high-level and its corresponding low-level features.