• Proceedings of the Korean Society for Bioinformatics Conference
• /
• 2005.09a
• /
• pp.339-343
• /
• 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.

• Proceedings of the Korea Information Assurance Society Conference
• /
• 2004.05a
• /
• pp.35-42
• /
• 2004

Ubiquitous Computing is gradually accepting in our real society. Already some Advanced State has studying of Ubiquitous for more convenience Ubiquitous environment. Anywhere, Anytime user can be provided information and service that he want, but it has some problem such as Rogue AP, IP spoofing, DoS attack, Warm which can causing social confusion in Ubiquitous society. In this situation we must analytics that security requirement in the Ubiquitous network environment and investigate 'Ad hoc' and RFID which is main technique for network infra construction.struction.

• IEIE Transactions on Smart Processing and Computing
• /
• v.5 no.4
• /
• pp.289-293
• /
• 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.

• Journal of the Korea Computer Industry Society
• /
• v.3 no.2
• /
• pp.199-208
• /
• 2002

Because of the change of computing environment, an agent technology is spotlighted recently. By deploying agent distributedly in network and offering the necessary service quickly in place easing realization of service, the enlargement of the effectiveness of network is necessary more and more. In this paper, agents programmed in java are distributed in place easing realization of service in network. And a manager connects to agents and informs URL of service code of the agent. This paper implements distributed computing environment in which agent downloads service code from URL of service code, executes the code, and returns the result of execution to manager.

• Convergence Security Journal
• /
• v.5 no.4
• /
• pp.59-65
• /
• 2005

Ubiquitous Computing is gradually accepting in our real society. Already some Advanced State has studying of Ubiquitous for more convenience Ubiquitous environment. Anywhere, Anytime user can be provided information and service that he want, but it has some problem such as Rogue AP, IP spoofing, DoS attack, Warm which can causing social confusion in Ubiquitous society. In this paper we must analysis that security requirement in the Ubiquitous network environment and investigate 'Ad hoc' and RFID which is main technique for network infra construction.

• Journal of Communications and Networks
• /
• v.15 no.2
• /
• pp.207-216
• /
• 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.

• Journal of Advanced Navigation Technology
• /
• v.14 no.6
• /
• pp.984-989
• /
• 2010

In this paper, parallel programming technique by using Java Thread is introduced so as to develop parallel design tool to analyze the small micro flow sensor. To estimate computing time for Thread-level parallelism, the performances of two experimental models for potential problem subject to Thermal transfer equation are examined. As a result, if the number of network PC is increase, computing time for parallelism on network environment is enhanced to be almost n times. The micro sensor design tool based on distributed computing can be utilized to analyze a large scale problem.

• Electronics and Telecommunications Trends
• /
• v.34 no.1
• /
• pp.23-35
• /
• 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
• /
• v.44 no.2
• /
• pp.194-207
• /
• 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
• /
• v.9 no.4
• /
• pp.204-213
• /
• 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.