• Journal of The Institute of Information and Telecommunication Facilities Engineering
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• v.3 no.1
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• pp.15-24
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• 2004

'The delay and control overhead during route discovery for destinations outside ad hoc networks are major obstacle to achieving scalability in the Internet. To solve this issue, we propose a novel route discovery scheme equipped with two augmented functions. In this paper, the Internet gateway maintains an address cache of Internet nodes frequently accessed from the ad hoc network and replies with an extended Route Response (RREP) message to the Route Request (RREQ) message based on its routing table and the address cache called EXIT(EXternal node Information Table). These augmented functions make the source node determine the location of the destination as fast as possible. Through simulations, the proposed route discovery scheme using both EXIT and extended RREP message shows considerable' reduction in both route discovery time and control message overhead.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.10
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• pp.4889-4902
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• 2018

It is a challenging issue to improve the energy efficiency of neighbor discovery in WSNs. This paper proposes an optimal discovery model (ODM) for the first time. Based on the model, we investigate the influence of the relative size of two unequal active slots on the energy efficiency. ODM provides the energy optimal value of the length of the larger active slot at a given duty cycle. Other than existing methods, the worst-case latency bound of ODM is only one period. This is a subversive conclusion, because almost all other related methods are based on a wake-up schedule that contains several periods. We theoretically deduce that ODM can reduce worst-case discovery latency by 43.89% compared to Searchlight-Trim when their duty cycles are the same. The simulations verify the advantage of ODM.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.41 no.12
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• pp.33-40
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• 2004

IPv6 defines relation between surrounding node using Neighbor Discovery protocol. Used Neighbor Discovery messages, grasp surrounding node, include important informations about network. These network information outcrops can give rise in network attack and also service that use network will paralysis. Various kinds of security limitation was found in Present Neighbor Discovery protocol therefore security function to supplement this problem was required. In this thesis, Secure Neighbor Discovery protocol that add with suity function was design and embody by CGA module and SEND module.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.1B
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• pp.9-14
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• 2012

The neighbor discovery using directional antennas in mmWave band is a prerequisite for communications and this issue is crucial and urgent. In this paper, the synchronized, direct, two-way directional neighbor discovery process is analyzed mathematically for mmWave WPANs. The analysis is based on the values which are derived from the effect of using directional antennas. The neighbor discovery probability for a given amount of time is considered and several performance measures such as the optimal sojourn time are derived in closed forms. Numerical results are obtained using parameters based on the IEEE 802.15.3c. The mathematical analysis provides the theoretical basis for the directional neighbor discovery process.

• The Journal of Society for e-Business Studies
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• v.9 no.4
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• pp.85-102
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• 2004

Currently, developed technologies for semantic web services discovery are based on ontologies. These ontologies are DAML-S(DARPA Agent Markup Language for Services) and Process Handbook Project of MIT. These technologies have some problems for intelligent web services discovery. So, in this paper we analyzed those ontologies and proposed TM-S, Topic Maps for Services. TM-S is the presentation model for semantic web services. And TM-S includes benefits and complements weaknesses of those ontologies. And we proposed TMS-QL, TM-S Query Language. TMS-QL is query language for intelligent web services discovery. At last, we designed and implemented intelligent web service discovery system that deals TM-S ontology and TMS-QL

• Proceedings of the Korean Information Science Society Conference
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• 2007.10b
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• pp.17-21
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• 2007

Drug discovery is a long process with a low rate of successful new therapeutic discovery regardless of the advances in information technologies. Identification of candidate proteins is an essential step for the drug discovery and it usually requires considerable time and efforts in the drug discovery. The drug discovery is not a logical, but a fortuitous process. Nevertheless, considerable amount of information on drugs are accumulated in UniProt, NCBI, or DrugBank. As a result, it has become possible to try to devise new computational methods classifying drug target candidates extracting the common features of known drug target proteins. In this paper, we devise a method for drug target protein classification by using weighted feature summation and Support Vector Machine. According to our evaluation, the method is revealed to show moderate accuracy $85{\sim}90%$. This indicates that if the devised method is used appropriately, it can contribute in reducing the time and cost of the drug discovery process, particularly in identifying new drug target proteins.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.11
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• pp.3935-3949
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• 2021

With the rapid development of Internet and big data technology, various online social network platforms have been established, producing massive information every day. Hot topic discovery aims to dig out meaningful content that users commonly concern about from the massive information on the Internet. Most of the existing hot topic discovery methods focus on a single network data source, and can hardly grasp hot spots as a whole, nor meet the challenges of text sparsity and topic hotness evaluation in cross-network scenarios. This paper proposes a novel hot topic discovery method across social network based on an im-proved LDA model, which first integrates the text information from multiple social network platforms into a unified data set, then obtains the potential topic distribution in the text through the improved LDA model. Finally, it adopts a heat evaluation method based on the word frequency of topic label words to take the latent topic with the highest heat value as a hot topic. This paper obtains data from the online social networks and constructs a cross-network topic discovery data set. The experimental results demonstrate the superiority of the proposed method compared to baseline methods.

• Journal of Multimedia Information System
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• v.8 no.4
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• pp.259-266
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• 2021

Security vulnerabilities have been reported in major design software systems such as Adobe Photoshop and Illustrator, which are recognized as de facto standard design tools in most of the design industries. Companies need to evaluate and manage their risk levels posed by those vulnerabilities, so that they could mitigate the potential security bridges in advance. In general, security vulnerabilities are discovered throughout their life cycles repeatedly if software systems are continually used. Hence, in this study, we empirically analyze risk levels for the three major graphical design software systems, namely Photoshop, Illustrator and GIMP with respect to a software vulnerability discovery model. The analysis reveals that the Alhazmi-Malaiya Logistic model tends to describe the vulnerability discovery patterns significantly. This indicates that the vulnerability discovery model makes it possible to predict vulnerability discovery in advance for the software systems. Also, we found that none of the examined vulnerabilities requires even a single authentication step for successful attacks, which suggests that adding an authentication process in software systems dramatically reduce the probability of exploitations. The analysis also discloses that, for all the three software systems, the predictions with evenly distributed and daily based datasets perform better than the estimations with the datasets of vulnerability reporting dates only. The observed outcome from the analysis allows software development managers to prepare proactively for a hostile environment by deploying necessary resources before the expected time of vulnerability discovery. In addition, it can periodically remind designers who use the software systems to be aware of security risk, related to their digital work environments.

• KNOM Review
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• v.24 no.2
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• pp.48-55
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• 2021

Ethereum is an open software platform based on blockchain technology that enables the construction and distribution of distributed applications. Ethereum uses a fully distributed connection method in which all participating nodes participate in the network with equal authority and rights. Ethereum networks use Kademlia-based node discovery protocols to retrieve and store node information. Ethereum is striving to stabilize the entire network topology by implementing node discovery protocols, but systems for monitoring are insufficient. This paper develops a WireShark dissector that can receive packet information in the Ethereum node discovery process and provides network packet measurement results. It can be used as basic data for the research on network performance improvement and vulnerability by analyzing the Ethereum node discovery process.

• Computers and Concrete
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• v.31 no.4
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• pp.277-292
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• 2023

Much of our experiments are designed to uncover the cause(s) and effect(s) behind a phenomenon (i.e., data generating mechanism) we happen to be interested in. Uncovering such relationships allows us to identify the true workings of a phenomenon and, most importantly, to realize and articulate a model to explore the phenomenon on hand and/or allow us to predict it accurately. Fundamentally, such models are likely to be derived via a causal approach (as opposed to an observational or empirical mean). In this approach, causal discovery is required to create a causal model, which can then be applied to infer the influence of interventions, and answer any hypothetical questions (i.e., in the form of What ifs? Etc.) that commonly used prediction- and statistical-based models may not be able to address. From this lens, this paper builds a case for causal discovery and causal inference and contrasts that against common machine learning approaches - all from a civil and structural engineering perspective. More specifically, this paper outlines the key principles of causality and the most commonly used algorithms and packages for causal discovery and causal inference. Finally, this paper also presents a series of examples and case studies of how causal concepts can be adopted for our domain.