• Journal of the Korea Society for Simulation
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• v.5 no.2
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• pp.25-40
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• 1996

Modeling of systems for intensive knowledge-based processing requires a modeling methodology that makes efficient access to the information in huge data base models. The proposed level access mothodology is a modeling approach applicable to systems where data is stored in a hierarchical and modular modules of active memory cells(processor/memory pairs). It significantly reduces the effort required to create discrete event simulation models constructed in hierarchical, modular fashion for above application. Level access mothodology achieves parallel access to models within the modular, hierarchical modules(clusters) by broadcasting the desired operations(e.g. querying information, storing data and so on) to all the cells below a certain desired hierarchical level. Level access methodology exploits the capabilities of object-oriented programming to provide a flexible communication paradigm that combines port-to-port coupling with name-directed massaging. Several examples are given to illustrate the utility of the methodology.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.1241-1244
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• 1996

This paper present a performance evaluation method for Timed Place Petri Nets modeled by Live-and-Bounded Circuits (LB-circuits) through a bottom-up approach. The method can handle the case for the nets having common resources(CR). The target system is divided into the sub-systems by disconnecting the common Transition-Transition-Path(TTP) or Place-Place-Path (PPP) between sub-systems. The common PPP pattern is classified into Parallel Common Resource (PCR) and Sequential Common Resource (SCR) in detail for handling common resources. We evaluate the performance of each divided sub-system, and calculate the sub-systems affect on the performance of the whole system. The facts are generalized as a theorem. The developed theorem are applied into the performance evaluation of an automated assembly system shown in an example. All the results are verified by simulation.

• Journal of the Korean Institute of Telematics and Electronics A
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• v.33A no.2
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• pp.50-58
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• 1996

This paper proposes the channel-access protocol suitable to a very high speed photonic WDM network with star configuration, which can provide a high channel utilization and insensitivity to the propagation delay. The proposed protocol employs a control channel and a simple status table to deal with the propagation delay which has been a major limiting factor in the performance of the very high speed optical communication networks. The control channel transmits control information in order to reserve access on data channels, and each node constitutes a status table after the reception of control pckets which holds information about the availbility of destination node and data channel. The proposed protocol is insensitive to the propagation delay time by removing necessity of the retransmission and by allowing parallel transmission of control packet and data packets. It is proved in analysis and discrete event simulation that the proposed protocol is superior in throughput and mean delay, especially at the high load conditions compared to the existing high speed channel-access protocols.

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 2005.10a
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• pp.375-380
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• 2005

In Korea, most landslides are occurred during the rainy season from June to September and have a shallow failure plane parallel to the slope. For these types of rainfall-induced failures, the most important factors triggering slope unstability is not the increase of pore water pressure but the decrease of the matric suction of unsaturated soils by rainfall infiltration. So it is essential to landslide hazard assessment that defines the characteristics of infiltration in natural slopes. In this study, field measurements have been carried out in order to monitor in-situ volumetric water contents and ground water table, at several depths and locations on a natural slope. The results show that rainfall infiltration is correlated with antecedent water contents, rainfall intensity and total rainfall. The ground water table was varied sensitively by every rainfall event.

• Bulletin of the Korean Space Science Society
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• 2009.10a
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• pp.44.1-44.1
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• 2009

Identification of wave characteristics during current disruption events in the near-Earth geomagnetic tail region (~ 10 RE) is important to understand the substorm onset mechanism. In this paper, linear stability analysis in the ion-cyclotron grequency range, considering temperature anisotropy and cross-field flow is presented. It is found that the ion-cyclotron drift waves propagating in a quasi-perpendicular direction with respect to the ambient magnetic field are characterized by low frequencies ($\omega$ < $0.5{\Omega}ci$), while quasi-parallel waves have frequencies close to the ion-cyclotron frequency ($\omega{\sim}{\Omega}ci$). This finding is consistent with the observation by THEMIS spacecraft of a current disruption event in which a similar high- and low-frequency band structure is also present [A. T. Y. Lui, et al., J. Geophys. Res. 113, A00C06 (2008)]. It is also found that the quasi-perpendicular mode is excited by the ion cross-field flow.

• Journal of the Korean Institute of Telematics and Electronics A
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• v.31A no.8
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• pp.100-110
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• 1994

This paper proposes an efficient traceback method for parallel hardware implementation of the Viterbi algorithm. Compared to the conventional Viterbi algorithm where initial state for traceback is selected arbitrarily the proposed algorithm decides decoding output by analyzing the survivor paths of consecutive tracebacks. This makes Viterbi algorithm more efficient in error correction event when more than one survivor path exists. The proposed traceback algorithm together with its hardware realization is presented in this paper. Experimental results show tht the proposed algorithms is efficient in error correction in noisy channels compared to the existing algorithms.

• East Asian mathematical journal
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• v.37 no.5
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• pp.599-612
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• 2021

In China, Weibo is one of the social platforms with more users. It has the characteristics of fast information transmission and wide coverage. People can comment on a certain event on Weibo to express their emotions and attitudes. Judging the emotional tendency of users' comments is not only beneficial to the monitoring of the management department, but also has very high application value for rumor suppression, public opinion guidance, and marketing. This paper proposes a two-input Adaboost model based on TextCNN and BiLSTM. Use the TextCNN model that can perform local feature extraction and the BiLSTM model that can perform global feature extraction to process comment data in parallel. Finally, the classification results of the two models are fused through the improved Adaboost algorithm to improve the accuracy of text classification.

• Journal of Digital Convergence
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• v.12 no.8
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• pp.351-359
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• 2014

The log data generated by security equipment have been synthetically analyzed on the ESM(Enterprise Security Management) base so far, but due to its limitations of the capacity and processing performance, it is not suited for big data processing. Therefore the another way of technology on the big data platform is necessary. Big Data platform can achieve a large amount of data collection, storage, processing, retrieval, analysis, and visualization by using Hadoop Ecosystem. Currently ESM technology has developed in the way of SIEM (Security Information & Event Management) technology, and to implement security technology in SIEM way, Big Data platform technology is essential that can handle large log data which occurs in the current security devices. In this paper, we have a big data platform Hadoop Ecosystem technology for analyzing the security log for sure how to implement the system model is studied.

• The Journal of the Korea Contents Association
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• v.13 no.6
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• pp.9-19
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• 2013

In this paper, we develop a Spatio-Temporal graph as of a key component of our knowledge representation Scheme. We design an integrated representation scheme to depict not only present and past but future in parallel with the spaces in an effective and intuitive manner. An event in general occupies not only a space but a time. Hence a crucial premise for the simulation of virtual situations is to position events in the multi-dimensional context, that is, 3-D space extended by the temporal dimension. Furthermore an event tends to have physical, social and mental aspects intertwined. As a result we need diverse information structures and functions to model entities and relations associated with events and to describe situations in different stances or perspectives of the virtual agents. These structures and functions are implemented in terms of integrated and intuitive representation schemes at different levels such as Ontology View, Instance View, ST View, Reality View. The resulting multi-dimensional comprehensive knowledge structure accommodates multi-layered virtual world developing in the time to maximize the diversity of situations in the historical context. The viability of this knowledge representation scheme is demonstrated with a typical scenario applied to a simulator implemented based on the ST Graph. The virtual stage based on the ST graph can be used to provide natural contexts for situated learning or next-generation simulation games.

• Journal of KIISE
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• v.43 no.4
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• pp.470-479
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• 2016

As the number of various types of media data such as UCC (User Created Contents) increases, research is actively being carried out in many different fields so as to provide meaningful media services. Amidst these studies, a semantic web-based media classification approach has been proposed; however, it encounters some limitations in video classification because of its underlying ontology derived from meta-information such as video tag and title. In this paper, we define recognized objects in a video and activity that is composed of video objects in a shot, and introduce a reasoning approach based on description logic. We define sequential rules for a sequence of shots in a video and describe how to classify it. For processing the large amount of increasing media data, we utilize Spark streaming, and a distributed in-memory big data processing framework, and describe how to classify media data in parallel. To evaluate the efficiency of the proposed approach, we conducted an experiment using a large amount of media ontology extracted from Youtube videos.