• Journal of the Institute of Electronics Engineers of Korea CI
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• v.38 no.4
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• pp.59-71
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• 2001

The Current CORBA shows some limitations for its successful deployment in real time system applications. Recently, OMG adopted Real-Time CORBA specification, which is defined as an extension to CORBA. The goal of the Real-Time CORBA is to provide a standard for CORBA ORB implementations that support 'end to end predictability'. In order to support 'end-to-end predictability', Real Time CORBA specifies many components such as priority model, communication protocol configuration, thread management, and etc. Among them, 'priority model' is the most important mechanism for avoiding or bounding priority inversion in CORBA invocations. In this paper, we present our efforts on a design ,and implementation of the Priority Model in Real-Time CORBA specification. The implementation is done as an extension of omniORB2(v.3.0.0), a popular open source non real time ORB. Experiment results demonstrate that our priority model implementation shows better performance and predictability than the non real-time ORB.

• Journal of KIISE
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• v.44 no.7
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• pp.742-747
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• 2017

In this paper, we propose a novel approach to improve the performance of the Convolutional Neural Network(CNN) word embedding model on top of word2vec with the result of performing like doc2vec in conducting a document classification task. The Word Piece Model(WPM) is empirically proven to outperform other tokenization methods such as the phrase unit, a part-of-speech tagger with substantial experimental evidence (classification rate: 79.5%). Further, we conducted an experiment to classify ten categories of news articles written in Korean by feeding words and document vectors generated by an application of WPM to the baseline and the proposed model. From the results of the experiment, we report the model we proposed showed a higher classification rate (89.88%) than its counterpart model (86.89%), achieving a 22.80% improvement. Throughout this research, it is demonstrated that applying doc2vec in the document classification task yields more effective results because doc2vec generates similar document vector representation for documents belonging to the same category.

• The Transactions of the Korea Information Processing Society
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• v.6 no.4
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• pp.1058-1070
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• 1999

In this paper, we propose a control model which can control the burst input messages of the BSC(Base Station controller) in mobile communication systems more efficiently and reliably, by dividing the input messages characteristically and using multiprocessor system. Using M/M/c/K queueing model, we briefly analyze proposed model to get characteristic parameters which are required to performance improvement. On the base of the results, we compare our proposed model with the conventional one by using SLAM II with regard to the following factors : the call blocking rate of the input message, the distribution of average queue length, the utilization of process controller(server), and the distribution of average waiting time in queue. In addition, we modified our model which has overload control function for burst input messages, and analyzed its performance.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.47 no.3
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• pp.76-82
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• 2010

In this paper, we develop Linguistic Model (LM) based on information granules as a systematic approach to generating fuzzy if-then rules from a given input-output data. The LM introduced by Pedrycz is performed by fuzzy information granulation obtained from Context-based Fuzzy Clustering(CFC). This clustering estimates clusters by preserving the homogeneity of the clustered patterns associated with the input and output data. Although the effectiveness of LM has been demonstrated in the previous works, it needs to improve in the sense of performance. Therefore, we focus on the automatic generation of linguistic contexts, addition of bias term, and the transformed form of consequent parameter to improve both approximation and generalization capability of the conventional LM. The experimental results revealed that the improved LM yielded a better performance in comparison with LM and the conventional works for automobile MPG(miles per gallon) predication and Boston housing data.

• Journal of Korea Port Economic Association
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• v.26 no.4
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• pp.168-187
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• 2010

The mutual risk management in port is really important for operating the enterprise between container terminals who provide port service and shipping liners who use the port service. This study is performed to contribute to obtain the competitive power of domestic shipping and harbor industry by getting solution of mutual risk management which can make Win-Win strategy on each other as an alternative idea. We suggested two kinds of management models to promote common benefits between container terminals and shipping liners. It is necessary to push positive support and cooperation from government and belonging related organizations for activating the Gwangyang port. In this study, we presented the efficient method to manage mutual risks between container terminals and shipping liners.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.36S no.7
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• pp.104-115
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• 1999

This paper proposes an efficient method for 3D modeling of a human face from trinocular images by reconstructing face surface using range data. By using a trinocular camera system, we mitigated the tradeoff between the occlusion problem and the range resolution limitation which is the critical limitation in binocular camera system. We also propose an MPC_MBS (Matching Pixel Count Multiple Baseline Stereo) area-based matching method to reduce boundary overreach phenomenon and to improve both of accuracy and precision in matching. In this method, the computing time can be reduced significantly by removing the redundancies. In the model generation sub-pixel accurate surface data are achieved by 2D interpolation of disparity values, and are sampled to make regular triangular meshes. The data size of the triangular mesh model can be controlled by merging the vertices that lie on the same plane within user defined error threshold.

• Proceedings of the Korea Water Resources Association Conference
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• 2012.05a
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• pp.92-92
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• 2012

경안천은 팔당호로 직접 유입되는 하천 중 유입량 대비 오염도가 매우 높아 상수원보호를 위한 특별관리가 요구되는 지역이다. 또한 경안천 유역은 수도권과 인접하고 있어, 도시화에 의해 토지이용이 지속적으로 변화되고 있다. 이러한 토지이용의 변화는 불투수면적을 증가시켜 강우시 유출 및 수질오염 발생량을 증가시킨다. 그러므로 불투수면적 증가에 따른 영향을 줄이기 위해서는 친환경 도시개발에 적용하고 있는 LID기법을 도입하여, 개발지역의 불투수면적 발생을 최소화하여야 하며 오염원 발생을 사전에 제어해야 한다. 그러나 도시개발시 무분별한 LID기법 도입은 정부의 막대한 예산 및 인력낭비를 초래하므로 현장적용 전 모델링을 통해 LID기법의 기대효과 및 비용을 산출하여야 하며, 도시 계획 수립시 가장 효과적인 LID기법을 제시하여야 한다. 따라서 효과적인 LID기법을 제시하기 위해서는 LID기법 평가가 가능한 SWMM모형을 이용해야 한다. 하지만 경안천 유역과 같이 유역 내 도시 와 비도시지역이 혼재되어 있는 우리나라의 대부분의 유역은 SWMM모형만으로는 유역의 강우-유출 및 수질 평가가 불가능하기 때문에, 유역 내 도시와 비도시지역의 유출 및 수질관리 평가가 가능한 SWAT-SWMM 연계모델을 이용하여 유출량 및 수질관리 효과를 분석해야 한다. 본 연구에서는 SWAT-SWMM 연계모델을 이용하여 LID기법 별 시나리오를 구축하였고, 시나리오별 유출량 및 수질오염 발생량을 모의하여 분석하였다. 분석결과 상당량의 유출량 저감 및 수질개선 효과가 나타났다. 또한 SWAT-SWMM 연계모델을 이용하여 모의된 수질자료는 환경부에서 제시하고 있는 단위유역 대표지점 수질환경기준 달성의 객관적인 평가를 가능하게 한다. 향후 LID를 적용한 SWAT-SWMM 연계모델을 이용하여 정부에서 규제하는 개발제한구역이 포함된 유역에서의 도시개발시 수질환경기준에 맞는 친환경적인 개발을 할 수 있을 것이라 기대된다.

• The Journal of Society for e-Business Studies
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• v.26 no.3
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• pp.67-80
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• 2021

The node embedding technique for learning graph representation plays an important role in obtaining good quality results in graph mining. Until now, representative node embedding techniques have been studied for homogeneous graphs, and thus it is difficult to learn knowledge graphs with unique meanings for each edge. To resolve this problem, the conventional Triple2Vec technique builds an embedding model by learning a triple graph having a node pair and an edge of the knowledge graph as one node. However, the Triple2 Vec embedding model has limitations in improving performance because it calculates the relationship between triple nodes as a simple measure. Therefore, this paper proposes a feature extraction technique based on a graph convolutional neural network to improve the Triple2Vec embedding model. The proposed method extracts the neighborliness vector of the triple graph and learns the relationship between neighboring nodes for each node in the triple graph. We proves that the embedding model applying the proposed method is superior to the existing Triple2Vec model through category classification experiments using DBLP, DBpedia, and IMDB datasets.

• Journal of the Korea Society for Simulation
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• v.30 no.1
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• pp.75-85
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• 2021

In this paper, we present a deep neural network-based prediction model that processes and analyzes the corporate credit and personal credit information of individual business owners as a new method to predict the default rate of individual business more accurately. In modeling research in various fields, feature selection techniques have been actively studied as a method for improving performance, especially in predictive models including many features. In this paper, after statistical verification of macroeconomic indicators (macro variables) and credit information (micro variables), which are input variables used in the default rate prediction model, additionally, through the credit information feature selection method, the final feature set that improves prediction performance was identified. The proposed credit information feature selection method as an iterative & hybrid method that combines the filter-based and wrapper-based method builds submodels, constructs subsets by extracting important variables of the maximum performance submodels, and determines the final feature set through prediction performance analysis of the subset and the subset combined set.

• Journal of the Korean Society of Propulsion Engineers
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• v.24 no.6
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• pp.28-44
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• 2020

In this paper, the scavenging process of various transfer ports was evaluated to improve the performance of a small two-stroke engine for unmanned aerial vehicles. Three-dimensional computational fluid dynamics simulations were performed to four transfer ports for the evaluation, and a three-phase scavenging model was developed and applied to the simulation results for the quantitative comparison of scavenging performance. the short-circuit of fresh charge was restrained and an in-cylinder turbulent kinetic energy was enhanced by changing the transfer port. Also, a difference in the scavenging for each port were confirmed by applying the three-phase model to the simulation results.