• Journal of Information Management
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• v.42 no.2
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• pp.193-210
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• 2011

The study was applied to context window sizes and weighting method to obtain the best performance of word sense disambiguation using support vector machine. The context window sizes were used to a 3-word, sentence, 50-bytes, and document window around the targeted word. The weighting methods were used to Binary, Term Frequency(TF), TF ${\times}$ Inverse Document Frequency(IDF), and Log TF ${\times}$ IDF. As a result, the performance of 50-bytes in the context window size was best. The Binary weighting method showed the best performance.

• 한국HCI학회:학술대회논문집
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• 2009.02a
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• pp.685-689
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• 2009

Evolutionary Computation is not only widely used in optimization and machine learning, but also being applied in creating novel structures and entities. This paper proposes evolutionary topic maps that can suggest new and creative knowledge not easily producible by humans. Interactive evolutionary computation method is applied into topic maps in order to accept human evaluation on feasibility of intermediate topic maps. Evolutionary topic maps are creativity support tools, helping users to encounter new and creative knowledge. Further work can greatly improve the system by providing more operations, preventing over-convergence, and overcoming user fatigue problem by providing more intuitive user interface, better visualization, and interpolation mechanisms.

• IE interfaces
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• v.16 no.4
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• pp.485-495
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• 2003

This paper reports on the application of the integration of mathematical programming model and database in a decision support system (DSS) for the planning of the multi-reservoir operating system. The DSS is based on a multi-objective, mixed-integer goal programming (MIGP) model, which can generate efficient solutions via the weighted-sums method (WSM). The major concern of this study is seamless, efficient integration between the mathematical model and the database, because there are significant differences in structure and content between the data for a mathematical model and the data for a conventional database application. In order to load the external optimization results on the database, we developed a systematic way of naming variable/constraint so that a rapid identification of variables/constraints is possible. An efficient database structure for planning of the multi-reservoir operating system is presented by taking advantage of the naming convention of the variable/constraint.

• Journal of Electrical Engineering and Technology
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• v.12 no.6
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• pp.2166-2176
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• 2017

Electricity price prediction plays a crucial part in making the schedule and managing the risk to the competitive electricity market participants. However, it is a difficult and challenging task owing to the characteristics of the nonlinearity, non-stationarity and uncertainty of the price series. This study proposes a hybrid improved strategy which incorporates data preprocessor components and a forecasting engine component to enhance the forecasting accuracy of the electricity price. In the developed forecasting procedure, the Seasonal Adjustment (SA) method and the Ensemble Empirical Mode Decomposition (EEMD) technique are synthesized as the data preprocessing component; the Coupled Simulated Annealing (CSA) optimization method and the Least Square Support Vector Regression (LSSVR) algorithm construct the prediction engine. The proposed hybrid approach is verified with electricity price data sampled from the power market of New South Wales in Australia. The simulation outcome manifests that the proposed hybrid approach obtains the observable improvement in the forecasting accuracy compared with other approaches, which suggests that the proposed combinational approach occupies preferable predication ability and enough precision.

• Journal of the Korean Society for Precision Engineering
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• v.33 no.10
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• pp.797-804
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• 2016

The vehicle-mounted radar system (VMRS) including its electronic parts must be designed so that its performance is maintained under varying environmental conditions. The important aspects are typically weight and safety. Since many rotating VMRSs have been developed, discussion about the vibration and shock requirements for the transportation conditions has occurred: in addition, the dynamic unpaved, paved, and off-road effects have been emphasized with respect to lightweight designs. A lightweight-design VMRS should be capable of operating stably under the wind condition with the support of the vehicle structure. In this paper, a structural analysis regarding the support of the VMRS is performed, whereby the real-load conditions for three types of road and pressure were employed in terms of the wind condition. The structural analysis for the safety of the VMRS is performed, and the structural-integrity analytical processes of the VMRS are presented for different load conditions.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2007.05a
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• pp.588-593
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• 2007

Fiber reinforced polymer(FRP) composite decks are new to bridge applications and hence not much literature exists on their structural mechanical behavior. As there are many differences between numerical displacements through static analysis of the primary model and experimental displacements through static load tests, system identification (SI)techniques such as Neural Networks (NN) and support vector machines (SVM) utilized in the optimization of the FE model. During the process of identification, displacements were used as input while stiffness as outputs. Through the comparison of numerical displacements after SI and experimental displacements, it can note that NN and SVM would be effective SI methods in modeling an FRP deck. Moreover, two methods such as response surface method and iteration were proposed to optimize the estimated stiffness. Finally, the results were compared through the mean square error (MSE) of the differences between numerical displacements and experimental displacements at 6 points.

• Journal of Korean Society for Atmospheric Environment
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• v.20 no.2
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• pp.185-193
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• 2004

A catalyst with CuO ceramic filter for simultaneous treatment of dust and HAP was prepared and characterized. Catalytic ceramic filter can not only potentially achieve the substantial savings in energy but provide with effective optimization and integration of process for simultaneous removal of SO$_2$, NO$_{x}$ and particulates from flue gases. Catalytic ceramic filters remove simultaneously particulates on exterior surface of filters and reduce NO to $N_2$ and $H_2O$ by SCR (Selective Catalytic Reduction) process. Preparation of catalyst impregnated ceramic filter with disk shape (Ψ 50) follow the processing of alumino-silicate ceramic filter, support impregnation and catalyst impregnation (copper oxide). Preparation routes of alumino-silicate catalyst carrier suitable for production of catalytic filters practically were studied and developed using the sol-gel and colloidal processing, homogeneous precipitation and impregnation method. Characterization of the catalyst, catalyst carrier catalytic filter materials have been performed the using various techniques such as BET, XRD, TGA, SEM. Combination of the sol-gel and colloidal processing and impregnation method is recommended to prepare catalyst carriers economically for catalytic filter applications.s.

• Proceedings of the KAIS Fall Conference
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• 2007.05a
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• pp.203-207
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• 2007

NEMO 기본 지원 (NEMO-BS, NEMO Basic Support) 프로토콜에서 MNN(Mobile Network Node)가 CN(Correspondent Node) 과 통신을 하기 위해서는 항상 MR(Mobile Router)과 HA(Home Agent) 사이의 양방향 터널을 이용해야 한다. 그러나 NEMO-BS 방식은 노드 간 데이터 전송 지연과 부분 구간에 대한 공격 가능성이 존재한다. 따라서 본 논문에서는 NEMO를 위한 인증된 경로 최적화(ATRO) 프로토콜을 제안한다. MR은 홈 링크로부터 멀어졌다고 판단되면 MNN으로부터 위임 권한을 얻기 위해 권한 위임 프로토콜을 수행한다. 그런 후에 MR과 CN은 공개키 암호 방식을 이용하여 자신의 의탁주소(CoA, Care-of Address)를 MNN의 홈 주소(HoA, Home-of Address)와 매핑하기 위한 등록 과정을 수행한다. 이때 각 노드의 주소 소유권 증명을 위해 암호학적으로 생성한 주소(CGA, Cryptographically Generated Address)를 이용한다. 성능분석에서는 구간별 안전성과 종단간 패킷 전송 지연 시간을 통해 프로토콜을 분석한다.

• JSTS:Journal of Semiconductor Technology and Science
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• v.17 no.4
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• pp.505-513
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• 2017

As mobile systems are performing various functionality in the IoT (Internet of Things) era, network-on-chip (NoC) plays a pivotal role to support communication between the tens and in the future potentially hundreds of interacting modules in system-on-chips (SoCs). Owing to intensive research efforts more than a decade, NoCs are now widely adopted in various SoC designs. Especially, studies on application-specific NoCs (ASNoCs) that consider the heterogeneous nature of modern SoCs contribute a significant share to use of NoCs in actual SoCs, i.e., ASNoC connects non-uniform processing units, memory, and other intellectual properties (IPs) using flexible router positions and communication paths. Although it is not difficult to find the prior works on ASNoC synthesis and optimization, little research has addressed the issues how to convert different protocols and data widths to make a NoC compatible with various IPs. Thus, in this paper, we address important issues on ASNoC implementation to support and convert multiple interfaces. Based on the in-depth discussions, we finally introduce our FPGA-proven full-custom ASNoC.

• Nuclear Engineering and Technology
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• v.52 no.7
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• pp.1436-1442
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• 2020

Nuclear infrastructure systems play an important role in national security. The functions and missions of nuclear infrastructure systems are vital to government, businesses, society and citizen's lives. It is crucial to design nuclear infrastructure for scalability, reliability and robustness. To do this, we can use machine learning, which is a state of the art technology used in various fields ranging from voice recognition, Internet of Things (IoT) device management and autonomous vehicles. In this paper, we propose to design and develop a machine learning algorithm to perform predictive maintenance of nuclear infrastructure. Support vector machine and logistic regression algorithms will be used to perform the prediction. These machine learning techniques have been used to explore and compare rare events that could occur in nuclear infrastructure. As per our literature review, support vector machines provide better performance metrics. In this paper, we have performed parameter optimization for both algorithms mentioned. Existing research has been done in conditions with a great volume of data, but this paper presents a novel approach to correlate nuclear infrastructure data samples where the density of probability is very low. This paper also identifies the respective motivations and distinguishes between benefits and drawbacks of the selected machine learning algorithms.