• Clean Technology
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• v.28 no.2
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• pp.138-146
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• 2022

Electricity has become a factor that dramatically affects the market economy. The day-ahead system marginal price determines electricity prices, and system marginal price forecasting is critical in maintaining energy management systems. There have been several studies using mathematics and machine learning models to forecast the system marginal price, but few studies have been conducted to develop, compare, and analyze various machine learning and deep learning models based on a data-driven framework. Therefore, in this study, different machine learning algorithms (i.e., autoregressive-based models such as the autoregressive integrated moving average model) and deep learning networks (i.e., recurrent neural network-based models such as the long short-term memory and gated recurrent unit model) are considered and integrated evaluation metrics including a forecasting test and information criteria are proposed to discern the optimal forecasting model. A case study of South Korea using long-term time-series system marginal price data from 2016 to 2021 was applied to the developed framework. The results of the study indicate that the autoregressive integrated moving average model (R-squared score: 0.97) and the gated recurrent unit model (R-squared score: 0.94) are appropriate for system marginal price forecasting. This study is expected to contribute significantly to energy management systems and the suggested framework can be explicitly applied for renewable energy networks.

• Science of Emotion and Sensibility
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• v.20 no.3
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• pp.23-38
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• 2017

In order to identify effective approaches for creating more viral Facebook posts, this research conducted an empirical content analysis of leading Korean brands' Facebook fan-pages (Samsung Mobile, SK Telecom, Kia Motors, and POSCO). Their distinctive visual storytelling and communication patterns were investigated as effective user engagement triggers. Through analysis of the research results, it was statistically proved that the different industrial attributes of the four brands, which are primarily characterized by their product (or service) types, affect their Facebook posting patterns by showing different engaging rates (measured by like, comment, and share metrics). In addition, the user engagement rates of the posts were influenced by their visual storytelling factors (i.e. ad objective, value scale, and visual media types). In line with these statistical findings, the distinctive visual storytelling strategies of the four brands were identified. Moreover, competitive and uncompetitive visual storytelling tactics were suggested according to the ad objectives and visual media types on Facebook.

• 한국신재생에너지학회:학술대회논문집
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• 2009.06a
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• pp.467-470
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• 2009

Variable loads along the drive-train are attributed to frequent failures of gears, bearings, and other components. Wind parameters cannot be controlled and therefore any turbine load-reducing remedies must be established based on proper insights into the wind-turbine interactions. A novel control concept to performance optimization of wind turbines is presented. This proposed concept is based on analysis of the turbine status reflected in the SCADA data. Modern computational techniques are used to optimize performance of a wind turbine from tree basic perspectives: drive-train, power output, and power quality. The proposed approach demonstrates that gains in the metrics representing the three perspectives and the corresponding control goals can be significantly improved for any wind turbine. The solution is applicable different turbine types operating in different wind regimes, e.g., winds of different speeds and variability. Simple and transparent parameters allow an operator to determine a balance between the operations and maintenance, technical, business objectives. The proposed modeling framework was embedded in software. The software tool has been tested on the data collected from 1.5 MW wind turbines.

• Journal of the Korean Mathematical Society
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• v.45 no.3
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• pp.859-870
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• 2008

In the present paper, we investigate the geometric structures of the Dirichlet manifold composed of the Dirichlet distribution. We show that the Dirichlet distribution is an exponential family distribution. We consider its dual structures and give its geometric metrics, and obtain the geometric structures of the lower dimension cases of the Dirichlet manifold. In particularly, the Beta distribution is a 2-dimensional Dirich-let distribution. Also, we construct an affine immersion of the Dirichlet manifold. At last, we give the e-flat hierarchical structures and the orthogonal foliations of the Dirichlet manifold. All these work will enrich the theoretical work of the Dirichlet distribution and will be great help for its further applications.

• Proceedings of the Korean Information Science Society Conference
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• 2002.04b
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• pp.388-390
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• 2002

Java 2 Enterprise Edition(J2EE)의 등장으로 국내.외 수많은 기업들은 J27E의 모델에 맞게 엔터프라이즈 어플리케이션을 개발하고 있다. 이것은 J2EE의 핵심 기술 요소인 Enterprise JavaBeans(EJB)의 컴포넌트모델이 분산 객체 어플리케이션의 개발 과정을 간단하게 해주기 때문이다. EJB 어플리케이션은 여러 개의 빈들로 구성된다. EJB 어플리케이션의 서비스는 클라이언트의 요청에 따른 빈의 비즈니스 메소드의 실행으로 이루어진다. 따라서 EJB 어플리케이션의 성능은 클라이언트의 요청에 따라 처리하는 빈에서의 측정과 요청을 처리하는 비즈니스 메소드의 측정에 매우 중요하다. 본 논문에서는 EJB 어플리케이션에서 클라이언트의 서비스 요정에 따라 수행하는 빈 단위에서의 성능 메트릭을 제시한다. 클라이언트의 서비스 요청은 요청을 받은 번에서의 메소드 실행으로 나타난다. 메소드의 유형을 분류하고, 각 유형에 따른 메트릭을 제시한다.

• Proceedings of the IEEK Conference
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• 2007.07a
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• pp.17-18
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• 2007

Mobile WiMAX technology is the key solution for providing high speed internet services at anytime and anywhere in mobile environments as 3.5th wireless technology between 3rd generation and 4th generation wireless technology, and the service demands shall be rapidly increased in future some years. This paper analyzed the QoS performance of Mobile WiMAX technology recommended by IEEE 802.16e specification under the restricted simulation conditions and environments. The major metrics for analyzing QoS performance of Mobile WiMAX are 1) total packet drop rates; 2) transmission / receive packet sizes; 3) end to end delay in node 2; 4) packet drop rate by mobile terminal mobility.

• Journal of Information Processing Systems
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• v.15 no.4
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• pp.931-956
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• 2019

Documents contain information that can be used for various applications, such as question answering (QA) system, information retrieval (IR) system, and recommendation system. To use the information, it is necessary to develop a method of extracting such information from the documents written in a form of natural language. There are several kinds of the information (e.g., temporal information, spatial information, semantic role information), where different kinds of information will be extracted with different methods. In this paper, the existing studies about the methods of extracting the temporal information are reported and several related issues are discussed. The issues are about the task boundary of the temporal information extraction, the history of the annotation languages and shared tasks, the research issues, the applications using the temporal information, and evaluation metrics. Although the history of the tasks of temporal information extraction is not long, there have been many studies that tried various methods. This paper gives which approach is known to be the better way of extracting a particular part of the temporal information, and also provides a future research direction.

• Journal of Multimedia Information System
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• v.1 no.1
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• pp.67-75
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• 2014

Nowadays as video services grow rapidly, it is important for the service providers to provide customized services. Video ranking plays a key role for the service providers to attract the subscribers. In this paper we propose a weekly video ranking mechanism based on the quantified user engagement. The traditional QoE ranking mechanism is relatively subjective and usually is accomplished by grading, while QoS is relatively objective and is accomplished by analyzing the quality metrics. The goal of this paper is to establish a ranking mechanism which combines the both advantages of QoS and QoE according to the third-party data collection platform. We use data mining method to classify and analyze the collected data. In order to apply into the actual situation, we first group the videos and then use the regression tree and the decision tree (CART) to narrow down the number of them to a reasonable scale. After that we introduce the analytic hierarchy process (AHP) model and use Elo rating system to improve the fairness of our system. Questionnaire results verify that the proposed solution not only simplifies the computation but also increases the credibility of the system.

• Journal of Electrical Engineering and Technology
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• v.13 no.3
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• pp.1202-1211
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• 2018

In this paper, induction machine operation efficiency and torque is improved using Machine Learning based Gene Optimization (ML-GO) Technique is introduced. Optimized Genetic Algorithm (OGA) is used to select the optimal induction machine data. In OGA, selection, crossover and mutation process is carried out to find the optimal electrical machine data for induction machine design. Initially, many number of induction machine data are given as input for OGA. Then, fitness value is calculated for all induction machine data to find whether the criterion is satisfied or not through fitness function (i.e., objective function such as starting to full load torque ratio, rotor current, power factor and maximum flux density of stator and rotor teeth). When the criterion is not satisfied, annealed selection approach in OGA is used to move the selection criteria from exploration to exploitation to attain the optimal solution (i.e., efficient machine data). After the selection process, two point crossovers is carried out to select two crossover points within a chromosomes (i.e., design variables) and then swaps two parent's chromosomes for producing two new offspring. Finally, Adaptive Levy Mutation is used in OGA to select any value in random manner and gets mutated to obtain the optimal value. This process gets iterated till finding the optimal value for induction machine design. Experimental evaluation of ML-GO technique is carried out with performance metrics such as torque, rotor current, induction machine operation efficiency and rotor power factor compared to the state-of-the-art works.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.4
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• pp.235-250
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• 2021

The overarching question of this study is how a typical rice cultivation system in Gimje, Korea was keeping up with the triple-win challenge of climate-smart agriculture (CSA). To answer this question, we have employed (1) quantitative data from direct measurement of energy, water, carbon and information flows in and out of a rice cultivation system and (2) appropriate metrics to assess production, efficiency, GHG fluxes, and resilience. The study site was one of the Korean Network of Flux measurement (KoFlux) sites (i.e., GRK) located at Gimje, Korea, managed by National Academy of Agricultural Science, Rural Development Administration. Fluxes of energy, water, carbon dioxide (CO₂) and methane (CH₄) were directly measured using eddy-covariance technique during the growing seasons of 2011, 2012 and 2014. The production indicators include gross primary productivity (GPP), grain yield, light use efficiency (LUE), water use efficiency (WUE), and carbon uptake efficiency (CUE). The GHG mitigation was assessed with indicators such as fluxes of carbon dioxide (F_CO2), methane (F_CH4), and nitrous oxide (F_N2O). Resilience was assessed in terms of self-organization (S), using information-theoretic approach. Overall, the results demonstrated that the rice cultivation system at GRK was climate-smart in 2011 in a relative sense but failed to maintain in the following years. Resilience was high and changed little for three year. However, the apparent competing goals or trade-offs between productivity and GHG mitigation were found within individual years as well as between the years, causing difficulties in achieving the triple-win scenario. The pursuit of CSA requires for stakeholders to prioritize their goals (i.e., governance) and to practice opportune interventions (i.e., management) based on the feedback from real-time assessment of the CSA indicators (i.e., monitoring) - i.e., a purpose-driven visioneering.