• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.10 no.6
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• pp.126-139
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• 2011

In the traditional four-step travel demand models, each step is sequentially conducted following the model estimation at the previous step. The accuracy of the following model is partly dependent on whether the model at the former stage was properly established or not. Therefore, trip generation, which is the first step in this conventional model, has great effects on the modeling process and forecasting results. Linear regression models for trip generation of Seoul Metropolitan Area might increase the forcasting errors, since a variety of land-use characteristics are not considered. Hence, in this study, zonal factors such as socioeconomic and land use variables are included to improve the elaboration of trip generation. Comparing the %RMSE with the existing models, which contain bigger errors in the zones highly based on the secondary and tertiary industries than residence-based, the trip generation models including those variables seem more appropriate overall.

• Fashion & Textile Research Journal
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• v.7 no.3
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• pp.291-300
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• 2005

The purpose of this study is to investigate, analyze the purchasing behavior of fashion goods according to life style and role model of preteen generation, and provide manager or marketing planner for the reference data so that they can understand preteen generation and make proper strategy efficiently. So called, preteen market focusing on 1014 generation (from ten to fourteen years old) is highlighted. This generation created between the year 1989 to 1993 after Seoul Olympic monopolize parent's love in abundant economic environment and rise to the core of consumption subject. Products aiming at this preteen generation continuously though consumption mind was shrunk greatly due to recession. Only 2~3 years before preteen market was regarded as grey zone which doesn't belong to not only children (between six and nine years old) but also teenagers (between fifteen to eighteen years old). But in recent day their purchasing powers have increased rapidly and age group is divided on details, so that preteen market has become a niche market. Subjects were 333 persons consisting of students in the 4th~6th grade of primary school and the 1st~2nd grade of middle school in Daegu city. Measuring instruments are as follows: 5questions to differentiate preteen generation, 22 questions to measure life style, 17questions (which have six sub-factors such as purchase motive, factor of product selection, utilization of informant, purchase time, purchase place, and purchase method) to measure the purchase behavior of fashion goods measurement, and 16 questions (which have four sub-factors such as parent, entertainer & sports stars, brothers and sisters, friends) to measure model of role. Statistical data were processed by SPSS 10.0 programs. Frequencies, Factor analysis, Cluster analysis, ANOVA, Cross analysis, Multiplex regression analysis, and Duncan's multiple range test were carried out.

• Journal of Digital Contents Society
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• v.17 no.3
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• pp.157-164
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• 2016

Solar power generation influenced by the weather. Using the weather forecast information, it is possible to predict the short-term solar power generation in the future. However, in limited circumstances such as islands or mountains, it can not be use weather forecast information by the disconnection of the network, it is impossible to use solar power generation prediction model using weather forecast. Therefore, in this paper, we propose a system that can predict the short-term solar power generation by using the information that can be collected by the system itself. We developed a short-term prediction model using the prior information of temperature and power generation amount to improve the accuracy of the prediction. We showed the usefulness of proposed prediction model by applying to actual solar power generation data.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.8
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• pp.1496-1503
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• 2009

The United Nations Framework Convention on Climate Change (UNFCCC) is an international environmental treaty to stabilize greenhouse gas concentrations in the atmosphere. In order to fulfil the commitments of the countries in an economically efficient way, the UNFCCC adapted the emission trading scheme in the Kyoto Protocol. If the UNFCCC's scheme is enforced in the country, considerable changes in electric power industry are expected due to the imposed greenhouse gas emission reduction. This paper proposes a game theoretic model of the case when generation companies participate in both competitive electricity market and emission market simultaneously. The model is designed such that generation companies select strategically between power quantity and greenhouse gas reduction to maximize their profits in both markets. Demand function and Environmental Welfare of emission trading market is proposed in this model. From the simulation results using the proposed model the impact of the emission trading on generation companies seems very severe in case that the emission prices are significantly high.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2007.04a
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• pp.809-812
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• 2007

Automatic generation method of structural analysis model data for a truss bridge is presented through 3-D bridge information modeling based on Industry Foundation Classes(IFC). The mapping schema is proposed between a steel bridge information model based on STEP and a truss bridge information model based on the IFC. The geometry information from mapping is presented by IFC model, and SAP 2000 that can import the IFC file performs the structural analysis. Numerical analysis for a truss bridge is performed in this paper.

• Journal of Electrical Engineering and Technology
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• v.11 no.4
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• pp.869-877
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• 2016

Necessity has compelled man to improve upon the art of tapping wind energy for power generation; an apt reliever of strain exerted on the non-renewable fossil fuel. The power generation in a Wind Farm (WF) depends on site and wind velocity which varies with time and season which in turn determine wind power modeling. It implies, the development of an accurate wind speed model to predict wind power fluctuations at a particular site is significant. In this paper, Box-Jenkins ARIMA (Auto Regressive Integrated Moving Average) time series model for wind speed is developed for a 99MW wind farm in the southern region of India. Because of the uncertainty in wind power developed, the economic viability and reliability of power generation is significant. Life Cycle Costing (LCC) method is used to determine the economic viability of WF generated power. Reliability models of WF are developed with the help of load curve of the utility grid and Capacity Outage Probability Table (COPT). ARIMA wind speed model is used for developing COPT. The values of annual reliability indices and variations of risk index of the WF with system peak load are calculated. Such reliability models of large WF can be used in generation system planning.

• Smart Media Journal
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• v.10 no.1
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• pp.79-87
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• 2021

In recent years research on automatic font generation with machine learning mainly focus on using transformation-based methods, in comparison, generative model-based methods of font generation have received less attention. Transformation-based methods learn a mapping of the transformations from an existing input to a target. This makes them ambiguous because in some cases a single input reference may correspond to multiple possible outputs. In this work, we focus on font generation using the generative model-based methods which learn the buildup of the characters from noise-to-image. We propose a novel way to train a conditional generative deep neural model so that we can achieve font style control on the generated font images. Our research demonstrates how to generate new font images conditioned on both character class labels and character style labels when using the generative model-based methods. We achieve this by introducing a modified generator network which is given inputs noise, character class, and style, which help us to calculate losses separately for the character class labels and character style labels. We show that adding the character style vector on top of the character class vector separately gives the model rich information about the font and enables us to explicitly specify not only the character class but also the character style that we want the model to generate.

• Journal of the Korean Society of Industry Convergence
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• v.26 no.4_2
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• pp.715-724
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• 2023

Solar energy forecasting is essential for (1) power system planning, management, and operation, requiring accurate predictions. It is crucial for (2) ensuring a continuous and sustainable power supply to customers and (3) optimizing the operation and control of renewable energy systems and the electricity market. Recently, research has been focusing on developing solar energy forecasting models that can provide daily plans for power usage and production and be verified in the electricity market. In these prediction models, various data, including solar energy generation and climate data, are chosen to be utilized in the forecasting process. The most commonly used climate data (such as temperature, relative humidity, precipitation, solar radiation, and wind speed) significantly influence the fluctuations in solar energy generation based on weather conditions. Therefore, this paper proposes a hybrid forecasting model by combining the strengths of the Prophet model and the GRU model, which exhibits excellent predictive performance. The forecasting periods for solar energy generation are tested in short-term (2 days, 7 days) and medium-term (15 days, 30 days) scenarios. The experimental results demonstrate that the proposed approach outperforms the conventional Prophet model by more than twice in terms of Root Mean Square Error (RMSE) and surpasses the modified GRU model by more than 1.5 times, showcasing superior performance.

• Journal of the Korean Data and Information Science Society
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• v.28 no.6
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• pp.1447-1456
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• 2017

Accurate inference of parameters of a stochastic rainfall generation model is essential to improve the applicability of the rainfall generation model which modeled the rainfall process and the structure of rainfall events. In this study, the model parameters of a stochastic rainfall generation model, NSRPM (Neyman-Scott rectangular pulse model), were estimated using DFP (Davidon-Fletcher-Powell), GA (genetic algorithm), Nelder-Mead, and DE (differential evolution) methods. Summer season hourly rainfall data of 20 rainfall observation sites within the Nakdong river basin from 1973 to 2017 were used to estimate parameters and the regional applicability of inference methods were analyzed. Overall results demonstrated that DE and Nelder-Mead methods generate better results than that of DFP and GA methods.

• Journal of Hydrogen and New Energy
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• v.33 no.6
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• pp.723-732
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• 2022

Cracking ammonia inside solid oxide fuel cell (SOFC) stack is a compact and simple way. To prevent sharp temperature fluctuation and increase cell efficiency, the decomposition reaction should be spread on whole cell area. This leading to a question that, how does anode thickness affect the conversion rate of ammonia and the cell voltage? Since the 0D model of SOFC is useful for system level simulation, how accurate is it to use equilibrium solver for internal ammonia cracking reaction? The 1D model of ammonia fed SOFC was used to simulate the diffusion and reaction of ammonia inside the anode electrode, then the partial pressure of hydrogen and steam at triple phase boundary was used for cell voltage calculation. The result shows that, the ammonia conversion rate increases and reaches saturated value as anode thickness increase, and the saturated thickness is bigger for lower operating temperature. The similar cell voltage between 1D and 0D models can be reached with NH₃ conversion rate above 90%. The 0D model and 1D model of SOFC showed similar conversion rate at temperature over 750℃.