• 한국연소학회:학술대회논문집
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• 2014.11a
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• pp.19-20
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• 2014

Boiler design should be desinged to maximize thermal efficiency of the system under imposed load requirement and a boiler should be validated for transient operation. If a proper prediction is possible on the transient behavior and transient characteristics of a boiler, one may asses the performance of boiler component, control logics and operation procedures. In this work, dynamic modeling method of boiler is presented and dynamic simulation of load runback scenario was carried out on suprecritical oil-firing boiler.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.368-368
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• 2000

In this paper, we present forecasting ozone concentration with decision support system. Since the mechanism of ozone concentration is highly complex, nonlinear, and nonstationary, modeling of ozone prediction system has many problems and results of prediction are not good performance so far. Forecasting ozone concentration with decision support system is acquired to information from human knowledge and experiment data. Fuzzy clustering method uses the acquisition and dynamic polynomial neural network gives us a good performance for ozone prediction with ability of superior data approximation and self-organization.

• Journal of KIISE
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• v.44 no.4
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• pp.411-416
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• 2017

The spectral radiance received by an infrared (IR) sensor is mainly influenced by the surface temperature of the target itself. Therefore, the precise temperature prediction is important for generating an IR target image. In this paper, we implement the combined three-dimensional surface temperature prediction module against target attitudes, environments and properties of a material for generating a realistic IR signal. In order to verify the calculated surface temperature, we are using the well-known IR signature analysis software, OKTAL-SE and compare the result with that. In addition, IR signal modeling is performed using the result of the surface temperature through coupling with OKTAL-SE.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2000.10a
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• pp.180-185
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• 2000

In this paper, we designed 2303.15MHz Sequency synthesizer for the purpose of the phase noise prediction. For the modeling of phase noise Oersted in the designed system through inooducing the noise-modeling method suggested by Lascari we analyzied a variation of phase noise as according as that of offest frequency. Especially, for the third-order system of the PLL among some kinds of phase noise generated from VCO we analyzed the aspect of 1/f-noise appearing troubles in the low frequency band. Since it is difficult to analyze mathematically 1/f-noise in the third-order system of the PLL, introducing the concept of pseudo-damping factor has made an ease of the access of the 1/f-noise variance. we showed a numerical formula of 1/f-noise variance in the third-order system of the PLL which is compared with that of 1/f-noise variance in the second-order system of the PLL

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2000.04a
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• pp.659-664
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• 2000

In this paper, We have discussed on the modeling of machined outer geometry which was established for the case of round shape machining, also the effects of externally machined profile are analyzed and its modeling realiability was verified by the experiments of roundness testing, especially in lathe operation. In this study, we established harmonic geometric model with the parameter harmonic function. In general, we can calculate the theoretical roundness profile with arbitrary multilobe parameter. But in real experiments, only 2-5 lobe profile was frequently measured. the most frequently ones are 3 and 5 lobe profile in experiments. With this results, we can predict that these results may be applies to round shape machining such as turning, drilling, boring, ball screw and cylindrical grinding operation in bearing and shaft making operation with the same method. In this study, simulation and experimental work were performed to show the profile behaviors. we can apply these new modeling method in real process for the prediction of part profile behaviors machined such as in round shape machining operation.

• Journal of Information Technology Services
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• v.16 no.3
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• pp.113-125
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• 2017

Customers have been affected by others' opinions when they make a purchase. Thanks to the development of technologies, people are sharing their experiences such as reviews or ratings through online or social network services, However, although ratings are intuitive information for others, many reviews include only texts without ratings. Also, because of huge amount of reviews, customers and companies can't read all of them so they are hard to evaluate to a product without ratings. Therefore, in this study, we propose a methodology to predict ratings based on reviews for a product. In a methodology, we first estimate the topic-review matrix using the Latent Dirichlet Allocation technic which is widely used in topic modeling. Next, we predict ratings based on the topic-review matrix using the artificial neural network model which is based on the backpropagation algorithm. Through experiments with actual reviews, we find that our methodology can predict ratings based on customers' reviews. And our methodology performs better with reviews which include certain opinions. As a result, our study can be used for customers and companies that want to know exactly a product with ratings. Moreover, we hope that our study leads to the implementation of future studies that combine machine learning and topic modeling.

• Journal of the Korean Society of Clothing and Textiles
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• v.42 no.3
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• pp.503-515
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• 2018

In this research, the three-dimensional structural and colorimetrical modeling of yarn-dyed woven fabrics was conducted based on the Kubelka-Munk theory (K-M theory) for their accurate color predictions. In the K-M theory for textile color formulation, the absorption and scattering coefficients, denoted K and S, respectively, of a colored fabric are represented using those of the individual colorants or color components used. One-hundred forty woven fabric samples were produced in a wide range of structures and colors using red, yellow, green, and blue yarns. Through the optimization of previous two-dimensional color prediction models by considering the key three-dimensional structural parameters of woven fabrics, three three-dimensional K/S-based color prediction models, that is, linear K/S, linear log K/S, and exponential K/S models, were developed. To evaluate the performance of the three-dimensional color prediction models, the color differences, ${\Delta}L^*$, ${\Delta}C^*$, ${\Delta}h^{\circ}$, and ${\Delta}E_{CMC(2:1)}$, between the predicted and the measured colors of the samples were calculated as error values and then compared with those of previous two-dimensional models. As a result, three-dimensional models have proved to be of substantially higher predictive accuracy than two-dimensional models in all lightness, chroma, and hue predictions with much lower ${\Delta}L^*$, ${\Delta}C^*$, ${\Delta}h^{\circ}$, and the resultant ${\Delta}E_{CMC(2:1)}$ values.

• Journal of Information Technology Services
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• v.19 no.6
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• pp.131-144
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• 2020

As the blockchain technology attracts attention, interest in cryptocurrency that is received as a reward is also increasing. Currently, investments and transactions are continuing with the expectation and increasing value of cryptocurrency. Accordingly, prediction for cryptocurrency price has been attempted through artificial intelligence technology and social sentiment analysis. The purpose of this paper is to develop a deep learning ensemble model for predicting the price fluctuations and one-day lag price of cryptocurrency based on the design science research method. This paper intends to perform predictive modeling on Ethereum among cryptocurrencies to make predictions more efficiently and accurately than existing models. Therefore, it collects data for five years related to Ethereum price and performs pre-processing through customized functions. In the model development stage, four LSTM models, which are efficient for time series data processing, are utilized to build an ensemble model with the optimal combination of hyperparameters found in the experimental process. Then, based on the performance evaluation scale, the superiority of the model is evaluated through comparison with other deep learning models. The results of this paper have a practical contribution that can be used as a model that shows high performance and predictive rate for cryptocurrency price prediction and price fluctuations. Besides, it shows academic contribution in that it improves the quality of research by following scientific design research procedures that solve scientific problems and create and evaluate new and innovative products in the field of information systems.

• Smart Structures and Systems
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• v.15 no.3
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• pp.717-733
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• 2015

Modal parameters of a structure are commonly used quantities for system identification and damage detection. With a limited number of studies on the statistics assessment of modal parameters, this paper presents procedures to properly account for the uncertainties present in the process of extracting modal parameters. Particularly, this paper focuses on how to deal with the measurement error in an ambient vibration test and the modeling error resulting from a modal parameter extraction process. A bootstrap approach is adopted, when an ensemble of a limited number of noised time-history response recordings is available. To estimate the modeling error associated with the extraction process, a model prediction expansion approach is adopted where the modeling error is considered as an "adjustment" to the prediction obtained from the extraction process. The proposed procedures can be further incorporated into the probabilistic analysis of applications where the modal parameters are used. This study considers the effects of the measurement and modeling errors and can provide guidance in allocating resources to improve the estimation accuracy of the modal data. As an illustration, the proposed procedures are applied to extract the modal data of a damaged beam, and the extracted modal data are used to detect potential damage locations using a damage detection method. It is shown that the variability in the modal parameters can be considered to be quite low due to the measurement and modeling errors; however, this low variability has a significant impact on the damage detection results for the studied beam.

• The Journal of the Acoustical Society of Korea
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• v.22 no.1E
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• pp.33-42
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• 2003

Kuo et al proposed an on-line method for an adaptive prediction error filter for improving secondary path modeling performance in the modeling method of the secondary path. This method have some disadvantages, namely having to use additive noise with the result that noise control performance is not good since it is focused on the estimated performance of the secondary path. In this paper, we proposes a modified Kuo model using gain control parameter and delay. It uses a reference signal for additive noise to improve the problems in the existing Kuo model.