• Proceeding of EDISON Challenge
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• 2016.03a
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• pp.55-61
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• 2016

$A{\beta}_{40}$ peptides form oligomers that later aggregate into a plaque, which is deemed to be a leading cause of Alzheimer's Disease. Its non-crystalline morphology has limited an understanding of comprehensive structural study. In this research, computational biomolecular simulations were performed in the following order: solvent and ion addition in a box, energy minimization of protein, equilibration, and periodic boundary condition disaggregation of a monomer from fibril. The result founded the two-fold model is 25% more stable in the simulation environment, and the steric zippers held on most tightly until 220 ps of simulation. The study supports the previous findings that two-fold aggregate $A{\beta}_{40}$ is more stable at 310 K and discusses further how much contribution steric-zipper and hydrogen bonding are making.

• Environmental and Resource Economics Review
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• v.31 no.4
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• pp.849-864
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• 2022

In case of a specific sector being divided into sub-sectors, this study presents a process for estimating an input-output table, which is frequently used as basic data in fields of energy and environment economics. RAS method, which is universally used for this case, requires information on production, intermediate input sum, and intermediate demand sum for each sector in the new table. But in many cases, it is difficult to secure information on intermediate demand sum by sector. This study suggests a process for estimating a new input-output table without using information of intermediate demand sum in the case of sector separation, under the assumption that information of production value and intermediate input sum by sector are available. The key idea is that the values of many elements in the input-output table after disaggregation are the same as those in the table before disaggregation and that the sum of the elements after disaggregation, equals the values of the elements before disaggregation. The process of estimating the intemediate transaction matrix or the input coefficient matrix is presented by using these information instead of intermediate demand sum information. A small-scale simulation shows that the average error rate of the process proposed in this study is about 11.23% in estimating input coefficients, which is smaller than the 11.30% estimation error of RAS using the information of intermediate demand sum. However, since it is known in the literature that using additional information does not always improve estimation performance compared to not using it, additional research on various simulations is needed to apply the method of this study to reality.

• Journal of the Korea Convergence Society
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• v.12 no.2
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• pp.21-28
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• 2021

In this study, we propose a deep learning-based NILM technique using actual measured power data for 5 kinds of home appliances and verify its effectiveness. For about 3 weeks, the active power of the central power measuring device and five kinds of home appliances (refrigerator, induction, TV, washing machine, air cleaner) was individually measured. The preprocessing method of the measured data was introduced, and characteristics of each household appliance were analyzed through spectogram analysis. The characteristics of each household appliance are organized into a learning data set. All the power data measured by the central power measuring device and 5 kinds of home appliances were time-series mapping, and training was performed using a LSTM neural network, which is excellent for time series data prediction. An algorithm that can disaggregate five types of energies using only the power data of the main central power measuring device is proposed.

• Fashion & Textile Research Journal
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• v.10 no.3
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• pp.394-398
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• 2008

One of barely water soluble ketones, acetophenone (AP) was dissolved in methanol and then was mixed with aqueous solution of C. I. Red Acid 114. In order to find out the role of AP in the dyeing process the rate constants and the activation parameters were calculated. The rate for the dyeing with AP was faster than that without it. Because of the reduced temperature dependence by AP the activation energy ($E_a$) for the dyeing with AP was smaller than that without it. With increasing temperature the activation enthalpy (${\Delta}H^*$), the activation entropy (${\Delta}S^*$), and the activation free energy ($G^*$) decreased, which was more noticeable in dyeing with AP. The rate constants and the activation parameters agreed well with the results from the previous reports that the ability of AP to increase disaggregation of dye molecules, loosening the wool fiber, and wickabilty of dyeing solution made it possible to dye wool fiber at low temperature.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.3
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• pp.183-191
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• 2021

Non-intrusive load monitoring is a technology that can be used for predicting and classifying the type of appliances through real-time monitoring of user power consumption, and it has recently got interested as a means of energy-saving. In this paper, we propose a system for classifying appliances from user consumption data by combining GAF(Gramian angular field) technique that can be used for converting one-dimensional data to the two-dimensional matrix with convolutional neural networks. We use REDD(residential energy disaggregation dataset) that is the public appliances power data and confirm the classification accuracy of the GASF(Gramian angular summation field) and GADF(Gramian angular difference field). Simulation results show that both models showed 94% accuracy on appliances with binary-state(on/off) and that GASF showed 93.5% accuracy that is 3% higher than GADF on appliances with multi-state. In later studies, we plan to increase the dataset and optimize the model to improve accuracy and speed.

• Fashion & Textile Research Journal
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• v.11 no.4
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• pp.672-677
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• 2009

To reduce the dependence of wool dyeing on the temperature several solvents with different properties and structures were added to the dye bath of C. I. Acid Yellow 42. Nearly the same total solubility parameters(${\delta}_t$) of solvents as those of wool fiber and hydrophobic part of the dyestuff were needed to increase disaggregation of dye molecules, loosening the wool fiber and wickabilty of dyeing solution; besides, the large surface tension(${\gamma}$) value of the solvents and the well balanced values of the three-component Hansen solubility parameters such as dispersion(${\delta}_d$), polar(${\delta}_p$), and hydrogen(${\delta}_h$) bonding parameters were required. Among the added solvents dimethyl phthalate(DMP) and acetophenone(AP) were satisfied with these conditions and worked the most successfully in the low temperature wool dyeing. Their effectiveness proven by the dyeing rate and the activation energy ($E_a$) of the dyeing was in the order of DMP > AP > DBE > CH > M >NONE. In conclusion the total solubility parameters(${\delta}_t$), the three-component Hansen parameters and the surface tension(${\gamma}$) of DMP and AP could be the guidelines to select suitable solvents for low temperature wool dyeing.