• Korean Chemical Engineering Research
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• v.57 no.2
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• pp.239-243
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• 2019

In this study, anthraquinone-2,7-disulfonic acid (2,7-AQDS) is used as negative active material and Tiron is used as positive active material for aqueous redox flow battery (RFB). In previous results that used the 2,7-AQDS and Tiron, sulfuric acid ($H_2SO_4$) was a supporting electrolyte. However, in this study, ammonium chloride ($NH_4Cl$) is suggested as the electrolyte for the first time. By changing the supporting electrolyte from $H_2SO_4$ to $NH_4Cl$, the cell voltage of RFB is improved from 0.76 V to 1.01 V. To investigate the effect of $NH_4Cl$ supporting electrolyte of the performance of RFB, the full-cell tests of RFB using 2,7-AQDS and Tiron that are dissolved in $NH_4Cl$ supporting electrolyte are carried out, while cut-off voltage range is a main parameter to determine their performance. When the cut-off voltage range is 0.2~1.6 V, the hydrogen evolution occurs during charging step. To address the side reaction effect, the cut-off voltage range is changed to 0.2~1.2 V. When the revised cut-off voltage range is used and the current density of $40mA/cm^2$ is applied, hydrogen evolution is not observed and the optimal RFB shows the charge efficiency of 99% and discharge capacity of 3.3 Ah/L at 10cycle.

• Korean Journal of Food Science and Technology
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• v.53 no.5
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• pp.553-560
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• 2021

Theanine, the major amino acid and a sweet umami component of green tea, has anti-stress effects in humans. From green tea, theanine was extracted at 80℃ for 2 h using a low temperature, high pressure extractor, and caffeine was removed using an HP-20 column with 80% ethanol. Theanine extracts were applied to produce functional jelly using three kinds of gelling agents (I, II, and III) or various concentrations of theanine extracts (10-50%). Theanine jelly was characterized with respect to its physical properties, product stability, and physiological function. Gelling agent III (tamarind gum, xanthan gum, and locust bean gum=2:3:5, w/w/w) and S3 (35% theanine extracts) jelly exhibited the optimum textural properties with lower hardness and high springiness. Among theanine jellies, S3 exhibited optimum product stability, high 1,1-diphenyl-2-picrylhydrazyl (DPPH) scavenging, and acetylcholinesterase inhibitory activity. These results indicate that the anine extracts could be used as a neuroprotective source in the food industry.

• Journal of Apiculture
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• v.32 no.3
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• pp.181-189
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• 2017

Lysinibacillus fusiformis has been suspected to be a pathogen of Bombus terrestris in Korea since 2008. In this study, we developed the rapid detection method for the L. fusiformis by utilizing the Ultra-rapid PCR. After optimizing of L. fusiformis-specific Ultra-rapid PCR, it can detect the existence of $1.0{\times}10^8$ L. fusiformis-specific DNA molecules in 4 minute and 22 seconds. Even, only 10 molecules could be detected quantitatively using this method. In addition, for the first time, in our knowledge, L. fusiformis was detected using proposed method from bumblebee produced commercially in Korea. Not only in the laboratory but also in the field, L. fusiformis-specific Ultra-rapid PCR would be applied and might be expected as convenient tools at production of bumblebee or inspection for the import and export of bumblebee.

• Journal of the Korea Convergence Society
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• v.12 no.6
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• pp.9-16
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• 2021

In the past, there have been various studies on predicting the stock market by machine learning techniques using stock price data and financial big data. As stock index ETFs that can be traded through HTS and MTS are created, research on predicting stock indices has recently attracted attention. In this paper, machine learning models for KOSPI's up and down predictions are implemented separately. These models are optimized through a grid search of their control parameters. In addition, a hybrid machine learning model that combines individual models is proposed to improve the precision and increase the ETF trading return. The performance of the predictiion models is evaluated by the accuracy and the precision that determines the ETF trading return. The accuracy and precision of the hybrid up prediction model are 72.1 % and 63.8 %, and those of the down prediction model are 79.8% and 64.3%. The precision of the hybrid down prediction model is improved by at least 14.3 % and at most 20.5 %. The hybrid up and down prediction models show an ETF trading return of 10.49%, and 25.91%, respectively. Trading inverse×2 and leverage ETF can increase the return by 1.5 to 2 times. Further research on a down prediction machine learning model is expected to increase the rate of return.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.12
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• pp.320-330
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• 2020

Fire is difficult to achieve good performance in image detection using deep learning because of its high irregularity. In particular, there is little data on fire detection in underground utility facilities, which have poor light conditions and many objects similar to fire. These make fire detection challenging and cause low performance of deep learning models. Therefore, this study proposed a fire detection model using deep learning and estimated the performance of the model. The proposed model was designed using a combination of a basic convolutional neural network, Inception block of GoogleNet, and Skip connection of ResNet to optimize the deep learning model for fire detection under underground utility facilities. In addition, a training technique for the model was proposed. To examine the effectiveness of the method, the trained model was applied to fire images, which included fire and non-fire (which can be misunderstood as a fire) objects under the underground facilities or similar conditions, and results were analyzed. Metrics, such as precision and recall from deep learning models of other studies, were compared with those of the proposed model to estimate the model performance qualitatively. The results showed that the proposed model has high precision and recall for fire detection under low light intensity and both low erroneous and missing detection capabilities for things similar to fire.

• Journal of Korea Water Resources Association
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• v.55 no.7
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• pp.545-556
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• 2022

It is recommended to use long-term hydrometeorological data for more than the service life of the hydraulic structures and water resource planning. For the purpose of expanding rainfall data, stochastic simulation models, such as Modified Bartlett-Lewis Rectangular Pulse (BLRP) and Neyman-Scott Rectangular Pulse (NSRP) models, have been widely used. The optimal parameters of the model can be estimated by repeatedly comparing the statistical moments defined through a combination of parameters of the probability distribution in the optimization context. However, parameter estimation using relatively small observed rainfall statistics corresponds to an ill-posed problem, leading to an increase in uncertainty in the parameter estimation process. In addition, as shown in previous studies, extreme values are underestimated because objective functions are typically defined by the first and second statistical moments (i.e., mean and variance). In this regard, this study estimated the parameters of the NSRP model using the objective function with the third moment and compared it with the existing approach based on the first and second moments in terms of estimation of extreme rainfall. It was found that the first and second moments did not show a significant difference depending on whether or not the skewness was considered in the objective function. However, the proposed model showed significantly improved performance in terms of estimation of design rainfalls.

• Journal of Mushroom
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• v.20 no.3
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• pp.178-182
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• 2022

Despite the long history of mushroom use, studies examining the genetic function of mushrooms and the development of new varieties via bio-molecular methods are significantly lacking compared to those examining other organisms. However, owing to recent developments, attempts have been made to use a novel gene-editing technique involving CRISPR/Cas9 technology and genetic scissors in mushroom studies. In particular, research is actively being conducted to utilize ribonucleoprotein particles (RNPs) that can be genetically edited with high efficiency without foreign gene insertion for ease of selection. However, RNPs are too large for Cas9 protein to pass through the cell membrane of the protoplasmic reticulum. Furthermore, guide RNA is unstable and can be easily decomposed, which remarkably affects gene editing efficiency. In this study, nanoparticles were used to mitigate the shortcomings of RNP-based gene editing techniques and to obtain transformants stably. We used Lentinula edodes (shiitake mushroom) Sanjo705-13 monokaryon strain, which has been successfully used in previous genome editing experiments. To identify a suitable osmotic buffer for the isolation of protoplast, 0.6 M and 1.2 M sucrose, mannitol, sorbitol, and KCl were treated, respectively. In addition, with various nanoparticle-forming materials, experiments were conducted to confirm genome editing efficiency via the formation of nanoparticles with calcium phosphate (CaP), which can be bound to Cas9 protein without any additional amino acid modification. RNPs/NP complex was successfully formed and protected nuclease activity with nucleotide sequence specificity.

• Economic Analysis
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• v.27 no.4
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• pp.1-42
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• 2021

The outlook for Korea's consumer price inflation rate has a profound impact not only on the Bank of Korea's operation of the inflation target system but also on the overall economy, including the bond market and private consumption and investment. This study presents the prediction results of consumer price inflation in Korea for the next three years. To this end, first, model selection is performed based on the out-of-sample predictive power of autoregressive distributed lag (ADL) models, AR models, small-scale vector autoregressive (VAR) models, and large-scale VAR models. Since there are many potential predictors of inflation, a Bayesian variable selection technique was introduced for 12 macro variables, and a precise tuning process was performed to improve predictive power. In the case of the VAR model, the Minnesota prior distribution was applied to solve the dimensional curse problem. Looking at the results of long-term and short-term out-of-sample predictions for the last five years, the ADL model was generally superior to other competing models in both point and distribution prediction. As a result of forecasting through the combination of predictions from the above models, the inflation rate is expected to maintain the current level of around 2% until the second half of 2022, and is expected to drop to around 1% from the first half of 2023.

• Korean Chemical Engineering Research
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• v.61 no.1
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• pp.123-141
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• 2023

A sufficient amount of data with quality is needed for training artificial neural networks (ANNs). However, developing ANN models with a small amount of data often appears in engineering fields. This paper presented an ANN model to improve prediction performance of the ammonia emission amount with 83 data. The ammonia emission rate included eleven inputs and two outputs (maximum ammonia loss, N_max and time to reach half of N_max, K_m). Categorical input variables were transformed into multi-dimensional equal-distance variables, and 13 data were added into 66 training data using a generative adversarial network. Hyperparameters (number of layers, number of neurons, and activation function) of ANN were optimized using Gaussian process. Using 17 test data, the previous ANN model (Lim et al., 2007) showed the mean absolute error (MAE) of K_m and N_max to 0.0668 and 0.1860, respectively. The present ANN outperformed the previous model, reducing MAE by 38% and 56%.

• Korean Journal of Microbiology
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• v.40 no.2
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• pp.147-153
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• 2004

Protein modification by N-acetyl-${\beta}$-D-glucosamine (O-G1cNAc) on the hydroxyl groups of Ser or Thr ubiq-uitously occurs in eukaryotic cells and is involved in many cellular phenomena. The level of O-G1cNAc-mod-ified protein is regulated by OGT and O-GlcNAcase enzymes. We have tried to produce recombinant O-GlcNAcase in E. coli as an effort to establish in vitro screening system for modulators of O-GlcNAcase. The culture conditions for improvement of O-GlcNAcase productivity, were as follows: induction temperature, $30^{\circ}C$; the concentration of L-arabinose, 0.02% and induction time, 5 hr. Under these culture conditions, E. coli cells containing O-GlcNAcase gene had no enzyme activity until up to 3 hr culture. However, O-GlcNAcase activity dramatically increased from 3 to 5 hr culture. It almost maintained the same level after 5 hr culture. Western blot analysis verified the amount of expressed O-GlcNAcase increased with culture time, being con-sistent with activity data. The optimal reaction condition determined in this study was as follows: protein quan-tity, $5{\mu}g$; reaction time, 30 min; reaction temperature, $45^{\circ}C$; substrate concentration, 2 mM; reaction pH, 6.5. Methanol had little effect on O-GlcNAcase activity and 90% of activity were retained at 10%. Only 15% resid-ual activity were detected at 5% of chloroform.