• Journal of Microbiology
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• v.44 no.1
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• pp.64-71
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• 2006

To investigate the effects of the nucleotide sequences in Shine-Dalgarno (SD) and the spacer region (SD-ATG) on bovine growth hormone (bGH) gene expression, the expression vectors under the control of the T7 promoter (pT7-7 vector) were constructed using bGH derivatives (bGH1 & bGH14) which have different 5'-coding regions and were induced in E. coli BL21 (DE3). Oligonucleotides containing random SD sequences and a spacer region were chemically synthesized and the distance between the SD region and the initiation codon were fixed to nine bases in length. The oligonucleotides were annealed and fused to the bGH1 and bGH14 cDNA, respectively. When the bGH gene was induced with IPTG in E. coli BL21(DE3), some clones containing only bGH14 cDNA produced considerable levels of bGH in the range of $6.9\%\;to\;8.5\%$ of total cell proteins by SDS-PAGE and Western blot. Otherwise, the bGH was not detected in any clones with bGH1 cDNA. Accordingly, the nucleotide sequences of SD and the spacer region affect on bGH expression indicates that the sequences sufficiently destabilize the mRNA secondary structure of the bGH14 gene. When the free energy was calculated from the transcription initiation site to the +51 nucleotide of bGH cDNA using a program of nucleic acid folding and hybridization prediction, the constructs with values below -26.3 kcal/mole (toward minus direction) were not expressed. The constructs with the original sequence of bGH cDNA also did not show any expression, regardless of the free energy values. Thus, the disruption of the mRNA secondary structure may be a major factor regulating bGH expression in the translation initiation process. Accordingly, the first stem-loop among two secondary structures present in the 5'-end region of the bGH gene should be disrupted for the effective expression of bGH.

• Journal of the Korean Society of Marine Environment & Safety
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• v.26 no.2
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• pp.139-148
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• 2020

The most important factor affecting the berthing energy generated when a ship berths is the berthing velocity. Thus, an accident may occur if the berthing velocity is extremely high. Several ship features influence the determination of the berthing velocity. However, previous studies have mostly focused on the size of the vessel. Therefore, the aim of this study is to analyze various features that influence berthing velocity and determine their respective importance. The data used in the analysis was based on the berthing velocity of a ship on a jetty in Korea. Using the collected data, machine learning classification algorithms were compared and analyzed, such as decision tree, random forest, logistic regression, and perceptron. As an algorithm evaluation method, indexes according to the confusion matrix were used. Consequently, perceptron demonstrated the best performance, and the feature importance was in the following order: DWT, jetty number, and state. Hence, when berthing a ship, the berthing velocity should be determined in consideration of various features, such as the size of the ship, position of the jetty, and loading condition of the cargo.

• Journal of the Korean Ceramic Society
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• v.38 no.9
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• pp.799-805
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• 2001

Two commercial alumina powders having different particle size of $0.5{\mu}m$ and 3${\mu}$m were presintered at 1120$^{\circ}$C for 2h and then lanthanum aluminosilicate glass was infiltrated at 1100$^{\circ}$C for up to 4h to obtain the densified glass-alumina composites. The effect of alumina particle size on packing factor, microstructure, wetting, porosity and pore size, and mechanical properties of the composite was investigated. The optimum mechanical properties and compaction behavior were observed for the 3${\mu}$m alumina particle dispersed composite. The 3${\mu}$m alumina particle size and distribution for he preform were within 0.1 to 48${\mu}$m and bimodal and random orientation. The strength and the fracture toughness of the composite having 3${\mu}$m alumina particles were 519MPa and $4.5MPa{\cdot}m^{1/2}$, respectively.

• 한국노년학
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• v.37 no.3
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• pp.727-745
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• 2017

This study purported to examine the effects of precarious employment and social capital on the changes of self-rated health status among the middle aged and the young-old population in South Korea. The study analyzed 12 year follow-up data generated by the Korean Labor and Income Panel Study(KLIPS 6-17), which included 10532 employed subjects aged 55 to 75. Multi-level growth curve modeling was performed by fixed and random effect models using STATA 13.0 program. Afterwards, Hausman test was performed, which resulted in support of the estimation by fixed effect model. The results showed that a day labor position was significant factor affecting the deteriorated changes of self-rated health status over time. In addition, wage, weekly working hours, and private/relational social capitals were also found to be significant factors affecting the changes of the self-rated health status. The results supported the divergence hypothesis as well as the cumulative advantage theory. Efforts should be made to develop and implement various employment support policies and social service programs to alleviate the health inequality of the employed workers over their middle-aged to young-old period.

• Korean Security Journal
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• no.57
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• pp.205-230
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• 2018

The purpose of this study is to examine the structural model between servant - transformational leadership, organizational citizenship behavior and organizational performance of private security organizations. The security guards working in private security companies in Seoul and Gyeonggi - do were selected as population, random sampling method. The survey was conducted from September 1, 2016 to November 30. Accoridng to the purpose of the study, this study conducted factor analysis(EFA/CFA), reliability analysis, convergence and discriminant validity analysis, and covariance structure analysis using SPSSWIN 21.0 and AMOS 21.0. The conclusions of this study are as follows. First, servant leadership has a positive (+) effect (${\beta}=.406$) on organizational citizenship behavior statistically at .001 level. Second, transformational leadership has a positive (+) effect (${\beta}=.373$) on organizational citizenship behavior statistically at .001 level. Third, organizational citizenship behavior has a positive (+) effect (${\beta}=.615$) on organizational performance statistically at .01 level. Fourth, servant leadership does not affect the organizational performance statistically (${\beta}=.211$). Fifth, transformational leadership does not affect the organizational performance statistically (${\beta}=.058$). Sixth, organizational citizenship behavior has statistically positive (+) mediation effect (${\beta}=.249$) in the relationship between servant leadership and organizational performance. Seventh, organizational citizenship behavior has statistically positive (+) mediating effects (${\beta}=.230$) on the relationship between transformational leadership and organizational performance. Innovation and Improvement of National Emergency Management System in Korea.

• Journal of The Korean Society of Agricultural Engineers
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• v.63 no.6
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• pp.77-88
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• 2021

Duck industry had a rapid growth in recent years. Nevertheless, researches to improve duck house environment are still not sufficient enough. Moisture generation of duck house litter is an important factor because it may cause severe illness and low productivity. However, the measuring process is difficult because it could be disturbed with animal excrements and other factors. Therefore, it has to be calculated according to the environmental data around the duck house litter. To cut through all these procedures, we built several machine learning regression model forecasting moisture generation of litter by measured environment data (air temperature, relative humidity, wind velocity and water contents). 5 models (Multi Linear Regression, k-Nearest Neighbors, Support Vector Regression, Random Forest and Deep Neural Network). have been selected for regression. By using R-Square, RMSE and MAE as evaluation metrics, the best accurate model was estimated according to the variables for each machine learning model. In addition, to address the small amount of data acquired through lab experiments, bootstrapping method, a technique utilized in statistics, was used. As a result, the most accurate model selected was Random Forest, with parameters of n-estimator 200 by bootstrapping the original data nine times.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.12
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• pp.485-496
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• 2018

So far, the deep learning, a field of artificial intelligence, has achieved remarkable results in solving problems from unstructured data. However, it is difficult to comprehensively judge situations like humans, and did not reach the level of intelligence that deduced their relations and predicted the next situation. Recently, deep neural networks show that artificial intelligence can possess powerful relational reasoning that is core intellectual ability of human being. In this paper, to analyze and observe the performance of Relation Networks (RN) among the neural networks for relational reasoning, two types of RN-based deep neural network models were constructed and compared with the baseline model. One is a visual question answering RN model using Sort-of-CLEVR and the other is a text-based question answering RN model using bAbI task. In order to maximize the performance of the RN-based model, various performance improvement experiments such as hyper parameters tuning have been proposed and performed. The effectiveness of the proposed performance improvement methods has been verified by applying to the visual QA RN model and the text-based QA RN model, and the new domain model using the dialogue-based LL dataset. As a result of the various experiments, it is found that the initial learning rate is a key factor in determining the performance of the model in both types of RN models. We have observed that the optimal initial learning rate setting found by the proposed random search method can improve the performance of the model up to 99.8%.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.6
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• pp.121-132
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• 2018

Weather is an important factor affecting roadway transportation in many aspects such as traffic flow, driver 's driving patterns, and crashes. This study focuses on the relationship between weather and road surface condition and develops a model to estimate the road surface condition using machine learning. A road surface sensor was attached to the probe vehicle to collect road surface condition classified into three categories as 'dry', 'moist' and 'wet'. Road geometry information (curvature, gradient), traffic information (link speed), weather information (rainfall, humidity, temperature, wind speed) are utilized as variables to estimate the road surface condition. A variety of machine learning algorithms examined for predicting the road surface condition, and a two - stage classification model based on 'Random forest' which has the highest accuracy was constructed. 14 days of data were used to train the model and 2 days of data were used to test the accuracy of the model. As a result, a road surface state prediction model with 81.74% accuracy was constructed. The result of this study shows the possibility of estimating the road surface condition using the existing weather and traffic information without installing new equipment or sensors.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.18 no.5
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• pp.93-101
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• 2008

Side channel attacks are well known as one of the most powerful physical attacks against low-power cryptographic devices and do not take into account of the target's theoretical security. As an important succeeding factor in side channel attacks (specifically in DPAs), exact time-axis alignment methods are used to overcome misalignments caused by trigger jittering, noise and even some countermeasures intentionally applied to defend against side channel attacks such as random clock generation. However, the currently existing alignment methods consider only on the position of signals on time-axis, which is ineffective for certain countermeasures based on time-axis misalignments. This paper proposes a new signal alignment method based on interpolation and decimation techniques. Our proposal can align the size as well as the signals' position on time-axis. The validity of our proposed method is then evaluated experimentally with a smart card chip, and the results demonstrated that the proposed method is more efficient than the existing alignment methods.

• Korean Journal of Optics and Photonics
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• v.32 no.3
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• pp.120-125
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• 2021

We have developed a linearly polarized high-power Yb-doped fiber laser in the master oscillator power amplifier (MOPA) scheme for efficient spectral beam combining. We modulated the phase of the seed laser by pseudo-random binary sequence (PRBS), with the bit length optimized to suppress stimulated Brillouin scattering (SBS), and subsequently amplified seed power in a 3-stage amplifier system. We have constructed by coiling the polarization-maintaining (PM) Yb-doped fiber, with core and cladding diameters of 20 ㎛ and 400 ㎛ respectively, to a diameter of 9-12 cm for suppression of the mode instability (MI). Finally, we obtained an output power of 1.004 kW with a slope efficiency of 83.7% in the main amplification stage. The beam quality factor M² and the polarization extinction ratio (PER) were measured to be 1.12 and 21.5 dB respectively. Furthermore, the peak-intensity difference between the Rayleigh signal and SBS signal was observed to be 2.36 dB in the backward spectra, indicating that SBS is successfully suppressed. In addition, it can be expected that the MI does not occur because not only there is no decrease in slope efficiency, but also the beam quality for each amplified output is maintained.