• Korean Journal of Plant Resources
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• v.24 no.6
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• pp.666-674
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• 2011

To elucidate genetic diversity of common reed in Korea, we collected a total of 674 common reed plants from 27 regions in South Korea. Hierarchical clustering using 7 morphological traits divided the 27 common reed populations into 7 groups. Random amplified polymorphic DNA (RAPD) results identified three distinct groups of common reed. Common reed accessions in group I mostly inhabit coastal areas. Group II includes reeds mostly collected from inland areas. Group III consists of common reed accessions collected from inland and coastal areas, suggesting that this group might contain hybrids. In summary, we suggest that parapatric speciation might be an important factor in the genetic diversity of common reed and geographical speciation of common reed that might be also affected by environmental gradients.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.9
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• pp.995-1000
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• 2016

The maximum cross-correntropy (MCC) algorithm with a set of random symbols keeps its optimum weights undisturbed from impulsive noise unlike MSE-based algorithms and its main factor has been known to be the input magnitude controller (IMC) that adjusts the input intensity according to error power. In this paper, a normalization of the step size of the MCC algorithm by the power of IMC output is proposed. The IMC output power is tracked recursively through a single-pole low-pass filter. In the simulation under impulsive noise with two different multipath channels, the steady state MSE and convergence speed of the proposed algorithm is found to be enhanced by about 1 dB and 500 samples, respectively, compared to the conventional MCC algorithm.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.12
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• pp.1899-1904
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• 2017

This paper presents a new one-time password approach generated based on $4{\times}4$ pattern schedule. It demonstrates generation of passkey from initial seed of random codes and mapping out in table pattern schedule which will produce a new form of OTP scheme in protecting information or data. The OTP-2FA has been recognized by many organizations as a landmark to authentication techniques. OTP is the solution to the shortcomings of the traditional user name/password authentication. With the application of OTP, some have benefited already while others have had second thoughts because of some considerations like cryptographic issue. This paper presents a new method of algorithmic approach based on table schedule (grid authentication). The generation of OTP will be based on the random parameters that will be mapped out in rows and columns allowing the user to form the XY values to get the appropriate values. The algorithm will capture the values and extract the predefined characters that produce the OTP codes. This scheme can work in any information verification system to enhance the security, trust and confidence of the user.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2006.05a
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• pp.37-42
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• 2006

The voltage drop in electrical circuits causes a heat accumulation of wiring. This heat could change the specific character of an insulator and deteriorate the dielectric strength so that it could affect an electric leakage, an electric ant a power failure and a fire caused by the short circuit. Therefore optimal design for voltage drop in electrical wiring became an important factor to ensure the safety and economical efficiency of electrical facilities. So in this paper, I analyzed consequences that voltage drop affects in electrical wiring in multi-distributed system used for low electrical system such as road lights, Building, subway station which needs securities for the public society. And I proposed voltage drops computation program and optimal design to ensure the safety and economical efficiency.

• Annual Conference of KIPS
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• 2019.10a
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• pp.585-588
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• 2019

Predictions of Energy Consumption for Industries gain an important place in energy management and control system, as there are dynamic and seasonal changes in the demand and supply of energy. This paper presents and discusses the predictive models for energy consumption of the steel industry. Data used includes lagging and leading current reactive power, lagging and leading current power factor, carbon dioxide (tCO2) emission and load type. In the test set, four statistical models are trained and evaluated: (a) Linear regression (LR), (b) Support Vector Machine with radial kernel (SVM RBF), (c) Gradient Boosting Machine (GBM), (d) random forest (RF). Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) are used to measure the prediction efficiency of regression designs. When using all the predictors, the best model RF can provide RMSE value 7.33 in the test set.

• Clinical and Experimental Pediatrics
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• v.64 no.10
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• pp.538-542
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• 2021

Background: The relationship between small for gestational age (SGA) and autism spectrum disorders (ASDs) remains unknown. Purpose: This meta-analysis aimed to investigate the relationship between SGA and the risk of ASD. Methods: We searched PubMed, Web of Science, and Scopus databases from inception to November 2020. The heterogeneity across studies was explored using the I² statistic. The possibility of publication bias was assessed using Begg test. The results were reported using the odds ratio (OR) and 95% confidence interval (CI) using a random-effects model. Results: The literature search yielded 824 articles with 8,752,138 participants. We assessed the association between SGA and the risk of ASD in cohort and case-control studies. Based on the random-effects model, compared with SGA, the estimated OR of the risk of ASD was 1.17 (95% CI, 1.09-1.24). Therefore, there was a significant association between SGA and the risk of ASD. Conclusion: Based on OR reports in epidemiological studies, we showed that SGA is a risk factor for and can increase the risk of ASD. The association between SGA and ASD risk has further relevance to the current public health emphasis on appropriate prepregnancy weight and pregnancy weight gain.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.7
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• pp.79-90
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• 2021

The purpose of this research is to identify factors affecting the acceptance and use of e-tax services among medium taxpayers in Phnom Penh, Cambodia. The researcher conducted the study based on a quantitative approach by using multi-stage sampling method, which selects a sample size by two or more stages. The first stage sampling was the stratified random sampling and the subsequent stage was purposive sampling. In this study, the stratified random sampling was first used, followed by purposive sampling. The data were collected from 450 medium taxpayers who experienced using e-tax services located in three tax branches in Phnom Penh. This study adapted the confirmatory factor analysis (CFA) and structural equation model (SEM) to analyze the model accuracy, reliability and influence of various variables. The primary result showed that behavioral intention has a significant effect on user behavior of e-tax services among medium taxpayers in Phnom Penh, Cambodia. Moreover, the results revealed that performance expectancy, effort expectancy, social influence, and anxiety have significant impact on behavioral intention. In addition, social influence has the strongest impact on behavioral intention, followed by anxiety, performance expectancy and effort expectancy. Conversely, facilitating conditions, trust in government, and trust in internet do not influence behavioral intention.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.7
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• pp.225-234
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• 2021

This research aimed to investigate the key factors affecting student satisfaction and loyalty in selected private universities in Cambodia. The study implemented a quantitative survey designed and guided by seven hypotheses to test the causal relationships among variables, such as academic experience, faculty services, campus life, social integration, student support facilities, student satisfaction, university image, and student loyalty. The research applied a multi-stage sampling technique of probability procedures to guarantee the presence of the research population. Confirmatory Factor Analysis (CFA) and Structural Equation Modeling (SEM) were applied for data analysis to test the hypotheses model. The collected survey totaled 543 from three universities. The purposive sampling methods were used to select the three target universities, based on their reputation, the number of students, year of establishment, and the recognition from the Ministry of Education, Youth, and Sport. The stratified random sampling method was employed to select target respondents for data collection by dividing the population into subgroups to ensure a random sample. The results showed that student support facilities, campus life, and social integration, faculty services, and university image play very important roles in the satisfaction and loyalty of the students in three universities.

• International Journal of Computer Science & Network Security
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• v.21 no.4
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• pp.84-92
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• 2021

Care services are a significant asset in human life. Care in its overall nature focuses on human needs and covers several aspects such as health care, homes, personal care, and education. In fact, care deals with many dimensions: physical, psychological, and social interconnections. Very little information is available on estimating the cost of care services that provided to orphans and abandoned children. Prediction of the cost of the care system delivered by governmental or non-governmental organizations to support orphans and abandoned children is increasingly needed. The purpose of this study is to analyze the care cost for orphanage organizations in Saudi Arabia to forecast the cost as well as explore the most influence factor on the cost. By using business analytic process that applied statistical and machine learning techniques, we proposed a model includes simple linear regression, Naive Bayes classifier, and Random Forest algorithms. The finding of our predictive model shows that Naive Bayes has addressed the highest accuracy equals to 87% in predicting the total care cost. Our model offers predictive approach in the perspective of business analytics.

• Journal of the Korean Geotechnical Society
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• v.23 no.3
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• pp.111-121
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• 2007

The paper presents a reliability-based method that can capture the impact of uncertainty of seismic loadings. The proposed method incorporates probabilistic concepts into the classical limit equilibrium and the Newmark-type deformation techniques. The risk of damage is then computed by Monte Carlo simulation. Random process and RMS hazard method are introduced to produce seismic motions and also to use them in the seismic slope analyses. The geotechnical variability and sampling errors are also considered. The results of reliability analyses indicate that in a highly seismically active region, characterization of earthquake hazard is the more critical factor, and characterization of soil properties has a relatively small effect on the computed risk of slope failure and excessive slope deformations. The results can be applicable to both circular and non-circular slip surface failure modes.