• Korean Chemical Engineering Research
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• v.43 no.2
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• pp.313-317
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• 2005

Synthetic gene carriers such as poly-cationic polymers easily form complexes with plasmid DNA which contains negative charge. Chitosan is a polysaccharide that demonstrates much potential as a gene delivery system. The ability of depolymerized chitosan to condense DNA was determined using electrophoresis. Dynamic laser scattering and scanning electron microscopy were used to examine the size and the morphology of the chitosan/DNA complex. Parameters such as chitosan molecular weight and charge density influenced the complex size and the DNA amount condensed with chitosan. The cell viabilities in the presence of chitosan ranged between 84-108% of the control in all experiments. Gene expression efficacy using chitosan/DNA complex was enhanced in 293 cells relative to that using naked DNA, although it was lower than that using lipofecamine. Transfection efficacy using low molecular weight chitosan (Mw=8,517) was higher than those of the control and the other chitosan (MW=4,078). The low molecular weight chitosan (MW=8,517) with a high charge density (18.32 mV) fulfilled the requirements for a suitable model gene delivery system with respect to the condensing ability of DNA, complex formation, and transfection efficacy.

• Journal of rehabilitation welfare engineering & assistive technology
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• v.7 no.1
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• pp.59-66
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• 2013

This study rehabilitation assistive technology system for people with disabilities employed by a company in the field of occupations and job satisfaction have adapted to, and for the rehabilitation assistive technology support(rehabilitation assistive technology hardware and the software) and service quality based on the quality of convenient for employees to work life by analyzing the factors that can act on adaptation employees on behavioral intentions was to determine the overall impact. Seoul, Gyeonggi, Incheon companies based in vocational education and training received in the employment of the disabled subject questionnaires were distributed, and finally 594 valid questionnaires were minor. In order to test the hypothesis SEM(structural equation model) were used, the results of this study can be summarized as follows. First, rehabilitation assistive technology hardware quality of the quality of rehabilitation assistive technology software affected. Second, rehabilitation assistive technology software quality on the quality of the service quality affected. Third, rehabilitation assistive technology hardware quality on the quality of the service quality affected. Fourth, quality of service, the quality of the adaptation of action for employees affected also. Fifth, rehabilitation assistive technology software for adaptive quality of the employees also had an impact on behavior. Sixth, rehabilitation assistive technology hardware to adapt the quality of the employees affected. And parameters (quality of service quality) influenced to as indirect effects. The results of this study support the rehabilitation assistive technology and rehabilitation assistive technology hardware and software) based, quality of service and quality of fused form acceptable to, the degree of action for employees to adapt more implications that may affect have provided.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.19 no.2
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• pp.89-103
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• 2020

As we face an aging society, the demand for personal mobility for disabled and aged people is increasing. In fact, as of 2017, the number of electric wheelchair in the country continues to increase to 90,000. However, people with disabilities and seniors are more likely to have accidents while driving, because their judgment and coordination are inferior to normal people. One of the causes of the accident is the interference of personal vehicle steering control due to unbalanced road surface conditions. In this paper, we introduce a encoder type semantic segmentation algorithm that can recognize road conditions at high speed to prevent such accidents. To this end, more than 1,500 training data and 150 test data including road surface damage were newly secured. With the data, we proposed a deep neural network composed of encoder stages, unlike the Auto-encoding type consisting of encoder and decoder stages. Compared to the conventional method, this deep neural network has a 4.45% increase in mean accuracy, a 59.2% decrease in parameters, and an 11.9% increase in computation speed. It is expected that safe personal transportation will be come soon by utilizing such high speed algorithm.

• Korean Journal of Applied Biomechanics
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• v.20 no.4
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• pp.395-406
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• 2010

The purpose of this study was to analyze the changes in kinematic and kinetic parameters of lower extremity joint according to rehabilitation period. Fourteen collegiate male athletes(age: $22.1{\pm}1.35$ years, height: $182.46{\pm}9.45cm$, weight: $88.63{\pm}9.25kg$) and fourteen collegiate athletes on functional ankle instability(age: $21.5{\pm}1.35$ years, height: $184.45{\pm}9.42cm$, weight: $92.85{\pm}10.85kg$) with the right leg as dominant were chosen. The subjects performed drop landing. The date were collected by using VICON with 8 camera to analyze kinematic variables and force platform to analyze kinetic variables. There are two approaches of this study, one is to compare between groups, the other is to find changes of lower extremity joint after rehabilitation. In comparison to the control group, FAI group showed more increased PF & Inversion at IC and decreased full ROM when drop landing. Regarding the peak force and loading rate, it resulted in higher PVGRF and loading. FAI group used more increased knee and hip ROM because of decreased ankle ROM to absorb the shock. And it used sagittal movement to stabilize. In terms of rehabilitation period, FAI group showed that landing patterns were changed and it increased total ankle excursion and used all lower extremity joint close to normal ankle. Regarding the peak force and loading rate, FAI group decreased PVGRF and loading rate. and also showed shock absorption using increased ankle movement. And COP variable showed that proprioception training increased stability during 8 weeks. The results of this study suggest that 8 weeks rehabilitation period is worthwhile to be considered as a way to improve neuromuscular control and to prevent sports injuries.

• Journal of Nutrition and Health
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• v.38 no.8
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• pp.626-636
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• 2005

This study was undertaken to examine effects of dietary intake of garcinia cambogia extract, soy peptide and L-carnitine mixture on body weight gain and obesity-related bio-markers in rats fed high-fat diet for 9 weeks with or without regular treadmill exercise. Forty 5-week-old male Sprague-Dawley rats were randomly divided into four groups; sedentary control group (SC), exercised control group (EC), sedentary formula-fed group (SF), and exercised formula-fed group (EF). The SC and EC rats were fed high-fat control diet (fat comprises$40\%$ of total caloris), and SF and EF rats were fed high-fat formula (composed of garcinia cambogia, soy peptide and L-carnitine) supplemented diet. Statistical analyses by two-way ANOVA indicated that the regular treadmill exercise significantly lowered cumulative body weight gain, total visceral fat mass, and epididymal, perirenal and retroperitoneal fat pad weights, and serum concentrations of total cholesterol and LDL + VLDL cholesterol, insulin, c-peptide and leptin. Feeding the formula also resulted in significant reductions in cumulative body weight gain and visceral fat pad weights, along with other related parameters including serum total and LDL + VLDL cholesterol levels, and hepatic enzyme activities involved in fatty acid synthesis. Statistical analyses by one-way ANOVA revealed that the formula consumption significantly improved body weight gain ($18\%$ reduction), total visceral fat weight ($20\%$ reductions), and serum total ($43\%$ reduction) and LDL + VLDL cholesterol ($54\%$ reduction) levels, as well as serum levels of insulin ($49\%$ reduction), and c-peptide ($41\%$ reduction) in sedentary rats, but failed to exhibit significant reductions in these indices in animals under treadmill exercise program. Taken together, these results suggest that the treadmill exercise per n exhibited significant improvements in body fat reduction and other related bio-markers, and so the formula consumption did not achieve a further significant reductions in these bio-markers in exercised rats. Nevertheless, animals fed the formula with regular exercise showed the most efficient weight reduction compared to other groups either fed formula without exercise or received regular exercise without dietary supplementation.

• Journal of KIISE:Software and Applications
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• v.26 no.11
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• pp.1372-1380
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• 1999

본 논문에서는 자연언어 문장의 객관적 정량적인 질 측정 방법의 구축에 대해 설명하고, 이를 문장 퇴고 시스템의 사례에 적용해 본다. 문장의 질을 평가한다는 것은 본질적으로 주관적이고 정량화가 어려운 작업이기 때문에, 이 과정에서 질의 객관적 계량화가 가능한지 여부가 가장 중요한 문제가 된다. 이 논문에서는 이러한 문제를 해결하기 위해 유전자 알고리즘을 이용한 진화적 접근 방법을 통해 객관적이고 정량적인 질의 측정 공식을 유도하는 방법론을 제시하였다. 이 논문에서 제시한 방법론의 핵심은 간단히 말해서 사람이 행하는 정성적인 판단을, 이에 가장 근접하는 정량적 측정 체계로 전환시키는 것이라고 보면 된다. 이것을 위해 정량화 문제를 문장의 단순 언어 특징들의 변화값을 이용한 최적화 문제로 환원시키고, 다시 이 최적화 문제를 유전자 알고리즘을 이용해 해결함으로써 문제를 효과적으로 해결할 수 있었다. 실험 결과를 보면, 본 논문에서 제시한 최적화 방법은 주어진 훈련용 예제와 검증용 예제 중 각각 99.84%, 99.88%를 만족시키는 해를 찾아내었으므로 정량적 질 평가 공식의 유도에 매우 효과적임을 알 수 있었다. 또한 도출된 측정 공식을 이용해서 실제 퇴고 시스템 평가에 적용한 결과 문장 질의 측정에 매우 유용하게 이용될 수 있음을 알 수 있었다. 이와 같이 질의 정량적 평가가 가능하다는 사실이 갖는 또 한가지 중요한 의미는 최종 사용자의 구매 의사나 개발자의 공학적 의사 결정을 위한 객관적 성능 평가 자료의 제공에 이 방법이 유용하게 사용될 수 있다는 점이다.Abstract This paper describes a method of building a quantitative measure of the quality of natural language sentences, particularly produced by document revision systems. Evaluating the quality of natural language sentences is intrinsically subjective, so what is most important as to the evaluation is whether the quality can be measured objectively. To solve such problem of objective measurability, genetic algorithm, an evolutionary learning method, is employed in this paper. The underlying standpoint of this approach is that building the quality measures is a task of constructing a formulae that produces as close results as can to the qualitative decisions made by humans. For doing this, the problem of measurability has been simply reduced to an optimization problem using the change of the values of simple linguistic parameters found in sentences, and the reduced problem has been solved effectively by the genetic algorithm. Experimental result shows that the optimization task satisfied 99.84% and 99.88% of the given objectives for training and validation samples, respectively, which means the method is quite effective in constructing the quantitative measure of the quality of natural language sentences. The actual evaluation result of a revision system shows that the measure is useful to quantize the quality of sentences. Another important contribution of this measure would be to provide an objective performance evaluation data of natural language systems on a basis of which end-users and developers can make their decision to fit their own needs.

• Journal of Korea Society of Industrial Information Systems
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• v.23 no.6
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• pp.67-77
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• 2018

Currently, there are many problems due to the decline in school-age population. Moreover, Korea has the largest number of universities compared to the population, and the university enrollment rate is also the highest in the world. As a result, the minimum student retention rate required for the survival of each university is becoming increasingly important. The purpose of this study was to examine the effects of reducing the number of graduates of education and the social climate that prioritizes employment. And to determine what the basic direction is for students to manage the student retention rate, which can be maintained from admission to graduation, to determine the optimal input variables, Based on the input parameters, we will make associative analysis using apriori algorithm to collect training data that is most suitable for maintenance rate management and make base data for development of the most efficient Deep Learning module based on it. The accuracy of Deep Learning was 75%, which is a measure of graduation using decision trees. In decision tree, factors that determine whether to graduate are graduated from general high school and students who are female and high in residence in urban area have high probability of graduation. As a result, the Deep Learning module developed rather than the decision tree was identified as a model for evaluating the graduation of students more efficiently.

• Journal of the Korean Institute of Gas
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• v.22 no.6
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• pp.90-103
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• 2018

This paper presents the development of two ANN models which can predict an optimum production rate by controlling choke size in oil well, and gas injection rate in gas-lift well. The input data was solution gas-oil ratio, water cut, reservoir pressure, and choke size or gas injection rate. The output data was wellhead pressure and production rate. Firstly, a range of each parameters was decided by conducting sensitive analysis of input data for onshore oil well. In addition, 1,715 sets training data for choke size decision model and 1,225 sets for gas injection rate decision model were generated by nodal analysis. From the results of comparing between the nodal analysis and the ANN on the same reservoir system showed that the correlation factors were very high(>0.99). Mean absolute error of wellhead pressure and oil production rate was 0.55%, 1.05% with the choke size model, respectively. And the gas injection rate model showed the errors of 1.23%, 2.67%. It was found that the developed models had been highly accurate.

• Journal of Internet Computing and Services
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• v.23 no.4
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• pp.45-55
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• 2022

The size of the cryptocurrency market is growing. For example, market capitalization of bitcoin exceeded 500 trillion won. Accordingly, many studies have been conducted to predict the price of cryptocurrency, and most of them have similar methodology of predicting stock prices. However, unlike stock price predictions, machine learning become best model in cryptocurrency price predictions, conceptually cryptocurrency has no passive income from ownership, and statistically, cryptocurrency has at least three times higher liquidity than stocks. Thats why we argue that a methodology different from stock price prediction should be applied to cryptocurrency price prediction studies. We propose Reverse Walk-forward Validation (RWFV), which modifies Walk-forward Validation (WFV). Unlike WFV, RWFV measures accuracy for Validation by pinning the Validation dataset directly in front of the Test dataset in time series, and gradually increasing the size of the Training dataset in front of it in time series. Train data were cut according to the size of the Train dataset with the highest accuracy among all measured Validation accuracy, and then combined with Validation data to measure the accuracy of the Test data. Logistic regression analysis and Support Vector Machine (SVM) were used as the analysis model, and various algorithms and parameters such as L1, L2, rbf, and poly were applied for the reliability of our proposed RWFV. As a result, it was confirmed that all analysis models showed improved accuracy compared to existing studies, and on average, the accuracy increased by 1.23%p. This is a significant improvement in accuracy, given that most of the accuracy of cryptocurrency price prediction remains between 50% and 60% through previous studies.

• The Korean Journal of Applied Statistics
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• v.35 no.4
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• pp.543-552
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• 2022

When frost occurs, crops are directly damaged. When crops come into contact with low temperatures, tissues freeze, which hardens and destroys the cell membranes or chloroplasts, or dry cells to death. In July 2020, a sudden sub-zero weather and frost hit the Minas Gerais state of Brazil, the world's largest coffee producer, damaging about 30% of local coffee trees. As a result, coffee prices have risen significantly due to the damage, and farmers with severe damage can produce coffee only after three years for crops to recover, which is expected to cause long-term damage. In this paper, we tried to predict frost using frost generation data and weather observation data provided by the Korea Meteorological Administration to prevent severe frost. A model was constructed by reflecting weather factors such as wind speed, temperature, humidity, precipitation, and cloudiness. Using XGB(eXtreme Gradient Boosting), SVM(Support Vector Machine), Random Forest, and MLP(Multi Layer perceptron) models, various hyper parameters were applied as training data to select the best model for each model. Finally, the results were evaluated as accuracy(acc) and CSI(Critical Success Index) in test data. XGB was the best model compared to other models with 90.4% ac and 64.4% CSI, followed by SVM with 89.7% ac and 61.2% CSI. Random Forest and MLP showed similar performance with about 89% ac and about 60% CSI.