• Journal of Korean Home Economics Education Association
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• v.33 no.1
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• pp.169-185
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• 2021

The purpose of this study is to apply the clothing teaching-learning plan from a critical science perspective developed to improve teenager's empowerment, and to examine it's effects. A total of 12 plans of 5 modules(Module A to E) developed from critical science perspective were implemented for four weeks. Second-year students (N 42) of K Middle School located in Y-si, Gyeonggi-do participated in the study in the study, and the survey results were analyzed quantitatively using t-tests. For the quality analysis, The student interview data, action reports and etc. were collected, and qualitative analysis was conducted using empowerment model as the analysis framework. The findings of study are follows. First, two hours each for modules A to D, and four hours for module E were assigned, because module E included an action project. In the action projects by for groups, students were expected to take the lead in conducting the activities such as developing promotional posters, posting opinions online, promoting videos, informing how to make recyclables, and donating to the community. Second, as a result of analyzing the pre-implementation vs post-implementation empowerment scores, a significant difference was found in social-political empowerment (t=-2.06, p<0.05). According to the analysis of student interviews and students project's reports, students were found to become aware of empowerment through the instruction. On the intrapersonal level, positive self-awareness and self-efficacy, and on the interpersonal level, smooth communication and democratic decision-making were confirmed. This study is meaningful in that regular a home economics instruction class from a critical science perspective have made a quantitative and qualitative impact on teenagers' improvement empowerment, providing opportunities to find their roles in the soceity, cooperate with others, and behave responsibly as members of society.

• International Journal of Automotive Technology
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• v.4 no.4
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• pp.181-191
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• 2003

Since the nonlinearity and uncertainties which inherently exist in vehicle system need to be considered in active suspension control law design, this paper proposes a new control strategy for active vehicle suspension systems by using a combined control scheme, i.e., respectively using a genetic algorithm (GA) based self-tuning PID controller and a fuzzy logic controller in two loops. In the control scheme, the PID controller is used to minimize vehicle body vertical acceleration, the fuzzy logic controller is to minimize pitch acceleration and meanwhile to attenuate vehicle body vertical acceleration further by tuning weighting factors. In order to improve the adaptability to the changes of plant parameters, based on the defined objectives, a genetic algorithm is introduced to tune the parameters of PID controller, the scaling factors, the gain values and the membership functions of fuzzy logic controller on-line. Taking a four degree-of-freedom nonlinear vehicle model as example, the proposed control scheme is applied and the simulations are carried out in different road disturbance input conditions. Simulation results show that the present control scheme is very effective in reducing peak values of vehicle body accelerations, especially within the most sensitive frequency range of human response, and in attenuating the excessive dynamic tire load to enhance road holding performance. The stability and adaptability are also showed even when the system is subject to severe road conditions, such as a pothole, an obstacle or a step input. Compared with conventional passive suspensions and the active vehicle suspension systems by using, e.g., linear fuzzy logic control, the combined PID and fuzzy control without parameters self-tuning, the new proposed control system with GA-based self-learning ability can improve vehicle ride comfort performance significantly and offer better system robustness.

• The Journal of Korean Association of Computer Education
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• v.10 no.1
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• pp.9-19
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• 2007

Recently, since the importance of the information communication ethics education for primary and secondary schools has being highlighted, researches and new instructional materials have been published actively. On the other hand, with the advance of e-learning, the various kinds of educational methods which consider the characteristics and the requirement of students are being developed. The educational method for Information Communication Ethics education is no exception. If the practical method for Information Communication Ethics education which considers students' requirements is developed, then a better educational effect can be acquired. In this paper, we decompose instructional design features into 4 components such as goal, model, contents, and media in order to design a better instruction for Information Communication Ethics education. And then, we analyze the relative importance of the instructional design components by using Conjoint method based on our questionnaire result. Finally, we propose an instructional design method for Information Communication Ethics as well as examine the differences among the instructional design components.

• Journal of the Korea Convergence Society
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• v.11 no.1
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• pp.131-139
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• 2020

Gamers switch their games to a new when get bored or encounter more attractive ones. Switching cost varies by gamers and depends on how they are satisfied with their current game. This study evaluates the satisfaction with current games as the miltiple basic psychological need in the self-determination theory and suggests 'needs-costs' causality research model that explain the variety of gamer's switching behavior. As the empirical test to domestic mobile gamers, the autonomy fulfillment to current game affect reversely with those of autonomy and relatedness. Those relationships between need satisfaction and perceptual switching cost vary according to their age and game genre preference. The results would be applied to understand gamers' switching behavior.

• Journal of Integrative Natural Science
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• v.9 no.1
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• pp.72-79
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• 2016

Hexane, ethyl acetate and methanol extracts of whole plant of Boerhavia diffusa were screened for phytochemical and biological activities. Qualitative phytochemical screening via colorimetric method and the quantitative estimation of phenolic and flavonoid content were performed. Antioxidant assay using DPPH scavenging method was studied. Antimicrobial screening of plant extracts was done by cup diffusion technique. Cytotoxic activity of B. diffusa was studied by brine shrimp bioassay and anthelminthic activity was evaluated in vitro in Pheretima posthuma. This study revealed B. diffusa as a source of various phyto-constituents such as alkaloids, glycosides, saponins, tannins, carbohydrates, cardiac glycosides, flavonoids and terpenoids. Quantitative estimation of total phenol was found to be maximum in BEE i.e. $29.73{\pm}0.88$, BME $19.8{\pm}2.02$ and in BHE $9.15{\pm}0.304mgGAE/g$. Similarly, the total flavonoid content was found to be $17.44{\pm}0.75$ in BEE, $14.43{\pm}0.23$ in BHE and 3.678 mg QE/g in BME. Ethyl acetate extract showed its antibacterial activity against all tested pathogens except Escherichia coli whereas Staphylococcus aureus and Salmonella Typhi were resistant to methanol and hexane extract. The zone of inhibition (ZOI) of ethyl acetate extract against S. Typhi and B. cereus was found to be 18 mm and 14 mm respectively. The MIC value of BEE in S. Typhi was $3.125{\mu}g/ml$ and in B. cereus was $12.5{\mu}g/ml$. The preliminary screening of anticancer property of B. diffusa i.e. BSLT in methanol was found to be $165.19{\mu}g/ml$. B. diffusa was also found to contain anthelmintic property. The study helped in further exploration of medicinal properties of B. diffusa by phytochemical screening and biological activities paving the path for study and investigation in this plant.

• Journal of the Korea Convergence Society
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• v.12 no.7
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• pp.133-142
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• 2021

This study aims to expand the effect of online education into the aspect of active exploration and sharing of class-related issues by learners. Based on theoretical discussions, Two types of motivation (personal and social) to explore issues, engagement, attitude toward issue content, and eWOM model were verified. As a result of the study, it was found that the impact of personal and social motivations that online education has on engagement on specific issues, and the positive(+) influence on attitudes toward issue content and word of mouth intentions on SNS, considering engagement as a parameter. In this study, the role of engagement in inducing the next learning by oneself was confirmed, and it can be seen that social and personal motives for issues and class content should be utilized to increase engagement.

• Geomechanics and Engineering
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• v.37 no.3
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• pp.223-241
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• 2024

Evaluating the performance of Tunnel Boring Machines (TBMs) stands as a pivotal juncture in the domain of hard rock mechanized tunneling, essential for achieving both a dependable construction timeline and utilization rate. In this investigation, three advanced artificial neural networks namely, gated recurrent unit (GRU), back propagation neural network (BPNN), and simple recurrent neural network (SRNN) were crafted to prognosticate TBM-rate of penetration (ROP). Drawing from a dataset comprising 1125 data points amassed during the construction of the Alborze Service Tunnel, the study commenced. Initially, five geomechanical parameters were scrutinized for their impact on TBM-ROP efficiency. Subsequent statistical analyses narrowed down the effective parameters to three, including uniaxial compressive strength (UCS), peak slope index (PSI), and Brazilian tensile strength (BTS). Among the methodologies employed, GRU emerged as the most robust model, demonstrating exceptional predictive prowess for TBM-ROP with staggering accuracy metrics on the testing subset (R² = 0.87, NRMSE = 6.76E-04, MAD = 2.85E-05). The proposed models present viable solutions for analogous ground and TBM tunneling scenarios, particularly beneficial in routes predominantly composed of volcanic and sedimentary rock formations. Leveraging forecasted parameters holds the promise of enhancing both machine efficiency and construction safety within TBM tunneling endeavors.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.125-140
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• 2013

We call a data set in which the number of records belonging to a certain class far outnumbers the number of records belonging to the other class, 'imbalanced data set'. Most of the classification techniques perform poorly on imbalanced data sets. When we evaluate the performance of a certain classification technique, we need to measure not only 'accuracy' but also 'sensitivity' and 'specificity'. In a customer churn prediction problem, 'retention' records account for the majority class, and 'churn' records account for the minority class. Sensitivity measures the proportion of actual retentions which are correctly identified as such. Specificity measures the proportion of churns which are correctly identified as such. The poor performance of the classification techniques on imbalanced data sets is due to the low value of specificity. Many previous researches on imbalanced data sets employed 'oversampling' technique where members of the minority class are sampled more than those of the majority class in order to make a relatively balanced data set. When a classification model is constructed using this oversampled balanced data set, specificity can be improved but sensitivity will be decreased. In this research, we developed a hybrid model of support vector machine (SVM), artificial neural network (ANN) and decision tree, that improves specificity while maintaining sensitivity. We named this hybrid model 'hybrid SVM model.' The process of construction and prediction of our hybrid SVM model is as follows. By oversampling from the original imbalanced data set, a balanced data set is prepared. SVM_I model and ANN_I model are constructed using the imbalanced data set, and SVM_B model is constructed using the balanced data set. SVM_I model is superior in sensitivity and SVM_B model is superior in specificity. For a record on which both SVM_I model and SVM_B model make the same prediction, that prediction becomes the final solution. If they make different prediction, the final solution is determined by the discrimination rules obtained by ANN and decision tree. For a record on which SVM_I model and SVM_B model make different predictions, a decision tree model is constructed using ANN_I output value as input and actual retention or churn as target. We obtained the following two discrimination rules: 'IF ANN_I output value <0.285, THEN Final Solution = Retention' and 'IF ANN_I output value ${\geq}0.285$, THEN Final Solution = Churn.' The threshold 0.285 is the value optimized for the data used in this research. The result we present in this research is the structure or framework of our hybrid SVM model, not a specific threshold value such as 0.285. Therefore, the threshold value in the above discrimination rules can be changed to any value depending on the data. In order to evaluate the performance of our hybrid SVM model, we used the 'churn data set' in UCI Machine Learning Repository, that consists of 85% retention customers and 15% churn customers. Accuracy of the hybrid SVM model is 91.08% that is better than that of SVM_I model or SVM_B model. The points worth noticing here are its sensitivity, 95.02%, and specificity, 69.24%. The sensitivity of SVM_I model is 94.65%, and the specificity of SVM_B model is 67.00%. Therefore the hybrid SVM model developed in this research improves the specificity of SVM_B model while maintaining the sensitivity of SVM_I model.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.57-78
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• 2020

With the development of the Internet, consumers have had an opportunity to check product information easily through E-Commerce. Product reviews used in the process of purchasing goods are based on user experience, allowing consumers to engage as producers of information as well as refer to information. This can be a way to increase the efficiency of purchasing decisions from the perspective of consumers, and from the seller's point of view, it can help develop products and strengthen their competitiveness. However, it takes a lot of time and effort to understand the overall assessment and assessment dimensions of the products that I think are important in reading the vast amount of product reviews offered by E-Commerce for the products consumers want to compare. This is because product reviews are unstructured information and it is difficult to read sentiment of reviews and assessment dimension immediately. For example, consumers who want to purchase a laptop would like to check the assessment of comparative products at each dimension, such as performance, weight, delivery, speed, and design. Therefore, in this paper, we would like to propose a method to automatically generate multi-dimensional product assessment scores in product reviews that we would like to compare. The methods presented in this study consist largely of two phases. One is the pre-preparation phase and the second is the individual product scoring phase. In the pre-preparation phase, a dimensioned classification model and a sentiment analysis model are created based on a review of the large category product group review. By combining word embedding and association analysis, the dimensioned classification model complements the limitation that word embedding methods for finding relevance between dimensions and words in existing studies see only the distance of words in sentences. Sentiment analysis models generate CNN models by organizing learning data tagged with positives and negatives on a phrase unit for accurate polarity detection. Through this, the individual product scoring phase applies the models pre-prepared for the phrase unit review. Multi-dimensional assessment scores can be obtained by aggregating them by assessment dimension according to the proportion of reviews organized like this, which are grouped among those that are judged to describe a specific dimension for each phrase. In the experiment of this paper, approximately 260,000 reviews of the large category product group are collected to form a dimensioned classification model and a sentiment analysis model. In addition, reviews of the laptops of S and L companies selling at E-Commerce are collected and used as experimental data, respectively. The dimensioned classification model classified individual product reviews broken down into phrases into six assessment dimensions and combined the existing word embedding method with an association analysis indicating frequency between words and dimensions. As a result of combining word embedding and association analysis, the accuracy of the model increased by 13.7%. The sentiment analysis models could be seen to closely analyze the assessment when they were taught in a phrase unit rather than in sentences. As a result, it was confirmed that the accuracy was 29.4% higher than the sentence-based model. Through this study, both sellers and consumers can expect efficient decision making in purchasing and product development, given that they can make multi-dimensional comparisons of products. In addition, text reviews, which are unstructured data, were transformed into objective values such as frequency and morpheme, and they were analysed together using word embedding and association analysis to improve the objectivity aspects of more precise multi-dimensional analysis and research. This will be an attractive analysis model in terms of not only enabling more effective service deployment during the evolving E-Commerce market and fierce competition, but also satisfying both customers.

• The Journal of Society for e-Business Studies
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• v.22 no.4
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• pp.71-101
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• 2017

Since late 2016, protectionism has been a major trend in world trade with the Great Britain exiting the European Union and the United States electing Donald Trump as the 45th president. Consequently, there has been a huge public outcry regarding the negative prospects of heavy industry firms in Korea, which are highly dependent upon international trade with Western countries including the United States. In light of such trend and concerns, we have tried to predict business performance of heavy industry firms in Korea with data regarding trade policy of the United States. United States International Trade Commission (USITC) levies countervailing duties and anti-dumping duties to firms that violate its fair-trade regulations. In this study, we have performed data analysis with past records of countervailing duties and anti-dumping duties. With results from clustering analysis, it could be concluded that trade policy trends of the Unites States significantly affects the business performance of heavy industry firms in Korea. Furthermore, we have attempted to quantify such effects by employing long short-term memory (LSTM), a popular neural networks model that is well-suited to deal with sequential data. Our major contribution is that we have succeeded in empirically validating the intuitive argument and also predicting the future trend with rigorous data mining techniques. With some improvements, our results are expected to be highly relevant to designing regulations regarding heavy industry in Korea.