• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.1
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• pp.184-192
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• 2011

In this study, we introduce a identification methodology for FCM-based fuzzy model. The two underlying design mechanisms of such networks involve Fuzzy C-Means (FCM) clustering method and Particle Swarm Optimization(PSO). The proposed algorithm is based on FCM clustering method for efficient processing of data and the optimization of model was carried out using PSO. The premise part of fuzzy rules does not construct as any fixed membership functions such as triangular, gaussian, ellipsoidal because we build up the premise part of fuzzy rules using FCM. As a result, the proposed model can lead to the compact architecture of network. In this study, as the consequence part of fuzzy rules, we are able to use four types of polynomials such as simplified, linear, quadratic, modified quadratic. In addition, a Weighted Least Square Estimation to estimate the coefficients of polynomials, which are the consequent parts of fuzzy model, can decouple each fuzzy rule from the other fuzzy rules. Therefore, a local learning capability and an interpretability of the proposed fuzzy model are improved. Also, the parameters of the proposed fuzzy model such as a fuzzification coefficient of FCM clustering, the number of clusters of FCM clustering, and the polynomial type of the consequent part of fuzzy rules are adjusted using PSO. The proposed model is illustrated with the use of Automobile Miles per Gallon(MPG) and Boston housing called Machine Learning dataset. A comparative analysis reveals that the proposed FCM-based fuzzy model exhibits higher accuracy and superb predictive capability in comparison to some previous models available in the literature.

• International Journal of Highway Engineering
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• v.20 no.2
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• pp.57-65
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• 2018

PURPOSES : The study aims to predict the service life of national highway asphalt pavements through deep learning methods by using maintenance history data of the National Highway Pavement Management System. METHODS : For the configuration of a deep learning network, this study used Tensorflow 1.5, an open source program which has excellent usability among deep learning frameworks. For the analysis, nine variables of cumulative annual average daily traffic, cumulative equivalent single axle loads, maintenance layer, surface, base, subbase, anti-frost layer, structural number of pavement, and region were selected as input data, while service life was chosen to construct the input layer and output layers as output data. Additionally, for scenario analysis, in this study, a model was formed with four different numbers of 1, 2, 4, and 8 hidden layers and a simulation analysis was performed according to the applicability of the over fitting resolution algorithm. RESULTS : The results of the analysis have shown that regardless of the number of hidden layers, when an over fitting resolution algorithm, such as dropout, is applied, the prediction capability is improved as the coefficient of determination ($R^2$) of the test data increases. Furthermore, the result of the sensitivity analysis of the applicability of region variables demonstrates that estimating service life requires sufficient consideration of regional characteristics as $R^2$ had a maximum of between 0.73 and 0.84, when regional variables where taken into consideration. CONCLUSIONS : As a result, this study proposes that it is possible to precisely predict the service life of national highway pavement sections with the consideration of traffic, pavement thickness, and regional factors and concludes that the use of the prediction of service life is fundamental data in decision making within pavement management systems.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.5
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• pp.406-412
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• 2001

In this paper, a new fuzzy single layer learning algorithm is proposed, which shows improved learning time and convergence property than that of the conventional fuzzy single layer perceptron algorithms. First, we investigate the structure of physiological neurons of the nervous system and propose new neuron structures based on fuzzy logic. And by using the proposed fuzzy neuron structures, the model and learning algorithm of Physiological Fuzzy Single Layer Perceptron(P-FSLP) are proposed. For the evaluation of performance of the P-FSLP algorithm, we applied the conventional fuzzy single layer perceptron algorithms and the P-FSLP algorithm to three experiments including Exclusive OR problem, the 3-bit parity bit problem and the recognition of car licence plates, which is an application of image recognition, and evaluated the performance of the algorithms. The experimentation results showed that the proposed P-FSLP algorithm reduces the possibility of local minima more than the conventional fuzzy single layer perceptrons do, and enhances the time and convergence for learning. Furthermore, we found that the P-FSLP algorithm has the great capability for image recognition applications.

• Journal of The Korean Association For Science Education
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• v.13 no.2
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• pp.296-307
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• 1993

The purpose of this study was to identify concretely how to improve inquiry learning. To put the purpose in detail : 1) to define the inquiry skills 2) to select the factors of inquiry skills appropriate to the content of Science I (earth science) textbook 3) to develop items which consist of experimental inquiry and concept inquiry in due proportion, to evalute inquiry skills 4) to analyze the relationship between high school students' cognitive levels and the achievement of science inquiry skills. To achieve these objectives, the investigator sampled 558 students in eleventh grade, living in Seoul, Chung-Ju and Kwang-Ju, and evaluated their cognitive levels and the achievement of science inquiry skills. The results of this study showed that the cognitive levels of students were lower than those identified in Piaget's work and that the achievement of science inquiry skills were low also. It may be thought that one of most important reasons to bring about those results is lacking in adaptation capability of science inquiry items and inquiry learning. So, it can be recommended as a way to heighten cognitive levels to make inquiry learning using the textbook content. In conclusion, the investigator make suggestions as follows : 1) to give inquiry learning which consist of experimental inquiry and concept inquiry in due proportion 2) to develop inquiry items to include content for evaluating inquiry learning, and test items for psycho-motor areas 3) to publish textbooks which motivate students' inquiry activities and develop their creative thinking, considering students' cognitive levels and inquiry skills.

• Korean Journal of Social Welfare
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• v.45
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• pp.341-373
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• 2001

This study seeks to identify the reform of vocational education plans so as to bring about a seamless transition from school to work. It puts forward a number of suggestions based upon an analysis of vocational education policies in Korean and U.S. schools, concerning the government's role, partnerships between education and industry, the educational system, curriculum and work-based learning. First, not only government initiatives but also close partnerships between education and industry are essential to help vocational education in school the transition to employment. Education and industry should work closely together to standardize certificate related skills and to have these skills reflected in the curriculum. Also the government should strive to provide guidelines for work-based learning and formulate standards for supervision and evaluation. Second, to facilitate the school to work transition, comprehensive schools should be promoted so that students have access to a greater ranger of vocational education. At the same time, an assessment system that certifies a mastering of the basic skills of those who undergo the education should be introduced, and it should be related to earn these certificates. Third, standardized vocational skills should be included in the curriculum so that students can acquire skills that are useful for industry. All the students in vocational and general high schools should have access both to general education, the foundation for lifelong learning and for employ ability, and to basic occupational skills which empower students in dealing with rapid changes of technology. Also a range of specialized vocational curricula should be offered so that students can opt for more specialized occupations; and they can select careers appropriate to their capability. Fourth, so that all students to have the opportunity to take part in work-based education, which is closely related to employment, various work-based learning programs should be offered to meet the needs of students and their educational conditions. Companies should for their part train students thoroughly in accordance with the standards of work-based education. In addition, supervisors should be stationed both in schools and companies in order to administer the students' work-based learning.

• Management & Information Systems Review
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• v.32 no.3
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• pp.43-59
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• 2013

The most important thing in training of organization is that how effectively it can be made most of the performance among the staff. It will be useless if the knowledge which gaining after training can not be applied. Therefore the transfer of learning is studied since it is important for decision of training. We studied the factors of transfer of learning and carried out a survey targeting the public officials of Gangwon province with the factors we made a study. We define the factor of both promoted and interrupted in training and suggest the way of improving it. The first, the modeling of competency can stimulate the desire of achievement and complete a course of training among staff of organizations. The second, the construction of training program and organizational culture just for Gangwon province can increase the satisfaction of training among the learners. The third, the establishment of management system after training can reinforce the capability making use of train. The sharing of each information with boss at the office can help to stimulate the function of feedback after training as well.

• The Journal of Information Systems
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• v.30 no.3
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• pp.93-112
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• 2021

Purpose Recently, many organizations are actively adopting enterprise architecture (EA) as a methodology to manage IT assets and build IT-based business system. This study intended to empirically examine how the role of EA operating unit and utilization capability of organizational members impact on system performance at the post-adoption stage. A balanced score card (BSC) is being used as a framework for a company's key performance indicator (KPI). Design/methodology/approach This study tried to investigate the causal relationship between the four perspectives of the balanced scorecard as an influencing factor of the introduction of the Enterprise Resource Planning (ERP) on the financial value. In particular, the mediating effect between the customer's point of view and the business process point of view was investigated between the learning growth point of view and the financial point of view, and the interaction effect (regulating effect) of strategic linkage in the system introduction process was investigated. Findings The results of the study were first, that the organizational learning and growth perspective had a positive effect on the customer perspective, business process, and financial perspective. In addition, the customer perspective and the process perspective also had a positive influence on the financial perspective. Second, between the learning growth and financial perspectives, the customer perspective and the process perspective showed a partial mediating effect. Third, as for strategic linkage, the interaction effect between the customer perspective, the learning growth perspective, and the process perspective and the financial perspective was not significant. The results of this study are expected to provide a framework for performance evaluation to organizations that have introduced ERP systems.

• International Area Studies Review
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• v.14 no.3
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• pp.388-419
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• 2010

The aim of the study is to create a theoretical model and hypotheses on competitive advantages of exporting SMEs. For this we have proposed an integrated model in which learning competences and dynamic capabilities should have an influence on competitive advantages of the SMEs. This study have examined the influence of integrating and reconfigurating capability respectively. As a result, the learning competences had positive influences in dynamic capabilities and to the cost and service competitive advantage. To integrating capabilities had positive influences in competitive advantage. Besides, dynamic capabilities playing significant intermediate role only for the cost advantage through the analysis of intermediate effects of learning competence to the dynamic capabilities.

• Smart Structures and Systems
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• v.31 no.5
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• pp.485-500
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• 2023

Machine learning-based structural health monitoring (ML-based SHM) methods are researched extensively in the recent decade due to the availability of advanced information and sensing technology. ML methods are well-known for their pattern recognition capability for complex problems. However, the main obstacle of ML-based SHM is that it often requires pre-collected historical data for model training. In most actual scenarios, damage presence can be detected using the unsupervised learning method through anomaly detection, but to further identify the damage types would require prior knowledge or historical events as references. This creates the cold-start problem, especially for new and unobserved structures. Modal-based methods identify damages based on the changes in the structural global properties but often require dense measurements for accurate results. Therefore, a two-stage hybrid modal-machine learning damage detection scheme is proposed. The first stage detects damage presence using Principal Component Analysis-Frequency Response Function (PCA-FRF) in an unsupervised manner, whereas the second stage further identifies the damage. To solve the cold-start problem, mode shape assessment using the first mode is initiated when no trained model is available yet in the second stage. The damage identified by the modal-based method would be stored for future training. This work highlights the performance of the scheme in alleviating the cold-start issue as it transitions through different phases, starting from zero damage sample available. Results showed that single and multiple damages can be identified at an acceptable accuracy level even when training samples are limited.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.6
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• pp.1530-1544
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• 2023

Data monitoring is an important foundation of modern science. In most cases, the monitoring data is time-series data, which has high application value. The deep learning algorithm has a strong nonlinear fitting capability, which enables the recognition of time series by capturing anomalous information in time series. At present, the research of time series recognition based on deep learning is especially important for data monitoring. Deep learning algorithms require a large amount of data for training. However, abnormal sample is a small sample in time series, which means the number of abnormal time series can seriously affect the accuracy of recognition algorithm because of class imbalance. In order to increase the number of abnormal sample, a data augmentation method called GANBATS (GAN-based Bi-LSTM and Attention for Time Series) is proposed. In GANBATS, Bi-LSTM is introduced to extract the timing features and then transfer features to the generator network of GANBATS.GANBATS also modifies the discriminator network by adding an attention mechanism to achieve global attention for time series. At the end of discriminator, GANBATS is adding averagepooling layer, which merges temporal features to boost the operational efficiency. In this paper, four time series datasets and five data augmentation algorithms are used for comparison experiments. The generated data are measured by PRD(Percent Root Mean Square Difference) and DTW(Dynamic Time Warping). The experimental results show that GANBATS reduces up to 26.22 in PRD metric and 9.45 in DTW metric. In addition, this paper uses different algorithms to reconstruct the datasets and compare them by classification accuracy. The classification accuracy is improved by 6.44%-12.96% on four time series datasets.