• School Mathematics
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• v.7 no.3
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• pp.269-286
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• 2005

The purpose of this paper is to study the spatial sense that is introduced for the first time in our 7th mathematics curriculum. For this purpose, we first investigated the factors of the spatial sense ability and with this factors, we analyze the errors those was founded in elementary school students' carrying out tasks related to the spatial sense, and the contents of elementary mathematics textbook. From the analysis, we found that the teaching topics in the spatial sense is disagreed with the students' learning level and for each similar topics is cut off into not adjacent grades, connecting these topics to each other and to the other traditional geo-metric topics is not easy. we must consider this findings in the future revision of mathematics curriculum.

• Computers and Concrete
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• v.33 no.1
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• pp.55-75
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• 2024

The present study introduces an optimum performance soft computing model for predicting the compressive strength of high-performance concrete (HPC) by comparing models based on conventional (kernel-based, covariance function-based, and tree-based), advanced machine (least square support vector machine-LSSVM and minimax probability machine regressor-MPMR), and deep (artificial neural network-ANN) learning approaches using a common database for the first time. A compressive strength database, having results of 1030 concrete samples, has been compiled from the literature and preprocessed. For the purpose of training, testing, and validation of soft computing models, 803, 101, and 101 data points have been selected arbitrarily from preprocessed data points, i.e., 1005. Thirteen performance metrics, including three new metrics, i.e., a20-index, index of agreement, and index of scatter, have been implemented for each model. The performance comparison reveals that the SVM (kernel-based), ET (tree-based), MPMR (advanced), and ANN (deep) models have achieved higher performance in predicting the compressive strength of HPC. From the overall analysis of performance, accuracy, Taylor plot, accuracy metric, regression error characteristics curve, Anderson-Darling, Wilcoxon, Uncertainty, and reliability, it has been observed that model CS4 based on the ensemble tree has been recognized as an optimum performance model with higher performance, i.e., a correlation coefficient of 0.9352, root mean square error of 5.76 MPa, and mean absolute error of 4.1069 MPa. The present study also reveals that multicollinearity affects the prediction accuracy of Gaussian process regression, decision tree, multilinear regression, and adaptive boosting regressor models, novel research in compressive strength prediction of HPC. The cosine sensitivity analysis reveals that the prediction of compressive strength of HPC is highly affected by cement content, fine aggregate, coarse aggregate, and water content.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.2
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• pp.907-923
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• 2020

For the severe haze situation in the Beijing-Tianjin-Hebei region, conventional fine particulate matter (PM2.5) concentration prediction methods based on pollutant data face problems such as incomplete data, which may lead to poor prediction performance. Therefore, this paper proposes a method of predicting the PM2.5 concentration based on image analysis technology that combines image data, which can reflect the original weather conditions, with currently popular machine learning methods. First, based on local parameter estimation, autoregressive (AR) model analysis and local estimation of the increase in image blur, we extract features from the weather images using an approach inspired by free energy and a no-reference robust metric model. Next, we compare the coefficient energy and contrast difference of each pixel in the AR model and then use the percentages to calculate the image sharpness to derive the overall mass fraction. Furthermore, the results are compared. The relationship between residual value and PM2.5 concentration is fitted by generalized Gauss distribution (GGD) model. Finally, nonlinear mapping is performed via the wavelet neural network (WNN) method to obtain the PM2.5 concentration. Experimental results obtained on real data show that the proposed method offers an improved prediction accuracy and lower root mean square error (RMSE).

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2001.01a
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• pp.285-291
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• 2001

This paper describes a natural language question answering system that can be used by students in getting as solution to their queries. Unlike AI question answering system that focus on the generation of new answers, the present system retrieves existing ones from question-answer files. Unlike information retrieval approaches that rely on a purely lexical metric of similarity between query and document, it uses a semantic knowledge base (WordNet) to improve its ability to match question. Paper describes the design and the current implementation of the system as an intelligent tutoring system. Main drawback of the existing tutoring systems is that the computer poses a question to the students and guides them in reaching the solution to the problem. In the present approach, a student asks any question related to the topic and gets a suitable reply. Based on his query, he can either get a direct answer to his question or a set of questions (to a maximum of 3 or 4) which bear the greatest resemblance to the user input. We further analyze-application fields for such kind of a system and discuss the scope for future research in this area.

• Journal of the Korean Society for Precision Engineering
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• v.15 no.12
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• pp.226-235
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• 1998

This study proposes the analysis and evaluation of method of time series of ultrasonic signal using the chaotic feature extraction for ultrasonic pattern recognition. The features are extracted from time series data for analysis of weld defects quantitatively. For this purpose, analysis objectives in this study are fractal dimension, Lyapunov exponent, and strange attractor on hyperspace. The Lyapunov exponent is a measure of rate in which phase space diverges nearby trajectories. Chaotic trajectories have at least one positive Lyapunov exponent, and the fractal dimension appears as a metric space such as the phase space trajectory of a dynamical system. In experiment, fractal(correlation) dimensions and Lyapunov exponents show the mean value of 4.663, and 0.093 relatively in case of learning, while the mean value of 4.926, and 0.090 in case of testing in slag inclusion(weld defects) are shown. Therefore, the proposed chaotic feature extraction can be enhancement of precision rate for ultrasonic pattern recognition in defecting signals of weld zone, such as slag inclusion.

• ETRI Journal
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• v.37 no.3
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• pp.541-550
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• 2015

In this paper, we propose a classification-based approach for hybridizing statistical machine translation and rulebased machine translation. Both the training dataset used in the learning of our proposed classifier and our feature extraction method affect the hybridization quality. To create one such training dataset, a previous approach used auto-evaluation metrics to determine from a set of component machine translation (MT) systems which gave the more accurate translation (by a comparative method). Once this had been determined, the most accurate translation was then labelled in such a way so as to indicate the MT system from which it came. In this previous approach, when the metric evaluation scores were low, there existed a high level of uncertainty as to which of the component MT systems was actually producing the better translation. To relax such uncertainty or error in classification, we propose an alternative approach to such labeling; that is, a cut-off method. In our experiments, using the aforementioned cut-off method in our proposed classifier, we managed to achieve a translation accuracy of 81.5% - a 5.0% improvement over existing methods.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.25 no.4
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• pp.296-311
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• 2021

Topological Data Analysis (TDA), a relatively new field of data analysis, has proved very useful in a variety of applications. The main persistence tool from TDA is persistent homology in which data structure is examined at many scales. Representations of persistent homology include persistence barcodes and persistence diagrams, both of which are not straightforward to reconcile with traditional machine learning algorithms as they are sets of intervals or multisets. The problem of faithfully representing barcodes and persistent diagrams has been pursued along two main avenues: kernel methods and vectorizations. One vectorization is the Betti sequence, or Betti curve, derived from the persistence barcode. While the Betti sequence has been used in classification problems in various applications, to our knowledge, the stability of the sequence has never before been discussed. In this paper we show that the Betti sequence is unstable under the 1-Wasserstein metric with regards to small perturbations in the barcode from which it is calculated. In addition, we propose a novel stabilized version of the Betti sequence based on the Gaussian smoothing seen in the Stable Persistence Bag of Words for persistent homology. We then introduce the normalized cumulative Betti sequence and provide numerical examples that support the main statement of the paper.

• Journal of Korea Multimedia Society
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• v.24 no.7
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• pp.880-889
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• 2021

In everyday life, recognizing people's emotions from their frames is essential and is a popular research domain in the area of computer vision. Visual emotion has a severe class imbalance in which most of the data are distributed in specific categories. The existing methods do not consider class imbalance and used accuracy as the performance metric, which is not suitable for evaluating the performance of the imbalanced dataset. Therefore, we proposed a method for recognizing visual emotion using balanced data augmentation to address the class imbalance. The proposed method generates a balanced dataset by adopting the random over-sampling and image transformation methods. Also, the proposed method uses the Focal loss as a loss function, which can mitigate the class imbalance by down weighting the well-classified samples. EfficientNet, which is the state-of-the-art method for image classification is used to recognize visual emotion. We compare the performance of the proposed method with that of conventional methods by using a public dataset. The experimental results show that the proposed method increases the F1 score by 40% compared with the method without data augmentation, mitigating class imbalance without loss of classification accuracy.

• Journal of Korean Society for Quality Management
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• v.47 no.2
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• pp.271-281
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• 2019

Purpose: The purpose of this study is to suggest a more efficient distance measure taking into account the relationship between categorical variables for categorical data cluster analysis. Methods: In this study, the association-based dissimilarity was employed to calculate the distance between two categorical data observations and the distance obtained from the association-based dissimilarity was applied to the PAM cluster algorithms to verify its effectiveness. The strength of association between two different categorical variables can be calculated using a mixture of dissimilarities between the conditional probability distributions of other categorical variables, given these two categorical values. In particular, this method is suitable for datasets whose categorical variables are highly correlated. Results: The simulation results using several real life data showed that the proposed distance which considered relationships among the categorical variables generally yielded better clustering performance than the Hamming distance. In addition, as the number of correlated variables was increasing, the difference in the performance of the two clustering methods based on different distance measures became statistically more significant. Conclusion: This study revealed that the adoption of the relationship between categorical variables using our proposed method positively affected the results of cluster analysis.

• Journal of the Korean Society of Systems Engineering
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• v.16 no.2
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• pp.67-77
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• 2020

Traditionally nuclear thermal hydraulic and nuclear safety has relied on numerical simulations to predict the system response of a nuclear power plant either under normal operation or accident condition. However, this approach may sometimes be rather time consuming particularly for design and optimization problems. To expedite the decision-making process data-driven models can be used to deduce the statistical relationships between inputs and outputs rather than solving physics-based models. Compared to the traditional approach, data driven models can provide a fast and cost-effective framework to predict the behavior of highly complex and non-linear systems where otherwise great computational efforts would be required. The objective of this work is to develop an AI algorithm to predict the peak fuel cladding temperature as a metric for the successful implementation of FLEX strategies under extended station black out. To achieve this, the model requires to be conditioned using pre-existing database created using the thermal-hydraulic analysis code, MARS-KS. In the development stage, the model hyper-parameters are tuned and optimized using the talos tool.