• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.139-156
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• 2021

The biggest reason for using a deep learning model in image classification is that it is possible to consider the relationship between each region by extracting each region's features from the overall information of the image. However, the CNN model may not be suitable for emotional image data without the image's regional features. To solve the difficulty of classifying emotion images, many researchers each year propose a CNN-based architecture suitable for emotion images. Studies on the relationship between color and human emotion were also conducted, and results were derived that different emotions are induced according to color. In studies using deep learning, there have been studies that apply color information to image subtraction classification. The case where the image's color information is additionally used than the case where the classification model is trained with only the image improves the accuracy of classifying image emotions. This study proposes two ways to increase the accuracy by incorporating the result value after the model classifies an image's emotion. Both methods improve accuracy by modifying the result value based on statistics using the color of the picture. When performing the test by finding the two-color combinations most distributed for all training data, the two-color combinations most distributed for each test data image were found. The result values were corrected according to the color combination distribution. This method weights the result value obtained after the model classifies an image's emotion by creating an expression based on the log function and the exponential function. Emotion6, classified into six emotions, and Artphoto classified into eight categories were used for the image data. Densenet169, Mnasnet, Resnet101, Resnet152, and Vgg19 architectures were used for the CNN model, and the performance evaluation was compared before and after applying the two-stage learning to the CNN model. Inspired by color psychology, which deals with the relationship between colors and emotions, when creating a model that classifies an image's sentiment, we studied how to improve accuracy by modifying the result values based on color. Sixteen colors were used: red, orange, yellow, green, blue, indigo, purple, turquoise, pink, magenta, brown, gray, silver, gold, white, and black. It has meaning. Using Scikit-learn's Clustering, the seven colors that are primarily distributed in the image are checked. Then, the RGB coordinate values of the colors from the image are compared with the RGB coordinate values of the 16 colors presented in the above data. That is, it was converted to the closest color. Suppose three or more color combinations are selected. In that case, too many color combinations occur, resulting in a problem in which the distribution is scattered, so a situation fewer influences the result value. Therefore, to solve this problem, two-color combinations were found and weighted to the model. Before training, the most distributed color combinations were found for all training data images. The distribution of color combinations for each class was stored in a Python dictionary format to be used during testing. During the test, the two-color combinations that are most distributed for each test data image are found. After that, we checked how the color combinations were distributed in the training data and corrected the result. We devised several equations to weight the result value from the model based on the extracted color as described above. The data set was randomly divided by 80:20, and the model was verified using 20% of the data as a test set. After splitting the remaining 80% of the data into five divisions to perform 5-fold cross-validation, the model was trained five times using different verification datasets. Finally, the performance was checked using the test dataset that was previously separated. Adam was used as the activation function, and the learning rate was set to 0.01. The training was performed as much as 20 epochs, and if the validation loss value did not decrease during five epochs of learning, the experiment was stopped. Early tapping was set to load the model with the best validation loss value. The classification accuracy was better when the extracted information using color properties was used together than the case using only the CNN architecture.

• Geophysics and Geophysical Exploration
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• v.13 no.1
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• pp.118-127
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• 2010

Resistivity logging instruments are designed to measure the electrical resistivity of a formation, and this can be directly interpreted to provide a water-saturation profile. However, resistivity logs are sensitive to borehole and shoulder-bed effects, which often result in misinterpretation of the results. These effects are emphasised more in the presence of tool eccentricity. For precise interpretation of short- and long-normal logging measurements in the presence of tool eccentricity, we simulate and analyse eccentricity effects by combining the use of a Fourier series expansion in a new system of coordinates with a 2D goal-oriented high-order self-adaptive hp finite-element refinement strategy, where h denotes the element size and p the polynomial order of approximation within each element. The algorithm automatically performs local mesh refinement to construct an optimal grid for the problem under consideration. In addition, the proper combination of h and p refinements produces highly accurate simulations even in the presence of high electrical resistivity contrasts. Numerical results demonstrate that our algorithm provides highly accurate and reliable simulation results. Eccentricity effects are more noticeable when the borehole is large or resistive, or when the formation is highly conductive.

• Communications of Mathematical Education
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• v.25 no.1
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• pp.261-288
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• 2011

We examined the effects of pair assistant collaborative learning on academic achievement of the 2nd year middle school students in the three subjects such as the Probability, Properties of Figures and Similarities of Figures. In order to carry out this study, we selected 2 classes of 2nd year students in a girls middle school in the Fall semester of 2009 and set up the experiment group and comparison group by the result of academic achievement tests given in the end of the Spring semester of 2009. Pair assistant collaborative learning was adopted for students in problem solving 2 or 3 times per a week in the experiment group and each academic achievement was given at the end of each subject in both groups. Also, we had a final survey to find out students' attitude to this collaborative learning. The achievement and survey were analysed by statistical methods. We conclude that our pair assistant collaborative learning was effective in Probability and Similarities of Figures Units. According to the result of survey, this collaborative learning brought about an opportunity to promote students' community spirit through reflecting each one's role in the group.

• Journal of the Korean Institute of Gas
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• v.18 no.1
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• pp.46-51
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• 2014

The purpose of this paper is to study of the vehicle lamp manufacturing system composing ultrasonic waves connection process. Making lamp assembly plant, it was produced in the separate process as the injection molding, ultrasonic waves bonding, annealing in the constant temperature, lamp assembling and packing. But the improvement method producing the lamp was added with one-step process by one automation technique. As a result, welding with ultrasonic waves process, the method decreased the energy consumption and noise during ultrasonic waves welding. Therefore, this method used the mathematics modeling for checking validity, it selected the stability and suitable controller using transfer function of plant and bode chart. In this study, the $180^{\circ}$ revolution control system to turn injection part upside down was $M_{eq}\;lcos{\theta}(t)$ because of gravity influence. It effected to unstable condition a system. For solving this problem, it aimed the linearization and stabilization of system by elimination $M_{eq}\;lcos{\theta}(t)$ as applying Free-forward control technique.

• Journal of Biomedical Engineering Research
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• v.19 no.6
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• pp.551-562
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• 1998

Amultiresolution filtering algorithm based on the physical SPECT lesion detachability provides and optimal solution for SPECT reconstruction problem. Related to the previous study, we estimated the SPECT lesion detection capability by m minimum detectable lesion sizes (MDLSs), and generated m reconstruction filters which are designed to maximize the smoothing effect at a fixed MDLS-dependent resolution level $\frac{MDLS}{4\sqrt{2In2}}$. The proposed multiresolution filtering algorithm used a coarse-to-fine approach for the m-level resolution filter images obtained from these m filters for a given projection image. First, the local homogeneity is determined for every pixel of the filter images, by comparing the local variance value computed in a window centered at the pixel and the mode determined from the distribution of the local variances. Based on the local homogeneity, the pixels declared as homogeneous are chosen from the filter image of the lowest resolution, and for the other pixels the same process is repeated for the higher resolution filter images. For the non-homogeneous pixels after this pixels after this repetition process ends, the pixel values of the highest resolution filter image are substituted. From the results of the simulated experiments, the proposed multiresolution filtering algorithm showed a strong smoothing effect in the homogeneous regions and a significant resolution improvement near the edge regions of the projection images, and so produced good adaptability effects in the reconstructed images.

• The Journal of the Korea Contents Association
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• v.17 no.7
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• pp.113-123
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• 2017

This research is designed to review the systems thinking and STEAM theory while ascertaining the effects of the classroom application of the STEAM programs based on systems thinking appropriate for studying climate change. The systems thinking based STEAM program has been developed by researchers and experts, who had participated in expert meetings in a continued manner. The program was applied to science classes over the course of eight weeks. Therefore, the application effects of the systems thinking based STEAM program were analyzed in students' systems thinking, STEAM semantics survey, and students' academic achievement. The findings are as follows. First, the test group has shown a statistically meaningful difference in the systems thinking analysis compared to the control group in the four subcategories of 'Systems Analysis', 'Personal Mastery', 'Shared Vision' and 'Team Learning' except for 'Mental Model'. Second, in the pre- and post-knowledge tests, the independent sample t-test results in the areas of science, technology, engineering, art and mathematics show statistically meaningful differences compared to the control group. Third, in the academic performance test regarding climate change, the test group displayed higher achievement than the control group. In conclusion, the system-based STEAM program is considered appropriate to enhance amalgamative thinking skills based on systems thinking. In addition, the program is expected to improve creative thinking and problem-solving abilities by offering new ideas based on climate change science.

• The Journal of Korean Association of Computer Education
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• v.23 no.4
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• pp.1-11
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• 2020

The purpose of this study is to propose the direction of AI education in K-12 education through investigating and analyzing aspects of the purpose, content, and methods of AI education as the curriculum and teacher training factors. We collected and analyzed 9 papers as the primary literature and 11 domestic and foreign policy reports as the secondary literature. The collected literatures were analyzed by applying a descriptive reviews, and the implications were derived by analyzing the curriculum components and TPACK elements for multi-dimensional analysis. As a result of this study, AI education targets were divided into three steps: AI users, utilizer, and developers. In K-12 education, the user and utilizer stages are appropriate, and artificial intelligence literacy must be included for user education. Based on the current computing thinking ability and coding ability for utilizer education, the implication was derived that it is necessary to target the ability to create creative output by applying the functions of artificial intelligence. In addition to the pedagogical knowledge and the ability to use the platform, The teacher training is necessary because teachers need content knowledge such as problem-solving, reasoning, learning, perception, and some applied mathematics, cognitive / psychological / ethical of AI.

• Journal of the Korea Society of Computer and Information
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• v.19 no.4
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• pp.149-158
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• 2014

The rapidly changing 21st-century knowledge and information society is emphasizing converged education that crosses various academic fields. In particular, the society expected the cultivation of the talent who balance scientific creativity and artistic sensitivity by adding arts to the existing converged education revolving around science and technology. However, at present, most STEAM education has been actively conducted with a focus on science and technology, whereas the subject of arts has been regarded or utilized as a supplementary means. Its problem is that the educational characteristics and values of art education have not been effectively utilized in educational terms and this could lead to superficial integrated education. In this respect, this study had the knowledge of various fields, such as science, technology, and mathematics, utilized usefully during the process of experiencing and creating arts. Accordingly, this study designed an education programs as with the case of Nam-Jun Baek who expanded the dominion of arts by creatively utilizing his own time's scientific technologies. In this educational process, the target program was developed in a manner that enables EPL to be utilized essentially as the study's knowledge-based tool and medium. The results of applying this educational program in 5th-grade elementary school students showed that the program has positive effects on the creative attributes of the students.

• Journal of The Korean Association For Science Education
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• v.40 no.4
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• pp.359-374
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• 2020

This study is a complex type consisting of survey study and self-study. The former investigated elementary teachers' epistemological beliefs on convergence knowledge and teaching. As a representative of the result of survey study I, as a teacher as well as a researcher, was the participant of the self-study, which investigated my epistemological belief on convergence knowledge and teaching and my execution of convergent science teaching based on family resemblance of mathematics, science, and physical education. A set of open-ended written questionnaires was administered to 28 elementary teachers. Participating teachers considered convergent teaching as discipline-using or multi-disciplinary teaching. They also have epistemological beliefs in which they conceived convergence knowledge as aggregation of diverse disciplinary knowledge and students could get it through their own problem solving processes. As a teacher and researcher I have similar epistemological belief as the other teachers. During the self-study, I tried to apply convergence knowledge system based on the family resemblance analysis among math, science, and PE to my teaching. Inter-disciplinary approach to convergence teaching was not easy for me to conduct. Mathematical units, ratio and rate were linked to science concept of velocity so that it was effective to converge two disciplines. Moreover PE offered specific context where the concepts of math and science were connected convergently so that PE facilitated inter-disciplinary convergent teaching. The gaps between my epistemological belief and inter-disciplinary convergence knowledge based on family resemblance and the cases of how to bridge the gap by my experience were discussed.

• Communications of Mathematical Education
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• v.27 no.4
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• pp.357-367
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• 2013

It is important to make students do research for oneself. But the practice of inquiry activity is not easy in the mathematics education field. Intellectual curiosities of students are unpredictable. It is important to meet intellectual curiosities of students. We could get a sequence in the process solving a problem. This sequence was expressed in a form of the recurrence relation $a_n=a_{n-1}+a_{n-3}$ ($n{\geq}4$), $a_1=a_2=a_3=1$. We tried to look for the general terms of this sequence. This sequence is similar to Fibonacci sequence, but the process finding the general terms is never similar to Fibonacci sequence. We can get two general terms expressed in different form after our a great deal of effort. We hope that this study will give the spot of education energy.