• Journal of Korea Multimedia Society
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• 제10권8호
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• pp.1068-1077
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• 2007

This paper proposes a method that extracts characteristics of cracks such as length, thickness and direction from a concrete slab surface image with image processing techniques. These techniques extract the cracks from the concrete surface image in variable conditions including bad image conditions) using the ART2-based RBF network to recognize the dominant directions -45 degree, 45 degree, horizontal and vertical) of the extracted cracks from the automatically calculated specifications like the lengths, directions and widths of the cracks. Our proposed extraction algorithms and analysis of the concrete cracks used a Robert operation to emphasize the cracks, and a Multiple operation to increase the difference in brightness between the cracks and background. After these treatments, the cracks can be extracted from the image by using an iterated binarization technique. Noise reduction techniques are used three separate times on this binarized image, and the specifications of the cracks are extracted form this noiseless image. The dominant directions can be recognized by using the ART2-based RBF network. In this method, the ART2 is used between the input layer and the middle layer to learn, and the Delta learning method is used between the middle layer and the output layer. The experiments using real concrete images showed that the cracks were effectively extracted, and the Proposed ART2-based RBF network effectively recognized the directions of the extracted cracks.

• Hwankyungkyoyuk
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• 제24권4호
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• pp.111-128
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• 2011

This study was aimed at developing a common curriculum for the department of environmental education from 5 colleges of education. The need and background of curriculum reform can be summarized as follow; first, it has been recognized that new national curriculum of 2009 and 2011 created need for training teachers equipped with more integrated competency. Second, global environmental problems such as climate change and energy crisis asked for more responsible choice and action from all citizens. Third, the extremely low hiring rate resulted in the consideration of new working fields for teacher students majoring in environmental education. Fourth, the expansion of new environmental education paradigms including education for sustainable development called for practicing reconstruction of both contends and methods. From a series of research processes including analysis of current curriculum, DACUM, opinion survey and interest groups review, several new approaches for developing new curriculum had been identified as follow; first, content areas of environmental education should be extended beyond environmental natural science. Second, new learning approaches such as project-based learning need to be emphasized for strengthening the identity of environment as a separate subject. Third, more selective majoring system need to be applied in connection with environment government officials, researchers, and social environmental educators. It was recommended that the application of new curriculum developed by the study would be evaluated and managed by teaching conditions surrounding each of the five university members joined this developing processes. However, it needs to be noted that there is not much time because we had experienced zero hiring rate for the last 4 years and environmental policy and education programs are moving rapidly toward sustainable development.

• Korean Parent-Child Health Journal
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• 제3권2호
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• pp.49-66
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• 2000

Pregnancy is a task of creation in which a women mobilizes her self and the resources available to her in the generation of a new person. Through the pregnancy, a mother has formed the new human relationship with a fetus. Maternal-fetal relationship is considered one of mechanism making the relationship of mother and child. It is important to well-being of mother and fetus, too. The earliest interaction between a mother and her child is during prenatal period. Maternal-fetal dyad is unique and perceived interactions with the fetus make the pregnancy real for the mother. Maternal behavior is "instinctive" and is formed in early childhood by copy of the mother. But, Rubin argues that this behavior is an open intellectual system rather than a prepackaged bundle of traits. There is openness to new learning and a high value placed on knowing which occurs with silent organization in thought. Thus, nurses and other health professionals provide prenatal care that optimally is part of the environment in which the maternal-fetal dyad develops. Thus it is appropriate for nurses to increases their understanding of the dyad and to explore ways to enhance its development. This study focusses on the interaction ability and response of fetus, and the maternal-fetal interaction. The research of fetal responses that involve physiological changes and motor movement have been shown to coccur to both external sensory stimuli and to maternal emotional states. The fetus does also have sensory capacity to be aware of some maternal behaviors, and the motor ability to respond in a way the mother can notice. Thus, very rudimentary interactions appear to be possible. Maternal awareness of fetal activity was supported by several studies. More interesting to the present study are description of maternal-fetal interaction and the finding that there appear to be levels of sensitivity to the fetus involved in maternal-fetal interactions. First, recognition comes that the fetus is separate from the maternal self. Next, the fetus engages in. Lastly, the parent may describe active interaction with the fetus, believing that mother and fetus are communicating on a meaningful level. Several interventions, developed to promote more active interaction between mother and fetus, have been reviewed. In general, the parents were taught to stimulate the fetus and to notice the fetus' responses. This type of intervention might increase the mother's sensitivity to her unborn baby, and she may have a head start toward learning how to res pond sensitivity to the newborn infant. Research In the area of maternal-fetal interaction is scarce. Sensitive behavior is construed as an appropriate and timely response to a signal of need from another person, but no such signal of need can be claimed regarding the fetus. The highest level of maternal-fetal interaction, therefore, might be based more on maternal representations of the imagined fetus than on factual evidence of fetal participation.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• 제14권2호
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• pp.115-121
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• 2021

This paper used big data and artificial intelligence technology to predict the rapidly increasing internet traffic. There have been various studies on traffic prediction in the past, but they have not been able to reflect the increasing factors that induce huge Internet traffic such as smartphones and streaming in recent years. In addition, event-like factors such as the release of large-capacity popular games or the provision of new contents by OTT (Over the Top) operators are more difficult to predict in advance. Due to these characteristics, it was impossible for an ISP (Internet Service Provider) to reflect real-time service quality management or traffic forecasts in the network business environment with the existing method. Therefore, in this study, in order to solve this problem, an Internet traffic collection system was constructed that searches, discriminates and collects traffic data in real time, separate from the existing NMS. Through this, the flexibility and elasticity to automatically register the data of the collection target are secured, and real-time network quality monitoring is possible. In addition, a large amount of traffic data collected from the system was analyzed by machine learning (AI) to predict future traffic of OTT operators. Through this, more scientific and systematic prediction was possible, and in addition, it was possible to optimize the interworking between ISP operators and to secure the quality of large-scale OTT services.

• Journal of Internet Computing and Services
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• 제19권6호
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• pp.91-99
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• 2018

Sketch-based 3D object retrieval is a convenient way to search for various 3D data using human-drawn sketches as query. In this paper, we propose a new method of using Sketch CNN, Wasserstein CNN and Wasserstein center loss for sketch-based 3D object search. Specifically, Wasserstein center loss is a method of learning the center of each object category and reducing the Wasserstein distance between center and features of the same category. To do this, the proposed 3D object retrieval is performed as follows. Firstly, Wasserstein CNN extracts 2D images taken from various directions of 3D object using CNN, and extracts features of 3D data by computing the Wasserstein barycenters of features of each image. Secondly, the features of the sketch are extracted using a separate Sketch CNN. Finally, we learn the features of the extracted 3D object and the features of the sketch using the proposed Wasserstein center loss. In order to demonstrate the superiority of the proposed method, we evaluated two sets of benchmark data sets, SHREC 13 and SHREC 14, and the proposed method shows better performance in all conventional metrics compared to the state of the art methods.

• Knowledge Management Research
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• 제22권2호
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• pp.1-17
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• 2021

In this study, we predicted the bitcoin prices of Bithum and Coinbase, a leading exchange in Korea and USA, using ARIMA and Recurrent Neural Networks(RNNs). And we used news articles from each country to suggest a separated RNN model. The suggested model identifies the datasets based on the changing trend of prices in the training data, and then applies time series prediction technique(RNNs) to create multiple models. Then we used daily news data to create a term-based dictionary for each trend change point. We explored trend change points in the test data using the daily news keyword data of testset and term-based dictionary, and apply a matching model to produce prediction results. With this approach we obtained higher accuracy than the model which predicted price by applying just time series prediction technique. This study presents that the limitations of the time series prediction techniques could be overcome by exploring trend change points using news data and various time series prediction techniques with text mining techniques could be applied to improve the performance of the model in the further research.

• Journal of Digital Convergence
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• 제20권2호
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• pp.499-509
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• 2022

This study was conducted to understand the effect of health assessment practical education on class participation, problem-solving process, academic self-efficacy, and academic achievement of nursing students. This study used a nonequivalent control group pretest-posttest design. The participants were 69 nursing university students located in C city. Data were collected on two separate occasions before and after the application of the program from February 2021 to July 2021. Data were analyzed by chi-square test, independent t-test, and ANCOVA using SPSS WIN 23.0. There were significant differences in class participation(F=15.003, p<.001), academic self-efficacy(F=13.288, p=.001) and academic achievement(F=19.755, p<.001) between the experimental group and the control group. In the problem-solving process, the experimental group was significantly higher than the control group in decision-making(F=6.948, p=.010), applying the solution(F=6.232, p=.015) and evaluation-reflection(F=5.364, p=.024). It is necessary to expand case-based learning to increase the problem-solving process, class participation, academic self-efficacy, and academic achievement of nursing students.

• Journal of Korean Home Economics Education Association
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• 제17권2호
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• pp.239-255
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• 2005

The purpose of this study was to understand middle school Home Economics teachers' family value and needs on learning objectives of family life area, and to identify the relationship between the two. Data was collected from the survey mailed to the teachers responsible for family life area in $\lceil$Technology/Home Economics$\rfloor$ in middle schools in Korea. The collected 312 questionnaires were used for final analysis. SPSS/WIN program was used for calculating average, standard deviation, percentage, t-test, ANOVA with post-verification scheffe., and correlation analysis. Followings are the summary of the results of this study. Firstly, the family value of middle school Home Economics teachers was relatively modern. They had very modem sense of value in all of the sub-areas such as sense of value on marriage. on gender role. on children, on filial love, and familism. Secondly, regarding needs on family life area of learning objects of Home Economics teachers, the requirement on emancipatory system of action was the highest. technical system of action was the next, and Communicative System of Action was the lowest. Thirdly, in the relationship between the needs of teaming objects of family life area and the family value, the needs of technical and interpretive behavioral system had few things to do with the family value. However. the needs on teaming object needs of emancipatory system of action was higher as the family value was modern. The trend in the relationship with needs was same in all the sub-areas such as sense of value on marriage, on gender role, on children. on filial love, and familism. However, the family value and the achievement level of family life area goals did not show significant correlation. Fourthly, regarding the family value and the needs on teaming objectives of family life area of middle school Home Economics teachers, those who were female, who had certificates for Home Economics Teaching, who were young and who had less experiences in teaching had more modem family value and required more teaming objectives in emancipatory system of action. Considering the results of the study, it is needed to emphasize the learning objects of emancipatory system of action in family life education by inducing consensus on the proposition that Home Economics subject is a critical and practical subject. To do this. it is needed to provide Home Economics teachers with emancipatory interest and mature family value through educating and refreshing them. It is desirable to separate Technology and Home Economics so that certificated Home Economics teachers could teach family life area. In that case they can teach the subject in the point of practical criticism. If the area is to be taught by other subject teachers there should be enough understanding on the philosophy and nature of Home Economics subject beforehand.

• Korean Journal of Remote Sensing
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• 제38권6_1호
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• pp.1181-1189
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• 2022

Clouds are composed of tiny water droplets, ice crystals, or mixtures suspended in the atmosphere and cover about two-thirds of the Earth's surface. Cloud detection in satellite images is a very difficult task to separate clouds and non-cloud areas because of similar reflectance characteristics to some other ground objects or the ground surface. In contrast to thick clouds, which have distinct characteristics, thin transparent clouds have weak contrast between clouds and background in satellite images and appear mixed with the ground surface. In order to overcome the limitations of transparent clouds in cloud detection, this study conducted cloud detection focusing on transparent clouds using machine learning techniques (Random Forest [RF], Convolutional Neural Networks [CNN]). As reference data, Cloud Mask and Cirrus Mask were used in MOD35 data provided by MOderate Resolution Imaging Spectroradiometer (MODIS), and the pixel ratio of training data was configured to be about 1:1:1 for clouds, transparent clouds, and clear sky for model training considering transparent cloud pixels. As a result of the qualitative comparison of the study, bothRF and CNN successfully detected various types of clouds, including transparent clouds, and in the case of RF+CNN, which mixed the results of the RF model and the CNN model, the cloud detection was well performed, and was confirmed that the limitations of the model were improved. As a quantitative result of the study, the overall accuracy (OA) value of RF was 92%, CNN showed 94.11%, and RF+CNN showed 94.29% accuracy.

• Korean Journal of Agricultural and Forest Meteorology
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• 제25권1호
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• pp.17-27
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• 2023

Weeds are one of the factors that reduce crop yield through nutrient and photosynthetic competition. Quantification of weed density are an important part of making accurate decisions for precision weeding. In this study, we tried to quantify the density of weeds in images of maize fields taken by unmanned aerial vehicle (UAV). UAV image data collection took place in maize fields from May 17 to June 4, 2021, when maize was in its early growth stage. UAV images were labeled with pixels from maize and those without and the cropped to be used as the input data of the semantic segmentation network for the maize detection model. We trained a model to separate maize from background using the deep learning segmentation networks DeepLabV3+, U-Net, Linknet, and FPN. All four models showed pixel accuracy of 0.97, and the mIOU score was 0.76 and 0.74 in DeepLabV3+ and U-Net, higher than 0.69 for Linknet and FPN. Weed density was calculated as the difference between the green area classified as ExGR (Excess green-Excess red) and the maize area predicted by the model. Each image evaluated for weed density was recombined to quantify and visualize the distribution and density of weeds in a wide range of maize fields. We propose a method to quantify weed density for accurate weeding by effectively separating weeds, maize, and background from UAV images of maize fields.