• Journal of the Korean Society of Earth Science Education
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• v.11 no.1
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• pp.55-62
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• 2018

The purpose of this study was to examine the pre-service elementary teachers' views on the necessity of science learning. The eighty five student teachers in their second year of studies were participated in this study. The participants freely wrote their thoughts on a question'Why should students learn science?' The results of the study are as follows: (1) The participants' responses were very diverse, so their responses contained almost all kind of values or arguments about the science learning suggested by researchers, and there was no difference in their response ratio between views of focusing on intrinsic values and views of focusing on extrinsic values; (2) About 30% of the participants had the biased conceptions on the necessity of science learning and they would be likely to explain their biased conceptions to their future students. The educational implications and the suggestions for further studies are also presented in this paper.

• Journal of Korean Elementary Science Education
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• v.32 no.4
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• pp.415-422
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• 2013

The purpose of this study was to investigate the effects of scientific program for gifted elementary students using metacognition on learning-flow and lingual interaction. For the purpose, the two classes for elementary science of P Institute for Gifted Education located in Pusan were assigned to experimental and control groups, respectively. The experimental and controls groups received eight sessions applying a program for scientifically gifted students using metacognition and same program without using it, respectively. The learning-flow test and the lingual interaction observed among students and between teacher and students pre and post program were compared and analyzed. The results shows that there was no significant difference between pretest and posttest in experimental group, indicating that learning-flow is not a construct obtained by short-term using of metacognition. For the lingual interactions, however, the amounts of conversation and the ratio of high level conversation in experimental group were 1.6 and 1.5 times higher than those in control group. This suggests that the lingual interaction may be varied among classes even with same experiments, by instruction method. The lingual interaction is not active in control group where the experimental results may be obtained easily only with sincere attitude while, in experimental group, questions using metacognition and providing intellectual stimulation is continuously presented, leading to high level of lingual interaction, therefore it is considered that the development of scientific program for gifted elementary students using these advantages is needed.

• Journal of the Korean School Mathematics Society
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• v.23 no.1
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• pp.89-109
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• 2020

This study aims to improve teacher education by analyzing the causes and backgrounds of which pre-service mathematics teachers experience learning difficulties on the topic of discrete mathematics. To this end, we conducted a questionnaire and an evaluation on the topic of discrete mathematics, and the obtained data were analyzed. The results show that (1) pre-service mathematics teachers need to share their perceptions of the need for discrete mathematics education; (2) a review of the adequacy of the discrete mathematical content and its credits are required; (3) the causes of their learning difficulties need to be looked at from a different perspective than the learning factors. And two implications were obtained. First, it is necessary to study the systematicity and sequence of content elements of discrete mathematics in the aspect of its continuity of curriculum of secondary school and university. Second, it is required consideration for adjusting the ratio of discrete mathematics to secondary teachers' employment examination.

• Journal of Korea Multimedia Society
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• v.8 no.2
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• pp.269-278
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• 2005

In order to process video data effectively, it is required that the content information of video data is loaded in database and semantics-based retrieval method can be available for various queries of users. In this paper, we propose a video retrieval system which support semantics retrieval of various users for massive video data by user's keywords and comparison area learning based on automatic agent. By user's fundamental query and selection of image for key frame that extracted from query, the agent gives the detail shape for annotation of extracted key frame. Also, key frame selected by user becomes a query image and searches the most similar key frame through color histogram comparison and comparison area learning method that proposed. From experiment, the designed and implemented system showed high precision ratio in performance assessment more than 93 percents.

• Imaging Science in Dentistry
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• v.52 no.2
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• pp.219-224
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• 2022

Purpose: This study aimed to evaluate the performance of transfer learning in a deep convolutional neural network for classifying implant fixtures. Materials and Methods: Periapical radiographs of implant fixtures obtained using the Superline (Dentium Co. Ltd., Seoul, Korea), TS III(Osstem Implant Co. Ltd., Seoul, Korea), and Bone Level Implant(Institut Straumann AG, Basel, Switzerland) systems were selected from patients who underwent dental implant treatment. All 355 implant fixtures comprised the total dataset and were annotated with the name of the system. The total dataset was split into a training dataset and a test dataset at a ratio of 8 to 2, respectively. YOLOv3 (You Only Look Once version 3, available at https://pjreddie.com/darknet/yolo/), a deep convolutional neural network that has been pretrained with a large image dataset of objects, was used to train the model to classify fixtures in periapical images, in a process called transfer learning. This network was trained with the training dataset for 100, 200, and 300 epochs. Using the test dataset, the performance of the network was evaluated in terms of sensitivity, specificity, and accuracy. Results: When YOLOv3 was trained for 200 epochs, the sensitivity, specificity, accuracy, and confidence score were the highest for all systems, with overall results of 94.4%, 97.9%, 96.7%, and 0.75, respectively. The network showed the best performance in classifying Bone Level Implant fixtures, with 100.0% sensitivity, specificity, and accuracy. Conclusion: Through transfer learning, high performance could be achieved with YOLOv3, even using a small amount of data.

• Tunnel and Underground Space
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• v.31 no.6
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• pp.508-519
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• 2021

In this study, a model was developed to predict the peak particle velocity (PPV) that affects people and the surrounding environment during blasting. Four machine learning models using the k-nearest neighbors (kNN), classification and regression tree (CART), support vector regression (SVR), and particle swarm optimization (PSO)-SVR algorithms were developed and compared with each other to predict the PPV. Mt. Yogmang located in Changwon-si, Gyeongsangnam-do was selected as a study area, and 1048 blasting data were acquired to train the machine learning models. The blasting data consisted of hole length, burden, spacing, maximum charge per delay, powder factor, number of holes, ratio of emulsion, monitoring distance and PPV. To evaluate the performance of the trained models, the mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE) were used. The PSO-SVR model showed superior performance with MAE, MSE and RMSE of 0.0348, 0.0021 and 0.0458, respectively. Finally, a method was proposed to predict the degree of influence on the surrounding environment using the developed machine learning models.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.4
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• pp.78-96
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• 2023

Urban traffic information is collected by various systems such as VDS, DSRC, and radar. Recently, with the development of deep learning technology, smart intersection systems are expanding, are more widely distributed, and it is possible to collect a variety of information such as traffic volume, and vehicle type and speed. However, as a result of reviewing related literature, the performance evaluation criteria so far are rbs-based evaluation systems that do not consider the deep learning area, and only consider the percent error of 'reference value-measured value'. Therefore, a new performance evaluation method is needed. Therefore, in this study, individual error, interval error, and overall error are calculated by using a formula that considers deep learning performance indicators such as precision and recall based on data ratio and weight. As a result, error rates for measurement value 1 were 3.99 and 3.54, and rates for measurement value 2 were 5.34 and 5.07.

• Annals of Hepato-Biliary-Pancreatic Surgery
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• v.27 no.2
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• pp.195-200
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• 2023

Backgrounds/Aims: We aimed to build a machine learning tool to help predict low-grade intraductal papillary mucinous neoplasms (IPMNs) in order to avoid unnecessary surgical resection. IPMNs are precursors to pancreatic cancer. Surgical resection remains the only recognized treatment for IPMNs yet carries some risks of morbidity and potential mortality. Existing clinical guidelines are imperfect in distinguishing low-risk cysts from high-risk cysts that warrant resection. Methods: We built a linear support vector machine (SVM) learning model using a prospectively maintained surgical database of patients with resected IPMNs. Input variables included 18 demographic, clinical, and imaging characteristics. The outcome variable was the presence of low-grade or high-grade IPMN based on post-operative pathology results. Data were divided into a training/validation set and a testing set at a ratio of 4:1. Receiver operating characteristics analysis was used to assess classification performance. Results: A total of 575 patients with resected IPMNs were identified. Of them, 53.4% had low-grade disease on final pathology. After classifier training and testing, a linear SVM-based model (IPMN-LEARN) was applied on the validation set. It achieved an accuracy of 77.4%, with a positive predictive value of 83%, a specificity of 72%, and a sensitivity of 83% in predicting low-grade disease in patients with IPMN. The model predicted low-grade lesions with an area under the curve of 0.82. Conclusions: A linear SVM learning model can identify low-grade IPMNs with good sensitivity and specificity. It may be used as a complement to existing guidelines to identify patients who could avoid unnecessary surgical resection.

• Korean Journal of Cognitive Science
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• v.34 no.1
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• pp.39-66
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• 2023

New media moved the place of social exchange to the Internet, allowing large groups to communicate in one place beyond the limits of time and space. Recent studies have also reported cases in which human social abilities do not keep up with the expansion of group scale through social media. In this context, current study investigated how human perception of social information is affected by the expansion of the group scale in the context of majority bias. Using Internet-based task, the psychological processes that group ratio and group size are perceived and affect majority-biased social information use were investigated, and whether group scale moderates those processes was examined. The group ratio has a positive effect on the majority bias, and the relationship was partially mediated by ratio perception. Group scale did not moderate the relationship between group ratio and ratio perception. On the other hand, the correlation between group size and majority-biased social information use was not significant. Group scale moderates group size perception. The group size and size perception showed positive correlation under the smaller group scale condition. However under the extended group scale condition, the perceived group size became significantly lower and lost its correlation with group size. These results provide evidence that the psychological mechanism related to group size perception was not properly responding to the expansion of the group scale. Furthermore, the possibility of a specific psychological mechanism for processing group size information and the form of information input specifically accepted by majority bias were discussed from perspective of evolutionary psychology.

• International Journal of Internet, Broadcasting and Communication
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• v.12 no.4
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• pp.83-92
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• 2020

A recent study to determine the fall is focused on analyzing fall motions using a recurrent neural network (RNN), and uses a deep learning approach to get good results for detecting human poses in 2D from a mono color image. In this paper, we investigated the improved detection method to estimate the position of the head and shoulder key points and the acceleration of position change using the skeletal key points information extracted using PoseNet from the image obtained from the 2D RGB low-cost camera, and to increase the accuracy of the fall judgment. In particular, we propose a fall detection method based on the characteristics of post-fall posture in the fall motion analysis method and on the velocity of human body skeleton key points change as well as the ratio change of body bounding box's width and height. The public data set was used to extract human skeletal features and to train deep learning, GRU, and as a result of an experiment to find a feature extraction method that can achieve high classification accuracy, the proposed method showed a 99.8% success rate in detecting falls more effectively than the conventional primitive skeletal data use method.