• Journal of The Korean Association For Science Education
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• v.29 no.2
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• pp.179-192
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• 2009

The purpose of this study was to examine the effects of Small-Scale Chemistry (below SSC) Lab Program with respect to students' extroversions and introversions. For this study, an SSC Lab Program was developed on the basis of analyzing the chemistry part of the high school science textbook in the 7th curriculum. The experimental group received SSC experiment lessons, and the comparison group received traditional experiment lessons based on textbook for 5 class periods. Afterwards, students were grouped into extrovert and introvert according to their personality test scores, the differences between the two groups were investigated using 2-way ANCOVA. Prior to the instructions, three test regarding the scientific attitude and academic self-efficacy were administered. After the instructions, the scientific attitude, academic self-efficacy, and students' perceptions on SSC Lab Program were examined. The scores in mid-term and end-of-term science exams were used as pre-test and post-test science achievement scores, respectively. Two-way ANCOVA results revealed that there were effects in the score of the academic achievement score, but there was no interactive effect between extroversion/introversion and treatment. In addition, a significant interactive effect was found in the scientific attitude, but there was no significant main effect. It was interpreted that extrovert students had many opportunities in SSC experiment classes and were able to experiment with initiative, but introverts would feel the responsibility and the pressure owing to the small group experiment. There were no main and interactive effects in the score of the academic self-efficacy test. Survey of students' perceptions on SSC Lab Program revealed that both over 90% extrovert and introvert students showed very positive perceptions in 'three-membered small group composition,' 'understanding,' and 'convenience' items. It was found to be a very different perception between extrovert and introvert students in 'comparing result with other students' item.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.29 no.4
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• pp.429-438
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• 2011

LiDAR technology is a combination of laser ranging, satellite positioning technology and digital image technology for study and determination with high accuracy of the true earth surface features in 3 D. Laser scanning data is typically a points cloud on the ground, including coordinates, altitude and intensity of laser from the object on the ground to the sensor (Wehr & Lohr, 1999). Data from laser scanning can produce products such as digital elevation model (DEM), digital surface model (DSM) and the intensity data. In Vietnam, the LiDAR technology has been applied since 2005. However, the application of LiDAR in Vietnam is mostly for topological mapping and DEM establishment using point cloud 3D coordinate. In this study, another application of LiDAR data are present. The study use the intensity image combine with some other data sets (elevation data, Panchromatic image, RGB image) in Bacgiang City to perform land cover classification using neural network method. The results show that it is possible to obtain land cover classes from LiDAR data. However, the highest accurate classification can be obtained using LiDAR data with other data set and the neural network classification is more appropriate approach to conventional method such as maximum likelyhood classification.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.11
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• pp.2793-2803
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• 2013

In H.264, three different data partition types are used, which have unequal importance to the reconstructed video quality. To improve the performance of H.264 video streaming transmission over IEEE 802.11e Wireless Local Area Networks, a prioritization mechanism that categorizes different partition types to different priority classes according to the calculated distortion within one Group of Pictures. In the proposed scheme, video streams have been encoded based on the H.264 codec with its data partition enabled. The dynamic scheduling scheme based on Enhanced Distributed Channel Access has been configured to differentiate the data partitions according to their distortion impact and the queue utilization ratio. Simulation results show that the proposed scheme improves the received video quality by 1dB in PSNR compared with the existing Enhanced Distributed Channel Access static mapping scheme.

• Geomechanics and Engineering
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• v.11 no.4
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• pp.515-530
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• 2016

In this paper, five different quantitative parameters were proposed for the characterization of the primary roughness which is the component of surface morphology that prevails during large strike-slip faults of more than 50 m. These parameters are mostly the anisotropic properties of rock surface morphology at various scales: (i) coefficient ($k_a$) and degree (${\delta}_a$) of apparent structural anisotropy of surface; (ii) coefficient ($k_r$) and degree (${\delta}_r$) of real structural anisotropy of surface; (iii) surface anisotropy function P(${\varphi}$); and (iv) degree of surface waviness ($W_s$). The coefficient and degree of apparent structural anisotropy allow qualifying the anisotropy/isotropy of a discontinuity according to a classification into four classes: anisotropic, moderately anisotropic/isotropic and isotropic. The coefficient and degree of real structural anisotropy of surface captures directly the actual surface anisotropy using geostatistical method. The anisotropy function predicts directional geometric properties of a surface of discontinuity from measurements in two orthogonal directions. These predicted data may subsequently be used to highlight the anisotropy/isotropy of the surface (radar plot). The degree of surface waviness allows qualifying the undulation of anisotropic surfaces. The proposed quantitative parameters allows their application at both lab and field scales.

• Journal of the Korean earth science society
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• v.33 no.5
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• pp.459-466
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• 2012

This study aimed to investigate the characteristics of pre-service elementary teachers' understanding about scientific inquiry in terms of designing exploration and reasoning that is used to formulate explanations based on evidence. The research context was an open inquiry with using the Science Writing Heuristic (SWH) template in which participant students were not provided with inquiry questions. As data, lab. 39 pre-service elementary teachers participated in this study while taking their science methods course. Analyses of the reports were framed by the cognitive processes of inquiry (Chinn and Malhotra, 2002) and each report was coded and analyzed by the framework of inquiry (Tytler and Peterson, 2004). Results showed that groups' works that utilized the SWH template encouraged the participants to interact each other about scientific inquiry. They came up with more relevant and testable questions for their scientific inquiry. It implicates that children will be able to have chances of testing their own questions more properly by using the SWH template in science classes just as the participants did in this study. The use of the SWH template would help pre-service teachers to teach appropriately how to test inquiry questions to their students in the future. Discussion was made to figure out the characteristics or Korean pre-service elementary teachers' understanding about scientific inquiry.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2000.11a
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• pp.59-60
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• 2000

The past few years have seen a dramatic increase in gene expression data on the basis of DNA microarrays or DNA chips. Going beyond a generic view on the genome, microarray data are able to distinguish between gene populations in different tissues of the same organism and in different states of cells belonging to the same tissue. This affords a cell-wide view of the metabolic and regulatory processes under different conditions, building an effective basis for new diagnoses and therapies of diseases. In this talk we present machine learning techniques for effective mining of DNA microarray data. A brief introduction to the research field of machine learning from the computer science and artificial intelligence point of view is followed by a review of recently-developed learning algorithms applied to the analysis of DNA chip gene expression data. Emphasis is put on graphical models, such as Bayesian networks, latent variable models, and generative topographic mapping. Finally, we report on our own results of applying these learning methods to two important problems: the identification of cell cycle-regulated genes and the discovery of cancer classes by gene expression monitoring. The data sets are provided by the competition CAMDA-2000, the Critical Assessment of Techniques for Microarray Data Mining.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.34 no.6
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• pp.393-400
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• 2021

As the recent climate problems are getting worse year after year, the demands for clean energy materials have highly increased in modern society. However, the candidate material classes for clean energy expand rapidly and the outcomes are too complex to be interpreted at laboratory scale (e.g., multicomponent materials). In order to overcome these issues, the first-principles calculations are becoming attractive in the field of material science. The calculations can be performed rapidly using virtual environments without physical limitations in a vast candidate pool, and theory can address the origin of activity through the calculations of electronic structure of materials, even if the structure of material is too complex. Therefore, in terms of the latest trends, we report academic progress related to the first-principles calculations for design of efficient electrocatalysts. The basic background for theory and specific research examples are reported together with the perspective on the design of novel materials using first-principles calculations.

• Journal of Korean Elementary Science Education
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• v.38 no.2
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• pp.244-262
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• 2019

The purpose of this study is to explore how the practical knowledge used by an elementary school science teacher during learner-centered science instruction can promote elementary students' construction of positive emotion. Using an auto-ethnographic approach over a period of three months, the researchers collected students' interest diaries, post interviews with students, video recordings in science classes, and students' personal diaries and analyzed them by means of the constant comparative method. In this way, the researchers categorized the structure of the practical knowledge held by the teacher and explained how it was applied in learner-centered science instruction to promote students' construction of positive emotion. Three images of an elementary science teacher's practical knowledge emerged and can be categorized under the following headings: 1) 'From science classroom to science $caf{\acute{e}}$', 2) 'Pleasant experiment class for all students and the teacher', and 3) 'A science class for students who were marginalized'. These images were backed up by principles and rules, and the teacher came to embody these images as he implemented these rules. This study also discusses how the impact of a science teacher's practical knowledge on students' construction of positive emotions can be interpreted as promoting positive outcomes rather than negative sanctions, meeting students' expectation from lab activities, and meeting the specific needs of marginalized students in a science class.

• Korean Journal of Remote Sensing
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• v.37 no.4
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• pp.747-761
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• 2021

With the increasing application of UAVs in intelligent transportation systems, vehicle detection for aerial images has become an essential engineering technology and has academic research significance. In this paper, a vehicle detection method for aerial images based on the YOLOv4 deep learning algorithm is presented. At present, the most known datasets are VOC (The PASCAL Visual Object Classes Challenge), ImageNet, and COCO (Microsoft Common Objects in Context), which comply with the vehicle detection from UAV. An integrated dataset not only reflects its quantity and photo quality but also its diversity which affects the detection accuracy. The method integrates three public aerial image datasets VAID, UAVD, DOTA suitable for YOLOv4. The training model presents good test results especially for small objects, rotating objects, as well as compact and dense objects, and meets the real-time detection requirements. For future work, we will integrate one more aerial image dataset acquired by our lab to increase the number and diversity of training samples, at the same time, while meeting the real-time requirements.

• International Journal of Computer Science & Network Security
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• v.22 no.10
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• pp.406-412
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• 2022

Scene classification of very high-resolution (VHR) imagery can attribute semantics to land cover in a variety of domains. Real-world application requirements have not been addressed by conventional techniques for remote sensing image classification. Recent research has demonstrated that deep convolutional neural networks (CNNs) are effective at extracting features due to their strong feature extraction capabilities. In order to improve classification performance, these approaches rely primarily on semantic information. Since the abstract and global semantic information makes it difficult for the network to correctly classify scene images with similar structures and high interclass similarity, it achieves a low classification accuracy. We propose a VHR remote sensing image classification model that uses extracts the global feature from the original VHR image using an EfficientNet-V2L CNN pre-trained to detect similar classes. The image is then classified using a multilayer perceptron (MLP). This method was evaluated using two benchmark remote sensing datasets: the 21-class UC Merced, and the 38-class PatternNet. As compared to other state-of-the-art models, the proposed model significantly improves performance.