• Title/Summary/Keyword: learning physics

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Analysis of Reflective Thinking Characteristic of College Students in General Physics Experiment (일반물리학실험에 나타난 대학생의 반성적 사고 특징 분석)

  • Kim, Hee Jung;Choi, Kyoulee;Oh, Yoonjeong
    • Journal of Science Education
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    • v.44 no.2
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    • pp.225-239
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    • 2020
  • This study aims to analyze the characteristics of reflective thoughts of students during experiments in general physics experiments. The participants were 32 college students, they submitted 10 experiment reports including answers to questions for reflective thinking. The results were as follows: First, students show reflective thinking broadly in the experiment, levels were followed by practical reflection, technical reflection, and critical reflection. Also, they actively accepted the knowledge related to experiments, but were passive in connecting new knowledge and experiences obtained through the experiments or forming new questions. Second, the reflective thinking of students show high correlation with experiments related to prior knowledge or the easy-to-understand process. Third, through the qualitative analysis of open-ended questions, it was confirmed that technical reflection occurs in individual evaluation, practical reflection in group evaluation, and practical reflection and critical reflection in improvement proposal. While the students' reflective thinking were superficial or mainly functional, however, they recursively examined the learning contents and the experimental process concurrently.

Elementary School Students' Images of Science Class and Factors Influencing Their Formations (초등학생들의 과학 수업에 대한 이미지와 이미지 형성에 영향을 미치는 요인)

  • Kang, Hun-Sik;Lee, Ji-Young
    • Journal of The Korean Association For Science Education
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    • v.30 no.4
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    • pp.519-531
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    • 2010
  • In this study, we investigated the elementary school students' images of science class and the factors influencing their formations. 280 sixth graders were selected from nine elementary schools in Gyeonggi province and Gangwon province and the DASCT-C (Draw-A-Science-Class-Test Checklist) was administered. In addition, four students were individually interviewed in order to investigate their responses deeply. Analyses of the results revealed that the students' images of science class for four science subjects (physics, chemistry, biology, and earth science) were more 'student-centered' than 'teacher-centered' or 'neutral'. The students of the teacher with student-centered image of science class had also more student-centered images than those with teacher-centered images. Many students answered that the main factors affecting their images of science class were the experiences of impressed or funny science classes, the perceptions of wanted science classes, the active science learning experiences, the educational experiences outside the school curriculum, and the negative science learning experiences. Educational implications of these findings are discussed.

Actual Use of Internet in Curriculum Study of Students in Radiology (방사선 재학생 전공교과목 학습에서 인터넷 활용 실태)

  • Kim, Min-Cheol;Huang, Yuxin;Choi, Ji Hoon;Jung, Hong Ryang;Park, Hae-Ri;Yang, Oh-Nam
    • Journal of radiological science and technology
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    • v.41 no.5
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    • pp.487-491
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    • 2018
  • The purpose of this study was to analyze questionnaires of 161 college students attending radiology departments in order to investigate the actual condition of internet use of radiology students. As a result, 95% of college students using the Internet showed 5.8% of general knowledge, 56.9% of radiation major, and 45.8% of general education. In the field of Internet use, basic medicine was 71.2%, anatomy 59.5% and physiology 51.6%. Radiation theory was 39.9% in radiation physics, 31.4% in radiation biology, and 18.3% in radiation management. The radiological applications were followed by radiography and radiography in order of 31.4% and 20.3%, respectively. The radiological imaging was 45.8%, MRI was 37.9%, CT was 37.3%, ultrasound was 24.2%, And radiation nuclear medicine 25.5%. The results of the descriptive statistics of the satisfaction of the contents using the Internet media showed that the overall satisfaction was below 2.5 Based on the results of this study, it is necessary to develop a program with high accessibility to provide various opportunities for internet-based opportunities to increase the academic achievement value of major subjects through the internet and to solve the difficulties in the major subject.

The Effect of Web-Aided Laboratory on Molecular Dynamics of High School Physics Course (고등학교 물리의 기체 분자 운동론에서 웹 활용 모의실험이 학습에 미치는 효과)

  • Roh, Hack-Kie;Kong, Youn-Sig;Park, Chang-Young;Chung, Ki-Soo
    • Journal of The Korean Association For Science Education
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    • v.25 no.5
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    • pp.547-554
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    • 2005
  • A developed Web-aided laboratory program visualized invisible gas. In the Web-aided laboratory temperature and pressure were controlled and the resultant findings were presented as types of graphs, disclosed in the form of an analyzed report. A Web-aided laboratory experiment and traditional experiment group(2 classes) were assembled from a farming village co-educational high school and taught the motion of molecule lesson for 2 class hours. Before actual class instruction, to survey learner motivation characteristics, the short-version GALT, the test of attitudes toward science instruction, was administered. After instruction, student learning achievement, TOSRA, and IMMS, were administered to the two groups. To analyze data ANCOVA was administrated. Result found that attitudes towards science instruction did not significantly differ, but learning motivation and achievement were significantly altered.

Distance Learning for Higher Education Applicants in War: Information Competence

  • Hanna, Truba;Iryna, Radziievska;Mykhailo, Sherman;Nataliia, Morska;Alla, Kulichenko;Nataliia, Havryliuk
    • International Journal of Computer Science & Network Security
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    • v.22 no.10
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    • pp.291-297
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    • 2022
  • Modern challenges in the educational environment force scientists and practitioners to search for an adequate answer. In particular, the war in Ukraine demonstrated the importance of developing information competence as one of the main means of distinguishing true information from a whole stream of fake news. This is especially relevant in connection with the introduction of distance learning when students must find and process a large amount of information on their own. Therefore, the purpose of the article is to analyze the training of higher education students through the prism of acquiring the necessary level of informational competence in war conditions. For this, general scientific and special research methods, as well as the experimental method, were used. In the results, the peculiarities of the interpretation of information competence in the distance form of education among modern researchers are determined, the psychological components of resistance to fakes are analyzed. Based on the conducted empirical measurements, it was established that thorough work on student education gives positive skills when working independently with Internet materials, strengthens the ability to distinguish false information and propaganda from the real state of affairs. The conclusions summarize the results of the empirical research and suggest ways to improve the situation with the formation of information competence.

Can AI-generated EUV images be used for determining DEMs of solar corona?

  • Park, Eunsu;Lee, Jin-Yi;Moon, Yong-Jae;Lee, Kyoung-Sun;Lee, Harim;Cho, Il-Hyun;Lim, Daye
    • The Bulletin of The Korean Astronomical Society
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    • v.46 no.1
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    • pp.60.2-60.2
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    • 2021
  • In this study, we determinate the differential emission measure(DEM) of solar corona using three SDO/AIA EUV channel images and three AI-generated ones. To generate the AI-generated images, we apply a deep learning model based on multi-layer perceptrons by assuming that all pixels in solar EUV images are independent of one another. For the input data, we use three SDO/AIA EUV channels (171, 193, and 211). For the target data, we use other three SDO/AIA EUV channels (94, 131, and 335). We train the model using 358 pairs of SDO/AIA EUV images at every 00:00 UT in 2011. We use SDO/AIA pixels within 1.2 solar radii to consider not only the solar disk but also above the limb. We apply our model to several brightening patches and loops in SDO/AIA images for the determination of DEMs. Our main results from this study are as follows. First, our model successfully generates three solar EUV channel images using the other three channel images. Second, the noises in the AI-generated EUV channel images are greatly reduced compared to the original target ones. Third, the estimated DEMs using three SDO/AIA images and three AI-generated ones are similar to those using three SDO/AIA images and three stacked (50 frames) ones. These results imply that our deep learning model is able to analyze temperature response functions of SDO/AIA channel images, showing a sufficient possibility that AI-generated data can be used for multi-wavelength studies of various scientific fields. SDO: Solar Dynamics Observatory AIA: Atmospheric Imaging Assembly EUV: Extreme Ultra Violet DEM: Diffrential Emission Measure

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Autoencoder Based N-Segmentation Frequency Domain Anomaly Detection for Optimization of Facility Defect Identification (설비 결함 식별 최적화를 위한 오토인코더 기반 N 분할 주파수 영역 이상 탐지)

  • Kichang Park;Yongkwan Lee
    • The Transactions of the Korea Information Processing Society
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    • v.13 no.3
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    • pp.130-139
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    • 2024
  • Artificial intelligence models are being used to detect facility anomalies using physics data such as vibration, current, and temperature for predictive maintenance in the manufacturing industry. Since the types of facility anomalies, such as facility defects and failures, anomaly detection methods using autoencoder-based unsupervised learning models have been mainly applied. Normal or abnormal facility conditions can be effectively classified using the reconstruction error of the autoencoder, but there is a limit to identifying facility anomalies specifically. When facility anomalies such as unbalance, misalignment, and looseness occur, the facility vibration frequency shows a pattern different from the normal state in a specific frequency range. This paper presents an N-segmentation anomaly detection method that performs anomaly detection by dividing the entire vibration frequency range into N regions. Experiments on nine kinds of anomaly data with different frequencies and amplitudes using vibration data from a compressor showed better performance when N-segmentation was applied. The proposed method helps materialize them after detecting facility anomalies.

Identification of primary input parameters affecting evacuation in ventilated main control room through CFAST simulations and application of a machine learning algorithm to replace CFAST model

  • Sumit Kumar Singh;Jinsoo Bae;Yu Zhang;Saerin Lim;Jongkook Heo;Seoung Bum Kim;Weon Gyu Shin
    • Nuclear Engineering and Technology
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    • v.56 no.9
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    • pp.3717-3729
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    • 2024
  • Accurately predicting evacuation time in a ventilated main control room (MCR) during fire emergencies is crucial for ensuring the safety of personnel at nuclear power plants. This study proposes to use neural networks alongside consolidated fire and smoke transport (CFAST) simulations to serve as a surrogate model for physics-based simulation tools. Our neural networks can promptly predict the evacuation time in MCRs, proving to be a valuable asset in fire emergencies and eliminating the need for time-consuming rollouts of the CFAST simulations. The CFAST model simulates fire and evacuation scenarios in a ventilated MCR with variations in input parameters such as door conditions, ventilation flow rate, leakage area, and fire propagation time. Target output parameters, such as hot gas layer temperature (HGLT), heat flux (HF), and optical density (OD), are used alongside standardized evacuation variables to train a machine learning model for predicting evacuation time. The findings suggest that high ventilation flow rates help to dilute smoke and discharge hot gas, leading to lower target output parameters and quicker evacuation. Standardized evacuation variables exceed the required abandonment criteria for all door conditions, indicating the importance of proper evacuation procedures. The results show that neural networks can generate evacuation times close to those obtained from CFAST simulations.

Prediction of Target Motion Using Neural Network for 4-dimensional Radiation Therapy (신경회로망을 이용한 4차원 방사선치료에서의 조사 표적 움직임 예측)

  • Lee, Sang-Kyung;Kim, Yong-Nam;Park, Kyung-Ran;Jeong, Kyeong-Keun;Lee, Chang-Geol;Lee, Ik-Jae;Seong, Jin-Sil;Choi, Won-Hoon;Chung, Yoon-Sun;Park, Sung-Ho
    • Progress in Medical Physics
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    • v.20 no.3
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    • pp.132-138
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    • 2009
  • Studies on target motion in 4-dimensional radiotherapy are being world-widely conducted to enhance treatment record and protection of normal organs. Prediction of tumor motion might be very useful and/or essential for especially free-breathing system during radiation delivery such as respiratory gating system and tumor tracking system. Neural network is powerful to express a time series with nonlinearity because its prediction algorithm is not governed by statistic formula but finds a rule of data expression. This study intended to assess applicability of neural network method to predict tumor motion in 4-dimensional radiotherapy. Scaled Conjugate Gradient algorithm was employed as a learning algorithm. Considering reparation data for 10 patients, prediction by the neural network algorithms was compared with the measurement by the real-time position management (RPM) system. The results showed that the neural network algorithm has the excellent accuracy of maximum absolute error smaller than 3 mm, except for the cases in which the maximum amplitude of respiration is over the range of respiration used in the learning process of neural network. It indicates the insufficient learning of the neural network for extrapolation. The problem could be solved by acquiring a full range of respiration before learning procedure. Further works are programmed to verify a feasibility of practical application for 4-dimensional treatment system, including prediction performance according to various system latency and irregular patterns of respiration.

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Intelligent Washing Machine: A Bioinspired and Multi-objective Approach

  • Milasi, Rasoul Mohammadi;Jamali, Mohammad Reza;Lucas, Caro
    • International Journal of Control, Automation, and Systems
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    • v.5 no.4
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    • pp.436-443
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    • 2007
  • In this paper, an intelligent method called BELBIC (Brain Emotional Learning Based Intelligent Controller) is used to control of Locally Linear Neuro-Fuzzy Model (LOLIMOT) of Washing Machine. The Locally Linear Neuro-Fuzzy Model of Washing Machine is obtained based on previously extracted data. One of the important issues in using BELBIC is its parameters setting. On the other hand, the controller design for Washing Machine is a multi objective problem. Indeed, the two objectives, energy consumption and effectiveness of washing process, are main issues in this problem, and these two objectives are in contrast. Due to these challenges, a Multi Objective Genetic Algorithm is used for tuning the BELBIC parameters. The algorithm provides a set of non-dominated set points rather than a single point, so the designer has the advantage of selecting the desired set point. With considering the proper parameters after using additional assumptions, the simulation results show that this controller with optimal parameters has very good performance and considerable saving in energy consumption.