• Journal of the Korean Society of Earth Science Education
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• v.16 no.1
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• pp.153-165
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• 2023

This study investigates learners' environmental literacy, classifies the results by factors of environmental literacy, and then investigates the differences in the students' environmental behavior practice experiences according to the classification by factor. The study was conducted with 47 6th grade students from D elementary school located in P metropolitan city as the subject of final analysis, and environmental literacy questionnaires and environmental behavior practice experience questionnaires were used as the main data. As a result of the study, the learners were classified into three groups according to the factors of environmental literacy, and they were respectively named as the "High environmental literacy group", "low environmental literacy group", and "Low Function and Affectif group". A Word network was formed using the descriptions of environmental behavior practice experiences for each cluster, and a Degree Centrality Analysis was performed to visualize and then analyze. As a result of the analysis, "High environmental literacy group" was confirmed, 1) recognized the subjects of environmental action practice as individuals and families, 2) described his experience of environmental action practice in relation to all elements of environmental literacy, and had a relatively pessimistic view. "low environmental literacy group", and "Low Function and Affectif group" were confirmed 1) perceive the subject of environmental behavior practice as a relatively social problem, 2) the description of the experience of environmental behavior practice is relatively biased specific factors, and the "Low Function and Affectif group" is particularly focused on the knowledge element. And 3) it was confirmed that they were aware of climate change from a relatively optimistic perspective. Based on this conclusion, suggestions were made from the perspective of environmental education.

• Journal of the Korean Applied Science and Technology
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• v.41 no.2
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• pp.472-483
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• 2024

This study evaluated the attitudes of the Korean population towards sunscreen use through the Health Belief Model (HBM) construct and investigated the psychological factors that influence sunscreen use. For this purpose, an online survey was conducted from 1 November 2023 to 1 January 2024, and a total of 303 participants were collected. The collected data were analysed using SPSS v. 25.0 programme using Cronbach's 𝛼, frequency analysis, descriptive statistics, correlation analysis, independent samples t-test, one way ANOVA, Scheffe's test, and multiple regression analysis. The results of the study showed that the mean score of sunscreen use was 3.26±1.384 out of 5, and there was a significant correlation between the variables of the health belief model and sunscreen use (p<.01). Gender, age, and skin colour were also associated with each variable, with women, the elderly, and those with lighter skin tending to be more proactive in sun protection. Multiple regression analyses revealed that self-efficacy (𝛽=.629, p<.001) and perceived vulnerability (𝛽=.139, p<.001), sub-factors of the Health Belief Model, had a statistically significant positive effect on sunscreen use, while perceived barriers (𝛽=-.261, p<.001) had a statistically significant negative effect on sunscreen use. These results may have important theoretical implications for the development and implementation of educational programmes to promote sunscreen use by providing insight into the psychosocial factors that influence sun protection.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1283-1297
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• 2023

Mid-wave infrared (MWIR) imagery, due to its ability to capture the temperature of land cover and objects, serves as a crucial data source in various fields including environmental monitoring and defense. The KOMPSAT-3A satellite acquires MWIR imagery with high spatial resolution compared to other satellites. However, the limited spatial resolution of MWIR imagery, in comparison to electro-optical (EO) imagery, constrains the optimal utilization of the KOMPSAT-3A data. This study aims to create a highly visible MWIR fusion image by leveraging the edge information from the KOMPSAT-3A panchromatic (PAN) image. Preprocessing is implemented to mitigate the relative geometric errors between the PAN and MWIR images. Subsequently, we employ a pre-trained pixel difference network (PiDiNet), a deep learning-based edge information extraction technique, to extract the boundaries of objects from the preprocessed PAN images. The MWIR fusion imagery is then generated by emphasizing the brightness value corresponding to the edge information of the PAN image. To evaluate the proposed method, the MWIR fusion images were generated in three different sites. As a result, the boundaries of terrain and objects in the MWIR fusion images were emphasized to provide detailed thermal information of the interest area. Especially, the MWIR fusion image provided the thermal information of objects such as airplanes and ships which are hard to detect in the original MWIR images. This study demonstrated that the proposed method could generate a single image that combines visible details from an EO image and thermal information from an MWIR image, which contributes to increasing the usage of MWIR imagery.

• Proceedings of the Korean Vacuum Society Conference
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• 2000.02a
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• pp.46-46
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• 2000

과학기술 및 산업의 발달로 인하여 실험 중 또는 공정 상에서 in situ, 실시간으로 측정하고 분석하며 이를 되먹임하는 품질제어의 중요성이 대두되고 있다. 반도체 공정 또는 박막제조 공정 중에서 박막의 두께, 굴절율, 물질의 조성비 등을 알아내는 것이 긴요한 과제로 대두되고 있으며, 이를 위하여 공정중인 제품의 품질을 실시간으로 평가하는 장비가 요구되고 있는 것이다. 나아가 공정중의 예상하지 못한 시료의 특성변화를 그대로 감지하여 적절히 보정해 주는 되먹임 기법은 높은 수율을 보장하는 첨단기법이라 할 수 있다. 이러한요구에 부응할 수 있는 본 제품(Elli-situ 2000)은 박막의 두께 표면변화를 sub 의 정밀도를 가지고 in situ, 실시간으로 정밀 측정할 수 있는 첨단 계측장비로서 빛의 편광상태 변화를 측정하기 때문에 공정 중의 시료에 영향을 주지 않는 비간섭 특성과 비접촉 특성의 장점 뿐만 아니라 공기중에서는 물론 진공이나 액체 등의 매질에서도 사용될 수 있어서 매질에 대한 제약이 거의 없다는 장점도 가지고 있다. 편광상태의 제어 및 측정을 필요한 광학장비의 경우, 제작이 까다롭기 때문에 대부분 가격이 높은 편이고 사용방법이나 측정 데이터에 대한 해석이 어렵다는 단점이 있으나, Elli-situ 2000의 경우 상용화된 외국제품(국내제품은 없슴)과 비교하여 성능 및 가격경쟁력에 있어서도 우위에 있으며 간단, 명료한 장비조작 및 컴퓨터를 사용한 구동의 전자동화를 이룸으로써 초보자도 쉽게 측정하고 데이터를 처리할 수 있도록 하였다. 또한 취부대의 경우, 진공포트 플랜지의 표준규격(2-3" Del-Seal 플랜지 규격)에 맞춤으로써 기존의 진공챔버에 부착하여 진공에 전혀 영향을 주지 않는 상태에서 시료의 변화를 in situ, 실시간으로 정밀 측정할 수 있도록 하였다. 하였다.O 박막은 산소 가스압력과 기판온도, 인가 전류를 변화시켜가며 증착하였으며 이에 따른 박막의 결정성 변화를 알아보았다. 기판온도를 실온에서 점차 증가시켜나가면 $\Delta$$\theta$50은 급격히 감소하며 30$0^{\circ}C$에서는 결정성이 우수한 막을 얻을 수 있었다. 또한 산소 가스 압력이 0.5~1mTorr에서 $\Delta$$\theta$50은 양호한 값을 나타내었지만 그 이상에서는 c-축 배향성이 나빠짐을 확인하였다. 따라서 대향타겟식스퍼터 장치를 이용하여 ZnO 박막을 증착시 가스압력 0.5~1mTorr, 기판온도 20$0^{\circ}C$이상의 막 제작조건에서 결정성이 우수하게 나타나는 것을 확인할 수 있었다. gluten이 단단해졌음을 알수 있었다. 유화제 stearly 칼슘, 혹은 hemicellulase를 amarans 10% 대체한 밀가루에 첨가하면 확연히 비용적을 증대시킬 수 있다는 사실을 알 수 있었다. quinoa는 명아주과 Chenopodium에 속하고 페루, 볼리비아 등의 고산지에서 재배 되어지는 것을 시료로 사용하였다. quinoa 분말은 중량의 5-20%을 quinoa를 대체하고 더욱이 분말중량에 대하여 0-200ppm의 lipase를 lipid(밀가루의 2-3배)에 대하여 품질개량제로서 이용했다. 그 결과 quinoa 대량 7.5%에서 비용적, gas cell이 가장 긍정적 결과를 산출했고 반죽의 조직구조가 강화되었다. 또 quinoa 대체에 의해 전분-지질 복합제의 흡열량이 증대된 것으로부터 전분-지질복합제의 형성 촉진이 시사되었다.이것으로 인하여 호화억제에 의한 노화 방지효과가 기대되었지만 실제로 빵의 노화는 현저히 진행되었다. 이것은

• Journal of the Korean Society of Radiology
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• v.15 no.4
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• pp.455-461
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• 2021

Cardiomegaly is one of the most common diseases seen on chest X-rays, but if it is not detected early, it can cause serious complications. In view of this, in recent years, many researches on image analysis in which deep learning algorithms using artificial intelligence are applied to medical care have been conducted with the development of various science and technology fields. In this paper, we would like to evaluate whether the Inception V3 deep learning model is a useful model for the classification of Cardiomegaly using chest X-ray images. For the images used, a total of 1026 chest X-ray images of patients diagnosed with normal heart and those diagnosed with Cardiomegaly in Kyungpook National University Hospital were used. As a result of the experiment, the classification accuracy and loss of the Inception V3 deep learning model according to the presence or absence of Cardiomegaly were 96.0% and 0.22%, respectively. From the research results, it was found that the Inception V3 deep learning model is an excellent deep learning model for feature extraction and classification of chest image data. The Inception V3 deep learning model is considered to be a useful deep learning model for classification of chest diseases, and if such excellent research results are obtained by conducting research using a little more variety of medical image data, I think it will be great help for doctor's diagnosis in future.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.3
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• pp.141-148
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• 2022

Recently, reinforcement learning combined with deep neural network technology has achieved remarkable success in various fields such as board games such as Go and chess, computer games such as Atari and StartCraft, and robot object manipulation tasks. However, such deep reinforcement learning describes states, actions, and policies in vector representation. Therefore, the existing deep reinforcement learning has some limitations in generality and interpretability of the learned policy, and it is difficult to effectively incorporate domain knowledge into policy learning. On the other hand, dNL-RRL, a new relational reinforcement learning framework proposed to solve these problems, uses a kind of vector representation for sensor input data and lower-level motion control as in the existing deep reinforcement learning. However, for states, actions, and learned policies, It uses a relational representation with logic predicates and rules. In this paper, we present dNL-RRL-based policy learning for transportation mobile robots in a manufacturing environment. In particular, this study proposes a effective method to utilize the prior domain knowledge of human experts to improve the efficiency of relational reinforcement learning. Through various experiments, we demonstrate the performance improvement of the relational reinforcement learning by using domain knowledge as proposed in this paper.

• Journal of Convergence for Information Technology
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• v.11 no.5
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• pp.111-117
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• 2021

With the rapid development of advanced science and technology including the 4th industrial revolution, the future battlefield environment is evolving at a rapid pace. In order to actively respond to issues such as reduction of military resources and shortening of service period, and to emphasize the realization of human-centered values, the Ministry of National Defense is re-establishing the role of the Army in accordance with the defense reform and is promoting the Warrior Platform, a next-generation individual combat system. In this paper, we intend to present the optimal warrior platform architecture suitable for the Korean Army by realizing the concept of future ground operations and analyzing overseas cases. We analyze the essential abilities required of individual combatants and the abilities required for each unit type, and specifically presents a plan for integration and linkage of warrior platform equipment. We also propose an efficient business promotion direction by presenting the data flow and power connection diagram between the devices that need integration and interworking.

• Journal of Science Education
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• v.45 no.3
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• pp.292-303
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• 2021

This study identifies the terms frequently used together with energy in online science news articles and topics of the news reports to find out how the term energy is used in everyday life and to draw implications for science curriculum and instruction about energy. A total of 2,171 online news articles in science category published by 11 major newspaper companies in Korea for one year from March 1, 2018 were selected by using energy as a search term. As a result of natural language processing, a total of 51,224 sentences consisting of 507,901 words were compiled for analysis. Using the R program, term frequency analysis, semantic network analysis, and structural topic modeling were performed. The results show that the terms with exceptionally high frequencies were technology, research, and development, which reflected the characteristics of news articles that report new findings. On the other hand, terms used more than once per two articles were industry-related terms (industry, product, system, production, market) and terms that were sufficiently expected as energy-related terms such as 'electricity' and 'environment.' Meanwhile, 'sun', 'heat', 'temperature', and 'power generation', which are frequently used in energy-related science classes, also appeared as terms belonging to the highest frequency. From a network analysis, two clusters were found including terms related to industry and technology and terms related to basic science and research. From the analysis of terms paired with energy, it was also found that terms related to the use of energy such as 'energy efficiency,' 'energy saving,' and 'energy consumption' were the most frequently used. Out of 16 topics found, four contexts of energy were drawn including 'high-tech industry,' 'industry,' 'basic science,' and 'environment and health.' The results suggest that the introduction of the concept of energy degradation as a starting point for energy classes can be effective. It also shows the need to introduce high-tech industries or the context of environment and health into energy learning.

• Spatial Information Research
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• v.22 no.4
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• pp.11-20
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• 2014

This study aims to provide information about the research-trends in the area of Geospatial Information using text-mining methods. We derived the National R&D Reports and papers from NDSL(National Discovery for Science Leaders) site. And then we preprocessed their key-words and classified those in separable sectors. We investigated the appearance rates and changes of key-words for R&D reports and papers. As a result, we conformed that the researches concerning applications are increasing, while the researches dealing with systems are decreasing. Especially, with in the framework of the keyword, '3D-GIS', 'sensor' and 'service' xcept ITS are emerging. It could be helpful to investigate research items later.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2016.05a
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• pp.191-193
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• 2016

인적오류 예방은 해양사고 예방에 가장 중요한 이슈로 현재 인식되고 있다. 현재 이러한 인적오류를 예방하기 위한 다양한 과학적인 기법들이 등장하고 있으나, 실제 인적오류를 예방할 수 있는 기법은 아직 개발되어 있지 못한 실정이다. 그 이유는 인적오류의 발생 원인과 특징이 사람을 대상으로 하기 때문에 실로 방대하고 원인식별이 어려우며, 원인과 결과 사이의 인과관계 구축에는 한계가 있기 때문이다. 기존 개발된 다양한 기법들은 이론적으로는 완벽할 수 있으나, 실제 방대한 원인과 결과 사이에 형성된 연계체인을 모두 흡수하기가 곤란하기 때문이다. 현재 IMO의 공식안전성평가(FSA) 기법이 해상분야에 널리 적용되고 있으나 구체적으로 어떠한 기법을 적용하여 인적오류를 적용할 수 있는지에 대해서는 아직도 애매모호한 실정이다. FTA, ETA, FEMA, SWIFT 등 다양한 분석기법의 등장과 AI, Fuzzy, MMC, Kalman 등 기초과학분야의 기본적인 이론과 기술을 적용할 수 있으나 인간의 인적오류 식별과 분석 및 평가와 예측에는 한계가 있는 것이 현재의 실정이다. 한편 최근에는 기존에 많은 문제점을 내포하고 있는 것으로 고려되었던 베이지안 네트워크(Bayesian Network, BN)가 다시 분석과 예측 분야에 등장하고 있는데, BN의 장점을 수용하고 단점을 해결할 수 있는 방법들이 연구되고 있기 때문이다. BN의 장점은 전방추론과 후방추론을 적용하여 사고의 원인과 결과를 분석한 후, 이에 대한 해결 방안을 식별할 수 있기 때문이다. BN의 단점은 이진(binary) 구조의 데이터만을 수용할 수 있기 때문에 상관 변수들이 방대한 경우 계산시간이 방대해지고 이를 모두 수용할 수 있는 방법이 없기 때문이다. 따라서 BN 구조를 어떻게 설계하는냐가 최근의 이수로 등장하고 있다. 본 연구에서는 이러한 제 문제점을 고찰하고 인적오류 모델 개발에 최적인 방법 또는 기술을 모색하는데 있다.