• Geomechanics and Engineering
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• 제37권3호
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• pp.263-277
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• 2024

Artificial Neural Networks (ANNs) are artificial learning algorithms that provide successful results in solving many machine learning problems such as classification, prediction, object detection, object segmentation, image and video classification. There is an increasing number of studies that use ANNs as a prediction tool in soil classification. The aim of this research was to understand the role of hyperparameter optimization in enhancing the accuracy of ANNs for soil type classification. The research results has shown that the hyperparameter optimization and hyperparamter optimized ANNs can be utilized as an efficient mechanism for increasing the estimation accuracy for this problem. It is observed that the developed hyperparameter tool (HyperNetExplorer) that is utilizing the Covariance Matrix Adaptation Evolution Strategy (CMAES), Genetic Algorithm (GA) and Jaya Algorithm (JA) optimization techniques can be successfully used for the discovery of hyperparameter optimized ANNs, which can accomplish soil classification with 100% accuracy.

• 통합자연과학논문집
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• 제17권1호
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• pp.21-30
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• 2024

Several anti-inflammatory small molecules have been found in the process of the inflammatory response, and these small molecules have been used to treat some inflammatory and autoimmune diseases. Numerous tools for predicting anti-inflammatory peptides (AIPs) have emerged in recent years. However, conducting experimental validations in the lab is both resource-intensive and time-consuming. Current therapies for inflammatory and autoimmune disorders often involve nonspecific anti-inflammatory drugs and immunosuppressants, often with potential side effects. AIPs have been used in treating inflammatory illnesses like Alzheimer's disease and can limit the expression of inflammatory promoters. Recent advances in adverse incident predictions (AIPs) have been made, but it is crucial to acknowledge limitations and imperfections in existing methodologies.

• 통합자연과학논문집
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• 제16권4호
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• pp.111-122
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• 2023

Invasive species are organisms that are introduced into places outside of their natural distribution range. The global pet trade is facilitating the introduction of invasive species into new countries and areas. Among the introduced alien species, turtles are one of the most common animal groups whether lives in wetland ecosystems, such as wetlands or reservoirs. Like other countries around the world, exotic turtles is becoming a growing concern for the wetland ecosystem in South Korea. In this study, we report new reports of subspecies of Painted turtle (Chrysemys spp.): Chrysemys picta marginata, C. p. bellii and C. dorsalis, from the reservoirs in downtown Cheongju and Gwangju, South Korea. We used morphological features, such as the characteristics of the legs, plastron, and carapace, to identify the turtles. It is assumed that all turtles were artificially released into nature. Considering the increasing number of reports on the introduction of alien invasive turtles in Korean wetlands, we recommend the formulation of an immediate and systematic management plan for pet trades and organized continuous monitoring programs.

• 한국재료학회지
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• 제33권5호
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• pp.175-188
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• 2023

Water electrolysis holds great potential as a method for producing renewable hydrogen fuel at large-scale, and to replace the fossil fuels responsible for greenhouse gases emissions and global climate change. To reduce the cost of hydrogen and make it competitive against fossil fuels, the efficiency of green hydrogen production should be maximized. This requires superior electrocatalysts to reduce the reaction energy barriers. The development of catalytic materials has mostly relied on empirical, trial-and-error methods because of the complicated, multidimensional, and dynamic nature of catalysis, requiring significant time and effort to find optimized multicomponent catalysts under a variety of reaction conditions. The ultimate goal for all researchers in the materials science and engineering field is the rational and efficient design of materials with desired performance. Discovering and understanding new catalysts with desired properties is at the heart of materials science research. This process can benefit from machine learning (ML), given the complex nature of catalytic reactions and vast range of candidate materials. This review summarizes recent achievements in catalysts discovery for the hydrogen evolution reaction (HER) and oxygen evolution reaction (OER). The basic concepts of ML algorithms and practical guides for materials scientists are also demonstrated. The challenges and strategies of applying ML are discussed, which should be collaboratively addressed by materials scientists and ML communities. The ultimate integration of ML in catalyst development is expected to accelerate the design, discovery, optimization, and interpretation of superior electrocatalysts, to realize a carbon-free ecosystem based on green hydrogen.

• 한국임상약학회지
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• 제34권2호
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• pp.134-139
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• 2024

Background: Increasing numbers of studies and research about artificial intelligence (AI) and machine learning (ML) have led to their application in clinical trials. The purpose of this study is to analyze computer-based new technologies (AI/ML) applied on clinical trials registered on ClinicalTrials.gov to elucidate current usage of these technologies. Methods: As of March 1st, 2023, protocols listed on ClinicalTrials.gov that claimed to use AI/ML and included at least one of the following interventions-Drug, Biological, Dietary Supplement, or Combination Product-were selected. The selected protocols were classified according to their context of use: 1) drug discovery; 2) toxicity prediction; 3) enrichment; 4) risk stratification/management; 5) dose selection/optimization; 6) adherence; 7) synthetic control; 8) endpoint assessment; 9) postmarketing surveillance; and 10) drug selection. Results: The applications of AI/ML were explored in 131 clinical trial protocols. The areas where AI/ML was most frequently utilized in clinical trials included endpoint assessment (n=80), followed by dose selection/optimization (n=15), risk stratification/management (n=13), drug discovery (n=4), adherence (n=4), drug selection (n=1) and enrichment (n=1). Conclusion: The most frequent application of AI/ML in clinical trials is in the fields of endpoint assessment, where the utilization is primarily focuses on the diagnosis of disease by imaging or video analyses. The number of clinical trials using artificial intelligence will increase as the technology continues to develop rapidly, making it necessary for regulatory associates to establish proper regulations for these clinical trials.

• 스마트미디어저널
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• 제13권7호
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• pp.36-44
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• 2024

Identifying plant species and diseases is crucial for maintaining biodiversity and achieving optimal crop yields, making it a topic of significant practical importance. Recent studies have extended plant disease recognition from traditional closed-set scenarios to open-set environments, where the goal is to reject samples that do not belong to known categories. However, in open-world tasks, it is essential not only to define unknown samples as "unknown" but also to classify them further. This task assumes that images and labels of known categories are available and that samples of unknown categories can be accessed. The model classifies unknown samples by learning the prior knowledge of known categories. To the best of our knowledge, there is no existing research on this topic in plant-related recognition tasks. To address this gap, this paper utilizes knowledge distillation to model the category space relationships between known and unknown categories. Specifically, we identify similarities between different species or diseases. By leveraging a fine-tuned model on known categories, we generate pseudo-labels for unknown categories. Additionally, we enhance the baseline method's performance by using a larger pre-trained model, dino-v2. We evaluate the effectiveness of our method on the large plant specimen dataset Herbarium 19 and the disease dataset Plant Village. Notably, our method outperforms the baseline by 1% to 20% in terms of accuracy for novel category classification. We believe this study will contribute to the community.

• International Journal of Internet, Broadcasting and Communication
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• 제12권4호
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• pp.232-237
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• 2020

Given the recent pace of development and expansion of Artificial Intelligence (AI) technology, the influence and ripple effects of AI technology on the whole of our lives will be very large and spread rapidly. The National Artificial Intelligence R&D Strategy, published in 2019, emphasizes the importance of artificial intelligence education for K-12 students. It also mentions STEM education, AI convergence curriculum, and budget for supporting the development of teaching materials and tools. However, it is necessary to create a new type of curriculum at a time when artificial intelligence curriculum has never existed before. With many attempts and discussions going very fast in all countries on almost the same starting line. Also, there is no suitable professor for K-12 students, and it is difficult to make K-12 students understand the concept of AI. In particular, it is difficult to teach elementary school students through professional programming in AI education. It is also difficult to learn tools that can teach AI concepts. In this paper, we propose an educational model for elementary school students to improve their understanding of AI through play or experience. This an experiential education model that combineds exploratory learning and discovery learning using multi-intelligence and the PLAY teaching-learning model to undertand the importance of data training or data required for AI education. This educational model is designed to learn how a computer that knows only binary numbers through UA recognizes images. Through code.org, students were trained to learn AI robots and configured to understand data bias like play. In addition, by learning images directly on a computer through TeachableMachine, a tool capable of supervised learning, to understand the concept of dataset, learning process, and accuracy, and proposed the process of AI inference.

• 한국초등과학교육학회지:초등과학교육
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• 제25권2호
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• pp.167-178
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• 2006

The purpose of this study was to explore characteristics of sixth grade students' opistemological beliefs in science and the relationship to learner's characteristics: learning motivation, learning strategies, and logical thinking. The subjects were 265 sixth graders and data was collected through two types of questionnaires, translated and modified by researchers: opistemological beliefs regarding science, learning motivation & strategies. The results of this study were as follows. The students believed that the goals of science were related to activations such as 'Science is experiment', or 'Science is invention: These beliefs were connected with the emphasis of science classes or the focus of the science curriculum. However, the students' beliefs related to the changeability of science knowledge, the source of science knowledge, and the role of experiments in developing knowledge were oriented to modern opistemological views. Moreover, the beliefs were meaningfully related to students' characteristics: learning motivation, learning strategies, and logical thinking. Among the students' characteristics, logical thinking was especially related to all of the factors of students' beliefs: the changeability of science knowledge, the source of science knowledge, and the role of experiments in developing knowledge. However, the students who believed that scientific knowledge came from scientists, science teachers, or science textbooks had high levels of self-efficacy. Therefore, the belief that scientific knowledge is formed by self-discovery, in order to generate high self-efficacy, needs to be encouraged. From the results, it is possible to check the orientation of current science education based on the students' opistemological beliefs. In addition, the resources can be accumulated for persevering in our efforts to achieve a positive orientation for science education.

• 한국수학교육학회지시리즈E:수학교육논문집
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• 제24권1호
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• pp.259-282
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• 2010

본 연구자들은 이 논문에서 다변수미적분학 수업에 적용한 변형 무어교수법(Modified Moore Method)을 소개하고, 이 교수법을 적용한 수업에서 학습에 대한 학생들의 인식변화와 학습 효과를 관찰하여 효과적인 교수 학습을 논의하였다. 본 연구는 3주 기간의 여름계절학기 강좌로 개설된 다변수미적분학 수업을 수강한 15명의 학생들을 대상으로 실시되었다. 학생들의 능동적 예습을 안내하기 위하여 주요 수학 개념에 관련된 단계별로 구조화된 발문 형식의 예습자료를 미리 제시하였다. 수업 중 학생들의 소그룹 협력학습 과정과 발표를 관찰하고, 매 수업 후반에 작성한 학생들의 강의일지와 학기말에 실시한 설문 조사를 분석한 결과에 의하면, 학생들은 스스로 탐구하여 발견하는 학습을 통하여 주제 개념에 대하여 보다 깊이 이해할 수 있음을 인식하게 되었고, 동료와의 토론 및 상호 가르침을 통하여 다양한 내용의 학습과 반성적 사고를 경험할 수 있었다.

• 영재교육연구
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• 제11권3호
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• pp.23-44
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• 2001

기존의 교실에서 교사가 학생들에게 무엇을 또 어떻게 탐구할 것인가에 대한 절차적인 지식을 가르칠 수는 있었으나, 그러한 가운데 우리가 놓치고 있었던 것은 과학을 하는 것(doing science)에 대한 본질이었다. 특히 영재들의 학문적 욕구는 평범한 아이들과는 질적으로 다르기 때문에, 과학 영재들에게 그러한 요구와 필요성은 상당히 큰 것이었으나 그 요구와 필요성에 부합하는 연구와 프로그램의 개발이 뒷받침되어주지 못한 것이 현실이다. 과학 영재를 위한 교과과정은 학습 내용, 학습과정, 산출물에 있어서 영재들을 위해 특수화가 필요하다. 이러한 현실적 문제에 봉착하여, 본 연구는 하나의 작은 시도로서 과학영재들을 위한 프로젝트형 탐구학습의 프로그램을 개발, 현장에 적용, 그리고 제한적이나 그 효과성도 부분적으로 검토하였다. 실제 문제들과 이슈들을 다루는 프로젝트형 탐구학습을 수행함으로서 학생들은 고차원적인 사고 능력을 기르며, 소집단활동을 통해서 학생들이 복잡한 실제 문제들을 창의적으로 해결할 수 있음을 보여주었다. 학생들은 이러한 형식의 수업을 통하여 또한 내용을 배우고 적용할 수 있게 되며 비판적 사고력을 배양하고 평생학습자로서의 자질을 키우며 의사소통능력과 상호협동능력을 자연스럽게 익힐 수 있음을 제시했다. 프로젝트형 탐구학습은 최근 여러 학문영역에서 널리 사용되고 있다. 하지만 아직 영재교육프로그램에서 그 사용은 그리 활발하게 이루어지고 있지 않는 실정이다 프로젝트형 탐구학습은 과학영재들의 특성과 잘 부합되므로 과학영재들을 위한 프로그램에 성공적으로 사용되어질 수 있음을 본 연구는 시사하고 있다.