• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.2
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• pp.1-12
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• 2023

To make semiconductor chips, a number of complex semiconductor manufacturing processes are required. Semiconductor chips that have undergone complex processes are subjected to EDS(Electrical Die Sorting) tests to check product quality, and a wafer bin map reflecting the information about the normal and defective chips is created. Defective chips found in the wafer bin map form various patterns, which are called defective patterns, and the defective patterns are a very important clue in determining the cause of defects in the process and design of semiconductors. Therefore, it is desired to automatically and quickly detect defective patterns in the field, and various methods have been proposed to detect defective patterns. Existing methods have considered simple, complex, and new defect patterns, but they had the disadvantage of being unable to provide field engineers the evidence of classification results through deep learning. It is necessary to supplement this and provide detailed information on the size, location, and patterns of the defects. In this paper, we propose an anomaly detection framework that can be explained through FCDD(Fully Convolutional Data Description) trained only with normal data to provide field engineers with details such as detection results of abnormal defect patterns, defect size, and location of defect patterns on wafer bin map. The results are analyzed using open dataset, providing prominent results of the proposed anomaly detection framework.

• Journal of Ginseng Research
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• v.48 no.3
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• pp.286-297
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• 2024

Brain plasticity refers to the brain's ability to modify its structure, accompanied by its functional changes. It is influenced by learning, experiences, and dietary factors, even in later life. Accumulated researches have indicated that ginseng may protect the brain and enhance its function in pathological conditions. There is a compelling need for a more comprehensive understanding of ginseng's role in the physiological condition because many individuals without specific diseases seek to improve their health by incorporating ginseng into their routines. This review aims to deepen our understanding of how ginseng affects brain plasticity of people undergoing normal aging process. We provided a summary of studies that reported the impact of ginseng on brain plasticity and related factors in human clinical studies. Furthermore, we explored researches focused on the molecular mechanisms underpinning the influence of ginseng on brain plasticity and factors contributing to brain plasticity. Evidences indicate that ginseng has the potential to enhance brain plasticity in the context of normal aging by mediating both central and peripheral systems, thereby expecting to improve age-related declines in brain function. Moreover, given modern western diet can damage neuroplasticity in the long term, ginseng can be a beneficial supplement for better brain health.

• Journal of Advanced Navigation Technology
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• v.25 no.1
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• pp.115-123
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• 2021

Pine nuts are Korea's representative nut forest products and profitable crops. However, pine nuts are harvested by climbing the trees themselves, thus the risk is high. In order to solve this problem, it is necessary to harvest pine nuts using a robot or an unmanned aerial vehicle(UAV). In this paper, we propose a deep learning based detection method for harvesting pine nut in UAV aerial images. For this, a video was recorded in a real pine forest using UAV, and a data augmentation technique was used to supplement a small number of data. As the data for 3D detection, Unity3D was used to model the virtual pine nut and the virtual environment, and the labeling was acquired using the 3D transformation method of the coordinate system. Deep learning algorithms for detection of pine nuts distribution area and 2D and 3D detection of pine nuts objects were used DeepLabV3+, YOLOv4, and CenterNet, respectively. As a result of the experiment, the detection rate of pine nuts distribution area was 82.15%, the 2D detection rate was 86.93%, and the 3D detection rate was 59.45%.

• Geophysics and Geophysical Exploration
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• v.25 no.4
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• pp.227-241
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• 2022

Full waveform inversion (FWI) in the field of seismic data processing is an inversion technique that is used to estimate the velocity model of the subsurface for oil and gas exploration. Recently, deep learning (DL) technology has been increasingly used for seismic data processing, and its combination with FWI has attracted remarkable research efforts. For example, DL-based data processing techniques have been utilized for preprocessing input data for FWI, enabling the direct implementation of FWI through DL technology. DL-based FWI can be divided into the following methods: pure data-based, physics-based neural network, encoder-decoder, reparameterized FWI, and physics-informed neural network. In this review, we describe the theory and characteristics of the methods by systematizing them in the order of advancements. In the early days of DL-based FWI, the DL model predicted the velocity model by preparing a large training data set to adopt faithfully the basic principles of data science and apply a pure data-based prediction model. The current research trend is to supplement the shortcomings of the pure data-based approach using the loss function consisting of seismic data or physical information from the wave equation itself in deep neural networks. Based on these developments, DL-based FWI has evolved to not require a large amount of learning data, alleviating the cycle-skipping problem, which is an intrinsic limitation of FWI, and reducing computation times dramatically. The value of DL-based FWI is expected to increase continually in the processing of seismic data.

• Education of Primary School Mathematics
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• v.26 no.4
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• pp.299-316
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• 2023

With the importance and necessity of using AI in the field of education, this study aims to explore elementary school teachers' perceptions of using Artificial Intelligence (AI) in mathematics education. For this purpose, we conducted a survey using a 5-point Likert scale with 161 elementary school teachers and analyzed their perceptions of mathematics education with AI via four categories (i.e., Attitude of using AI, AI for teaching mathematics, AI for learning mathematics, and AI for assessing mathematics performance). As a result, elementary school teachers displayed positive perceptions of the usefulness of AI applications to teaching, learning, and assessment of mathematics. Specifically, they strongly agreed that AI could assist personalized teaching and learning, supplement prerequisite learning, and analyze the results of assessment. They also agreed that AI in mathematics education would not replace the teacher's role. The results of this study also showed that the teachers exhibited diverse perceptions ranging from negative to neutral to positive. The teachers reported that they were less confident and prepared to teach mathematics using AI, with significant differences in their perceptions depending on whether they enacted mathematics lessons with AI or received professional training courses related to AI. We discuss the implications for the role of teachers and pedagogical supports to effectively utilize AI in mathematics education.

• Communications of Mathematical Education
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• v.19 no.4 s.24
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• pp.733-767
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• 2005

During the past decades, there has been a fundamental change in the objectives and nature of mathematics education, as well as a shift in research paradigms. The changes in mathematics education emphasize learning mathematics from realistic situations, students' invention or construction solution procedures, and interaction with other students of the teacher. This shifted perspective has many similarities with the theoretical . perspective of Realistic Mathematics Education (RME) developed by Freudental. The RME theory focused the guide reinvention through mathematizing and takes into account students' informal solution strategies and interpretation through experientially real context problems. The heart of this reinvention process involves mathematizing activities in problem situations that are experientially real to students. It is important to note that reinvention in a collective, as well as individual activity, in which whole-class discussions centering on conjecture, explanation, and justification play a crucial role. The overall purpose of this study is to examine the developmental research efforts to adpat the instructional design perspective of RME to the teaching and learning of differential equation is collegiate mathematics education. Informed by the instructional design theory of RME and capitalizes on the potential technology to incorporate qualitative and numerical approaches, this study offers as approach for conceptualizing the learning and teaching of differential equation that is different from the traditional approach. Data were collected through participatory observation in a differential equations course at a university through a fall semester in 2003. All class sessions were video recorded and transcribed for later detailed analysis. Interviews were conducted systematically to probe the students' conceptual understanding and problem solving of differential equations. All the interviews were video recorded. In addition, students' works such as exams, journals and worksheets were collected for supplement the analysis of data from class observation and interview. Informed by the instructional design theory of RME, theoretical perspectives on emerging analyses of student thinking, this paper outlines an approach for conceptualizing inquiry-oriented differential equations that is different from traditional approaches and current reform efforts. One way of the wars in which thus approach complements current reform-oriented approaches 10 differential equations centers on a particular principled approach to mathematization. The findings of this research will provide insights into the role of the mathematics teacher, instructional materials, and technology, which will provide mathematics educators and instructional designers with new ways of thinking about their educational practice and new ways to foster students' mathematical justifications and ultimately improvement of educational practice in mathematics classes.

• Journal of Software Engineering Society
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• v.27 no.1
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• pp.8-14
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• 2018

As cloud computing has emerged as a hot trend in the IT industry, various types of cloud services have emerged. In addition, cloud service broker (CSB) technology has emerged to alleviate the complexity of the process of selecting the desired service that user wants among the various cloud services. One of the key features of the CSB is to recommend the best cloud services to users. In general, CSB can use a method to evaluate a service by receiving feedback about a service from users in order to recommend a cloud service. However, since each user has different criteria for giving a rating, there is a problem that reliability of service evaluation can be low when the rating is only used. In this paper, a method is proposed to supplement evaluation of rating based service by applying machine learning based sentiment analysis to cloud service user's review. In addition, the CSB prototype is implemented based on proposed method. Further, the results of comparing the performance of various learning algorithms is proposed that can be used for sentiment analysis through experiments using actual cloud service review as learning data. The proposed service evaluation method complements the disadvantages of the existing rating-based service evaluation and can reflect the service quality in terms of user experience.

• Journal of the Korean Chemical Society
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• v.47 no.6
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• pp.645-658
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• 2003

This study was carried out to analyze inquiry area of the chemistry (II) textbooks which were published by the 7th curriculum. The study attempts to analyze the degree to which chemistry (II) textbooks reflected the guidelines of the 7th science curriculum and propose educational suggestions for the inquiry learning. The analysis of the inquiry area was carried out based on the suggested inquiry elements of the 7th science curriculum. Overall, for the analysis of inquiry elements, basic inquiry elements except classifying suggested by the 7th science curriculum were well reflected on the textbooks. However, for the integrated inquiry elements, interpreting data takes almost half of the total integrated inquiry elements. Other integrated inquiry elements except drawing conclusion and transforming data were reflected less than ten percent. Investigation was also reflected less than ten percent of all inquiry activity. And inquiry activities were limited in terms of variety with few projects and no field trip. The main essence of the 7th science curriculum is the emphasis on total inquiry learning through various integrated inquiry elements and inquiry activities for higher grade students. Thus it is suggested that teachers provide inquiry learning which can supplement the textbook.

• Journal of Elementary Mathematics Education in Korea
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• v.21 no.1
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• pp.215-241
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• 2017

This study analyzed teachers' Pedagogical Content Knowledge (PCK) regarding the pedagogical aspect of the instruction of ratio and rate in order to look into teachers' problems during the process of teaching ratio and rate. This study aims to clarify problems in teachers' PCK and promote the consideration of the materialization of an effective and practical class in teaching ratio and rate by identifying the improvements based on problems indicated in PCK. We subdivided teachers' PCK into four areas: mathematical content knowledge, teaching method and evaluation knowledge, understanding knowledge about students' learning, and class situation knowledge. The conclusion of this study based on analysis of the results is as follows. First, in the 'mathematical content knowledge' aspect of PCK, teachers need to understand the concept of ratio from the perspective of multiplicative comparison of two quantities, and the concept of rate based on understanding of two quantities that are related proportionally. Also, teachers need to introduce ratio and rate by providing students with real-life context, differentiate ratios from fractions, and teach the usefulness of percentage in real life. Second, in the 'teaching method and evaluation knowledge' aspect of PCK, teachers need to establish teaching goals about the students' comprehension of the concept of ratio and rate and need to operate performance evaluation of the students' understanding of ratio and rate. Also, teachers need to improve their teaching methods such as discovery learning, research study and activity oriented methods. Third, in the 'understanding knowledge about students' learning' aspect of PCK, teachers need to diversify their teaching methods for correcting errors by suggesting activities to explore students' own errors rather than using explanation oriented correction. Also, teachers need to reflect students' affective aspects in mathematics class. Fourth, in the 'class situation knowledge' aspect of PCK, teachers need to supplement textbook activities with independent consciousness and need to diversify the form of class groups according to the character of the activities.

• Journal of the Korean Society of Earth Science Education
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• v.12 no.1
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• pp.27-39
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• 2019

The purpose of this study is to develop a STEAM program for teaching climate change through CLAMP (Climate-Leaf Analysis Multivariate Program) paleoclimate inquiry in connection with high school 'Integrated Science' subject. In order to do so, we analyzed the 2015 revised national curriculum and science textbook in terms of the PDIE instructional design model, and developed the teaching-learning materials for 10 class hours through expert panel discussion and pilot test. According to the STEAM class procedure, in the situation presentation stage, the fossil leaves were collected from the dicotyledon plants near school, and the LMA (Leaf Margin Analysis) climate inquiry activity. was presented as the learning goal. During the creative design stage, students were taught about geology and leaf fossils in the study region, and CLAMP input data (31 characteristics of morphotype and leaf architectural of fossil leaves) were given. In the emotional experience and new challenge stage, we collected leaf fossils for outdoor learning, explored paleoclimate with CLAMP method, and promoted climatic literacy in the process of discussing tendencies and causes of Cenozoic's climate change. The validity of the development program was assessed (CVI .84) as being suitable for development purpose in all items through the process of establishing reliability among expert panel. In order to apply the program to the high school, a pilot test was conducted to supplement the discrepancies and to review the suitability. The satisfaction rate of the participants was 4.48, and the program was complemented with their opinions. This study will enable high school students to have practical knowledge and reacting volition for climate change, and contribute to fostering students' climate literacy.