• Korean Journal of Remote Sensing
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• v.39 no.5_1
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• pp.621-635
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• 2023

The assessment of structural condition is a crucial process for evaluating its usability and determining the diagnostic cycle. The currently employed manpower-based methods suffer from issues related to safety, efficiency, and objectivity. To address these concerns, research based on deep learning using images is being conducted. However, acquiring structural damage data is challenging, making it difficult to construct a substantial amount of training data, thus limiting the effectiveness of deep learning-based condition assessment. In this study, we propose a foundation model-based 2-step structural damage analysis to overcome the lack of training data in image-based structural condition assessments. We subdivided the elements of structural condition assessment into instantiation and quantification. In the quantification step, we applied a foundation model for image segmentation. Our method demonstrated a 10%-point increase in mean intersection over union compared to conventional image segmentation techniques, with a notable 40%-point improvement in the case of rebar exposure. We anticipate that our proposed approach will enhance performance in domains where acquiring training data is challenging.

• Journal of The Korean Association For Science Education
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• v.30 no.5
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• pp.557-575
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• 2010

The purpose of the study was to suggest the characteristics and goals of the science teaching model for use as criteria in selecting the appropriate teaching model for science in secondary schools. These characteristics and the goals have been organized based on the analyses of the literature on the teaching and/or instructional model. The teaching models have been classified into four areas, and the characteristics and goals of each area have been summarized as follows: $\cdot$ Traditional models: teaching of scientific knowledge through lectures, acquisition of scientific knowledge through discovery, acquisition of inquiry process skills through inquiry-based teaching/learning $\cdot$ Transitional models: demonstration and discovery as teaching strategies, acquisition of inquiry process skills through inquiry approach, acquisition and change of scientific knowledge $\cdot$ Modernistic model - conceptual change models: differentiation of scientific knowledge, exchange of misconceptions for scientific concepts - learning cycle models: conceptual differentiation, exchange of misconceptions, acquisition of science process skills Also described in this paper are the model's characteristics and goals that can be used as the criteria for selecting the appropriate teaching model for the subject that will be taught.

• International journal of advanced smart convergence
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• v.12 no.4
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• pp.75-87
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• 2023

We designed to employ an Artificial Intelligence learning model to predict real estate prices and determine the reasons behind their changes, with the goal of using the results as a guide for policy. Numerous studies have already been conducted in an effort to develop a real estate price prediction model. The price prediction power of conventional time series analysis techniques (such as the widely-used ARIMA and VAR models for univariate time series analysis) and the more recently-discussed LSTM techniques is compared and analyzed in this study in order to forecast real estate prices. There is currently a period of rising volatility in the real estate market as a result of both internal and external factors. Predicting the movement of real estate values during times of heightened volatility is more challenging than it is during times of persistent general trends. According to the real estate market cycle, this study focuses on the three times of extreme volatility. It was established that the LSTM, VAR, and ARIMA models have strong predictive capacity by successfully forecasting the trading price index during a period of unusually high volatility. We explores potential synergies between the hybrid artificial intelligence learning model and the conventional statistical prediction model.

• Journal of Fisheries and Marine Sciences Education
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• v.25 no.4
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• pp.878-890
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• 2013

The purpose of this study is to apply the PEOE class model that can enhance students' scientific creative problem-solving ability and self-directed learning ability in the middle school science subject and analyze the effects of it on students' long- and short-term retention, scientific creative problem-solving ability, and self-directed learning characteristics. And the paper has gained the following results: First, according to the result of analysis through the pre-test, post-test, and delay test to examine the effects of PEOE-based class on learners' long- and short-term retention, it is found to be statistically significant in the significant level of .05. In other words, the class using PEOE influences learners' short-term retention significantly, but it is even more effective in transmitting the concept that students acquire into their long-term memory. Second, according to the result of analysis through the pre-test and post-test to examine the effects of PEOE-based class on learners' scientific creative problem-solving ability, it is found to be statistically significant in the significant level of .05 in general. However, 'elaboration' and 'originality', the subfactors of scientific creative problem-solving ability, do not indicate significant effects. Third, according to the result of analysis through the pre-test and post-test to examine the effects of PEOE-based class on learners' self-directed learning characteristics, it is found to be statistically significant in the significant level of .05 as a whole. However, 'openness' and 'future-oriented self-understanding', the subfactors of self-directed learning characteristics, do not exert significant effects. Based on the above study results, it can be concluded that PEOE-based class is more effective for learners' academic achievement in science, scientific creative problem-solving ability, and self-directed learning characteristics than lecture-method instruction regarding the middle school science unit of 'The Properties of Air and Weather Change'.

• Korean Journal of Child Education & Care
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• v.17 no.4
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• pp.91-119
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• 2017

The purpose of this study is to determine whether using a dietary education program based on learning cycle model has any significant effect on enhancing their nutrition knowledge, dietary behavior, science-process skill and scientific attitude. The subjects of this study were children in H and G daycare center in G City. The experiment group of this study was 16 children in the class of five-year-olds and 7 children in the class of four-year-olds who passed their birthday and became five-year-olds in H daycare center. The Analysis of Covariance(ANCOVA) and Pared t-test was conducted using SPSS WINDOWS 20.0 program. The results of applying dietary education program were as follows. Experimental group indicated enhancements between pre and post test of Nutrition Achievement Test, Nutrition Quotient for Preschooler, Science Process Skill and Scientific Attitude Assessment compare to comparative group. Therefore, we can conclude that the dietary education program does have effects on enhancing of nutrition knowledge, dietary behavior, science process skill and scientific attitude. The result of this study can be used as basic data to study dietary related factors that present importance of health dietary life of young children and need to provide educational experience of healthy diet for young children.

• Journal of Mechanical Science and Technology
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• v.17 no.12
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• pp.1969-1976
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• 2003

In the early phases of the product life cycle, Life Cycle Assessment (LCA) is recently used to support the decision-making for the product concepts, and the best alternative can be selected based on its estimated LCA and benefits. Both the lack of detailed information and time for a full LCA for a various range of design concepts need a new approach for the environmental analysis. This paper explores a new approximate LCA methodology for the product concepts by grouping products according to their environmental characteristics and by mapping product attributes into environmental impact driver (EID) index. The relationship is statistically verified by exploring the correlation between total impact indicator and energy impact category. Then, a neural network approach is developed to predict an approximate LCA of grouping products in conceptual design. Trained learning algorithms for the known characteristics of existing products will quickly give the result of LCA for newly designed products. The training is generalized by using product attributes for an EID in a group as well as another product attributes for the other EIDs in other groups. The neural network model with back propagation algorithm is used, and the results are compared with those of multiple regression analysis. The proposed approach does not replace the full LCA but it would give some useful guidelines for the design of environmentally conscious products in conceptual design phase.

• Journal of the Korean Society for Precision Engineering
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• v.20 no.3
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• pp.221-229
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• 2003

In the early phases of the product life cycle, Life Cycle Assessment (LCA) is recently used to support the decision-making fer the conceptual product design and the best alternative can be selected based on its estimated LCA and its benefits. Both the lack of detailed information and time for a full LCA fur a various range of design concepts need the new approach fer the environmental analysis. This paper suggests a novel approximate LCA methodology for the conceptual design stage by grouping products according to their environmental characteristics and by mapping product attributes into impact driver index. The relationship is statistically verified by exploring the correlation between total impact indicator and energy impact category. Then a neural network approach is developed to predict an approximate LCA of grouping products in conceptual design. Trained learning algorithms for the known characteristics of existing products will quickly give the result of LCA for new design products. The training is generalized by using product attributes for an ID in a group as well as another product attributes for another IDs in other groups. The neural network model with back propagation algorithm is used and the results are compared with those of multiple regression analysis. The proposed approach does not replace the full LCA but it would give some useful guidelines fer the design of environmentally conscious products in conceptual design phase.

• Nuclear Engineering and Technology
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• v.54 no.2
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• pp.644-652
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• 2022

During the pyroprocessing operation, various signals can be collected by process monitoring (PM). These signals are utilized to diagnose process states. In this study, feasibility of using PM for nuclear safeguards of electrorefining operation was examined based on the use of machine learning for detecting off-normal operations. The off-normal operation, in this study, is defined as co-deposition of key elements through reduction on cathode. The monitored process signal selected for PM was cathode potential. The necessary data were produced through electrodeposition experiments in a laboratory molten salt system. Model-based cathodic surface area data were also generated and used to support model development. Computer models for classification were developed using a series of recurrent neural network architectures. The concept of transfer learning was also employed by combining pre-training and fine-tuning to minimize data requirement for training. The resulting models were found to classify the normal and the off-normal operation states with a 95% accuracy. With the availability of more process data, the approach is expected to have higher reliability.

• Journal of the Korea Society of Computer and Information
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• v.23 no.11
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• pp.103-109
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• 2018

This paper presents an idea and framework to automate a full cycle business process management and re-engineering by integrating traditional business process management systems, process mining, data mining, machine learning, and simulation. We build our framework on the cloud-based platform such that various data sources can be incorporated. We design our systems to be extensible so that not only beneficial for practitioners of BPM, but also for researchers. Our framework can be used as a test bed for researchers without the complication of system integration. The automation of redesigning phase and selecting a baseline process model for deployment are the two main contributions of this study. In the redesigning phase, we deal with both the analysis of the existing process model and what-if analysis on how to improve the process at the same time, Additionally, improving a business process can be applied in a case by case basis that needs a lot of trial and error and huge data. In selecting the baseline process model, we need to compare many probable routes of business execution and calculate the most efficient one in respect to production cost and execution time. We also discuss the challenges and limitation of the framework, including the systems adoptability, technical difficulties and human factors.

• Journal of Korean Elementary Science Education
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• v.33 no.2
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• pp.306-321
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• 2014

This study aims to analyze difficulties that pre-service elementary teachers experience in investigating the life cycle of a common cabbage butterfly in person. As difficulties they face during the process of this research, they pointed out collecting eggs, observing molting, creating environments for a breeding cage, feeding, building a breeding cage, and making butterfly specimens. Out of all the environmental difficulties related to their school fields, they pointed out a difficulty of time management most of all, followed by placing a breeding cage in the classroom and the lack of microscopes for observation. In regard to difficulties related to their evaluations on students' activities, they found it difficult to evaluate students' activity with the life cycle of an insect in the aspect of knowledge and even to set evaluation criteria. Besides, many of them responded that it would be appropriate to evaluate a research on the life cycle of an insect through a portfolio or an observation journal. In regard to difficulties in terms of teachers' knowledge, they found it difficult to understand insect molting, metamorphoses, complete metamorphoses, incomplete metamorphoses, the structure of an insect body, and how to distinguish a female insect from a male one. In regard to the application of class models, they knew it is important for students to have various experiences through direct observation, so the experience-based learning model was proper for the process of observing the life cycle of a common cabbage butterfly. However, they found it difficult for students to observe each stage of the life cycle in person.