• Toxicological Research
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• v.33 no.2
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• pp.165-171
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• 2017

Bisphenol A (BPA) is a monomer used in a polymerization reaction in the production of polycarbonate plastics. It has been used in many consumer products, including plastics, polyvinyl chloride, food packaging, dental sealants, and thermal receipts. However, there is little information available on the inhalation toxicity of BPA. Therefore, the aim of this study was to determine its inhalation toxicity and effects on the estrous cycle, spatial learning, and memory. Sprague-Dawley rats were exposed to 0, 10, 30, and $90mg/m^3$ BPA, 6 hr/day, 5 days/week for 8 weeks via whole-body inhalation. Mortality, clinical signs, body weight, hematology, serum chemistry, estrous cycle parameters, performance in the Morris water maze test, and organ weights, as well as gross and histopathological findings, were compared between the control and BPA exposure groups. Statistically significant changes were observed in serum chemistry and organ weights upon exposure to BPA. However, there was no BPA-related toxic effect on the body weight, food consumption, hematology, serum chemistry, organ weights, estrous cycle, performance in the Morris water maze test, or gross or histopathological lesions in any male or female rats in the BPA exposure groups. In conclusion, the results of this study suggested that the no observable adverse effect level (NOAEL) for BPA in rats is above $90mg/m^3$/6 hr/day, 5 days/week upon 8-week exposure. Furthermore, BPA did not affect the estrous cycle, spatial learning, or memory in rats.

• Journal of Fisheries and Marine Sciences Education
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• v.28 no.5
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• pp.1183-1197
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• 2016

The purpose of this study is to develop a Puzzle-Based Computer Science Instruction Model and Learning Program and to confirm the effects. To do so, we selected 2 classes with a similar level of pre-computational thinking in elementary schools in the Jeju Province. After that, from 2 classes, we designated the 5th grade students in 'D' elementary school as group A and designated students of the same grade in 'J' elementary school as group B. In a total of 28 sessions during an 18 week period, a Puzzle-Based Computer Science Learning Program was used with 31 students in group A, and the traditional computer science course was used with 25 students in group B. The results showed that there were significant improvements in computational thinking, which is computational cognition and its creativity, of the students in group A compared to students in group B. Also, this study proved that the Puzzle-Based program correlated with positive changes group A students' Science-Related Affective Domain. In this paper, on the basis of proven effectiveness, we introduce the Puzzle-Based Computer Science Instruction Model and Learning Program as an alternative to traditional, computer science education.

• Korean System Dynamics Review
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• v.13 no.4
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• pp.81-99
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• 2012

The main purpose of this study is to explore the effects of the teaching and learning strategy for systems thinking education in elementary students. For this, we developed the teaching and learning material for the systems thinking education based on the book, namely "The tip of the iceberg," and applied to the control group(N=97) of the all students(N=201). The results were as follows. Firstly, the products of the control groups showed more cycle loops than non-control groups. Secondly, the prominent difference of the number of cycle loops was displayed by the 5th graders between control and non-control groups. Thirdly, in this study, applying the teaching and learning strategy for systems thinking education didn't increase the students' thinking ability in terms of quantity. Consequently, this study showed that improving systems thinking ability of higher elementary students is possible through the teleological education.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.20 no.4
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• pp.2752-2759
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• 1996

This paper presents crack growth analysis approach on the basis of neural networks, a branch of cognitive science to high temperature low cycle fatigue that shows strong nonlinearity in material behavior. As the number of data patterns on crack growth increase, pattern classification occurs well and two point representation scheme with gradient of crack growth curve simulates crack growth rate better than one point representation scheme. Optimal number of learning data exists and excessive number of learning data increases estimated mean error with remarkable learning time J-da/dt relation predicted by neural networks shows that test condition with unlearned data is simulated well within estimated mean error(5%).

• Journal of the Korean Society for Precision Engineering
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• v.21 no.9
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• pp.85-94
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• 2004

As over 70% of the total life cycle cost (LCC) of a product is committed at the early design stage, designers are in an important position to substantially reduce the LCC of the products they design by giving due to life cycle implications of their design decisions. During early design stages, there may be competing concepts with dramatic differences. In addition, the detailed information is scarce and decisions must be made quickly. Thus, both the overhead in developing parametric LCC models fur a wide range of concepts, and the lack of detailed information make the application of traditional LCC models impractical. A different approach is needed, because a traditional LCC method is to be incorporated in the very early design stages. This paper explores an approximate method for providing the preliminary LCC, Learning algorithms trained to use the known characteristics of existing products might allow the LCC of new products to be approximated quickly during the conceptual design phase without the overhead of defining new LCC models. Artificial neural networks are trained to generalize product attributes and LCC data from pre-existing LCC studies. Then the product designers query the trained artificial model with new high-level product attribute data to quickly obtain an LCC for a new product concept. Foundations fur the learning LCC approach are established, and then an application is provided.

• Journal of Ocean Engineering and Technology
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• v.36 no.1
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• pp.32-40
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• 2022

Underwater optical images face various limitations that degrade the image quality compared with optical images taken in our atmosphere. Attenuation according to the wavelength of light and reflection by very small floating objects cause low contrast, blurry clarity, and color degradation in underwater images. We constructed an image data of the Korean sea and enhanced it by learning the characteristics of underwater images using the deep learning techniques of CycleGAN (cycle-consistent adversarial network), UGAN (underwater GAN), FUnIE-GAN (fast underwater image enhancement GAN). In addition, the underwater optical image was enhanced using the image processing technique of Image Fusion. For a quantitative performance comparison, UIQM (underwater image quality measure), which evaluates the performance of the enhancement in terms of colorfulness, sharpness, and contrast, and UCIQE (underwater color image quality evaluation), which evaluates the performance in terms of chroma, luminance, and saturation were calculated. For 100 underwater images taken in Korean seas, the average UIQMs of CycleGAN, UGAN, and FUnIE-GAN were 3.91, 3.42, and 2.66, respectively, and the average UCIQEs were measured to be 29.9, 26.77, and 22.88, respectively. The average UIQM and UCIQE of Image Fusion were 3.63 and 23.59, respectively. CycleGAN and UGAN qualitatively and quantitatively improved the image quality in various underwater environments, and FUnIE-GAN had performance differences depending on the underwater environment. Image Fusion showed good performance in terms of color correction and sharpness enhancement. It is expected that this method can be used for monitoring underwater works and the autonomous operation of unmanned vehicles by improving the visibility of underwater situations more accurately.

• Journal of the Korean Society of Radiology
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• v.18 no.1
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• pp.37-44
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• 2024

Even though MR can reveal excellent soft-tissue contrast and functional information, CT is also required for electron density information for accurate dose calculation in Radiotherapy. For the fusion of MRI and CT images in RT treatment planning workflow, patients are normally scanned on both MRI and CT imaging modalities. Recently deep-learning-based generations of CT images from MR images became possible owing to machine learning technology. This eliminated CT scanning work. This study implemented a CycleGan deep-learning-based CT image generation from MR images. Three CT generators whose learning is based on T1- , T2- , or T1-&T2-weighted MR images were created, respectively. We found that the T1-weighted MR image-based generator can generate better than other CT generators when T1-weighted MR images are input. In contrast, a T2-weighted MR image-based generator can generate better than other CT generators do when T2-weighted MR images are input. The results say that the CT generator from MR images is just outside the practical clinics and the specific weight MR image-based machine-learning generator can generate better CT images than other sequence MR image-based generators do.

• Journal of IKEEE
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• v.23 no.2
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• pp.529-533
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• 2019

Combined cycle power plants are often used to produce power. These days prediction of power plant output based on operating parameters is a major concern. This paper presents an approach to using computational intelligence technique to predict the output power of combined cycle power plant. Computational intelligence techniques have been developed and applied to many real world problems. In this paper, tree architectures of fuzzy neural networks are considered to predict the output power. Tree architectures of fuzzy neural networks have an advantage of reducing the number of rules by selecting fuzzy neurons as nodes and relevant inputs as leaves optimally. For the optimization of the networks, two-step optimization method is used. Genetic algorithms optimize the binary structure of the networks by selecting the nodes and leaves as binary, and followed by random signal-based learning further refines the optimized binary connections in the unit interval. To verify the effectiveness of the proposed method, combined cycle power plant dataset obtained from the UCI Machine Learning Repository Database is considered.

• Structural Engineering and Mechanics
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• v.83 no.3
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• pp.293-304
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• 2022

In this work, a novel reliability approach for combined high and low cycle fatigue (CCF) estimation is developed by combining active learning strategy with least squares support vector machines (LS-SVM) (named as ALS-SVM) surrogate model to address the multi-resources uncertainties, including working loads, material properties and model itself. Initially, a new active learner function combining LS-SVM approach with Monte Carlo simulation (MCS) is presented to improve computational efficiency with fewer calls to the performance function. To consider the uncertainty of surrogate model at candidate sample points, the learning function employs k-fold cross validation method and introduces the predicted variance to sequentially select sampling. Following that, low cycle fatigue (LCF) loads and high cycle fatigue (HCF) loads are firstly estimated based on the training samples extracted from finite element (FE) simulations, and their simulated responses together with the sample points of model parameters in Coffin-Manson formula are selected as the MC samples to establish ALS-SVM model. In this analysis, the MC samples are substituted to predict the CCF reliability of turbine blades by using the built ALS-SVM model. Through the comparison of the two approaches, it is indicated that the reliability model by linear cumulative damage rule provides a non-conservative result compared with that by the proposed one. In addition, the results demonstrate that ALS-SVM is an effective analysis method holding high computational efficiency with small training samples to gain accurate fatigue reliability.

• Journal of Korean Elementary Science Education
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• v.35 no.2
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• pp.166-180
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• 2016

This paper examines the effects on academic achievement, scientific process skills and affective domain for elementary students learning the 'Chicken life cycle' through traditional science class versus a smart media based STEAM approach. Students designed and built a hatching jar and created a smart media content for chickens using time-lapse technology. This STEAM program was developed to improve their scientific concepts of animals over nine periods of classes using integrated education methods. The experimental study took place in the third grade of public schools in a province, with the STEAM approach applied in 2 classes (44 students) and the traditional discipline approach implemented in 2 classes (46 students). The STEAM education significantly influenced the improvement of academic achievements, basic scientific process skills and affective domain. The results suggest that this STEAM approach for teaching scientific concepts of animal life cycles has the performance in terms of knowledge, skills and affect gain achievements in elementary school students' learning when compared to a traditional approach. Moreover, the smart media based STEAM program is helpful to lead students to engage in integrated problem-solving designs and learning science and technology.