• 한국수소및신에너지학회논문집
• /
• 제32권6호
• /
• pp.592-611
• /
• 2021

In this study a rarely available high-throughput cycling data set of 124 commercial lithium iron phosphate/graphite cells cycled under fast-charging conditions, with widely varying cycle lives ranging from 150 to 2,300 cycles including in-cycle temperature and per-cycle IR measurements. We worked out own Python codes which reproduced the various data plots and machine learning approaches for cycle life prediction using early cycles and more details not presented in the article and the supplementary information. Particularly, we applied regularized ridge, lasso and elastic net linear regression models using features extracted from capacity fade curves, discharge voltage curves, and other data such as internal resistance and cell can temperature. We found that due to the limitation in the quantity and quality of the data from costly and lengthy battery testing a careful hyperparameter tuning may be required and that model features need to be extracted based on the domain knowledge.

• Toxicological Research
• /
• 제33권2호
• /
• pp.165-171
• /
• 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.

• 수산해양교육연구
• /
• 제28권5호
• /
• pp.1183-1197
• /
• 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.

• 한국시스템다이내믹스연구
• /
• 제13권4호
• /
• pp.81-99
• /
• 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.

• 대한기계학회논문집A
• /
• 제20권4호
• /
• pp.2752-2759
• /
• 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%).

• 한국정밀공학회지
• /
• 제21권9호
• /
• pp.85-94
• /
• 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.

• 한국해양공학회지
• /
• 제36권1호
• /
• pp.32-40
• /
• 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.

• 한국방사선학회논문지
• /
• 제18권1호
• /
• pp.37-44
• /
• 2024

MR은 우수한 연조직 대비와 기능 정보를 보여줄 수 있지만, 방사선치료에서 정확한 선량 계산을 위해서는 CT영상의 전자밀도 정보가 필요하다. 방사선치료(Radiotherapy) 계획 워크플로우에서 MR영상과 CT영상을 융합하기 위해 환자는 일반적으로 MR과 CT영상 방식 모두에서 스캔된다. 최근에 딥러닝기술 덕분에 MR영상에서 딥러닝 기반의 CT영상 생성이 가능해졌다. 이로 인해 CT 스캔 작업을 할 필요가 없게 된다. 본 연구에서는 MR영상으로부터 CycleGan 딥러닝 기반 CT영상생성을 구현했다. T1가중이나 T2가중 중에 한 가지 또는 그 둘다의 MR영상을 가지고 합습한 3가지의 인공지능 CT생성기를 만들었다. 결과에서 우리는 T1가중 MR 영상 기반으로 학습한 생성기가 T1가중 MR영상이 입력될 때 다른 CT생성기보다 더 나은 결과를 생성할 수 있음을 발견했다. 반면, T2가중 MR영상 기반 CT생성기는 T2가중 MR영상을 입력 받을 때, 다른 시퀀스기반 CT생성기보다 더 나은 결과를 생성할 수 있습니다. MR영상을 기반으로 한 CT생성기는 곧 임상현장에 적용될 수 있는 기술이다. 특정 시퀀스 MR영상으로 학습한 머신러닝 CT생성기는 다른 시퀀스 MR영상으로 학습한 생성기보다 더 그 특정 시퀀스와 같은 MR영상을 입력받을 때 더 나은 CT영상을 생성할 수 있음을 보여주었다.

• 전기전자학회논문지
• /
• 제23권2호
• /
• pp.529-533
• /
• 2019

오늘날 복합 화력 발전소는 전력 생산을 위해 많이 사용되고 있고, 최근에는 운전 매개 변수를 기반으로 발전 출력을 예측하는 것이 주요 관심사이다. 본 논문에서는 복합 화력 발전소의 출력을 예측하기 위해 컴퓨터 지능 기법을 이용하는 방법을 제시한다. 컴퓨터 지능 기술은 지속적으로 발전되어 많은 실제 문제에 적용되어 왔다. 본 논문에서는 트리 구조의 퍼지 뉴럴 네트워크를 이용하여 발전 출력을 예측하고자 한다. 트리 구조의 퍼지 뉴럴 네트워크는 퍼지 뉴런을 노드로 선택하고 관련 입력을 최적으로 선택하여 규칙 수를 줄이는 장점이 있다. 네트워크의 최적화를 위해 2 단계 최적화 방법이 사용된다. 유전 알고리즘은 최적의 노드와 리프를 선택하여 네트워크의 이진 구조를 최적화 한 다음 랜덤 신호 기반 학습을 수행하여 최적화 된 이진 연결을 단위 구간에서 미세 학습한다. 제안 된 방법의 유용성을 검증하기 위해 UCI Machine Learning Repository Database에서 얻은 복합 화력 발전소 데이터를 사용한다.

• Structural Engineering and Mechanics
• /
• 제83권3호
• /
• pp.293-304
• /
• 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.