Gyeong-Geun Lee;Suk-Min Hong;Ji-Su Kim;Dong-Hyun Ahn;Jong-Min Kim
Transactions of the Korean Society of Pressure Vessels and Piping
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제20권1호
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pp.56-65
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2024
Cast austenitic stainless steels (CASS) and austenitic stainless steel weldments with a ferrite-austenite duplex structure are widely used in nuclear power plants, incorporating ferrite phase to enhance strength, stress relief, and corrosion resistance. Thermal aging at 290-325℃ can induce embrittlement, primarily due to spinodal decomposition and G-phase precipitation in the ferrite phase. This study evaluates the effects of thermal aging by collecting and analyzing various mechanical properties, such as Charpy impact energy, ferrite microhardness, and tensile strength, from various literature sources. Different model expressions, including hyperbolic tangent and phase transformation equations, are applied to calculate activation energy (Q) of room-temperature impact energies, and the results are compared. Additionally, predictive models for Q based on material composition are evaluated, and the potential of machine learning techniques for improving prediction accuracy is explored. The study also examines the use of ferrite microhardness and tensile strength in calculating Q and assessing thermal embrittlement. The findings provide insights for developing advanced prediction models for the thermal embrittlement behavior of CASS and the weldments of austenitic steels, contributing to the safety and reliability of nuclear power plant components.
International Journal of Computer Science & Network Security
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제23권12호
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pp.151-160
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2023
Breast cancer is the most dangerous and deadly form of cancer. Initial detection of breast cancer can significantly improve treatment effectiveness. The second most common cancer among Indian women in rural areas. Early detection of symptoms and signs is the most important technique to effectively treat breast cancer, as it enhances the odds of receiving an earlier, more specialist care. As a result, it has the possible to significantly improve survival odds by delaying or entirely eliminating cancer. Mammography is a high-resolution radiography technique that is an important factor in avoiding and diagnosing cancer at an early stage. Automatic segmentation of the breast part using Mammography pictures can help reduce the area available for cancer search while also saving time and effort compared to manual segmentation. Autoencoder-like convolutional and deconvolutional neural networks (CN-DCNN) were utilised in previous studies to automatically segment the breast area in Mammography pictures. We present Automatic SegmenAN, a unique end-to-end adversarial neural network for the job of medical image segmentation, in this paper. Because image segmentation necessitates extensive, pixel-level labelling, a standard GAN's discriminator's single scalar real/fake output may be inefficient in providing steady and appropriate gradient feedback to the networks. Instead of utilising a fully convolutional neural network as the segmentor, we suggested a new adversarial critic network with a multi-scale L1 loss function to force the critic and segmentor to learn both global and local attributes that collect long- and short-range spatial relations among pixels. We demonstrate that an Automatic SegmenAN perspective is more up to date and reliable for segmentation tasks than the state-of-the-art U-net segmentation technique.
International Journal of Computer Science & Network Security
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제23권12호
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pp.101-106
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2023
Chronic illnesses are among the most common serious problems affecting human health. Early diagnosis of chronic diseases can assist to avoid or mitigate their consequences, potentially decreasing mortality rates. Using machine learning algorithms to identify risk factors is an exciting strategy. The issue with existing feature selection approaches is that each method provides a distinct set of properties that affect model correctness, and present methods cannot perform well on huge multidimensional datasets. We would like to introduce a novel model that contains a feature selection approach that selects optimal characteristics from big multidimensional data sets to provide reliable predictions of chronic illnesses without sacrificing data uniqueness.[1] To ensure the success of our proposed model, we employed balanced classes by employing hybrid balanced class sampling methods on the original dataset, as well as methods for data pre-processing and data transformation, to provide credible data for the training model. We ran and assessed our model on datasets with binary and multivalued classifications. We have used multiple datasets (Parkinson, arrythmia, breast cancer, kidney, diabetes). Suitable features are selected by using the Hybrid feature model consists of Lassocv, decision tree, random forest, gradient boosting,Adaboost, stochastic gradient descent and done voting of attributes which are common output from these methods.Accuracy of original dataset before applying framework is recorded and evaluated against reduced data set of attributes accuracy. The results are shown separately to provide comparisons. Based on the result analysis, we can conclude that our proposed model produced the highest accuracy on multi valued class datasets than on binary class attributes.[1]
Jiyeon Kim;Ji Myung Choi;Ji-Hyun Kim;Qi Qi Pang;Jung Min Oh;Ji Hyun Kim;Hyun Young Kim;Eun Ju Cho
Nutrition Research and Practice
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제18권4호
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pp.464-478
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2024
BACKGROUND/OBJECTIVES: Chronic alcohol consumption causes oxidative stress in the body, which may accumulate excessively and cause a decline in memory; problem-solving, learning, and exercise abilities; and permanent damage to brain structure and function. Consequently, chronic alcohol consumption can cause alcohol-related diseases. MATERIALS/METHODS: In this study, the protective effects of Phyllostachys edulis (Carrière) J. Houz (PE) against alcohol-induced neuroinflammation and cognitive impairment were evaluated using a mouse model. Alcohol (16%, 5 g/kg/day for 6 weeks) and PE (100, 250, and 500 mg/kg/day for 21 days) were administered intragastrically to mice. RESULTS: PE showed a protective effect against memory deficits and cognitive dysfunction caused by alcohol consumption, confirmed through behavioral tests such as the T-maze, object recognition, and Morris water maze tests. Additionally, PE attenuated oxidative stress by reducing lipid oxidation, nitric oxide, and reactive oxygen species levels in the mice's brains, livers, and kidneys. Improvement of neurotrophic factors and downregulation of apoptosis-related proteins were confirmed in the brains of mice fed low and medium concentrations of PE. Additionally, expression of antioxidant enzyme-related proteins GPx-1 and SOD-1 was enhanced in the liver of PE-treated mice, related to their inhibitory effect on oxidative stress. CONCLUSION: This suggests that PE has both neuroregenerative and antioxidant effects. Collectively, these behavioral and histological results confirmed that PE could improve alcohol-induced cognitive deficits through brain neurotrophic and apoptosis protection and modulation of oxidative stress.
Students' perception on a science program for gifted was investigated. The whole program was designed in consistency and integrity based on the Autonomous Learner Model suggested by Betts & Kercher(1999). 7th, 8th and 9th grade students were enrolled in this program, offered by G Education Institute for Gifted(GEI) located in Seoul. A survey was done to ask students' perception regarding the effect of the program. The survey consisted of statements about the expected effects of the program and students were asked if they agreed with the statements. Most students strongly agreed that GEI's program has positive effects. Students replied that they learned useful and interesting science contents, enjoyed meaningful experience of cooperating with members in small groups, and were challenged by the inquiry tasks. They recognized that they were being trained to become autonomous learners. They also said that their choices and decisions were respected, which resulted in positive effects on their ability to negotiate or to inquire actively. These implies that Autonomous Learner Model had been successfully applied. Although it was not clear autonomy of students was fully grown, the possibility of becoming an autonomous learner was evident. Satisfaction level is higher for the older students, implying that the integrity in the program gave accumulating effect. Students response showed that three sub-programs of GEI, the classes of each subject, conference at the end of the year and autonomous learner training played equally important role for students to learn the process of scientific inquiry and autonomous learning. This was a positive sign that the strategies for scientific inquiry and autonomous learning were embedded and integrated deeply in the program. The results of current research suggests that the integrity of a program based on a specific education model for the gifted could provide better education environment for the gifted students.
Vision and voice-based technologies are commonly utilized for human-robot interaction. But it is widely recognized that the performance of vision and voice-based interaction systems is deteriorated by a large margin in the real-world situations due to environmental and user variances. Human users need to be very cooperative to get reasonable performance, which significantly limits the usability of the vision and voice-based human-robot interaction technologies. As a result, touch screens are still the major medium of human-robot interaction for the real-world applications. To empower the usability of robots for various services, alternative interaction technologies should be developed to complement the problems of vision and voice-based technologies. In this paper, we propose the use of accelerometer-based gesture interface as one of the alternative technologies, because accelerometers are effective in detecting the movements of human body, while their performance is not limited by environmental contexts such as lighting conditions or camera's field-of-view. Moreover, accelerometers are widely available nowadays in many mobile devices. We tackle the problem of classifying acceleration signal patterns of 26 English alphabets, which is one of the essential repertoires for the realization of education services based on robots. Recognizing 26 English handwriting patterns based on accelerometers is a very difficult task to take over because of its large scale of pattern classes and the complexity of each pattern. The most difficult problem that has been undertaken which is similar to our problem was recognizing acceleration signal patterns of 10 handwritten digits. Most previous studies dealt with pattern sets of 8~10 simple and easily distinguishable gestures that are useful for controlling home appliances, computer applications, robots etc. Good features are essential for the success of pattern recognition. To promote the discriminative power upon complex English alphabet patterns, we extracted 'motion trajectories' out of input acceleration signal and used them as the main feature. Investigative experiments showed that classifiers based on trajectory performed 3%~5% better than those with raw features e.g. acceleration signal itself or statistical figures. To minimize the distortion of trajectories, we applied a simple but effective set of smoothing filters and band-pass filters. It is well known that acceleration patterns for the same gesture is very different among different performers. To tackle the problem, online incremental learning is applied for our system to make it adaptive to the users' distinctive motion properties. Our system is based on instance-based learning (IBL) where each training sample is memorized as a reference pattern. Brute-force incremental learning in IBL continuously accumulates reference patterns, which is a problem because it not only slows down the classification but also downgrades the recall performance. Regarding the latter phenomenon, we observed a tendency that as the number of reference patterns grows, some reference patterns contribute more to the false positive classification. Thus, we devised an algorithm for optimizing the reference pattern set based on the positive and negative contribution of each reference pattern. The algorithm is performed periodically to remove reference patterns that have a very low positive contribution or a high negative contribution. Experiments were performed on 6500 gesture patterns collected from 50 adults of 30~50 years old. Each alphabet was performed 5 times per participant using $Nintendo{(R)}$$Wii^{TM}$ remote. Acceleration signal was sampled in 100hz on 3 axes. Mean recall rate for all the alphabets was 95.48%. Some alphabets recorded very low recall rate and exhibited very high pairwise confusion rate. Major confusion pairs are D(88%) and P(74%), I(81%) and U(75%), N(88%) and W(100%). Though W was recalled perfectly, it contributed much to the false positive classification of N. By comparison with major previous results from VTT (96% for 8 control gestures), CMU (97% for 10 control gestures) and Samsung Electronics(97% for 10 digits and a control gesture), we could find that the performance of our system is superior regarding the number of pattern classes and the complexity of patterns. Using our gesture interaction system, we conducted 2 case studies of robot-based edutainment services. The services were implemented on various robot platforms and mobile devices including $iPhone^{TM}$. The participating children exhibited improved concentration and active reaction on the service with our gesture interface. To prove the effectiveness of our gesture interface, a test was taken by the children after experiencing an English teaching service. The test result showed that those who played with the gesture interface-based robot content marked 10% better score than those with conventional teaching. We conclude that the accelerometer-based gesture interface is a promising technology for flourishing real-world robot-based services and content by complementing the limits of today's conventional interfaces e.g. touch screen, vision and voice.
Journal of the Korea Academia-Industrial cooperation Society
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제13권6호
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pp.2607-2616
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2012
This study was designed to figure out patient safety culture of medical institutions and try to utilize the study results as basic data for analyzing doctor's awareness of patient safety culture. To this end, questionnaire survey was conducted from August 1st to September 5th, 2011, targeting doctors working at senior general hospitals located in G city, and 194 questionnaires were utilized for final analysis. The research results are as follows. First, there was a difference in awareness of deployment of staffs depending on gender, age, term of service in the hospital, contact with patients and working hours per week in relationship between subjects, wards and hospital safety culture, and organizational learning and teamwork in the ward turned out to be significant in accordance with working hours per week, and all sub-areas of the ward safety culture by departments. Second, feedback about the malpractice, communication, report on malpractice frequency and overall safety awareness were found to be significant by departments in relationship of subjects, medical incident reporting system, patient safety evaluation and overall level of consciousness, and the overall safety awareness showed significant results according to contact with patients and working hours per week. Third, there was a positive corelation in sub-areas of the ward and hospital safety culture awareness, overall recognition and patient safety evaluation, and a positive corelation with medical incident reporting system was found in all areas except for attitude of managers/immediate supervisors and that of hospital executives. Fourth, sub-areas of patient safety culture which has a effect on patient safety showed significant results in organizational learning, openness of communication, overall safety awareness, systematic cooperation between departments, feedback/communication and non-punitive response. In conclusion, to increase the level of the ward and hospital patient safety culture of doctors and implement medical incident reporting system faithfully, it is necessary to activate teamwork through organizational learning in the ward based on the adequate staffing and working hours, promote open communication between departments and provide feedback on medical malpractice, thereby establishing a cooperative system by departments and active support of hospital executives for patient safet.
Ocean salinity affects ocean circulation on a global scale and low salinity water around coastal areas often has an impact on aquaculture and fisheries. Microwave satellite sensors (e.g., Soil Moisture Active Passive [SMAP]) have provided sea surface salinity (SSS) based on the dielectric characteristics of water associated with SSS and sea surface temperature (SST). In this study, a Light Gradient Boosting Machine (LGBM)-based model for generating high resolution SSS from Geostationary Ocean Color Imager (GOCI) data was proposed, having machine learning-based improved SMAP SSS by Jang et al. (2022) as reference data (SMAP SSS (Jang)). Three schemes with different input variables were tested, and scheme 3 with all variables including Multi-scale Ultra-high Resolution SST yielded the best performance (coefficient of determination = 0.60, root mean square error = 0.91 psu). The proposed LGBM-based GOCI SSS had a similar spatiotemporal pattern with SMAP SSS (Jang), with much higher spatial resolution even in coastal areas, where SMAP SSS (Jang) was not available. In addition, when tested for the great flood occurred in Southern China in August 2020, GOCI SSS well simulated the spatial and temporal change of Changjiang Diluted Water. This research provided a potential that optical satellite data can be used to generate high resolution SSS associated with the improved microwave-based SSS especially in coastal areas.
Journal of The Korean Association For Science Education
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제29권8호
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pp.990-1010
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2009
To derive brain-based evolutionary educational principles, this study examined the studies on the structural and functional characteristics of human brain, the biological evolution occurring between- and within-organism, and the evolutionary attributes embedded in science itself and individual scientist's scientific activities. On the basis of the core characteristics of human brain and the framework of universal Darwinism or universal selectionism consisted of generation-test-retention (g-t-r) processes, a Model of Brain-based Evolutionary Scientific Teaching for Learning (BEST-L) was developed. The model consists of three components, three steps, and assessment part. The three components are the affective (A), behavioral (B), and cognitive (C) components. Each component consists of three steps of Diversifying $\rightarrow$ Emulating (Executing, Estimating, Evaluating) $\rightarrow$ Furthering (ABC-DEF). The model is 'brain-based' in the aspect of consecutive incorporation of the affective component which is based on limbic system of human brain associated with emotions, the behavioral component which is associated with the occipital lobes performing visual processing, temporal lobes performing functions of language generation and understanding, and parietal lobes, which receive and process sensory information and execute motor activities of the body, and the cognitive component which is based on the prefrontal lobes involved in thinking, planning, judging, and problem solving. On the other hand, the model is 'evolutionary' in the aspect of proceeding according to the processes of the diversifying step to generate variants in each component, the emulating step to test and select useful or valuable things among the variants, and the furthering step to extend or apply the selected things. For three components of ABC, to reflect the importance of emotional factors as a starting point in scientific activity as well as the dominant role of limbic system relative to cortex of brain, the model emphasizes the DARWIN (Driving Affective Realm for Whole Intellectual Network) approach.
Journal of the Korean Society of Earth Science Education
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제10권2호
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pp.173-184
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2017
The purpose of this study is to examine the effects of science classes using the geological materials of a locality on academic achievement and scientific attitude of elementary school students. For this study, the class unit is 'stratum and fossils', 2nd semester of 3rd grade, and the geological materials of a locality is applied for class. The geological materials used in teaching and learning of 'stratum and fossil' unit are photographs & video data relating to geological phenomenon, and rock & fossils samples collected in Jeollanam-do province. This study has been aimed at 2 classes 47 students of 3rd grade in G elementary school of G metropolitan. One class 23 students are the research group to apply science class using the geological materials in a locality, on the order hand another class 24 students are the comparison group to apply general science classes. The results of this study are follows. First, a positive relationship was identified between academic achievement and science class applying the geological materials in a locality in the research group. This shows that academic achievement was improved by science class applying the geological materials in a locality. Second, a positive relationship was identified between scientific attitude and science class applying the geological materials in a locality in the research group. This shows that scientific attitude was improved by science class applying the geological materials in a locality. Third, by the results of interview with students who was participated in science class applying the geological materials in a locality, it shows that they have interest and curiosity about local geology. Above results means that science class applying the geological materials in a locality help elementary schools students improve the educational effect about 'stratum and fossils' unit. Thus, it is needed to use the geological materials of a locality in science class relating to the geology units of elementary school science in order to improve academic achievement and scientific attitude of elementary school students.
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