• Computers and Concrete
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• v.30 no.5
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• pp.301-310
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• 2022

Cement-based sensors have been widely used as structural health monitoring systems, however, their long-term sensing performance have not actively investigated. In this study, a deep learning-based methodology is adopted to predict the long-term piezoresistive properties of cement-based sensors. Samples with different multi-walled carbon nanotube contents (0.1, 0.3, and 0.5 wt.%) are fabricated, and piezoresistive tests are conducted over 10,000 loading cycles to obtain the training data. Time-dependent degradation is predicted using a modified long short-term memory (LSTM) model. The effects of different model variables including the amount of training data, number of epochs, and dropout ratio on the accuracy of predictions are analyzed. Finally, the effectiveness of the proposed approach is evaluated by comparing the predictions for long-term piezoresistive sensing performance with untrained experimental data. A sensitivity of 6% is experimentally examined in the sample containing 0.1 wt.% of MWCNTs, and predictions with accuracy up to 98% are found using the proposed LSTM model. Based on the experimental results, the proposed model is expected to be applied in the structural health monitoring systems to predict their long-term piezoresistice sensing performances during their service life.

• Journal of Korean Society of Water and Wastewater
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• v.37 no.5
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• pp.271-279
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• 2023

Fouling is an inevitable problem in membrane water treatment plant. It can be measured by trans-membrane pressure (TMP) in the constant flux operation, and chemical cleaning is carried out when TMP reaches a critical value. An early fouilng alarm is defined as warning the critical TMP value appearance in advance. The alarming method was developed using one of machine learning algorithms, decision tree, and applied to a ceramic microfiltration (MF) pilot plant. First, the decision tree model that classifies the normal/abnormal state of the filtration cycle of the ceramic MF pilot plant was developed and it was then used to make the early fouling alarm method. The accuracy of the classification model was up to 96.2% and the time for the early warning was when abnormal cycles occurred three times in a row. The early fouling alram can expect reaching a limit TMP in advance (e.g., 15-174 hours). By adopting TMP increasing rate and backwash efficiency as machine learning variables, the model accuracy and the reliability of the early fouling alarm method were increased, respectively.

• Journal of the Korean earth science society
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• v.42 no.1
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• pp.88-101
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• 2021

The purpose of this study was to qualitatively examine the meaning of students' drawings in outdoor classes and modeling-based learning cycles. Ten students were observed in a gifted education center in Seoul. Under the theme of the Hantan River, three outdoor classes and three modeling activities were conducted. Data were collected to document all student activities during field trips and classroom modeling activities using simultaneous video and audio recording and observation notes made by the researcher and students. Please note it is unclear what this citation refers to. If it is the previous sentence it should be placed within that sentence's punctuation. Hatisaru (2020) Ddrawing typess were classified by modifying the representations in a learning context in geological field trips. We used deductive content analysis to describe the drawing characteristics, including students writing. The results suggest that students have symbolic images that consist of geologic concepts, visual images that describe topographical features, and affective images that express students' emotion domains. The characteristics were classified into explanation, generality, elaboration, evidence, coherence, and state-of-mind. The characteristics and drawing types are consecutive in the modeling-based learning cycle and reflect the students' positive attitude and cognitive scientific domain. Drawing is a useful tool for reflecting students' thoughts and opinions in both outdoor class and classroom modeling activities. This study provides implications for emphasizing the importance of drawing activities.

• Journal of The Korean Association For Science Education
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• v.37 no.1
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• pp.135-142
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• 2017

The Trends in International Mathematics and Science Study (TIMSS) announced the TIMSS 2015 results at the end of 2016. In this research, we analyzed the relationship between Korean eighth grade students' attitude toward science and science achievement, trends in students' attitude toward science based on common items used in three to four cycles of TIMSS, and trends across grades in students' attitude toward science. According to the results, Korean eighth grade students showed the lowest level of confidence with science, interest in learning science, and valuing science among the 15 top performing countries as well as all the participant countries. In addition, according to the analysis result of common items, Korean students' confidence with science and interest in learning science have decreased, whereas students' valuing science with instrumental values has increased between TIMSS 2011 and TIMSS 2015. According to trends across grades, the cohort of students, assessed at the fourth grade in TIMSS 2011 and moved to the eighth grade four years later in 2015, decreased in their confidence with science and interest in learning science. Discussed in the conclusion are further studies and ways to improve science teaching and learning to improve students' attitude toward science.

• International Journal of Quality Innovation
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• v.9 no.2
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• pp.99-117
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• 2008

High-tech industry has been the principal economic source for Taiwan in recent years. The characteristics of high-tech industries in Taiwan are changeable product markets, short product life cycles and high company attrition rate. In the globalization trend, the high-tech industry has gradually increased corporate competitiveness and reached the goal of sustainable operations through knowledge management, knowledge sharing and new product research and development. Firms have aggressively strengthened and integrated their internal and external resources and enhanced knowledge sharing to increase industry operational performance. Effectively strengthening the knowledge management operation and performance evaluation of knowledge sharing in Taiwan's high-tech industry has become a critical issue. In the selection of knowledge sharing Key Performance Indicators (KPI), this research divided the knowledge sharing indicators into representative strategic indicators such as organizational knowledge learning, organizational knowledge resources, organizational information capacity and organizational knowledge performance through screening using Factor Analysis. The characteristics of the constructs were interdependent. This research calculated and adjusted the correlation among the key performance knowledge sharing indicators using ANP and determined the relative weight of knowledge sharing.

• Proceedings of the KIEE Conference
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• 1998.07b
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• pp.740-743
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• 1998

This paper presents an optimization algorithm for a stable Dynamic Neural Network (DNN) using genetic algorithm. Optimized DNN is applied to a problem of controlling nonlinear dynamical systems. DNN is dynamic mapping and is better suited for dynamical systems than static forward neural network. The real time implementation is very important, and thus the neuro controller also needs to be designed such that it converges with a relatively small number of training cycles. SDNN has considerably fewer weights than DNN. The object of proposed algorithm is to the number of self dynamic neuron node and the gradient of activation functions are simultaneously optimized by genetic algorithms. To guarantee convergence, an analytic method based on the Lyapunov function is used to find a stable learning for the SDNN. The ability and effectiveness of identifying and controlling, a nonlinear dynamic system using the proposed optimized SDNN considering stability' is demonstrated by case studies.

• Clinical and Experimental Pediatrics
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• v.50 no.8
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• pp.711-717
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• 2007

Sleep is a vital, highly organized process regulated by complex systems of neuronal networks and neurotransmitters. Normal sleep comprises non-rapid eye movement (NREM) and REM periods that alternate through the night. Sleep usually begins in NREM and progresses through deeper NREM stages (2, 3, and 4 stages), but newborns enter REM sleep (active sleep) first before NREM (quiet sleep). A period of NREM and REM sleep cycle is approximately 90 minutes, but newborn have a shorter sleep cycle (50 minutes). As children mature, sleep changes as an adult pattern: shorter sleep duration, longer sleep cycles and less daytime sleep. REM sleep is approximately 50% of total sleep in newborn and dramatically decreases over the first 2 years into adulthood (20% to 25%). An initial predominant of slow wave sleep (stage 3 and 4) that peaks in early childhood, drops off abruptly after adolescence by 40% from preteen years, and then declines over the life span. The hypothalamus is recognized as a key area of brain involved in regulation of sleep and wakefulness. The basic function of sleep largely remains elusive, but it is clear that sleep plays an important role in the regulation of CNS and body physiologic processes. Understanding of the architecture of sleep and basic mechanisms that regulate sleep and wake cycle are essential to evaluate normal or abnormal development of sleep pattern changes with age. Reduction or disruption of sleep can have a significant impact on daytime functioning and development, including learning, growth, behavior, and emotional regulation.

• The Transactions of the Korean Institute of Power Electronics
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• v.26 no.1
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• pp.1-8
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• 2021

For the safe and reliable operation of lithium-ion batteries in electric vehicles or energy storage systems, having accurate information of the battery, such as the state of charge (SOC), is essential. Many different techniques of battery SOC estimation have been developed, such as the Kalman filter. However, when this filter is applied to multiple batteries, it has difficulty maintaining the accuracy of the estimation over all cells owing to the difference in parameter values of each cell. The difference in the parameter of each cell may increase as the operation time accumulates due to aging. In this paper, a novel deep neural network (DNN)-based SOC estimation method for multi-cell application is proposed. In the proposed method, DNN is implemented to determine the nonlinear relationships of the voltage and current at different SOCs and temperatures. In the training, the voltage and current data obtained at different temperatures during charge/discharge cycles are used. After the comprehensive training with the data obtained from the cycle test with a cell, the resulting algorithm is applied to estimate the SOC of other cells. Experimental results show that the mean absolute error of the estimation is 1.213% at 25℃ with the proposed DNN-based SOC estimation method.

• Korean Journal of Remote Sensing
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• v.37 no.5_3
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• pp.1405-1423
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• 2021

Precipitation is one of the main factors that affect water and energy cycles, and its estimation plays a very important role in securing water resources and timely responding to water disasters. Satellite-based quantitative precipitation estimation (QPE) has the advantage of covering large areas at high spatiotemporal resolution. In this study, machine learning-based rainfall intensity models were developed using Himawari-8 Advanced Himawari Imager (AHI) water vapor channel (6.7 ㎛), infrared channel (10.8 ㎛), and weather radar Column Max (CMAX) composite data based on random forest (RF). The target variables were weather radar reflectivity (dBZ) and rainfall intensity (mm/hr) converted by the Z-R relationship. The results showed that the model which learned CMAX reflectivity produced the Critical Success Index (CSI) of 0.34 and the Mean-Absolute-Error (MAE) of 4.82 mm/hr. When compared to the GeoKompsat-2 and Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN)-Cloud Classification System (CCS) rainfall intensity products, the accuracies improved by 21.73% and 10.81% for CSI, and 31.33% and 23.49% for MAE, respectively. The spatial distribution of the estimated rainfall intensity was much more similar to the radar data than the existing products.

• Journal of Korean Academy of Nursing Administration
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• v.19 no.1
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• pp.95-107
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• 2013

Purpose: The purpose of this study was to examine the effectiveness of Action Research (AR) approach in nursing. Methods: Participants were 64 perioperative nurses recruited from C hospital in Gwangju, Korea. The nurses were engaged in the project through 2 cycles of planning, acting, observing, and reflecting. A mixed-methods design was used to examine changes in participants and their knowledge management practice. Quantitative data were analyzed using SPSS 20.0 program and qualitative reflection data underwent content analysis. Results: During the project, participants developed standardized pre-operative checklists and opened an Internet Cafe to better manage their perioperative nursing information. At the end of the project, there was a significant increase in nurses' knowledge management (p=.015) and the rate of surgical material prescription errors decreased from 8.0% to 2.9%. Core AR project team members' teamwork skills and organizational commitment increased significantly (p=.040, p=.301, respectively). The main themes that emerged from the qualitative data were learning how to solve problems in practice, facilitating team activities through motivation, barriers of large participation, and rewarded efforts and inflated expectations. Conclusion: The AR project contributed to empowering participants to solve local problems. AR is a useful methodology to promote changes in practices and research participants.