• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.11
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• pp.554-560
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• 2020

In the manufacturing process of flat panel displays, a high-precision planar motion stage is used to position a specimen. Stages of this type typically use frictionless linear motors and air bearings, and laser interferometers. Real-time dynamic correction of the yaw motion error is very important because the inevitable yaw motion error of the stage means a change in the specimen orientation. Gantry control is generally used to compensate for yaw motion errors. Flexure units that allow rotational motion are applied to the stage to apply this method to a stage using an air-bearing guide. This paper proposes a method to improve the constant speed motion performance of a H-type XY stage equipped with air bearing and flexure units. When applying the gantry control to the stage, including the flexure units, the cause of the mutual ripple generated from the linear motors is analyzed, and adaptive learning control is proposed to compensate for the mutual ripple. A simulation was performed to verify the proposed method. The speed ripple was reduced to approximately the 22 % level. The ripple reduction was verified by simulating the stage state where yaw motion error occurs.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.11
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• pp.206-216
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• 2013

The emotion plays a critical role in human's daily life including learning, action, decision and communication. In this paper, emotion discrimination classifier is designed to reduce system complexity through reduced selection of dominant features from biosignals. The photoplethysmography(PPG), skin temperature, skin conductance, fontal and parietal electroencephalography(EEG) signals were measured during 4 types of movie watching associated with the induction of neutral, sad, fear joy emotions. The genetic algorithm with support vector machine(SVM) based fitness function was designed to determine dominant features among 24 parameters extracted from measured biosignals. It shows maximum classification accuracy of 96.4%, which is 17% higher than that of SVM alone. The minimum error features selected are the mean and NN50 of heart rate variability from PPG signal, the mean of PPG induced pulse transit time, the mean of skin resistance, and ${\delta}$ and ${\beta}$ frequency band powers of parietal EEG. The combination of parietal EEG, PPG, and skin resistance is recommendable in high accuracy instrumentation, while the combinational use of PPG and skin conductance(79% accuracy) is affordable in simplified instrumentation.

• Journal of the Korea Society of Computer and Information
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• v.26 no.11
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• pp.41-49
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• 2021

All application programs, including malware, call the Application Programming Interface (API) upon execution. Recently, using those characteristics, attempts to detect and classify malware based on API Call information have been actively studied. However, datasets containing API Call information require a large amount of computational cost and processing time. In addition, information that does not significantly affect the classification of malware may affect the classification accuracy of the learning model. Therefore, in this paper, we propose a method of extracting a essential feature set after reducing the dimensionality of API Call information by applying various feature selection methods. We used CICAndMal2020, a recently announced Android malware dataset, for the experiment. After extracting the essential feature set through various feature selection methods, Android malware classification was conducted using CNN (Convolutional Neural Network) and the results were analyzed. The results showed that the selected feature set or weight priority varies according to the feature selection methods. And, in the case of binary classification, malware was classified with 97% accuracy even if the feature set was reduced to 15% of the total size. In the case of multiclass classification, an average accuracy of 83% was achieved while reducing the feature set to 8% of the total size.

• Korean Journal of Child Education & Care
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• v.19 no.2
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• pp.19-33
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• 2019

Objective: The purpose of this study is to understand how the experience of studying mathematics education for young children based on picture books is affecting pre-service early childhood teachers with their attitude toward mathematics and mathematics teaching efficiency. Methods: For this, a total of 39 pre-service early childhood teachers majoring in early childhood education at S university located in G metropolitan city participated in the study. 20 of them are randomly assigned to the experimental group while the rest 19 were assigned to the control group. The experimental group participated in the classes of mathematics education for young children based on picture books for 15 weeks, while the control group attended the normal classes of mathematics education for young children for the same period of time. By using SPSS 18.0 Program for the collected data, t-test was conducted for differences in the results of attitude toward mathematics and mathematics teaching efficiency. Results: The results of this study are as follows. Firstly, the experience of studying mathematics education for young children based on picture books had a positive effects on pre-service early childhood teachers' attitude toward mathematics, improved values and interest in mathematics, and reduced anxiety about mathematics. Secondly, the experience of studying mathematics education for young children based on picture books had a positive effects on improving pre-service early childhood teachers' mathematics teaching efficiency. Also, both faith in ability and faith in results have improved significantly. Conclusion/Implications: These results imply that the experience of studying mathematics education for young children based on picture books is an effective teaching-learning method in improving pre-service early childhood teachers' attitude toward mathematics and mathematics teaching efficiency.

• Journal of the Korea Society of Computer and Information
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• v.27 no.7
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• pp.1-7
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• 2022

Recently, as the use of applications such as big data programs and machine learning programs that are driven while generating large amounts of data in the program itself becomes common, the existing main memory alone lacks memory, making it difficult to execute the program quickly. In particular, the need to derive results more quickly has emerged in a situation where it is necessary to analyze whether the entire sequence is genetically altered due to the outbreak of the coronavirus. As a result of measuring performance by applying large-capacity data to a computing system equipped with a self-developed memory pool MOCA host adapter instead of processing large-capacity data from an existing SSD, performance improved by 16% compared to the existing SSD system. In addition, in various other benchmark tests, IO performance was 92.8%, 80.6%, and 32.8% faster than SSD in computing systems equipped with memory pool MOCA host adapters such as SortSampleBam, ApplyBQSR, and GatherBamFiles by task of workflow. When analyzing large amounts of data, such as electrical dielectric pipeline analysis, it is judged that the measurement delay occurring at runtime can be reduced in the computing system equipped with the memory pool MOCA host adapter developed in this research.

• Korean Chemical Engineering Research
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• v.62 no.3
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• pp.274-280
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• 2024

Machine learning techniques utilizing neural networks have been employed in various fields such as disease gene discovery and diagnosis, drug development, and prediction of drug-induced liver injury. Disease features can be investigated by molecular information of DNA. In this study, we developed a neural network to predict the length of DNA and the number of DNA species in mixture solution which are representative molecular information of DNA. In order to address the time-consuming limitations of gel electrophoresis as conventional analysis, we analyzed the dynamic data of a microfluidic concentrating device. The dynamic data were reconstructed into a spatiotemporal map, which reduced the computational cost required for training and prediction. We employed a convolutional neural network to enhance the accuracy to analyze the spatiotemporal map. As a result, we successfully performed single DNA length prediction as single-variable regression, simultaneous prediction of multiple DNA lengths as multivariable regression, and prediction of the number of DNA species in mixture as binary classification. Additionally, based on the composition of training data, we proposed a solution to resolve the problem of prediction bias. By utilizing this study, it would be effectively performed that medical diagnosis using optical measurement such as liquid biopsy of cell-free DNA, cancer diagnosis, etc.

• Journal of the Korean association of regional geographers
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• v.2 no.2
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• pp.219-231
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• 1996

The aim here is to analyse the system of description of GIS in all of the high school textbooks passed with the official approval, to find the degree to which teachers understand about GIS, and to consider the present condition of GIS instruction. Most of the authors of textbooks generally underestimate importance of GIS, and there is difference among their awareness. In the system of description of GIS, there are only a few kinds of textbooks in which explanation of GIS is made coherently from the purpose of instruction aim through the chapter summary and to overall test in both of the Korean Geography and the World Geography. This trend is due to the degree of distribution of the GIS specialists in writing a textbook while the other texts books shows just a brief introduction of GIS concept. Although there is the limit for teachers to study how to teach GIS due to its very technological aspect as well as few previous training and teacher's guide. Thus it is evident that about a half of teachers who responded taught high school students without a knowledge on GIS, and a few of them even never referred to that concept. These facts may negatively affect the status of a geography in the society of information. For the solution of these issues, it is considered how to repair the description system and its contents. Besides, the variation among textbooks is reduced at the further revision of the 7th curriculum. And the printed matters of GIS are sufficiently provided for the teachers to use as their teaching aids. It is desirable that the GIS instruction models should be further developed for college education, and the programs for the on-the-job teachers training should be arranged. Besides, the previous training for the on-the-job teachers should be achieved more practically with enough time before the revision of curriculum.

• Korean Journal of Remote Sensing
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• v.38 no.3
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• pp.265-282
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• 2022

Sea surface temperature (SST) is a factor that greatly influences ocean circulation and ecosystems in the Earth system. As global warming causes changes in the SST near the Korean Peninsula, abnormal water temperature phenomena (high water temperature, low water temperature) occurs, causing continuous damage to the marine ecosystem and the fishery industry. Therefore, this study proposes a methodology to predict the SST near the Korean Peninsula and prevent damage by predicting abnormal water temperature phenomena. The study area was set near the Korean Peninsula, and ERA5 data from the European Center for Medium-Range Weather Forecasts (ECMWF) was used to utilize SST data at the same time period. As a research method, Long Short-Term Memory (LSTM) algorithm specialized for time series data prediction among deep learning models was used in consideration of the time series characteristics of SST data. The prediction model predicts the SST near the Korean Peninsula after 1- to 7-days and predicts the high water temperature or low water temperature phenomenon. To evaluate the accuracy of SST prediction, Coefficient of determination (R²), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE) indicators were used. The summer (JAS) 1-day prediction result of the prediction model, R²=0.996, RMSE=0.119℃, MAPE=0.352% and the winter (JFM) 1-day prediction result is R²=0.999, RMSE=0.063℃, MAPE=0.646%. Using the predicted SST, the accuracy of abnormal sea surface temperature prediction was evaluated with an F1 Score (F1 Score=0.98 for high water temperature prediction in summer (2021/08/05), F1 Score=1.0 for low water temperature prediction in winter (2021/02/19)). As the prediction period increased, the prediction model showed a tendency to underestimate the SST, which also reduced the accuracy of the abnormal water temperature prediction. Therefore, it is judged that it is necessary to analyze the cause of underestimation of the predictive model in the future and study to improve the prediction accuracy.

• Sleep Medicine and Psychophysiology
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• v.8 no.2
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• pp.129-137
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• 2001

Objectives: The purpose of this study is to investigate the effects of 38-hour sleep deprivation on fine motor performance. The Motor Performance Series (MPS) in the Vienna Test System (computerized neurocognitive function tests) was used in this study. Methods: Twenty four subjects participated in this study. Subjects had no past history of psychiatric disorders and physical illness. Subjects had normal sleep-waking cycle without current sleep disturbances and were all right-handed (Annett's Hand Preference Questionnaire: above +9 points). To minimize the learning effects, familiarization with the Vienna Test System was performed one day before the study. Subjects were to get up at 6:00 in the morning after getting enough sleep according to his or her usual sleep-wake cycle. After awakening, subjects remained awake for 38 hours under continuous surveillance. During two consecutive study days, the subjects tested MPS at 7 AM and 7 PM each day, which means the MPS was done four times in total. During the experiment, anything that could affect the subjects' sleep such as coffee, tea, alcohol, a nap, tiring sports, and all medications were prohibited. Results: In MPS, the fine motor functions of both hands decreased after 38 hours of sleep deprivation. The decrement in motor performance was prominent in the dominant right hand. In the right hand, the total number of tapping was reduced (p<.005), and the number of misses (p<.05) and the length of misses (p<.05) of line tracking, the total length of inserting a short pin (p<.01), the total length of inserting a long pin (p<.05), and the number of misses in aiming (p<.05) increased. Such performance decrement was distinct in the morning sessions. Conclusions: These results suggest that fine motor performance decrement during sleep deprivation is predominant in the right hand, which exerts maximal motor function. The finding of decrement in motor function in tapping during sleep deprivation also suggested that the time required for exhaustion of muscles is shortened during sleep deprivation. More deterioration of motor performance was shown in the morning, which could be explained as circadian rhythm effects.