• Communications of Mathematical Education
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• v.35 no.3
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• pp.295-322
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• 2021

This study was conducted for one semester through one group pretest-posttest design with 49 first-year middle school students to explore the effects of mathematics-centered STEAM class experiences on students' mathematical modeling abilities. The main results of this study are as follows: First, the results of the pre and post-mathematical modeling ability tests showed that the average score of posttest was improved compared to the pretest, and that the experiences of mathematics-centered STEAM classes provided in this study had a positive effect on improving the mathematical modeling ability of first-year middle school students. Second, STEAM classes were more effective in solving mathematical modeling problems that require students' creative and divergent thinking. Third, the content analysis of student responses for each subquestion showed that STEAM classes were especially more helpful in activating students' mathematical model construction and validating steps.

• Journal of the Korea Society of Computer and Information
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• v.26 no.9
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• pp.1-12
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• 2021

In this paper, we propose a tool called ARCAV (Atomatic Recovery of CUDA Atomicity violation) to automatically repair atomicity violations in GPU (Graphics Processing Unit) program. ARCAV monitors information of every barrier and memory to make actual memory writes occur at the end of the barrier region or to make the program execute barrier region again. Existing methods do not repair atomicity violations but only detect the atomicity violations in GPU programs because GPU programs generally do not support lock and sleep instructions which are necessary for repairing the atomicity violations. Proposed ARCAV is designed for GPU execution model. ARCAV detects and repairs four patterns of atomicity violations which represent real-world cases. Moreover, ARCAV is independent of memory hierarchy and thread configuration. Our experiments show that the performance of ARCAV is stable regardless of the number of threads or blocks. The overhead of ARCAV is evaluated using four real-world kernels, and its slowdown is 2.1x, in average, of native execution time.

• Journal of the Korea Computer Graphics Society
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• v.24 no.5
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• pp.41-48
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• 2018

The most natural way to increase immersion and provide free interaction in a virtual environment is to provide a gesture interface using the user's hand. However, most studies about hand gesture recognition require specialized sensors or equipment, or show low recognition rates. This paper proposes a three-dimensional DenseNet Convolutional Neural Network that enables recognition of hand gestures with no sensors or equipment other than an RGB camera for hand gesture input and introduces a virtual reality game based on it. Experimental results on 4 static hand gestures and 6 dynamic hand gestures showed that they could be used as real-time user interfaces for virtual reality games with an average recognition rate of 94.2% at 50ms. Results of this research can be used as a hand gesture interface not only for games but also for education, medicine, and shopping.

• Journal of IKEEE
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• v.22 no.4
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• pp.933-940
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• 2018

In this paper, we propose a multi-block deep learning structure for improving face recognition rate. The recognition structure of the proposed deep learning consists of three steps: multi-blocking of the input image, multi-block selection by facial feature numerical analysis, and perform deep learning of the selected multi-block. First, the input image is divided into 4 blocks by multi-block. Secondly, in the multi-block selection by feature analysis, the feature values of the quadruple multi-blocks are checked, and only the blocks with many features are selected. The third step is to perform deep learning with the selected multi-block, and the result is obtained as an efficient block with high feature value by performing recognition on the deep learning model in which the selected multi-block part is learned. To evaluate the performance of the proposed deep learning structure, we used CAS-PEAL face database. Experimental results show that the proposed multi-block deep learning structure shows 2.3% higher face recognition rate than the existing deep learning structure.

• Journal of Convergence for Information Technology
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• v.9 no.8
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• pp.63-73
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• 2019

The purpose of this study is to examine the effects of nursing work environment and reciprocity on job embeddedness in the small and medium size hospital nurses. The data were collected from questionnaires filled out by 206 nurses. Data collection was performed from March 4 to 22, 2019. The collected data were analyzed by SPSS 25.0 program. As a result of the research, the study model accounted for 66.0% of job embeddedness. The most powerful variable affecting job embeddedness was support system of nurse, among sub - variables of nursing work environment. Therefore, nursing managers should improve the nurse's job embeddedness by creating nursing work environment that supports nurses such as salary improvement, professional development and promotion opportunities. In addition, it suggests that improvement of hospital and nursing organization system is needed to maintain cooperative relationship with nursing team or other health care professionals.

• Composites Research
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• v.32 no.3
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• pp.163-169
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• 2019

In this paper, we constructed a molecular dynamics (MD) polymer model of PMMA with 95% of conversion by using dynamic polymerization algorithm of a thermoplastic polymer based on free radical polymerization. In this algorithm, we introduced a united-atom level coarse-grained force field that combines the non-bonded terms from the TraPPE-UA force field and the bonded terms from the PCFF force field to alleviate the computation efforts. The molecular weight distribution and the average molecular weight of the polymer were calculated by investigating each chain generated from the free radical polymerization simulation. The molecular weight of the polymer was controlled by the number of initiator radicals presented in the initial state and molecular weight effect to the density, the glass transition temperature, and the mechanical properties were studied.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.4
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• pp.7-12
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• 2019

Ambient backscatter communication is limited to channel estimation technique through a pilot signal, which is a channel estimation method in current RF communication, due to transmission power efficiency. In a limited transmission power environment, the research of traditional ambient backscatter communication has been studied assuming that it is an ideal channel without signal distortions due to channel conditions. In this paper, we propose an expectation-maximization(EM) algorithm, one of the blind channel estimation techniques, as a channel estimation method in ambient backscatter communication system which is the state of channel following normal distribution. In the proposed system model, the simulations confirm that channel estimate through EM algorithm is approaching the lower bound of the mean square error compared with the Bayesian Cramer-Rao Boundary(BCRB) to check performance. It shows that the channel parameter can be estimated in the ambient backscatter communication system.

• Journal of Advanced Navigation Technology
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• v.23 no.1
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• pp.1-7
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• 2019

Thisstudy presentsthe development of a fast-time airport surface simulator. The simulator usesthe output from a first-come first-served (FCFS) scheduler and has adopted one-dimensional dynamic model to simulate the movement of the aircraft on the surface. Higher collision risks situations in the airport surface traffic are analyzed to classify those situations into six cases. A conflict detection and resolution algorithm is implemented to maintain separation distance and to prevent deadlock. The simulator was tested with a scenario at the Incheon International Airport that contains 72 aircraft. Without the conflict detection and resolution, various conflict situations are identified. When the conflict detection and resolution algorithm is managing the traffic, it is confirmed that the conflicts are removed at the price of additional delays. In the conflict resolution algorithm, three prioritization strategies are implemented, and delayed aircraft count and average additional delays are compared. Prioritization based on remaining time or distance showed smaller total additional delay compared to choosing minimum delay priority for each situation.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.10
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• pp.1341-1347
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• 2020

Legacy studies for classifying arrhythmia have been studied in order to improve the accuracy of classification, Neural Network, Fuzzy, Machine Learning, etc. In particular, deep learning is most frequently used for arrhythmia classification using error backpropagation algorithm by solving the limit of hidden layer number, which is a problem of neural network. In order to apply a deep learning model to an ECG signal, it is necessary to select an optimal model and parameters. In this paper, we propose parameter extraction based on AR and arrhythmia classification through a deep learning. For this purpose, the R-wave is detected in the ECG signal from which noise has been removed, QRS and RR interval is modelled. And then, the weights were learned by supervised learning method through deep learning and the model was evaluated by the verification data. The classification rate of PVC is evaluated through MIT-BIH arrhythmia database. The achieved scores indicate arrhythmia classification rate of over 97%.

• Journal of IKEEE
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• v.24 no.4
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• pp.1141-1147
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• 2020

The hornet species are so similar in shape that they are difficult for non-experts to classify, and because the size of the objects is small and move fast, it is more difficult to detect and classify the species in real time. In this paper, we developed a system that classifies hornets species in real time based on a deep learning algorithm using a boundary box. In order to minimize the background area included in the bounding box when labeling the training image, we propose a method of selecting only the head and body of the hornet. It also experimentally compares existing boundary box-based object recognition algorithms to find the best algorithms that can detect wasps in real time and classify their species. As a result of the experiment, when the mish function was applied as the activation function of the convolution layer and the hornet images were tested using the YOLOv4 model with the Spatial Attention Module (SAM) applied before the object detection block, the average precision was 97.89% and the average recall was 98.69%.