• Journal of the Korean Society of Physical Medicine
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• v.10 no.1
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• pp.23-35
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• 2015

PURPOSE: People with physical disabilities such as cerebral palsy usually experience obstacles when interacting with computer through conventional keyboard because of their motor disabilities. The purpose of this study is empirically compare of text entry(alphabet and word) speed and accuracy using the three different keyboard type on four students(male 2 and female 2) with cerebral palsy. METHODS: This research design used a replicated single-case experimental approach to compare the individual performance. An alternating treatments design was used to examine the effectiveness of standard QWERTY keyboard and alternative keyboard(mini and big keyboard) on computer access for students with cerebral palsy. To avoid changes in posture that influence a keyboard character entry training and evaluation was carried out using his sitting in a wheelchair. Compass software program used in this study as an assessment tool to measure speed and accuracy when performance of text entry(alphabet and word). This was repeated until the stable status of reaction time. RESULTS: As a result, the alternative keyboard seems to be the most effective device for students with cerebral palsy to perform text entry. But various factors such as peculiarity of motor disabilities, experience and preferences of the user are heavily related. CONCLUSION: Thus, we must perform the objective and systematic assessment for computer access and if sustained training is accomplished, it could to improve speed and accuracy of text entry(alphabet and word).

• Journal of the Korea Society of Computer and Information
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• v.20 no.5
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• pp.73-81
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• 2015

Most of the software fault prediction studies focused on the binary classification model that predicts whether an input entity has faults or not. However the ability to predict entity fault-proneness in various severity categories is more useful because not all faults have the same severity. In this paper, we propose fault prediction models at different severity levels of faults using traditional size and complexity metrics. They are ternary classification models and use four machine learning algorithms for their training. Empirical analysis is performed using two NASA public data sets and a performance measure, accuracy. The evaluation results show that backpropagation neural network model outperforms other models on both data sets, with about 81% and 88% in terms of accuracy score respectively.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.23 no.7
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• pp.815-821
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• 2012

A CW Doppler radar can measure radial velocity of an object. It detects a Doppler frequency shift that is proportioned to radial velocity of a moving object. To detect a Doppler frequency shift, FFT(Fast Fourier Transform) is conducted. In this process, the time domain received signal is transformed to a frequency domain. A number of FFT affects not only the time resolution but also signal to noise ratio of received signal. So finally it is related with a radial velocity accuracy. Therefore in this paper, it is described the relation of time resolution and the radial velocity accuracy.

• Journal of Veterinary Clinics
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• v.32 no.4
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• pp.319-323
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• 2015

The performance of diagnostic test accuracy is usually summarized by a variety of statistics such as sensitivity, specificity, predictive value, likelihood ratio, and kappa. These indices are most commonly presented when evaluations of competing diagnostic tests are reported, and it is of utmost importance to compare the accuracies of diagnostic tests to decide on the best available test for certain medical disorder. However, it is important to emphasize that specific point values of these indices are merely estimates. If parameter estimates are reported without a measure of uncertainty (precision), knowledgeable readers cannot know the range within which the true values of the indices are likely to lie. Therefore, when evaluations of diagnostic accuracy are reported the precision of estimates should be stated in parallel. To reflect the precision of any estimate of a diagnostic performance characteristic or of the difference between performance characteristics, the computation of confidential interval (CI), an indicator of precision, is widely used in medical literatures in that CIs are more informative to interpret test results than the simple point estimates. The majority of peer-reviewed journals usually require CIs to be specified for descriptive estimates, whereas domestic veterinary journals seem less vigilant on this issues. This paper describes how to calculate the indices and associated CIs using practical examples when assessing diagnostic test performance.

• Current Optics and Photonics
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• v.4 no.6
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• pp.500-508
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• 2020

The rifling angle of artillery is an important parameter, and its determination plays a key role in the stability, hit rate, accuracy and service life of artillery. In this study, we propose an optical measurement method for the rifling angle based on angle error correction. The method is based on the principle of geometrical optics imaging, where the rifling on the inner wall of the artillery barrel is imaged on a CCD camera target surface by an optical system. When the measurement system moves in the barrel, the rifling image rotates accordingly. According to the relationship between the rotation angle of the rifling image and the travel distance of the measurement system, different types of rifling equations are established. Solving equations of the rifling angle are deduced according to the definition of the rifling angle. Furthermore, we added an angle error correction function to the method that is based on the theory of dynamic optics. This function can measure and correct the angle error caused by the posture change of the measurement system. Thus, the rifling angle measurement accuracy is effectively improved. Finally, we simulated and analyzed the influence of parameter changes of the measurement system on rifling angle measurement accuracy. The simulation results show that the rifling angle measurement method has high measurement accuracy, and the method can be applied to different types of rifling angle measurements. The method provides the theoretical basis for the development of a high-precision rifling measurement system in the future.

• Journal of the Korea Society of Computer and Information
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• v.26 no.6
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• pp.37-46
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• 2021

In this paper, we designed and implemented a program to measure and to judge the accuracy of yoga postures using Azure Kinect. The program measures all joint positions of the user through Azure Kinect Camera and sensors. The measured values of joints are used as data to determine accuracy in two ways. The measured joint data are determined by trigonometry and Pythagoras theorem to determine the angle of the joint. In addition, the measured joint value is changed to relative position value. The calculated and obtained values are compared to the joint values and relative position values of the desired posture to determine the accuracy. Azure Kinect Camera organizes the screen so that users can check their posture and gives feedback on the user's posture accuracy to improve their posture.

• Nuclear Engineering and Technology
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• v.54 no.8
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• pp.3027-3033
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• 2022

Background: In this study, various types of deep-learning models for predicting in vitro radiosensitivity from gene-expression profiling were compared. Methods: The clonogenic surviving fractions at 2 Gy from previous publications and microarray gene-expression data from the National Cancer Institute-60 cell lines were used to measure the radiosensitivity. Seven different prediction models including three distinct multi-layered perceptrons (MLP), four different convolutional neural networks (CNN) were compared. Folded cross-validation was applied to train and evaluate model performance. The criteria for correct prediction were absolute error < 0.02 or relative error < 10%. The models were compared in terms of prediction accuracy, training time per epoch, training fluctuations, and required calculation resources. Results: The strength of MLP-based models was their fast initial convergence and short training time per epoch. They represented significantly different prediction accuracy depending on the model configuration. The CNN-based models showed relatively high prediction accuracy, low training fluctuations, and a relatively small increase in the memory requirement as the model deepens. Conclusion: Our findings suggest that a CNN-based model with moderate depth would be appropriate when the prediction accuracy is important, and a shallow MLP-based model can be recommended when either the training resources or time are limited.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2022.11a
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• pp.29-30
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• 2022

The unit-water content has a very significant effect on the durability of the construction structure and the quality of concrete. Although there are various methods for measuring the unit-water content, there are problems of time required for measurement, precision, and reproducibility. Recently, there is an FDR sensor capable of measuring moisture content in real time through an apparent dielectric constant change of electromagnetic waves. In addition, various artificial intelligence techniques that can non-linearly supplement the accuracy of FDR sensors are being studied. In this study, the accuracy of unit-water content measurement was compared and evaluated using machine learning and deep learning techniques after normalizing the data secured in concrete using frequency domain reflectometry (FDR) sensors used to measure soil moisture at home and abroad. The result of comparing the accuracy of machine learning and deep learning is judged to be excellent in the accuracy of deep learning, which can well express the nonlinear relationship between FDR sensor data and concrete unit-water content.

• International conference on construction engineering and project management
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• 2013.01a
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• pp.616-623
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• 2013

One of the oldest, most common engineering problems is measuring the dimensions of different objects and the distances between locations. In AEC/FM, related uses vary from large-scale applications such as measuring distances between cities to small-scale applications such as measuring the depth of a crack or the width of a welded joint. Within the last few years, advances in applying new technologies have prompted the development of new measuring devices such as ultrasound and laser-based measurers. Because of wide varieties in type, associated costs, and levels of accuracy, the selection of an optimal measuring technology is challenging for construction engineers and facility managers. To tackle this issue, we present an overview of various measuring technologies adopted by experts in the area of AEC/FM. As the next step, to evaluate the performance of these technologies, we select one indoor and one outdoor case and measure several dimensions using six categories of technologies: tapes, total stations, laser measurers, ultrasound devices, laser scanners, and image-based technologies. Then we evaluate the results according to various metrics such as accuracy, ease of use, operation time, associated costs, compare these results, and recommend optimal technologies for specific applications. The results also revealed that in most applications, computer vision-based technologies outperform traditional devices in terms of ease of use, associated costs, and accuracy.

• Earthquakes and Structures
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• v.26 no.2
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• pp.117-130
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• 2024

Probabilistic model of seismic demand is the main tool used for seismic demand estimation, which is a fundamental component of the new performance-based design method. This model seeks to mathematically relate the seismic demand parameter and the ground motion intensity measure. This study is intended to use Bayesian analysis to evaluate the accuracy of the seismic demand estimation of Steel moment resisting frames (SMRFs) through a completely Bayesian method in statistical calculations. In this study, two types of intensity measures (earthquake intensity-related indices such as magnitude and distance and intensity indices related to ground motion and spectral response including peak ground acceleration (PGA) and spectral acceleration (SA)) have been used to form the models. In addition, an extensive database consisting of sixty accelerograms was used for time-series analysis, and the target structures included five SMRFs of three, six, nine, twelve and fifteen stories. The results of this study showed that for low-rise frames, first mode spectral acceleration index is sufficient to accurately estimate demand. However, for high-rise frames, two parameters should be used to increase the accuracy. In addition, adding the product of the square of earthquake magnitude multiplied by distance to the model can significantly increase the accuracy of seismic demand estimation.