• Progress in Medical Physics
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• v.29 no.4
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• pp.150-156
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• 2018

This study aims to develop an improved Feldkamp-Davis-Kress (FDK) reconstruction algorithm using anisotropic total variation (ATV) minimization to enhance the image quality of low-dose cone-beam computed tomography (CBCT). The algorithm first applies a filter that integrates the Shepp-Logan filter into a cosine window function on all projections for impulse noise removal. A total variation objective function with anisotropic penalty is then minimized to enhance the difference between the real structure and noise using the steepest gradient descent optimization with adaptive step sizes. The preserving parameter to adjust the separation between the noise-free and noisy areas is determined by calculating the cumulative distribution function of the gradient magnitude of the filtered image obtained by the application of the filtering operation on each projection. With these minimized ATV projections, voxel-driven backprojection is finally performed to generate the reconstructed images. The performance of the proposed algorithm was evaluated with the catphan503 phantom dataset acquired with the use of a low-dose protocol. Qualitative and quantitative analyses showed that the proposed ATV minimization provides enhanced CBCT reconstruction images compared with those generated by the conventional FDK algorithm, with a higher contrast-to-noise ratio (CNR), lower root-mean-square-error, and higher correlation. The proposed algorithm not only leads to a potential imaging dose reduction in repeated CBCT scans via lower mA levels, but also elicits high CNR values by removing noisy corrupted areas and by avoiding the heavy penalization of striking features.

• International journal of advanced smart convergence
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• v.8 no.3
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• pp.184-192
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• 2019

The existing digital signage systems inside the elevators are a lack of tailored contents appropriate to the space and environment inside or outside the elevators. Also, they almost impossible to flexibly respond to various contents disclosure according to the demand of the consumers or the elevator markets. Therefore we design and implement an IoT(Internet of Things)-based smart digital signage system for the safety of elevator passengers.. In order to provide IoT-based information to the smart digital signage within the elevator, we propose an IoT system as a set-top box with gyroscope sensor, acceleration sensor, RFID(Radio-Frequency Identification), fine dust sensor, etc., which processes various data collected by the sensors and provides the elevator passengers with various tailored contents such as elevator driving information, environmental information inside and outside the elevator, and disaster information in addition to simple advertisement information. The proposed IoT system is a set-top box that operates the smart digital signage and has an independent information control processor based on the IoT sensors that do not depend on the elevator control system. For the proposed smart digital signage, it supports an operating system that is independent of the elevator driving service as well as the media service. So the smart signage system has a characteristic that it does not depend on the elevator control system since it is a stand-along IoT-based information control system. With the proposed system providing intuitive content for the surge, steep descent, and radical movements of an elevator due to an emergency situation, the elevator passengers should be able to recognize the situation quickly and respond accordingly. In the near future, the proposed system will expand the market of digital signage applied in conjunction with the development of contents in the disaster, safety and environment fields, and expect expected to revitalize related industries associated with signage.

• PNF and Movement
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• v.17 no.1
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• pp.137-144
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• 2019

Purpose: This study examined the effects of the abdominal drawing-in maneuver (ADIM) on muscle activity in the trunk and legs while subjects walk on a ramp. Methods: The subjects were healthy adult males (n=15) and females (n=8) in their twenties. The subjects were asked to maintain the ADIM contraction for 15 minutes using a pressure biofeedback unit. Their muscle activity was then measured while ascending or descending the ramp with or without the ADIM contraction maintained. Activity in the sternocleidomastoid, splenius capitis, rectus abdominis, external oblique abdominal, transversus abdominis, erector spinae, vastus medialis, and vastus lateralis muscles was measured using surface electromyography (TM DTS, Noraxon, USA). A paired t-test was conducted using SPSS 18.0 (IBM) for statistical data processing. Results: Maintaining the ADIM contraction during ascension led to a significant increase (p<0.05) in muscle activity for the rectus abdominis, transversus abdominis, vastus medialis, and vastus lateralis, but a significant decrease (p<0.05) in muscle activity for the erector spinae, when compared to the same activity without the ADIM maintained. Furthermore, maintaining the ADIM contraction during descent led to a significant increase (p<0.05) in muscle activity for the rectus abdominis, external abdominal oblique, transversus abdominis, vastus medialis, and vastus lateralis, but a significant decrease (p<0.05) in muscle activity for the erector spinae, when compared to the same activity without the ADIM maintained. Conclusion: As a result of this study, it maintains the ADIM and reduces lumbar muscle activity at the waist and increases muscle activity in the legs when walking on a ramp. Therefore, maintaining the ADIM contraction during ramp walking is recommended as training to improve the function of patients' muscular skeleton.

• Korean Journal of Optics and Photonics
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• v.33 no.6
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• pp.245-251
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• 2022

We have developed a multi-channel fiber laser for tiled laser beam combining and a laser output array system for multi-beam alignment. The fiber laser is a master oscillator power amplifier configuration that has a common seed, a preamplifier, and a 7-channel amplifier. The output power of each channel is more than 10 W. The laser output array system is a packed cylindrical configuration for a high fill-factor, and it has capabilities for collimation and tilt control with built-in PZT. Multi-beam alignment to a target is successfully implemented using PZT controlled with a stochastic parallel gradient descent (SPGD) algorithm.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.1
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• pp.125-134
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• 2021

With the rapid development of the film industry, the number of films is significantly increasing and movie recommendation system can help user to predict the preferences of users based on their past behavior or feedback. This paper proposes a movie recommendation system based on the latent factor model with the adjustment of mean and bias in rating. Singular value decomposition is used to decompose the rating matrix and stochastic gradient descent is used to optimize the parameters for least-square loss function. And root mean square error is used to evaluate the performance of the proposed system. We implement the proposed system with Surprise package. The simulation results shows that root mean square error is 0.671 and the proposed system has good performance compared to other papers.

• Journal of the Korea Society of Computer and Information
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• v.26 no.7
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• pp.1-7
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• 2021

In this paper, we propose a feature selection technique for multi-label classification. Many existing feature selection techniques have selected features by calculating the relation between features and labels such as a mutual information scale. However, since the mutual information measure requires a joint probability, it is difficult to calculate the joint probability from an actual premise feature set. Therefore, it has the disadvantage that only a few features can be calculated and only local optimization is possible. Away from this regional optimization problem, we propose a feature selection technique that constructs a low-rank space in the entire given feature space and selects features with sparsity. To this end, we designed a regression-based objective function using Nuclear norm, and proposed an algorithm of gradient descent method to solve the optimization problem of this objective function. Based on the results of multi-label classification experiments on four data and three multi-label classification performance, the proposed methodology showed better performance than the existing feature selection technique. In addition, it was showed by experimental results that the performance change is insensitive even to the parameter value change of the proposed objective function.

• Journal of Digital Convergence
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• v.20 no.4
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• pp.389-396
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• 2022

Deep Learning is based on a perceptron, and is currently being used in various fields such as image recognition, voice recognition, object detection, and drug development. Accordingly, a variety of learning algorithms have been proposed, and the number of neurons constituting a neural network varies greatly among researchers. This study analyzed the learning characteristics according to the number of neurons of the currently used SGD, momentum methods, AdaGrad, RMSProp, and Adam methods. To this end, a neural network was constructed with one input layer, three hidden layers, and one output layer. ReLU was applied to the activation function, cross entropy error (CEE) was applied to the loss function, and MNIST was used for the experimental dataset. As a result, it was concluded that the number of neurons 100-300, the algorithm Adam, and the number of learning (iteraction) 200 would be the most efficient in deep learning learning. This study will provide implications for the algorithm to be developed and the reference value of the number of neurons given new learning data in the future.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.12
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• pp.1749-1755
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• 2021

Virtual reality is actively applied in the fields of games, entertainment, communication, sports, and architecture. In particular, many virtual reality-based education systems are being developed in the field of education, creating efficient learning effects. In addition, virtual reality-based education is used in areas such as maintenance, fighter control, medical care, and firefighting as it can maximize the educational effect through the mastery process of the function itself through the curriculum as well as indirect experiences of dangerous situations that are difficult to experience. However, due to technical limitations, lack of contents, and lack of theoretical research, the level of application of military education and training is still insufficient. This paper aim to contribute to the development of a virtual reality-based education system as a military training system by developing a high-quality drop training system applicable to military group descent training, studying key technologies and implementation methods necessary for development.

• Journal of Internet of Things and Convergence
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• v.8 no.5
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• pp.127-132
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• 2022

As the IoT technology is more developed, it is more important for the accuracy of IoT data. Since the IoT data supports a different formats and protocols, it is often happened that the IoT system is failed or the incorrect data is generated with the unreliable IoT devices(sensor, actuator). Because the abnormality of IoT device or the user situation is not detected correctly, this problem makes the user to be unsatisfied with the IoT system. This study proposes the decision methodology of IoT data consistency whether the IoT data is generated in normal range or not by using the mathematical functions('gradient descent function' and 'linear regression function'). It may be concluded that the gradient function method is suitable for the IoT data which the 'increasing velocity' is related with the next generated pattern(eg. sensor devices), the linear regression function method is suitable for the IoT data which the 'the difference from linear regression function' is related with the next generated pattern in case the data has a linear pattern(eg. water meter, electric meter).

• Journal of Advanced Navigation Technology
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• v.26 no.5
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• pp.281-288
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• 2022

This study quantitatively analyzed the human casualties that can occur when a multicopter-type Urban Air Mobility (UAM) with a weight of about 1 ton and a speed of about 100 km/h falls in an urban area. Based on the population density and building database in Seoul, the population exposed to collisions in the event of a UAM crash was derived. Through the ballistic descent model, the accident impact radius of the UAM fall was calculated. In addition, the change in human casualties on the ground was analyzed when the accident impact radius increased. Finally, the ground risk map was created for Seoul, and it was confirmed that about 1 to 10 people could be injured when a UAM crash.