• Journal of The Korean Society of Integrative Medicine
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• v.7 no.2
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• pp.181-187
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• 2019

Purpose: The purpose of this study was to investigate the activity of respiratory muscle and lung capacity during deep breathing with electrical stimulation of the vagus nerve. Methods: This study was conducted on 30 healthy adults in their 20s. Subjects were randomly performed to deep breathing or deep breathing with vagus nerve electrical stimulation. All subjects' diaphragm and internal oblique muscle activity were measured during deep breathing by electromyography, and lung capacity was measured by spirometry immediately after beep breathing. In the vagus nerve stimulation method, the surface electrode was cut into the left ear and then electrically stimulated using a needle electric stimulator. Results: The activity of diaphragm was significantly increased in deep breathing with vagus nerve electrical stimulation than in deep breathing. However, lung capacity did not show any significant difference according to the condition. Conclusion: Vagus nerve electrical stimulation could induce diaphragm activity more than deep breathing alone. Deep breathing with vagus nerve electrical stimulation may enhance the activity of the respiratory muscles and is expected to be an effective treatment for the elderly or COPD patients with poor breathing ability.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.11
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• pp.4246-4267
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• 2020

As the network goes deep into all aspects of people's lives, the number and the complexity of network traffic is increasing, and traffic classification becomes more and more important. How to classify them effectively is an important prerequisite for network management and planning, and ensuring network security. With the continuous development of deep learning, more and more traffic classification begins to use it as the main method, which achieves better results than traditional classification methods. In this paper, we provide a comprehensive review of network traffic classification based on deep learning. Firstly, we introduce the research background and progress of network traffic classification. Then, we summarize and compare traffic classification based on deep learning such as stack autoencoder, one-dimensional convolution neural network, two-dimensional convolution neural network, three-dimensional convolution neural network, long short-term memory network and Deep Belief Networks. In addition, we compare traffic classification based on deep learning with other methods such as based on port number, deep packets detection and machine learning. Finally, the future research directions of network traffic classification based on deep learning are prospected.

• Journal of information and communication convergence engineering
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• v.20 no.3
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• pp.219-225
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• 2022

In this study, we propose training a support vector machine (SVM) model on top of deep networks for detecting Covid-19 from chest X-ray images. We started by gathering a real chest X-ray image dataset, including positive Covid-19, normal cases, and other lung diseases not caused by Covid-19. Instead of training deep networks from scratch, we fine-tuned recent pre-trained deep network models, such as DenseNet121, MobileNet v2, Inception v3, Xception, ResNet50, VGG16, and VGG19, to classify chest X-ray images into one of three classes (Covid-19, normal, and other lung). We propose training an SVM model on top of deep networks to perform a nonlinear combination of deep network outputs, improving classification over any single deep network. The empirical test results on the real chest X-ray image dataset show that deep network models, with an exception of ResNet50 with 82.44%, provide an accuracy of at least 92% on the test set. The proposed SVM on top of the deep network achieved the highest accuracy of 96.16%.

• International Journal of Computer Science & Network Security
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• v.22 no.5
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• pp.73-78
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• 2022

The aim of this paper is to investigate the prevalence of Deep Learning in the literature on Fire & Rescue Service. It is found that deep learning techniques are only beginning to benefit the firefighters. The popular areas where deep learning techniques are making an impact are situational awareness, decision making, mental stress, injuries, well-being of the firefighter such as his sudden fall, inability to move and breathlessness, path planning by the firefighters while getting to an fire scene, wayfinding, tracking firefighters, firefighter physical fitness, employment, prediction of firefighter intervention, firefighter operations such as object recognition in smoky areas, firefighter efficacy, smart firefighting using edge computing, firefighting in teams, and firefighter clothing and safety. The techniques that were found applied in firefighting were Deep learning, Traditional K-Means clustering with engineered time and frequency domain features, Convolutional autoencoders, Long Short-Term Memory (LSTM), Deep Neural Networks, Simulation, VR, ANN, Deep Q Learning, Deep learning based on conditional generative adversarial networks, Decision Trees, Kalman Filters, Computational models, Partial Least Squares, Logistic Regression, Random Forest, Edge computing, C5 Decision Tree, Restricted Boltzmann Machine, Reinforcement Learning, and Recurrent LSTM. The literature review is centered on Firefighters/firemen not involved in wildland fires. The focus was also not on the fire itself. It must also be noted that several deep learning techniques such as CNN were mostly used in fire behavior, fire imaging and identification as well. Those papers that deal with fire behavior were also not part of this literature review.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.2
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• pp.299-304
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• 2023

Among artificial intelligence techniques, deep learning is a model that has been used in many places and has proven its effectiveness. However, deep learning models are not used effectively in everywhere. In this paper, we will show the limitations of deep learning models through comparison of regression analysis and deep learning models, and present a guide for effective use of deep learning models. In addition, among various techniques used for optimization of deep learning models, data normalization and data shuffling techniques, which are widely used, are compared and evaluated based on actual data to provide guidelines for increasing the accuracy and value of deep learning models.

• Advances in concrete construction
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• v.13 no.3
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• pp.243-254
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• 2022

Reinforced concrete (RC) deep beams are critical structural elements used in offshore pile caps, rectangular cross-section water tanks, silo structures, transfer beams in high-rise buildings, and bent caps. As a result of the low shear span ratio to effective depth (a/d) in deep beams, arch action occurs, which leads to shear failure. Several studies have been carried out to improve the shear resistance of RC deep beams and avoid brittle fracture behavior in recent years. This study was performed to investigate the behavior of RC deep beams numerically and experimentally with different reinforcement arrangements. Deep beams with four different reinforcement arrangements were produced and tested under monotonic static loading in the study's scope. The horizontal and vertical shear reinforcement members were changed in the test specimens to obtain the effects of different reinforcement arrangements. However, the rebars used for tension and the vertical shear reinforcement ratio were constant. In addition, the behavior of each deep beam was obtained numerically with commercial finite element analysis (FEA) software ABAQUS, and the findings were compared with the experimental results. The results showed that the reinforcements placed diagonally significantly increased the load-carrying and energy absorption capacities of RC deep beams. Moreover, an apparent plastic plateau was seen in the load-displacement curves of these test specimens in question (DE-2 and DE-3). This finding also indicated that diagonally located reinforcements improve displacement ductility. Also, the numerical results showed that the FEM method could be used to accurately predict RC deep beams'behavior with different reinforcement arrangements.

• Proceedings of the Korean Geotechical Society Conference
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• 1999.03a
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• pp.301-308
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• 1999

The construction of great deep tunnels has become an important part in tunnel construction especially in the mountain area. Therefore, it is necessary to establish the proper method of the stability analysis for great deep tunnels. In this paper presents the study result on the followings: (1) Evaluation of practical problem on the stability analysis of great deep tunnels. (2) Proposal of the proper on method for great deep tunnels analysis considering the depth of overburden. (3) Understanding of the ground behavior of the great deep tunnel through the sensitivity analysis and the parametric study.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2008.10a
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• pp.130-133
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• 2008

The formability of DP steels can be affected by not only initial texture but also deformation texture evolved during plastic deformation. To investigate the evolution of deformation texture during deep drawing, deep drawing process for DP steels was carried out experimentally. A rate sensitive polycrystal model was used to predict texture evolution during deep drawing process. In order to evaluate the strain path during deep drawing, a steady state was assumed in the flange part of deep drawn cup. A rate sensitive polycrystal model successfully predicted the texture development in DP steels during deep drawing process. It was found that the final stable orientations were strongly dependent on the initial location in the blank.

• The Journal of Korean Academy of Orthopedic Manual Physical Therapy
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• v.10 no.1
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• pp.57-65
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• 2004

The main purpose of this article is to make a handy Deep Friction Massager by using DFM in based on Dr. Cyriax's manual medicine. Also this study's aimed to heal soft tissue lesions - low back pain, neck pain, tennis elbow, golfer's elbow, frozen shoulder, myofibrosis etc. - which has resolved adhesion scar tissue problem in soft tissue. The results of this study were as followings ; 1. Deep friction massager has a effect not only massage but also healing, because it is broken the physiologic bridge of scar tissue in soft tissue. 2. It is possible to reduce the fatigue and effort of therapists during the deep friction massage. 3. Deep friction massager is made of handy form, so it is very convenient of using and application to patients.

• Steel and Composite Structures
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• v.16 no.5
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• pp.481-489
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• 2014

The function of carbon fibre reinforced polymer (CFRP) reinforcement in increasing the ductility of reinforced concrete (RC) deep beam is important in such shear-sensitive RC member. This paper aims to investigate the effect of CFRP-strengthening on the energy absorption of RC deep beams. Six ordinary RC deep beams and six CFRP-strengthened RC deep beams with shear span to the effective depth ratio of 0.75, 1.00, 1.25, 1.50, 1.75, and 2.00 were tested till failure in this research. An empirical relationship was established to obtain the energy absorption of CFRP-strengthened RC deep beams. The shear span to the effective depth ratio and growth of energy absorption of CFRP-strengthened deep beam were the significant factors to establish this relationship.