• The Journal of Korean Physical Therapy
• /
• v.28 no.6
• /
• pp.349-354
• /
• 2016

Purpose: The purpose of the study was to investigate the thickness of deep neck flexors (DNF) and sternocleidomastoid muscle (SCM) bilaterally during deep neck flexor endurance test using ultrasonography images. Methods: Healthy volunteers (n=22), 20-25 (mean 22.2) years old, were recruited for the study. Participants were asked to perform the craniocervical flexion test (CCFT) in a seated position to measure deep neck flexor endurance. The thickness of DNF and SCM was assessed bilaterally and was measured using ultrasonography images at resting, contracted, pre-terminal and terminal phases of the neck muscle endurance test. Muscle contraction pattern was also observed along with the changes in muscle thickness from the resting phase to the terminal phase. Repeated-measure ANOVA was employed to compare muscle thickness bilaterally at each phase. Results: The thickness of right and left muscles was found to be significantly different in DNF both at resting and contracted phases (p=0.02, p<0.01, respectively), whereas no significant difference was observed in SCM at resting or contracted phases (p=0.59, p=0.18, respectively). Thickness changes from resting to contracted phase were not significantly different both in DNF and SCM (p=0.18, p=0.22, respectively). Muscle contraction patterns in right and left muscles were shown to be similar. Conclusion: The current study, performed on (with) healthy subjects, significantly detected different right and left muscle thickness in DNF, but the muscle contraction patterns were similar in DNF and SCM bilaterally. Further study is required to investigate DNF and SCM muscle size and function in people with neck pain.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.13 no.10
• /
• pp.4833-4848
• /
• 2019

This paper introduces the structure and effects of Kakao's news curation algorithm, which is created based on the Deep Reading Index (DRI). The DRI examines the extent of deep reading through content reading time, that is, the duration of reader engagement with an article. Current news curation algorithms focus on reader choice, with the click-through rate or pageviews as the gauge for consumption frequency. DRI is a product of the challenge of introducing and adopting a new factor called 'consumption time' instead of 'frequency of consumption', which is the basis of existing curation algorithms. The analysis of DRI-based services proves that the new algorithm can act as a curation system that is more effective in providing in-depth and quality news reports.

• Transactions of Materials Processing
• /
• v.3 no.4
• /
• pp.440-453
• /
• 1994

The multi-stage deep-drawing processes including normal-drawing, reverse-drawing, and re-drawing are analyzed by use of the rigid-plastic finite element method. Computational results on the punch/die loads and thickness distributions were compared with the experiments of the current drawing processes. Deep-drawing processes of the redesigned shell to improve the specific strength and stiffness were simulated with the numerical method developed. With varying several process parameters such as blank size, corner radii of tools, and clearances, the simulation results showed the improvements in reducing the forming loads. Also forming defects were found during simulation and appropriate blank size could be verified.

• Proceedings of the Korea Concrete Institute Conference
• /
• 1996.10a
• /
• pp.337-344
• /
• 1996

Reinforced concrete deep beams with conpressive strengths in the range of 500kg/$\textrm{cm}^3$~750kg/$\textrm{cm}^3$ were tested under two-point loding. All the beams were singly reinforced with main steel percent $\rho$=1.29% and with nominal percentage of vertical shear reinflrcements $\rho_v$=0.26%. According to shear-span to depth ratio a/d. The beams were tested for four horizontal shear reinforcement ratio $\rho_h$, ranging from$\rho_h$=0.0 to $\rho_h$=0.53. The results indicate that the horizontal shear reinforcements of beams have an effect on failure load and on ductile behavior of deep beams. The test results are compared with predictions based on the current ACI Building Code. The computated reports in the paper will have designers assured for design of high strength concrete deep beam. Though ACI Code is relatively conservative and tend to non-economical, ACI Code has the merit that is easy to use.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
• /
• 2004.05a
• /
• pp.565-569
• /
• 2004

This paper deals with the characteristics of potential rise and effective impulse impedance of deep-driven ground rods that are used in high resistivity soil or in confined places such as downtown. Also the effects of the impulse and fault currents on the deep-driven ground rods combined with different type grounding electrodes like as mesh grids and counterpoises are described. The $8/20{\mu}s$ impulse current and other wave currents with different rise times are injected into the test ground rod and the effective impedances are examined. The most effective way to obtain the fine transient impedance behaviors of deep-driven ground rods is to reduce the inductive component of grounding electrode systems combined with other ground electrodes.

• Journal of Information Processing Systems
• /
• v.18 no.4
• /
• pp.587-598
• /
• 2022

To increase building change recognition accuracy, we present a deep learning-based building change detection using remote sensing images. In the proposed approach, by merging pixel-level and object-level information of multitemporal remote sensing images, we create the difference image (DI), and the frequency-domain significance technique is used to generate the DI saliency map. The fuzzy C-means clustering technique pre-classifies the coarse change detection map by defining the DI saliency map threshold. We then extract the neighborhood features of the unchanged pixels and the changed (buildings) from pixel-level and object-level feature images, which are then used as valid deep neural network (DNN) training samples. The trained DNNs are then utilized to identify changes in DI. The suggested strategy was evaluated and compared to current detection methods using two datasets. The results suggest that our proposed technique can detect more building change information and improve change detection accuracy.

• Nuclear Engineering and Technology
• /
• v.55 no.1
• /
• pp.285-294
• /
• 2023

Autonomous radiation source detection has long been studied for radiation emergencies. Compared to conventional data-driven or path planning methods, deep reinforcement learning shows a strong capacity in source detection while still lacking the generalized ability to the geometry in unknown environments. In this work, the detection task is decomposed into two subtasks: exploration and localization. A hierarchical control policy (HC) is proposed to perform the subtasks at different stages. The low-level controller learns how to execute the individual subtasks by deep reinforcement learning, and the high-level controller determines which subtasks should be executed at the current stage. In experimental tests under different geometrical conditions, HC achieves the best performance among the autonomous decision policies. The robustness and generalized ability of the hierarchy have been demonstrated.

• Steel and Composite Structures
• /
• v.47 no.3
• /
• pp.383-393
• /
• 2023

Conventional reinforced concrete design codes assume ideal strain evolution in semi-deep beams with externally bonded fiber-reinforced polymer (EB-FRP) web strips. However, there is a strain interaction between internal stirrups and web strips, leading to a notable difference between code-based and experimental shear strengths. Current study provides an experiment-verified detailed numerical framework to assess the potential strain interaction under quasi-static monotonic load. Based on the observations, steel stirrups are effective only for low EB-FRP amounts and the over-strengthening of semi-deep beams prevents the stirrups from yielding, reducing its shear strength contribution. A notable difference is detected between the code-based and the study-based EB-FRP strain values, which is a function of the normalized FRP stress parameter. Semi-analytical relations are proposed to estimate the effective strain and stress of the components considering the potential strain interaction. For the sake of simplification, a linearized correction factor is proposed for the EB-FRP web strip strain, assuming its restraining effect as constant for all steel stirrup amounts.

• International Journal of Computer Science & Network Security
• /
• v.23 no.6
• /
• pp.140-146
• /
• 2023

A detailed survey, applications and challenges of video encoding-decoding systems is discussed in this paper. A novel architecture has also been set aside for future work in the same direction. The literature reviews span the years 1960 to the present, highlighting the benchmark methods proposed by notable academics in the field of video compression. The timeline used to illustrate the review is divided into three sections. Classical methods, conventional heuristic methods, and current deep learning algorithms are all used for video compression in these categories. The milestone contributions are discussed for each category. The methods are summarized in various tables, along with their benefits and drawbacks. The summary also includes some comments regarding specific approaches. Existing studies' shortcomings are thoroughly described, allowing potential researchers to plot a course for future research. Finally, a closing note is made, as well as future work in the same direction.

• International Journal of Computer Science & Network Security
• /
• v.23 no.12
• /
• pp.107-114
• /
• 2023

The attack technique by the malware distribution form is a dangerous, difficult to detect and prevent attack method. Current malware detection studies and proposals are often based on two main methods: using sign sets and analyzing abnormal behaviors using machine learning or deep learning techniques. This paper will propose a method to detect malware on Endpoints based on Event IDs using deep learning. Event IDs are behaviors of malware tracked and collected on Endpoints' operating system kernel. The malware detection proposal based on Event IDs is a new research approach that has not been studied and proposed much. To achieve this purpose, this paper proposes to combine different data mining methods and deep learning algorithms. The data mining process is presented in detail in section 2 of the paper.