• Structural Engineering and Mechanics
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• v.13 no.2
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• pp.135-154
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• 2002

This paper presents the convected material frame approach to study the nonlinear behavior of inelastic frame structures. The convected material frame approach is a modification of the co-rotational approximation by incorporating an adaptive convected material frame in the basic definition of the displacement vector and strain tensor. In the formulation, each discrete element is associated with a local coordinate system that rotates and translates with the element. For each load increment, the corresponding strain-displacement and nodal force-stress relationships are defined in the updated local coordinates, and based on the updated element geometry. The rigid body motion and deformation displacements are decoupled for each increment. This modified approach incorporates the geometrical nonlinearities through the continuous updating of the material frame geometry. A generalized nonlinear function is used to derive the inelastic constitutive relation and the kinematic hardening is considered. The equation of motion is integrated by an explicit procedure and it involves only vector assemblage and vector storage in the analysis by assuming a lumped mass matrix of diagonal form. Several numerical examples are demonstrated in close agreement with the solutions obtained by the ANSYS code. Numerical studies show that the proposed approach is capable of investigating large deflection of inelastic planar structures and providing an excellent numerical performance.

• Journal of the Korean Society of Radiology
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• v.5 no.4
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• pp.189-194
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• 2011

This study used magnetic resonance diffusion tensor imaging (DTI) to quantitatively analyze the neural fiber tractography according to the age of normal corpus callosum and to evaluate of usefulness. The research was intended for the applicants of 60 persons that was in a good state of health with not brain or other disease. The test parameters were TR: 6650 ms, TE: 66 ms, FA: $90^{\circ}$, NEX: 2, thickness: 2 mm, no gap, FOV: 220 mm, b-value: $800s/mm^2$, sense factor: 2, acquisition matrix size: $2{\times}2{\times}2mm^3$, and the test time was 3 minutes 46 seconds. The evaluation method was constructed the color-cored FA map include to the skull vertex from the skull base in scan range. We set up the five ROI of corpus callosum of genu, anterior-mid body, posterior-mid body, isthmus, and splenium, tracking, respectively, and to quantitatively measured the length of neural fiber. As a result, the length of neural fiber, for the corpus callosum of genu was 20's: $61.8{\pm}6.8$, 30's: $63.9{\pm}3.8$, 40's: $65.5{\pm}6.4$, 50's: $57.8{\pm}6.0$, 60's: $58.9{\pm}4.5$, more than 70's: $54.1{\pm}8.1mm$, for the anterior-mid body was 20's: $54.8{\pm}8.8$, 30's: $58.5{\pm}7.9$, 40's: $54.8{\pm}7.8$, 50's: $56.1{\pm}10.2$, 60's: $48.5{\pm}6.2$, more than 70's: $48.6{\pm}8.3mm$, for the posterior-mid body was 20's: $72.7{\pm}9.1$, 30's: $61.6{\pm}9.1$, 40's: $60.9{\pm}10.5$, 50's: $61.4{\pm}11.7$, 60's: $54.9{\pm}10.0$, more than 70's: $53.1{\pm}10.5mm$, for the isthmus was 20's: $71.5{\pm}17.4$, 30's: $74.1{\pm}14.9$, 40's: $73.6{\pm}14.2$, 50's: $66.3{\pm}12.9$, 60's: $56.5{\pm}11.2$, more than 70's: $56.8{\pm}11.3mm$, and for the splenium was 20's: $82.6{\pm}6.8$, 30's: $86.9{\pm}6.4$, 40's: $83.1{\pm}7.1$, 50's: $81.5{\pm}7.4$, 60's: $78.6{\pm}6.0$, more than 70's: $80.55{\pm}8.6mm$. The length of neural fiber for normal corpus callosum were statistically significant in the genu(P=0.001), posterior-mid body(P=0.009), and istumus(P=0.012) of corpus callosum. In order of age, the length of neural fiber increased from 30s to 40s, as one grows older tended to decrease. For this reason, the nerve cells of brain could be confirmed through the neural fiber tractography to progress actively in middle age.

• Physical Therapy Korea
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• v.30 no.1
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• pp.68-77
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• 2023

Background: The weakness of the gluteus medius (GM) is associated with various musculoskeletal disorders. The increasing GM activity without synergistic dominance should be considered when prescribing pelvic drop exercise (PD). Isometric hip extension or flexion of the non-weight bearing leg using thera-band at the ankle during PD may influence hip abductor activities. Objects: To determine how isometric hip extension or flexion of the non-weight bearing leg using thera-band at the ankle during PD influences the activities of three subdivisions of GM (anterior, GMa; middle, GMm; posterior, GMp), tensor fasciae latae (TFL), contralateral quadratus lumborum (QL), and GMp/TFL, GMm/QL activity ratios in patients with GM weakness. Methods: Twenty-three patients with GM weakness were recruited. Three types of PD were performed: PD, PD with an isometric hip extension of the non-weight bearing leg (PDE), and PD with an isometric hip flexion of the non-weight bearing leg (PDF). Surface electromyography (SEMG) was used to measure hip abductor activities. One-way repeated-measures analysis of variance was used to assess the statistical significance of muscle activities and muscle activity ratios. Results: GMa, GMm, and GMp activities were significantly greater during PDF than during PD and PDE (p < 0.001, p = 0.001; p = 0.001, p = 0.005; p = 0.004, p = 0.004; respectively). TFL activity was significantly greater during PDE than during PD and PDF (p < 0.001, p < 0.001, respectively). QL activity was significantly greater during PDF than during PD (p = 0.003). GMp/TFL activity ratio was significantly lower during PDE than during PD and PDF (p = 0.001, p = 0.001, respectively). There were no significant differences in the GMm/QL activity ratio. Conclusion: PDF may be an effective exercise to increase the activities of all three GM subdivisions while minimizing the TFL activity in patients with GM weakness.

• Structural Engineering and Mechanics
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• v.90 no.3
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• pp.219-232
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• 2024

In current study, for the first time, Nonlinear Bending of a skew microplate made of a laminated composite strengthened with graphene nanosheets is investigated. A mixture of mechanical and thermal stresses is applied to the plate, and the reaction is analyzed using the First Shear Deformation Theory (FSDT). Since different percentages of graphene sheets are included in the multilayer structure of the composite, the characteristics of the composite are functionally graded throughout its thickness. Halpin-Tsai models are used to characterize mechanical qualities, whereas Schapery models are used to characterize thermal properties. The microplate's non-linear strain is first calculated by calculating the plate shear deformation and using the Green-Lagrange tensor and von Karman assumptions. Then the elements of the Couple and Cauchy stress tensors using the Modified Coupled Stress Theory (MCST) are derived. Next, using the Hamilton Principle, the microplate's governing equations and associated boundary conditions are calculated. The nonlinear differential equations are linearized by utilizing auxiliary variables in the nonlinear solution by applying the Frechet approach. The linearized equations are rectified via an iterative loop to precisely solve the problem. For this, the Differential Quadrature Method (DQM) is utilized, and the outcomes are shown for the basic support boundary condition. To ascertain the maximum values of microplate deflection for a range of circumstances-such as skew angles, volume fractions, configurations, temperatures, and length scales-a parametric analysis is carried out. To shed light on how the microplate behaves in these various circumstances, the resulting results are analyzed.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.1-19
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• 2019

Artificial intelligence (AI) is one of the main driving forces leading the Fourth Industrial Revolution. The technologies associated with AI have already shown superior abilities that are equal to or better than people in many fields including image and speech recognition. Particularly, many efforts have been actively given to identify the current technology trends and analyze development directions of it, because AI technologies can be utilized in a wide range of fields including medical, financial, manufacturing, service, and education fields. Major platforms that can develop complex AI algorithms for learning, reasoning, and recognition have been open to the public as open source projects. As a result, technologies and services that utilize them have increased rapidly. It has been confirmed as one of the major reasons for the fast development of AI technologies. Additionally, the spread of the technology is greatly in debt to open source software, developed by major global companies, supporting natural language recognition, speech recognition, and image recognition. Therefore, this study aimed to identify the practical trend of AI technology development by analyzing OSS projects associated with AI, which have been developed by the online collaboration of many parties. This study searched and collected a list of major projects related to AI, which were generated from 2000 to July 2018 on Github. This study confirmed the development trends of major technologies in detail by applying text mining technique targeting topic information, which indicates the characteristics of the collected projects and technical fields. The results of the analysis showed that the number of software development projects by year was less than 100 projects per year until 2013. However, it increased to 229 projects in 2014 and 597 projects in 2015. Particularly, the number of open source projects related to AI increased rapidly in 2016 (2,559 OSS projects). It was confirmed that the number of projects initiated in 2017 was 14,213, which is almost four-folds of the number of total projects generated from 2009 to 2016 (3,555 projects). The number of projects initiated from Jan to Jul 2018 was 8,737. The development trend of AI-related technologies was evaluated by dividing the study period into three phases. The appearance frequency of topics indicate the technology trends of AI-related OSS projects. The results showed that the natural language processing technology has continued to be at the top in all years. It implied that OSS had been developed continuously. Until 2015, Python, C ++, and Java, programming languages, were listed as the top ten frequently appeared topics. However, after 2016, programming languages other than Python disappeared from the top ten topics. Instead of them, platforms supporting the development of AI algorithms, such as TensorFlow and Keras, are showing high appearance frequency. Additionally, reinforcement learning algorithms and convolutional neural networks, which have been used in various fields, were frequently appeared topics. The results of topic network analysis showed that the most important topics of degree centrality were similar to those of appearance frequency. The main difference was that visualization and medical imaging topics were found at the top of the list, although they were not in the top of the list from 2009 to 2012. The results indicated that OSS was developed in the medical field in order to utilize the AI technology. Moreover, although the computer vision was in the top 10 of the appearance frequency list from 2013 to 2015, they were not in the top 10 of the degree centrality. The topics at the top of the degree centrality list were similar to those at the top of the appearance frequency list. It was found that the ranks of the composite neural network and reinforcement learning were changed slightly. The trend of technology development was examined using the appearance frequency of topics and degree centrality. The results showed that machine learning revealed the highest frequency and the highest degree centrality in all years. Moreover, it is noteworthy that, although the deep learning topic showed a low frequency and a low degree centrality between 2009 and 2012, their ranks abruptly increased between 2013 and 2015. It was confirmed that in recent years both technologies had high appearance frequency and degree centrality. TensorFlow first appeared during the phase of 2013-2015, and the appearance frequency and degree centrality of it soared between 2016 and 2018 to be at the top of the lists after deep learning, python. Computer vision and reinforcement learning did not show an abrupt increase or decrease, and they had relatively low appearance frequency and degree centrality compared with the above-mentioned topics. Based on these analysis results, it is possible to identify the fields in which AI technologies are actively developed. The results of this study can be used as a baseline dataset for more empirical analysis on future technology trends that can be converged.

• Tunnel and Underground Space
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• v.23 no.5
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• pp.383-391
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• 2013

A number of true triaxial tests on rock samples have been conducted since the late 1960 and their results strongly suggest that the intermediate principal stress has a considerable effect on rock strength. Based on these experimental evidence, various 3-D rock failure criteria accounting for the effect of the intermediate principal stress have been proposed. Most of the 3-D failure criteria, however, are focused on the phenomenological description of the rock strength from the true triaxial tests, so that the associated strength parameters have little physical meaning. In order to confirm the likelihood that the intermediate principal stress dependency of rock strength is related to the presence of weak planes and their distribution to the preferred orientation, true triaxial tests are simulated with the transversely isotropic rock model. The conventional Mohr-Coulomb criterion is extended to its anisotropic version by incorporating the concept of microstructure tensor. With the anisotropic Mohr-Coulomb criterion, the critical plane approach is applied to calculate the strength of the transversely isotropic rock model and the orientation of the fracture plane. This investigation hints that the spatial distribution of microstructural planes with respect to the principal stress triad is closely related to the intermediate principal stress dependency of rock strength.

• Journal of the Korean Society of Radiology
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• v.15 no.4
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• pp.555-564
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• 2021

Thalamus is known to play an important role in the regulation of nerve function. Thalamus, located in the center of the brain, is involved in sleep, arousal, and emotional regulation, and has been reported to be associated with multiple sclerosis, essential tremors, and neurodegenerative diseases such as Parkinson's disease. In addition, it has been reported that iron deposits in the thalamus can cause depressive symptoms with age. Although there are discrepancies between studies, it can be deduced that the thalamus region has a clear effect on neurological disorders due to a strong relationship between the thalamus and neurological functions such as emotional control and processing. Through tractography analysis, the connectivity between the detailed areas of each subcortical region was investigated in the form of a matrix, showing strong connectivity and weak interhemispheric connectivity. In the 59> group, the WM connectivity of thalamus was found to be weaker than those of the two groups. Comparisons between the two groups showed that the young groups (10-39 and 40-59) had higher connection intensity than the 59> group and that statistically significant differences in 3 connection pathways were found in each hemisphere. A decrease in thalamus-related connection strength in aging has shown that it can affect emotional and neurological disorders such as anxiety and depression, and network measurements can help assess cognitive impairment across clinical conditions.