• Tunnel and Underground Space
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• v.15 no.1 s.54
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• pp.47-54
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• 2005

One of the standard procedures of discontinuity survey is the joint set identification from the population of field orientation data. Discontinuity set identification is fundamental to rock engineering tasks such as rock mass classification, discrete element analysis, key block analysis. and discrete fracture network modeling. Conventionally, manual method using contour plot had been widely used for this task, but this method has some short-comings such as yielding subjective identification results, manual operations, and so on. In this study, the method of discontinuity set identification using genetic algorithm was introduced, but slightly modified to handle the orientation data. Finally, based on the genetic algorithm, we developed a FORTRAN program, Genetic Algorithm based Clustering(GAC) and applied it to two different discontinuity data sets. Genetic Algorithm based Clustering(GAC) was proved to be a fast and efficient method for the discontinuity set identification task. In addition, fitness function based on variance showed more efficient performance in finding the optimal number of clusters when compared with Davis - Bouldin index.

• Journal of KIISE:Computer Systems and Theory
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• v.35 no.5
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• pp.187-198
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• 2008

This paper develops a framework for efficient resource management within the grid service environment. Resource management is the core element of the grid service; therefore, GridRMF(Grid Resource Management Framework) is modeled and developed in order to respond to such variable characteristics of resources as accordingly as possible. GridRMF uses the participation level of grid resource as a basis of its hierarchical management. This hierarchical management divides managing domains into two parts: VMS(Virtual Organization Management System) for virtual organization management and RMS(Resource Management System) for metadata management. VMS mediates resources according to optimal virtual organization selection mechanism, and responds to malfunctions of the virtual organization by LRM(Local Resource Manager) automatic recovery mechanism. RMS, on the other hand, responds to load balance and fault by applying resource status monitoring information into adaptive performance-based task allocation algorithm.

• The Korean Journal of Emergency Medical Services
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• v.19 no.2
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• pp.83-97
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• 2015

Purpose: Although appointed as a national competency standards (NCS) based reserves department, the department of emergency medical technology, an NCS-based emergency department, is mainly focused on subject deduction for a NCS-based curriculum. Methods: Job models were formed and verified by combining the competency unit of NCS and the duty of Developing a curriculum (DACUM) based on the development procedure indicated in the guidelines for a NCS-based curriculum. The mapping method of the subject was performed by deducting necessary competency units (duty) and competency unit elements (task) by connecting with the composition items of NCS and DACUM. Results: Job models combined with job analysis for the NCS and DACUM were reduced to 13 competency units (duty) and 79 competency unit elements (task). A modified method such as the 1:N method was mainly applied as a subject-matching method with consideration of the competency level and size of the competency unit. Conclusion: It would be a desirable direction to develop a NCS-based curriculum in the center of the practice subject in consideration of the size of the competency unit and competency level of the competency unit element. The existing curriculum should be promoted as a field-oriented curriculum at the complementary level.

• Convergence Security Journal
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• v.22 no.2
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• pp.21-26
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• 2022

Wearing face masks is an effective measure to prevent COVID-19 infection. Infrared thermal image based temperature measurement and identity recognition system has been widely used in many large enterprises and universities in China, so it is totally necessary to research the face mask detection of thermal infrared imaging. Recently introduced MTCNN (Multi-task Cascaded Convolutional Networks)presents a conceptually simple, flexible, general framework for instance segmentation of objects. In this paper, we propose an algorithm for efficiently searching objects of images, while creating a segmentation of heat generation part for an instance which is a heating element in a heat sensed image acquired from a thermal infrared camera. This method called a mask MTCNN is an algorithm that extends MTCNN by adding a branch for predicting an object mask in parallel with an existing branch for recognition of a bounding box. It is easy to generalize the R-CNN to other tasks. In this paper, we proposed an infrared image detection algorithm based on R-CNN and detect heating elements which can not be distinguished by RGB images.

• Nuclear Engineering and Technology
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• v.55 no.1
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• pp.339-352
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• 2023

Prediction of the time-related traits of pebble flow inside pebble-bed HTGRs is of great significance for reactor operation and design. In this work, an image-driven approach with the aid of a convolutional neural network (CNN) is proposed to predict the remaining time of initially loaded pebbles and the time interval of paired flow images of the pebble bed. Two types of strategies are put forward: one is adding FC layers to the classic classification CNN models and using regression training, and the other is CNN-based deep expectation (DEX) by regarding the time prediction as a deep classification task followed by softmax expected value refinements. The current dataset is obtained from the discrete element method (DEM) simulations. Results show that the CNN-aided models generally make satisfactory predictions on the remaining time with the determination coefficient larger than 0.99. Among these models, the VGG19+DEX performs the best and its CumScore (proportion of test set with prediction error within 0.5s) can reach 0.939. Besides, the remaining time of additional test sets and new cases can also be well predicted, indicating good generalization ability of the model. In the task of predicting the time interval of image pairs, the VGG19+DEX model has also generated satisfactory results. Particularly, the trained model, with promising generalization ability, has demonstrated great potential in accurately and instantaneously predicting the traits of interest, without the need for additional computational intensive DEM simulations. Nevertheless, the issues of data diversity and model optimization need to be improved to achieve the full potential of the CNN-aided prediction tool.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.5
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• pp.599-604
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• 2019

This study examined how the characteristics of smartphone affect the functional performance of smartphones. In particular, this study focused on an understanding of the correlation between smartphone functional factors and usage habits. Functionality is defined as 11 kinds of functional elements. The characteristics of the smartphones were defined as the hardware characteristics and the user habits characteristics. Eighty subjects were organized to collect actual data by the smartphone function. The actual time required to perform each function was measured and observed five times for each functional element. Regression analysis was performed using Minitab ver.14 by classifying the measured values of the functional elements as dependent variables, the hardware characteristics collected through the questionnaire, and the user's usage habits as 12 independent variables. Overall, it is difficult to conclude that demographic and hardware characteristics of smartphone users have a significant effect on the performance. On the other hand, the variables related to smartphone usage habits have had a great impact on the performance of smartphone tasks, and as a result, the task execution time has increased. In simple input variables or viewing variables, the effects on usability was relatively small, but in all active variables, the execution time increased 10% - 30% in all tasks except for phone calls, seeking phone numbers, and dictionary search. Thus far, if the smartphone user interface has been provided uniformly in a large and simple manner, users with various usage habits can be utilized even if the input method and task processing method are more complicated and various interface types are provided.

• Geotechnical Engineering
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• v.14 no.5
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• pp.129-142
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• 1998

This research is focused on the inducement of the constitutive equation considering the creep strain component and on the development of a finite element method program. The purpose of this research was to contribute to the design of construction structures or to the construction management in soft clay ground through predicting the long-term strain of construction structures reasonably bused on the above program. Modified Cam Clay model was adopted to describe the elastic-plastic behavior of clayey soil. And in the calculation of the creep sprain, the secondary coefficient of consolidation C. was applied for considering the volumetric creep element and the constants m, $\alpha$, A were rosed by the empirical creep equation proposed by Singh 8E Mitchell for considering the deviatoric creep element. To examine the reliability of the program which is developed in this study, the estimated values by this program were compared with the theoretical solution and the experimental results. And the applicability of the developed program was found to be reliable from the sensitive analysis of each parameters used in this study. According to the results obtained from the application of the program on the field measurement data, the estimated values by the program were found with be consistent with the actual values. And from the analysis of the displacement of embankments, the case of considering the creep behavior induced much fower errors than the case of neglecting it. But the results obtained from considering the volumetric creep behavior only were slightly underestimated the results from considering the deviator creep behavior showed the slightly overestimated values. Therefore, it remains the task of further studios to develop the laboratory test devices to obtain the reliable creep parameters, and to select the appropriate soil parameters, etc.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.34 no.5
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• pp.583-588
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• 2010

The nonlinearity and high deformability of rubber make accurate analysis of the behavior of cord-rubber composites a challenging task. Some researchers have adopted the third phase between cord and rubber and have carried out three-phase modeling. However, it is difficult to determine the thickness and properties of the interface in cord-rubber composites. In this study, a two-dimensional finite-element method (2D FEM) is used to investigate the effective and normalized moduli of cord-rubber composites having interfaces of various thicknesses; this model takes into account the 2D generalized plane strain and a plane strain element. The neo-Hookean model is used for the properties of rubber, several interface properties are assumed and three loading directions are selected. It is found that the properties and thickness of the interface can affect the nonlinearity and the effective modulus of cord-rubber composites.

• Earthquakes and Structures
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• v.1 no.1
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• pp.109-127
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• 2010

This work aims at introducing structural sensitivity analysis capabilities into existing commercial finite element software codes for the purpose of mapping retrofit strategies for a broad group of structures including heritage-type buildings. More specifically, the first stage sensitivity analysis is implemented for the standard deterministic environment, followed by stochastic structural sensitivity analysis defined for the probabilistic environment in a subsequent, second phase. It is believed that this new generation of software that will be released by the industrial partner will address the needs of a rapidly developing specialty within the engineering design profession, namely commercial retrofit and rehabilitation activities. In congested urban areas, these activities are carried out in reference to a certain percentage of the contemporary building stock that can no longer be demolished to give room for new construction because of economical, historical or cultural reasons. Furthermore, such analysis tools are becoming essential in reference to a new generation of national codes that spell out in detail how retrofit strategies ought to be implemented. More specifically, our work focuses on identifying the minimum-cost intervention on a given structure undergoing retrofit. Finally, an additional factor that arises in earthquake-prone regions across the world is the random nature of seismic activity that further complicates the task of determining the dynamic overstress that is being induced in the building stock and the additional demands placed on the supporting structural system.

• Smart Structures and Systems
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• v.10 no.4_5
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• pp.375-391
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• 2012

This paper reports the structural health monitoring benchmark study results for the Canton Tower using Bayesian methods. In this study, output-only modal identification and finite element model updating are considered using a given set of structural acceleration measurements and the corresponding ambient conditions of 24 hours. In the first stage, the Bayesian spectral density approach is used for output-only modal identification with the acceleration time histories as the excitation to the tower is unknown. The modal parameters and the associated uncertainty can be estimated through Bayesian inference. Uncertainty quantification is important for determination of statistically significant change of the modal parameters and for weighting assignment in the subsequent stage of model updating. In the second stage, a Bayesian model updating approach is utilized to update the finite element model of the tower. The uncertain stiffness parameters can be obtained by minimizing an objective function that is a weighted sum of the square of the differences (residuals) between the identified modal parameters and the corresponding values of the model. The weightings distinguish the contribution of different residuals with different uncertain levels. They are obtained using the Bayesian spectral density approach in the first stage. Again, uncertainty of the stiffness parameters can be quantified with Bayesian inference. Finally, this Bayesian framework is applied to the 24-hour field measurements to investigate the variation of the modal and stiffness parameters under changing ambient conditions. Results show that the Bayesian framework successfully achieves the goal of the first task of this benchmark study.