• Journal of Korean Society for Quality Management
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• v.31 no.1
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• pp.11-20
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• 2003

In the development of a complex systems, the early prototypes generally have reliability problems, and, consequently these systems are subjected to a reliability growth program to find problems and take corrective action. A variety of models have been proposed to account for the reliability growth phenomena. Clear guidelines need to be established to assist the reliability engineers for model selection. In this paper, some of more well-known growth models are surveyed and classified. These models are classified based upon distinguishing model features. A procedure for model selection is introduced which is based on this classification.

• Korean Journal of Computational Design and Engineering
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• v.17 no.1
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• pp.7-16
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• 2012

One distinguishing feature of BIM(Building Information Modeling) is the objectification of spatial elements independently, which makes it easy to represent spatial network. From this perspective, this study aimed to develop the spatial network analysis tool based on open BIM technologies. From the literature review, an object model of spatial network with nodes and links and a process model from construction to visualization were established. A prototype system implementing the proposed models, named SNAT(Spatial Network Analysis Tool), was developed in Java platform with using its open source packages. SNAT can create a spatial network from IFC-BIM model, calculate the indices of spatial network analysis, and visualize it with the representing types(map, graph, matrix and table).

• Journal of KIBIM
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• v.8 no.1
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• pp.33-42
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• 2018

The consistency between a building information model(BIM) and drawings are very important and has a critical impact on the usage of BIM throughout the project life-cycle. The purpose of this study is to develop a checklist for the consistency check between BIM and drawings. In this research, requirements for drawings in the construction documentation phase were analyzed. In generating drawings based on BIM, a checklist can help practitioners confirm what to modify in the BIM model and what to modify in CAD drawings. Finally, it is expected that the work efficiency will be improved by reducing the unnecessary work by distinguishing the elements that can be extracted from the BIM model from the elements that requires additional works when generating BIM-based drawings.

• Journal of Information Technology Applications and Management
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• v.26 no.3
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• pp.19-27
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• 2019

In this study, we propose a methodology for predicting the movie audience based on movie information that can be easily acquired before opening and effectively distinguishing qualitative variables. In addition, we constructed a model to estimate the number of movie audiences at the time of data acquisition through the configured variables. Another purpose of this study is to provide a criterion for categorizing success of movies with qualitative characteristics. As an evaluation criterion, we used information gain ratio which is the node selection criterion of C4.5 algorithm. Through the procedure we have selected 416 movie data features. As a result of the multiple linear regression model, the performance of the regression model using the variables selection method based on the information gain ratio was excellent.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.05a
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• pp.107-108
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• 2016

Our study proposes the methods of distinguishing vehicle types using the interval and size of the car. The car videos converts the basic RGB model to Gray model for use and through Canny Edge Direction, it eliminates the background of the car while obtaining feature points through the detection of contours.

• Imaging Science in Dentistry
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• v.54 no.1
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• pp.33-41
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• 2024

Purpose: The aims of this study were to create a deep learning model to distinguish between nasopalatine duct cysts (NDCs), radicular cysts, and no-lesions (normal) in the midline region of the anterior maxilla on panoramic radiographs and to compare its performance with that of dental residents. Materials and Methods: One hundred patients with a confirmed diagnosis of NDC (53 men, 47 women; average age, 44.6±16.5 years), 100 with radicular cysts (49 men, 51 women; average age, 47.5±16.4 years), and 100 with normal groups (56 men, 44 women; average age, 34.4±14.6 years) were enrolled in this study. Cases were randomly assigned to the training datasets (80%) and the test dataset (20%). Then, 20% of the training data were randomly assigned as validation data. A learning model was created using a customized DetectNet built in Digits version 5.0 (NVIDIA, Santa Clara, USA). The performance of the deep learning system was assessed and compared with that of two dental residents. Results: The performance of the deep learning system was superior to that of the dental residents except for the recall of radicular cysts. The areas under the curve (AUCs) for NDCs and radicular cysts in the deep learning system were significantly higher than those of the dental residents. The results for the dental residents revealed a significant difference in AUC between NDCs and normal groups. Conclusion: This study showed superior performance in detecting NDCs and radicular cysts and in distinguishing between these lesions and normal groups.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.6
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• pp.3273-3308
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• 2017

Building appropriate trust evaluation models is an important research issue for security guarantee in social networks. Most of the existing works usually consider the trust values at the current time slot, and model trust as the stochastic variable. However, in fact, trust evolves over time, and trust is a stochastic process. In this paper, we propose a novel time-variant stochastic trust evaluation (TSTE) model, which models trust over time and captures trust evolution by a stochastic process. Based on the proposed model, we derive the time-variant bound of untrustworthy probability, which provides stochastic trust guarantee. On one hand, the time-variant trust level of each node can be measured by our model. Meanwhile, by tolerating nodes with relatively poor performance, our model can effectively improve the node resource utilization rate. Numerical simulations are conducted to verify the accuracy and consistency of the analytical bounds on distinguishing misbehaved nodes from normal ones. Moreover, simulation results on social network dataset show the tradeoff between trust level and resource utilization rate, and verify that the successful transmission rate can be improved by our model.

• Journal of Korean Society for Quality Management
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• v.42 no.3
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• pp.477-496
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• 2014

Purpose: This paper has examined the impact of open innovation business model in technology commercialization with the data from 30 companies of manufacturing firms in South Korea. Methods: The findings provide support for distinguishing five hypotheses relating to development time, IP management, sales, firm size and R&D intensity. To test the hypotheses, data were collected using via e-mail and fax. Small and medium-sized (less than 300 employees) and large industrial firms were chosen for this study. Results: The result shows that openness in its business model is positively associated with successful technology commercialization. Conclusion: The major findings and the implications are: First, as the business model gets more open, development period of technology will be more favorable which gets benefit from rising costs of innovation. Second, as the business model gets more open, large portion of sales are created from new products. Thus, the problem of shorter product life in the market which affects large portion of market revenue can be solved through an open business model. Third, in general, R&D intensity, firm size and the level of IP management affect determination of business model types. The findings also suggest that companies need to increasingly address their external technology exploitation process instead of focusing on their internal innovation processes.

• The Journal of Korea Robotics Society
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• v.13 no.1
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• pp.8-15
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• 2018

This paper proposes a convolutional neural network model for distinguishing areas occupied by obstacles from a LiDAR image converted from a 3D point cloud. The channels of a LiDAR image used as input consist of the distances to 3D points, the reflectivities of 3D points, and the heights of 3D points from the ground. The proposed model uses a LiDAR image as an input and outputs a result of a segmented LiDAR image. The proposed model adopts refinement modules with skip connections to segment a LiDAR image. The refinement modules with skip connections in the proposed model make it possible to construct a complex structure with a small number of parameters than a convolutional neural network model with a linear structure. Using the proposed model, it is possible to distinguish areas in a LiDAR image occupied by obstacles such as vehicles, pedestrians, and bicyclists. The proposed model can be applied to recognize surrounding obstacles and to search for safe paths.

• Structural Engineering and Mechanics
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• v.71 no.5
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• pp.469-483
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• 2019

In this paper, an improved mathematical model is presented for the bending analysis of doubly curved functionally graded material (FGM) sandwich rhombic conoids. The mathematical model includes expansion of Taylor's series up to the third degree in thickness coordinate and normal curvatures in in-plane displacement fields. The condition of zero-transverse shear strain at upper and lower surface of rhombic conoids is implemented in the present model. The newly introduced feature in the present mathematical model is the simultaneous inclusion of normal curvatures in deformation field and twist curvature in strain-displacement equations. This unique introduction permits the new 2D mathematical model to solve problems of moderately thick and deep doubly curved FGM sandwich rhombic conoids. The distinguishing feature of present shell from the other shells is that maximum transverse deflection does not occur at its center. The proposed new mathematical model is implemented in finite element code written in FORTRAN. The obtained numerical results are compared with the results available in the literature. Once validated, the current model was employed to solve numerous bending problems by varying different parameters like volume fraction indices, skew angles, boundary conditions, thickness scheme, and several geometric parameters.