• Journal of Institute of Control, Robotics and Systems
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• v.7 no.8
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• pp.689-696
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• 2001

Large-scale Multi-disciplinary Systems(LMS) such as transportation, aerospace, defense etc. are complex systems in which there are many subsystems, interfaces, functions and demanding performance requirements. Because many contractors participate in the development, it is necessary to apply methods of sharing common objectives and communicating design status effectively among all of the stakeholders. The processes and methods of systems engineering which includes system requirement analysis; functional analysis; architecting; system analysis; interface control; and system specification development provide a success-oriented disciplined approach to the project. This paper shows not only the methodology and the results of model-based systems engineering to Automated Guided Transit(AGT) system as one of LMS systems, but also propose the extension of the model-based tool to help manage a project by linking WBS (Work Breakdown Structure), work organization, and PBS (Product Breakdown Structure). In performing the model-based functional analysis, the focus was on the operation concept of an example rail system at the top-level and the propulsion/braking function, a key function of the modern automated rail system. The model-based behavior analysis approach that applies a discrete-event simulation method facilitates the system functional definition and the test and verification activities. The first application of computer-aided tool, RDD-100, in the railway industry demonstrates the capability to model product design knowledge and decisions concerning key issues such as the rationale for architecting the top-level system. The model-based product design knowledge will be essential in integrating the follow-on life-cycle phase activities. production through operation and support, over the life of the AGT system. Additionally, when a new generation train system is required, the reuse of the model-based database can increase the system design productivity and effectiveness significantly.

• Journal of the Korean Society for Railway
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• v.4 no.2
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• pp.47-54
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• 2001

Railway companies have been faced with many changes in the railway environment. To cope immediately with the influence of environment and to improve productivity, an efficient train operation system and related core technologies must be introduced. The railway system is composed of large scale infrastructures and high-cost trains. Simulation method is one of core technologies and also efficient tool for planning and analyzing these kinds of complex system. In this research, we review basic simulation programming models and present a modeling for the elements of railway system such as rail-line infrastructure, train, time table and operational route. Additionally, some considerations on the development of multi-train traffic simulator for KyongBu-line are discussed.

• Nuclear Engineering and Technology
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• v.49 no.6
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• pp.1339-1345
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• 2017

In the framework of the EU funded project NURESAFE, the subchannel code CTF and the neutronics code DYN3D were integrated and coupled on the NURESIM platform. The developments achieved during this 3-year project include assembly-level and pin-by-pin multiphysics thermal hydraulics/neutron kinetics coupling. In order to test this coupling, a PWR rod ejection transient was simulated on a MOX/UOX minicore. The transient is simulated using two different models of the minicore. In the first simulation, both codes model the core with an assembly-wise resolution. In the second simulation, a pin-by-pin fuel-centered model is used in CTF for the central assembly, and a pin power reconstruction method is applied in DYN3D. The analysis shows the influence of the different models on global parameters, such as the power and the average fuel temperature, but also on local parameters such as the maximum fuel temperature.

• Proceedings of the KIEE Conference
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• 2003.07c
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• pp.1738-1740
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• 2003

This paper deals with the characteristic analysis of multi-grounding system. In order to assess the effectiveness of bonding two grounding systems, the scale model grounding simulation system and grid grounding conductors which were downsized as a scale factor of 100:1 were designed and fabricated. A profile of GPR(Grounding Potential Rise) of each case was measured. Also the measured results were compared with the analysis results.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.10
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• pp.5179-5196
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• 2019

To explore an effective non-invasion medical imaging diagnostics approach for hepatocellular carcinoma (HCC), we propose a method based on adopting the multiple technologies with the multi-parametric data fusion, transfer learning, and multi-scale deep feature extraction. Firstly, to make full use of complementary and enhancing the contribution of different modalities viz. multi-parametric MRI images in the lesion diagnosis, we propose a data-level fusion strategy. Secondly, based on the fusion data as the input, the multi-scale residual neural network with SPP (Spatial Pyramid Pooling) is utilized for the discriminative feature representation learning. Thirdly, to mitigate the impact of the lack of training samples, we do the pre-training of the proposed multi-scale residual neural network model on the natural image dataset and the fine-tuning with the chosen multi-parametric MRI images as complementary data. The comparative experiment results on the dataset from the clinical cases show that our proposed approach by employing the multiple strategies achieves the highest accuracy of 0.847±0.023 in the classification problem on the HCC differentiation. In the problem of discriminating the HCC lesion from the non-tumor area, we achieve a good performance with accuracy, sensitivity, specificity and AUC (area under the ROC curve) being 0.981±0.002, 0.981±0.002, 0.991±0.007 and 0.999±0.0008, respectively.

• Phonetics and Speech Sciences
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• v.11 no.4
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• pp.89-96
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• 2019

In this paper, we propose a deep learning architecture that can effectively detect speech segmentation in broadcast contents. We also propose a multi-scale time-dilated layer for learning the temporal changes of feature vectors. We implement several comparison models to verify the performance of proposed model and calculated the frame-by-frame F-score, precision, and recall. Both the proposed model and the comparison model are trained with the same training data, and we train the model using 32 hours of Korean broadcast data which is composed of various genres (drama, news, documentary, and so on). Our proposed model shows the best performance with F-score 91.7% in Korean broadcast data. The British and Spanish broadcast data also show the highest performance with F-score 87.9% and 92.6%. As a result, our proposed model can contribute to the improvement of performance of speech detection by learning the temporal changes of the feature vectors.

• Journal of computational fluids engineering
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• v.2 no.1
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• pp.21-28
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• 1997

This paper presents a two-dimensional horizontal implicit model to general circulation in estuaries and coastal seas. The model is developed in non-orthogonal curvilinear coordinates system, using the Interpolating Matrix Method (IMM), in combination with a technique of multi-domain. In the propose model, the Saint-Venant equations are solved by a splitting-up technique, in the successive steps; convection, diffusion and wave propagation. The ability of the proposed model to deal with full scale nature is illustrated by the interpretation of a dye-tracing experiment in the Gironde estuary.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2020.11a
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• pp.25-28
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• 2020

Automatic segmentation of brain tissues such as WM, GM, and CSF from brain MRI scans is helpful for the diagnosis of many neurological disorders. Accurate segmentation of these brain structures is a very challenging task due to low tissue contrast, bias filed, and partial volume effects. With the aim to improve brain MRI segmentation accuracy, we propose an end-to-end convolutional based U-SegNet architecture designed with multi-scale kernels, which includes cascaded dilated convolutions for the task of brain MRI segmentation. The multi-scale convolution kernels are designed to extract abundant semantic features and capture context information at different scales. Further, the cascaded dilated convolution scheme helps to alleviate the vanishing gradient problem in the proposed model. Experimental outcomes indicate that the proposed architecture is superior to the traditional deep-learning methods such as Segnet, U-net, and U-Segnet and achieves high performance with an average DSC of 93% and 86% of JI value for brain MRI segmentation.

• Steel and Composite Structures
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• v.46 no.6
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• pp.823-837
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• 2023

This paper presents the seismic performance of seven beam-column joints with an eccentricity between beam depths under cyclic loadings. The failure modes of the panel zone were divided into two types. One was the shear force failure that appeared in the entire panel zone (SFEPZ), the other was the shear force failure that appeared in the partial panel zone (SFPPZ). Seven finite element models were established using multi-scale methods. Compared with the experimental specimens, the hysteretic loops exhibited a similar trend. The multi-scale models could accurately simulate the experimental results. Furthermore, the calculation formulas of yield and plastic shear capacity of unequal-depth joints with outer annular stiffener were proposed.

• Journal of the Korean Society for Precision Engineering
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• v.30 no.9
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• pp.915-921
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• 2013

Most of large scale solar panel handling robots adopt the timing-belt drive system for its driveline because of the simplicity and the easiness of implementation. The vibration caused by the flexure of the timing belt would increase as the size and the weight of the panel that the robot handles increase and the vibration would deteriorate the precision and/or productivity of the whole robot system. For the development of a proper control system and for the improvement of the design of the robot it is important to estimate the oscillatory response of the robot system including the flexible drive system properly. In this paper a flexible multi-body dynamics model of a large-scale solar-panel-handling robot with the flexible timing-belt drive system is developed using a generic multi-body dynamics analysis program, RecurDyn.