• Transactions of the Korean Society of Mechanical Engineers A
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• v.25 no.6
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• pp.1004-1014
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• 2001

The fully automatic algorithm from initial finite element mesh generation to remeshing in two dimensional geometry is introduced using bubble packing method (BPM) for finite element analysis. BPM determines the node placement by force-balancing configuration of bubbles and the triangular meshes are made by Delaunay triangulation with advancing front concept. In BPM, we suggest two node-search algorithms and the adaptive/recursive bubble controls to search the optimal nodal position. To use the automatically generated mesh information in FEA, the new enhanced bandwidth minimization scheme with high efficiency in CPU time is developed. In the remeshing stage, the mesh refinement is incorporated by the control of bubble size using two parameters. And Superconvergent Patch Recovery (SPR) technique is used for error estimation. To verify the capability of this algorithm, we consider two elasticity problems, one is the bending problem of short cantilever beam and the tension problem of infinite plate with hole. The numerical results indicate that the algorithm by BPM is able to refine the mesh based on a posteriori error and control the mesh size easily by two parameters.

• International conference on construction engineering and project management
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• 2020.12a
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• pp.225-234
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• 2020

Reliability-based structural optimization usually requires designers or engineers model different designs manually, which is considered very time consuming and all possibilities cannot be fully explored. Otherwise, a lot of time are needed for designers or engineers to learn mathematical modeling and programming skills. Therefore, a framework that integrates generative design, structural simulation and reliability theory is proposed. With the proposed framework, various designs are generated based on a set of rules and parameters defined based on visual programming, and their structural performance are simulated by OpenSees. Then, reliability of each design is evaluated based on the simulation results, and an optimal design can be found. The proposed framework and prototype are tested in the optimization of a steel frame structure, and results illustrate that generative design based on visual programming is user friendly and different design possibilities can be explored in an efficient way. It is also reported that structural reliability can be assessed in an automatic way by integrating Dynamo and OpenSees. This research contributes to the body of knowledge by providing a novel framework for automatic reliability evaluation and structural optimization.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.1
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• pp.105-125
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• 2024

Gliomas are the most common malignant brain tumor and cause the most deaths. Manual brain tumor segmentation is expensive, time-consuming, error-prone, and dependent on the radiologist's expertise and experience. Manual brain tumor segmentation outcomes by different radiologists for the same patient may differ. Thus, more robust, and dependable methods are needed. Medical imaging researchers produced numerous semi-automatic and fully automatic brain tumor segmentation algorithms using ML pipelines and accurate (handcrafted feature-based, etc.) or data-driven strategies. Current methods use CNN or handmade features such symmetry analysis, alignment-based features analysis, or textural qualities. CNN approaches provide unsupervised features, while manual features model domain knowledge. Cascaded algorithms may outperform feature-based or data-driven like CNN methods. A revolutionary cascaded strategy is presented that intelligently supplies CNN with past information from handmade feature-based ML algorithms. Each patient receives manual ground truth and four MRI modalities (T1, T1c, T2, and FLAIR). Handcrafted characteristics and deep learning are used to segment brain tumors in a Global Convolutional Neural Network (GCNN). The proposed GCNN architecture with two parallel CNNs, CSPathways CNN (CSPCNN) and MRI Pathways CNN (MRIPCNN), segmented BraTS brain tumors with high accuracy. The proposed model achieved a Dice score of 87% higher than the state of the art. This research could improve brain tumor segmentation, helping clinicians diagnose and treat patients.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.38 no.7
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• pp.716-726
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• 2010

In this research, design optimization of an aircraft wing has been performed using the fully automated Multidisciplinary Design Optimization (MDO) framework, which integrates aerodynamic and structural analysis considering nonlinear structural behavior. A computational fluid dynamics (CFD) mesh is generated automatically from parametric modeling using CATIA and Gambit, followed by an automatic flow analysis using FLUENT. A computational structure mechanics (CSM) mesh is generated automatically by the parametric method of the CATIA and visual basic script of NASTRAN-FX. The structure is analyzed by ABAQUS. Interaction between CFD and CSM is performed by a fully automated system. The Response Surface Method (RSM) is applied for optimization, helping to achieve the global optimum. The optimization design result demonstrates successful application of the fully automated MDO framework.

• Journal of the Korea Society of Computer and Information
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• v.26 no.7
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• pp.37-44
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• 2021

Automatic classification of brain MRI images play an important role in early diagnosis of brain tumors. In this work, we present a deep learning-based brain tumor classification model in MRI images using ensemble of deep features. In our proposed framework, three different deep features from brain MR image are extracted using three different pre-trained models. After that, the extracted deep features are fed to the classification module. In the classification module, the three different deep features are first fed into the fully-connected layers individually to reduce the dimension of the features. After that, the output features from the fully-connected layers are concatenated and fed into the fully-connected layer to predict the final output. To evaluate our proposed model, we use openly accessible brain MRI dataset from web. Experimental results show that our proposed model outperforms other machine learning-based models.

• Composites Research
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• v.32 no.6
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• pp.314-318
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• 2019

A simple and very flexible automatic dipping machine was constructed for producing functional multilayer films on wide substrates via the layer-by-layer (LbL) assembly technique. The proposed machine exhibits several features that allow a fully automated coating operation, such as various depositing recipes, control of the dipping depth and time, operating speed, and rinsing flow, air-assist drying nozzles, and an operation display. The machine uniformly dips a substrate into aqueous mixtures containing complementary (e.g., oppositely charged, capable of hydrogen bonding, or capable of covalent bonding) species. Between the dipping of each species, the sample is spray cleaned with deionized water and blow-dried with air. The dipping, rinsing, and drying areas and times are adjustable by a computer program. Graphene-based thin films up to ten-bilayers were prepared and characterized. This film exhibits the highly filled multilayer structures and low thermal resistance, indicating that the robotic dipping system is simple to produce functional thin film coatings with a variety of different layers.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.8
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• pp.106-112
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• 1998

Recently, the amount of image and audio data has been increasing dramatically for high performance. According to this trend, a high-density magnetic recording system is necessitated and the tracks of magnetic tapes are getting narrower. This, in turn, requires the capstan servo system of the magnetic recording system such as DVCR to control precisely the speed and position of the capstan motor. Especially, in case of play-back, the capstan servo system should be able to position and maintain the head on the desired place of the track. To meet this requirement, digital camcorders use ATF (Automatic Track Following). In this paper, a new ATF block using discrete Fourier transform is proposed. The proposed ATF block was designed and implemented in ALTERA FPGA chips and fully tested in a real DVCR system. It is shown through experiments that the new ATF block is more cost-effective than other existing ATF blocks using digital lowpass filters. In particular, the number of logic gates can be reduced by 20% in average, compared to the existing ATF's.

• The KIPS Transactions:PartB
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• v.14B no.2
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• pp.89-98
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• 2007

For browsing, searching, and manipulating video documents, an indexing technique to describe video contents is required. Until now, the indexing process is mostly carried out by specialists who manually assign a few keywords to the video contents and thereby this work becomes an expensive and time consuming task. Therefore, automatic classification of video content is necessary. We propose a fully automatic and computationally efficient method for analysis and summarization of spots news video for 5 spots news video such as soccer, golf, baseball, basketball and volleyball. First of all, spots news videos are classified as anchor-person Shots, and the other shots are classified as news reports shots. Shot classification is based on image preprocessing and color features of the anchor-person shots. We then use the dominant color of the field and motion features for analysis of sports shots, Finally, sports shots are classified into five genre type. We achieved an overall average classification accuracy of 75% on sports news videos with 241 scenes. Therefore, the proposed method can be further used to search news video for individual sports news and sports highlights.

• Journal of the Korea Society of Computer and Information
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• v.15 no.6
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• pp.1-9
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• 2010

This paper proposes a test method for compliance of SMS encoder and decoder modules with 3GPP (3rd Generation Partnership Project) specification on SMS PDU (Protocol Data Unit). The existing tools have focused on providing an SMS gateway and on helping to view and edit a single SMS PDU, which rarely help to resolve the compliance test problem. The proposed compliance test method is based on an automatic generation of SMS PDUs fully compliant with the 3GPP specification by using QuickCheck library written in Haskell. By applying the proposed method to a C-based SMS encoder and decoder in Linux Mobile platform, we have found out several critical bugs such as wrong interpretation of time stamps in BCD format. The automatic SMS PDU generator is reusable in that it only depends on the 3GPP SMS specification. The QuickCheck library is also applicable for testing other network protocol data encoders and decoders, as used in this paper.

• The KIPS Transactions:PartB
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• v.16B no.1
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• pp.47-54
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• 2009

Automatic object recognition in 3D measuring data is of great interest in many application fields e.g. computer vision, reverse engineering and digital factory. In this paper we present a software tool for a fully automatic object detection and parameter estimation in unordered and noisy point clouds with a large number of data points. The software consists of three interactive modules each for model selection, point segmentation and model fitting, in which the orthogonal distance fitting (ODF) plays an important role. The ODF algorithms estimate model parameters by minimizing the square sum of the shortest distances between model feature and measurement points. The local quadric surface fitted through ODF to a randomly touched small initial patch of the point cloud provides the necessary initial information for the overall procedures of model selection, point segmentation and model fitting. The performance of the presented software tool will be demonstrated by applying to point clouds.