• Journal of Korean Tunnelling and Underground Space Association
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• v.9 no.1
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• pp.19-28
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• 2007

Tunnel management is a time-consuming and expensive task. In particular, visual analysis of tunnel inspection often requires extended time and cost and shows problems on data gathering, storage and analysis. This study proposes a new approach to extract information for tunnel management by using a laser scanning technology. A prototype tunnel laser scanner developed was used to obtain point clouds of a railway tunnel surface. Initial processing of laser scanning data was to separate those laser pulses returned from the installations attached to tunnel liner using radiometric and geometric characteristics of laser returns. Once the laser returns from the installations were separated and removed, physically damaged parts on tunnel lining are detected. Based on the plane formed by laser scanner data, damaged parts are detected by analysis of proximity. The algorithms presented in this study successfully detect the physically damaged parts which can be verified by the digital photography of the corresponding location on the tunnel surface.

• Journal of Biomedical Engineering Research
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• v.32 no.2
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• pp.134-143
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• 2011

The selection of meaningful clinical tests and its reference values from a high-dimensional clinical data with imbalanced class distribution, one class is represented by a large number of examples while the other is represented by only a few, is an important issue for differential diagnosis between similar diseases, but difficult. For this purpose, this study introduces methods based on the concepts of both discernibility matrix and function in rough set theory (RST) with two discretization approaches, equal width and frequency discretization. Here these discretization approaches are used to define the reference values for clinical tests, and the discernibility matrix and function are used to extract a subset of significant clinical tests from the translated nominal attribute values. To show its applicability in the differential diagnosis problem, we have applied it to extract the significant clinical tests and its reference values between normal (N = 351) and abnormal group (N = 101) with either cholecystitis or cholelithiasis disease. In addition, we investigated not only the selected significant clinical tests and the variations of its reference values, but also the average predictive accuracies on four evaluation criteria, i.e., accuracy, sensitivity, specificity, and geometric mean, during l0-fold cross validation. From the experimental results, we confirmed that two discretization approaches based rough set approximation methods with relative frequency give better results than those with absolute frequency, in the evaluation criteria (i.e., average geometric mean). Thus it shows that the prediction model using relative frequency can be used effectively in classification and prediction problems of the clinical data with imbalanced class distribution.

• Journal of the Korea Society of Computer and Information
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• v.27 no.5
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• pp.85-92
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• 2022

In this paper, we propose a technique that can express low-resolution hair and fur simulations in high-resolution without noise using ConvNet and geometric images of strands in the form of lines. Pairs between low-resolution and high-resolution data can be obtained through physics-based simulation, and a low-resolution-high-resolution data pair is established using the obtained data. The data used for training is used by converting the position of the hair strands into a geometric image. The hair and fur network proposed in this paper is used for an image synthesizer that upscales a low-resolution image to a high-resolution image. If the high-resolution geometry image obtained as a result of the test is converted back to high-resolution hair, it is possible to express the elastic movement of hair, which is difficult to express with a single mapping function. As for the performance of the synthesis result, it showed faster performance than the traditional physics-based simulation, and it can be easily executed without knowing complex numerical analysis.

• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.136-139
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• 2004

The extraction of 'end-pixels' (i.e. end-members) aims to quantify the abundance of different materials in a single pixel, which becomes popular in the subpixel analysis for hyperspectral dataset. In this paper, we present a new concept called 'End-Pixel of Features (EPF)' to extends the concept of end-pixels for multispectral data and even panchromatic data. The algorithm combines the advantages of previous simplex and clustering methods to search the EPFs in the feature space and reduce the effects of noise. Some experimental results show that, the proposed methodology can be successfully used to hyperspectral data and other remote sensing data.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.8
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• pp.783-789
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• 2011

Target tracking performance improvement using multi-sensor data fusion is a challenging work. However, biases in the measurements should be removed before various data fusion techniques are applied. In this paper, a bias removing algorithm using measurement data from multi-radar tracking systems is proposed and evaluated by computer simulation. To predict bias estimation performance in various geometric relations between the radar systems and target, a system observability index is proposed and tested via computer simulation results. It is also studied that target tracking which utilizes multi-sensor data fusion with bias-removed measurements results in better performance.

• Korean Journal of Computational Design and Engineering
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• v.10 no.5
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• pp.314-327
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• 2005

This paper describes the volumetric data modeling and analysis methods that employ volumetric NURBS or VNURBS that represents heterogeneous objects or fields in multidimensional space. For volumetric data modeling, we formulate the construction algorithms involving the scattered data approximation and the curvilinear grid data interpolation. And then the computational algorithms are presented for the geometric and mathematical analysis of the volume data set with the VNURBS model. Finally, we apply the modeling and analysis methods to various field applications including grid generation, flow visualization, implicit surface modeling, and image morphing. Those application examples verify the usefulness and extensibility of our VNUBRS representation in the context of volume modeling and analysis.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.12
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• pp.505-518
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• 2023

3D point cloud semantic segmentation is a computer vision task that involves dividing the point cloud into different objects and regions by predicting the class label of each point. Existing 3D semantic segmentation models have some limitations in performing sufficient fusion of multi-modal features while ensuring both characteristics of 2D visual features extracted from RGB images and 3D geometric features extracted from point cloud. Therefore, in this paper, we propose MMCA-Net, a novel 3D semantic segmentation model using 2D-3D multi-modal features. The proposed model effectively fuses two heterogeneous 2D visual features and 3D geometric features by using an intermediate fusion strategy and a multi-modal cross attention-based fusion operation. Also, the proposed model extracts context-rich 3D geometric features from input point cloud consisting of irregularly distributed points by adopting PTv2 as 3D geometric encoder. In this paper, we conducted both quantitative and qualitative experiments with the benchmark dataset, ScanNetv2 in order to analyze the performance of the proposed model. In terms of the metric mIoU, the proposed model showed a 9.2% performance improvement over the PTv2 model using only 3D geometric features, and a 12.12% performance improvement over the MVPNet model using 2D-3D multi-modal features. As a result, we proved the effectiveness and usefulness of the proposed model.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.10a
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• pp.965-969
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• 1995

This paper deals with effective using the IGES file for flexible data exchange among the other CAD/CAM system. If a data exchange between STL file and the neutral IGES file in Stereolithography system is available, a product design becomes more flexible. THere can be many restrictions and difficulties intranslating these data. First, an STL file follows two rules, such as right-hand rule, and vertex-to-vertex rule, thus requires a structural verification. Second, translation should be performed with minmal errors. It becomes very important to translate IGES file of limited kinds of entities fot the purpose of geometric information into STL file with minor data manipulation. The developed system suggests a good approach of translating the sampled B-spline surface of IGES and shows a potential linkage between IGES and STL format file.

• Proceedings of the KSRS Conference
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• v.1
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• pp.67-70
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• 2006

KARI is developing Image Data Acquisition and Control System (IDACS) for pre-processing meteorological and ocean data acquired on geostationary orbit. This paper describes the functions and architecture of IDACS and gives its operation policy including backup operation to overcome limitation of single-configured antenna system. The COMS IDACS provides the capability to receive the raw sensor data and disseminate processed MI data to users via a satellite. From the processed image data, users can produce a set of meteorological and ocean products for a wide range of applications. Most of IDACS subsystems are being developed by Korean technologies and experience acquired from previous projects. In case of COMS geometric correction software module, as it is closely dependent on the characteristics of imagers and spacecraft bus system, it is being co-developed with overseas prime contractor who develops spacecraft bus system.

• Journal of Radiation Protection and Research
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• v.26 no.3
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• pp.215-224
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• 2001

In order to comply with revised national regulationson radiological protection and to implement recent nuclear data and dose conversion factors, KOPEC developed a new point kernel gamma and beta ray shielding analysis computer program. This new code, named VisualShield, adopted mass attenuation coefficient and buildup factors from recent ANSI/ANS standards and flux-to-dose conversion factors from the International Commission on Radiological Protection (ICRP) Publication 74 for estimation of effective/equivalent dose recommended in ICRP 60. VisualShieid utilizes graphical user interfaces and 3-D visualization of the geometric configuration for preparing input data sets and analyzing results, which leads users to error free processing with visual effects. Code validation and data analysis were performed by comparing the results of various calculations to the data outputs of previous programs such as MCNP 4B, ISOSHLD-II, QAD-CGGP, etc.