• Journal of Drive and Control
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• v.9 no.3
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• pp.1-7
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• 2012

This paper deals with the issue of model reference adaptive control strategy to control the injection molding machine. Prior to controller design, a pair of transfer functions are derived for the injection and dwelling process based on mathematical models of components. As external disturbances to examine the robustness of the proposed controller, nozzle clogging and contraction of molded objects are considered and realized by proportional valve. The overall simulation system, consisting of hydraulic components, controller and sensors, is implemented using the components of commercial software SimulationX. The simulation results confirm the proposed scheme's efficiency and robustness.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.22 no.6
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• pp.937-944
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• 2013

This paper describes an improved model that can be used for configuring a non-contact pneumatic head to handle a large sheet of glass. The cylindrical head model is of a large size (70 mm). It operates on vortex flow, which can simultaneously generate suction and repulsion over the flat object's surface. The head allows for the minimal non-contact lifting of objects weighing over 3N by using reference conditions (working pressure and head dimensions). Additionally, a functional flow-guide is applied for inducing a developing tangential vortex flow to increase suction and repulsion to the reference head. The cylindrical flow-guide is associated with relatively low tangential velocity. The improved model generates greater lifting force than the reference model, as verified experimentally.

• Journal of the Institute of Convergence Signal Processing
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• v.19 no.1
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• pp.1-6
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• 2018

In this paper, we propose the method to eliminate repetitive processes for key-point detection on multi-scale image space. The proposed method detects key-points from the original image, and select a good key-points using the cluster filters, and create the key-point clusters. And it select reference objects by using direction angles of the key-point clusters, predict the scale of the original image by using the distributed distance ratio. It transform the scale of the reference image, and apply the detection of key-points to the transformed reference image. In the results of the experiment, the proposed method can be found to improve the key-points detection time by 75 % and 71 % compared to SIFT method and scaled ORB method using the multi-scale images.

• Korean Journal of Remote Sensing
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• v.32 no.1
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• pp.13-24
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• 2016

This paper presents a method to evaluate the performance of subpixel localization operators using target image data. Subpixel localization of edges is important to extract the precise shape of objects from images. In this study, each target image was designed to provide reference lines and edges to which the localization operators can be applied. We selected two types of moment-based operators: Gray-level Moment (GM) operator and Spatial Moment (SM) operator for comparison. The original edge localization operators with kernel size 5 are tested and their extended versions with kernel size 7 are also tested. Target images were collected with varying Camera-to-Object Distance (COD). From the target images, reference lines are estimated and edge profiles along the estimated reference lines are accumulated. Then, evaluation of the performance of edge localization operators was performed by comparing the locations calculated by each operator and by superimposing them on edge profiles. Also, enhancement of edge localization by increasing the kernel size was also quantified. The experimental result shows that the SM operator whose kernel size is 7 provides higher accuracy than other operators implemented in this study.

• ETRI Journal
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• v.41 no.1
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• pp.84-92
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• 2019

Recent advances in digital holography in the far-infrared region of the spectrum have demonstrated the potential use of digital holography in homeland security as a tool to observe hostile environments in which smoke, flames, and dust impair vision. However, to make this application practical, it is necessary to simplify the optical setup. Here, we show an off-axis, self-reference scheme that spills the reference beam out from the object beam itself and avoids the need for a complex interferometric arrangement. We demonstrate that this scheme allows the reconstruction of high-quality holograms of objects captured under visible as well as far-infrared light exposure. This could pave the way to the industrialization of holographic systems to enable users to see through fire. Moreover, the quantitative nature of the holographic signal is preserved. Thus, the reported results demonstrate the possibility to use this setup for optical metrology.

• The Journal of the Korea Contents Association
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• v.7 no.2
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• pp.40-50
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• 2007

In this paper, we propose an efficient method for detecting and tracking multiple moving objects based on morphological region merging from real-time video sequences. The proposed approach consists of adaptive threshold extraction, morphological region merging and detecting and tracking of objects. Firstly, input frame is separated into moving regions and static regions using the difference of images between two consecutive frames. Secondly, objects are segmented with a reference background image and adaptive threshold values, then, the segmentation result is refined by morphological region merge algorithm. Lastly, each object segmented in a previous step is assigned a consistent identification over time, based on its spatio-temporal information. The experimental results show that a proposed method is efficient and useful in terms of real-time multiple objects detecting and tracking.

• Proceedings of the KSRS Conference
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• v.1
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• pp.106-109
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• 2006

Over the years, many researches have been performed to create 3D digital maps. Nevertheless, it is still time-consuming and involves a high cost because a large part of 3D digital mapping is conducted manually. To compensate this limitation, we propose methodologies to represent 3D objects as 3D symbols and locate these symbols into a base map automatically. First of all, we constructed the 3D symbol library to represent 3D objects as 3D symbols. In the 3D symbol library, the attribute and geometry information are stored, which defines factors related to the types of symbols and related to the shapes respectively. These factors were used to match 3D objects and 3D symbols. For automatic mapping of 3D symbols into a base map, we used predefined parameters such as the size, the height, the rotation angle and the center of gravity of 3D objects which are extracted from Light Detection and Ranging (LIDAR) data and 2D digital maps. Finally, the 3D map in urban area was constructed and the mapping results were tested using aerial photos as reference data. Through this research, we can identify that the developed the algorithms can be used as effective techniques for 3D digital cartographic techniques

• Journal of Korea Spatial Information System Society
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• v.2 no.1 s.3
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• pp.57-62
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• 2000

Open GIS Consortium(OGC) provides with Simple Features Specification for OLE/COM which is a system object technology of interoperability and reusable capability. In this research, the Spatial Reference System(SRS) component is developed based on the OGC specification using ATL. The component presents 44 map projections and transformations between different geographic coordinate systems utilizing the seven parameter(Bursa Wolf) and Molodenski's methods, a user can set up all objects and its attributes comprising SRS and can create SRS and save its setting using predefined text, WellKnownText. The Spatial Reference System component can be easily implemented into the variety of GIS software so that it reduces the developing time for a system and defines new reference system without difficulty.

• Journal of Cadastre & Land InformatiX
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• v.44 no.1
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• pp.127-138
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• 2014

It can be effective to extract and apply the updated information from the newly updated map data for updating road data of topographic map. In this study, update target data and update reference data are overlaid and the update objects are explored using network matching technique. And the network objects are classified into five matching and update cases and the update processes for each case are applied to the test data. For this study, road centerline data of digital topographic map is used as an update target data and road data of Korean Address Information System is used as an update reference data. The buffer-based network matching method is applied to the two data and the matching and update cases are classified after calculating the overlaid ratio of length. The newly updated road centerline data of digital topographic map is generated from the application of update process for each case. As a result, the update information can be extracted from the different map dataset and applied to the road network data updating.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.36 no.5
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• pp.423-432
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• 2018

Surface water mapping has been widely used in various remote sensing applications. Water indices have been commonly used to distinguish water bodies from land; however, determining the optimal threshold and discriminating water bodies from similar objects such as shadows and snow is difficult. Deep learning algorithms have greatly advanced image segmentation and classification. In particular, FCN (Fully Convolutional Network) is state-of-the-art in per-pixel image segmentation and are used in most benchmarks such as PASCAL VOC2012 and Microsoft COCO (Common Objects in Context). However, these data sets are designed for daily scenarios and a few studies have conducted on applications of FCN using large scale remotely sensed data set. This paper aims to fine-tune the pre-trained FCN network using the CRMS (Coastwide Reference Monitoring System) data set for surface water mapping. The CRMS provides color infrared aerial photos and ground truth maps for the monitoring and restoration of wetlands in Louisiana, USA. To effectively learn the characteristics of surface water, we used pre-trained the DeepWaterMap network, which classifies water, land, snow, ice, clouds, and shadows using Landsat satellite images. Furthermore, the DeepWaterMap network was fine-tuned for the CRMS data set using two classes: water and land. The fine-tuned network finally classifies surface water without any additional learning process. The experimental results show that the proposed method enables high-quality surface mapping from CRMS data set and show the suitability of pre-trained FCN networks using remote sensing data for surface water mapping.