• ETRI Journal
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• v.30 no.1
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• pp.101-112
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• 2008

In this paper, we present a method to customize the ground color in outdoor sports video to provide TV viewers with a better viewing experience or subjective satisfaction. This issue, related to content personalization, is becoming critical with the advent of mobile TV and interactive TV. In outdoor sports video, such as soccer video, it is sometimes observed that the ground color is not satisfactory to viewers. In this work, the proposed algorithm is focused on customizing the ground color to deliver a better viewing experience for viewers. The algorithm comprises three modules: ground detection, shot classification, and ground color customization. We customize the ground color by considering the difference between ground colors from both input video and the target ground patch. Experimental results show that the proposed scheme offers useful tools to provide a more comfortable viewing experience and that it is amenable to real-time performance, even in a software-based implementation.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2006.04a
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• pp.469-473
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• 2006

In general, a classification process between ground data and non-ground data, which include building objects, is required prior to producing a DEM for a certain surface reconstruction from LiDAR data in which the DEM can be produced from the ground data, and certain objects like buildings can be reconstructed using non-ground data. Thus, an exact classification between ground and non-ground data from LiDAR data is the most important factor in the ground reconstruction process using LiDAR data. In particular, building objects can be largely used as digital maps, orthophotos, and urban planning regarding the object in the ground and become an essential to providing three dimensional information for certain urban areas. In this study, an entropy theory, which has been used as a standard of disorder or uncertainty for data used in the information theory, is used to apply a more objective and generalized method in the recognition and segmentation of buildings from raw LiDAR data. In particular, a method that directly uses the raw LiDAR data, which is a type of point shape vector data, without any changes, to a type of normal lattices was proposed, and the existing algorithm that segments LiDAR data into ground and non-ground data as a binarization manner was improved. In addition, this study proposes a generalized building extraction method that excludes precedent information for buildings and topographies and subsidiary materials, which have different data sources.

• Spatial Information Research
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• v.9 no.1
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• pp.125-135
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• 2001

Ground failure such as landslide, rock fall land subsidence by heavy rainfall have damaged to people and property. Especially, the damage to important facility such as dam, bridge, tunnel and industrial complex may be possible. Therefore the ground failure must be assessed and counter plan must be prepared. So, the object of this study is to develop the spatial information system for regional ground stability assessment. For this, the topographic, geologic, soil, forest, land use, rainfall frequency map, and satellite image near 40 dams were collected and constructed to the spatial information system. The spatial information system was developed using Avenue in ArcView 3.2 environment and consists of pull down menus and icons. For application of the spatial information system, regional ground stability was assessed in Andong dam. The assessment was ground failure susceptibility and possibility. The spatial information can be used for regional ground stability assessment, prevention and mitigation of hazard, and management of ground as basic data.

• Journal of information and communication convergence engineering
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• v.1 no.1
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• pp.44-47
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• 2003

The split ground structure for microstrip structure can be used to protect analog/RF signal from SSN interference of digital circuits on PCB with common ground. However, the split ground structure gives rise to undesired emissions that may interfere with nearby circuitry due to the ground discontinuity. In this paper, we have proposed the modified structure, Dumbbell shaped SGS and the method to reduce the radiation by adding the lumped resistor on the proposed Dumbbell shaped SGS.

• Proceedings of the Korean Geotechical Society Conference
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• 2009.09a
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• pp.863-874
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• 2009

The engineering characteristics and the reinforcement effect of the elephant foot method were discussed with parametric study. The elephant foot method is adopted to support the loads transferred from tunnel crown and improve bearing capacity of elephant foot in poor ground condition. The evaluation of reinforcement effect, which has the mechanical relationship between ground condition, footing size and reinforcement system, was carried out through the previous research and numerical analysis. In addition, the simple design chart was proposed to estimate the applicability of the elephant foot reinforcement method. It will be practical for the engineer to determine the optimum reinforcement method for safe tunnelling in soft ground condition.

• Journal of Information Processing Systems
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• v.13 no.3
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• pp.491-499
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• 2017

In this study, we proposed a new approach to segment ground and nonground points gained from a 3D laser range sensor. The primary aim of this research was to provide a fast and effective method for ground segmentation. In each frame, we divide the point cloud into small groups. All threshold points and start-ground points in each group are then analyzed. To determine threshold points we depend on three features: gradient, lost threshold points, and abnormalities in the distance between the sensor and a particular threshold point. After a threshold point is determined, a start-ground point is then identified by considering the height difference between two consecutive points. All points from a start-ground point to the next threshold point are ground points. Other points are nonground. This process is then repeated until all points are labelled.

• Journal of the Korean GEO-environmental Society
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• v.20 no.3
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• pp.15-20
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• 2019

A large amount of site investigation data is essential to obtain reliable design value. However, site investigations are generally insufficient due to economic problems. It is important to estimate the ground profile information in unboring region for accurate earthwork-volume prediction, and such ground profile information can be estimated by using the geo-statistical approach. Furthermore, the ground profile information in unboring region can be estimated by training a model via machine learning technique such as artificial neural network. In this paper, artificial neural network-based model estimated the ground profile information in unboring region, and this results were compared with that of ordinary kriging technique, which is referred to the geo-statistical approach. Accordingly, a total of 84 ground profile information in an actual bridge environment was split into 75 training and 9 test databases. The observed ground profile information of the test database was compared with those of the ordinary kriging technique and artificial neural network.

• Journal of information and communication convergence engineering
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• v.9 no.2
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• pp.150-154
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• 2011

In this paper a four-pole elliptic function bandpass filter is designed with two ground slots. A research of microstrip bandpass filters (BPF) using defected ground structures (DGS) is presented. DGS technique allows designs of tight couplings without the necessity of using very narrow coupling gaps. The simulator Sonnet is used to design the resonator and to calculate the coupling coefficient of the basic coupling structure. Compared to similar microstrip filters without defected ground structure, the simulated performances of these novel structures indicate some technological advantages.

• Proceedings of the Korea Information Processing Society Conference
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• 2016.10a
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• pp.681-682
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• 2016

In this study we propose a novel approach to segment a terrain in two parts: ground and none-ground. The terrain is gained by a multi-channel 3D laser range sensor. We process each vertical line in each frame data. The vertical line is bounded by the sensor's position and a point in the largest circle of the frame. We consider each pair of two consecutive points in each line to find begin-ground and end-ground points. All points placed between a begin-ground point and an end-ground point are ground ones. The other points are none-ground. After examining all vertical lines in the frame, we obtain the terrain segmentation result.

• Journal of Information Processing Systems
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• v.14 no.6
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• pp.1445-1456
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• 2018

Environment perception and three-dimensional (3D) reconstruction tasks are used to provide unmanned ground vehicle (UGV) with driving awareness interfaces. The speed of obstacle segmentation and surrounding terrain reconstruction crucially influences decision making in UGVs. To increase the processing speed of environment information analysis, we develop a CPU-GPU hybrid system of automatic environment perception and 3D terrain reconstruction based on the integration of multiple sensors. The system consists of three functional modules, namely, multi-sensor data collection and pre-processing, environment perception, and 3D reconstruction. To integrate individual datasets collected from different sensors, the pre-processing function registers the sensed LiDAR (light detection and ranging) point clouds, video sequences, and motion information into a global terrain model after filtering redundant and noise data according to the redundancy removal principle. In the environment perception module, the registered discrete points are clustered into ground surface and individual objects by using a ground segmentation method and a connected component labeling algorithm. The estimated ground surface and non-ground objects indicate the terrain to be traversed and obstacles in the environment, thus creating driving awareness. The 3D reconstruction module calibrates the projection matrix between the mounted LiDAR and cameras to map the local point clouds onto the captured video images. Texture meshes and color particle models are used to reconstruct the ground surface and objects of the 3D terrain model, respectively. To accelerate the proposed system, we apply the GPU parallel computation method to implement the applied computer graphics and image processing algorithms in parallel.