• Journal of the Korea Institute of Military Science and Technology
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• v.8 no.4 s.23
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• pp.14-23
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• 2005

In this paper we propose a sensor fusion method for the navigation algorithm which can be used to estimate state vectors such as position and velocity for its motion control using multi-sensor output measurements. The output measurement we will use in estimating the state is a series of known multi-sensor asynchronous outputs with measurement noise. This paper investigates the Extended Kalman Filtering method to merge asynchronous heading, heading rate, velocity of DVL, and SSBL information to produce a single state vector. Different complexity of Kalman Filter, with. biases and measurement noise, are investigated with theoretically data from MOERI's SAUV. All levels of complexity of the Kalman Filters are shown to be much more close and smooth to real trajectories then the basic underwater acoustic navigation system commonly used aboard underwater vehicle.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.175-175
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• 2000

In this Paper we propose a navigation algorithm which can be used to estimate state vectors such as position and velocity for its motion control using multi-sensor output measurements. The output measurement we will use in estimating the state is a series of known multi-sensor asynchronous outputs with measurement noise. This paper investigates the Extended Kalman Filtering method to merge asynchronous heading, heading rate, velocity of DVL, and SSBL information to produce a single state vector. Different complexity of Kalman Filter, with biases and measurement noise, are investigated with theoretically data from KRISO's AUV. All levels of complexity of the Kalman Filters are shown to be much more close and smooth to real trajectories then the basic underwater acoustic navigation system comment)'used aboard underwater vehicle.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2012.07a
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• pp.361-364
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• 2012

MPEG과 ITU-T에서 최근 표준화가 진행되고 있는 HEVC는 H.264/AVC에 비해, CU(coding unit), PU(prediction unit), TU(transform unit)의 다양한 형태 분할 단위를 갖는 것을 큰 특징으로 한다. 이 중, CU와 TU는 쿼드트리 형태의 재귀적 분할 구조를 가지도록 구성되는데, 압축 효율은 향상시키지만 높은 부호화 복잡도를 갖는 단점이 있다. 본 논문에서는 이러한 재귀적 분할 구조를 변환하여 가장 작은 CU의 정보를 병합하여 큰 CU의 정보를 빠르게 결정하는 방법을 제안한다. 제안한 방법을 HEVC의 CU 부호화에 적용한 결과, 부호화 복잡도를 32-45% 가량 감소시키면서 압축 효율 하락은 0.6-0.9%로 억제할 수 있었다. 또한, HM6.1에 구현되어 있는 고속 탐색 알고리즘과 비교 할 경우, 압축 효율 하락을 0.2-0.3%로 억제하면서 부호화 복잡도를 8-12% 감소시킬 수 있었다.

• Proceedings of the IEEK Conference
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• 2000.07b
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• pp.835-838
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• 2000

Popular methods for extracting a text region in video images are in general based on analysis of a whole image such as merge and split method, and comparison of two frames. Thus, they take long computing time due to the use of a whole image. Therefore, this paper suggests the faster method of extracting a text region without processing a whole image. The proposed method uses line sampling methods, FFT and neural networks in order to extract texts in real time. In general, text areas are found in the higher frequency domain, thus, can be characterized using FFT The candidate text areas can be thus found by applying the higher frequency characteristics to neural network. Therefore, the final text area is extracted by verifying the candidate areas. Experimental results show a perfect candidate extraction rate and about 92% text extraction rate. The strength of the proposed algorithm is its simplicity, real-time processing by not processing the entire image, and fast skipping of the images that do not contain a text.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.150-152
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• 2003

Laser scanned lidar data record 3D surface information in detail. Exploring valuable spatial information from lidar data is a prerequisite task for its applications, such as DEM generation and 3D building model reconstruction. However, the inherent spatial information is implicit in the abundant, densely and randomly distributed point cloud. This paper proposes a novel method to organize point cloud data, so that further analysis or feature extraction can proceed based on a well organized data model. The principle of the proposed algorithm is to segment point cloud into 3D planes. A split and merge segmentation based on the octree structure is developed for the implementation. Some practical airborne and ground lidar data are tested for demonstration and discussion. We expect this data organization could provide a stepping stone for extracting spatial information from lidar data.

• Proceedings of the Korea Information Processing Society Conference
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• 2011.04a
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• pp.92-96
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• 2011

Nowadays, the researches study high rate and real-time query applications seem to be real-time query scheduling protocols and energy aware real time query protocols. Also the WSNs should provide the quality of data in real time query applications that is more and more popular for wireless sensor networks (WSNs). Thus we propose the quality of data function to merge into energy efficiency called energy join quality aware realtime query scheduling (EJQRTQ). Our work calculate the energy ratio that considers interference of queries, and then compute the expected quality of query and allocate slots to real-time preemptive query scheduler.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.11a
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• pp.374-376
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• 2022

Community detection is meaningful research in social network analysis, and many existing studies use graph theory analysis methods to detect communities. This paper proposes a method to detect community by detecting maximal cliques and obtain the high influence cliques by high influence nodes, then merge the cliques with high similarity in social network.

• Journal of Korean Society of Transportation
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• v.26 no.5
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• pp.73-80
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• 2008

ALINEA algorithm, which is one of the best on-ramp metering algorithms, was designed to control the traffic volume from on-ramp in order to maintain the optimal occupancy rate of the detectors installed downstream of the merge area. But, the reliability of occupancy rate estimated from the loop detectors, which are used most commonly in Korea, is relatively lower than other parameters such as speed and volume. Moreover, because occupancy rate depends on the length of loop detectors and site, lots of calibration work is required whenever they are installed in order to estimate the occupancy rate. Therefore, there exists room for improvement of ALINEA algorithm because only occupancy rate having some problems is considered as a control parameter in ALINEA algorithm. Practically it is difficult to measure or perceive the occupancy rate for traffic engineers and drivers. On the other hand, speed can be good alternative which can overcome the defect induced by using occupancy. In this study, occupancy based ALINEA algorithm is converted to speed based ALINEA assuming the linear relationship between density and speed.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.1
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• pp.70-77
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• 2014

This paper proposes a novel localization algorithm based on ego-motion which used Lucas-Kanade Optical Flow and warping image obtained through fish-eye lenses mounted on the robots. The omnidirectional image sensor is a desirable sensor for real-time view-based recognition of a robot because the all information around the robot can be obtained simultaneously. The preprocessing (distortion correction, image merge, etc.) of the omnidirectional image which obtained by camera using reflect in mirror or by connection of multiple camera images is essential because it is difficult to obtain information from the original image. The core of the proposed algorithm may be summarized as follows: First, we capture instantaneous $360^{\circ}$ panoramic images around a robot through fish-eye lenses which are mounted in the bottom direction. Second, we extract motion vectors using Lucas-Kanade Optical Flow in preprocessed image. Third, we estimate the robot position and angle using ego-motion method which used direction of vector and vanishing point obtained by RANSAC. We confirmed the reliability of localization algorithm using ego-motion based on fisheye warping image through comparison between results (position and angle) of the experiment obtained using the proposed algorithm and results of the experiment measured from Global Vision Localization System.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.5
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• pp.610-616
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• 2012

In this paper, we present a method to detect an object such as ship, rock and buoy from sea IR image for the safety navigation. To this end, we do the image smoothing first and the apply watershed algorithm to segment image into subregions. Since watershed algorithm almost always produces over-segmented regions, it requires posterior merging process to get meaningful segmented regions. We propose an efficient merger algorithm that requires only two times of direct access to the pixels regardless of the number of regions. Also by analyzing IR image obtained from sea environments, we could find out that most horizontal edge come out from object regions. For the given input IR image we extract horizontal edge and eliminate isolated edges produced from background and noises by adopting morphological operator. Among the segmented regions, the regions that have horizontal edges are extracted as final results. Experimental results show the adequacy of the proposed method.