• Journal of Institute of Control, Robotics and Systems
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• v.18 no.7
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• pp.658-663
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• 2012

An accurate outdoor localization method using line/arc features is suggested for mobile robots with LRFs (Laser Range Finders) and odometry. Localization is a key process for outdoor mobile robots which are used for autonomous navigation, exploration and so on. In this paper, an accurate pose correction algorithm is proposed for mobile robots using LRFs, which use three feature types: line, circle, and arc. Using this method we can reduce the number of singular cases that robots couldn't find their pose. Finally we have got simulation results to validate the proposed algorithm.

• Korean Journal of Geomatics
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• v.1 no.1
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• pp.27-33
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• 2001

There are invisible wars going on to preoccupy required satellite information for national defense, industry and living in the out space. Therefore, Korea has developed and successfully launched KOMPSAT (Korea Multi-Purpose SATellite), Korea's first multi-pur pose applications satellite, on December 21, 1999. In the course of geometric corrections with KOMPSAT-1 images, an accuracy of GCP collections is analyzed by the coordinated of digital map respective and an accuracy according to the GCP disposition was analyzed as well. For disposition of GCP, it turned out that even distribution on the whole screen contributes to promote accuracy. These are expected to used as basic data in putting the KOMPSAT-1 geometric correction into practical use.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.8
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• pp.678-687
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• 2005

This paper describes a relative localization algorithm using odometry data and consecutive local maps. The purpose of this paper is the odometry error correction using the area matching of two consecutive local maps. The local map is built up using a sensor module with dual laser beams and USB camera. The range data form the sensor module is measured using the structured lighting technique (active stereo method). The advantage in using the sensor module is to be able to get a local map at once within the camera view angle. With this advantage, we propose the AVS (Aligned View Sector) matching algorithm for. correction of the pose error (translational and rotational error). In order to evaluate the proposed algorithm, experiments are performed in real environment.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.44 no.7
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• pp.545-551
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• 2016

Surface temperature of supersonic wind tunnel model was measured using an infrared thermography technique. To measure the temperature quantitatively, various calibration techniques such as blackbody calibration which converts detected camera signal to temperature, distortion correction due to the camera lens and an imbalance of camera pose, and emissivity calibration which considers viewing angles to the model surface, were employed. Throughout the study, for the quantitative as well as qualitative surface temperature measurement, it was verified that the distortion correction must be considered even for the use of two-dimensional model in aerodynamics testing.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.1
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• pp.225-229
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• 2020

Recently, convolutional neural network (CNN) have been showed outstanding performance in the field of image recognition, image processing and computer vision, etc. In this paper, we propose a CNN-based image rotation correction algorithm as a solution to image rotation problem, which is one of the factors that reduce the recognition rate in image recognition system using CNN. In this paper, we trained our deep learning model with Leeds Sports Pose dataset to extract the information of the rotated angle, which is randomly set in specific range. The trained model is evaluated with mean absolute error (MAE) value over 100 test data images, and it is obtained 4.5951.

• Smart Media Journal
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• v.6 no.3
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• pp.49-56
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• 2017

Images and Videos that include the human face contain a lot of information. Therefore, accurately extracting human face is a very important issue in the field of computer vision. However, in real life, human faces have various shapes and textures. To adapt to these variations, A model-based approach is one of the best ways in which unknown data can be represented by the model in which it is built. However, the model-based approach has its weaknesses when the motion between two frames is big, it can be either a sudden change of pose or moving with fast speed. In this paper, we propose an enhanced human face-tracking model. This approach included human face detection and motion estimation using Cascaded Convolutional Neural Networks, and continuous human face tracking and modeling correction steps using the Active Appearance Model. A proposed system detects human face in the first input frame and initializes the models. On later frames, Cascaded CNN face detection is used to estimate the target motion such as location or pose before applying the old model and fit new target.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.4 no.4
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• pp.231-235
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• 2011

In these day, the number of people who have an abnormal posture caused by bad habit are increasing. Therefore, people suffer various disease and symptoms. For correcting the posture to cure, we need continuous monitor, expenditure of time and money. In this study, we develop a posture correcting aid system in other to monitor a posture continuously and leads to pose correctly and records postural variation which are attached to the neck and the waist. The devised system showed good potential for the correct posture guide and a cure of postural defect.

• Proceedings of the Korean Society of Soil and Groundwater Environment Conference
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• 1996.12a
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• pp.97-101
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• 1996

Municipal and sanitary landfills can pose environmental problems due to leachate, landfill gas md unstable geotechnical properties. Most governmental bodies delay the correction of landfill problems or landfill replacement until a crises stage is reached. The replacement of a landfill is often made difficult due to costly regulatory controls, public opposition to siting and the high cost of closure for the previous landfill unit. Solutions to extending landfill life and capacity Involve waste minimization by recycling, refuse compaction and waste-to-energy incineration. Incineration can reduce the volume of refuse by 50-95%. The largest installed bases of municipal waste Incinerators are located in Japan and the U.S. The volume of waste contained in a landfill can be estimated by load count tabulations, weight-and-volume measurements or a material balance analysis based on the trash profile of user categories. for an existing landfill, core samples may be collected and analyzed for use in a material balance analysis. Newly generated refuse contains approximately 50% of the heating value of coal. However, landfill properties vary significantly due to the waste profile of the contributors and biodegradation due to time and weathering. The volume of the Nanji-do landfill

• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.634-636
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• 2023

Surveillance systems play a pivotal role in ensuring the safety and security of various environments, including public spaces, critical infrastructure, and private properties. However, detecting abnormal human behavior in lowlight conditions is a critical yet challenging task due to the inherent limitations of visual data acquisition in such scenarios. This paper introduces a spatiotemporal framework designed to address the unique challenges posed by low-light environments, enhancing the accuracy and efficiency of human abnormality detection in surveillance camera systems. We proposed the pre-processing using lightweight exposure correction, patched frames pose estimation, and optical flow to extract the human behavior flow through t-seconds of frames. After that, we train the estimated-action-flow into autoencoder for abnormal behavior classification to get normal loss as metrics decision for normal/abnormal behavior.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.5
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• pp.177-183
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• 2022

Recently, interesting of home training is getting bigger due to COVID-19. Accordingly, research on applying HAR(human activity recognition) technology to home training has been conducted. However, existing paper of HAR proposed static activity instead of dynamic activity. In this paper, the deep learning model where dynamic exercise posture can be analyzed and the accuracy of the user's exercise posture can be shown is proposed. Fitness images of AI-hub are analyzed by blaze pose. The experiment is compared with three types of deep learning model: RNN(recurrent neural network), LSTM(long short-term memory), CNN(convolution neural network). In simulation results, it was shown that the f1-score of RNN, LSTM and CNN is 0.49, 0.87 and 0.98, respectively. It was confirmed that CNN is more suitable for human activity recognition than other models from simulation results. More exercise postures can be analyzed using a variety learning data.