• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.5
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• pp.48-54
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• 2018

With the 4th industry era, smart factories are emerging. In the era of multi-product small scale production, unmanned transportation vehicles are rapidly increasing in utilization of unmanned transportation vehicles that carry and arrange goods in the work space. The conventional unmanned vehicle detected its position by using the guided line method and the position based method for indoor location recognition and movement. This method has disadvantages of initial high cost and maintenance / maintenance. In this paper, to solve the disadvantages, the method of predicting the direct path of the unmanned vehicle through the Kalman filter is verified using the white LED marker of the warehouse and the position data and the image data of the white LED marker recognition image. Through this, the reliability of the linear movement which occupies the most part in the lattice structure is secured. It is also expected that the reliance on additional position sensors will also be reduced.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.16 no.5
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• pp.99-104
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• 2016

The walking exercise can alleviate stress and also it can improve health fortheir lifetime. Recent development in Information and Communication Technologies (ICT) has laid the foundation for Internet of Things (IoT) to become the future technology. IoT has many applications in industry automation, security, smart homes and cities, education, health etc. In personal health-care domain, IoT is mainly used for monitoring fitness condition by observing current activity of individual. In this paper, we have proposed a novel IoT based personal wellness care system. Proposed system not only keep track of current fitness level but also provide future activity prediction based on history data along with standard recommendations. Predicted activity helps in motivating the individual to achieve the desired fitness level. Initially, we consider only walking activity for testing purpose and later, other types of activities/exercise will be captured for improved health care support.

• Journal of Korea Water Resources Association
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• v.33 no.3
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• pp.341-350
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• 2000

The objectives of this study are to develop a state-space form of stochastic dynamic storage function routing model and to test the model performance for real-time flow forecast. The selected study area is the main Han River starting from Paldang Dam site to Indogyo station and the 13 flood events occurred from 1987 to 1998 are selected for computing model parameters and testing the model performance. It was shown that the optimal model parameters are quite different depending on Hood events, but the values used on field work also give reasonable results in this study area. It is also obvious that the model performance from the stochastic-dynamic model developed in this study gives more accurate and reliable results than that from the existing deterministic model. Analysis for allowable forecast lead time leads that under the current time step the reasonable predicted downstream flows in 5 hours time advance are obtained from the stochastic dynamic model on relatively less lateral inflow event in the study area.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.1
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• pp.141-144
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• 2006

The deviations in the injection orbital parameters, resulting from launcher dispersions, need to be estimated and used for autonomous satellite operations. For the proposed small satellite mission of the university there will be two GPS receivers onboard the satellite to provide the instantaneous orbital state to the onboard data handling system. In order to meet the power requirements, the satellite will be sun-tracking whenever there is no imaging operation. For imaging activities, the satellite will be maneuvered to nadir-pointing mode. Due to such different modes of orientation the geometry for the GPS receivers will not be favorable at all times and there will be instances of poor geometry resulting in no output from the GPS receivers. Onboard the satellite, the orbital information should be continuously available for autonomous switching on/off of various subsystems. The paper presents the strategies to make use of small arcs of data from GPS receivers to compute the mean orbital parameters and use the updated orbital parameters to calculate the position and velocity whenever the same is not available from GPS receiver. Thus the navigation message from the GPS receiver, namely the position vector in Earth-Centered-Earth-Fixed (ECEF) frame, is used as measurements. As for estimation, two techniques - (1) batch least squares method, and (2) Kalman Filter method are used for orbit estimation (in real time). The performance of the onboard orbit estimation has been assessed based on hardware based multi-channel GPS Signal simulator. The results indicate good converge even with short arcs of data as the GPS navigation data are generally very accurate and the data rate is also fast (typically 1Hz).

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.4
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• pp.438-444
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• 2004

The intelligent robots that will be needed in the near future are human-friendly robots that are able to coexist with humans and support humans effectively. To realize this, robots need to recognize their position and posture in known environment as well as unknown environment. Moreover, it is necessary for their localization to occur naturally. It is desirable for a robot to estimate of his position by solving uncertainty for mobile robot navigation, as one of the best important problems. In this paper, we describe a method for the localization of a mobile robot using image information of a moving object. This method combines the observed position from dead-reckoning sensors and the estimated position from the images captured by a fixed camera to localize a mobile robot. Using the a priori known path of a moving object in the world coordinates and a perspective camera model, we derive the geometric constraint equations which represent the relation between image frame coordinates for a moving object and the estimated robot's position. Since the equations are based or the estimated position, the measurement error may exist all the time. The proposed method utilizes the error between the observed and estimated image coordinates to localize the mobile robot. The Kalman filter scheme is applied for this method. its performance is verified by the computer simulation and the experiment.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.46 no.2
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• pp.84-91
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• 2009

This paper presents an online partial 3D modeling methodology that uses a mobile augmented reality system and aerial photographs, and a tracking methodology that compares the 3D model with a video image. Instead of relying on models which are created in advance, the system generates a 3D model for a real building on the fly by combining frontal and aerial views. A user's initial pose is estimated using an aerial photograph, which is retrieved from a database according to the user's GPS coordinates, and an inertial sensor which measures pitch. We detect edges of the rooftop based on Graph cut, and find edges and a corner of the bottom by minimizing the proposed cost function. To track the user's position and orientation in real-time, feature-based tracking is carried out based on salient points on the edges and the sides of a building the user is keeping in view. We implemented camera pose estimators using both a least squares estimator and an unscented Kalman filter (UKF). We evaluated the speed and accuracy of both approaches, and we demonstrated the usefulness of our computations as important building blocks for an Anywhere Augmentation scenario.

• Journal of Korea Multimedia Society
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• v.16 no.2
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• pp.138-146
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• 2013

In this paper, we propose a new 3D PUI(Physical User Interface) system in which the index fingertip points and moves a mouse position on a given monitor screen. There are two 3D PUI schemes to control smart devices like smart TVs remotely, the relative pointing one and the absolute pointing one. The former has a problem in that it does not match the human perception process, and the latter requires excessive movement of the body. We combined the relative one and the absolute one, and develop a new intuitive and user-friendly pointing method, 3D PUI. It requires an establishment of a pyramid shape visible area (view volume) to point a mouse position on a screen with the dominant eye. In order to maintain the real-time view volume, however, it requires large computation depending on the movement of the dominant eye. We optimized the computation of the view volume in which it determines the internal and external position on the screen. In addition, Kalman filter is applied with tracing of the mouse pointer position to stabilize the trembling of the pointer and offers the user ease of use.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.5
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• pp.444-450
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• 2015

The accuracy of small and low cost CCD camera is insufficient to provide data for precisely tracking unmanned aerial vehicles(UAVs). This study shows how UAV can hover on a human targeted tracking object by using CCD camera rather than imprecise GPS data. To realize this, UAVs need to recognize their attitude and position in known environment as well as unknown environment. Moreover, it is necessary for their localization to occur naturally. It is desirable for an UAV to estimate of his attitude by environment recognition for UAV hovering, as one of the best important problems. In this paper, we describe a method for the attitude of an UAV using image information of a maker on the floor. This method combines the observed position from GPS sensors and the estimated attitude from the images captured by a fixed camera to estimate an UAV. Using the a priori known path of an UAV in the world coordinates and a perspective camera model, we derive the geometric constraint equations which represent the relation between image frame coordinates for a marker on the floor and the estimated UAV's attitude. Since the equations are based on the estimated position, the measurement error may exist all the time. The proposed method utilizes the error between the observed and estimated image coordinates to localize the UAV. The Kalman filter scheme is applied for this method. its performance is verified by the image processing results and the experiment.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.10
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• pp.609-617
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• 2016

In general, Personal Navigation Systems (PNSs) can be defined systems to acquire pedestrian positional information. GPS is an example of PNS. However, GPS can only be used where the GPS signal can be received. Pedestrian Dead Reckoning (PDR) can estimate the positional information of pedestrians using Inertial Measurement Unit (IMU). Therefore, PDR can be used for GPS-disabled areas. This paper proposes a PDR scheme considering various movement types over GPS-disabled areas as combat environments. We propose a movement distance estimation scheme and movement direction estimation scheme as pedestrian's various movement types such as walking, running and crawling using IMU. Also, we propose a fusion algorithm between GPS and PDR to mitigate the lack of accuracy of positional information at the entrance to the building. The proposed algorithm has been tested in a real test bed. In the experimental results, the proposed algorithms exhibited an average position error distance of 5.64m and position error rate in goal point of 3.41% as a pedestrian traveled 0.6km.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.1D
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• pp.135-143
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• 2009

Integrating the Global Positioning System (GPS) and Inertial Navigation System (INS) sensor technologies using the precise GPS Carrier phase measurements is a methodology that has been widely applied in those application fields requiring accurate and reliable positioning and attitude determination; ranging from 'kinematic geodesy', to mobile mapping and imaging, to precise navigation. However, such integrated system may not fulfil the demanding performance requirements when the baseline length between reference and mobil user GPS receiver is grater than a few tens of kilometers. This is because their positioning/attitude determination is still very dependent on the errors of the GPS observations, so-called "baseline dependent errors". This limitation can be remedied by the integration of GPS and INS sensors, using multiple reference stations. Hence, in order to derive the GPS distance dependent errors, this research proposes measurement processing algorithms for multiple reference stations, such as a reference station ambiguity resolution procedure using linear combination techniques, a error estimation based on Kalman filter and a error interpolation. In addition, all the algorithms are evaluated by processing real observations and results are summarized in this paper.