• Journal of Institute of Control, Robotics and Systems
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• v.2 no.3
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• pp.188-193
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• 1996

In this paper, an integrated navigation system based on GPS(Global Positioning System) and Dead-Reckoning (DR) is designed. For the calibration of DR, a self-calibration method and a GPS-based calibration method are proposed. From the field-test results, it is shown that DR can be successfully calibrated by the two proposed calibration methods. Also, a cascaded filter approach and a mixed-measurement algorithm are employed for GPS/DR integration. By using the newly proposed mixed-measurement algorithm, it is shown in simulation that the position error becomes smaller than by using only DR even if the number of visible GPS satellites is less than 4.

• International journal of advanced smart convergence
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• v.7 no.1
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• pp.24-32
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• 2018

An outlier detection method using mixed prediction model has been described in this paper. The mixed prediction model consists of time-series model and regression model. The parameter estimation of the prediction model was performed using supervised learning and a genetic algorithm is adopted for a learning method. The experiments were performed in artificial and real data set. The prediction performance is compared with the existing prediction methods using artificial data. Outlier detection is conducted using the real sensor measurements in a dam. The validity of the proposed method was shown in the experiments.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.245-248
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• 1997

The control modes used in teleoperation are position control mode, and rate control mode. This paper presents the problems of the conventional control modes, through simulation, proposes an intelligent mixed control mode that converts the operation mode between the position mode and the rate mode intelligently by judging the operator's intention using the real-time measurement data. The effectiveness of the proposed intelligent mixed control mode is demonstrated and compared to other typical control modes through simulation and actual experiment.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.22 no.10
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• pp.687-696
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• 2010

In this study, FDD algorithm was developed using the normalized distance method and general pattern classifier method that can be applied to constant air volume air handling unit(CAV AHU) system. The simulation model using TRNSYS and EES was developed in order to obtain characteristic data of CAV AHU system under the normal and the faulty operation. Sensitivity analysis of fault detection was carried out with respect to fault progress. When differential pressure of mixed air filter increased by more than about 105 pascal, FDD algorithm was able to detect the fault. The return air temperature is very important measurement parameter controlling cooling capacity. Therefore, it is important to detect measurement error of the return air temperature. Measurement error of the return air temperature sensor can be detected at below $1.2^{\circ}C$ by FDD algorithm. FDD algorithm developed in this study was found to indicate each failure modes accurately.

• Journal of the Korea Institute of Military Science and Technology
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• v.18 no.5
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• pp.517-524
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• 2015

In this paper, we propose a new target tracking filter architecture using active and passive sensors in underwater environment. A passive sensor for target tracking needs a bearing measurement of target. And target tracking filter for using passive sensor has the observability problem. On the other hand, an active sensor does not have the problem associated with system observability problem because an active sensor uses bearing and range measurement. In this paper, the tracking filter algorithm that could be used in the active and passive sensor system is proposed to analyze maneuvering target and to improve target tracking performance. The proposed tracking filter algorithm is tested by a series of computer simulation runs and the results are analyzed and compared with existing algorithm.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.22 no.8
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• pp.1152-1163
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• 1998

In the present work, the image processing technique for measurement of drop sizes has been improved. Firstly, the local processing concept was adopted in addition to the global processing technique to take account of non-uniformity of the illumination intensity ; thereby, basically, the measurement error can be reduced. Also, the unfocussed image of drops can be eliminated more precisely since the elimination process is based on the local normalized contrast. Secondly the algorithms to process the partially detected or overlapped drop images and the non-spherical drop images were developed. Finally, the improved algorithm was tested by using an artificially prepared image-frame, where the partial or overlapped particles and the non-spherical particles are mixed with the normal spherical ones (with their true size-distributions known a priori). The results showed that both the recognition rate of the number of particles and the measurement accuracy were improved prominently.

• 한국가시화정보학회:학술대회논문집
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• 2006.12a
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• pp.1-6
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• 2006

Single-Camera Stereoscopic Vision three-dimensional measurement system has been developed based upon 30-PTV algorithm. The system consists of one camera $(1k\times1k)$ and a host computer. To attain three-dimensional measurements a plate having stereo holes has been installed inside of the lens system. Three-dimensional measurements was successfully attained by adopting the conventional 30-PTV camera calibration methods. As applications of the constructed measurement system, a water droplet mixed with alcohol was constructed on a transparent plastic plate with the contacted fluid diameter 4mm, and the particles motions inside of the droplet have been investigated with the constructed measurement system. The measurement uncertainty of the constructed system was 0.04mm, 0.04mm and 0.09mm for X, Y and Z coordinates.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.7
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• pp.1774-1794
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• 2024

In the field of environmental sensing, it is necessary to develop radio planning techniques for the next generation Internet of Things (IoT) networks over mixed terrains. Such techniques are needed for smart remote monitoring of utility supplies, with links situated close to but out of range of cellular networks. In this paper, a three-dimension (3-D) geometric optimization algorithm is proposed, considering the positions of edge IoT devices and antenna coupling factors. Firstly, a multi-level single linkage (MLSL) iteration method, based on geometric objectives, is derived to evaluate the data rates over ISM 915 MHz channels, utilizing optimized power-distance profiles of continuous waves. Subsequently, a federated learning (FL) data selection algorithm is designed based on the 3-D geometric positions. Finally, a measurement example is taken in a meadow biome of the Mexican Colima district, which is prone to fluvial floods. The empirical path loss model has been enhanced, demonstrating the accuracy of the proposed optimization algorithm as well as the possibility of further prediction work.

• Korean Journal of Remote Sensing
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• v.19 no.1
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• pp.1-9
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• 2003

MODIS LAI product has been one of key variable for analyzing the quantitative aspects of terrestrial ecology at global scale. This study was designed to validate MODIS global LAI product for regional application. To examine the quality of MODIS LAI data, we developed a reference LAI surface that was derived by relating the ground LAI measurements to Landsat ETM+ reflectance. The study area, the Kwangneung Experiment Forest in Korea, covers mixed deciduous and coniferous species of temperate forest. Ground measurements of LAI were conducted at 30 sample plots by using a photo-optical instrument during the growing season of 2002. Ground measured LAI data were then related to the ETM+ reflectance to produce a continuous map of LAI surface over the study area. From the comparison between the MODIS LAI and the reference LAI, it was found that the MODIS LAI values were slightly higher at the forestland. Considering the limitations of producing the reference LAI surface and the uncertainty of the input variable for the MODIS LAI algorithm, such small discrepancy mal not be significant.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.10
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• pp.839-846
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• 2016

Bin picking is a task of picking a small object from a bin. For accurate bin picking, the 3D pose information, position, and orientation of a small object is required because the object is mixed with other objects of the same type in the bin. Using this 3D pose information, a robotic gripper can pick an object using exact distance and orientation measurements. In this paper, we propose a 3D vision technique for accurate measurement of 3D position and orientation of small objects, on which a paper label is stuck to the surface. We use a maximally stable extremal regions (MSERs) algorithm to detect the label areas in a left bin image acquired from a stereo camera. In each label area, image features are detected and their correlation with a right image is determined by a stereo vision technique. Then, the 3D position and orientation of the objects are measured accurately using a transformation from the camera coordinate system to the new label coordinate system. For stable measurement during a bin picking task, the pose information is filtered by averaging at fixed time intervals. Our experimental results indicate that the proposed technique yields pose accuracy between 0.4~0.5mm in positional measurements and $0.2-0.6^{\circ}$ in angle measurements.