• 대한기계학회논문집B
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• 제32권11호
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• pp.863-869
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• 2008

This paper investigated the new calibration algorithm of a straight-type five-hole pressure probe for measuring three-dimensional flow velocity components. This new calibration algorithm was used for velocity data reduction from the calibration map and based on the combination of a look-up, a binary search algorithm and a geometry transformation including the translation and reflection of nodes in a calibration map. The calibration map was expanded up to the application angle, ${\pm}55^{\circ}$ of a probe. This velocity data reduction method showed a perfect performance without any kind of interpolating errors in calculating yaw and pitch angles from the calibration map. Moreover, when it was applied to an actual flow field including a swirling flow, a good result came out on the whole.

• 한국해양공학회지
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• 제35권3호
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• pp.183-190
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• 2021

Tomographic particle image velocimetry (PIV) is a widely used method that measures a three-dimensional (3D) flow field by reconstructing camera images into voxel images. In 3D measurements, the setting and calibration of the camera's mapping function significantly impact the obtained results. In this study, a camera self-calibration technique is applied to tomographic PIV to reduce the occurrence of errors arising from such functions. The measured 3D particles are superimposed on the image to create a disparity map. Camera self-calibration is performed by reflecting the error of the disparity map to the center value of the particles. Vortex ring synthetic images are generated and the developed algorithm is applied. The optimal result is obtained by applying self-calibration once when the center error is less than 1 pixel and by applying self-calibration 2-3 times when it was more than 1 pixel; the maximum recovery ratio is 96%. Further self-correlation did not improve the results. The algorithm is evaluated by performing an actual rotational flow experiment, and the optimal result was obtained when self-calibration was applied once, as shown in the virtual image result. Therefore, the developed algorithm is expected to be utilized for the performance improvement of 3D flow measurements.

• 한국통신학회논문지
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• 제42권2호
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• pp.536-544
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• 2017

무선 RSSI fingerprinting 방식은 기존 무선 인프라를 이용하면서 적정수준의 정확도를 얻을 수 있는 실내위치인식 방법 중의 하나이다. 하지만 라디오 맵 구성( fingerprint calibration) 과정에서 목표 환경의 다양한 위치에서 정확한 물리적 좌표와 무선 신호를 측정해야 하므로 시간과 노력이 많이 소요된다. 이 논문은 이러한 방식으로 위치 정보를 수집하지 않고 반지도식 자기조직화지도 학습 알고리즘을 사용하여 labeled RSSI를 얻고 RSSI 조합으로부터 맵을 구성하는 방법을 제안한다. 모의 데이터에 대한 실험을 통해 제안 방법이 fingerprint 데이터베이스로 부터 1%의 RSSI 샘플을 가지고 효과적인 전체 맵을 얻을 수 있다는 결론을 얻었다.

• 동력기계공학회지
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• 제12권4호
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• pp.18-25
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• 2008

This paper investigated the new calibration algorithm of a straight-type five-hole pressure probe necessary for calculating three-dimensional flow velocity components. The new data reduction method Includes a look-up, a geometry transformation such as the translation and reflection of nodes, and a binary search algorithm. This new calibration map was applied up to the application angle, ${\pm}55^{\circ}$ of a probe. As a result, this data reduction method showed a perfect performance without any kind of interpolation errors In calculating yaw and pitch angle from the calibration map.

• 동력기계공학회지
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• 제18권1호
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• pp.22-31
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• 2014

This paper introduced the new calibration algorithm of a straight-type five-hole pressure probe necessary for calculating three-dimensional flow velocity components. The new velocity data reduction method using both a commercial two-dimensional curve-fitting program and the zone partition method of a calibration map was firstly introduced in this study. This new calibration method can be applied up to the wide flow angle of ${\pm}80^{\circ}$ despite of using a five-hole pressure probe because this data reduction method showed a comparatively good performance in calculating yaw and pitch angles from the calibration map.

• 한국농공학회논문집
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• 제51권4호
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• pp.15-20
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• 2009

The purpose of this study was to test the applicability of the HSPEXP model performance criteria for calibrating hydrologic parameters of HSPF. Baran watershed, located at Whasung city, was selected as a study watershed in this study. Input data for the HSPF model were obtained from the digital elevation map, landuse map, soil map and others. Water flow data from 1996 to 2000 was used for calibration and from 2002 to 2007 was for validation. Using the HSPEXP decision-support software, hydrology parameters were adjusted based on total volume, then low flows, storm flows, and finally seasonal flows. Suggested criteria for each model performance variables were referenced from the previous research. For the calibration period, all the HSPEXP model performance criteria were satisfied while two criteria were slightly violated for the validation period.

• 한국농공학회논문집
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• 제55권4호
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• pp.13-20
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• 2013

The purpose of this study was to evaluate the applicability of the HSPEXP expert system for the calibration of the Hydrological Simulation Program - Fortran (HSPF) for the study watershed. HSPEXP offers advice to the modeler, suggesting parameter changes that might result in better representation of a river basin and provides explanations supporting the recommended parameter changes. The study watershed, Sancheong, is located within the Nakdong River Basin and having the size of $1,072.4km^2$. Input data for the HSPF model were obtained from the landuse map, digital elevation map, meteorological data and others. Water flow data from 2006 to 2008 were used for calibration and from 2009 to 2010 were for validation. Using the HSPEXP expert system, hydrological parameters were adjusted based on total volume, then low flows, storm flows, and finally seasonal flows. For the calibration and validation period, all the HSPEXP model performance criteria were satisfied.

• 로봇학회논문지
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• 제4권4호
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• pp.298-304
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• 2009

This paper describes an algorithm that improves 3D reconstruction result using a multi-sensor fusion disparity map. We can project LRF (Laser Range Finder) 3D points onto image pixel coordinatesusing extrinsic calibration matrixes of a camera-LRF (${\Phi}$, ${\Delta}$) and a camera calibration matrix (K). The LRF disparity map can be generated by interpolating projected LRF points. In the stereo reconstruction, we can compensate invalid points caused by repeated pattern and textureless region using the LRF disparity map. The result disparity map of compensation process is the multi-sensor fusion disparity map. We can refine the multi-sensor 3D reconstruction based on stereo vision and LRF using the multi-sensor fusion disparity map. The refinement algorithm of multi-sensor based 3D reconstruction is specified in four subsections dealing with virtual LRF stereo image generation, LRF disparity map generation, multi-sensor fusion disparity map generation, and 3D reconstruction process. It has been tested by synchronized stereo image pair and LRF 3D scan data.

• 제어로봇시스템학회논문지
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• 제19권4호
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• pp.290-298
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• 2013

Geomagnetic is refracted by building's wall and pillar. Therefore refracted geomagnetic is able to be used as feature point. In a specific space, a mobile device that is equipped with magnetic sensor array measures 3-axis magnetic field for each point. Magnetic field map is acquired by collecting the every sample point in the magnetic field. The measured magnetic field must be calibrated, because each magnetic sensor has a distortion. For this reason, sensor distortion model and sensor calibration method are proposed in this paper. Magnetic field that is measured by mobile device matches magnetic field map. Result of the matching is used for position estimation. This paper implements hardware system for position estimation method using magnetic field map.

• ETRI Journal
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• 제38권3호
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• pp.551-559
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• 2016

In this paper, we propose a hybrid sensor calibration scheme for mobile crowdsensing applications. As the number of newly produced mobile devices containing embedded sensors continues to rise, the potential to use mobile devices as a sensor data source increases. However, because mobile device sensors are generally of a lower performance and cost than dedicated sensors, sensor calibration is crucial. To enable more accurate measurements of natural phenomena through the use of mobile device sensors, we propose a hybrid sensor calibration scheme for such sensors; the scheme makes use of mobile device sensors and existing sensing infrastructure, such as weather stations, to obtain dense data. Simulation results show that the proposed scheme supports low mean square errors. As a practical application of our proposed scheme, we built a temperature map of a city using six mobile phone sensors and six reference sensors. Thanks to the mobility of the sensors and the proposed scheme, our map presents more detailed information than infrastructure-based measurements.