Earticle

지자기 벡터는 센서가 바라보고 있는 방향에 따라 그 값이 달라지는 특성이 있다. 본 논문에서는 그런 문제를 최소 화하여 지자기 기반 실내 위치 추정에 사용될 수 있도록 지자기 벡터 보정법을 제안한다. 지자기 기반 실내 위치 추 정에서 사용되는 핑거프린팅 기법은 자기장 지도와 현재 위치에서의 자기장 값을 매칭하여 위치를 추정해낸다. 이 때, 자기장 센서는 사용자의 이동 방향에 따라 읽어 들이는 자기장 벡터 값이 달라지기 때문에 위치 추정 정확도가 낮아진다. 이를 해결하기 위해 많은 연구들은 자기장 벡터 크기를 사용하지만, 이는 지문의 고유성을 감소시킨다. 따라서 본 논문에서는 지문의 고유성을 유지할 수 있는 자기장 벡터를 그대로 사용하되, 벡터 크기처럼 사용자의 이 동방향에 영향을 받지 않도록 벡터 값을 보정하는 방법을 제안한다. 임의의 방향으로 걸어본 결과, 본 연구에서 제 안된 보정법을 사용하면 자기장 지도와의 매칭 정확도가 높아지는 것을 확인하였다.

Magnetic sensors have the disadvantage that their vector values differ depending on the direction. In this paper, we propose a magnetic vector calibration method for geomagnetic-based indoor localization estimates. The fingerprinting technique used in geomagnetic-based indoor localization the position by matching the magnetic field map and the magnetic sensor value. However, since the moving direction of the current user may be different from the moving direction of the person who creates the magnetic field map at the collection time, the sampled magnetic vector may have different values from the vector values recorded in the field map. This may substantially lower the positioning accuracy. To avoid this problem, the existing studies use only the magnitude of magnetic vector, but this reduces the uniqueness of the fingerprint, which may also degrade the positioning accuracy. In this paper we propose a vector calibration algorithm which can adjust the sampled magnetic vector values to the vector direction of the magnetic field map by using the parametric equation of a circle. This can minimize the inaccuracy caused by the direction mismatch.

In this study we present a new improved nonlinear calibration method for vector magnetograms made by the Solar Flare Telescope of BOAO. To identify Fe I 6302.5 line, we have scanned monochromatic images of the line integrated over filter passband, changing the location of the central transmission wavelength of a Lyot filter. Then we obtained a filter-convolved line profile, which is in good agreement with spectral atlas data provided by the Sacramento Peak Solar Observatory. The line profile has been used to derive calibration coefficients of longitudinal and transverse fields, employing the conventional line slope method under the weak field approximation. Our improved nonlinear calibration method has also been used to calculate theoretical Stokes polarization signals with various angles of inclination of magnetic fields. For its numerical test, we have compared input magnetic fields with the calibrated ones, which have been derived from the new improved non-linear method and the conventional method respectively. The numerical test shows that the calibrated fields obtained from the improved method are consistent with the input fields, but not with those from the conventional method. Finally, we applied our new improved method to a dipole model which characterizes a typical field configuration of a single, round sunspot. It is noted that the conventional method remarkably underestimates the transverse field component near the inner penumbra.

The position of a 3-dimensional(3D) point can be measured by using calibrated stereo camera. To obtain more accurate measurement ,more accurate camera calibration is required. There are many existing methods to calibrate camera. The simple linear methods are usually not accurate due to nonlinear lens distortion. The nonlinear methods are accurate more than linear method, but it increase computational cost and good initial guess is needed. The multi step methods need to know some camera parameters of used camera. Recent years, these explicit model based camera calibration work with the development of more precise camera models involving correction of lens distortion. But these explicit model based camera calibration have disadvantages. So implicit camera calibration methods have been derived. One of the popular implicit camera calibration method is to use neural network. In this paper, we propose implicit stereo camera calibration method for 3D reconstruction using support vector machine. SVM can learn the relationship between 3D coordinate and image coordinate, and it shows the robust property with the presence of noise and lens distortion, results of simulation are shown in section 4.

In nuclear power plants (NPPs), periodic sensor calibrations are required to assure that sensors are operating correctly. By checking the sensor's operating status at every fuel outage, faulty sensors may remain undetected for periods of up to 24 months. Moreover, typically, only a few faulty sensors are found to be calibrated. For the safe operation of NPP and the reduction of unnecessary calibration, on-line instrument calibration monitoring is needed. In this study, principal component-based auto-associative support vector regression (PCSVR) using response surface methodology (RSM) is proposed for the sensor signal validation of NPPs. This paper describes the design of a PCSVR-based sensor validation system for a power generation system. RSM is employed to determine the optimal values of SVR hyperparameters and is compared to the genetic algorithm (GA). The proposed PCSVR model is confirmed with the actual plant data of Kori Nuclear Power Plant Unit 3 and is compared with the Auto-Associative support vector regression (AASVR) and the auto-associative neural network (AANN) model. The auto-sensitivity of AASVR is improved by around six times by using a PCA, resulting in good detection of sensor drift. Compared to AANN, accuracy and cross-sensitivity are better while the auto-sensitivity is almost the same. Meanwhile, the proposed RSM for the optimization of the PCSVR algorithm performs even better in terms of accuracy, auto-sensitivity, and averaged maximum error, except in averaged RMS error, and this method is much more time efficient compared to the conventional GA method.

영상의 두점과 카메라 초점을 지나는 벡터들간의 사잇각을 기반한 방정식은 카메라 위치와 제세가 독립적으로 분리시킬 수 있다. 본 논문은 이 방정식의 두번째 응용으로써, 벡터내적 기반 방정식에 의해 생성된 곡면 분석을 통한 카메라와 레이져 라인 스캐너간의 상대적인 외부표정 계산을 소개한다.

The equation based on vector inner product by the angles between pairs of two image rays can independently separate the position and pose of a camera. As our second application, the exterior calibration between a camera and a laser range finder is proposed here through analysis of surfaces created by the equation.

Multi-axis magnetic and accelerometer sensor are widely used in consumer product such as smart phones. The vector output of multi-axis sensors have errors on each axis such as offset error, scale error, non-orthogonality. These errors cause many problems on the performance of the applications. In this paper, we designed the effective inline compensation algorithm for calibrating of 3 axis sensors using ellipsoid for mass production of multi-axis sensors. The outputs with those kinds of errors can be modeled by ellipsoid, and the proposed algorithm makes sequential mappings of the virtual ellipsoid to perfect sphere which is calibrated function of the sensor on three-dimensional space. The proposed calibrating process composed of four main stages and is very straightforward and effective. In addition, another imperfection of the sensor such as the drift from temperature can be easily inserted in each mapping stage. Numerical simulation and experimental results shows great performance of the proposed compensation algorithm.