COLOURIMETRIC CALIBRATION FOR PHOTOGRAPHY, PHOTOGRAMMETRY, AND PHOTOMODELLING WITHIN ARCHITECTURAL SURVEY

In accordance with the “Declaration of Rome on architectural survey”, we can affirm that recording and interpretation of colour information in photographic surveying, in photogrammetric surveying and in photomodelling requires careful planning of Colour Imaging processes. Information acquired by an optical sensor is influenced not just by the actual photographed scene, but also by the spectral sensitivity of the sensor. We have adopted, from the field of Cultural Heritage, a method of colourimetric calibration for digital photographs and have proposed some adjustments to finalise this process for the purposes of Architectural Survey. With the use of a colourimetric target and a non-linear transformation algorithm, our Colour Imaging method statistically reconstructs colours conventionally unrecordable by a commercial camera. In addition, this method reconstructs colours as if the photographed object were exposed to a standard illuminant, assessing a colour error parameter value for each photo. By including the colourimetric target in every shot and by applying the calibration algorithm to all photographs taken, the process correlates all data sets to a single standard illuminant: regarding photomodelling, this leads to a more uniform and detailed representation of the surfaces of virtual models. We present two successful examples of application: one focused on a design object with physioplastic decoration and another regarding a circular fountain in a historic villa.

Study on LT Accuracy Improvement by Calibration Based on Network Measurements

Laser trackers (LT) are widely used to calibrate other machines. Nevertheless, very little is known about calibrating an LT. There are some standards that allow us to evaluate the LT performance. However, they require specialized equipment. A calibration procedure to improve the LT accuracy in an easy and fast way is presented in this paper. This method is based on network measurements where a set of reflectors were measured from different LT positions in a working environment. The methodology proposed deal with the lack of nominal data of the reflector mesh. A measurement scenario was defined, based on error parameter dependence on distances and angles, thus, obtaining those positions more sensitive to errors. The influence of the incidence angle of the laser beam on the reflector was characterized, revealing that its contribution to the LT measurement error can be up to 13 µm. Error kinematic parameters were identified to provide the optimum value of an objective function, where the reflector mesh nominal data were unknown. The calibration procedure was validated with nominal data, by measuring a set of reflectors located on a coordinate measuring machine. The findings of this study suggested that the LT accuracy can be improved up to 25%. Moreover, the method can be carried out by the LT user without requiring specialized equipment.

Evaluation of methods for feasible parameter set estimation of Takagi-Sugeno models for nonlinear regression with bounded errors

In data-driven modeling besides the point estimate of the model parameters, an estimation of the parameter uncertainty is of great interest. For this, bounded error parameter estimation methods can be used. These are particularly interesting for problems where the stochastical properties of the random effects are unknown and cannot be determined. In this paper, different methods for obtaining a feasible parameter set are evaluated for the use with Takagi-Sugeno models. Case studies with simulated data and with measured data from a manufacturing process are presented.

A multi-objective sparse evolutionary framework for large-scale weapon target assignment based on a reward strategy

The asset-based weapon target assignment (ABWTA) problem is one of the important branches of the weapon target assignment (WTA) problem. Due to the current large-scale battlefield environment, the ABWTA problem is a multi-objective optimization problem (MOP) with strong constraints, large-scale and sparse properties. The novel model of the ABWTA problem with the operation error parameter is established. An evolutionary algorithm for large-scale sparse problems (SparseEA) is introduced as the main framework for solving large-scale sparse ABWTA problem. The proposed framework (SparseEA-ABWTA) mainly addresses the issue that problem-specific initialization method and genetic operators with a reward strategy can generate solutions efficiently considering the sparsity of variables and an improved non-dominated solution selection method is presented to handle the constraints. Under the premise of constructing large-scale cases by the specific case generator, two numerical experiments on four outstanding multi-objective evolutionary algorithms (MOEAs) show Runtime of SparseEA-ABWTA is faster nearly 50% than others under the same convergence and the gap between MOEAs improved by the mechanism of SparseEA-ABWTA and SparseEA-ABWTA is reduced to nearly 20% in the convergence and distribution.

Accuracy Assessment of Aircraft Positioning Using the Dual-Frequency GPS Code Observations in Aviation

This study publishes results of tests with regard to determination of the aircraft positioning accuracy by means of the GPS navigation in aviation. The research exploits the mathematical model of the linear combination "IonosphereFree" in order to designate the coordinates of an aircraft. The research uses the actual GPS code observations, recorded by a satellite receiver mounted in the Cessna 172, at the time of the experiment for the EPDE military aerodrome in Dęblin. The computations of the position of the Cessna 172 aircraft for the linear combination "Ionosphere-Free" were made in the APS Toolbox v.1.0.0. programme. Within evaluation of accuracy of the GPS positioning in aviation, the determined coordinates of the aircraft Cessna 172 from the APS programme were compared to an accurate reference position from the solution derived by the PPP measurement technique. In the research, the authors obtained an average positioning accuracy of approximately 5 m in the geocentric XYZ coordinates and approximately 4 m in the ellipsoidal BLh coordinates. In addition, the 3D-error parameter is lower than 7 m for the XYZ geocentric coordinates.

Wireless sensor indoor positioning based on an improved particle filter algorithm

Positioning by wireless sensor network is one of its main functions and has been widely used in many fields. However, when signal propagation is hindered, serious errors, non-line-of-sight errors, occur. In order to solve this problem, this article proposes an improved particle filter algorithm, which introduces the idea of residual analysis to improve reliability. The algorithm assigns weights to the particles based on the residuals and selects the appropriate particles. In addition, the non-line-of-sight error parameter [Formula: see text] is introduced, and the second selection is made according to [Formula: see text], which considers the influence of non-line-of-sight error. The non-line-of-sight error is greatly reduced after two selections. The simulation is performed under several different non-line-of-sight errors, and results show that the algorithm is superior to Kalman filter and particle filter.

Machine learning friendly set version of Johnson–Lindenstrauss lemma

The widely discussed and applied Johnson–Lindenstrauss (JL) Lemma has an existential form saying that for each set of data points Q in n-dimensional space, there exists a transformation f into an $$n'$$n′-dimensional space ($$n'<n$$n′<n) such that for each pair $$\mathbf {u},\mathbf {v} \in Q$$u,v∈Q$$(1-\delta )\Vert \mathbf {u}-\mathbf {v}\Vert ^2 \le \Vert f(\mathbf {u})-f(\mathbf {v})\Vert ^2 \le (1+\delta )\Vert \mathbf {u}-\mathbf {v}\Vert ^2 $$(1-δ)‖u-v‖2≤‖f(u)-f(v)‖2≤(1+δ)‖u-v‖2 for a user-defined error parameter $$\delta $$δ. Furthermore, it is asserted that with some finite probability the transformation f may be found as a random projection (with scaling) onto the $$n'$$n′ dimensional subspace so that after sufficiently many repetitions of random projection, f will be found with user-defined success rate $$1-\epsilon $$1-ϵ. In this paper, we make a novel use of the JL Lemma. We prove a theorem stating that we can choose the target dimensionality in a random projection-type JL linear transformation in such a way that with probability $$1-\epsilon $$1-ϵ all of data points from Q fall into predefined error range $$\delta $$δ for any user-predefined failure probability $$\epsilon $$ϵ when performing a single random projection. This result is important for applications such as data clustering where we want to have a priori dimensionality reducing transformation instead of attempting a (large) number of them, as with traditional Johnson–Lindenstrauss Lemma. Furthermore, we investigate an important issue whether or not the projection according to JL Lemma is really useful when conducting data processing, that is whether the solutions to the clustering in the projected space apply to the original space. In particular, we take a closer look at the k-means algorithm and prove that a good solution in the projected space is also a good solution in the original space. Furthermore, under proper assumptions local optima in the original space are also ones in the projected space. We investigate also a broader issue of preserving clusterability under JL Lemma projection. We define the conditions for which clusterability property of the original space is transmitted to the projected space, so that a broad class of clustering algorithms for the original space is applicable in the projected space.

Hybrid IRBM-BPNN Approach for Error Parameter Estimation of SINS on Aircraft

To realize the error parameter estimation of strap-down inertial navigation system (SINS) and improve the navigation accuracy for aircraft, a hybrid improved restricted Boltzmann machine BP neural network (IRBM-BPNN) approach, which combines restricted Boltzmann machine (RBM) and BP neural network (BPNN), is proposed to forecast the inertial measurement unit (IMU) instrument errors and initial alignment errors of SINS. Firstly, the error generation mechanism of SINS is analyzed, and initial alignment error model and IMU instrument error model are established. Secondly, an unsupervised RBM method is introduced to initialize BPNN to improve the forecast performance of the neural network. The RBM-BPNN model is constructed through the information fusion of SINS/GPS/CNS integrated navigation system by using the sum of position deviation, the sum of velocity deviation and the sum of attitude deviation as the inputs and by using the error parameters of SINS as the outputs. The RBM-BPNN structure is improved to enhance its forecast accuracy, and the pulse signal is increased as the input of the neural network. Finally, we conduct simulation experiments to forecast and compensate the error parameters of the proposed IRBM-BPNN method. Simulation results show that the artificial neural network method is feasible and effective in forecasting SINS error parameters, and the forecast accuracy of SINS error parameters can be effectively improved by combining RBM and BPNN methods and improving the neural network structure. The proposed IRBM-BPNN method has the optimal forecast accuracy of SINS error parameters and navigation accuracy of aircraft compared with the radial basis function neural network method and BPNN method.