An In-Orbit Stereo Navigation Camera Self-Calibration Method for Planetary Rovers with Multiple Constraints

In order to complete the high-precision calibration of the planetary rover navigation camera using limited initial data in-orbit, we proposed a joint adjustment model with additional multiple constraints. Specifically, a base model was first established based on the bundle adjustment model, second-order radial and tangential distortion parameters. Then, combining the constraints of collinearity, coplanarity, known distance and relative pose invariance, a joint adjustment model was constructed to realize the in orbit self-calibration of the navigation camera. Given the problem of directionality in line extraction of the solar panel due to large differences in the gradient amplitude, an adaptive brightness-weighted line extraction method was proposed. Lastly, the Levenberg-Marquardt algorithm for nonlinear least squares was used to obtain the optimal results. To verify the proposed method, field experiments and in-orbit experiments were carried out. The results suggested that the proposed method was more accurate than the self-calibration bundle adjustment method, CAHVOR method (a camera model used in machine vision for three-dimensional measurements), and vanishing points method. The average error for the flag of China and the optical solar reflector was only 1 mm and 0.7 mm, respectively. In addition, the proposed method has been implemented in China’s deep space exploration missions.

3D Reconstruction with Coronary Artery Based on Curve Descriptor and Projection Geometry-Constrained Vasculature Matching

This paper presents a novel method based on a curve descriptor and projection geometry constrained for vessel matching. First, an LM (Leveberg–Marquardt) algorithm is proposed to optimize the matrix of geometric transformation. Combining with parameter adjusting and the trust region method, the error between 3D reconstructed vessel projection and the actual vessel can be minimized. Then, CBOCD (curvature and brightness order curve descriptor) is proposed to indicate the degree of the self-occlusion of blood vessels during angiography. Next, the error matrix constructed from the error of epipolar matching is used in point pairs matching of the vascular through dynamic programming. Finally, the recorded radius of vessels helps to construct ellipse cross-sections and samples on it to get a point set around the centerline and the point set is converted to mesh for reconstructing the surface of vessels. The validity and applicability of the proposed methods have been verified through experiments that result in the significant improvement of 3D reconstruction accuracy in terms of average back-projection errors. Simultaneously, due to precise point-pair matching, the smoothness of the reconstructed 3D coronary artery is guaranteed.

A novel ensemble method based on SBLMD-ANN-MOPSO approach for predicting the milling stability regimes

In recent decades, lots of work has been done to mitigate self excited vibration effects in milling operations. Still, a robust methodology is yet to be developed that can suggest stability bounds pertaining to higher metal removal rate (MRR). In the present work, experimentally acquired acoustic signals in milling operation have been computed using a modified Local Mean Decomposition (SBLMD) technique in order to cite tool chatter features. Further, three artificial neural network (ANN) training algorithms viz. Resilient Propagation (RP), Conjugate Gradient-Based (CGP) and Levenberg-Marquardt Algorithm (LM) and two activation functions viz. Hyperbolic Tangent Sigmoid (TANSIG) and Log Sigmoid (LOGSIG) has been used to train the acquired chatter vibration and metal removal rate data set. Over-fitting or under-fitting issues may arise from the random selection of a number of hidden neurons. The solution to these problems is also proposed in this paper. Among these training algorithms and activation functions, a suitable one has been selected and further invoked to develop prediction models of chatter severity and metal removal rate. Finally, Multi-Objective Particle Swarm Optimization (MOPSO) has been invoked to optimize developed prediction models for obtaining the most favourable range of input parameters pertaining to stable milling with higher productivity.

Processing and Interpretation of UAV Magnetic Data: A Workflow Based on Improved Variational Mode Decomposition and Levenberg-Marquardt Algorithm

Unmanned aerial vehicles (UAVs) have become a research hotspot in the field of magnetic exploration because of their unique advantages, e.g., low cost, high safety, and easy to operate. However, the lack of effective data processing and interpretation method limits their further deployment. In view of this situation, a complete workflow of UAV magnetic data processing and interpretation is proposed in this paper, which can be divided into two steps: (1) the improved variational mode decomposition (VMD) is applied to the original data to improve its signal-to-noise ratio as much as possible, and the decomposition modes number K is determined adaptively according to the mode characteristics; (2) the parameters of target position and magnetic moment are obtained by Euler deconvolution first, and then used as the prior information of the Levenberg–Marquardt (LM) algorithm to further improve its accuracy. Experiments are carried out to verify the effectiveness of the proposed method. Results show that the proposed method can significantly improve the quality of the original data; by combining the Euler deconvolution and LM algorithm, the horizontal positioning error can be reduced from 15.31 cm to 4.05 cm, and the depth estimation error can be reduced from 16.2 cm to 5.4 cm. Moreover, the proposed method can be used not only for the detection and location of near-surface targets, but also for the follow-up work, such as the clearance of targets (e.g., the unexploded ordnance).

Multi-Gene Genetic Programming-Based Identification of a Dynamic Prediction Model of an Overhead Traveling Crane

This paper proposes a multi-gene genetic programming (MGGP) approach to identifying the dynamic prediction model for an overhead crane. The proposed method does not rely on expert knowledge of the system and therefore does not require a compromise between accuracy and complex, time-consuming modeling of nonlinear dynamics. MGGP is a multi-objective optimization problem, and both the mean square error (MSE) over the entire prediction horizon as well as the function complexity are minimized. In order to minimize the MSE an initial estimate of the gene weights is obtained by using the least squares approach, after which the Levenberg–Marquardt algorithm is used to find the local optimum for a k-step ahead predictor. The method was tested on both a simulation model obtained from the Euler–Lagrange equation with friction and the experimental stand. The simulation and the experimental stand were trained with varying control inputs, rope lengths and payload masses. The resulting predictor model was then validated on a testing set, and the results show the effectiveness of the proposed method.

Short Term load forecasting for a Captive Power Plant Using Artificial Neural Network

The irregularity of Indian grid system increases, with increase in the power demand. The quality of power supplied by the power grid is also poor due to continuous variation in frequency and voltage. To overcome this problem of power deficit, Captive Power Plants installed capacity has grown at a faster rate. Here short term load forecasting of Yara Fertilizers India Private limited installed at Babrala, Uttar Pradesh is performed using multi-layer feed-forward Neural network in MATLAB. The algorithm used is a Levenberg Marquardt algorithm. However, the training and results from ANN are very fast and accurate. Inputs given to the Neural Network are time, ambient air temperature from the compressor, cool air temperature at the compressor and IGV opening. The need, benefits and growth of CPP in India and use of ANN for short term load forecasting of CPP has been explained in detail in the paper.

A phenomenological theory-based viscosity model for shear thickening fluids

A viscosity model for shear thickening fluids (STFs) based on phenomenological theory is proposed. The model considers three characteristic regions of the typical material properties of STFs: a shear thinning region at low shear rates, followed by a sharp increase in viscosity above the critical shear rate, and subsequently a significant failure region at high shear rates. The typical S-shaped characteristic of the STF viscosity curve is represented using the logistic function, and suitable constraints are applied to satisfy the continuity of the viscosity model. Then, the Levenberg–Marquardt algorithm is introduced to fit the constitutive model parameters based on experimental data. Verification against experimental data shows that the model can predict the viscosity behavior of STF systems composed of different materials with different mass concentrations and temperatures. The proposed viscosity model provides a calculation basis for the engineering applications of STFs (e.g., in increasing impact resistance and reducing vibration).