Iterative Initial Value Research Articles

Spatial positioning method based on range-only measurement of multi-station radar

Aiming at the characteristics that affect the high ranging accuracy of shipborne radar, a multi station radar spatial positioning method based on ranging is proposed. This method is based on the ECEF (Earth Centered Earth Fixed) coordinate system, and adopts the method of intersecting the detection distances of multiple radars to the target, and directly calculates the spatial position of the target. This method only needs the geodetic coordinates of each radar station and the detection range of the target to calculate the geodetic coordinates of the target, which can effectively solve the coordinate conversion problem caused by the curvature of the earth, and does not involve higher-order equations and multi-value solutions, and it does not involve iterative initial value calculation. The positioning system can not only obtain high positioning accuracy, but also has a long operating distance, which can better solve the multi-radar range measurement intersection positioning problem.

Path Tracking Controller Design of Automated Parking Systems via NMPC with an Instructible Solution

Parking difficulties have become a social issue that people have to solve. Automated parking system is practicable for quick par operations without a driver which can also greatly reduces the probability of parking accidents. The paper proposes a Lyapunov-based nonlinear model predictive controller embedding an instructable solution which is generated by the modified rear-wheel feedback method (RF-LNMPC) in order to improve the overall path tracking accuracy in parking conditions. Firstly, A discrete-time RF-LNMPC considering the position and attitude of the parking vehicle is proposed to increase the success rate of automated parking effectively. Secondly, the RF-LNMPC problem with a multi-objective cost function is solved by the Interior-Point Optimization, of which the iterative initial values are described as the instructable solutions calculated by combining modified rear-wheel feedback to improve the performance of local optimal solution. Thirdly, the details on the computation of the terminal constraint and terminal cost for the linear time-varying case is presented. The closed-loop stability is verified via Lyapunov techniques by considering the terminal constraint and terminal cost theoretically. Finally, the proposed RF-LNMPC is implemented on a self-driving Lincoln MKZ platform and the experiment results have shown improved performance in parallel and vertical parking conditions. The Monte Carlo analysis also demonstrates good stability and repeatability of the proposed method which can be applied in practical use in the near future.

Noise-Adaptive State Estimators with Change-Point Detection.

Aiming at tracking sharply maneuvering targets, this paper develops novel variational adaptive state estimators for joint target state and process noise parameter estimation for a class of linear state-space models with abruptly changing parameters. By combining variational inference with change-point detection in an online Bayesian fashion, two adaptive estimators-a change-point-based adaptive Kalman filter (CPAKF) and a change-point-based adaptive Kalman smoother (CPAKS)-are proposed in a recursive detection and estimation process. In each iteration, the run-length probability of the current maneuver mode is first calculated, and then the joint posterior of the target state and process noise parameter conditioned on the run length is approximated by variational inference. Compared with existing variational noise-adaptive Kalman filters, the proposed methods are robust to initial iterative value settings, improving their capability of tracking sharply maneuvering targets. Meanwhile, the change-point detection divides the non-stationary time sequence into several stationary segments, allowing for an adaptive sliding length in the CPAKS method. The tracking performance of the proposed methods is investigated using both synthetic and real-world datasets of maneuvering targets.

Real-time decomposition technique for compressive light field display using the multiplex correlations

How to compress and decompose the high-dimensional light field information in real time is still a challenging task for compressive light field display. Traditional iterative algorithms suffer from slow convergence speed and limited image quality. Therefore, a real-time decomposition technique for compressive light field display using multiplex correlations is proposed. Firstly, the iteration initial value of the algorithm is optimized, by utilizing the spatial correlations of pixel multiplex light fields, which significantly improves the convergence speed and reduces noise. Secondly, the iterative task of high-dimensional matrix in the non-negative matrix factorization (NMF) algorithm is divided into highly parallel linear iterative tasks. A stochastic gradient descent (SGD) optimizer and GPU are used to parallel compress and decompose the light fields. Thirdly, addresses of light field data are reordered using the sign distance field (SDF) transformation in sheared camera frustum space, making the addressing process of compression and decomposition more efficient. A rendering pipeline is constructed that renders the compressive light fields using 3D model data directly. For a light field containing 5 × 5 viewpoints and 1024 × 1024 × 2 pixels, only 2-3 iterations are needed to approach the optimal solution. The decomposition efficiency is increased by 15.24 times. The frame rate of decomposition exceeds 30 frames per second (fps). A compressive light field display system has been built to realize 3D display, verifying the feasibility of the technique.

Automatic crack tip localization in enormous DIC images to in-situ characterize high-temperature fatigue crack growth behavior

Digital Image Correlation (DIC), with its advantages of full-field, non-contact and applicability to extreme environments, is a powerful deformation measurement technique to in-situ monitor the high-temperature fatigue crack propagation behavior. For the DIC-based fracture mechanics analyses of cracking, crack tip position in DIC images, which is usually determined visually and manually, plays a significant role. However, the long-term fatigue-DIC experiment at both room-and high-temperatures poses a real challenge due to the huge amount of DIC images, lower image quality at high-temperature, and even the invisibility of cracks. In this paper, a fully automated algorithm for localizing the crack tip is proposed based on the crack tip displacement field and the immune algorithm. Compared to traditional algorithms, it does not necessitate iterative initial values of crack tip location, and permits precise and fast determination of critical fracture mechanics parameters, such as the Stress Intensity Factors (SIFs). The algorithm reliability is validated by both numerical simulation and fatigue testing of nickel-based superalloy GH4169 at room temperature and 650℃. It is shown that crack tip position and SIF values can be precisely and automatically determined within 7 s per image using an ordinary low-cost computer, which significantly improves the analyzing efficiency.

Analysis of DC bias characteristics of transformer by using fixed-point time-step FEM and dynamic J–A hysteresis model

The injection of the Direct Current (DC) bias component will lead to oversaturation of the transformer core and affect its safe operation. In this paper, the vector magnetic potential A and winding current I are used as the variables solved in the time-step finite element method to simulate the nonlinear transient magnetization characteristics of the transformer under DC bias, and the fixed-point reluctivity is introduced to deal with the nonlinear iterative problem. The field-circuit coupling relationship is established through the electromagnetic induction law, and the two-dimensional time-step finite element model of the transformer core is found. The dynamic J–A hysteresis model is used to deal with the hysteresis effect of iron cores. The fixed-point reluctivity of difference form is introduced to solve the problem of uneven transition and multi-value of magnetization curve with hysteresis in the iterative process. The determination scheme for the equivalent thickness of the core model and the initial iterative value are discussed. A laminated core transformer is made for experiments, and the accuracy and applicability of the model proposed in this paper are verified by comparing the calculation and experimental results. The influence of different voltages and different DC bias components on the electromagnetic characteristics of the transformer is analyzed.

Ripple force compensation based on ILC–ASMC for linear servo system

The ripple force, which displays intricate dynamic characteristics and is fundamentally nonlinear in linear servo systems, poses challenges in modeling and compensation. To achieve superior suppression of the ripple force, this paper proposes a new scheme called the iterative learning control (ILC)–adaptive sliding-mode control (ASMC), which combines ASMC and ILC. Initially, the servo system model is established, followed by an analysis of the ripple force mechanism and obtaining a simplified model. Then, an adaptive sliding-mode structure with a feed-forward component is proposed to enhance the servo dynamic response based on the design of a sliding-mode surface. In addition, a new ripple force compensator based on the ILC is designed, allowing for iterative learning from the previous period to accurately estimate the ripple force online. It is worth noting that the process is divided into the 0th iteration and the ith ( i ≥ 1) iteration to avoid the selection for initial values. The 0th iteration ensures initial system stability and provides the initial iteration values, while subsequent iterations approximate the real ripple force through iterative learning. Importantly, the controller and the compensator are combined through the same sliding-mode surface, obviously simplifying the system structure. Finally, experimental results validate the effectiveness of the proposed scheme.

Reconstruction refinement of hybrid background-oriented schlieren tomography

This paper introduces a hybrid method that leverages the advantages of both the window-based separate algorithm and the regularization-based unifying algorithm to refine the three-dimensional refractive index field measured by the background-oriented schlieren (BOS) tomography. Two image-warping approaches, under paraxial and non-paraxial ray-tracing assumption, are developed as a connecting link between coarse and refined reconstruction. An orthogonal test is conducted to examine the influence of various factors on the reconstruction fields in the hybrid approach, providing insights into the impact of factors such as the ray-tracing assumption, initial value for refractive index iteration, and image displacement algorithm. Various synthetic and real BOS cases demonstrate that our hybrid method reconstructs a more detailed and robust field, outperforming conventional BOS reconstruction methods in scenarios with small or large density gradients, as well as laminar or turbulent structures.

Optimization Path of Innovation and Entrepreneurship Ability of College Students in the Context of High-Quality Development

Abstract Studying the influencing factors of college students’ innovation and entrepreneurship ability is crucial to optimizing the innovation and entrepreneurship of college students in the context of high-quality development. Based on this, this paper constructs an SEM structural equation model and improves the convergence of the PLS algorithm using the least squares solution and optimal iteration initial value for the PLS algorithm in the SEM model. Then, the correlation analysis is done between six influencing factors, such as national policy, birthplace, and entrepreneurial activities, and college students’ innovation and entrepreneurship ability, and finally, the hypothesis of the influence of innovation and entrepreneurship ability is verified by the SEM model test. The main results are as follows: this paper constructed a model of influence factors on college students’ innovation and entrepreneurship ability, with five levels and 18 factors, and ranked their importance. The SEM model’s fit coefficient of 0.927>0.9 is a sign of a high degree of fit in the structural equation model. The regression coefficients of the five dimensions calculated according to the SEM model are 0.781, 0.939, 0.243, 0.935, and 0.839. All five dimensions have a significant positive impact on innovation and entrepreneurship. This paper finds that there is a positive correlation between college students’ innovation and entrepreneurship ability and national innovation and entrepreneurship policy, place of origin, entrepreneurial activities, education, and internship experience, and constructs the optimization path of college students’ innovation and entrepreneurship ability.

Efficient and Low-Complex Signal Detection with Iterative Feedback in Wireless MIMO-OFDM Systems.

To solve error propagation and exorbitant computational complexity of signal detection in wireless multiple-input multiple-output-orthogonal frequency division multiplexing (MIMO-OFDM) systems, a low-complex and efficient signal detection with iterative feedback is proposed via a constellation point feedback optimization of minimum mean square error-ordered successive interference cancellation (MMSE-OSIC) to approach the optimal detection. The candidate vectors are formed by selecting the candidate constellation points. Additionally, the vector most approaching received signals is chosen by the maximum likelihood (ML) criterion in formed candidate vectors to reduce the error propagation by previous erroneous decision, thus improving the detection performance. Under a large number of matrix inversion operations in the above iterative MMSE process, effective and fast signal detection is hard to be achieved. Then, a symmetric successive relaxation iterative algorithm is proposed to avoid the complex matrix inversion calculation process. The relaxation factor and initial iteration value are reasonably configured with low computational complexity to achieve good detection close to that of the MMSE with fewer iterations. Simultaneously, the error diffusion and complexity accumulation caused by the successive detection of the subsequent OSIC scheme are also improved. In addition, a method via a parallel coarse and fine detection deals with several layers to both reduce iterations and improve performance. Therefore, the proposed scheme significantly promotes the MIMO-OFDM performance and thus plays an irreplaceable role in the future sixth generation (6G) mobile communications and wireless sensor networks, and so on.

Modelling of African Vulture Optimization Algorithm with Deep Learning-based Object Classification for Intelligent Manufacturing Systems

Abstract Intelligent manufacturing system incorporates a number of sensors including IoT devices, cameras, and scanners, for capturing real-time data about the manufacturing process. Based on their physical properties, colours, dimensions, or other relevant characteristics, these sensors can be used to track and identify waste objects. Waste object classification in intelligent manufacturing includes the usage of recent systems and technologies to detect and classify waste materials or objects produced during the manufacturing process. The objective is to enable effective waste management and recycling practices, optimizing resource utilization and reducing environmental impact. Manual waste classification is a laborious and expensive task, which results in the development of automatic waste classification models using artificial intelligence (AI) techniques. It remains a challenging process due to the significant variations in the solid waste because of varying shapes, colours, and sizes. Therefore, recent advances in deep learning (DL) methods can be employed to accomplish the solid waste classification process. The study introduces a chaotic African vulture optimization algorithm with a deep learning-based solid waste classification (CAVOA-DLSWC) system. The CAVOA-DLSWC technique aims to automatically detect waste objects and classify them into different categories using DL models. In the presented CAVOADLSWC approach, two major processes are involved such as object classification and detection. For the object detection method, the CAVOA-DLSWC technique uses a lightweight RetinaNet model with CAVOA based hyperparameter tuning process. The CAVOA is derived by integrating the chaotic concepts into the initial iteration values of the AVOA. Once the waste objects are identified, the classification process can be performed by the use of convolutional long short-term memory (CLSTM) network. The experimental values of the CAVOA-DLSWC approach can be tested employing the solid waste database including diverse kinds of waste objects. The comparative results show the remarkable performance of the CAVOA-DLSWC method over other techniques.

Embedding Kalker's variational theory into railway vehicle system dynamics and its efficiency improvement

The wheel–rail contact plays a vital role in railway transportation, hence an accurate wheel–rail contact model is the key to the simulation of railway vehicle dynamics. Though Kalker's variational theory is the canon, fewer works try to embed it into railway vehicle dynamics, mainly due to the computational efficiency problem and the complex wheel–rail geometry processing. This paper proposes a wheel–rail geometry processing method to embed KVT into railway vehicle multi-body dynamics based on the trajectory coordinate formulation, which conveniently introduces the wheel–rail contact surface pair for tracking the potential contact areas. After this prior detection, coarse detection is performed to narrow the range of penetrated elements. Multiple contact patches can also be identified based on the connection topology of discrete elements of wheel–rail surfaces. Thus, Kalker's variational theory can be used on each separate patch for local contact evaluation. An initial prediction method for the contact status is embedded in the multi-body dynamics simulation to accelerate the solution of Kalker's variational problem, in which the tractions with contact divisions obtained from the neighbouring system states are selected as the iterative initial value. The importance of predicting the contact tractions and demarcation for the convergence of the iteration is revealed. The proposed method is fully demonstrated by the Manchester contact benchmark and simulations of a railway vehicle passing through a curved track. As an advantage of embedding Kalker's variational theory, the precise traction pattern is obtained simultaneously along with the vehicle dynamics simulation, and the influencing mechanism of the track geometry on the vehicle system is analysed. The improvement of solution efficiency is also verified in simulations.

Improved SSOR-based signal detector by using based on gauss-seidel method for large-scale MIMO systems

As the core technology in large-scale MIMO system, the reliability of signal detection has an important impact on the whole system. Traditional linear detectors, such as the zero-forcing (ZF), often involve very complex matrix inverse calculations when the number of antennas in large-scale MIMO systems is too large. In this paper, we first introduce the traditional signal detector based on SSOR method, the iterative operation is used to replace the complex matrix inverse calculation, thus reducing the computational complexity. Then, the Gauss-Seidel method is proposed to calculate the initial iteration value, and the simulation results show that the bit error rate (BER) performance of the improved SSOR method is greatly improved.

Dynamic displacement measurement of hoisting rope in lateral and longitudinal direction by improving Lucas–Kanade algorithm

The hoisting rope has complex dynamic characteristics under external excitation during operation, characterized by large longitudinal movement and minor lateral oscillation. However, there are no appropriate approaches to acquire the dynamic displacement of the rope currently. In this study, a fast vision-based measurement framework, including initial value forecast (IVF) and target tracking based on Lucas–Kanade (LK) algorithm, called IVF-LK, is proposed to obtain the lateral-longitudinal displacement of the rope. Firstly, an IVF method is proposed to obtain the initial value of iteration, overcoming the problem of non-convergence of LK algorithm under large displacement. Secondly, a parameter update strategy makes the convergence speed of LK significantly improved. Then, the performance of IVF-LK algorithm was tested in the experimental bench, which reveal its superior capacity in rope-structure dynamic measurement both in efficiency and precision. Finally, a field experiment was implemented to analyze the dynamic displacement of the hoisting rope.

A 3D reconstruction method of bubble flow field based on multi-view images by bi-direction filtering maximum likelihood expectation maximization algorithm

Aiming at the problem of the three-dimensional (3D) reconstruction of bubble flow field, a Bi-Direction Filtering Maximum Likelihood Expectation Maximization (BDF-MLEM) algorithm based on multi-view bubble images was proposed in this work. The calibration method for the multi-view was studied to unify the world coordinate systems and obtain the mapping relationship between the 3D world and the projection images. Combined with the calibration results, a 3D reconstruction algorithm of BDF-MLEM for bubble flow field was developed from MLEM. In BDF-MLEM, the setting method of iterative initial value was optimized and the projection and back projection methods in the iterative process was improved based on bi-direction filtering. The experimental results have shown that, compared with SART algorithm and MLEM algorithm, both the reconstruction speed and reconstruction accuracy of the proposed BDF-MLEM algorithm were greatly improved. In addition, the reconstruction results of overlapped bubbles have been improved to some extent by using the proposed method.

A method of measuring complex ermittivity of nonmagnetic materials and selecting its initial value based on iterative inversion

Complex permittivity is an important parameter to reflect the macroscopic electromagnetic properties of material. The selection, design, and application of related materials and devices in the electromagnetic field must be based on the electromagnetic parameters of materials in the working frequency band. The numerical iterative method is an important method to inverse and calculate the complex permittivity of material. But there always exists a difficult problem that the initial value of iteration is difficult to give accurately. Based on the relationship between the complex permittivity and the reflectivity of absorbing material, an initial value selecting method for iteration to invert permittivity of absorbing material is proposed in this work. For absorbing materials with arbitrary thickness at a certain frequency, the complex permittivity must be near the complex permittivity of perfect matching layer. Therefore, the permittivity of perfect matching layer can be used as the initial value of the iterative algorithm. On this basis, a method of calculating the complex permittivity of monolayer absorbing material by using the reflection parameter is constructed. The experimental results show that the complex permittivity of lossy material can be calculated more accurately at the frequency with absorbing performance. However a great error can be caused at the frequency where the reflectivity is close to zero. Based on the iterative solution of the complex permittivity of single-layer absorbing material, a new method of measuring the complex permittivity of material is constructed by combining multi-layer absorbing materials to enhance full-band reflectivity. Given the electromagnetic parameters of other layer materials , the permittivity of absorbing materials and low-loss materials can be obtained accurately.

Block Optimization and Outlier Detection in GEO SAR Turbulent Tropospheric Error Compensation

Geosynchronous Synthetic Aperture Radar (GEO SAR) has important potential applications in the fields of atmospheric water cycle and digital weather forecast, and establishing the connection between GEO SAR imaging and atmospheric water vapor inversion is a key step. The turbulent variation of atmospheric phase delay will cause the SAR image defocus, and turbulent tropospheric delay phase estimation based on block autofocus method in low-orbit high resolution space-borne SAR can be used for reference, but this method is very sensitive to the data block selection, modulation frequency (FM) outlier detection and correction. This Letter gives some considerations to these two problems of block selection and outlier correction. Based on the idea of Extreme Learning Machine (ELM), a mathematical model with nonlinearity as the core and linear model as easy to solve is established. When the parameters affecting the turbulent troposphere are known, the model can be directly used to obtain the block optimization selection, namely, the number and size of blocks. When the parameters are unknown, the model can be used to roughly inverse the parameters and provide initial values for iteration. For the second problem, using the prior information of the correlation of the FM estimated by the adjacent sub-blocks, we proposes a Local Outlier Factor (LOF) method to identify the error point by defining the distance threshold. The effectiveness of the proposed algorithm is verified by Sentinel-1 data injected with tropospheric error.

A novel line parameter adaptive phasor domain fault location method for double-circuit transmission lines

• Accurate phasor domain fault location for double-circuit transmission line. • Fault location without acquiring information of line parameters. • Uniqueness and convergence of the fault location equation solution. Phasor domain methods had been commonly used in power transmission line fault location. To improve the accuracy and line parameter adaptive ability of phasor domain fault location, this paper studies a two-terminal phasor domain line parameter adaptive fault location method. First, this paper analyses the convergence issue of original multi-dimensional non-linear fault location equations. Then, by regarding the four complexes consist of fault distance and line parameters as the new unknowns and thereby supplementing a constraint equation about the new unknowns, a quadratic fault location equation set is reconstructed. Furthermore, it is mathematically proven that the four new unknowns are all within the first quadrant. Therefore, the elements in the Jacobian matrix of the reconstructed equation set are all proven to be of symbolic invariance within the solution space. Thus, by using Newton-Raphson method and setting the initial iteration value of the four unknowns to any complexes within the first quadrant, the iteration solution of the reconstructed equation set will converge to the true solution. Finally, electro-magnetic fault simulations are used to evaluate the proposed method. The test results demonstrate that the proposed method is able to guarantee fault location accuracy under different circumstances without knowing line parameters.

3-D Computerized Ionospheric Tomography With GPS, SAR, and Ionosonde

The GPS-based computerized ionospheric tomography (CIT) has the capacity to reconstruct the three-dimensional ionosphere (i.e., electron density distribution), making it one of the most important techniques for ionospheric observation. However, the CIT technology is unable of high vertical resolution due to the restricted viewing angle. Therefore, the precision of CIT cannot be substantially enhanced with GPS only. Aiming at this issue, this paper proposes a bi-iteration algorithm that integrates GPS, Phased Array L-band Synthetic Aperture Radar on board the Advanced Land Observing Satellite (ALOS PALSAR), and ionosonde data. The key is that the joint retrieval of PALSAR and ionosonde may offer high-precision one-dimensional electron density profile along the whole path, which can effectively enhance the authenticity of the iterative initial value. The corrected initial value is then fused into the process of CIT, which can finally improve the precision of vertical resolution after two iterations. Experimental verification demonstrates that due to the ability to obtain more realistic initial values, the reconstruction accuracy of the algorithm proposed in this paper is 51.4 percent and 45.1 percent higher than the accuracy with GPS alone and with GPS and ionosonde data, respectively. This indicates that the combination of these three kinds of data can effectively improve the precision of CIT.

A Novel Predefined Time PD-Type ILC Paradigm for Nonlinear Systems

Intelligent robotics has drawn a great deal of attention due to its high precision, stability, and reliability, which are the basic key factors for industrial automation. This paper proposes an iterative learning control (ILC) technique with predefined-time convergence as a solution to an applied engineering problem, namely, that local time cannot be preset when a second-order nonlinear system undertakes control of the accurate tracking of local time under any initial iterative value. A time-varying sliding surface with an initial value of zero was designed, and it was theoretically proven that the trajectory tracking error in the sliding surface could converge to zero within a predefined time. The iterative control problem of trajectory tracking was thus changed to an iterative control problem of time-varying sliding-mode surface tracing with a starting value of zero. A PD-type closed-loop ILC with a time-varying sliding mode surface was designed such that the trajectory tracking error converged and stabilized on the sliding mode surface after a finite number of learning iterations. The control goal for the system’s output was the ability to track the desired trajectory accurately within a predefined time interval, and it was achieved by combining this with the predefined time convergence characteristics of the time-varying sliding mode surface. Numerical simulation of trajectory tracking control of a repetitive motion manipulator was used to verify the effectiveness of the proposed controller and its robustness in the face of external disturbances.