Iteration Time Research Articles

Design of autonomous vehicle trajectory tracking controller based on Neural Network Predictive Control

When the linear Model Predictive Control (MPC) algorithm is used for trajectory tracking control of autonomous vehicles in high-speed turning conditions, the tracking accuracy is usually decreased due to the low fidelity of the predictive model. To deal with this problem, a trajectory tracking controller based on Neural Network Predictive Control (NNPC) is designed in this paper. The data collected from driving simulation experiments is used for the Back Propagation Neural Network (BPNN) training process to obtain the vehicle state predictive models. To reduce the iteration times of the rolling optimization module of NNPC, Particle Swarm Optimization (PSO) with Fitness Allocating Inertia Weights (PSO-FAIW) algorithm is proposed, which can allocate inertia weights in light of the distance between each particle’s fitness and the best fitness of the swarm. Finally, the performance of the controller designed in this paper is verified based on MATLAB /Simulink. The simulation results demonstrate that the controller proposed in this paper can give consideration to tracking accuracy and real-time performance.

Analysis of Efficient and Fast Prediction Method for the Kinematics Solution of the Steel Bar Grinding Robot

Aiming at the robotization of the grinding process in the steel bar finishing process, the steel bar grinding robot can achieve the goal of fast, efficient, and accurate online grinding operation, a multi-layer forward propagating deep neural network (DNN) method is proposed to efficiently predict the kinematic solution of grinding robot. The process and kinematics model of the grinding robot are introduced. Based on the proposed method, simulations of the end position and orientation, and joint angle of the grinding robot are given. Three different methods, including SGD + tanh, Nadam + tanh, Nadam + ELU, are used to test the DNN calculation process results show that the method combining Nadam with ELU function has the fastest solution speed and higher accuracy can be obtained with the increase in iteration times. Finally, the Nadam optimizer is used to optimize the calculation results of the example. The optimization results show that this method accelerates the convergence rate of trajectory prediction error and improves the accuracy of trajectory prediction. Thus, the proposed method in this paper is an effective method to predict the kinematic solution when the grinding robot works online.

Binary operations on neuromorphic hardware with application to linear algebraic operations and stochastic equations

Non-von Neumann computational hardware, based on neuron-inspired, non-linear elements connected via linear, weighted synapses—so-called neuromorphic systems—is a viable computational substrate. Since neuromorphic systems have been shown to use less power than CPUs for many applications, they are of potential use in autonomous systems such as robots, drones, and satellites, for which power resources are at a premium. The power used by neuromorphic systems is approximately proportional to the number of spiking events produced by neurons on-chip. However, typical information encoding on these chips is in the form of firing rates that unarily encode information. That is, the number of spikes generated by a neuron is meant to be proportional to an encoded value used in a computation or algorithm. Unary encoding is less efficient (produces more spikes) than binary encoding. For this reason, here we present neuromorphic computational mechanisms for implementing binary two’s complement operations. We use the mechanisms to construct a neuromorphic, binary matrix multiplication algorithm that may be used as a primitive for linear differential equation integration, deep networks, and other standard calculations. We also construct a random walk circuit and apply it in Brownian motion simulations. We study how both algorithms scale in circuit size and iteration time.

Equilibrium electron beam parameters of the High Energy Photon Source

PurposeThe physics design of the High Energy Photon Source (HEPS) was finished after many times of iteration. Hereby, the typical equilibrium electron beam parameters corresponding to the proposed two baseline operation modes in the baseline design of HEPS are presented.MethodsTo compute the equilibrium parameters of the electron beam, the lattice parameters, RF parameters, and the parameters of the insertion devices (IDs) were determined first. Furthermore, it is more precise to use the full-current electron beam parameters in the estimations of the performance of the synchrotron light. Therefore, not only the single-particle dynamics but also the current-dependent collective effects need to be considered in the computations of the full-current, equilibrium parameters of the electron beam. Both analytic computations and multi-particle tracking simulations were carried out.ResultsThe full-current, equilibrium parameters of the electron beams in the HEPS storage ring are presented in this paper. Moreover, the main beam parameters in the injector (the booster and the LINAC), corresponding to the two baseline operation modes of the storage ring, are also presented.ConclusionThe typical electron beam parameters corresponding to the two baseline operation modes are given in detail in this paper.

A smoothing Levenberg-Marquardt algorithm for linear weighted complementarity problem

<abstract> <p>In this paper, we consider the solution of linear weighted complementarity problem (denoted by LWCP). Firstly, we introduce a new class of weighted complementary functions and show that its continuously differentiable. On this basis, the LWCP is reconstructed as a smooth system of equations, and then solved by the Levenberg-Marquardt method. The convergence of the algorithm is proved theoretically and numerical experiments are carried out. The comparative experiments show that the algorithm has some advantages in computing time and iteration times.</p> </abstract>

Machine Learning for Relaying Topology: Optimization of IoT Networks With Energy Harvesting

In this paper, we examine Internet of Things (IoT) systems related to smart cities, smart factories, connected cars, etc. To support such systems in a wide area with low power consumption, energy harvesting technology utilizing wireless charging infrastructure is necessary for the longevity of networks. Considering that the position and amount of energy charged for each device could be unbalanced according to the distribution of nodes and energy sources, maximizing the minimum throughput among all nodes has become an NP-hard challenging issue. To overcome this challenge, we propose a machine learning based relaying topology algorithm with a novel backward-pass rate assessment method to present proper learning direction and an iterative balancing time slot allocation algorithm which can utilize a node with sufficient energy as the relay. To validate our proposed scheme, we conducted simulations on our established system model; thus, we confirm that the proposed scheme is stable and superior to conventional schemes.

A novel algorithm for solving sum of several affine fractional functions

<abstract><p>By using the outer space branch-and-reduction scheme, we present a novel algorithm for globally optimizing the sum of several affine fractional functions problem (SAFFP) over a nonempty compact set. For providing the reliable lower bounds in the searching process of iterations, we devise a novel linearizing method to establish the affine relaxation problem (ARP) for the SAFFP. Thus, the main computational work involves solving a series of ARP. For improving the convergence speed of the algorithm, an outer space region reduction technique is proposed by utilizing the objective function characteristics. Through computational complexity analysis, we estimate the algorithmic maximum iteration times. Finally, numerical comparison results are given to reveal the algorithmic computational advantages.</p></abstract>

Modeling 3-D Elastic Wave Propagation in TI Media Using Discontinuous Galerkin Method on Tetrahedral Meshes

Transversely isotropic (TI) medium is a widely studied anisotropic solid medium in seismology. The numerical simulation of seismic wave propagation in TI media is an effective tool to analyze the mechanism of seismic waves in complex anisotropic media. In this study, we introduce a double-weighted Runge-Kutta discontinuous Galerkin (RKDG) method for numerically solving wave propagation problems in 3D TI media with surface topography. This method incorporates the discontinuous Galerkin spatial discretization with an explicit double-weighted two-step iteration time discretization. The local Lax-Friedrichs flux is used as the numerical flux in the formulations. This method can solve the first-order velocity-stress seismic wave equations including TI media as well as more general anisotropic media. Due to the large scale of 3D problems, the parallel technology is adopted. Regions with irregular boundaries are discretized into unstructured tetrahedral meshes. Numerical experiments for various anisotropic media including the vertical and tilted TI media are presented. The results demonstrate the effectiveness of the double-weighted RKDG method in wavefield simulations in 3D complicated anisotropic media.

An intelligence-based hybrid PSO-SA for mobile robot path planning in warehouse

Mobile robots play crucial roles in industry and commerce, and automatic guided vehicles (AGV) are one of the primary parts of smart manufactory and intelligent logistics. Path planning is the core task for the AGV system, and it generates the path from origin to destination. The motivation of the study is to improve the scalability, flexibility, adaptability, and performance of the robot path planning systems. We propose the hybrid PSO-SA algorithm for the optimization of AGV path planning. Compared with other heuristic algorithms by benchmark functions, including HS, FA, ABC and GA, the proposed algorithm shows excellent performance in dealing with optimization problems. It reduces the possibility of getting trapped in one local optimum and enhances the efficiency to get the best global solution with faster convergence and less time consumption. It is evaluated with multiple cost functions and path planning with simulations and experiments. The objective of the proposed algorithm is to minimize the path length and produce a smooth path without collision. The proposed PSO-SA algorithm is compared with PSO in the path planning application, and the mean runtime and iteration times are usually significantly lower than PSO.

A Novel Pressure Relief Hole Recognition Method of Drilling Robot Based on SinGAN and Improved Faster R-CNN

The drilling robot is the key equipment for pressure relief in rockburst mines, and the accurate recognition of a pressure relief hole is the premise for optimizing the layout of pressure relief holes and intelligent drilling. In view of this, a pressure relief hole recognition method for a drilling robot, based on single-image generative adversarial network (SinGAN) and improved faster region convolution neural network (Faster R-CNN), is proposed. Aiming at the problem of insufficient sample generation diversity and poor performance of the traditional SinGAN model, some improvement measures including image size adjustment, multi-stage training, and dynamically changing iteration times are designed as an improved SinGAN for the generation of pressure relief hole images. In addition, to solve the problem that the traditional depth neural network is not ideal for small-size target recognition, an improved Faster R-CNN based on multi-scale image input and multi-layer feature fusion is designed with the improved SqueezeNet as the framework, and the sample data collected from ground experiments are used for comparative analysis. The results indicate that the improved SinGAN model can improve the diversity of generated images on the premise of ensuring the quality of image samples, and can greatly improve the training speed of the model. The accuracy and recall rate of the improved Faster R-CNN model were able to reach 90.09% and 98.32%, respectively, and the average detection time was 0.19 s, which verifies the superiority of the improved Faster R-CNN model. To further verify the practicability of the proposed method, some field images were collected from the underground rockburst relief area in the coal mine, and a corresponding test analysis was carried out. Compared with three YOLO models, the accuracy and recall rate of improved Faster R-CNN model improved significantly, although the training time and recognition time increased to a certain extent, which proves the feasibility and effectiveness of the proposed method.

Research on Coal Dust Wettability Identification Based on GA-BP Model.

Aiming at the problems of the influencing factors of coal mine dust wettability not being clear and the identification process being complicated, this study proposed a coal mine dust wettability identification method based on a back propagation (BP) neural network optimized by a genetic algorithm (GA). Firstly, 13 parameters of the physical and chemical properties of coal dust, which affect the wettability of coal dust, were determined, and on this basis, the initial weight and threshold of the BP neural network were optimized by combining the parallelism and robustness of the genetic algorithm, etc., and an adaptive GA−BP model, which could reasonably identify the wettability of coal dust was constructed. The extreme learning machine (ELM) algorithm is a single hidden layer neural network, and the training speed is faster than traditional neural networks. The particle swarm optimization (PSO) algorithm optimizes the weight and threshold of the ELM, so PSO−ELM could also realize the identification of coal dust wettability. The results showed that by comparing the four different models, the accuracy of coal dust wettability identification was ranked as GA−BP > PSO−ELM > ELM > BP. When the maximum iteration times and population size of the PSO algorithm and the GA algorithm were the same, the running time of the different models was also different, and the time consumption was ranked as ELM < BP < PSO−ELM < GA−BP. The GA−BP model had the highest discrimination accuracy for coal mine dust wettability with an accuracy of 96.6%. This study enriched the theory and method of coal mine dust wettability identification and has important significance for the efficient prevention and control of coal mine dust as well as occupational safety and health development.

Fast Supraharmonic Estimation Algorithm Based on Simplified Compressed Sensing Model

The computational time of compressed sensing algorithms applied to supraharmonic needs to be improved in online applications. In this paper, a simplified supraharmonic compressive sensing model is proposed. The model first detects the supraharmonic raw spectral array to obtain the estimated sparsity and the index of supraharmonic emissions, which simplifies the sensing matrix in the iteration according to the index and then shortens the whole iteration time of compressed sensing. The simulation verifies that the model can reduce the computation time to less than half of the original compressed sensing model and does not affect the computation accuracy. Finally, the online application effect of the algorithm is verified by experiments.

An Accelerated Time-Domain Iterative Physical Optics Method for Analyzing Electrically Large and Complex Targets

A local area coupling-based time-domain iterative physical optics (TD-LIPO) method for the analysis of the scattering from electrically large and complex targets is proposed in this study. A modulated Gaussian-pulse plane wave is taken as the incident wave. Initially, via a ray-tracing mechanism, the current radiation iteration is limited to the local area, and the iteration times are determined by the bounce times. This approach can lower the enormous computation time. In addition, GPU parallel technology is also applied to accelerate the method. Finally, the TD-LIPO method is used to obtain the scattering echo data matrix of the target under different radar observation angles. An inverse fast Fourier transform (IFFT) is performed on the matrix to obtain the ISAR image under a small bandwidth and small angle. Numerical examples and ISAR images show that the accelerated local area coupling technique can significantly improve computational efficiency and verify feasibility in radar imaging.

Short-Term Load Forecasting Based on Wavelet Transform and Chaotic Bat Optimization Algorithm-Long Short-Term Memory Neural Network

To achieve an accurate forecast of short-term load, a new method via wavelet transform (WT) and chaotic bat optimization algorithm (CBA) and long short-term memory neural network (LSTM) is proposed. Firstly, the actual load data is decomposed by WT, and multiple groups of modal function components with different characteristics are obtained. Then the hidden neurons, initial learning rate, and iteration times in the LSTM regression model are optimized based on the CBA. Then the modal function components of each group are predicted respectively. Finally, the predicted modal component functions are reconstructed to achieve power load prediction. The results show that the WT-CBA-LSTM achieves accurate load prediction, with an average absolute error of 29.68 MW, a root mean square error of 52.14 MW, and an average absolute percentage error of 0.59%. However, the evaluation indicators of the traditional LSTM method, the WT-LSTM method and the CBA-LSTM method are greater than those of the WT-CBA-LSTM method, so the WT-CBA-LSTM has high accuracy in the process of load prediction.

Realistic 3D Face Reconstruction Method for Single Image

To solve the problem that the 3DMM parameter fitting methods cannot generate realistic 3D face, a single-image realistic 3D face reconstruction method based on deep learning is proposed. Firstly, the RP-Net regression network is constructed, and a dataset containing 50 000 face images is constructed at the same time. The parameters are learned from the input images, and the face model is fitted to generate the 3D face geometry. Secondly, weakly supervised learning is performed by constructing a multi-level loss function, which includes low-level pixel loss, landmark loss, and high-level identity loss. Thirdly, a realistic face texture is generated by means of texture mapping. Finally, two real face data and one generated data are used to compare experiments with recent 3D face reconstruction methods. The factors affecting the reconstruction such as lighting, expression, and steering are used to test proposed method, and quantitatively evaluate the reconstruction by SSIM and PSNR. These results show that proposed method can generate accurate face shapes and realistic face textures. Compared with the recent 3D face reconstruction method, the training time and number of iterations of the proposed method are reduced by 6% and 13%, respectively, the SSIM value is increased by 0.005‒0.010, and the PSNR value is increased by 0.03‒0.08 dB on average.

A Non-iterative Optimal Power Flow Calculation Method in Power System

The optimal power flow is very important in the study of power system, and it is of great significance for the economic operation of the power system to solve the active power output of the generator through the optimal power flow. In order to solve the problem that the traditional method is sensitive to the initial state and the large iteration times, this paper applies the holomorphic embedding method to solve the optimal power flow. By embedding holomorphic variables, the holomorphic embedding forms of constraints and KKT conditions are obtained. The power series coefficients of node voltage and generator active power output are calculated by non-iterative solution. Finally, the active power output of the generator is obtained by the method proposed in this paper, and then the lowest power generation cost of the system is calculated. Simulation results on IEEE study Case 3, Case 4, Case 30 and Case 39 verify the feasibility and accuracy of the proposed method.

Big Data-aided Study of the Physical Process of Volume Ignition

Volume ignition is a method of igniting a fuel as a whole by simultaneously achieving ignition conditions throughout the fuel zone. The basic criterion for ignition is that the thermonuclear energy is greater than the energy leakage at the fuel boundary, resulting in self-sustaining heating and deep combustion. Deuterium-tritium fuels are wrapped in medium to high Z media to reduce radiative leakage and achieve lower-temperature holistic ignition and non-equilibrium combustion, ultimately allowing the fuel to achieve high combustion efficiency. Volume ignition is the use of energy balance relations under the local thermodynamic equilibrium approximation to establish the energy balance equation for thermonuclear systems, and the system ignition threshold is obtained by solving this equation. By understanding the physical process, we believe that the non-equilibrium process is universal to the volume ignition process. Changes in external factors (density, boundaries, albedo, etc.) at the moment of ignition can have a significant impact on the development direction of the system, and an ignition system with a large surface density can nevertheless withstand a large amount of reverse work and continue to burn. The design of the ignition target tries to avoid these factors through margin design, but conversely, the rational use of these laws can further improve the design margin of the capsule. With the aid of big data, the volume ignition method is easier to calculate and has a shorter iteration time. The traditional way is to propose a model, set the material, and then perform the calculation while using big data can set any model and material for calculation. In this paper, a simple comparison will be made to find out that the efficiency of the physical design of a volume ignition target will be effectively improved with the aid of big data. Volume ignition targets can be used not only in Z-pinch-driven systems but also for laser-driven volume ignition.

An efficient surface profile measurement with the scanning path based on Hilbert and Gosper fractal curves

This paper proposes a non-contact efficient measurement method of surface morphology with the scanning path based on the Hilbert and Gosper fractal curves. By comparing the proposed two types of fractals scanning curves with the traditional reciprocating S-curves, it can be found that the plane distribution of measuring points of the fractal curves is much more uniform, and then the measurement performance is much improved. The two fractal curves with different iterative times are introduced as a measurement path of the surface morphology, and the local three-dimensional roughness and overall shape tolerances of the parts are determined. In the view of the measurement point uniformity, different total number of fractals of iterations are compared as the measurement path of the surface morphology. In order to verify the accuracy of the proposed method, a specialized measuring device is developed, and measurement verification is made with the optical profilometer.

Dynamic parameter estimation method of impact between bourrelet and barrel based on experimental value correction

The impact between bourrelet and barrel often involves material, geometric, and boundary condition nonlinearities. In the process of dynamic parameter estimation, the parameter iteration is highly sensitive to the initial value. Different initial values will lead to great differences in iteration time and estimation results. Therefore, an experimental method to fast acquire the initial value is established and applied to the dynamic parameter estimation of impact between bourrelet and barrel. According to the contact force model between bourrelet and barrel, the estimation method of dynamic parameters is established. The relationship between contact time and impact velocity in the contact force model is analyzed, and an impact test system is established to accurately measure the contact time and velocity. The dynamic parameters obtained from the inverse solution are used as the initial values in the estimation calculation process. The dynamic parameters are obtained by the linearization estimation and the theoretical initial value are compared respectively. The convergence time of dynamic parameters is shortened by about 1/3, and the relative error of the return value of contact force is about 5%, which improves the solution accuracy of dynamic parameters of impact for complex contacting surfaces.

Curvature-weighted nudged elastic band method using the Riemann curvature.

The nudged elastic band (NEB) method is utilized to find reaction paths (RPs) using discretized intermediate structures called "images" between a reactant and a product. In fact, NEB calculations do not always converge because of the bent of RPs. Mathematically, more images are needed for complex curves, and here, we focused on the curvature. In this study, we propose a new method for calculating the curvature of the RPs as well as a method for weighting the spring constant of the NEB with the curvature, which we named the curvature weighted NEB (CW-NEB) method. In addition, we will propose the CW-NEB method with the climbing image (CI) method (CW-CI-NEB). To show the efficiency of our method, calculations for the ene-reaction of the CW-CI-NEB method were performed and compared with those of the CI-NEB methods. The CW-CI-NEB methods converged in shorter iteration times than the CI-NEB calculation, which was found by gathering the bent of RPs.