Simulation Experimental Results Research Articles

Research on laser communication ranging Integration and VLC nonlinear compensation technology

Abstract Visible light communication (VLC) is a new spectrum communication technology that provides a new direction to solve spectrum resource problems. The nonlinearity problem in VLC will lead to a significant reduction in the performance of the communication system. To solve this problem, this paper studies the integration of laser communication ranging and VLC nonlinear compensation technology based on analyzing the principles of ranging methods and factors affecting ranging performance. Aiming at the problem that the complexity of the Random Fourier Features Kernel adaptive filter (RFF-KAF) method will increase significantly as the dimension of the mapping space increases, we introduce some second-order Volterra series square terms into the input vector of RFF-KAF and propose an improved prediction method. Experimental simulation results show that the method proposed in this article can greatly improve the performance of the RFF-KAF method while maintaining the same mapping space dimension. Compared with obtaining similar performance by increasing the mapping space dimension, the cost of this method is greatly reduced.

Behavioral propagation based on regret-hesitation psychology on multiple networks

There are many factors affecting the social propagation of adoption behavior, and the traditional propagation model is not enough to study the influence of human psychological factors. We introduce the psychological mechanism of regret and hesitation to explain the complexity of individual decision making and information propagation. In this paper, a method based on edge division theory is used to analyze the propagation process on multiple networks, and the experimental results show the phenomenon of mixed phase transition. When the system is dominated by regret psychology, the system can achieve global eruption, but when it is dominated by hesitation psychology, the system cannot achieve global eruption. The results of theoretical analysis and experimental simulation agree well. It is of great significance to the study of sociology, psychology and information propagation.

Microstructure and finite element analysis of Mo2C-diamond/Cu composites by spark plasma sintering

Abstract Mo2C layer was generated on the diamond surface via vacuum micro-evaporating, which was used as the reinforcement particles to fabricate diamond/Cu composites by spark plasma sintering (SPS). The effect of evaporation parameters on the forming of Mo2C, and the holding time on diamond/Cu composites fabrication is studied. Combined with the experiment and finite element analysis (FEA), the holding time on diamond/Cu composites influence on the thermal conductivity (TC) of composites is further discussed. The results show that the Mo2C area on the diamond surface would gradually enlarge and cover the diamond surface evenly with the increment in evaporation time and temperature, better vacuum micro-evaporating parameters were given as 1,000°C for 60 min. The fractures in the diamond/Cu composites are mainly ductile fractures on copper and diamond falling out from the Mo2C interface. It was found that sintering time would significantly influence the dissipation property of diamond/Cu composites. A comprehensive parameter for SPS was obtained at 900°C, 80 MPa for 10 min, the relative density (RD) and TC of the composites obtained under the parameter were 96.13% and 511 W/(m K). A longer sintering time would damage the Mo2C interlayer and further decrease the bonding between copper matrix and diamond particles, which would lower the RD and TC of composites. It can be obtained from the comparison of simulation results and experimental results that the FEA result is closer to the experimental results due to the gaps with low heat conduction, and the air in the gaps is added in the simulation process.

Multi-strategy Improved Pelican Optimization Algorithm for Mobile Robot Path Planning

In response to the problems of easily falling into local optima, low path planning accuracy, and slow convergence speed when applying the traditional pelican optimization algorithm to the mobile robot path planning problem, a multi-strategy improved pelican optimization algorithm (MPOA) is proposed. In the initialization stage, chaotic mapping is used to increase the diversity of the pelican population individuals. In the exploration stage, an adaptive feedback adjustment factor is proposed to adjust the local optima of pelican individuals’ positions and balance the algorithm’s local development capability. In the development stage, the Lévy flight strategy is introduced to adjust the domain radius of the pelican population individuals, and the Gaussian mutation mechanism is used to disturb individuals that have fallen into local optima. Simulation experimental results show that the improved algorithm has significantly improved and effectively shortened the length of the planned path.

Study on Multi-Scale Cloud Growth Characteristics of Frustoconical Dispersal Devices

This study aims to understand cloud growth behavior and enhance cloud safety and reliability by investigating the design of cloud dispersal devices. Based on the experimental results and simulation results, this study analyzes the dispersion characteristics of cloud materials within a frustoconical device with a semi-cone angle ranging from 0° to 10° across multiple scales. The collision aggregation model for cloud particles and the multi-scale coupling mechanism for cloud growth are established. The research shows that the semi-cone angle of the device extends the effective cloud growth duration and enlarges the cloud macroscopic size. At the mesoscopic scale, vortex phenomena are observed, causing particles to converge within the cloud, resulting in collisions and aggregation. The vortices enhance the continuity of the cloud concentration. The magnitude of these vortices demonstrates a positive correlation with the magnitude of the semi-cone angle of the dispersal device. For a macroscopically stable cloud, the high-concentration area within the cloud moves outward radially with an increase in the semi-cone angle. This study provides a theoretical foundation for cloud morphology control technology, contributing to enhancing the safety and reliability of cloud systems.

DEM simulation of microscopic structure and macroscopic mechanical behavior of clay in oedometer and triaxial compression tests

An updated DEM simulation scheme for clay is implemented by incorporating convex polygon shaped platelets and robust algorithm for physico-chemical–mechanical interaction between clay platelets. Clays with two typical microscopic structures, i.e., card-house structure and book-house structure, are simulated in oedometer test and triaxial compression test. Virtual Mercury Intrusion Porosimetry (MIP) and pore segmentation algorithms are utilized to extract pore statistics from the numerical samples. The simulation results and experimental results from the literature are thoroughly compared at both macroscopic and microscopic scales. It is found that the implemented DEM simulation scheme can satisfactorily reproduce the experimentally observed macroscopic behavior of clay. Especially, in triaxial compression tests, the critical state behavior, stress-dilatancy relationship and over-consolidation effects are generally consistent with existing experimental results. General consistency in pore size, orientation and shape distributions in DEM simulations and experiments can be observed. Clay platelets tend to rotate to align their normal directions along the major principal stress direction, while pores tend to align their long-axis directions in the confinement direction, leading to dynamically evolving fabric anisotropy. In the book-house structure clay, clay domains experience multiple deformation modes, including uniaxial compaction, shear distortion, spatial spin, and their combinations.

Optimizing Resource Consumption and Reducing Power Usage in Data Centers, A Novel Mathematical VM Replacement Model and Efficient Algorithm

This study addresses the issue of power consumption in virtualized cloud data centers by proposing a virtual machine (VM) replacement model and a corresponding algorithm. The model incorporates multi-objective functions, aiming to optimize VM selection based on weights and minimize resource utilization disparities across hosts. Constraints are incorporated to ensure that CPU utilization remains close to the average CPU usage while mitigating overutilization in memory and network bandwidth usage. The proposed algorithm offers a fast and efficient solution with minimal VM replacements. The experimental simulation results demonstrate significant reductions in power consumption compared with a benchmark model. The proposed model and algorithm have been implemented and operated within a real-world cloud infrastructure, emphasizing their practicality.

A naïve Bayes regularized logistic regression estimator for low-dimensional classification

To reduce the estimator's variance and prevent overfitting, regularization techniques have attracted great interest from the statistics and machine learning communities. Most existing regularized methods rely on the sparsity assumption that a model with fewer parameters predicts better than one with many parameters. This assumption works particularly well in high-dimensional problems. However, the sparsity assumption may not be necessary when the number of predictors is relatively small compared to the number of training instances. This paper argues that shrinking the coefficients towards a low-variance data-driven estimate could be a better regularization strategy for such situations. For low-dimensional classification problems, we propose a naïve Bayes regularized logistic regression (NBRLR) that shrinks the logistic regression coefficients toward the naïve Bayes estimate to provide a reduction in variance. Our approach is primarily motivated by the fact that naïve Bayes is functionally equivalent to logistic regression if naïve Bayes' conditional independence assumption holds. Under standard conditions, we prove the consistency of the NBRLR estimator. Extensive simulation and empirical experimental results show that NBRLR is a competitive alternative to various state-of-the-art classifiers, especially on low-dimensional datasets.

Globally Guided Deep V-Network-Based Motion Planning Algorithm for Fixed-Wing Unmanned Aerial Vehicles.

Fixed-wing UAVs have shown great potential in both military and civilian applications. However, achieving safe and collision-free flight in complex obstacle environments is still a challenging problem. This paper proposed a hierarchical two-layer fixed-wing UAV motion planning algorithm based on a global planner and a local reinforcement learning (RL) planner in the presence of static obstacles and other UAVs. Considering the kinematic constraints, a global planner is designed to provide reference guidance for ego-UAV with respect to static obstacles. On this basis, a local RL planner is designed to accomplish kino-dynamic feasible and collision-free motion planning that incorporates dynamic obstacles within the sensing range. Finally, in the simulation training phase, a multi-stage, multi-scenario training strategy is adopted, and the simulation experimental results show that the performance of the proposed algorithm is significantly better than that of the baseline method.

A Steering-Vector-Based Matrix Information Geometry Method for Space–Time Adaptive Detection in Heterogeneous Environments

Plagued by heterogeneous clutter, it is a serious challenge for airborne radars to detect low-altitude, weak targets. To overcome this problem, a novel matrix information geometry detector for airborne multi-channel radar is proposed in this paper. The proposed detector applies the given steering vector and array structure information to the matrix information geometry detection method so that it can be used for space–time adaptive detection. While improving the detection performance, the matrix information geometry detector’s original anti-clutter advantage is enhanced as well. The simulation experiment results indicate that the proposed detector has advantages in several of the properties related to space–time adaptive detection, while its computational complexity does not increase significantly. Moreover, experiment results based on the measured data verify the superior performance of the proposed method. Sea-detecting data-sharing-program data, mountaintop data, and phased-array radar data are employed to verify the performance advantage of the proposed method in heterogeneous clutter and the ability for weak target detection.

Stability analysis of ultra-high and steep reinforced soil fills slopes based on strength reduction

The research focuses on solving the important problem of slope stability in the field of civil engineering. The study adopted an advanced strength double reduction coefficient method for slope stability analysis, which considers the different influence weights of cohesion and internal friction angle, and reduces them with different reduction coefficients to describe the stability of the slope. The simulation experiment results indicate that the attenuation degree of cohesion and internal friction angle affects slope stability. When the reduction coefficient of cohesion increases to 1.737 and the reduction coefficient of internal friction angle increases to 1.201, the slope is prone to instability and failure, and the safety factor of the calculated result is 1.493. Moreover, when the anti-slip pile is set in the middle of the slope (1/2), and the slope is in a critical state, the bending moment and shear force suffered by the anti-slip pile are both maximum, so the reinforcement effect is also the best.

An n-dimensional polynomial modulo chaotic map with controllable range of Lyapunov exponents and its application in color image encryption

In chaos-based image encryption, better performance of the chaotic system represents higher security of the encryption algorithm, and the Lyapunov exponent (LE) is an important measure to evaluate the chaotic performance. In this paper, an n-dimensional(n-D) polynomial modulo chaotic map (nD-PMCM) is proposed as a general model, whose range of Lyapunov exponent is controlled by the system parameters and the robust chaotic behavior with the desired complexity can be obtained. And an instantiated 2D-PMCM is constructed by setting the corresponding parameters in nD-PMCM, which exhibits superior chaotic performance compared to other chaotic maps. Then a novel color image encryption scheme based on the 2D-PMCM is also constructed, which mainly uses cross-plane cyclic shift permutation and cross-plane XOR diffusion. Different from the traditional encryption scheme with separate processing of color planes, the cross-plane operation disrupts the correlation between color image planes and has higher encryption efficiency due to the ability to disrupt the pixel position while also changing the pixel value. The experimental simulation results show that this encryption scheme has better security and encryption effects.

On the infiltration of cellular solids by sheet molding compound: process simulation and experimental validation

This study examines a numerical method to simulate the production of novel multi-material metal-composite components, where an additive-manufactured cellular solid is infiltrated by a sheet molding compound (SMC) in a single-step compression molding operation. A single-fiber numerical approach is adopted to predict microstructural changes, such as fiber orientation, fiber-matrix separation, and fiber volume content variations during molding. The accuracy of the numerical predictions is confirmed by physical samples using micro-computed tomography and optical microscopy investigations at both the qualitative and quantitative scales. From optical microscopy observations, there emerged a positive correlation between experimental outcomes and simulation results, accurately capturing fiber swirling, wrinkling, and draping that occurred during molding. At a quantitative scale, a 0.6% mismatch was observed when void volume and unfilled areas were compared, as measured by micro-computed tomography and numerical simulation.

An enhanced fireworks algorithm and its application in fault detection of the displacement sensor

Regarding the fault detection problem of the displacement sensor, an enhanced fireworks algorithm with information crossover and conversion factor (EFWA-IC) is proposed in this paper. In EFWA-IC, an information crossover strategy is proposed to maintain population diversity. This strategy is based on the predatory behavior of carnivores in nature. In addition, a conversion factor is set to control whether the explosion operator is executed or not to provide a more reasonable search. To fully evaluate the performance of EFWA-IC, a range of tests are carried out based on the CEC2017 and 23 classical test functions. The results show that the performance of EFWA-IC is better than other state-of-the-art optimization algorithms in terms of solution accuracy, convergence speed, and stability. Finally, EFWA-IC is utilized to optimize the particle filter (PF) to establish a fault detection model of displacement sensor in the continuous casting mold. The simulation experiment result of field data manifests that EFWA–IC–PF can accurately detect faults in the displacement sensor. For bias faults, the RMSE of the EFWA–IC–PF model is 0.14468, and the false alarm rate (FAR) and missed detection rate (MDR) are 1 % and 0.5 %, respectively. For stuck faults, the RMSE is 1.00148, and the FAR and MDR are 0.88 % and 1 %, respectively.

Impact of message fatigue in information-disease coupled dynamics on temporal simplicial networks

During information diffusion, individuals typically experience fatigue and mental exhaustion due to repeated exposure to similar information, which is a state called message fatigue. To address message fatigue in information diffusion and group interactions, we developed a novel coupled information-disease spreading model within the framework of multiplex temporal networks. Here, the information network simulates the process of disease-related information diffusion using a temporal simplicial network. Furthermore, we utilize a threshold model to characterize the effects of message fatigue. We describe the dynamic evolutionary equations of the model by the microscopic Markov chain (MMC) approach to determine the epidemic threshold. We also validated the proposed model using a number of Monte Carlo (MC) simulations. The experimental results of the MC simulations and MMC theoretical results are in good agreement. Our research has shown that message fatigue can lead to a two-stage change in the information-disease coupled spreading. Raising the threshold for message fatigue and preventing a large number of individuals from experiencing it can help inhibit the disease spreading. In short, excessive publicizing of related-disease information can backfire. The results can facilitate us to accurately capture the spreading characteristics of real-world diseases.

Dynamic characteristics optimization of elastomer for resistance strain force sensor

PurposeThis paper aims to provide a new method to study and improve the dynamic characteristics of the four-column resistance strain force sensor through the elastomer structure design and optimization.Design/methodology/approachBased on the mechanism analysis method, the authors first present a dynamic characteristic model of the four-column resistance strain force sensors’ elastomer. Then, the authors verified and modified the model according to the Solidworks finite element simulation results. Finally, the authors designed and optimized two types of four-column elastomers based on the dynamic characteristic model and verified the improvement of sensor dynamic performance through a hammer knock dynamic experiment.FindingsThe Solidworks finite element simulation and hammer knock dynamic experiment results show that the relative error of the model is less than 10%, which confirms the accuracy of the model. The dynamic performance of the sensors based on the model can be improved by more than 30%, which is a great improvement in sensor dynamic performance.Originality/valueThe authors first present a dynamic characteristic model of the four-column elastomer and optimize the four-column sensors successfully based on the mechanism analysis method. And a new method to study and improve the dynamic characteristics of the resistance is provided.

Channel multiplexing of FSO systems based on singular optics approaches

The article proposes two new methods for multiplexing-demultiplexing communication channels in the FSO system, based on singular optics approaches. The first makes use of computer generated vortex holograms installed in the transmitting and receiving modules of the systems, which make it possible to generate and separate communication channels formed by vortex beams with different topological charges. The second method, spatial singular multiplexing, is based on the property of singular beams to restore angular momentum when vortex beams propagate in a medium with physical disturbances. The method uses opaque screens installed in the transmitting block of the system, which diaphragm the vortex beams. As a result of this operation, the centers of the vortices in the receiving part of the system are displaced, which makes it possible to separate the beams that are differently blocked in the transmitting unit. Computer simulation data and experimental results are presented.

Effect of magnetic needle magnetic particle grinding process on the performance of metal aluminum plates

Magnetic needle grinding processing technology is one of the magnetic grinding processing techniques. It possesses the characteristics of micro-cutting removal, small increase in processing temperature, flexible processing, high-quality, and high-precision processing. It is mainly utilized to remove burrs at the edge of the workpiece and the edge of the hole, as well as to finish the surface of the workpiece. It is frequently employed in civil, aerospace, navigation, and other fields. Due to the randomness and complexity of magnetic needle movement in magnetic abrasive finishing, it is difficult to quantify the processing parameters and predict processing effects. Therefore, this paper establishes a simulation model of magnetic needle in magnetic abrasive finishing by the coupling numerical simulation method of fluid dynamics discrete element method (CFD-DEM) to analyze the working state parameters of the magnetic needle. Through the simulation of actual working conditions, the machining process and parameters of magnetic abrasive finishing are quantified and analyzed, and the motion trend of magnetic needles during the machining process is studied. Then, the residual stress of single magnetic needle impact is analyzed with ABAQUS, and the performance enhancement of the workpiece is predicted. Finally, observations of surface morphology and validation of residual stress prediction were conducted through experiments on an aluminum plate. The results show that the residual stress of the aluminum plate is positively correlated with the number of strikes of the magnetic needle. The residual stress changes from tensile stress (+0.1 MPa) to compressive stress (-16.5 MPa). The comparison between simulation results and experimental results is good, indicating that the simulation model can comprehensively consider multiple factors such as magnetic field, particle motion, and fluid flow, and establish a magnetic needle magnetic grinding process model that is suitable for actual working conditions.

A Hybrid Genetic Algorithm for Ground Station Scheduling Problems

In recent years, the substantial growth in satellite data transmission tasks and volume, coupled with the limited availability of ground station hardware resources, has exacerbated conflicts among missions and rendered traditional scheduling algorithms inadequate. To address this challenge, this paper introduces an improved tabu genetic hybrid algorithm (ITGA) integrated with heuristic rules for the first time. Firstly, a constraint satisfaction model for satellite data transmission tasks is established, considering multiple factors such as task execution windows, satellite–ground visibility, and ground station capabilities. Leveraging heuristic rules, an initial population of high-fitness chromosomes is selected for iterative refinement. Secondly, the proposed hybrid algorithm iteratively evolves this population towards optimal solutions. Finally, the scheduling plan with the highest fitness value is selected as the best strategy. Comparative simulation experimental results demonstrate that, across four distinct scenarios, our algorithm achieves improvements in the average task success rate ranging from 1.5% to 19.8% compared to alternative methods. Moreover, it reduces the average algorithm execution time by 0.5 s to 28.46 s and enhances algorithm stability by 0.8% to 27.7%. This research contributes a novel approach to the efficient scheduling of satellite data transmission tasks.

Parameter learning for Wiener systems with time‐delay state‐space model

AbstractThis paper discusses a novel scheme for learning the Wiener output error nonlinear system with time‐delay state‐space model. In the Wiener system, the dynamic linear block is approximated by time‐delay state‐space model, and the static nonlinear block is established using neural fuzzy network. Combined signals designed including separable signal and random signal are devoted to achieving parameters separation learning of the Wiener system, that is, the two blocks are learned independently. Firstly, using the properties of shift operator and transforming state‐space model with time‐delay into a representation with input and output, then linear dynamic block parameters are learned by the virtue of correlation analysis method in the condition of Gaussian signals. Moreover, a recursive extended least squares estimation is carried out to learn parameters of static nonlinear block and colored noise model under the condition of random signals. The efficiency and accuracy of proposed scheme are confirmed on experiment results of a numerical simulation and a typical practical nonlinear process, and experimental simulation results demonstrate that the learning scheme proposed obtains good learning precision.