Scale Factor Parameter Research Articles

A comprehensive optimization method for dual redundancy aviation electric propulsion control system

Abstract This paper comprehensively optimizes and improves the traditional vector control method to improve the control performance of dual-redundancy electric aircraft and ensure the stability of its system operation in complex meteorological environments. First, the PI controller of the speed loop is replaced with the designed fuzzy PI controller, and its quantization factor and scale factor parameters are adjusted using a beetle antennae search algorithm (BAS) to enhance the system’s ability to respond quickly and resist interference. Secondly, a sliding mode load observer was designed to feed forward the observed values to the speed loop output, solving the torque and current ripple problems caused by the direct input of the redundant electric propulsion system when the aircraft switches from a single-redundant cruise state to a dual-redundant high-speed state. Finally, the design method was simulated and validated using MATLAB/SIMULINK, verifying the feasibility of the design.

Pump System Model Parameter Identification Based on Experimental and Simulation Data

In this paper, the entire downhole fluid-sucker rod-pump system is replaced with a viscoelastic vibration model, namely a third-order differential equation with an inhomogeneous forcing term. Both Kelvin’s and Maxwell’s viscoelastic models can be implemented along with the dynamic behaviors of a mass point attached to the viscoelastic model. By employing the time-dependent polished rod force measured with a dynamometer as the input to the viscoelastic dynamic model, we have obtained the displacement responses, which match closely with the experimental measurements in actual operations, through an iterative process. The key discovery of this work is the feasibility of the so-called inverse optimization procedure, which can be utilized to identify the equivalent scaling factor and viscoelastic system parameters. The proposed Newton–Raphson iterative method, with some terms in the Jacobian matrix expressed with averaged rates of changes based on perturbations of up to two independent parameters, provides a feasible tool for optimization issues related to complex engineering problems with mere information of input and output data from either experiments or comprehensive simulations. The same inverse optimization procedure is also implemented to model the entire fluid delivery system of a very viscous non-Newtonian polymer modeled as a first-order ordinary differential equation (ODE) system similar to the transient entrance developing flow. The convergent parameter reproduces transient solutions that match very well with those from fully fledged computational fluid dynamics models with the required inlet volume flow rate and outlet pressure conditions.

Adaptation of the Scaling Factor Based on the Success Rate in Differential Evolution

Differential evolution is a popular heuristic black-box numerical optimization algorithm which is often used due to its simplicity and efficiency. Parameter adaptation is one of the main directions of study regarding the differential evolution algorithm. The main reason for this is that differential evolution is highly sensitive to the scaling factor and crossover rate parameters. In this study, a novel adaptation technique is proposed which uses the success rate to replace the popular success history-based adaptation for scaling factor tuning. In particular, the scaling factor is sampled with a Cauchy distribution, whose location parameter is set as an nth order root of the current success rate, i.e., the ratio of improved solutions to the current population size. The proposed technique is universal and can be applied to any differential evolution variant. Here it is tested with several state-of-the-art variants of differential evolution, and on two benchmark sets, CEC 2017 and CEC 2022. The performed experiments, which include modifications of algorithms developed by other authors, show that in many cases using the success rate to determine the scaling factor can be beneficial, especially with relatively small computational resource.

A Comprehensive Survey on Model Quantization for Deep Neural Networks in Image Classification

Recent advancements in machine learning achieved by Deep Neural Networks (DNNs) have been significant. While demonstrating high accuracy, DNNs are associated with a huge number of parameters and computations, which leads to high memory usage and energy consumption. As a result, deploying DNNs on devices with constrained hardware resources poses significant challenges. To overcome this, various compression techniques have been widely employed to optimize DNN accelerators. A promising approach is quantization, in which the full-precision values are stored in low bit-width precision. Quantization not only reduces memory requirements but also replaces high-cost operations with low-cost ones. DNN quantization offers flexibility and efficiency in hardware design, making it a widely adopted technique in various methods. Since quantization has been extensively utilized in previous works, there is a need for an integrated report that provides an understanding, analysis, and comparison of different quantization approaches. Consequently, we present a comprehensive survey of quantization concepts and methods, with a focus on image classification. We describe clustering-based quantization methods and explore the use of a scale factor parameter for approximating full-precision values. Moreover, we thoroughly review the training of a quantized DNN, including the use of a straight-through estimator and quantization regularization. We explain the replacement of floating-point operations with low-cost bitwise operations in a quantized DNN and the sensitivity of different layers in quantization. Furthermore, we highlight the evaluation metrics for quantization methods and important benchmarks in the image classification task. We also present the accuracy of the state-of-the-art methods on CIFAR-10 and ImageNet. This article attempts to make the readers familiar with the basic and advanced concepts of quantization, introduce important works in DNN quantization, and highlight challenges for future research in this field.

Adaptive super-resolution image reconstruction based on fractal theory

Image super-resolution (SR) reconstruction has been a significant research field in image processing, although the current approaches to dealing with complicated image reconstruction still have some issues. In order to overcome these problems, traditional approaches apply fractal geometry to image super-resolution reconstruction, either in textured or non-textured regions, treating the image as a single fractal set. However, it won't be possible to classify the image specifically, such as edge, common texture, and complex texture, unless identifying the texture complexity of all regions in the image. For the texture region, this paper considers the fractal as a perturbation function of rational interpolation and constructs a new interpolation model with scale factor parameter. The parameter is related to the local fractal dimension (LFD). This paper defines a new function between scale factor and local fractal dimension so as to select the optimal value adaptively. Considering the image edge quality, this paper combines the non-texture regions and edge regions, applies an improved edge interpolation algorithm and sets a new pixel mapping method to meet the needs of different zoom factors. Based on the above, this paper proposes an adaptive super-resolution reconstruction algorithm. Particularly, region segmentation with different texture complexity is a foundational step of our algorithm, this paper proposes an image segmentation algorithm based on the research of local fractal dimension which combines the multi-scale analysis capability of wavelets with the multi-scale self-similarity feature of fractals. The experimental results show that our algorithm can obtain accurate texture details, smooth edges and low noise subjectively, and achieve the best evaluation objectively. This paper lays the theoretical foundation for fractal applications in super-resolution image reconstruction.

A Study of Evolution of Cosmological Parameters Based on a Dark Energy Model in the Framework of Brans-Dicke Gravity

The objective of the present study is to find the characteristics of evolution of a homogeneous and isotropic universe in the framework of Brans-Dicke (BD) theory of gravity. FLRW space-time, with zero spatial curvature, has been used to obtain BD field equations. Scale factor and Hubble parameter have been obtained from an ansatz for the deceleration parameter, assumed on the basis of its property of signature flip indicating a change of phase from deceleration to acceleration. Validation of the model has been achieved by a suitable parametrization of that ansatz. Expressions for energy density, pressure, equation of state (EoS) parameter, cosmological constant, gravitational constant have been derived and depicted graphically. The gravitational constant is found to decrease with time at a gradually decreasing rate. The Hubble parameter, deceleration parameter and energy density decrease with time, which is in agreement with many other studies. The value of the EoS parameter at the present epoch is negative, and it becomes more negative with time. The cosmological constant increases very rapidly in the early universe from negative to smaller negative values, becoming positive finally, with a much slower change thereafter. A cosmographic and a geometrical analysis have been carried out. It is observed that a gradual transition takes place from a regime of quintessence to phantom dark energy. An important finding of this study is that the signature flip of the deceleration parameter takes place almost simultaneously with the signature flip of the cosmological constant, implying a connection between accelerated expansion and dark energy, which is represented here by the cosmological constant. Unlike the common practice of using arbitrary units, proper SI units for all measurable quantities have been used. This theoretical investigation provides the reader with a simple method to formulate models in the framework of BD theory.

Enhanced variable universe fuzzy PID control of the active suspension based on expansion factor parameters adaption and genetic algorithm

The optimal value of the expansion factor and quantization factor parameters is the key to determine the performance of the variable domain fuzzy proportion-integral-derivative (PID) controller. In view of the problem that the relevant parameters are fixed, difficult to determine and cannot be adjusted adaptively in the traditional variable domain fuzzy PID control, a kind of enhanced variable domain fuzzy PID control (EVUFP) is proposed to improve the ride comfort of vehicles. First, based on the traditional variable domain fuzzy PID control, an adaptive expansion factor controller is constructed to determine and adjust adaptively the parameters of the expansion factor in real time. Then the genetic algorithm is used to optimize the quantization factor and scale factor parameters to seek the optimal value of relevant parameters. And the obtained optimal parameters are substituted into the Simulink model to realize the design of the EVUFP controller. The simulation experiment results under different working conditions show that the proposed EVUFP control strategy can reduce the root mean square (RMS) values of the body acceleration, suspension dynamic deflection and tire dynamic load by more than 54%, 50% and 23%, respectively, improve the ride comfort of vehicles, and provide a new idea for the development of active suspensions.

Multimodal Public Transit Network Design Method Based on Hub-and-Spoke Infrastructure

This paper presents a two-phase method for generating a hub-and-spoke network for a multimodal public transit network (MPTN). The first phase addresses the problem of clustering in MPTNs, which is a key operation in solving the hub location problem (HLP) in MPTN design. However, previous work often oversimplifies or neglects the clustering phase, so this study presents a spatial clustering algorithm that can process real urban MPTNs without reducing their size or dividing them into zones. The algorithm is tested on four large city datasets and compared with popular spatial clustering algorithms. In the second phase, a heuristic algorithm is presented that aims to maximize network coverage. The proposed method is tested using the MPTN of Greater London and the resulting hub-and-spoke network is compared with the Journey API provided by Transport for London. The results show that the total travel time is improved by 16.9% with a strict hubbing policy. Transportation planners can adapt the proposed method to their specific needs by changing the size and number of clusters using the cluster scaling factor ([Formula: see text]) parameter and including or excluding any criteria in the decision equations.

Development and Traceability Method of a 100 kA Reference Impulse Current Measuring System

In order to improve the measurement accuracy of high impulse current, this paper presents the system composition, performance improvement and, traceability method of a 100 kA impulse current measuring system. Since the scale factor of the measuring system can be determined by the product of the scale factor of the components such as the shunt and the digital recorder, a method of component quantity traceability is proposed. Based on a shunt with rated current of 100 kA, a stable measuring system is developed. In addition, a reference impulse current calibrator and a step current generator for traceability are developed, so that the impulse scale factor of the shunt considering skin effect can be accurately calibrated. A single influence quantity independent quantification method was proposed. The influence of measurement error of 100 kA impulse current measuring system is analyzed quantitatively. Based on the high current test results and theoretical analysis, the measurement uncertainty components of peak current and time parameters are evaluated. The expanded uncertainty of the scale factor and time parameters were 0.4% ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> = 2) and 0.8% ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> = 2) respectively. Evaluation results demonstrate that the developed measuring system can accurately reproduce and measure the high exponential lightning impulse current.

A Review of MEMS Vibrating Gyroscopes and Their Reliability Issues in Harsh Environments

Micro-electromechanical systems (MEMS) vibrating gyroscopes have gained a lot of attention over the last two decades because of their low power consumption, easy integration, and low fabrication cost. The usage of the gyroscope equipped with an inertial measurement unit has increased tremendously, with applications ranging from household devices to smart electronics to military equipment. However, reliability issues are still a concern when operating this inertial sensor in harsh environments, such as to control the movement and alignment of mini-satellites in space, tracking firefighters at an elevated temperature, and assisting aircraft navigation in gusty turbulent air. This review paper focuses on the key fundamentals of the MEMS vibrating gyroscopes, first discussing popular designs including the tuning fork, gimbal, vibrating ring, and multi-axis gyroscopes. It further investigates how bias stability, angle random walk, scale factor, and other performance parameters are affected in harsh environments and then discusses the reliability issues of the gyroscopes.

Inverse identification of material constitutive parameters based on co-simulation

Establishing a material constitutive model reflecting the mechanical behavior of metal materials in the cutting process is the basis of cutting finite element simulation, and has an important guiding role in accurately describing the cutting thermo-mechanical coupling state variables and further optimizing process parameters. This paper proposes a reverse identification method of material constitutive parameters based on co-simulation. The influence of mass scaling factor, mesh size, and Johnson-Cook (J-C) constitutive parameters on cutting force was analyzed, and orthogonal cutting experiments were carried out for two difficult-to-machine materials, die steel H13 and titanium alloy Ti-6Al-4V. ABAQUS is redeveloped based on Python, and then the J-C constitutive parameters are directly optimized for the first time with the minimum error between the finite element simulation value and the experimental value of the cutting force as the objective function, the least square identification of J-C constitutive parameters based on co-simulation is realized by combining genetic algorithm. The results show that the reasonable setting of mass scaling factor and mesh size can significantly reduce the time of simulation calculation and improve the optimization efficiency of constitutive parameters. The yield stress, hardening coefficient, strain rate coefficient, hardening coefficient and softening coefficient of materials have different effects on cutting force, which is the key to effective inverse identification of constitutive parameters. Compared with different J-C parameters reported in the literature, it is found that the constitutive parameters obtained by inverse identification based on co-simulation are more accurate, and the prediction of residual stress and serrated chip characteristics also has good accuracy. This study provides a reference for the optimization of constitutive parameters, and the research results provide a new scheme for the efficient construction of material mechanical behavior required for cutting finite element simulation.

PARAMETER ESTIMATION OF THE CLASSICAL FRACTAL MAP BASED ON A GIVEN JULIA SET’S SHAPE

Julia set is one of the most important branches in fractal theory, and has achieved much progress on both theoretical analysis and application research. Nevertheless, most of the existing investigations relied on the systems with known parameters. Few works have been done on the inverse problem about how to determine the system parameter based on a given Julia set’s shape. This work aims to address this issue by starting with the most classical polynomial map. First, by constructing a proper fitness function measuring the Julia sets’ area error, the Julia sets’ parameter estimation is formulated as a kind of optimization problem. According to the known conditions of Julia set to be estimated, the optimization problem is classified into two cases. For one case, when both the shape and size of Julia set are given, we only need to estimate the system’s own parameter [Formula: see text]. For the other case, when the Julia set has only a shape, we redesign the problem into three dimensions by adding a new scaling factor parameter [Formula: see text] and applying the partial coverage principle. Second, a particle swarm optimization (PSO) approach including dynamically adjustment strategy is adopted to solve the proposed optimization problem. At last, we present seven groups of simulations in which both randomly generated Julia sets and those in existing literature (or on the internet) are included. The simulation results verify the effectiveness of the proposed parameter estimation scheme.

An Approach to Growth Delimitation of Straight Line Segment Classifiers Based on a Minimum Bounding Box.

Several supervised machine learning algorithms focused on binary classification for solving daily problems can be found in the literature. The straight-line segment classifier stands out for its low complexity and competitiveness, compared to well-knownconventional classifiers. This binary classifier is based on distances between points and two labeled sets of straight-line segments. Its training phase consists of finding the placement of labeled straight-line segment extremities (and consequently, their lengths) which gives the minimum mean square error. However, during the training phase, the straight-line segment lengths can grow significantly, giving a negative impact on the classification rate. Therefore, this paper proposes an approach for adjusting the placements of labeled straight-line segment extremities to build reliable classifiers in a constrained search space (tuned by a scale factor parameter) in order to restrict their lengths. Ten artificial and eight datasets from the UCI Machine Learning Repository were used to prove that our approach shows promising results, compared to other classifiers. We conclude that this classifier can be used in industry for decision-making problems, due to the straightforward interpretation and classification rates.

A Novel Extrinsic Calibration Method of Mobile Manipulator Camera and 2D-LiDAR via Arbitrary Trihedron-Based Reconstruction

Mobile manipulators are increasingly applied to improve the efficiency in industrial manufacturing. As a typical system using multi-sensor fusion technology, accurate extrinsic calibration of manipulator’s exteroceptive sensors like camera and 2D-LiDAR is essential for mobile manipulators to perform complicated task such as mobile assembly. However, most existing camera-LiDAR calibration methods require sophisticated artificial calibration targets, leading to implementation restrictions. In this paper, by reconstructing an arbitrary trihedron that existed in the general human-made environment, a novel method is presented to estimate the transformation between the manipulator camera and the 2D-LiDAR coordinate system. The proposed method is based on the use of point, line, and plane geometry constraints between the segmented 2D-LiDAR scan and the reconstructed trihedron features. Considering the metric of reconstruction, the extended hand-eye calibration framework is implemented to recover the scale factor and hand-eye parameters. Then, a new optimization model is presented to reconstruct the key features of arbitrary trihedron in a preset global coordinate system. Finally, with only one 2D-LiDAR measurement, camera-LiDAR transformation can be calculated by constructing point-to-line and line-to-plane geometric constraints. Further, the transformation between the manipulator kinematic base and 2D-LiDAR can also be calibrated. Both simulation and real-world experiments show that the proposed method can provide robust and accurate results.

A novel and robust scale-invariant WENO scheme for hyperbolic conservation laws

A novel, simple, robust, and effective modification in the nonlinear weights of the scale-invariant WENO operator is proposed that achieves an optimal order of accuracy with smooth function regardless of the critical point (Cp-property), a scale-invariant with an arbitrary scaling of a function (Si-property), an essentially non-oscillatory approximation of a discontinuous function (ENO-property), and, in some cases, a well-balanced WENO finite difference/volume scheme (WB-property) (up to machine rounding error numerically). The classical WENO-JS/Z/D operators do not satisfy the Si-property intrinsically due to a loss of sub-stencils' adaptivity in the WENO reconstruction of a discontinuous function when scaled by a small scaling factor. By introducing the descaling function, an average of the function values in the weights to build the scale-invariant WENO-JSm/Zm/Dm operators, the operators are independent of both the scaling factor and sensitivity parameter. The Si-property and Cp-property of the WENO operators are validated theoretically and numerically in quadruple-precision with small and large scaling factors and sensitivity parameters. The results show that the WENO-JSm/Zm/Dm operators satisfy the Si-property and the WENO-D/Dm operators satisfy the Cp-property. Furthermore, the ENO-property of the WENO-Zm/Dm schemes is illustrated via several one- and two-dimensional shock-tube problems. In solving the Euler equations under gravitational fields, the well-balanced scale-invariant WENO schemes achieve the WB-property intrinsically without imposing the stringent homogenization condition.

Comparison of numerical methods in estimating Weibull parameters to install a sustainable wind farm in mount Bamboutos, Cameroon

PurposeThe purpose of this paper is to evaluate the wind energy potential of Mount Bamboutos in Cameroon by comparing nine numerical methods in determining Weibull parameters for the installation of a sustainable wind farm.Design/methodology/approachBy using statistical analysis, the analysis of shape and scale parameters, the estimation of the available wind power density and wind direction frequency distributions, the objective of this paper is to compare nine numerical methods in estimating Weibull parameters for the installation of a sustainable wind farm in Mount Bamboutos, Cameroon.FindingsThe results suggested that the minimum and maximum values of the standard deviation occurred in the months of May and November 2016, respectively. The graphical method appeared to be the most effective method with the maximum value of variance and minimum values of chi-square and RMSE. The scale factor parameter values indicated that Mount Bamboutos hills were a potential site for electricity generation. The analysis of wind power density showed that it reached the maximum and minimum values in February and September, respectively. The wind direction frequency distributions showed that the prevailing wind directions were North-East.Originality/valueThe wind energy potential of Mount Bamboutos in Cameroon was performed by using nine numerical methods. Therefore, it could be effective to have a prediction model for the wind speed profile. The analysis of wind power density showed that it reached the maximum and minimum values in February and September, respectively. The wind direction frequency distributions showed that the prevailing wind directions were North-East.

An Online Gyro Scale Factor Error Calibration Method for Laser RINS

The rotational inertial navigation system (RINS) could greatly improve navigation performance by rotating the inertial measurement unit with gimbals. And the self-calibration of error parameters could be achieved conveniently. Among the rate-biased Ring laser gyro (RLG), the input angular velocity can reach tens of degrees per second. Therefore, the attitude errors caused by scale factor is dozens of times that of the other RINS. In this paper, gyro scale factor errors are calibrated through optimal estimation with attitude errors in dual-axis laser RINS. During the movement of vehicle, the dynamic gyro scale factor errors calibration can be performed online by adding parking points. Moreover, the parking points can be accurately determined by the statistical characteristics of accelerometers and gyros output information, without extern sensors. The experiment results indicate that the calibration repeatability scale factor parameters are less than 1ppm, and the attitude errors cause by the scale factor error is reduce from tens of arc seconds to 10 arc seconds, which verifies the effectiveness and accuracy of the method proposed in this paper. Through the compensation of calibration results, the attitude error of RINS is further improved in both the static and dynamic experiment.

Variation of Scale Factor and Deceleration Parameter Through the Cosmic Evolution of Time and Temperature

Variation of Scale Factor and Deceleration Parameter Through the Cosmic Evolution of Time and Temperature

Improved fuzzy-PID integrated control for vehicle active suspension based on road excitation

In order to improve the ride comfort of a car and its robustness under various road conditions, an improved fuzzy-PID integrated control strategy for the active suspension of the vehicle seat and the active suspension of the chassis is proposed. Compared with the general fuzzy-PID control method, the control strategy can adjust the quantisation factor, scale factor and PID parameter value of the controller in real time according to the random excitation of the road surface and the vertical speed of the seat, so as to achieve more accurate control of the control force. An 11-DOF vehicle ride comfort model is established to simulate the vehicle running on A, B, C and D grade road surface in MATLAB/Simulink. The results show that the improved fuzzy-PID integrated control of the vehicle active suspension can greatly improve the vehicle ride comfort and handling stability, and the effect is obviously better than that of the fuzzy-PID control.

Study on Comprehensive Calibration and Image Sieving for Coal-Gangue Separation Parallel Robot

Online sorting robots based on image recognition are key pieces of equipment for the intelligent washing of coal mines. In this paper, a Delta-type, coal gangue sorting, parallel robot is designed to automatically identify and sort scattered coal and gangue on conveyor belts by configuring the image recognition system. Robot calibration technology can reduce the influence of installation error on system accuracy and provides the basis for the robot to accurately track and grab gangue. Due to the fact that the angle deflection error between the conveyor belt coordinate system and the robot coordinate system is not considered in the traditional conveyor belt calibration method, an improved comprehensive calibration method is put forward in this paper. Firstly, the working principle and image recognition and positioning process of the Delta coal gangue sorting robot are introduced. The scale factor parameter Factorc of the conveyor encoder is adopted to characterize the relationship between the moving distance of the conveyor and the encoder. The conveyor belt calibration experiment is described in detail. The transformation matrix between the camera, the conveyor belt, and the robot are obtained after establishment of the three respective coordinate systems. The experimental results show that the maximum cumulative deviation of traditional calibration method is 13.841 mm and the comprehensive calibration method is 3.839 mm. The main innovation of the comprehensive calibration is such that the accurate position of each coordinate in the robot coordinate system can be determined. This comprehensive calibration method is simple and feasible, and can effectively improve system calibration accuracy and reduce robot installation error on the grasping accuracy. Moreover, a calculation method to eliminate duplicate images is put forward, with the frame rate of the vision system set at seven frames per second to avoid image repetition acquisition and missing images. The experimental results show that this calculation method effectively improves the processing efficiency of the recognition system, thereby meeting the demands of the grab precision of coal gangue separation engineering. The goal revolving around “safety with few people and safety with none” can therefore be achieved in coal gangue sorting using robots.