Search Results (22,064)

In this paper, nonlinear system identification using Bayesian network has been implemented to discover open-loop lateral-directional aerodynamic model parameters of an agile aircraft using a grey box modelling structure. Our novel technique has been demonstrated on simulated flight data from an F-16 nonlinear simulation of its Flight Dynamic Model (FDM). A mathematical model has been obtained using time series analysis of a Box–Jenkins (BJ) model structure, and parameter refinement has been performed using Bayesian mechanics. The aircraft nonlinear Flight Dynamic Model is adequately excited with doublet inputs, as per the dictates of its natural frequency, in accordance with non-parametric modelling (Finite Impulse Response) estimates. Time histories of optimized doublet inputs in the form of aileron and rudder deflections, and outputs in the form of roll and yaw rates are recorded. Dataset is pre-processed by implementing de-trending, smoothing, and filtering techniques. Blend of System Identification time-domain grey box modelling structures to include Output Error (OE) and Box–Jenkins (BJ) Models are stage-wise implemented in multiple flight conditions under varied stochastic models. Furthermore, a reduced order parsimonious model is obtained using Akaike information Criteria (AIC). Parameter error minimization activity is conducted using the Levenberg–Marquardt (L-M) Algorithm, and parameter refinement is performed using the Bayesian Algorithm due to its natural connection with grey box modelling. Comparative analysis of different nonlinear estimators is performed to obtain best estimates for the lateral–directional aerodynamic model of supersonic aircraft. Model Quality Assessment is conducted through statistical techniques namely: Residual Analysis, Best Fit Percentage, Fit Percentage Error, Mean Squared Error, and Model order. Results have shown promising one-step model predictions with an accuracy of 96.25%. Being a sequel to our previous research work for postulating longitudinal aerodynamic model of supersonic aircraft, this work completes the overall aerodynamic model, further leading towards insight to its flight control laws and subsequent simulator design. Full article

Acquiring knowledge of aircraft flight dynamics is crucial for simulation, control, mission performance and safety assurance analysis. In the fast-paced UAV market, long flight testing campaigns are hard to achieve, leaving limited controlled flight data and a significant amount of unplanned flight data. This work delves into the application of system identification techniques on unplanned flight data when faced with a shortage of dedicated flight test data. Based on a medium-sized, fixed-wing UAV, it focuses on the system identification of longitudinal dynamics using structural routine flight test data of pitch down and pitch up manoeuvres with no specific guidelines for the control inputs given. The proposed solution uses first- and second-order parameter-based models to build a non-linear dynamic model which, using a least square error optimisation algorithm in a time domain formulation, has its parameters tuned to converge the model behaviour with the real aircraft dynamics. The optimisation uses a combination of pitch, altitude, airspeed and pitch rate responses as a measure of model accuracy. Very significant improvements regarding the UAV model response are found when trimmed flight manoeuvres are used, resulting in proper estimation of important aerodynamic and control derivatives. Pitching moment and control derivatives are shown to be the crucial parameters. However, difficulties in estimation are shown for untrimmed flight manoeuvres. Better results were obtained when using multiple manoeuvres simultaneously in the optimisation error metric, as opposed to single manoeuvres that led to system bias. The proposed system identification procedure can be applied to any fixed-wing UAV without the need for specific flight testing campaigns. Full article

The energy transition toward low-carbon electricity systems has resulted in a steady increase in RESs. The expansion of RESs has been accompanied by a growing number of energy storage systems (ESSs) that smooth the demand curve or improve power quality. However, in order to investigate ESS benefits, it is necessary to determine their reliability. This article proposes a four-state reliability model of a battery ESS operating with a PV system for low-voltage grid end users: households and offices. The model assumes an integration scenario of an ESS and a PV system to maximize autoconsumption and determine generation reliability related to energy availability. The paper uses a simulation approach and proposes many variants of power source and storage capacity. Formulas to calculate the reliability parameters—the intensity of transition $\lambda$, resident time $T_i$, or stationary probabilities—are provided. The results show that increasing the BESS capacity above 80% of daily energy consumption does not improve the availability probability, but it may lead to an unnecessary cost increase; doubling the PV system capacity results in a decrease in the unavailability probability by almost half. The analysis of the results by season shows that it is impossible to achieve a high level of BESS reliability in winter in temperate climates. Full article

In order to solve the hysteresis nonlinearity and resonance problems of piezoelectric stages, this paper takes a three-degree-of-freedom tip-tilt-piston piezoelectric stage as the object, compensates for the hysteresis nonlinearity through inverse hysteresis model feedforward control, and then combines the composite control method of positive acceleration velocity position feedback damping control and high-gain integral feedback controller to suppress the resonance of the system and improve the tracking speed and positioning accuracy. Firstly, the three-degree-of-freedom motion of the end-pose is converted into the output of three sets of piezoelectric actuators and single-axis control is performed. Then, the rate-dependent Prandtl–Ishlinskii model is established and the parameters of the inverse model are identified. The accuracy and effectiveness of parameter identification are verified through open-loop and closed-loop compensation experiments. After that, for the third-order system, the parameters of positive acceleration velocity position feedback damping control and high-gain integral feedback controller are designed as a whole based on the pattern of the Butterworth filter. The effectiveness of the design method is proved by step signal and triangle wave signal trajectory tracking experiments, which suppresses the resonance of the system and improves the bandwidth of the system and the tracking speed of the stage. Full article

Wavelet analysis algorithms in combination with thresholding procedures are widely used in nonparametric regression problems when estimating a signal function from noisy data. The advantages of these methods lie in their computational efficiency and the ability to adapt to the local features of the estimated function. It is usually assumed that the signal function belongs to some special class. For example, it can be piecewise continuous or piecewise differentiable and have a compact support. These assumptions, as a rule, allow the signal function to be economically represented on some specially selected basis in such a way that the useful signal is concentrated in a relatively small number of large absolute value expansion coefficients. Then, thresholding is performed to remove the noise coefficients. Typically, the noise distribution is assumed to be additive and Gaussian. This model is well studied in the literature, and various types of thresholding and parameter selection strategies adapted for specific applications have been proposed. The risk analysis of thresholding methods is an important practical task, since it makes it possible to assess the quality of both the methods themselves and the equipment used for processing. Most of the studies in this area investigate the asymptotic order of the theoretical risk. In practical situations, the theoretical risk cannot be calculated because it depends explicitly on the unobserved, noise-free signal. However, a statistical risk estimate constructed on the basis of the observed data can also be used to assess the quality of noise reduction methods. In this paper, a model of a signal contaminated with additive Gaussian noise is considered, and the general formulation of the thresholding problem with threshold functions belonging to a special class is discussed. Lower bounds are obtained for the threshold values that minimize the unbiased risk estimate. Conditions are also given under which this risk estimate is asymptotically normal and strongly consistent. The results of these studies can provide the basis for further research in the field of constructing confidence intervals and obtaining estimates of the convergence rate, which, in turn, will make it possible to obtain specific values of errors in signal processing for a wide range of thresholding methods. Full article

With the development of rolled steel strips towards higher strength and thinner thickness, negative forward slip has been frequently observed during the process of cold rolling, and this phenomenon closely related to interface is believed to seriously influence rolling stability. However, the existing classic forward slip models are limited to calculating positive forward slip values and cannot reflect negative forward slip effects. Therefore, in this paper, based on BLAND-FORD forward slip theory, a novel modified forward slip model capable of predicting negative forward slip is established and verified, in which the corresponding flattened curve is characterized and a compensation coefficient related to actual tension and coil number is supplemented. Then, a dimensionless sensitivity factor is defined to compare and analyze the influences of various parameters on forward slip through the modified model, in order to pick a more effective and reasonable regulation approach. Finally, an idea of keeping stable forward slip through dynamic tension regulation is suggested and applied in the actual rolling process, and it is drawn that this strategy can be used to avoid fluctuations of process parameters and suppress mill chatter. As a result, the presented modified forward slip model can identify both positive and negative forward slips and is helpful in regulating the interface state and improving the stability of the rolling process. Full article

In order to ensure the safe operation of adjacent buried pipelines under blast vibration, it is of great practical engineering significance to accurately predict the peak vibration velocity ofburied pipelines under blasting loads. Relying on the test results of the buried steel pipe blast model test, a sensitivity analysis of relevant influencing factors was carried out by using the gray correlation analysis method. A least squares support vector machine (LS-SVM) model was established to predict the peak vibration velocity of the pipeline and determine the best parameter combination in the LS-SVM model through a local particle swarm optimization (PSO), and the results of the PSO-LSSVM model were predicted. These were compared with BP neural network model and Sa’s empirical formula. The results show that the fitting correlation coefficient (R2), root mean square error (RMSE), average relative error (MRE), and Nash coefficient (NSE) of the PSO-LSSVM model for the prediction of pipeline peak vibration velocity are 91.51%, 2.95%, 8.69%, and 99.03%, showing that the PSO-LSSVM model has a higher prediction accuracy and better generalization ability, which provides a new idea for the vibration velocity prediction of buried pipelines under complex blasting conditions. Full article

In order to improve the stability of semiconductor laser fiber coupling efficiency, based on the coupling principle, the optimal parameters for semiconductor laser fiber coupling were simulated to be θ = 45°, r = 3.25 μm, and z = 5.65 μm. By optimizing the structure and position of the lens fiber, it has been experimentally proven that the maximum fiber coupling efficiency of the 980 nm semiconductor laser can reach 87.1%, and the average coupling efficiency can also reach 84%. After temperature cycling and aging experiments, the average coupling efficiency of the device was 81.7%, indicating a decrease in coupling efficiency. At the same time, the effect of fiber stress on the reliability of coupling efficiency was analyzed, and the stability and consistency of the device before and after temperature cycling were explored. In future work, it will be necessary to further optimize the thermal stress caused by UV glue curing and tail pipe soldering, find suitable process parameters, and obtain stable and reliable coupling modules. Full article

The wall thickness of the TP2 copper tube casting billet is not uniform after a three-roll planetary rotational rolling, which affects the wall thickness uniformity of the copper tube in the subsequent process. In order to study the influence of wall thickness at different positions of copper pipe after rolling on the wall thickness of copper pipe after joint drawing, an online ultrasonic test platform was used to measure the wall thickness of copper pipe after tying, and based on the test data, a finite element model of copper pipe billet was established, and the numerical simulation of joint drawing wall thickness was conducted. Based on the results of the ultrasonic testing experiment and finite element simulation, different neural network models were used to predict the joint tensile wall thickness with the data of the ultrasonic testing experiment as input and the results of finite element simulation as output. The prediction effect of different neural network models was compared, and the results showed that the prediction and fitting effect of the SVM model was better, but overfitting occurred during the fitting process. Furthermore, particle swarm optimization is used to optimize the penalty parameter C and the kernel parameter g in the SVM model. Compared with the traditional SVM model, the PSO–SVM model is more suitable for the prediction of joint tensile wall thickness, which can better guide the production to solve this problem. Full article

This article explores the challenges of integrating two deep learning neural networks, YOLOv5 and RT-DETR, to enhance the recognition of unmanned aerial vehicles (UAVs) within the optical-electronic channels of Sensor Fusion systems. The authors conducted an experimental study to test YOLOv5 and Faster RT-DETR in order to identify the average accuracy of UAV recognition. A dataset in the form of images of two classes of objects, UAVs, and birds, was prepared in advance. The total number of images, including augmentation, amounted to 6337. The authors implemented training, verification, and testing of the neural networks exploiting PyCharm 2024 IDE. Inference testing was conducted using six videos with UAV flights. On all test videos, RT-DETR-R50 was more accurate by an average of 18.7% in terms of average classification accuracy (Pc). In terms of operating speed, YOLOv5 was 3.4 ms more efficient. It has been established that the use of RT-DETR as the only module for UAV classification in optical-electronic detection channels is not effective due to the large volumes of calculations, which is due to the relatively large number of parameters. Based on the obtained results, an algorithm for combining two neural networks is proposed, which allows for increasing the accuracy of UAV and bird classification without significant losses in speed. Full article

In order to solve the problem of mowing between plants in Xinjiang trunk orchards, an obstacle avoidance mower suitable for trunk orchard planting mode was designed. The whole structure, working principle and main parameter design of the obstacle avoidance mower are introduced. The finite element analysis and kinematic analysis of the cutter are carried out on the premise of using a Y-shaped cutter and its arrangement, and the condition that the inter-row mower does not leak is determined. Through the modal analysis of the frame, the range of the first six natural frequencies of the frame is determined and compared with the frequency of the main excitation source of the machine to determine the rationality of the frame design. On the premise of simplifying the inter-plant obstacle avoidance mechanism into a two-dimensional model for kinematics analysis, the motion parameters of the key components of the machine were determined. At the same time, the virtual kinematics simulation single-factor test of the designed inter-plant obstacle avoidance device was carried out with the help of ADAMS 2020 software. Through the reduction in and calculation of the motion trajectory of the simulation test, it was finally determined that the forward speed of the machine, the elastic coefficient of the reset spring and the compression speed of the hydraulic cylinder were the main influencing factors of the inter-plant obstacle avoidance mower. The orthogonal test was designed and the optimal solution of the three test factors was determined. The optimal solution is taken for further field test verification. The results show that when the tractor forward speed is 1.5 km∙h⁻¹, the hydraulic cylinder compression speed is 225 mm∙s⁻¹, and the elastic coefficient of the reset spring is 29 N∙mm⁻¹, the average leakage rate between the orchard plants is 7.64%, and the obstacle avoidance pass rate is 100%. The working stability is strong and meets the design requirements. Full article

In this paper we present a new extension of the truncated positive normal (TPN) model, called power truncated positive normal. This extension incorporates a shape parameter that provides more flexibility to the model. In addition, this new extension was reparameterized based on the p-th quantile of the distribution in order to perform quantile regression. The initial values were calculated from a modification of the moment estimators, which allowed the maximum likelihood estimators to be obtained. A simulation study was carried out which suggests good behavior of the maximum likelihood estimators in finite samples. Finally, two applications using health databases are presented. Full article

Closeness is a measure that quantifies how quickly information can spread from a given node to all other nodes in the network, reflecting the efficiency of communication within the network by indicating how close a node is to all other nodes. For a graph G, the subset S of vertices of $V\left(G\right)$ is called vertex cut of G if the graph $G-S$ becomes disconnected. The minimum cardinality of S for which $G-S$ is either disconnected or contains precisely one vertex is called connectivity of G. A graph is called k-connected if it stays connected even when any set of fewer than k vertices is removed. In communication networks, a k-connected graph improves network reliability; even if up to $k-1$ nodes fail, the network remains operational, maintaining connectivity between devices. This paper aims to study the concept of closeness within n-vertex graphs with fixed connectivity. First, we identify the graphs that maximize the closeness among all graphs of order n with fixed connectivity k. Then, we determine the graphs that achieve the maximum closeness within all k-connected graphs of order n, given specific fixed parameters such as diameter, independence number, and minimum degree. Full article

The paper is devoted to the parametric stability optimization of explicit Runge–Kutta methods with higher-order derivatives. The key feature of these methods is the dependence of the coefficients of their stability polynomials on free parameters. Thus, the integral characteristics of stability domains can be considered as functions of free parameters. The optimization is based on the numerical maximization of the area of the stability domain and the length of the stability interval. Runge–Kutta methods with higher-order derivatives, presented in previous works, are optimized. The optimal values of parameters are computed for methods of fourth, fifth, and sixth orders. In numerical experiments, optimal parameter values are used for the construction of high-order schemes for the method of lines for problems with partial differential equations. Problems for linear and nonlinear hyperbolic and parabolic equations are considered. Additionally, an optimized scheme is used in lattice Boltzmann simulations of gas flow. As the main result of computations and comparison with existing methods, it is demonstrated that optimized schemes have better stability properties and can be used in practice. Full article

In order to reduce the emissions caused by internal combustion engine vehicles, the industry is introducing more and more electric or hybrid vehicles to the market nowadays. The battery cells and modules of these vehicles require a lot of care, as improper or improperly maintained battery units can cause serious problems inside vehicles and can be extremely dangerous. The safest solution is to keep this unit of a vehicle under constant supervision so that it can be repaired immediately in case of an issue. Since all necessary data can be extracted from a vehicle’s communication network(s) through standard communication protocols, it is advisable to use them for continuous monitoring and diagnostics of units, while also considering cost-effectiveness and simplicity. The data received from here can also be used for measurement of electric powertrains and other parameters. However, since these data go through many conversions and computers (ECUs) before reaching us, their accuracy is questionable. In this study, we present our own custom battery diagnostic tool based on data extracted from a communication network. With the help of commercially available diagnostic tools, we also compare several measurements of the extent of the error limits of the data arriving at the communication network, how far they differ from the real values, and with the help of these, we analyze the accuracy of the device we have made. We present the commonly used Controller Area Network (CAN) communication protocol for passenger vehicles and briefly describe the construction of the high-voltage battery unit of the test vehicle. Full article