Search Results (8)

Road undulations have a significant impact on path lengths and energy consumption, so rough-terrain path planning for unmanned vehicles is of great research importance for performing more tasks with limited energy. This paper proposes a Deep Q-Network (DQN)-based path-planning method, which shapes the reward by introducing a slope penalty function and a terrain penalty function. For the problem of the low exploration efficiency of the ε-greedy strategy, a hybrid exploration strategy combining stochastic exploration and the A* algorithm is proposed, after which the agent is trained on rough terrain. The results show that the algorithm can efficiently plan energy-saving paths, converge quickly, and compared with the traditional A* algorithm and RRT algorithm, performs better under three-dimensional terrain and can choose paths more rationally. Full article

This review analyzes numerical techniques for frictional contact problems, highlighting their strengths and limitations in addressing inherent nonlinearities and computational demands. Finite element methods (FEM), while dominant due to versatility, often require computationally expensive iterative solutions. Alternative methods, like boundary element methods (BEM) and meshless methods, offer potential advantages but require further exploration for broader applicability. The choice of contact algorithm significantly impacts accuracy and efficiency; penalty methods, though computationally efficient, can lack accuracy at high friction coefficients; whereas, Lagrange multiplier methods, while more accurate, are computationally more demanding. The selection of an appropriate friction constitutive model is crucial; while the Coulomb friction law is common, more sophisticated models are necessary to represent real-world complexities, including surface roughness and temperature dependence. This review paper delves into the future research that prioritizes developing computationally efficient algorithms and parallel computing strategies. Advancements in constitutive modelling are vital for improved accuracy, along with enhanced contact detection algorithms for complex geometries and large deformations. Integrating experimental data and multiphysics capabilities will further enhance the reliability and applicability of these numerical techniques across various engineering applications. These advancements will ultimately improve the predictive power of simulations in diverse fields. Full article

Image formation in radio astronomy is often posed as a problem of constructing a nonnegative function from sparse samples of its Fourier transform. We explore an alternative approach that reformulates the problem in terms of estimating the entries of a diagonal covariance matrix from Gaussian data. Maximum-likelihood estimates of the covariance cannot be readily computed analytically; hence, we investigate an iterative algorithm originally proposed by Snyder, O’Sullivan, and Miller in the context of radar imaging. The resulting maximum-likelihood estimates tend to be unacceptably rough due to the ill-posed nature of the maximum-likelihood estimation of functions from limited data, so some kind of regularization is needed. We explore penalized likelihoods based on entropy functionals, a roughness penalty proposed by Silverman, and an information-theoretic formulation of Good’s roughness penalty crafted by O’Sullivan. We also investigate algorithm variations that perform a generic smoothing step at each iteration. The results illustrate that tuning parameters allow for a tradeoff between the noise and blurriness of the reconstruction. Full article

Signal propagation attenuation is greater in the post-disaster collapsed environment than that it is indoor or outdoor. The transmission environment is seriously affected by multi-path and non-line-of-sight transmission. When the signals penetrate the ruins and reach the receiver, their power may become very weak, which greatly affects the success rate of signal acquisition by the receiver. In the post-disaster environment, wireless signal propagation is severely blocked, which leads to serious signal attenuation and non-line-of-sight propagation, and signal acquisition distance and direction of detection equipment are limited. An optimization method of post-disaster wireless positioning detection equipment based on semi-deterministic programming was proposed, which allowed us to construct a location model of multiple detection equipment. The decision variable with variable nodes was generated. The reference node and penalty function were determined by fast rough positioning of the target. The node selection algorithm based on semi-definite programming was used to find the optimal node combination and complete the precise location. The performance of this algorithm was better than other known SDP optimization algorithms for reference nodes, and the correct selection accuracy of locating nodes was higher than 90%. Compared with the fixed main nodes, the positioning accuracy of the optimization algorithm was improved by 15.8%. Full article

Artificial roughness on the absorber of the solar air heater (SAH) is considered to be the best passive technology for performance improvement. The roughened SAHs perform better in comparison to conventional SAHs under the same operational conditions, with some penalty of higher pumping power requirements. Thermo-hydraulic performance, based on effective efficiency, is much more appropriate to design roughened SAH, as it considers both the requirement of pumping power and useful heat gain. The shape, size, and arrangement of artificial roughness are the most important factors for the performance optimization of SAHs. The parameters of artificial roughness and operating parameters, such as the Reynolds number (Re), temperature rise parameter (ΔT/I) and insolation (I) show a combined effect on the performance of SAH. In this case study, various performance parameters of SAH have been evaluated to show the effect of distinct artificial roughness, investigated previously. Therefore, thermal efficiency, thermal efficiency improvement factor (TEIF) and the effective efficiency of various roughened absorbers of SAH have been predicted. As a result, thermal and effective efficiencies strongly depend on the roughness parameter, Re and ΔT/I. Staggered, broken arc hybrid-rib roughness shows a higher value of TEIF, thermal and effective efficiencies consistently among all other distinct roughness geometries for the ascending values of ΔT/I. This roughness shows the maximum value of effective efficiency equals 74.63% at a ΔT/I = 0.01 K·m²/W. The unique combination of parameters p/e = 10, e/D_h = 0.043 and α = 60° are observed for best performance at a ΔT/I higher than 0.00789 K·m²/W. Full article

Motivated by mapping adverse artifactual events caused by body movements in electroencephalographic (EEG) signals, we present a functional independent component analysis based on the spectral decomposition of the kurtosis operator of a smoothed principal component expansion. A discrete roughness penalty is introduced in the orthonormality constraint of the covariance eigenfunctions in order to obtain the smoothed basis for the proposed independent component model. To select the tuning parameters, a cross-validation method that incorporates shrinkage is used to enhance the performance on functional representations with a large basis dimension. This method provides an estimation strategy to determine the penalty parameter and the optimal number of components. Our independent component approach is applied to real EEG data to estimate genuine brain potentials from a contaminated signal. As a result, it is possible to control high-frequency remnants of neural origin overlapping artifactual sources to optimize their removal from the signal. An R package implementing our methods is available at CRAN. Full article

Smoothing is one of the fundamental procedures in functional data analysis (FDA). The smoothing parameter $\lambda$ influences data smoothness and fitting, which is governed by selecting automatic methods, namely, cross-validation (CV) and generalized cross-validation (GCV) or subjective assessment. However, previous biomechanics research has only applied subjective assessment in choosing optimal $\lambda$ without using any automatic methods beforehand. None of that research demonstrated how the subjective assessment was made. Thus, the goal of this research was to apply the FDA method to smoothing and differentiating kinematic data, specifically right hip flexion/extension (F/E) angle during the American kettlebell swing (AKS) and determine the optimal $\lambda .$ CV and GCV were applied prior to the subjective assessment with various values of $\lambda$ together with cubic and quintic spline (B-spline) bases using the FDA approach. The selection of optimal $\lambda$ was based on smoothed and well-fitted first and second derivatives. The chosen optimal $\lambda$ was $1\times {10}^{-12}$ with a quintic spline (B-spline) basis and penalized fourth-order derivative. Quintic spline is a better smoothing and differentiation method compared to cubic spline, as it does not produce zero acceleration at endpoints. CV and GCV did not give optimal $\lambda ,$ forcing subjective assessment to be employed instead. Full article

As air pollution characterized by fine particulate matter has become one of the most serious environmental issues in China, a critical understanding of the behavior of major pollutant is increasingly becoming very important for air pollution prevention and control. The main concern of this study is, within the framework of functional data analysis, to compare the fluctuation patterns of PM_2.5 concentration between provinces from 1998 to 2016 in China, both spatially and temporally. By converting these discrete PM_2.5 concentration values into a smoothing curve with a roughness penalty, the continuous process of PM_2.5 concentration for each province was presented. The variance decomposition via functional principal component analysis indicates that the highest mean and largest variability of PM_2.5 concentration occurred during the period from 2003 to 2012, during which national environmental protection policies were intensively issued. However, the beginning and end stages indicate equal variability, which was far less than that of the middle stage. Since the PM_2.5 concentration curves showed different fluctuation patterns in each province, the adaptive clustering analysis combined with functional analysis of variance were adopted to explore the categories of PM_2.5 concentration curves. The classification result shows that: (1) there existed eight patterns of PM_2.5 concentration among 34 provinces, and the difference among different patterns was significant whether from a static perspective or multiple dynamic perspectives; (2) air pollution in China presents a characteristic of high-emission “club” agglomeration. Comparative analysis of PM_2.5 profiles showed that the heavy pollution areas could rapidly adjust their emission levels according to the environmental protection policies, whereas low pollution areas characterized by the tourism industry would rationally support the opportunity of developing the economy at the expense of environment and resources. This study not only introduces an advanced technique to extract additional information implied in the functions of PM_2.5 concentration, but also provides empirical suggestions for government policies directed to reduce or eliminate the haze pollution fundamentally. Full article