Search results

Dynamically tracking the target by unmanned ground vehicles (UGVs) plays a critical role in mobile drone recovery. This study aims to solve this challenge under diverse random disturbances, proposing a dynamic target tracking framework for UGVs based on target state estimation, trajectory prediction, and UGV control.

To mitigate the adverse effects of noise contamination in target detection, the authors use the extended Kalman filter (EKF) to improve the accuracy of locating unmanned aerial vehicles (UAVs). Furthermore, a robust motion prediction algorithm based on polynomial fitting is developed to reduce the impact of trajectory jitter caused by crosswinds, enhancing the stability of drone trajectory prediction. Regarding UGV control, a dynamic vehicle model featuring independent front and rear wheel steering is derived. Additionally, a linear time-varying model predictive control algorithm is proposed to minimize tracking errors for the UGV.

To validate the feasibility of the framework, the algorithms were deployed on the designed UGV. Experimental results demonstrate the effectiveness of the proposed dynamic tracking algorithm of UGV under random disturbances.

This paper proposes a tracking framework of UGV based on target state estimation, trajectory prediction and UGV predictive control, enabling the system to achieve dynamic tracking to the UAV under multiple disturbance conditions.

Concrete gravity dams are important structures for flood control and hydraulic power generation, but they can be vulnerable to seismic activity due to ground movements that trigger crack propagation.

To better understand the factors that affect the stability of concrete gravity dams against concrete fracture during earthquakes, a concrete plastic damage model has been utilized with two new expressions to simulate compressive and tensile damage variables.

The findings showed that the crack patterns were strongly influenced by the concrete’s strength. The simulation results led to the proposal of appropriate concrete properties aimed at minimizing damage. These findings, together with the proposed model, offer significant insights that can enhance the safety and stability of concrete gravity dam structures.

This study offers a comprehensive analysis of concrete behavior under varying grades and introduces simple and robust expressions for evaluating concrete parameters in plastic damage models. The versatility of these expressions enables accurate simulation of stress-strain curves for different grades, resulting in excellent agreement between model results and experimental findings. The simulation of the Koyna Dam case study demonstrates a similarity in crack patterns with previous simulations and field observations.

This study aims to study the influence of friction influencing factors between the wire rope and the liner on the safe use of the wire rope, which can provide guidance for the reliability design of the lifting system with strong dynamic response such as high speed, heavy load, etc., and improve the friction-driven stability of the system.

In this paper, the friction mechanism of wire rope and liner under the condition of excitation is investigated by means of wire rope-liner friction-vibration experimental platform and dynamic viscoelastic test of liner.

The results show that: With increasing excitation frequency, the friction between the three liner materials (G30, K25, PU) and the wire rope decreased, and the wear of the surface shape of the liners was greater. The dynamic thermomechanical analysis (DMA) test results showed that the viscoelasticity of the three liner materials increased when the frequency was increased.

Wire ropes are widely used in deep shaft hoisting and building elevators. Its operational reliability depends on whether there is sufficient friction between the wire rope and the friction liner, and whether the friction liner has good wear resistance. The study of the friction between the wire rope and the liner influencing factors is of great significance for the safe service of the wire rope.

The related results can provide guidance for the reliability design of lifting systems with strong dynamic response, such as high speed and heavy load, to improve the friction drive stability of the system.

With the increase of mining depth, to improve the transportation efficiency of the hoist used in deep and ultra-deep mines, as well as to ensure the safety and reliability of its operation, it is crucial that the large friction hoisting equipment has sufficient friction between the wire rope and the friction lining, as well as whether the friction lining has a good abrasion resistance.

In response to the issue of traditional algorithms often falling into local minima or failing to find feasible solutions in manipulator path planning. The purpose of this paper is to propose a 3D artificial moment method (3D-AMM) for obstacle avoidance for the robotic arm's end-effector.

A new method for constructing temporary attractive points in 3D has been introduced using the vector triple product approach, which generates the attractive moments that attract the end-effector to move toward it. Second, distance weight factorization and spatial projection methods are introduced to improve the solution of repulsive moments in multiobstacle scenarios. Third, a novel motion vector-solving mechanism is proposed to provide nonzero velocity for the end-effector to solve the problem of limiting the solution of the motion vector to a fixed coordinate plane due to dimensionality constraints.

A comparative analysis was conducted between the proposed algorithm and the existing methods, the improved artificial potential field method and the rapidly-random tree method under identical simulation conditions. The results indicate that the 3D-AMM method successfully plans paths with smoother trajectories and reduces the path length by 20.03% to 36.9%. Additionally, the experimental comparison outcomes affirm the feasibility and effectiveness of this method for obstacle avoidance in industrial scenarios.

This paper proposes a 3D-AMM algorithm for manipulator path planning in Cartesian space with multiple obstacles. This method effectively solves the problem of the artificial potential field method easily falling into local minimum points and the low path planning success rate of the rapidly-exploring random tree method.

Bionic flapping-wing aerial vehicles (FWAVs) mimic natural flyers to generate the lift and thrust, such as birds, bats and insects. As an important component of the FWAVs, the flapping wings are crucial for the flight performance. The aim of this paper is to study the effects of different wings on aerodynamic performance.

Inspired by the wings structure of birds, the authors design four cambered wings to analyze the effect of airfoils on the FWAVs aerodynamic performance. The authors design the motor-driven mechanism of flapping wings, and realize the control of flapping frequency. Combined with the wind tunnel equipment, the authors build the FWAVs force test platform to test the static and dynamic aerodynamic performance of different flapping wings under the state variables of flapping frequency, wind speed and inclined angle.

The results show that the aerodynamic performance of flapping wing with a camber of 20 mm is the best. Compared with flat wing, the average lift can be improved by 59.5%.

Different from the traditional flat wing design of FWAVs, different cambered flapping wings are given in this paper. The influence of airfoils on aerodynamic performance of FWAVs is analyzed and the optimal flapping wing is obtained.

This study aims to fill data gaps concerning solutions and practices used in sustainable food systems (SFS) in higher education (HE). The development of SFS is a vital global challenge in which HE may play a significant role.

Literature search and content analysis of found papers were performed. Additionally, the quantitative time trend of the emergence of research connected to SFS HE and the connectivity of content within the research papers about research questions were determined by regression analysis and data visualization, respectively.

It is evident that SFS education in universities (higher education institutes) is emerging, and the number of research papers is rapidly increasing. In the reviewed papers, universities recognized their significant role in managing wicked problems. The motivation for developing SFS education was high, with strong ambitions. SFS in HE includes developing education in-house through trans- and multidisciplinary solutions, developing education with stakeholders and supporting student growth to become responsible professionals and citizens.

When developing SFS education in HE both practical and theoretical research is needed. Ethical dimensions should be included in both research orientations because of the moral complexity that exists in SFS issues.

This study shows that competence in trans- and multidisciplinary working is needed. Concomitantly, the ability of cooperation between HE, business and society is vital when solving global food challenges. Also, the local tradition of food cultivation should be respected and maintained.

To the best of the authors’ knowledge, this is the first review on the development of SFS education in HEIs. A qualitative content analysis and data visualization were used to enrich the review.

Predicting commodity futures trading volumes represents an important matter to policymakers and a wide spectrum of market participants. The purpose of this study is to concentrate on the energy sector and explore the trading volume prediction issue for the thermal coal futures traded in Zhengzhou Commodity Exchange in China with daily data spanning January 2016–December 2020.

The nonlinear autoregressive neural network is adopted for this purpose and prediction performance is examined based upon a variety of settings over algorithms for model estimations, numbers of hidden neurons and delays and ratios for splitting the trading volume series into training, validation and testing phases.

A relatively simple model setting is arrived at that leads to predictions of good accuracy and stabilities and maintains small prediction errors up to the 99.273th quantile of the observed trading volume.

The results could, on one hand, serve as standalone technical trading volume predictions. They could, on the other hand, be combined with different (fundamental) prediction results for forming perspectives of trading trends and carrying out policy analysis.

This paper aims to estimate contact location from sparse and high-dimensional soft tactile array sensor data using the tactile image. The authors used three feature extraction methods: handcrafted features, convolutional features and autoencoder features. Subsequently, these features were mapped to contact locations through a contact location regression network. Finally, the network performance was evaluated using spherical fittings of three different radii to further determine the optimal feature extraction method.

This paper aims to estimate contact location from sparse and high-dimensional soft tactile array sensor data using the tactile image.

This research indicates that data collected by probes can be used for contact localization. Introducing a batch normalization layer after the feature extraction stage significantly enhances the model’s generalization performance. Through qualitative and quantitative analyses, the authors conclude that convolutional methods can more accurately estimate contact locations.

The paper provides both qualitative and quantitative analyses of the performance of three contact localization methods across different datasets. To address the challenge of obtaining accurate contact locations in quantitative analysis, an indirect measurement metric is proposed.

Finite element (FE) analysis can be used for both design and verification of components. In the case of 3D-printed materials, a proper characterization of properties, accounting for anisotropy and raster angles, can help develop efficient material models. This study aims to use compression tests to characterize short carbon-reinforced PA12 made by fused filament fabrication (FFF) and to model its behaviour by the FE method.

In this work, the authors focus on compression tests, using post-processed specimens to overcome external defects introduced by the FFF process. The material’s elastoplastic mechanical behaviour is modelled by an elastic stiffness matrix, Hill’s anisotropic yield criterion and Voce’s isotropic hardening law, considering the stacking sequence of raster angles. A FE analysis is conducted to reproduce the material’s compressive behaviour through the LS-DYNA software.

The proposed model can capture stress values at different deformation levels and peculiar aspects of deformed shapes until the onset of damage mechanisms. Deformation and damage mechanisms are strictly correlated to orientation and raster angle.

The paper aims to contribute to the understanding of 3D-printed material’s behaviour through compression tests on bulk 3D-printed material. The methodology proposed, enriched with an anisotropic damage criterion, could be effectively used for design and verification purposes in the field of 3D-printed components through FE analysis.

This paper aims to examine the dynamic relationship between economic growth and sustainable development, integrating the Environmental Kuznets Curve (EKC) in 22 Organization of Islamic Cooperation (OIC) member countries across income groups.

Using annual data between 1990 and 2022, the authors apply the cross-correlation coefficient (CCC) approach of Narayan et al. (Economic Modeling, 2016, 53, 388–397) to examine the lead/lag relationship between GDP per capita and sustainable development. This study further validates the findings through a panel Granger causality test and a fixed panel regression model.

This research provides evidence of a U-shaped EKC for only 1 out of 22 (5%) OIC countries. For 13 out of the 22 (59%) OIC countries, increasing income growth is expected to enhance sustainable development in the future. The results show that as income levels rise, there will be a more significant decline in sustainable development for high-income OIC countries in the future than for both middle-income groups, contradicting the EKC hypothesis. The findings from the panel Granger causality and panel regression models also support the CCC results.

This study proposes a reverse version of the EKC hypothesis and contributes to the literature on economic growth and environmental sustainability. With increasing economic growth, the results can assist OIC member governments and policy-makers in designing tailored policies and practical measures for future sustainable development.