141 research outputs found
Publisher Correction: Multi-step ahead forecasting of electrical conductivity in rivers by using a hybrid Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) model enhanced by Boruta-XGBoost feature selection algorithm
Get PDFCorrection to: Scientific Reportshttps://doi.org/10.1038/s41598-024-65837-0, published online 01 July 2024 In the original version of this Article, Changhyun Jun and Aitazaz Ahsan Farooque were omitted as corresponding authors. The correct corresponding authors for this Article are Masoud Karbasi, Changhyun Jun and Aitazaz Ahsan Farooque. The original Article has been corrected. © The Author(s) 2024
Background data for: "The instantaneous structure of a turbulent wall-bounded flow influenced by freestream turbulence: streamwise evolution"
No full textThis data set contains planar Particle Image Velocimetry measurement fields for the experiments described in the article titled "The instantaneous structure of a turbulent wall-bounded flow influenced by freestream turbulence: streamwise evolution" (doi:10.1017/jfm.2024.1008).
The experiments were conducted in a water channel at the Norwegian University of Science and Technology. The setup includes an active grid to control freestream conditions. To analyze the evolution of the flow, the boundary layer was tested at four different streamwise locations for three grid sequences with freestream turbulence intensities up to 10.9%. Careful preprocessing was implemented to ensure high accuracy and minimal uncertainties.
This work was funded by the Research Council of Norway (see funding information): Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the Research Council of Norway. The granting authority cannot be held responsible for them.</p
Autonomous cooperative visual navigation for planetary exploration robots
No full textPlanetary robotics navigation has attracted the great attention of many researchers in recent years. Localization is one of the most important problems for robots on another planet in the lack of GPS. The robots need to be able to know their location and the surrounding map in the environment concurrently, to work and communicate together on another planet. In the current work, a novel algorithm is designed to cooperatively localize a team of robots on another planet. Consequently, a robust algorithm is developed for cooperative Visual Odometry (VO) to localize each robot in a planetary environment while detecting both intra-loop closure and inter-loop closures using previously observed area by the robot and shared area from other robots, respectively. To validate the proposed algorithm, a comparison is provided between the proposed cooperative VO and the single version of VO. Accordingly, a planetary analogue real dataset is employed to investigate the accuracy of the proposed algorithm. The results promise the concept of cooperative VO to significantly increase the accuracy of localization
Importance Sampling for a Markov Modulated Queuing Network with Customer Impatience until the End of Service
Get PDFFor more than two decades, there has been a growing of interest in fast simulation techniques for estimating probabilities of rare events in queuing networks. Importance sampling is a variance reduction method for simulating rare events. The present paper carries out strict deadlines to the paper by Dupuis et al for a two node tandem network with feedback whose arrival and service rates are modulated by an exogenous finite state Markov process. We derive a closed form solution for the probability of missing deadlines. Then we have employed the results to an importance sampling technique to estimate the probability of total population overflow which is a rare event. We have also shown that the probability of this rare event may be affected by various deadline values.Importance Sampling, Queuing Network, Rare Event, Markov Process, Deadline
Electrophoretic deposition of Cu2ZnSn(S0.5Se0.5)4 films using solvothermal synthesized nanoparticles
No full textSLAM in Dynamic Environments: A Deep Learning Approach for Moving Object Tracking Using ML-RANSAC Algorithm
Get PDFThe important problem of Simultaneous Localization and Mapping (SLAM) in dynamic environments is less studied than the counterpart problem in static settings. In this paper, we present a solution for the feature-based SLAM problem in dynamic environments. We propose an algorithm that integrates SLAM with multi-target tracking (SLAMMTT) using a robust feature-tracking algorithm for dynamic environments. A novel implementation of RANdomSAmple Consensus (RANSAC) method referred to as multilevel-RANSAC (ML-RANSAC) within the Extended Kalman Filter (EKF) framework is applied for multi-target tracking (MTT). We also apply machine learning to detect features from the input data and to distinguish moving from stationary objects. The data stream from LIDAR and vision sensors are fused in real-time to detect objects and depth information. A practical experiment is designed to verify the performance of the algorithm in a dynamic environment. The unique feature of this algorithm is its ability to maintain tracking of features even when the observations are intermittent whereby many reported algorithms fail in such situations. Experimental validation indicates that the algorithm is able to perform consistent estimates in a fast and robust manner suggesting its feasibility for real-time applications
Robust Adaptive Fuzzy Fractional Control for Nonlinear Chaotic Systems with Uncertainties
Get PDFThe control of nonlinear chaotic systems with uncertainties is a challenging problem that has attracted the attention of researchers in recent years. In this paper, we propose a robust adaptive fuzzy fractional control strategy for stabilizing nonlinear chaotic systems with uncertainties. The proposed strategy combined a fuzzy logic controller with fractional-order calculus to accurately model the system’s behavior and adapt to uncertainties in real-time. The proposed controller was based on a supervised sliding mode controller and an optimal robust adaptive fractional PID controller subjected to fuzzy rules. The stability of the closed-loop system was guaranteed using Lyapunov theory. To evaluate the performance of the proposed controller, we applied it to the Duffing–Holmes oscillator. Simulation results demonstrated that the proposed control method outperformed a recently introduced controller in the literature. The response of the system was significantly improved, highlighting the effectiveness and robustness of the proposed approach. The presented results provide strong evidence of the potential of the proposed strategy in a range of applications involving nonlinear chaotic systems with uncertainties
Distributed Cooperative Visual Odometry For Planetary Exploration Rovers
No full textNavigation is a basic skill for planetary exploration robots. In the last years, planetary robotics navigation has become an important research field that includes all the robot capabilities such as perception, localisation, and mapping. This paper provides a novel algorithm to cooperatively localise a distributed team of robots on another planet when the initial pose of the robots is unknown. To perform this, a single Visual Odometry (VO) algorithm based on the conventional KLT feature tracker is employed to localise each single exploration rover in a planetary environment while detecting loop closures. The trajectory of the robots is described by a pose graph form in the designed Cooperative VO (CVO) algorithm. Detecting loop closure from the previously observed areas by the robots leads to triggering the optimisation process and improving the accuracy of the localisation. Accordingly, a planetary analogue real dataset is used to investigate the accuracy of the proposed algorithm. The superiority of the proposed distributed CVO is proved by comparing the obtained results with the single VO algorithm
Distributed Cooperative Visual Odometry For Planetary Exploration Rovers
No full textNavigation is a basic skill for planetary exploration robots. In the last years, planetary robotics navigation has become an important research field that includes all the robot capabilities such as perception, localisation, and mapping. This paper provides a novel algorithm to cooperatively localise a distributed team of robots on another planet when the initial pose of the robots is unknown. To perform this, a single Visual Odometry (VO) algorithm based on the conventional KLT feature tracker is employed to localise each single exploration rover in a planetary environment while detecting loop closures. The trajectory of the robots is described by a pose graph form in the designed Cooperative VO (CVO) algorithm. Detecting loop closure from the previously observed areas by the robots leads to triggering the optimisation process and improving the accuracy of the localisation. Accordingly, a planetary analogue real dataset is used to investigate the accuracy of the proposed algorithm. The superiority of the proposed distributed CVO is proved by comparing the obtained results with the single VO algorithm
- …
