120 research outputs found
Consistency of control performance in 3d overhead cranes under payload mass uncertainty
The paper addresses the problem of effectively and robustly controlling a 3D overhead crane under the payload mass uncertainty, where the control performance is shown to be consistent. It is proposed to employ the sliding mode control technique to design the closed-loop controller due to its robustness, regardless of the uncertainties and nonlinearities of the under-actuated crane system. The radial basis function neural network has been exploited to construct an adaptive mechanism for estimating the unknown dynamics. More importantly, the adaptation methods have been derived from the Lyapunov theory to not only guarantee stability of the closed-loop control system, but also approximate the unknown and uncertain payload mass and weight matrix, which maintains the consistency of the control performance, although the cargo mass can be varied. Furthermore, the results obtained by implementing the proposed algorithm in the simulations show the effectiveness of the proposed approach and the consistency of the control performance, although the payload mass is uncertain. © 2020 by the authors. Licensee MDPI, Basel, Switzerland
Depth-based Sampling and Steering Constraints for Memoryless Local Planners
By utilizing only depth information, the paper introduces a novel but
efficient local planning approach that enhances not only computational
efficiency but also planning performances for memoryless local planners. The
sampling is first proposed to be based on the depth data which can identify and
eliminate a specific type of in-collision trajectories in the sampled motion
primitive library. More specifically, all the obscured primitives' endpoints
are found through querying the depth values and excluded from the sampled set,
which can significantly reduce the computational workload required in collision
checking. On the other hand, we furthermore propose a steering mechanism also
based on the depth information to effectively prevent an autonomous vehicle
from getting stuck when facing a large convex obstacle, providing a higher
level of autonomy for a planning system. Our steering technique is
theoretically proved to be complete in scenarios of convex obstacles. To
evaluate effectiveness of the proposed DEpth based both Sampling and Steering
(DESS) methods, we implemented them in the synthetic environments where a
quadrotor was simulated flying through a cluttered region with multiple
size-different obstacles. The obtained results demonstrate that the proposed
approach can considerably decrease computing time in local planners, where more
trajectories can be evaluated while the best path with much lower cost can be
found. More importantly, the success rates calculated by the fact that the
robot successfully navigated to the destinations in different testing scenarios
are always higher than 99.6% on average.Comment: Submitted to the Journal of Intelligent & Robotic Systems (JINT
Modified Dijkstra's Routing Algorithm for Security with Different Trust Degrees
A great number of efficient methods to improve the performance of the networks have been proposed in physical-layer security for wireless communications. So far, the security and privacy in wireless communications is optimized based on a fixed assumption about the trustworthiness or trust degrees (TD) of certain wireless nodes. The nodes are often classified into different types such as eavesdroppers, untrusted relays, and trusted cooperative nodes. Wireless nodes in different networks do not completely trust each other when cooperating or relaying information for each other. Optimizing the network based on trust degrees plays an important role in improving the security and privacy for the modern wireless network. We proposed a novel algorithm to find the route with the smallest total transmission time from the source to the destination and still guarantee that the accumulated TD is larger than a trust degree threshold. Simulation results are presented to analyze the affects of the transmit SNR, node density, and TD threshold on different network performance elements
A depth-based hybrid approach for safe flight corridor generation in memoryless planning
This paper presents a depth-based hybrid method to generate safe flight corridors for a memoryless local navigation planner. It is first proposed to use raw depth images as inputs in the learning-based object-detection engine with no requirement for map fusion. We then employ an object-detection network to directly predict the base of polyhedral safe corridors in a new raw depth image. Furthermore, we apply a verification procedure to eliminate any false predictions so that the resulting collision-free corridors are guaranteed. More importantly, the proposed mechanism helps produce separate safe corridors with minimal overlap that are suitable to be used as space boundaries for path planning. The average intersection of union (IoU) of corridors obtained by the proposed algorithm is less than 2%. To evaluate the effectiveness of our method, we incorporated it into a memoryless planner with a straight-line path-planning algorithm. We then tested the entire system in both synthetic and real-world obstacle-dense environments. The obtained results with very high success rates demonstrate that the proposed approach is highly capable of producing safe corridors for memoryless local planning. © 2023 by the authors
Statistical evaluation of the geochemical data for prospecting polymetallic mineralization in the Suoi Thau – Sang Than region, Northeast Vietnam
In Northeast Vietnam, Suoi Thau-Sang Than is considered as a high potential area of polymetallic deposits. 1,720 geochemical samples were used to investigate polymetallic mineralization; thereby polymetallic ore occurrences in this study region were discovered and the statistical and multivariate analysis helps to define geochemical anomalies in some northeastern regions, namely Suoi Thau, Sang Than, and Ban Kep. The statistical method and cluster analysis of geochemical data indicate that the Cu, Pb, and Zn elements are good indicators, and most of them comply with the lognormal or gamma distribution. Based on the third-order threshold, the geochemical anomalies of the content of the Cu, Pb, and Zn elements reflect the concentration of copper forming ore bodies in the mineralized zone, and clearly show the concentration in three distinct zones. The trend surface analysis which was employed to determine spatial variations and relationships among these good indicator elements and anomalous areas revealed relative changes in the content of the indicator elements, and they can be considered as regular. Moreover, the goodness of fit obtained trend functions of Pb and Zn, and Cu elements is a third-degree trend surface model. These results indicate that the models can be useful in studying geochemical anomalies and analyzing the tendency of the concentration of indicator elements in the Suoi Thau-Sang Than region. Additionally, it is suggested that the statistical analysis shows a remarkable potential to use the bottom river sediments in the region to investigate polymetallic mineralization. Moreover, geochemical data can help to evaluate geochemical anomalies of the pathfinder elements and potential mineral mapping of the Suoi Thau-Sang Than region in Northeast Vietnam
Securing MIMO Wiretap Channel with Learning-Based Friendly Jamming under Imperfect CSI
Wireless communications are particularly vulnerable to eavesdropping attacks
due to their broadcast nature. To effectively deal with eavesdroppers, existing
security techniques usually require accurate channel state information (CSI),
e.g., for friendly jamming (FJ), and/or additional computing resources at
transceivers, e.g., cryptography-based solutions, which unfortunately may not
be feasible in practice. This challenge is even more acute in low-end IoT
devices. We thus introduce a novel deep learning-based FJ framework that can
effectively defeat eavesdropping attacks with imperfect CSI and even without
CSI of legitimate channels. In particular, we first develop an
autoencoder-based communication architecture with FJ, namely AEFJ, to jointly
maximize the secrecy rate and minimize the block error rate at the receiver
without requiring perfect CSI of the legitimate channels. In addition, to deal
with the case without CSI, we leverage the mutual information neural estimation
(MINE) concept and design a MINE-based FJ scheme that can achieve comparable
security performance to the conventional FJ methods that require perfect CSI.
Extensive simulations in a multiple-input multiple-output (MIMO) system
demonstrate that our proposed solution can effectively deal with eavesdropping
attacks in various settings. Moreover, the proposed framework can seamlessly
integrate MIMO security and detection tasks into a unified end-to-end learning
process. This integrated approach can significantly maximize the throughput and
minimize the block error rate, offering a good solution for enhancing
communication security in wireless communication systems.Comment: 12 pages, 15 figure
Deep Transfer Learning: A Novel Collaborative Learning Model for Cyberattack Detection Systems in IoT Networks
Federated Learning (FL) has recently become an effective approach for
cyberattack detection systems, especially in Internet-of-Things (IoT) networks.
By distributing the learning process across IoT gateways, FL can improve
learning efficiency, reduce communication overheads and enhance privacy for
cyberattack detection systems. Challenges in implementation of FL in such
systems include unavailability of labeled data and dissimilarity of data
features in different IoT networks. In this paper, we propose a novel
collaborative learning framework that leverages Transfer Learning (TL) to
overcome these challenges. Particularly, we develop a novel collaborative
learning approach that enables a target network with unlabeled data to
effectively and quickly learn knowledge from a source network that possesses
abundant labeled data. It is important that the state-of-the-art studies
require the participated datasets of networks to have the same features, thus
limiting the efficiency, flexibility as well as scalability of intrusion
detection systems. However, our proposed framework can address these problems
by exchanging the learning knowledge among various deep learning models, even
when their datasets have different features. Extensive experiments on recent
real-world cybersecurity datasets show that the proposed framework can improve
more than 40% as compared to the state-of-the-art deep learning based
approaches.Comment: 12 page
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