150 research outputs found
Development of a SMA-fishing-line-McKibben bending actuator
High power-to-weight ratio soft artificial muscles are of overarching importance to enable inherently safer solutions to human-robot interactions. Traditional air driven soft McKibben artificial muscles are linear actuators. It is impossible for them to realize bending motions through a single McKibben muscle. Over two McKibben muscles should normally be used to achieve bending or rotational motions, leading to heavier and larger systems. In addition, air driven McKibben muscles are highly nonlinear in nature, making them difficult to be controlled precisely. A SMA(shape memory alloy)–fishing–line–McKibben (SFLM) bending actuator has been developed. This novel artificial actuator, made of a SMA-fishing-line muscle and a McKibben muscle, was able to produce the maximum output force of 3.0 N and the maximum bending angle (the rotation of the end face) of 61°. This may promote the application of individual McKibben muscles or SMA-fishing-line muscles alone. An output force control method for SFLM is proposed, and based on MATLAB/Simulink software the experiment platform is set up, the effectiveness of control system is verified through output force experiments. A three-fingered SFLM gripper driven by three SFLMs has been designed for a case study, which the maximum carrying capacity is 650.4 ± 0.2 g
Influence of catwalk design parameters on the galloping of constructing main cables in long-span suspension bridges
A main cable of a long-span suspension bridge is semi-surrounded by a catwalk during construction. Thus, design parameters of a catwalk may have influences on the galloping stability of a main cable during construction. To study the influence of catwalk design parameters on the galloping of steepled main cables, two main foci have been conducted. Firstly, the aerodynamic coefficients of the catwalk with actual design parameters are obtained by numerical simulation based on computational fluid dynamics (CFD), and the numerical results are compared with those of the previous wind tunnel test. Several typical main cables with different cross sections of a long-span suspension bridge during construction are selected, and their Den Hartog coefficients are obtained based on the numerical simulation considering the aerodynamic influences of the catwalks. Then four typical working conditions of a main cable which have great potential to occur galloping are selected based on the galloping analyze, and their aerodynamic coefficients considering the influence of the catwalk with different design parameters are obtained. The influence of the catwalk design parameters on galloping of the main cables is analyzed based on the Den Hartog criterion. Results indicate that catwalk design parameters have evident influences on aerodynamic coefficients and galloping of the main cables. The parameters of the catwalk which are favorable for suppressing the galloping of the main cables are determined, which establish a good guideline for the galloping-resistant design of the catwalk-main cable system on suspension bridges
Fixed-time safe tracking control of uncertain high-order nonlinear pure-feedback systems via unified transformation functions
summary:In this paper, a fixed-time safe control problem is investigated for an uncertain high-order nonlinear pure-feedback system with state constraints. A new nonlinear transformation function is firstly proposed to handle both the constrained and unconstrained cases in a unified way. Further, a radial basis function neural network is constructed to approximate the unknown dynamics in the system and a fixed-time dynamic surface control (FDSC) technique is developed to facilitate the fixed-time control design for the uncertain high-order pure-feedback system. Combined with the proposed unified transformation function and the FDSC technique, an adaptive fixed-time control strategy is proposed to guarantee the fixed-time tracking. The novel original results of the paper allow to design the independent unified flexible fixed-time control strategy taking into account the actual possible constraints, either present or missing. Numerical examples are presented to demonstrate the proposed fixed-time tracking control strategy
Automated Federated Learning in Mobile Edge Networks -- Fast Adaptation and Convergence
Federated Learning (FL) can be used in mobile edge networks to train machine
learning models in a distributed manner. Recently, FL has been interpreted
within a Model-Agnostic Meta-Learning (MAML) framework, which brings FL
significant advantages in fast adaptation and convergence over heterogeneous
datasets. However, existing research simply combines MAML and FL without
explicitly addressing how much benefit MAML brings to FL and how to maximize
such benefit over mobile edge networks. In this paper, we quantify the benefit
from two aspects: optimizing FL hyperparameters (i.e., sampled data size and
the number of communication rounds) and resource allocation (i.e., transmit
power) in mobile edge networks. Specifically, we formulate the MAML-based FL
design as an overall learning time minimization problem, under the constraints
of model accuracy and energy consumption. Facilitated by the convergence
analysis of MAML-based FL, we decompose the formulated problem and then solve
it using analytical solutions and the coordinate descent method. With the
obtained FL hyperparameters and resource allocation, we design a MAML-based FL
algorithm, called Automated Federated Learning (AutoFL), that is able to
conduct fast adaptation and convergence. Extensive experimental results verify
that AutoFL outperforms other benchmark algorithms regarding the learning time
and convergence performance
Few-shot Message-Enhanced Contrastive Learning for Graph Anomaly Detection
Graph anomaly detection plays a crucial role in identifying exceptional
instances in graph data that deviate significantly from the majority. It has
gained substantial attention in various domains of information security,
including network intrusion, financial fraud, and malicious comments, et al.
Existing methods are primarily developed in an unsupervised manner due to the
challenge in obtaining labeled data. For lack of guidance from prior knowledge
in unsupervised manner, the identified anomalies may prove to be data noise or
individual data instances. In real-world scenarios, a limited batch of labeled
anomalies can be captured, making it crucial to investigate the few-shot
problem in graph anomaly detection. Taking advantage of this potential, we
propose a novel few-shot Graph Anomaly Detection model called FMGAD (Few-shot
Message-Enhanced Contrastive-based Graph Anomaly Detector). FMGAD leverages a
self-supervised contrastive learning strategy within and across views to
capture intrinsic and transferable structural representations. Furthermore, we
propose the Deep-GNN message-enhanced reconstruction module, which extensively
exploits the few-shot label information and enables long-range propagation to
disseminate supervision signals to deeper unlabeled nodes. This module in turn
assists in the training of self-supervised contrastive learning. Comprehensive
experimental results on six real-world datasets demonstrate that FMGAD can
achieve better performance than other state-of-the-art methods, regardless of
artificially injected anomalies or domain-organic anomalies
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