With the vigorous development of the photovoltaic industry, how to improve the efficiency of photovoltaic power generation has become an important issue, among which partial shadow occlusion is an important reason aff...
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In recent years, hashing methods have been popular in the large-scale media search for low storage and strong representation capabilities. To describe objects with similar overall appearance but subtle differences, mo...
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This paper provides a new monitoring method based on the attention mechanism and online sequential extreme learning machines(OS-ELM) for nonlinear dynamic process,referred as attentive ***-ELM provides the prediction ...
This paper provides a new monitoring method based on the attention mechanism and online sequential extreme learning machines(OS-ELM) for nonlinear dynamic process,referred as attentive ***-ELM provides the prediction of outputs and the update of parameters by sequential *** cope with nonlinearity,the attention mechanism is utilized to map data to a high-dimensional feature *** effect of the attention mechanism and the single hidden layer feedforward network trained with OS-ELM work collaboratively in monitoring the real-time process data to detect possible *** addition,fault detection rate and false alarm rate are adopted to evaluate the monitoring *** effectiveness of the proposed method is illustrated by a typically complicated industrial system.
In this article, the complex task assignment, scheduling and planning of complex heterogeneous multi-robot welding process are studied. For large workpiece welding tasks that involve complex heterogeneous multi-robot ...
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作者：
Le YuXinde LiPengfei ZhangZhentong ZhangFir DunkinSchool of Automation
Southeast University Nanjing China and Nanjing Center for Applied Mathematics Nanjing China and Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing China School of Automation
Southeast University Nanjing China and Nanjing Center for Applied Mathematics Nanjing China and Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing China and Southeast University Shenzhen Research Institute Shenzhen China School of Automation
Southeast University Nanjing China and Nanjing Center for Applied Mathematics Nanjing China
Model-Agnostic Meta-Learning (MAML) and its variants have shown remarkable performance in scenarios characterized by a scarcity of labeled data during the training phase of machine learning models. Despite these succe...
Model-Agnostic Meta-Learning (MAML) and its variants have shown remarkable performance in scenarios characterized by a scarcity of labeled data during the training phase of machine learning models. Despite these successes, MAMLbased approaches encounter significant challenges when there is a substantial discrepancy in the distribution of training and testing tasks, resulting in inefficient learning and limited generalization across domains. Inspired by classical proportional-integral-derivative (PID) control theory, this study introduces a Layer-Adaptive PID (LA-PID) Optimizer, a MAML-based optimizer that employs efficient parameter optimization methods to dynamically adjust task-specific PID control gains at each layer of the network, conducting a first-principles analysis of optimal convergence conditions. A series of experiments conducted on four standard benchmark datasets demonstrate the efficacy of the LA-PID optimizer, indicating that LA-PID achieves state-of-the-art performance in few-shot classification and cross-domain tasks, accomplishing these objectives with fewer training steps. Code is available on https://***/yuguopin/LA-PID.
This paper addresses the stabilization problem of time-varying delay systems subject to actuator saturation. A novel class of Lyapunov functional with a nonlinear metric is developed to guarantee that there exists a L...
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This paper addresses the stabilization problem of time-varying delay systems subject to actuator saturation. A novel class of Lyapunov functional with a nonlinear metric is developed to guarantee that there exists a Lyapunov-Razumikhin function that strictly decreases for the controlled system. Such a nonlinear metric is parameterized by a multi-dimensional Taylor network with concise topology, which brings into the high-order dynamic information and guarantees real-time performance. A sum-of-squares programming approach based on the designed Lyapunov functional is formulated to deduce the stability criteria, which maximizes the estimate of the region of attraction and ensures the uniform asymptotic stability of the closed-loop system theoretically. A numerical example demonstrates the effectiveness of the proposed stabilization control scheme.
Remotely controlled soft continuum robots with active steering capability have broad prospects in medical ***,conventional continuum robots have the miniaturization *** paper presents a microscale soft continuum micro...
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Remotely controlled soft continuum robots with active steering capability have broad prospects in medical ***,conventional continuum robots have the miniaturization *** paper presents a microscale soft continuum microrobot with steering and locomotion capabilties based on magnetic field *** magnetically driven soft continuum microrobot is made of NdFeB particles and polyd imethylsiloxane(PDMS),and it can be as small as 200 pum in ***,a hydrogel layer is covered on the surface of the microrobot,which not only overcomes the adhesion force between the microobjects and the soft tip but also reduces the friction between the microrobot and *** performance test indicates the soft continuum microrobot featured excellent control and steering *** experimental results demonstrate that the soft continuum microrobot can travel through the microfluidic channel by its own vibration and flexibly steer in a bifurcation ***,the micromanipulation of microbeads in the microfluidic channels proves that the proposed microscale soft continuum microrobot has a great potential for intravascu lar manipu lation.
Based on the port-controlled dissipative Hamiltonian(PCDH)system,a novel nonlinear(NN)H∞control is designed for the wind farm side(WFS)voltage source converter(VSC)of VSC-based high-voltage direct current(VSC-HVDC)tr...
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Based on the port-controlled dissipative Hamiltonian(PCDH)system,a novel nonlinear(NN)H∞control is designed for the wind farm side(WFS)voltage source converter(VSC)of VSC-based high-voltage direct current(VSC-HVDC)transmission system connected large wind ***,the uncertain nonlinear dynamical model of WFS VSC in the VSC-HVDC is established by taking external disturbances and parameter variations into ***,the dissipative Hamiltonian structure of the WFS VSC system is constructed by means of variable ***,on the basis of the obtained dissipative Hamiltonian structure,a NN H∞control is put ***,the simulation results show that the proposed NN H∞control gains the advantage over the interconnection and damping assignment passivity-based(IDA-PB)control.
Time series forecasting is crucial for many fields, such as disaster warning, weather prediction, and energy consumption. The Transformer-based models are considered to have revolutionized the field of sequence modeli...
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Autonomous driving still leaves a challenging task that how to apply the complementary information captured from different sensors, i.e. cameras and LiDAR, to handle place recognition task. In this paper, a brand new ...
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Autonomous driving still leaves a challenging task that how to apply the complementary information captured from different sensors, i.e. cameras and LiDAR, to handle place recognition task. In this paper, a brand new pipeline was designed to tackle the retrieval problem with a multi-modal late fusion. The light-weighted convolutional blocks is applied to encode features from images, followed by converting them into pseudo point clouds. Inspired by the progress of the Transformer, the network employs a residual point transformer module to extract feature vectors for 3D points and pseudo point clouds respectively. Finally, the two corresponding local descriptors are fused to get a robust fused global descriptor, which is capable of place recognition task after end-to-end training. Experimental results collected from sequence 00, 02, 05, 06 of KITTI dataset validate that fusing information from two modalities is able to enhance performance.
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