Industrial Internet of Things(IoT)connecting society and industrial systems represents a tremendous and promising paradigm *** IoT,multimodal and heterogeneous data from industrial devices can be easily collected,and ...
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Industrial Internet of Things(IoT)connecting society and industrial systems represents a tremendous and promising paradigm *** IoT,multimodal and heterogeneous data from industrial devices can be easily collected,and further analyzed to discover device maintenance and health related potential knowledge *** data-based fault diagnosis for industrial devices is very helpful to the sustainability and applicability of an IoT *** how to efficiently use and fuse this multimodal heterogeneous data to realize intelligent fault diagnosis is still a *** this paper,a novel Deep Multimodal Learning and Fusion(DMLF)based fault diagnosis method is proposed for addressing heterogeneous data from IoT environments where industrial devices ***,a DMLF model is designed by combining a Convolution Neural Network(CNN)and Stacked Denoising Autoencoder(SDAE)together to capture more comprehensive fault knowledge and extract features from different modal ***,these multimodal features are seamlessly integrated at a fusion layer and the resulting fused features are further used to train a classifier for recognizing potential ***,a two-stage training algorithm is proposed by combining supervised pre-training and fine-tuning to simplify the training process for deep structure models.A series of experiments are conducted over multimodal heterogeneous data from a gear device to verify our proposed fault diagnosis *** experimental results show that our method outperforms the benchmarking ones in fault diagnosis accuracy.
This paper addresses the stability of networked controlsystems with aperiodic sampling and time-varying network-induced delay. The sampling intervals are assumed to vary within a known interval. The transmission dela...
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In this article, an optimal switching integrity attack problem is investigated to study the response of feedback controlsystems under attack. The authors model the malicious attacks on sensors as additive norm bounde...
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This paper presents an interactive motion control method based on reinforcement learning, designed to assist children with autism who have social motor impairments through a mirror game intervention. The virtual teach...
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A multi-modal emotion recognition method based on facial multi-scale features and cross-modal attention (MS-FCA) network is proposed. The MSFCA model improves the traditional single-branch ViT network into a two-branc...
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ISBN:
(数字)9798331521950
ISBN:
(纸本)9798331521967
A multi-modal emotion recognition method based on facial multi-scale features and cross-modal attention (MS-FCA) network is proposed. The MSFCA model improves the traditional single-branch ViT network into a two-branch ViT architecture by using classification tokens in each branch to interact with picture embeddings in the other branch, which facilitates effective interactions between different scales of information. Subsequently, audio features are extracted using ResNet18 network. The cross-modal attention mechanism is used to obtain the weight matrices between different modal features, making full use of inter-modal correlation and effectively fusing visual and audio features for more accurate emotion recognition. Two datasets are used for the experiments: eNTERFACE'05 and REDVESS dataset. The experimental results show that the accuracy of the proposed method on the eNTERFACE'05 and REDVESS datasets is 85.42% and 83.84% respectively, which proves the effectiveness of the proposed method.
For a class of unstable discrete-time multi-variable switched systems with parametric uncertainty, an identification scheme is developed in this paper. Specifically, the identification algorithm is derived to identify...
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Analysis and design techniques for cooperative flocking of nonholonomic multi-robot systems with connectivity maintenance on directed graphs are presented. First, a set of bounded and smoothly distributed control prot...
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The problem of flocking of second-order multiagent systems with connectivity preservation is investigated in this paper. First, for estimating the algebraic connectivity as well as the corresponding eigenvector, a new...
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The problem of distributed coordinated tracking control for networked Euler-Lagrange systems without velocity measurements is investigated. Under the condition that only a portion of the followers have access to the l...
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In this paper,a fault tolerant control method based on active disturbance rejection control(ADRC) and radial basis function neural network(RBFNN) is proposed for a class of multi-input-multi-output nonlinear system wi...
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ISBN:
(纸本)9781538629185
In this paper,a fault tolerant control method based on active disturbance rejection control(ADRC) and radial basis function neural network(RBFNN) is proposed for a class of multi-input-multi-output nonlinear system with actuator faults,components faults and sensor *** proposed method does not rely on the plant *** regarding the faults and plant uncertainties as the disturbance,through the observation of extended state observer and the compensation of feedback control signal,this method achieves the fault tolerance control of the plant with component fault and actuator *** sensor faults,in this work,radial basis function neural network is applied to estimate the real output of the *** this output estimation is utilized by active disturbance rejection control to achieve the fault tolerance of ***,the effectiveness of the proposed method is validated by the simulation results of the three-tank system.
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