To address the issues of lacking datasets and low recognition accuracy for paint film defects, this paper proposes a denoising diffusion implicit model (DDIM) for data augmentation of paint film defects and innovative...
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ISBN:
(数字)9798350386905
ISBN:
(纸本)9798350386912
To address the issues of lacking datasets and low recognition accuracy for paint film defects, this paper proposes a denoising diffusion implicit model (DDIM) for data augmentation of paint film defects and innovatively suggests a classification method combining Selective Kernel Networks (SKNet) with SqueezeNet model. Initially, the DDIM is used for dataset expansion, followed by an evaluation of the similarity between generated and captured paint film defect images using multiscale structural similarity (MS-SSIM). The training and generation effects of DDIM are then compared with those of DCGAN. In the SqueezeNet model, a Selective Kernel module is added following the fire7 module to enhance the model’s attention mechanism. The results show that all types of paint film defect images generated by DDIM have MS-SSIM indices above 0.64, with most exceeding 0.7. The combined approach of Selective Kernel Networks(SKNet) and SqueezeNet outperforms other attention mechanisms, achieving an accuracy above 96.3%. The method demonstrates promising prospects for paint film defect detection, enhancing identification efficiency and accuracy while reducing detection costs, and is applicable to mobile or embedded devices.
Non-intrusive load monitoring (NILM) implements energy decomposition in a non-intrusive manner and provides a promising path to tap demand response potential for flexible load resources in residential and commercial b...
Non-intrusive load monitoring (NILM) implements energy decomposition in a non-intrusive manner and provides a promising path to tap demand response potential for flexible load resources in residential and commercial buildings. Modern deep neural networks (DNNs) have succeeded in NILM fields, especially under offline settings where the task for a network is fixed and all training data are provided simultaneously. However, these algorithms face an inevitable challenge. When new or unknown appliances are added, the learning algorithms mentioned above exhibit a pronounced susceptibility to catas-trophic forgetting as they undertake the incremental acquisition of knowledge associated with these new appliance classes. To tackle this challenge, a novel incremental learning method is proposed in this paper. An effective linear model is introduced to correct the strong deviation when learning unknown appliance class. Numerical results on the PLAID public datasets illustrate the effectiveness of the presented method.
This article considers a rough neurocomputing approach to the design of the classify layer of a Brooks architecture for a robot control system. In the case of the line-crawling robot (LCR) described in this article, r...
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In modern industry,data has become an indispensible resource for the whole process of production and *** the optimization of the control performance,the data-driven-based PID controller is introduced in this *** on th...
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ISBN:
(纸本)9781479970186
In modern industry,data has become an indispensible resource for the whole process of production and *** the optimization of the control performance,the data-driven-based PID controller is introduced in this *** on the data-driven control law,the parameters of the PID controller will be renewed automatically by using the real and historical input/output data of the *** then,the control action can be update in real *** the perspective of applications,the data-driven-based PID controller is researched as the controller in the bed temperature of circulating fluidized bed *** simulation results show that the data-driven-based PID controller is suitable for the bed temperature control,and its performance is better than that of the conventional controller.
Taste sensation can be objectively measured using electroencephalography (EEG) or electromyography (EMG). How-ever, it is still challenging to effectively utilize the complementary information from EEG and EMG signals...
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Taste sensation can be objectively measured using electroencephalography (EEG) or electromyography (EMG). How-ever, it is still challenging to effectively utilize the complementary information from EEG and EMG signals in taste sensation recognition. This paper proposes a bimodal fusion network (Bi-FusionNet) for recognizing basic taste sensations (sour, sweet, bitter, salty, umami, and blank). Two convolutional backbones with similar structures are designed to separately extract the single-modal features of EEG and EMG. Then, EEG and EMG features are concatenated for bimodal interaction and complementarity. Finally, three loss functions are adopted: a center loss for aggregating intra-class samples, a mean squared error loss for sequence positions for minimizing the difference between signals during the stimulation, and a softmax loss for minimizing the entropy of prediction and true labels. The results on the taste sensation dataset show that bimodal fusion improves recognition performance, and Bi-FusionNet outperforms single-modal methods and other fusion methods. Bi-FusionNet paves the way for the application of multimodal fusion in taste sensation recognition.
Image inpainting is a long-standing key problem in the field of computer vision, which aims to fill the missing parts of an image with visually realistic and semantically appropriate content. For a long time, in the r...
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We propose an algorithm for optimal input design in nonlinear stochastic dynamic systems. The approach relies on minimizing a function of the covariance of the parameter estimates of the system with respect to the inp...
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Lesion segmentation plays an important role in medical image processing and analysis. There exist several successful dynamic programming (DP) based segmentation methods for general images. In those methods, the gradie...
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Traditional diagnostic models for laser gyroscopes, widely utilized as high-precision angular velocity sensors in aerospace applications, often suffer from limited reliability and accuracy due to the difficulty of fea...
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In this paper, a new distributed algorithm is presented to achieve the consensus of time variations and that of initial time simultaneously. By combining both controller and estimator design methods, it obtains higher...
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ISBN:
(纸本)9781509035502
In this paper, a new distributed algorithm is presented to achieve the consensus of time variations and that of initial time simultaneously. By combining both controller and estimator design methods, it obtains higher synchronization precision with stronger robustness against noisy inputs resulted from crystal oscillators. Furthermore, the control input is ensured bounded to make our algorithm realizable in practical implementation. The implementation of the algorithm allows the receiving end to be event-triggered, while the transmitting end is executed periodically. The performance of the algorithm is illustrated by the given numerical simulations.
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