In this article,we use a convolutional autoencoder neural network to reduce data dimensioning and rebuild soliton dynamics in a passively mode-locked fiber *** on the particle characteristic in double solitons and tri...
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In this article,we use a convolutional autoencoder neural network to reduce data dimensioning and rebuild soliton dynamics in a passively mode-locked fiber *** on the particle characteristic in double solitons and triple solitons interactions,we found that there is a strict correspondence between the number of minimum compression parameters and the number of independent parameters of soliton *** shows that our network effectively coarsens the high-dimensional data in nonlinear *** work not only introduces new prospects for the laser self-optimization algorithm,but also brings new insights into the modeling of nonlinear systems and description of soliton interactions.
At present, computed tomography (CT) is widely used to assist disease diagnosis. Especially, computer aided diagnosis (CAD) based on artificial intelligence (AI) recently exhibits its importance in intelligent healthc...
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At present, computed tomography (CT) is widely used to assist disease diagnosis. Especially, computer aided diagnosis (CAD) based on artificial intelligence (AI) recently exhibits its importance in intelligent healthcare. However, it is a great challenge to establish an adequate labeled dataset for CT analysis assistance, due to the privacy and security issues. Therefore, this paper proposes a convolutionalautoencoder deep learning framework to support unsupervised image features learning for lung nodule through unlabeled data, which only needs a small amount of labeled data for efficient feature learning. Through comprehensive experiments, it shows that the proposed scheme is superior to other approaches, which effectively solves the intrinsic labor-intensive problem during artificial image labeling. Moreover, it verifies that the proposed convolutionalautoencoder approach can be extended for similarity measurement of lung nodules images. Especially, the features extracted through unsupervised learning are also applicable in other related scenarios.
During aero-engine production and design, optimizing engine performance frequently involves minimizing the gap between dynamic and static rotors, thereby elevating the likelihood of friction. Rubbing amplifies equipme...
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ISBN:
(纸本)9780791888032
During aero-engine production and design, optimizing engine performance frequently involves minimizing the gap between dynamic and static rotors, thereby elevating the likelihood of friction. Rubbing amplifies equipment vibration and, in severe instances, jeopardizes the entire shaft system, rendering it incapable of normal operation and incurring substantial economic losses. This paper presents a diagnostic method for identifying engine rotor rubbing faults using wavelet time frequency images with a deep convolutional autoencoder neural network. The method converts original vibration signals into two-dimensional time-frequency images via wavelet decomposition, serving as input for a deep neuralnetwork. Leveraging the autoencoder's characteristics with minimal dimensionality reduction parameters and the convolutionalnetwork's robust feature extraction capability for two-dimensional data, the network is trained to extract crucial image features. Subsequently, these features undergo classification and diagnosis of faults by training a Softmax classifier. An experimental system was established to validate the method's feasibility, simulating fault phenomena such as misalignment, looseness, cracks, and rubbing by adjusting the fixing degree of the rotor system support seat. Test data are collected using the data acquisition system, which also preprocesses the raw data by introducing noise and conducting wavelet transforms. Subsequently, the data undergo transformation into time frequency images, serving as inputs to the autoencoderneuralnetwork for extracting essential features. These features are inputted into this classification convolutionalnetwork, enabling fault diagnosis. The results could demonstrate the validity of the deep convolutional autoencoder neural network for detecting engine rotor rubbing faults.
Intelligent power management system is an important part of the smart grid, in which the non-intrusive load monitoring technology is one of the key technologies. However, most of the load monitoring methods regard thi...
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Intelligent power management system is an important part of the smart grid, in which the non-intrusive load monitoring technology is one of the key technologies. However, most of the load monitoring methods regard this task as a multi-classification problem, thus it is not effective to identify the unknown loads that did not participate in training. In this paper, an adaptive non-intrusive load monitoring method based on feature fusion has been proposed which utilizes both information of harmonic current feature and voltage-current (V-I) trajectory feature. The harmonic current feature is obtained from the high-frequency load sampling data through Fast Fourier Transform (FFT). At the same time, the V-I trajectory feature is obtained by using the pre-trained convolutional autoencoder neural network that was trained for feature extraction on a public dataset. After the feature extraction, the similarity between these two feature vectors and the available feature vectors in the database is calculated by TOPSIS algorithm. Then the load monitoring can be carried out according to the similarity. When the maximum similarity is greater or equal than the set threshold, the load is considered to be one of the existing loads in the database, otherwise, it will be considered as a new type of load, and the load feature database will be updated. The autoencoder model is trained by using the V-I trajectory from the BLUED dataset and the PLAID dataset is used to verify the identification accuracy of the proposed algorithm by comparison with different algorithms. On the PLAID dataset, the identification accuracy can reach above 97%. Finally, on the embedded Linux system with STM32MP1 as the core, some household electrical appliances are used for validation in real house environment. The results show that the proposed method has improved the capability of load identification by using the complementarity of different features. It can be carried out in real time on embedded system and
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