Wind turbine detection is essential for the power industry and regulatory agencies to efficiently and accurately determine their number and spatial distribution. Under complex underlying surface conditions, the detect...
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In the paper, we investigate the secure communication of multiple-input single-output (MISO) systems with multiple eavesdroppers. We jointly design the beamforming (BF) and the artificial noise (AN) in MISO systems wi...
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The development of deep learning models for intelligent vehicles rely on a large number of reliable data, among which large-scale and accurately labeled traffic scene image data is conducive to promoting the research ...
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Dual-comb spectroscopy is a competitive means of laser absorption spectroscopy owing to broad spectral range and high spectral resolution. Single-cavity dual-comb systems based on mode-locked lasers hold promising pot...
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USB interfaces have become ubiquitous in various Internet of Things (IoT) devices, all adhering to the same USB protocol. While enhancing convenience, they also widen the potential attack surface. Fuzzing is a proacti...
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Unsupervised person re-identification (re-ID) is a challenging task which has received extensive attention from the industry. State-of-the-arts unsupervised re-ID methods adopt clustering techniques to generate pseudo...
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Diagnosis of compound faults remains a challenge during fault diagnosis of bearings, owing to the different fault parameters coupling, fault characteristics diversity, and the exponential increasement of the number of...
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ISBN:
(纸本)9781665427470
Diagnosis of compound faults remains a challenge during fault diagnosis of bearings, owing to the different fault parameters coupling, fault characteristics diversity, and the exponential increasement of the number of possible failure modes. Current compound faults diagnostic methods, which are usually based on supervised or semi-supervised learning, require sufficient labeled or unlabeled training data for each compound faults. In industrial scenarios, neither labeled nor unlabeled training data of compound faults are usually difficult to collect and sometimes even inaccessible, whereas single faults samples are easy to obtain. Based on these issues, we construct a novel generative zero-shot learning (ZSL) compound faults diagnosis model identifies unseen compound faults using only single faults samples as training set. This model comprises several modules, namely semantic vector definition, feature extractor, generative adversarial modules. Firstly, we devise a unified semantic vector definition method for expressing single and compound faults based on theoretical correlation of characteristics between single fault and compound faults vibration data. Secondly, a CNN-based feature extractor is designed for extraction the fault features from the time-frequency domain of vibration data. Thirdly, a generative adversarial module performs adversarial training of semantic vectors and fault features of single faults to learn the mapping relationship between the fault features and the fault semantic vectors. Once trained, the generator is able to generate compound fault features using the compound fault semantic vectors, rather than any compound fault samples. Finally, the K-nearest neighbor method is adopted to identify the unseen compound faults by measuring the distance between the extracted feature from the testing compound fault samples and the generated features. The effectiveness of the proposed method is verified on a self-built bearing test stand. The results show
The virtual simulation experiment teaching system based on the Internet of Things simulates the Internet of Things sensing device in the form of software, and integrates relatively new teaching methods and theoretical...
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Recently,object detection based on convolutional neural networks(CNNs)has developed *** backbone networks for basic feature extraction are an important component of the whole detection ***,we present a new feature ext...
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Recently,object detection based on convolutional neural networks(CNNs)has developed *** backbone networks for basic feature extraction are an important component of the whole detection ***,we present a new feature extraction strategy in this paper,which name is *** this strategy,we design:1)a sandwich attention feature fusion module(SAFF module).Its purpose is to enhance the semantic information of shallow features and resolution of deep features,which is beneficial to small object detection after feature fusion.2)to add a new stage called D-block to alleviate the disadvantages of decreasing spatial resolution when the pooling layer increases the receptive *** method proposed in the new stage replaces the original method of obtaining the P6 feature map and uses the result as the input of the regional proposal network(RPN).In the experimental phase,we use the new strategy to extract *** experiment takes the public dataset of Microsoft Common Objects in Context(MS COCO)object detection and the dataset of Corona Virus Disease 2019(COVID-19)image classification as the experimental object *** results show that the average recognition accuracy of COVID-19 in the classification dataset is improved to 98.163%,and small object detection in object detection tasks is improved by 4.0%.
Relation prediction in knowledge graphs (KGs) aims at predicting missing relations in incomplete triples, whereas the dominant paradigm by KG embeddings has a limitation to predict the relation between unseen entities...
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