Real-Time Sensing and Fault Diagnosis for Transmission Lines

Shakiba, Fatemeh Mohammadi; Shojaee, Milad; Azizi, S. Mohsen; Zhou, Megnchu

doi:10.53941/ijndi0101004

Cited by 92 publications

(35 citation statements)

References 60 publications

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“…Recently, machine learning models have been gradually introduced into lunar and planetary research [52][53][54]. Further studies will explore the potential of more advanced machine learning models with regard to lunar brightness temperature analysis [55][56][57].…”

Section: Discussionmentioning

confidence: 99%

A Unified Brightness Temperature Features Analysis Framework for Mapping Mare Basalt Units Using Chang’e-2 Lunar Microwave Sounder (CELMS) Data

Yuan

Meng

et al. 2023

Remote Sensing

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The brightness temperature (TB) features extracted from Chang’e-2 Lunar Microwave Sounder (CELMS) data represent the passive microwave thermal emission (MTE) from the lunar regolith at different depths. However, there have been few studies assessing the importance and contribution of each TB feature for mapping mare basalt units. In this study, a unified framework of TB features analysis is proposed through a case study of Mare Fecunditatis, which is a large basalt basin on the eastern nearside of the Moon. Firstly, TB maps are generated from original CELMS data. Next, all TB features are evaluated systematically using a range of analytical approaches. The Pearson coefficient is used to compute the correlation of features and basalt classes. Two distance metrics, normalized distance and J-S divergence, are selected to measure the discrimination of basalt units by each TB feature. Their contributions to basalt classification are quantitatively evaluated by the ReliefF method and out-of-bag (OOB) importance index. Then, principal component analysis (PCA) is applied to reduce the dimension of TB features and analyze the feature space. Finally, a new geological map of Mare Fecunditatis is generated using CELMS data based on a random forest (RF) classifier. The results will be of great significance in utilizing CELMS data more widely as an additional tool to study the geological structure of the lunar basalt basin.

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Section: Discussionmentioning

confidence: 99%

A Unified Brightness Temperature Features Analysis Framework for Mapping Mare Basalt Units Using Chang’e-2 Lunar Microwave Sounder (CELMS) Data

Yuan

Meng

et al. 2023

Remote Sensing

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“…Recently, artificial neural networks have been successfully applied to RSs owing to their strong feature extraction abilities [7,22,35,39,42,47,49]. For example, a convolutional sequence embedding (Caser) RS has been proposed in [42] for product recommendation, where a convolutional neural network (CNN) is employed to capture the sequential features by analyzing the embedding matrix.…”

Section: Introductionmentioning

confidence: 99%

A unified approach to designing sequence-based personalized food recommendation systems: tackling dynamic user behaviors

Zhang

Wang

Liu

et al. 2023

Int. J. Mach. Learn. & Cyber.

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The recommender system (RS) is a well-known practical application of the state-of-the-art information filtering and machine learning technologies. Traditional recommendation approaches, including collaborative and content-based filtering techniques, have been widely employed to provide suggestions in RSs, where the user-item interaction matrix is the primary data source. In many application domains, interactions between users and items are more likely to be dynamic rather than static, and thus dynamic user behaviors should be taken into account when solving recommendation tasks in order to provide more accurate suggestions. In this work, we consider the sequentially ordered information from user-item interactions in the RSs where a sequence-based recommendation model is put forward with applications to the food recommendation scenario. Furthermore, the long short-term memory (LSTM) network is employed as the building block to establish such a recommendation model, and a collaborative filtering unit is adopted to make personalized food recommendation. The proposed LSTM-based RS is successfully applied to a real-world food recommendation data set. Experimental results demonstrate that the developed method outperforms some currently popular RSs in terms of precision, recall, mean average precision and mean reciprocal rank in food recommendation.

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“…Deep neural networks (DNNs) have achieved remarkable accomplishments in a variety of applications such as pattern recognition [5,18,31,41,60], and have also shown sustained superiorities in comparison to other methods. However, the large amount of model parameters and high performance demand on GPUs have also brought about great challenges on storage and time costs.…”

Section: Introductionmentioning

confidence: 99%

Differentiable channel pruning guided via attention mechanism: a novel neural network pruning approach

Cheng

Wang²,

Ma³

et al. 2023

Complex Intell. Syst.

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Neural network pruning offers great prospects for facilitating the deployment of deep neural networks on computational resource limited devices. Neural architecture search (NAS) provides an efficient way to automatically seek appropriate neural architecture design for compressed model. It is observed that, for existing NAS-based pruning methods, there is usually a lack of layer information when searching the optimal neural architecture. In this paper, we propose a new NAS approach, namely, differentiable channel pruning method guided via attention mechanism (DCP-A), where the adopted attention mechanism is able to provide layer information to guide the optimization of the pruning policy. The training process is differentiable with Gumbel-softmax sampling, while parameters are optimized under a two-stage training procedure. The neural network block with the shortcut is dedicatedly designed, which is of help to prune the network not only on its width but also on its depth. Extensive experiments are performed to verify the applicability and superiority of the proposed method. Detailed analysis with visualization of the pruned model architecture shows that our proposed DCP-A learns explainable pruning policies.

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Real-Time Sensing and Fault Diagnosis for Transmission Lines

Cited by 92 publications

References 60 publications

A Unified Brightness Temperature Features Analysis Framework for Mapping Mare Basalt Units Using Chang’e-2 Lunar Microwave Sounder (CELMS) Data

A Unified Brightness Temperature Features Analysis Framework for Mapping Mare Basalt Units Using Chang’e-2 Lunar Microwave Sounder (CELMS) Data

A unified approach to designing sequence-based personalized food recommendation systems: tackling dynamic user behaviors

Differentiable channel pruning guided via attention mechanism: a novel neural network pruning approach

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