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801
AUD-MTS: An Abnormal User Detection Approach Based on Power Load Multi-Step Clustering with Multiple Time Scales
Published 2019-08-01“…Firstly, we combine RBM (Restricted Boltzmann Machine) hidden feature learning with K-Means clustering to extract typical load patterns in the short-term. …”
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802
Cross-Modality Person Re-Identification Algorithm Based on Two-Branch Network
Published 2023-07-01“…Firstly, we use infrared image colorization technology to convert infrared images into color images to reduce the differences between modalities and propose a visible-infrared cross-modality person re-identification algorithm based on Two-Branch Network with Double Constraints (VI-TBNDC), which consists of two main components: a two-branch network for feature extraction and a double-constrained identity loss for feature learning. The two-branch network extracts the features of both data sets separately, and the double-constrained identity loss ensures that the learned feature representations are discriminative enough to distinguish different people from two different patterns. …”
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803
Automatic Transmission Bearing Fault Diagnosis Based on Comprehensive Index Method and Convolutional Neural Network
Published 2022-10-01“…Rolling-element bearing fault diagnosis has some problems in the applied environment, such as low signal-to-noise ratio, weak feature extraction, low efficiency of feature learning and the complex structure of diagnosis models. …”
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804
Model NOx, SO<sub>2</sub> Emissions Concentration and Thermal Efficiency of CFBB Based on a Hyper-Parameter Self-Optimized Broad Learning System
Published 2022-10-01“…A broad learning system (BLS) is a novel neural network algorithm, which shows good performance in multidimensional feature learning. However, the BLS has several hyper-parameters to be set in a wide range, so that the optimal combination between hyper-parameters is difficult to determine. …”
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805
Neighbor-Based Label Distribution Learning to Model Label Ambiguity for Aerial Scene Classification
Published 2021-02-01“…Experiments on the aerial image dataset (AID) and NWPU_RESISC45 (NR) datasets demonstrate that using the label distributions clearly improves the classification performance by assisting feature learning and mitigating over-fitting problems, and our method achieves state-of-the-art performance.…”
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806
Dual Modality Collaborative Learning for Cross-Source Remote Sensing Retrieval
Published 2022-03-01“…Finally, to supplement the specific knowledge to the common features, we develop modality transformation and the dual-modality feature learning modules. Their function is to transmit the specific knowledge from different sources mutually and fuse the specific and common features adaptively. …”
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807
Research on Virus Morphology Recognition Method Based on Enhanced Graph Convolutional Network
Published 2022-05-01“…Methods In this model, Convolutional Neural Network (CNN) was used to extract the local feature information between pixels, and GCN was used for graph feature learning combined with the nearest neighbor relationship between sample features. …”
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808
Multi-Label Attribute Selection of Arrhythmia for Electrocardiogram Signals with Fusion Learning
Published 2022-06-01“…The objective is to build a unified system with automatic feature learning which supports effective multi-label classification. …”
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809
Learning Attention-Aware Interactive Features for Fine-Grained Vegetable and Fruit Classification
Published 2021-07-01“…Specifically, we employ a region proposal network (RPN) to generate a collection of informative regions and apply a biattention module to learn global and local attentive feature maps, which are fused and fed into an interactive feature learning subnetwork. The novel neural structure is verified through extensive experiments and shows consistent performance improvement in comparison with the SOTA on the VegFru data set, demonstrating its superiority in fine-grained vegetable and fruit recognition. …”
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810
Development and validation of chest CT-based imaging biomarkers for early stage COVID-19 screening
Published 2022-09-01“…Furthermore, unsupervised feature learning led to the discovery of clinical-relevant imaging biomarkers from the vasculature-like signals for accurate and sensitive COVID-19 screening that have been double-blindly validated in an independent hospital (sensitivity: 0.941, specificity: 0.920, AUC: 0.971, accuracy 0.931, F1 score: 0.929). …”
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811
RPU-PVB: robust object detection based on a unified metric perspective with bilinear interpolation
Published 2023-12-01“…We propose the robustness optimization based on uniform metric perspective (RPU) for feature learning of clean and adversarial samples, drawing on the fine-grained idea. …”
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812
Dynamic Community Detection Method of a Social Network Based on Node Embedding Representation
Published 2022-12-01“…The node embedding method enables network structure feature learning and representation for social network community detection. …”
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813
Improving Urban Land Cover/Use Mapping by Integrating A Hybrid Convolutional Neural Network and An Automatic Training Sample Expanding Strategy
Published 2020-07-01“…To obtain a well-trained 2D ConvNet, a training sample expansion strategy was introduced to assist context feature learning. The H-ConvNet was tested in six highly heterogeneous urban regions around the world, and it was compared with support vector machine (SVM), object-based image analysis (OBIA), Markov random field model (MRF) and a newly proposed patch-based ConvNet system. …”
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814
A Multiscale Cross Interaction Attention Network for Hyperspectral Image Classification
Published 2023-01-01“…However, two challenging problems still exist: the first challenge is that redundant information is averse to feature learning, which damages the classification performance; the second challenge is that most of the existing classification methods only focus on single-scale feature extraction, resulting in underutilization of information. …”
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815
A Hybrid-Scale Feature Enhancement Network for Hyperspectral Image Classification
Published 2023-12-01“…Additionally, previous works usually focused on utilizing fixed-scale convolutional kernels or multiple available, receptive fields with varying scales to capture features, which leads to the underutilization of information and is vulnerable to feature learning. To remedy the above issues, we propose an innovative hybrid-scale feature enhancement network (HFENet) for HSI classification. …”
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816
A joint algorithm of multi-target detection and tracking for underground miners
Published 2022-10-01“…In the part of multi-target tracking, the omni-scale network (OSNet) is used to replace the shallow residual network in Deep SORT to carry out omni-directional feature learning. Therefore, pedestrian re-identification and target tracking precision are improved. …”
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817
DPSSD: Dual-Path Single-Shot Detector
Published 2022-06-01“…Our improved dual-path network is more adaptable to multi-scale object detection tasks, and we combine it with the feature fusion module to generate a multi-scale feature learning paradigm called the “Dual-Path Feature Pyramid”. …”
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818
Deep-Deterministic Policy Gradient Based Multi-Resource Allocation in Edge-Cloud System: A Distributed Approach
Published 2023-01-01“…To this end, we propose a deep-deterministic policy gradient (DDPG) based temporal feature learning attentional network (TFLAN) model to address the MRA problem. …”
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819
A Deep-Learning-Based Multimodal Data Fusion Framework for Urban Region Function Recognition
Published 2023-11-01“…In the first part of the complementary feature-learning and fusion module, we use a convolutional neural network (CNN) to extract visual features and social features. …”
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820
Learning from few subjects with large amounts of voice monitoring data
Published 2019Get full text
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