To develop a round-the-clock surveillance system, acoustic signal recognition-based strategies have been recently studied in [5][6] [7] [8]. Real experiments on four frequently used excavation
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Nov 03, 2021 · Any development style (no-code, low-code, to pro-code) and deployment choice (multi-tenant cloud or a dedicated tenant) is satisfied with the extensibility and breadth that Okta and Auth0 offer.
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proposed a deep network called multi-label extreme learning machine with ML-ELM-RBF for multi-label classi cation. Moreover, ML-ELM-RBF staked ELMs based on Auto Encoder (ELM-AE) in the rst layers is followed by a nal layer of RBF for classi cation. Lately, a ML-ELM [11{13] was proposed in which multiple layers of ELM-AE were used for
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Up to10%cash back · Oct 10, 2018 · The proposed MCEA multi-view network architecture is composed of four modules: shape rendering, multi-view CNNs, ELM-AE-based feature aggregation, and ELM classifier, as depicted in Fig. 1.The view-based shape representation task starts from multiple views of a 3D model, which is rendered with different virtual cameras.
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Sep 30, 2016 · the-clock recognition system for excavation de vices [8]–[10]. T o protect the underground pipeline network, Y ang et al. [ 8 ] investigated the recognition of three representati ve excava-
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Mar 09, 2018 · Up to10%cash back · Kasun et al. introduced the ELM auto-encoder (ELM-AE) and proposed multilayer ELM (ML-ELM) to perform layer-by-layer unsupervised learning. Yu et al. ( 2015 ) utilized stacked ELMs to learn deep representations of raw data.
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May 14, 2020 · 1. Introduction. Recognition of human activities has become very popular during the last decade, particularly in fields such as bio-medicine, elderly care, sports injury detection, entertainment, military devices, pattern recognition and soft Robotics, innumerable applications can be found,,,,,,, .This is mainly due to new advances in the area of machine learning and …
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We propose a new ELM Autoencoder (ELM-AE) based domain adaption algorithm which describes the subspaces of source and target domain by ELM-AE and then carries out subspace alignment to project different domains into a common new space. By leveraging nonlinear approximation ability and efficient one-pass learning ability of ELM-AE, the proposed domain …
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Jan 01, 2020 · 1. Introduction. UAC aims at the recognition of acoustic streams frequently encountered in the urban environment, which are found crucial to smart city engineering,,,,,,,,,, .For instances, Asensio organized a special issue on "acoustics in smart cities", which the recent achievements on urban sounds monitoring, soundscape assessment, analyses and …
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Local Subspace Classifier with Transformation Invariance for Appearance-Based Character Recognition in Natural Images (KH, SH), pp. 533–537. ICDAR-2013-HollausGS #documentation #image #multi Enhancement of Multispectral Images of Degraded Documents by Employing Spatial Information ( FH, MG, RS ), pp. 145–149.
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Feb 14, 2018 · 1. Introduction. Clustering is a task of finding a partition of data in an unsupervised manner such that the data in the same group are more similar to each other than to those in other groups .Many real world problems in a vast variety of fields, such as market research, document classification, and bioinformatics, can be formulated as the fundamental task of clustering.
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An ELM-AE model is created by training the AE using the ELM algorithm and serves as a feature extraction strategy [40]. Thus, the AE output weight matrix …
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Get a QuoteMuqing Deng, Tingchang Fan, Jiuwen Cao, Siu-Ying Fung, Jing Zhang: Human gait recognition based on deterministic learning and knowledge fusion through multiple walking views. J. Frankl. Inst. 357 (4): 2471-2491 (2020)
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The graph Laplacian-based manifold regularisation was added to the loss function of the ELM-AE (GELM-AE) in, where the Laplacian matrix was built for the reconstruction output. Based on the GELM-AE, the denoising GELM-AE has been developed in [ 14 ] by adding the mask noises into the inputs and reconstructing the noise-free inputs as the targets.
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Excavation Devices Recognition with ELM-AE based Multi-Feature Learning Algorithm. DSL 2018: 1-5 [c35] view. electronic edition via DOI; Semi-supervised learning using ensembles of multiple 1D-embedding-based label boosting. Int. J. Wavelets Multiresolution Inf. Process. 14 …
Get a QuoteAiming at problem of low efficacy of early warning of rock burst in coal mine, a multisystem and multiparameter integrated early warning method based on genetic algorithm (GA) is proposed. In this method, firstly, the temporal-spatial-intensity information of energy incubation process of rock burst is deeply mined, and the multidimensional precursory characteristic parameter …
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The graph Laplacian-based manifold regularisation was added to the loss function of the ELM-AE (GELM-AE) in, where the Laplacian matrix was built for the reconstruction output. Based on the GELM-AE, the denoising GELM-AE has been developed in [ 14 ] by adding the mask noises into the inputs and reconstructing the noise-free inputs as the targets.
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Excavation Devices Recognition with ELM-AE based Multi-Feature Learning Algorithm Conference Paper Nov 2018 Fei Cheng Jiuwen Cao Jianzhong Wang [] Pierre-Paul JL Vidal A novel excavation device
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May 27, 2020 · In geotechnical engineering, excavation process is affected by many factors, so that the evaluation of the excavatability is a difficult task in the field. In order to have a better equipment selection and minimize the excavation cost, it seems that there is a need to develop a more accurate evaluation model for excavatability. In this paper, a hybrid model based on …
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