Enhanced Capsule Style Housing components with multiple bedrooms
Product Details:
Place of origin: China
Certification: CE, FCC
Model Number: Model E7 Capsule | Model E5 Capsule | Apple Cabin | Model J-20 Capsule | Model O5 Capsule | QQ Cabin
Payment and shipping terms:
Minimum order quantity: 1 unit
Packaging Details: Film wrapping, foam and wooden box
Delivery time: 4-6 weeks after payment
Payment terms: T/T in advance
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Product Name
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Enhanced Capsule Style Housing components with multiple bedrooms |
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Exterior Equipment
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Galvanized steel frame; Fluorocarbon aluminum alloy shell; Insulated, waterproof and moisture-proof construction; Hollow tempered
glass windows; Hollow tempered laminated glass skylight; Stainless steel side-hinged entry door. |
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Interior Equipment
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Integrated modular ceiling &wall; Stone plastic composite floor; Privacy glass door for bathroom; Marble/tile floor for bathroom;
Washstand /washbasin /bathroom mirror; Toilet /faucet /shower /floor drain; Whole house lighting system; Whole house plumbing &electrical system; Blackout curtains; Air conditioner; Bar table; Entryway cabinet. |
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Room Control Unit
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Key card switch; Multiple scenario modes; Lights&curtains with intelligent integrated control; Intelligent voice control; Smart
lock. |
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Attention enhanced capsule network for text classification by
Website:europepmc.org
Introduction:This paper proposes an enhanced capsule network text classification model Syntax-AT-CapsNet for text classification tasks. The model first uses graph convolutional neural networks as submodules to encode syntactic dependency trees, extract syntactic information in text, and further integrate with sequence information and dependency
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SECaps: A Sequence Enhanced Capsule Model for- Springer
Website:link.springer.com
Introduction:we propose a Sequence Enhanced Capsule model, dubbed as SECaps model, to relieve this problem. Specifically, following the work of capsule networks, we propose the seq-caps layer, which considers sequence infor-mation and spatial information of legal texts simultaneously. Then we design an attention residual unit, which provides auxiliary
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ME-CapsNet: A Multi-Enhanced Capsule Networks with Routing
Website:arxiv.org
Introduction:In this section, the different components equipped by our work to complete the robust architecture is discussed exten-sively. Our architecture predominantly has three blocks to multi-enhance the feature relationship of CNN: the SE Primary Capsule (SE-PC) block, SE Classification Capsule (SE-CC) block and the Mask by Layer (MbL) block. That is
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Empirical Study on convergence of Capsule Networks with
Website:people.cs.vt.edu
Introduction:A capsule is a group of neurons whose outputs represent different properties of the same entity in the image. Each layer in the capsule network consists of many capsules. So capsule networks can be viewed nested neural networks. On a high level, the capsule networks,as described by Hinton et. al [8], works as follows: each capsule in a layer
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SECaps: A Sequence Enhanced Capsule Model for Charge
Website:link.springer.com
Introduction:SECaps model includes four parts: Input layer, Multiple seq-caps layer, Attention, Output layer. Figure 2 show the architecture of SECaps model. Input layer: In this part, we treat the fact description of a case as a sequence of words \ (x = \ {x_1, x_2, \cdots , x_n\}\), then, each word of the sequence is transformed to the primary capsule.
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Topic-Enhanced Capsule Network for Multi-Label Emotion
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(PDF) Feature Enhanced Capsule Networks for Robust Automatic
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Introduction:3.1 CapsRater Capsule Neural Networks were introduced to accurately identify hierarchical relation- ships between objects and the features that constitute those objects. A capsule is a struc- ture that essentially contains information about the probability and orientation of these features.
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Topic-Enhanced Capsule Network for Multi-Label Emotion
Website:dl.acm.org
Introduction:To tackle these problems, we propose a topic-enhanced capsule network, which contains two main parts: a variational autoencoder and a capsule module, for multi-label emotion detection task. Specifically, the variational autoencoder can learn the latent topic information of the text, and the capsule module can capture rich features for
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Diagnostics Free Full-Text Image-Enhanced Capsule- MDPI
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ME-CapsNet: A Multi-Enhanced Capsule Networks with Routing
Website:ui.adsabs.harvard.edu
Introduction:Convolutional Neural Networks need the construction of informative features, which are determined by channel-wise and spatial-wise information at the network's layers. In this research, we focus on bringing in a novel solution that uses sophisticated optimization for enhancing both the spatial and channel components inside each layer's receptive field. Capsule Networks were used to understand
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