scholarly journals Fast inverse lithography based on dual-channel model-driven deep learning

2020 ◽  
Vol 28 (14) ◽  
pp. 20404 ◽  
Author(s):  
Xu Ma ◽  
Xianqiang Zheng ◽  
Gonzalo R. Arce
Keyword(s):  
Deep Learning ◽  
Channel Model ◽  
Model Driven ◽  
Dual Channel

scholarly journals Automatic Detection of Arrhythmia Based on Multi-Resolution Representation of ECG Signal

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1579
Author(s):  
Dongqi Wang ◽  
Qinghua Meng ◽  
Dongming Chen ◽  
Hupo Zhang ◽  
Lisheng Xu
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Deep Neural Networks ◽  
Channel Model ◽  
Expert Knowledge ◽  
Automatic Detection ◽  
Data Representation ◽  
Learning Technology ◽  
Arrhythmia Detection ◽  
Automatic Feature Extraction

Automatic detection of arrhythmia is of great significance for early prevention and diagnosis of cardiovascular disease. Traditional feature engineering methods based on expert knowledge lack multidimensional and multi-view information abstraction and data representation ability, so the traditional research on pattern recognition of arrhythmia detection cannot achieve satisfactory results. Recently, with the increase of deep learning technology, automatic feature extraction of ECG data based on deep neural networks has been widely discussed. In order to utilize the complementary strength between different schemes, in this paper, we propose an arrhythmia detection method based on the multi-resolution representation (MRR) of ECG signals. This method utilizes four different up to date deep neural networks as four channel models for ECG vector representations learning. The deep learning based representations, together with hand-crafted features of ECG, forms the MRR, which is the input of the downstream classification strategy. The experimental results of big ECG dataset multi-label classification confirm that the F1 score of the proposed method is 0.9238, which is 1.31%, 0.62%, 1.18% and 0.6% higher than that of each channel model. From the perspective of architecture, this proposed method is highly scalable and can be employed as an example for arrhythmia recognition.

scholarly journals Mining and Reuse on Design Knowledge of Non-standard Special Tool Based on Deep Learning

2020 ◽  
Author(s):  
Mingwei Wang ◽  
Jingtao Zhou ◽  
Xiaoying Chen ◽  
Zeyu Li
Keyword(s):  
Deep Learning ◽  
Association Rule ◽  
Deep Belief Network ◽  
Design Knowledge ◽  
Machining Features ◽  
Belief Network ◽  
Dual Channel ◽  
Engineering Drawings ◽  
Low Efficiency

Abstract Aiming at the problems of design difficulty, low efficiency and unstable quality of non-standard special tools, facing the strong correlation between part machining features and tools, this article takes the two-dimensional engineering drawings of tools and parts as research objects, proposes the research on mining and reuse on design knowledge of non-standard special tool based on deep learning. Firstly, a dual-channel deep belief network is established to complete the feature modeling of machining features and tool features; secondly, the deep belief network is used to realize the association relationship mining between the machining features and tool features; thirdly, both the key local features of the tool and the overall similar design case of the tool are reused through association rule reasoning; finally, the non-standard special turning tool is used as an example to verify the effectiveness of the proposed method.

scholarly journals Energy and Exergy Analysis of Dual Channel Solar Air Collector with Different Absorber Plates Geometry

2018 ◽  
Vol 24 (4) ◽  
pp. 19
Author(s):  
Najim A. Jassim ◽  
Suhaib J. Shbailat
Keyword(s):  
Heat Transfer ◽  
Channel Model ◽  
Heat Removal ◽  
Wire Mesh ◽  
Plate Model ◽  
Absorber Plate ◽  
Dual Channel ◽  
Energy And Exergy Analysis ◽  
Mass Flow Rates ◽  
Flat Plate Collector

Flat-plate collector considers most common types of collectors, for ease of manufacturing and low price compared with other collectors. The main aim of the present work is to increase the efficiency of the collector, which can be achieved by improving the heat transfer and minimize heat loss experimentally. Five types of solar air collectors have been tested, which conventional channel with a smooth absorber plate (model I), dual channel with a smooth absorber plate (model II), dual channel with perforating “V” corrugated absorber plate (model III), dual channel with internal attached wire mesh (model Ⅳ), and dual channel with absorber sheet of transparent honeycomb, (model Ⅴ). The dual channel collector used for increasing heat transfer area and heat removal factor to improve thermal performance. The outdoor test was conducted during the period December (2016) to February (2017) at different mass flow rates 0.0217 kg/s, 0.0271 kg/s and 0.0325 kg/s. The experiments were carried out from 8:30 AM to 3:00 PM for clear days. Experimental results show that the average thermal efficiency was (72.2 %) for model (III), (40.2 %) for model (I), (51.6 %) for model (II), (65.1 %) for model (Ⅳ) and (59.7 %) for model (Ⅴ). At the last part of the study, the exergy analyses were derived for both collectors. The results of this part showed that the conventional channel model (I) is having largest irreversibility, and the dual channel collector model (III) is having a greatest exergetic efficiency.  

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