This paper proposes for the first time to combine the concept of deep learning to analyze the data under the energyPLAN *** the process of data simulation,the Generative Adversarial Networks(GAN) algorithm is introduc...
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ISBN:
(纸本)9781665431293
This paper proposes for the first time to combine the concept of deep learning to analyze the data under the energyPLAN *** the process of data simulation,the Generative Adversarial Networks(GAN) algorithm is introduced to compensate for the missing data of individual energy indicators,so as to realize the intelligent monitoring and renewable *** is significant for secondary utilization of *** innovation of Energy Management Module(EMM) and availability of renewable energy development,we have achieved phased results,but there are still challenges:(1) The feature extraction of the data is broken down or stolen,and the distortion problem *** original data of the training sample is abnormal,and the integrity of the data needs to be expanded;(2) Large-scale data relies solely on *** are still feasibility problems,and further verification is *** the article,the realtime power data and simulation collected by the international platform energyPLAN are used to verify the deep learning,and the compensation data is integrated into the verification at the same time.
Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by atypical functional integration of brain regions. The vast majority of neuroimaging studies of ASD have focused on older children, adole...
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Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by atypical functional integration of brain regions. The vast majority of neuroimaging studies of ASD have focused on older children, adolescents, and adults with the disorder. Very little work has explored whole-brain functional connectivity of young children with ASD. Here, we collected resting-state functional magnetic resonance imaging data from 58 young children (mean age 4.98 years;29 with ASD;29 matched healthy controls [HC]). All children were under sedation during scanning. A functional "connectedness" method was first used to seek for brain regions showing atypical functional connectivity (FC) in children with ASD. Then, a recurrent-seek strategy was applied to reveal atypical FC circuits in ASD children. FC matrices between regions-of-interest (ROIs) were compared between ASD and HC. Finally, a supportvectorregression (SVR) method was used to assess the relationship between the FC circuits and ASD symptom severity. Two atypical FC circuits comprising 23 ROIs in ASD were revealed: one predominantly comprised brain regions involved with social cognition showing under-connectivity in ASD;the other predominantly comprised sensory-motor and visual brain regions showing overconnectivity in ASD. The SVR analysis showed that the two FC circuits were separately related to social deficits and restricted behavior scores. These findings indicate disrupted FC of neural circuits involved in the social and sensorimotor processes in young children with ASD. The finding of the atypical FC patterns in young children with ASD underscores the utility of studying younger children with the disorder, and highlights nuanced patterns of brain connectivity underlying behavior closer to disorder onset. (C) 2018 International Society for Autism Research, Wiley Periodicals, Inc.
In the present work, a supportvectorregression (SVR) model is developed to predict the angular error in taper cutting using wire electrical discharge machining (WEDM) process. The model is developed based on the dat...
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In the present work, a supportvectorregression (SVR) model is developed to predict the angular error in taper cutting using wire electrical discharge machining (WEDM) process. The model is developed based on the data obtained from experimentation. A set of six important input parameters such as taper angle, part thickness, discharge current, pulse duration, wire speed and wire tension is chosen to conduct the tapering operation in WEDM. The model accuracy is evaluated by the three performance criteria including root mean square error (RMSE), NashSutcliffe efficiency co-efficient (E) and co-efficient of determination (R-2). Results show that SVR is an effective and simple approach in comparison to traditional prediction methods. The model provides an inexpensive and time saving alternative to study the angular error in taper cutting before actual cutting operation. (c) 2017 Elsevier Ltd. All rights reserved.
The Streptomyces lonarensis strain NCL 716 hydrolyses starch to produce a mixture of maltotriose (G3) and maltotetraose (G4) along with maltose (G2). The objective of the present work was to determine an optimum cost ...
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The Streptomyces lonarensis strain NCL 716 hydrolyses starch to produce a mixture of maltotriose (G3) and maltotetraose (G4) along with maltose (G2). The objective of the present work was to determine an optimum cost effective media composition for the production of a-amylase from this strain. The most influential factor was found to be starch while the least influential factor found was peptone by PlackettBurman method. Peptone amount was kept constant throughout the fermentation. Peptone, which is one of the expensive media components was used at a concentration of 1?g/L, which made the optimum media composition cost effective. A supportvectorregression-based process model was developed for approximating the non-linear relationship between the fermentation operating variables and the a-amylase yield. Multicanonical Jump Walk Annealing, a stochastic optimization technique is used to obtain optimal operating variables to maximize amylase yield. The maximum amylase activity thus obtained was in good agreement with the experimental values at the optimized levels. The optimum media composition obtained by this method was: yeast extract: 4.53?g/L, starch: 20.246?g/L, K2HPO4: 0.0827%, MgSO4: 0.15%, peptone: 1?g/L. A maximum enzyme activity of 297?U/mL, which was achieved using the above approaches compares well with the activity of reported amylases producing maltooligosaccharides.
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