This work proposed a LSTM(long short-term memory)model based on the double attention mechanism for power load prediction,to further improve the energy-saving potential and accurately control the distribution of power load into each department of the hospital.Firstly,the key influencing factors of the power loads were screened based on the grey relational degree analysis.Secondly,in view of the characteristics of the power loads affected by various factors and time series changes,the feature attention mechanism and sequential attention mechanism were introduced on the basis of LSTM network.The former was used to analyze the relationship between the historical information and input variables autonomously to extract important features,and the latter was used to select the historical information at critical moments of LSTM network to improve the stability of long-term prediction effects.In the end,the experimental results from the power loads of Shanxi Eye Hospital show that the LSTM model based on the double attention mechanism has the higher forecasting accuracy and stability than the conventional LSTM,CNN-LSTM and attention-LSTM models.
现实中基于树型层次结构的属性值分类是普遍存在的,反映这种树型层次结构的属性值分类法(Attribute Value Taxonomy,AVT)已被证明对数据的泛化上是有效的。部分数据集已具备相关专家提供的AVT,但大多数数据集不具备人为提供的AVT。为此,本文提出一种VDM-AVT学习器,即一种依据数据自动构造AVT的方法;为了评价所构造AVT的质量,基于VDM-AVT学习器提出了VDM-AVT-AGR模型。VDM-AVT学习器基于VDM距离,利用层次聚类将属性值抽象为树型层次结构,VDM-AVT-AGR模型利用VDM-AVT学习器得到的AVT对数据进行属性泛化约简。实验表明,利用VDM-AVTAGRD模型处理后的数据集相比原始数据集具有更好的分类性能和泛化能力。由此也可以证明VDM-AVT学习器得出的AVT是有效的。
Heavy metal pollution is becoming a prominent stress on plants.Plants contaminated with heavy metals undergo changes in external morphology and internal structure,and heavy metals can accumulate through the food chain,threatening human health.Detecting heavy metal stress on plants quickly,accurately,and nondestructively helps to achieve precise management of plant growth status and accelerate the breeding of heavy metal-resistant plant varieties.Traditional chemical reagent-based detection methods are laborious,destructive,time-consuming,and costly.The internal and external structures of plants can be altered by heavy metal contamination,which can lead to changes in plants'absorption and reflection of light.Visible/near-infrared(V/NIR)spectroscopy can obtain plant spectral information,and hyperspectral imaging(HSI)can obtain spectral and spatial information in simple,speedy,and nondestructive ways.These 2 technologies have been the most widely used high-throughput phenotyping technologies of plants.This review summarizes the application of V/NIR spectroscopy and HSI in plant heavy metal stress phenotype analysis as well as introduces the method of combining spectroscopy with machine learning approaches for high-throughput phenotyping of plant heavy metal stress,including unstressed and stressed identification,stress types identification,stress degrees identification,and heavy metal content estimation.The vegetation indexes,full-range spectra,and feature bands identified by different plant heavy metal stress phenotyping methods are reviewed.The advantages,limitations,challenges,and prospects of V/NIR spectroscopy and HSI for plant heavy metal stress phenotyping are discussed.Further studies are needed to promote the research and application of V/NIR spectroscopy and HSI for plant heavy metal stress phenotyping.
Introduction Plant phenotyping describes the result of the interaction of genotype with the environment[1].This is performed with high throughput in greenhouses by automated screening systems using different types of imaging and non-imaging sensors[2].The high-throughput imaging routines result in large amounts of data,which require sophisticated processing routines.Sharing and reusing phenotype-related data are not common,because its acquisition and processing are resource costly and technically intensive[3].
