Hyperspectral remote sensing identification of marine oil emulsions based on the fusion of spatial and spectral features

Marine oil spill emulsions are difficult to recover,and the damage to the environment is not easy to eliminate.The use of remote sensing to accurately identify oil spill emulsions is highly important for the protection of marine environments.However,the spectrum of oil emulsions changes due to different water content.Hyperspectral remote sensing and deep learning can use spectral and spatial information to identify different types of oil emulsions.Nonetheless,hyperspectral data can also cause information redundancy,reducing classification accuracy and efficiency,and even overfitting in machine learning models.To address these problems,an oil emulsion deep-learning identification model with spatial-spectral feature fusion is established,and feature bands that can distinguish between crude oil,seawater,water-in-oil emulsion(WO),and oil-in-water emulsion(OW)are filtered based on a standard deviation threshold–mutual information method.Using oil spill airborne hyperspectral data,we conducted identification experiments on oil emulsions in different background waters and under different spatial and temporal conditions,analyzed the transferability of the model,and explored the effects of feature band selection and spectral resolution on the identification of oil emulsions.The results show the following.(1)The standard deviation–mutual information feature selection method is able to effectively extract feature bands that can distinguish between WO,OW,oil slick,and seawater.The number of bands was reduced from 224 to 134 after feature selection on the Airborne Visible Infrared Imaging Spectrometer(AVIRIS)data and from 126 to 100 on the S185 data.(2)With feature selection,the overall accuracy and Kappa of the identification results for the training area are 91.80%and 0.86,respectively,improved by 2.62%and 0.04,and the overall accuracy and Kappa of the identification results for the migration area are 86.53%and 0.80,respectively,improved by 3.45%and 0.05.(3)The oil emulsion identification model has a certain degree of transferability and can effectively identify oil spill emulsions for AVIRIS data at different times and locations,with an overall accuracy of more than 80%,Kappa coefficient of more than 0.7,and F1 score of 0.75 or more for each category.(4)As the spectral resolution decreasing,the model yields different degrees of misclassification for areas with a mixed distribution of oil slick and seawater or mixed distribution of WO and OW.Based on the above experimental results,we demonstrate that the oil emulsion identification model with spatial–spectral feature fusion achieves a high accuracy rate in identifying oil emulsion using airborne hyperspectral data,and can be applied to images under different spatial and temporal conditions.Furthermore,we also elucidate the impact of factors such as spectral resolution and background water bodies on the identification process.These findings provide new reference for future endeavors in automated marine oil spill detection.

STGSA:A Novel Spatial-Temporal Graph Synchronous Aggregation Model for Traffic Prediction

The success of intelligent transportation systems relies heavily on accurate traffic prediction,in which how to model the underlying spatial-temporal information from traffic data has come under the spotlight.Most existing frameworks typically utilize separate modules for spatial and temporal correlations modeling.However,this stepwise pattern may limit the effectiveness and efficiency in spatial-temporal feature extraction and cause the overlook of important information in some steps.Furthermore,it is lacking sufficient guidance from prior information while modeling based on a given spatial adjacency graph(e.g.,deriving from the geodesic distance or approximate connectivity),and may not reflect the actual interaction between nodes.To overcome those limitations,our paper proposes a spatial-temporal graph synchronous aggregation(STGSA)model to extract the localized and long-term spatial-temporal dependencies simultaneously.Specifically,a tailored graph aggregation method in the vertex domain is designed to extract spatial and temporal features in one graph convolution process.In each STGSA block,we devise a directed temporal correlation graph to represent the localized and long-term dependencies between nodes,and the potential temporal dependence is further fine-tuned by an adaptive weighting operation.Meanwhile,we construct an elaborated spatial adjacency matrix to represent the road sensor graph by considering both physical distance and node similarity in a datadriven manner.Then,inspired by the multi-head attention mechanism which can jointly emphasize information from different r epresentation subspaces,we construct a multi-stream module based on the STGSA blocks to capture global information.It projects the embedding input repeatedly with multiple different channels.Finally,the predicted values are generated by stacking several multi-stream modules.Extensive experiments are constructed on six real-world datasets,and numerical results show that the proposed STGSA model significantly outperforms the benchmarks.

Spatial-temporal evolution of gas migration pathways in coal during shear loading

Custom designed and built meso shear test equipment was used to examine the shear crack propagation in gassy coal under different gas pressures.The spatial-temporal evolution of gas migration pathways in the coal during shear loading was also researched.The results show that gas pressure can hasten crack growth at the shear fracture surface,can reduce the shear strength of gassy coal,and can accelerate the shear failure process.Shear failure in gassy coal exhibits five stages:the pre-crack stage;the stable crack growth stage;the unsteady crack growth stage;the fracture stage;and,finally,the friction crack stage.The shear breaking creates two kinds of crack,shear cracks and tensile cracks.Cracks first appear in the shear plane at both ends and then extend toward the center until a shear fracture surface forms.The direction of shear crack propagation diverges from the predetermined shear plane by an angle of about 5°-10°.

Non-Intrusive Load Identification Model Based on 3D Spatial Feature and Convolutional Neural Network

Load identification method is one of the major technical difficulties of non-intrusive composite monitoring. Binary V-I trajectory image can reflect the original V-I trajectory characteristics to a large extent, so it is widely used in load identification. However, using single binary V-I trajectory feature for load identification has certain limitations. In order to improve the accuracy of load identification, the power feature is added on the basis of the binary V-I trajectory feature in this paper. We change the initial binary V-I trajectory into a new 3D feature by mapping the power feature to the third dimension. In order to reduce the impact of imbalance samples on load identification, the SVM SMOTE algorithm is used to balance the samples. Based on the deep learning method, the convolutional neural network model is used to extract the newly produced 3D feature to achieve load identification in this paper. The results indicate the new 3D feature has better observability and the proposed model has higher identification performance compared with other classification models on the public data set PLAID.

Online identification and extraction method of regional large-scale adjustable load-aggregation characteristics

This article introduces the concept of load aggregation,which involves a comprehensive analysis of loads to acquire their external characteristics for the purpose of modeling and analyzing power systems.The online identification method is a computer-involved approach for data collection,processing,and system identification,commonly used for adaptive control and prediction.This paper proposes a method for dynamically aggregating large-scale adjustable loads to support high proportions of new energy integration,aiming to study the aggregation characteristics of regional large-scale adjustable loads using online identification techniques and feature extraction methods.The experiment selected 300 central air conditioners as the research subject and analyzed their regulation characteristics,economic efficiency,and comfort.The experimental results show that as the adjustment time of the air conditioner increases from 5 minutes to 35 minutes,the stable adjustment quantity during the adjustment period decreases from 28.46 to 3.57,indicating that air conditioning loads can be controlled over a long period and have better adjustment effects in the short term.Overall,the experimental results of this paper demonstrate that analyzing the aggregation characteristics of regional large-scale adjustable loads using online identification techniques and feature extraction algorithms is effective.

Data-Driven Load Forecasting Using Machine Learning and Meteorological Data

Electrical load forecasting is very crucial for electrical power systems’planning and operation.Both electrical buildings’load demand and meteorological datasets may contain hidden patterns that are required to be investigated and studied to show their potential impact on load forecasting.The meteorological data are analyzed in this study through different data mining techniques aiming to predict the electrical load demand of a factory located in Riyadh,Saudi Arabia.The factory load and meteorological data used in this study are recorded hourly between 2016 and 2017.These data are provided by King Abdullah City for Atomic and Renewable Energy and Saudi Electricity Company at a site located in Riyadh.After applying the data pre-processing techniques to prepare the data,different machine learning algorithms,namely Artificial Neural Network and Support Vector Regression(SVR),are applied and compared to predict the factory load.In addition,for the sake of selecting the optimal set of features,13 different combinations of features are investigated in this study.The outcomes of this study emphasize selecting the optimal set of features as more features may add complexity to the learning process.Finally,the SVR algorithm with six features provides the most accurate prediction values to predict the factory load.

Short-Term Mosques Load Forecast Using Machine Learning and Meteorological Data

The tendency toward achieving more sustainable and green buildings turned several passive buildings into more dynamic ones.Mosques are the type of buildings that have a unique energy usage pattern.Nevertheless,these types of buildings have minimal consideration in the ongoing energy efficiency applications.This is due to the unpredictability in the electrical consumption of the mosques affecting the stability of the distribution networks.Therefore,this study addresses this issue by developing a framework for a short-term electricity load forecast for a mosque load located in Riyadh,Saudi Arabia.In this study,and by harvesting the load consumption of the mosque and meteorological datasets,the performance of four forecasting algorithms is investigated,namely Artificial Neural Network and Support Vector Regression(SVR)based on three kernel functions:Radial Basis(RB),Polynomial,and Linear.In addition,this research work examines the impact of 13 different combinations of input attributes since selecting the optimal features has a major influence on yielding precise forecasting outcomes.For the mosque load,the(SVR-RB)with eleven features appeared to be the best forecasting model with the lowest forecasting errors metrics giving RMSE,nRMSE,MAE,and nMAE values of 4.207 kW,2.522%,2.938 kW,and 1.761%,respectively.

地震荷载作用下土体变形试验研究

为研究不规则地震荷载对土体变形特性的影响,对砂土试样开展了系统的动三轴试验。试验中分别对3种不同密度砂土试样施加了4条具有不同峰值的不规则地震应力时程及其循环次数为20周的等幅正弦荷载,以便开展对比研究。结果表明:在地震荷载及其等幅循环荷载输入下,砂土试样的应变增长曲线差异显著,荷载类型及荷载波形是影响土单元应变时程发展特征的主要因素。考虑荷载不规则性得到的修正系数受荷载类型和砂土相对密实度的影响,不受输入荷载动应力幅值大小及砂土类型的影响。最后针对地震荷载下土单元变形的计算,给出了以常规的等幅三轴变形试验为依据的估算方法。

基于动态自适应图神经网络的电动汽车充电负荷预测

电动汽车充电站负荷波动的不确定性与长时间预测任务给提升充电负荷预测精度带来巨大的挑战。文中提出一种基于动态自适应图神经网络的电动汽车充电负荷预测算法。首先,构建了一个充电负荷信息时空关联特征提取层,将多头注意力机制与自适应相关图结合生成具有时空关联性的综合特征表达式,以捕获充电站负荷的波动性;然后,将提取的特征输入到时空卷积层,捕获时间和空间之间的耦合关系;最后,通过切比雪夫多项式图卷积与多尺度时间卷积提升模型耦合长时间序列之间的能力。以Palo Alto数据集为例,与现有方法相比,所提算法在4种波动情况下的平均预测误差大幅降低。

法庭口译话语特征研究——以运动员孙杨CAS兴奋剂仲裁案口译为例

孙杨兴奋剂仲裁案的听证会是具有重大国际影响的体育仲裁事件。过程中的口译质量问题一度成为口译和涉外法律研究的焦点话题。本文以法庭口译的质量要求为基准,以听证会的话轮和话语特征为研究对象,剖析法庭质证过程中的口译质量问题与存在原因。本研究旨在帮助译员熟悉范式、降低负荷、提供策略,促进法庭口译研究和人才培养,响应加速涉外法治人才培养的国家政策。

基于改进时间卷积网络的微电网超短期负荷预测

为了提高微电网中用电负荷超短期预测的准确性,对时间卷积网络进行特征增强和注意力增强改进,将时间卷积网络中的一维因果膨胀卷积替换为二维卷积,同时利用时间模式注意力机制对时间卷积网络的隐藏层加权处理,提取负荷的多维特征,挖掘序列中存在的潜藏联系。根据改进的方法建立预测模型并进行对比实验以验证方法的有效性,能够对用电负荷的不确定性进行有效的处理,拓宽特征向量的维度,有效捕捉负荷序列中与时间有关的特征,提高用电负荷的预测精度。

基于改进SVIT算法的非侵入式居民负荷监测方法

针对居民侧异常用电负荷对用户用电安全有重大影响的问题,提出了一种基于改进SVIT的非侵入式负荷监测方法。该方法从用户总线处对居民家庭用电负荷投切时的V-I数据进行特征提取;对提取出的V-I轨迹特征进行二进制化处理并绘制成V-I轨迹图像;利用改进的与三重注意力机制(Triplet Attention)相结合的SVIT的特征提取网络对其进行特征提取与映射;在此基础上,将处理后生成的新的特征向量进行聚类形成特征检索数据库,以识别出不在该特征检索数据库中的用电器V-I轨迹样本。通过利用PLAID数据集进行仿真实验并分析,验证了模型的有效性以及算法的优越性。

基于眼动数据分析交通环境要素对驾驶员视觉负荷的影响

研究旨在通过提出1种基于眼动数据得到的负荷评价指标—眼动离散程度,确定不同视觉负荷下眼动离散程度的划分阈值,并利用该指标探究交通环境要素对人工驾驶员视觉负荷影响的程度排序,进而为交通设计提供参考。研究方法为分析DADA2000数据集,利用语义分割算法与眼动特征识别算法,建立眼动离散程度变化机制模型,并将事故前3s眼动点集中区域与语义分割得到的要素进行匹配,进而得到不同视觉负荷下眼动离散程度的划分阈值和交通环境要素对驾驶员视觉负荷的影响程度排序。研究结果发现,在不同道路条件下,驾驶员的眼动离散程度和视觉负荷水平会有所变化,但事故前较短时间内眼动离散程度会降低至原来的1/3,眼动点也会集中在碰撞物或事故主要责任要素上,可通过机器学习算法对各交通环境要素影响的重要程度进行排序。分析研究结果可得到结论:利用眼动离散程度区分驾驶员视觉负荷是否过大的方法具有可行性,并可进一步用于事故防范与预测;驾驶员视觉负荷主要来源的前3位要素是自行车参与者、路侧标志杆、路面状况,影响重要度分别达到0.395、0.137、0.124。

应用n-LSTM的云平台任务CPU负载预测方法

云平台任务的CPU负载预测有助于云平台资源的优化配置,以改善资源利用率.它是有效管理云资源的重要手段.为提高任务CPU负载预测精度,本文主要做了以下工作:1)利用热度图提取用于进行CPU负载预测的资源使用特征;2)设计并实现了一种基于n-LSTM的云平台任务的CPU负载预测方法DPFE-n-LSTM;3)分别在阿里云平台数据集和Google云平台数据集上进行了实验,结果表明,相对于目前已经提出的CPU负载预测模型BP、LSTM和CNN-LSTM,DPFE-n-LSTM方法具有更好的预测性能.

基于在线特征库的非侵入式负荷特征提取方法

负荷特征是指负荷设备运行过程中具备某种统计规律的特殊标识,而包含负荷特征的数据库则是实现非侵入式负荷监测与分解的基本依据。对于纯阻性负荷设备,研究人员利用其运行状态切换过程中功率产生跃变的特点,可以准确提取相应状态下的负荷特征。然而,对非纯阻性设备,其负荷特征提取存在以下2个问题。问题1)运行状态切换过程中功率变化不显著,无法精准定位状态投切时刻点;问题2)负荷设备存在功率缓变化的运行状态,导致对应状态下的负荷特征不唯一,无法手动提取。为了解决非纯阻性设备负荷特征提取中存在的上述问题,提出一种基于在线特征库的非侵入式负荷特征提取方法,该方法分为2个阶段。第一阶段:基于负荷设备运行过程中的稳态周期电流数组建立在线特征库,通过改进Pearson相似系数构建滑窗函数,得到负荷设备运行时的周期电流数组与在线特征库的相似性,并同步判断在线特征库冗余性,实现负荷设备状态数据分割;第二阶段:计算在线特征库的特征矩阵,对特征矩阵进行K-means聚类分析,融合相似在线特征库,形成负荷设备的状态特征库,从而实现负荷设备特征电流数组的提取。在私人数据集和PLAID数据集上的测试结果证明,所提负荷特征提取方法在不同的用电场景下均有较好的鲁棒性。所提方法可大幅减少负荷特征提取阶段的人工参与,有利于缓解因负荷设备种类过多导致的负荷特征提取烦琐的问题,为后续建立负荷特征数据库提供了便利。

考虑天气特征与多变量相关性的配电网短期负荷预测

针对配电网短期负荷预测受到众多复杂天气特征等随机不确定性因素影响,以及传统预测模型难以有效分析不同特征序列之间的相关性等问题,提出一种考虑天气特征与多变量相关性的配电网短期负荷预测方法。首先,提出多变量快速最大信息系数(multi-variable rapid maximal information coefficient,MVRapidMIC)提取相关性高的天气特征序列。其次,引入探索性因子分析法(exploratory factor analysis,EFA),对高相关性特征序列进行降维处理。最后,将维度分段(dimension-segment-wise,DSW)机制和两阶段注意力(two-stage attention,TSA)机制与Informer模型结合,提高预测模型对不同特征序列相关性的分析能力。通过DTU 7K 47节点实际配电网的历史负荷数据开展仿真测试,验证所提方法的预测精度、鲁棒性和时效性。

基于变分模态分解的卷积长短时记忆网络短期电力负荷预测方法

电力负荷序列易受多重外部因素影响而呈现复杂性,不利于精准预测。为此,提出一种基于变分模态分解(variational mode decomposition,VMD)的卷积神经网络和长短期记忆网络(convolutional neural network and long short-term memory network,CNN-LSTM)相结合的短期电力负荷并行预测方法。先采用VMD将负荷数据分解为规律性强的各本征模态函数(intrinsic mode function,IMF)及残差;再将各分量分别输入到各自对应的CNN-LSTM混合预测网络,获得各初始预测值,并将该值与由气候、日期类型等组合得到的相关因素特征集相结合,进一步得出修正预测值;最终,叠加各分量修正预测值即得到完整预测结果。在实际负荷数据上做验证分析,结果表明,考虑相关外部因素特征集后日负荷预测平均相对误差均值可降低2.18%。与几种常规负荷预测方法进行效果对比,验证了该方法的有效性和可行性。

基于多尺度特征融合的负荷辨识及其可解释交互增强方法

负荷辨识技术可快速辨识电器类型,在家庭能量管理、危险用电预警、响应潜力评估等方面具有重要作用。针对现有负荷辨识方法多关注负荷长期或短期单尺度特征,导致特征表征能力不足而使模型识别精度和泛化性能受限的问题,提出一种基于多尺度特征融合的负荷辨识及其可解释交互增强方法。首先,从负荷采样数据中提取高频尺度的短期特征和中、低频尺度的长期特征,构建双塔结构的深层特征提取网络以利用网络的不同分支高效率挖掘各尺度深层特征。其次,设计自注意力与交叉注意力相结合的特征融合网络以实现负荷长、短期特征融合,提高模型的特征利用程度。然后,采用度量学习的训练方法,拉近同类型样本的特征距离,提升特征融合的效率和效果。最后,利用基于梯度的可解释分析方法量化特征的重要性,实现自适应的特征增强与结合专家交互的模型调优。实验结果说明所提模型识别精度与泛化能力均优于现有模型,且可解释分析验证了其有效性源于多尺度特征的充分利用。

多元数据融合的智能配电网负荷分析预测管理系统

针对多元大数据在智能配电网中的应用问题,为实现配电网的精益管理、科学预测和合理规划,文章开发了多元数据融合的智能配电网负荷分析预测管理系统。对软件系统的总体框架进行了设计;对软件系统的各功能模块进行开发和介绍;给出了软件系统的一个应用实例。该系统充分利用海量的历史负荷数据进行负荷特性分析,建立负荷特征库以及业扩信息库,通过对新接入用户进行信息匹配实现负荷管理及最大负荷预测。此外,该系统建立负荷预测方法模型库,可提供不同维度的负荷预测功能,从传统的地区负荷预测转变为馈线负荷预测,结合馈线现状以及业扩信息优化用户接入决策。总的来说,该系统具有功能模块数据链路互通、不同功能之间能提供信息支持、整体采用模块化设计思想等特点,可满足电网企业的日常应用需求。

基于混合特征的农电负荷智能优化识别方法研究

对农电负荷进行非侵入式负荷辨识,有助于掌握用户负荷信息,合理开展电网经济调度,达到降低网损、调节峰谷差等目的。本文针对农电多类型负荷同时运行时的负荷特征,以混合特征和智能优化算法为基础,提出1种基于频率加权因子遗传算法(Frequency Weighting Factor Genetic Algorithm,FWF-GA)的异种负荷同时识别方法。该方法以时域信号的有功功率、无功功率以及频域信号的幅频特性建立混合特征模型,以有功功率和无功功率构建同时识别的优化模型,并以混合特征模型构建遗传算法异种负荷同时识别的适应度函数。通过农村居民5种用电设备的负荷识别对所提方法进行验证。采用本文所提方法对5000组混合负荷进行识别,识别结果表明5种电器的单个识别准确率以及整体识别的平均准确率均在90%以上;采用3种不同适应度函数的识别方法对8组混合负荷进行识别,识别结果表明本文所提方法的识别效率最高。实例分析的结果表明,基于FWF-GA的异种负荷同时识别方法具有较好的识别效率和精度。