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°.

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.

Advanced 3D ordered electrodes for PEMFC applications: From structural features and fabrication methods to the controllable design of catalyst layers

The catalyst layers(CLs) electrode is the key component of the membrane electrode assembly(MEA) in proton exchange membrane fuel cells(PEMFCs). Conventional electrodes for PEMFCs are composed of carbon-supported, ionomer, and Pt nanoparticles, all immersed together and sprayed with a micron-level thickness of CLs. They have a performance trade-off where increasing the Pt loading leads to higher performance of abundant triple-phase boundary areas but increases the electrode cost. Major challenges must be overcome before realizing its wide commercialization. Literature research revealed that it is impossible to achieve performance and durability targets with only high-performance catalysts, so the controllable design of CLs architecture in MEAs for PEMFCs must now be the top priority to meet industry goals. From this perspective, a 3D ordered electrode circumvents this issue with a support-free architecture and ultrathin thickness while reducing noble metal Pt loadings. Herein, we discuss the motivation in-depth and summarize the necessary CLs structural features for designing ultralow Pt loading electrodes. Critical issues that remain in progress for 3D ordered CLs must be studied and characterized. Furthermore, approaches for 3D ordered CLs architecture electrode development, involving material design, structure optimization, preparation technology, and characterization techniques, are summarized and are expected to be next-generation CLs for PEMFCs. Finally, the review concludes with perspectives on possible research directions of CL architecture to address the significant challenges in the future.

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.

Regional Evolution Features and Coordinated Development Strategies for Northeast China

Northeast China, as the most important production base of agriculture, forestry, and livestock-breeding as well as the old industrial base in the whole country, has been playin a key role in the construction and development of China＇s economy. However, after the policy of reform and open-up was taken in China. the economic development speed and efficiency ofthis area have turned to be evidently lower than those of coastal area and the national average level as well, which is so-called ＇Northeast Phenomenon＇ and ＇Neo-Northeast Phenomenon＇. In terms of those phenomena, this paper firstly reviews the spatial and temporal features of the regional evolution of this area so as to unveil the profound forming causes of ＇Northeast Phenomena＇ and ＇Neo-Northeast Phenomena＇. And then the paper makes a further exploration into the status quo of this region and its forming causes by analyzing its economy gross, industrial structure, product structure, regional eco-categories, etc. At the end of the paper, the authors put forward the basic coordinated development strategies for Northeast China. namely we can revitalize this area by means of adjustment of economic structure, regional coordination, planning urban and rural areas as a whole, institutional innovation, etc.

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 Characteristics of Regional Extreme Low Temperature Events in China during 1960-2009

An objective identification technique is used to detect regional extreme low temperature events （RELTE） in China during 1960-2009. Their spatial-temporal characteristics are analyzed. The results indicate that the lowest temperatures of RELTE, together with the frequency distribution of the geometric latitude center, exhibit a double-peak feature. The RELTE frequently happen near the geometric area of 30°N and 42°N before the mid-1980s, but shifted afterwards to 30°N. During 1960-2009, the frequency~ intensity, and the maximum impacted area of RELTE show overall decreasing trends. Due to the contribution of RELTE, with long duratioh and large spatial range, which account for 10% of the total RELTE, there is a significant turning point in the late 1980s. A change to a much more steady state after the late 1990s is identified. In addition, the integrated indices of RELTE are classified and analyzed.

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.

融合卷积神经网络和注意力机制的负荷识别方法

对居民住宅进行非侵入式负荷监测(non-intrusive load monitoring,NILM)是智能电网用户需求侧的重要研究内容,居民负荷的能耗分析和用电管理是实现节能减排、可持续发展的关键环节。针对传统算法识别性能差、难以适应当下复杂用电环境的问题,文中从增强分类算法特征提取性能的优化思路出发,提出融合卷积神经网络(convolutional neural network,CNN)和自注意力机制的NILM负荷识别方法。首先,采集8种不同家用电器的电力数据,建立U-I轨迹曲线数据库;其次,采用挤压-激励网络(squeeze-and-excitation network,SENet)注意力机制提升CNN的特征聚合能力,完成对不同电器U-I轨迹曲线的特征提取和负荷识别;最后,对私有数据集和PLAID数据集进行测试,算例结果表明,所提方法在不同运行场景下均具有较高的识别准确率和较好的泛化性能。

基于余弦相似度和图卷积网络的电力负荷预测方法

针对现有电力负荷预测模型难以深入提取时空关联特征,模型泛化能力弱,无法同时胜任短期和长期的电力负荷预测的问题,提出一种面向多用户的基于余弦相似度和全局-局部协同图卷积网络的电力负荷预测方法。首先,利用余弦相似度来学习不同节点负荷数据之间的相似模式,以提取深层次的时空关联特征。其次,对影响电力负荷变化趋势的静态全局因素和动态局部因素进行协同建模,以提升模型的泛化能力。最后,通过在一个实测数据集上进行的大量实验,验证了该方法在短期和长期负荷序列预测任务中同时具备有效性和稳定性。

Optimization of support vector machine power load forecasting model based on data mining and Lyapunov exponents

According to the chaotic and non-linear characters of power load data,the time series matrix is established with the theory of phase-space reconstruction,and then Lyapunov exponents with chaotic time series are computed to determine the time delay and the embedding dimension.Due to different features of the data,data mining algorithm is conducted to classify the data into different groups.Redundant information is eliminated by the advantage of data mining technology,and the historical loads that have highly similar features with the forecasting day are searched by the system.As a result,the training data can be decreased and the computing speed can also be improved when constructing support vector machine(SVM) model.Then,SVM algorithm is used to predict power load with parameters that get in pretreatment.In order to prove the effectiveness of the new model,the calculation with data mining SVM algorithm is compared with that of single SVM and back propagation network.It can be seen that the new DSVM algorithm effectively improves the forecast accuracy by 0.75%,1.10% and 1.73% compared with SVM for two random dimensions of 11-dimension,14-dimension and BP network,respectively.This indicates that the DSVM gains perfect improvement effect in the short-term power load forecasting.

Study on dynamic response of multi-degree-of-freedom explosion vessel system under impact load

In order to study the dynamic response and calculate the axial dynamic coefficient of the monolayer cylindrical explosion vessel,the wall of vessel is simplified as a multi-degree-of-freedom(MDoF) undamped elastic foundation beam.Decoupling the coupled motion equation and using Duhamel's integrals,the solutions in generalized coordinates of the equations under exponentially decaying loads,square wave loads and triangular wave loads are calculated.These solutions are consistent in form with the solutions of single-degree-of-freedom(SDoF) undamped forced vibration simplified model.Based on the model,equivalent MDoF design method(also called MDoF dynamic coefficient method) of cylindrical explosion vessel is proposed.The traditional method can only predict the dynamic coefficient of torus portion around the explosion center,but this method can predict that of the vessel wall at any axial n dividing point position.It is verified that the prediction accuracy of this model is greatly improved compared with the SDoF model by comparing the results of this model with SDoF model and numerical simulation in different working conditions.However,the prediction accuracy decreases as the scaled distance decreases and approaches the end of the vessel,which is related to the accuracy of the empirical formula of the implosion load,the simplification of the explosion load direction,the boundary conditions,and the loading time difference.

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.

SELF-ALIGNING EVEN LOAD MECHANISM OF MULTI-ROW BEARINGS OF LARGE STRIP ROLLING MILL

The load distribution of multi-row bearings of large strip rolling mill is fully analyzed by 3D contact boundary element method （BEM）. It is found out that bearings are frequently worn out due to serious uneven load on the multi-row rollers. The constraint mechanism of the previous rolling system is found to be unreasonable by theoretical analysis on heavy machinery structure. A mechanism of self-aligning even load for workroll bearing of 2 050 mm hot rolling mill of Baoshan I＆S Co. is developed. This device is manufactured with particular regard to the structure of 2 050 mm hot rolling mill mentioned above. Hence, uneven load on multi-row bearings is greatly relieved and their lives are remarkably prolonged. Meanwhile, theoretical analysis and on-spot tests prove the rationality and validity of the device.

Optimal Load Forecasting Model for Peer-to-Peer Energy Trading in Smart Grids

Peer-to-Peer(P2P)electricity trading is a significant research area that offers maximum fulfilment for both prosumer and consumer.It also decreases the quantity of line loss incurred in Smart Grid(SG).But,uncertainities in demand and supply of the electricity might lead to instability in P2P market for both prosumer and consumer.In recent times,numerous Machine Learning(ML)-enabled load predictive techniques have been developed,while most of the existing studies did not consider its implicit features,optimal parameter selection,and prediction stability.In order to overcome fulfill this research gap,the current research paper presents a new Multi-Objective Grasshopper Optimisation Algorithm(MOGOA)with Deep Extreme Learning Machine(DELM)-based short-term load predictive technique i.e.,MOGOA-DELM model for P2P Energy Trading(ET)in SGs.The proposed MOGOA-DELM model involves four distinct stages of operations namely,data cleaning,Feature Selection(FS),prediction,and parameter optimization.In addition,MOGOA-based FS technique is utilized in the selection of optimum subset of features.Besides,DELM-based predictive model is also applied in forecasting the load requirements.The proposed MOGOA model is also applied in FS and the selection of optimalDELM parameters to improve the predictive outcome.To inspect the effectual outcome of the proposed MOGOA-DELM model,a series of simulations was performed using UK Smart Meter dataset.In the experimentation procedure,the proposed model achieved the highest accuracy of 85.80%and the results established the superiority of the proposed model in predicting the testing data.

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.

Analysis of Cooperation between Wind Power and Load Side Resources

Development of the intermittent energy is greatly promoted by change in energy, while consumption of large-scale intermittent energy is becoming a problem. With the development of smart grid technology, controllability of load side resources is becoming more and more important. Based on the wave characteristics of wind power, this paper indicates that wind energy has continuous output characteristics on the hour-time scale. Through analysis on loads characteristic of industry, public facility and resident, this paper gets comprehensive response of load side resources. Considering characteristics of wind power output, combined with different load side resources and DR program, this paper suggests cooperation between wind power and load side resources on different time scales.

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。

考虑动态时间锚点和典型特征约束的年日均负荷曲线预测

基于负荷趋势性、周期性和受日历特征影响的特点,考虑动态时间锚点和典型特征约束,实现年日均负荷曲线精确预测。首先,根据历史和预测年的日历关联关系建立动态时间锚点矩阵,结合标幺化和周期平滑处理后的历史年日均负荷形状因子曲线,提出DTA-Soft-DBA方法以获得预测年的日均负荷形状因子预测曲线;然后,进行反标幺化和反周期平滑处理,并结合电力电量特征预测值进行典型特征约束修正,获得年日均负荷预测曲线。基于某地区的算例结果表明,所提方法具有更高的预测精度,其结果与典型特征预测值相吻合,符合年内时序变化规律,能有效整合具有不同日历特征的历史样本时序共性规律。

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。