Aiming at the extraction of cavern surface deformation from three-dimensional laser scanning dense point clouds,we propose a method integrating the Multiscale Model-to-Model Cloud Comparison(M3C2)with an im-proved Alpha Shapes algorithm.First,the two-phase surface point cloud data are registered,and the improved Alpha Shapes algorithm is used to identify the outer contour point clouds.After the fine registration of these two-phase outer contour point clouds,the M3C2 algorithm calculates the deformation value of each point,and finally the continuous deformation regions are extracted through distance clustering.Experimental results show that the proposed method ef-fectively eliminates the points at small furrows as well as those affected by mixed pixels.Specifically,the removal rates of point clouds in the two phases within 10 m from the scanner to the cavern section are 14.17%and 13.52%,respectively,which are 6.25%and 6.42%within 70 m.This method accurately and efficiently extracts the cavern surface deformation regions with more than twice the registration error.
近年来,越来越多的科研工作者试图将催化活性与催化剂中氧空位的酸碱性联系起来,通过结合路易斯酸碱活性位点对氧空位缺陷在确定催化特性和电荷载流子动力学中所起作用的深入理解,可以为合理开发更有效的催化剂提供有价值的见解.氧空位是过渡金属氧化物中最常见的缺陷,含氧空位的催化剂与表面酸碱性如何影响反应物、中间体和固体表面产物的吸附、反应和解吸过程密切相关.氧空位位置和氧空位的酸碱性在多相催化的过程中一直起着核心作用,研究基于氧空位缺陷构建路易斯酸碱位点的催化剂,对于各类分子活化、催化转化反应的性能提升具有重要意义.多相催化剂的表面性质将在很大程度上决定表面活性位点的电子结构,因此本文以含氧空位的固体催化剂为研究对象,对关于氧空位构筑路易斯酸碱位点的研究进行综述,探究相应活性位点对于催化剂的催化效率、稳定性和化学选择性的影响,并总结了其在光催化领域中的相关应用.
The flight test data is characterized by multiple types,large data volume and complex algorithms,thus data processing is a key link in flight test.The flight test data processing system needs to have capabilities such as rapid analysis of test data and timely sharing of cross-regional data.Here,on the basis of analyzing current flight test data processing system,we propose new approaches such as data processing center&diversity points,sharing be-tween diversity points,horizontal flight data splitting,and separated processing,then design a new flight test data processing system based on edge cloud collaboration,which realizes functions of data processing,cross-regional data sharing,and performance deduction&analysis for the whole-process subject implementation.
To improve the accuracy and reliability of solar irradiance prediction for photovoltaic power system,we propose a model to forecast short-term solar irradiance based on improved Stacking ensemble learning and error cor-rection.First,the Gradient Boosting Decision Tree(GBDT)is used to perform feature selection and remove redun-dant characteristics of original data set,thus increase prediction accuracy and computing efficiency.Then,an im-proved Stacking irradiance prediction model is established.In accordance with the difference in prediction accuracy of prediction models in the primary layer under K-fold cross-validation,the prediction results are weighted,and the Box-Cox is employed to transform and process the training set data input from the first layer to the second layer of Stacking,so as to increase the normality and homoscedasticity of prediction.Finally,the historical prediction error data are extracted,and Random Forest is applied to construct an error model to further improve the prediction accu-racy.The experimental results show that,compared with traditional models and classic Stacking models,the proposed method significantly improves the prediction performance on solar irradiance.
In view of the complicated factors influencing the stock price,we revised the Long Short-Term Memory(LSTM)network,which is commonly used in time series,to predict stock prices under the condition of multivari-able.First,the Variance Inflation Factor(VIF)was used to screen variables,and then the adaptive promotion(Ada-boost)model was combined to check the importance of characteristic variables.Second,the crawler was used to con-duct text analysis of investor sentiment,calculate indicators including sentiment index,and reveal the relationship between them and stock price.Then,prices of three stocks including Gree Electric Appliances,Flyco Electric Appli-ances and Midea Group were predicted by Multilayer Perceptron(MLP)and LSTM,and the appropriate model was selected as the benchmark model.Finally,indicators of sentiment index and investor concern were added to the benchmark model to construct the LSTM-EM model,and the GM(1,1)model was used to correct the residual term after considering investor sentiment.The empirical results show that the proposed model can predict the stock price accurately.
To reduce the influence of system dynamic coupling and external disturbance on the performance of flight control system of fixed-wing UAV thus improve its flight control accuracy,this paper proposes a singular per-turbation model for fixed-wing UAV and then designs a sliding mode control approach based on disturbance observ-er.The velocities and attitudes of the fixed-wing UAV are modeled based on the dynamics of action.Then the dynam-ic model is transformed into a singular perturbation one and then decomposed to complete decoupling.Two reduced-order uncoupled subsystems are obtained,which is a fast subsystem with angular velocity as fast variable and a slow subsystem with linear velocity and attitude as slow variables.Then anti-disturbance sliding mode controllers are de-signed for angular velocity loop,and angle&attitude loop.Finally,the feasibility and effectiveness of the sliding mode control approach based on fast and slow decomposition are verified by Simulink simulation.
To accurately analyze the dynamics and changing trends of KPI(Key Performance Indicator)data in the daily monitoring of cloud computing clusters and predict its subsequent development to achieve high availability of cloud computing clusters,we propose a three-frequency cloud KPI data prediction approach based on combined attention model of EWT-ARIMA-Auto-TPA(EAAT for short).First,low,medium and high frequency Intrinsic Mode Variables(IMFs)of cloud KPI data are obtained via Empirical Wavelet Transform(EWT)to reduce the complexi-ty of data prediction.Second,according to the information characteristics of low,medium and high frequency IMFs obtained from the decomposition,models of ARIMA,Autoformer,and TPA-BiLSTM are used to predict each type of IMFs.Finally,the classification prediction results are combined through the Inverse EWT(IEWT)to obtain the pre-diction result of the KPI.The proposed prediction approach has been verified on four datasets from Google and Ama-zon.Whether the data is periodic and stable or not,the proposed approach outperforms comparison models.
Tripping is a common fault in power transmission and distribution systems.Protection measures against tripping used to be relaying operation and electrical component action,which have hysteresis in handling tripping faults.Therefore,the prediction of tripping faults plays a vital role in dealing with hidden problems and power recov-ery.Here,a method of power system tripping fault prediction based on multisource time series data is proposed.LSTM is used to extract the time characteristics of multisource data,which alleviates the problem of RNN gradient disappearance on long time series.A peephole connection structure is added to the three-layer grid to enable single units to check the LSTM unit status in the previous stage,thereby strengthening the network timing memory capabili-ty.Then L2 regularization measures such as parameter normalization are used to mitigate the impact of over fitting in fault prediction.Finally,support vector machine classifier is introduced to improve the generalization ability and ro-bustness of the overall model.The experimental data were obtained from relevant institutions of the State Grid of Chi-na.Experiment results show that the proposed method has higher classification accuracy compared with existing data mining methods.The practical application is discussed for its feasibility in actual scenarios.
To address the low efficiency and accuracy of smoke&fire detection due to the small size of target and the confusion of fire feature with actual scene in complex environment,a small scale smoke&fire target detection method based on improved YOLOv5 is proposed.First,a fourth detection layer is added to the third detection layer output in the original YOLOv5 model,so as to obtain a larger feature map for small target detection and strengthen the feature extraction capability of the network model.Second,to solve the easy missing detection of target in shiel-ded scene,DIoU_Loss is used to replace the GIoU_Loss in calculating the regression loss function of the target frame.Finally,TensorRT is used to compress and accelerate the optimization of the model,and then deployed to the Jetson TX2 development board for accelerated inference experiments.In addition,more smoke&fire scene data are constructed by replication enhancement.Experimental results show that the proposed method has fast convergence speed and high accuracy for small scale smoke&fire detection,possessing the prospect for popularization and appli-cation.
Hydrogen peroxide(H2O2)is an environmentally friendly and efficient oxidant,which is widely used in industries like medicine and semiconductor chip.The electrochemical synthesis of H2O2 by Oxygen Reduction Reac-tion(ORR)has great potential to replace traditional anthraquinone method.To commercialize this process,the de-velopment of 2e-ORR electrocatalysts with high activity,high selectivity and long-term stability is imminent.Here,we systematically present the research of currently available metal and non-metal based catalysts,with special em-phasis on the control strategy of surface groups,and resolves effects on bond binding strength and electron transfer pathways of intermediates in the reduction process.We focus on key strategies such as electronic and geometric effects,coordination heteroatom doping,and active sites of nonmetal-based materials,highlighting that appropriate meso-structural engineering and kinetic strategies can further optimize the catalytic activity and H2O2 selectivity of existing catalysts.Finally,we summarize the challenges in exploring the active centers of non-metallic catalysts,the influence of electrolyte environment on catalysts and industrial equipment design with large output power,and pros-pect the future development in electrocatalytic synthesis of hydrogen peroxide.
Energy system integrated new energies such as wind power and photovoltaic to achieve complementary energy supply of electricity,heat and cold has attracted much concern.When multiple investors are involved in the operation of integrated energy system as independent subjects,it is worthwhile to reasonably allocate the capacity to better absorb new energy and maximize the interests of each investor.Based on the Nash equilibrium principle of game theory,this study establishes a capacity allocation game model for the integrated energy system composed of wind power,photovoltaic,and a combined cooling heating and power system,and uses Particle Swarm Optimization(PSO)algorithm to solve it.The comparative analysis of three scenarios including non-game,non-cooperative game and cooperative game shows that in the cooperative game scenario,the system generates optimal results in investors'return,capacity allocation,and overall system return,thus each participant has obvious possibility to cooperate.This study provides a solution for multi-party participation in the energy supply market.
The tilting of vehicle towards the outside of the curve caused by high-speed turning will lead to a roll-over accident in severe cases.To address this problem,the Active Roll Control(ARC)of the vehicle body was stud-ied to improve the vehicle steering stability.A vehicle dynamic model with six Degrees of Freedom(DOFs)was es-tablished considering both yaw and roll motions.Then,the desired vehicle roll angle was determined,and an active roll controller was designed to make the actual roll angle approach the desired roll angle.Finally,simulations were carried out to obtain vehicle body roll angles,acceleration perceived by occupants and the lateral load transfer rates,and investigate the power consumption of active suspension for roll control as well as the dynamic deflection of the suspension due to the active roll under different driving conditions.The results show that the ARC can make the ac-tual roll angle rapidly approach the desired roll angle,and still ensure driving stability under complex driving condi-tions;the ARC reduces the peak value of the suspension dynamic deflection,and decreases the lateral acceleration perceived by occupants and the lateral load transfer to zero;the low power consumption of the active suspension for roll control ensures the vehicle's economic performance.
To examine the effects of gradual increase of atmospheric CO2 concentration on soil respiration of winter wheat(Triticum aestivum)field,a gradually increased CO2 concentration experiment was conducted with automatic control system of CO2 in open top chambers(OTCs)during 2017-2019 growing seasons.In this study,a gradual in-crease of atmospheric CO2 concentration(C80 and C120,an increase of 40 μmol·mol-1 year by year from 2016)was set up based on the ambient atmospheric CO2 concentration(CK).The soil respiration rate(Rs)was measured by static chamber-gas chromatograph method.The results showed that gradually increased CO2 did not alter the seasonal patterns of soil respiration,but had significant effect on Rs during winter wheat bloom-growth period.In 2018-2019 growing season,compared to CK,C120 treatment significantly increased Rs by 50.2%(P=0.008)at the heading-flowering stage,and significantly increased cumulative amount of CO2 emissions(CAC)by 25.9%(P=0.044)during the wheat growing season;while in 2017-2018 growing season,compared to CK,C80 treatment had no signifi-cant effect on Rs.A positive exponential relationship was found between soil respiration rate and soil temperature.Compared to CK,gradually increased CO2 concentration reduced the temperature sensitivity coefficient of soil respi-ration(Q10 values).In summary,a gradual increase of atmospheric CO2 concentration of 120 μmol·mol-1 increased CAC during the growing season of winter wheat.
To improve the poor convergence performance and escape from local optimum of Salp Swarm Algorithm(SSA),a Golden sine SSA with Multi-strategy(MGSSA)is proposed.First,the Selective Opposition-Based Learning(SOBL)strategy is used to improve the population quality by calculating selective opposite solutions for individuals in the population that completely deviate from the optimal individual search direction.Then the optimal individual and elite mean individual are added in the follower position update phase to speed up the convergence of the algorithm.Finally,the golden sine algorithm variation strategy is selected based on the probability to further im-prove the quality of the solution,and facilitate the algorithm to jump out of the local optimum later.In this study,ex-periments are conducted on 14 benchmark test functions to compare with other swarm intelligence optimization algo-rithms and novel improved SSA,and then the proposed approach is applied to test the solution of engineering optimi-zation problems in tension/compression spring design.The results show that the proposed MGSSA has high conver-gence accuracy and stability,and performs well in solving engineering problems.
To address the poor timeliness and simple prediction functions of stock forecasting models,we propose a model abbreviated as BiLSTM-SA-TCN,which combines Bi-directional Long Short-Term Memory(Bi-LSTM)neural network,Self-Attention(SA)and Temporal Convolution Network(TCN).The learning unit and prediction unit in the proposed model can effectively learn important stock data,capture long-term dependency information,and output the predicted next day close price.The experimental results indicate that the BiLSTM-SA-TCN model has more stable prediction results on multiple data sets and has higher modle generalization ability.Furthormore,incomparative experiment,the BiLSTM-SA-TCN model achieves the lowest root mean square error,the lowest mean absolute error,and the best fitting degree of R2 on the majority of datasets.
针对DDoS网络流量攻击检测效率低及误报率高的问题,本文提出一种基于离散小波变换(Discrete Wavelet Trans-form,DWT)和自适应知识蒸馏(Adaptive Knowledge Distillation,AKD)自动编码器神经网络的DDoS攻击检测方法.该方法利用离散小波变换提取频率特征,由自动编码器神经网络进行特征编码并实现分类,通过自适应知识蒸馏压缩模型,以实现高效检测DDoS攻击流量.研究结果表明,该方法对代理服务器攻击、数据库漏洞和TCP洪水攻击、UDP洪水攻击具有较高的检测效率,并且具有较低的误报率.
非侵入式负荷分解的本质是根据已知的总功率信号分解出单一的负荷设备的功率信号.目前基于深度学习模型大多存在网络模型负荷特征提取不充分、分解精度低、对使用频率较低的负荷设备分解误差大等问题.本文提出一种注意力时序网 络模型(Attention Recurrent Neural Network,ARNN)实现非侵入式负荷分解,它将回归网络与分类网络相结合来解决非侵入式负荷分解问题.该模型通过RNN网络实现对序列信号特征的提取,同时利用注意力机制定位输入序列中重要信息的位置,提高神经网络的表征能力.在公开数据集Wiki-Energy以及UK-DALE上进行的对比实验结果表明,本文提出的深度神经网络在所有考虑的实验条件下都是最优的.另外,通过注意力机制和辅助分类网络能够正确检测设备的开启或关闭,并定位高功耗的信号部分,提高了负荷分解的准确性.
为提高低压配电台区中分布式光伏(DPV)的接入容量,促进光伏消纳,本文提出一种计及源荷时序特性的低压台区DPV接入分布鲁棒优化方法.首先,针对低压台区中分布式光伏出力和负荷需求的不确定性,提出一种基于优化聚类的源-荷联合时序场景生成方法;其次,计及电压约束、线路容量约束、逆变器无功补偿约束及光伏消纳约束等,构建低压台区分布式光伏接入分布鲁棒优化模型,在保证各典型场景最恶劣概率分布下的弃光率期望值符合要求的情况下,最大化低压台区中分布式光伏接入容量;然后,建立低压台区分布式储能接入的数学模型,以研究储能接入及其充放电机制对低压台区分布式光伏接入的影响;最后,以实际配电台区为例进行仿真计算,验证了本文模型的有效性.
微表情是人们试图隐藏自己真实情绪时不由自主泄露出来的面部表情,是近年来情感计算领域的热点研究领域.微表情是一种细微的面部运动,难以捕捉其细微变化的特征.本文基于交叉注意力多尺度 ViT(CrossViT)在图像分类领域的优异性能以及能够捕捉细微特征信息的能力,将CrossViT作为主干网络,对网络中的交叉注意力机制进行改进,提出了DA模块(Dual Attention)以扩展传统交叉注意力机制,确定注意力结果之间的相关性,从而提升了微表情识别精度.本网络从三个光流特征(即光学应变、水平和垂直光流场)中学习,这些特征是由每个微表情序列的起始帧和峰值帧计算得出,最后通过Softmax进行微表情分类.在微表情融合数据集上,UF1 和UAR分别达到了 0.727 5 和 0.727 2,识别精度优于微表情领域的主流算法,验证了本文提出网络的有效性.
脑电信号容易记录且不易伪装,基于脑电信号的情感识别越来越受到人们的关注.然而,人类情感具有多样性和个体可变性,基于脑电信号的情感识别仍是情感计算领域的难题.本文提出一种多源域领域适应字典学习和稀疏表示方法.为减少源领域和目标领域数据分布的差异,将所有领域的数据投影到共享子空间,并在共享子空间中学习一个共有字典.根据稀疏重建的最小化类内误差和最大化类间误差准则,稀疏表示具有类别的分辨能力.另外,每个源域自适应学习领域权重,可以避免负迁移的发生.模型参数的求解通过参数交替优化方法,所有参数 可同时达到最优解.DEAP数据集的实验结果显示本文方法在所有对比方法中是最优的.
