An Image Fingerprint and Attention Mechanism Based Load Estimation Algorithm for Electric Power System

With the rapid development of electric power systems,load estimation plays an important role in system operation and planning.Usually,load estimation techniques contain traditional,time series,regression analysis-based,and machine learning-based estimation.Since the machine learning-based method can lead to better performance,in this paper,a deep learning-based load estimation algorithm using image fingerprint and attention mechanism is proposed.First,an image fingerprint construction is proposed for training data.After the data preprocessing,the training data matrix is constructed by the cyclic shift and cubic spline interpolation.Then,the linear mapping and the gray-color transformation method are proposed to form the color image fingerprint.Second,a convolutional neural network(CNN)combined with an attentionmechanism is proposed for training performance improvement.At last,an experiment is carried out to evaluate the estimation performance.Compared with the support vector machine method,CNN method and long short-term memory method,the proposed algorithm has the best load estimation performance.

Theoretical Framework and Method of Detecting Accounting Fraud

Accounting fraud has never been rooted out, which brings a lot of trouble to our society. Only through strengthening user＇s ability to discern, decode and feedback the false information can we maximally understand the information behind. Therefore, devoting ourselves to setting up a framework of detecting the accounting fraud has become a feasible and efficient way to solve this problem. Based on the ＂Three Elements Theory＂, this paper demonstrates a new way to discern the fraud by researching its origins. The authors combine the theoretical model and the applied technology together to design a fraud recognization system.

Liquefaction-induced damage evaluation of earth embankment and corresponding countermeasure

Liquefaction of sandy soils is a big threat to the stability and the safety of an earth embankment laid on saturated soils.A large number of liquefaction-induced damages on embankment due to different types of earthquakes have been reported worldwide.In this research,the dynamic behaviors of earth embankment and the reinforcement effects of grouting as remediation method,subjected to moderate earthquake EQ1 and strong earthquake EQ2,were numerically investigated.The seismic behaviors of ground composed of cohesionless sandy soil and cohesive clayey soil were uniformly described by the cyclic mobility(CM)model,which is capable of describing accurately the mechanical property of the soil due to monotonic and cyclic loadings by accounting for stress-induced anisotropy,over-consolidation,and soil structure.It is known from the numerical investigation that the embankment would experience destructive deformation,and that the collapse mode was closely related to the properties of input seismic motion because high intensities and long durations of an earthquake motion could lead to significant plastic deformation and prolonged soil liquefaction.Under the strong seismic loading of EQ2,a circular collapse surface,combined with huge settlement and lateral spread,occurred inside the liquefication zone and extended towards the embankment crest.In contrast,in moderate earthquake EQ1,upheaval was observed at each toe of the embankment,and instability occurred only in the liquefied ground.An anti-liquefaction remediation via grouting was determined to significantly reduce liquefaction-induced deformation(settlement,lateral spreading,and local uplift)and restrain the deep-seated circular sliding failure,even though the top sandy soil liquefied in both earthquakes.When the structure was subjected to EQ2 motion,local failure occurred on the embankment slope reinforced with grouting,and thus,an additional appropriate countermeasure should be implemented to further strengthen the slope.For both input motions,the surface deformation of the considered embankment decreased gradually as the thickness of reinforcement was increased,although the reinforcement effect was no longer significant once the thickness exceeded 6 m.