Direct Pointwise Comparison of FE Predictions to StereoDIC Measurements:Developments and Validation Using Double Edge-Notched Tensile Specimen

To compare finite element analysis(FEA)predictions and stereovision digital image correlation(StereoDIC)strain measurements at the same spatial positions throughout a region of interest,a field comparison procedure is developed.The procedure includes(a)conversion of the finite element data into a triangular mesh,(b)selection of a common coordinate system,(c)determination of the rigid body transformation to place both measurements and FEA data in the same system and(d)interpolation of the FEA nodal information to the same spatial locations as the StereoDIC measurements using barycentric coordinates.For an aluminum Al-6061 double edge notched tensile specimen,FEA results are obtained using both the von Mises isotropic yield criterion and Hill’s quadratic anisotropic yield criterion,with the unknown Hill model parameters determined using full-field specimen strain measurements for the nominally plane stress specimen.Using Hill’s quadratic anisotropic yield criterion,the point-by-point comparison of experimentally based full-field strains and stresses to finite element predictions are shown to be in excellent agreement,confirming the effectiveness of the field comparison process.

Simultaneous Measurement of Fringe Visibility and Path Predictability of Wave-Particle Duality

An experimental scheme to simultaneously obtain the information of fringe visibility and path predictability is designed. In a modified Young＇s double-slit experiment, two density filters rotating at different frequencies are placed before the two pineholes to encode path information. The spatial and temporal distributions of the output provide us with the wave and particle information of the single photons, respectively. The simultaneous measurement of the wave and particle information inevitably disturbs the system and thus causes some loss of the duality information, which is equal to the mixedness of the photonic state behind the density filters.

Employment of predictive search algorithm in digital image correlation

A predictive search algorithm to estimate the size and direction of displacement vectors was presented.The algorithm decreased the time of calculating the displacement of each pixel.In addition,the updating reference image scheme was used to update the reference image and to decrease the computation time when the displacement was larger than a certain number.In this way,the search range and computational complexity were cut down,and less EMS memory was occupied.The capability of proposed search algorithm was then verified by the results of both computer simulation and experiments.The results showed that the algorithm could improve the efficiency of correlation method and satisfy the accuracy requirement for practical displacement measuring.

Malicious Activities Prediction Over Online Social Networking Using Ensemble Model

With the vast advancements in Information Technology,the emergence of Online Social Networking(OSN)has also hit its peak and captured the atten-tion of the young generation people.The clone intends to replicate the users and inject massive malicious activities that pose a crucial security threat to the original user.However,the attackers also target this height of OSN utilization,explicitly creating the clones of the user’s account.Various clone detection mechanisms are designed based on social-network activities.For instance,monitoring the occur-rence of clone edges is done to restrict the generation of clone activities.However,this assumption is unsuitable for a real-time environment and works optimally during the simulation process.This research concentrates on modeling and effi-cient clone prediction and avoidance methods to help the social network activists and the victims enhance the clone prediction accuracy.This model does not rely on assumptions.Here,an ensemble Adaptive Random Subspace is used for clas-sifying the clone victims with k-Nearest Neighbour(k-NN)as a base classifier.The weighted clone nodes are analysed using the weighted graph theory concept based on the classified results.When the weighted node’s threshold value is high-er,the trust establishment is terminated,and the clones are ranked and sorted in the higher place for termination.Thus,the victims are alert to the clone propaga-tion over the online social networking end,and the validation is done using the MATLAB 2020a simulation environment.The model shows a better trade-off than existing approaches like Random Forest(RF),Naïve Bayes(NB),and the standard graph model.Various performance metrics like True Positive Rate(TPR),False Alarm Rate(FAR),Recall,Precision,F-measure,and ROC and run time analysis are evaluated to show the significance of the model.

Investigating the ENSO prediction skills of the Beijing Climate Center climate prediction system version 2

The El Niño-Southern Oscillation(ENSO)ensemble prediction skills of the Beijing Climate Center(BCC)climate prediction system version 2(BCC-CPS2)are examined for the period from 1991 to 2018.The upper-limit ENSO predictability of this system is quantified by measuring its“potential”predictability using information-based metrics,whereas the actual prediction skill is evaluated using deterministic and probabilistic skill measures.Results show that:(1)In general,the current operational BCC model achieves an effective 10-month lead predictability for ENSO.Moreover,prediction skills are up to 10–11 months for the warm and cold ENSO phases,while the normal phase has a prediction skill of just 6 months.(2)Similar to previous results of the intermediate coupled models,the relative entropy(RE)with a dominating ENSO signal component can more effectively quantify correlation-based prediction skills compared to the predictive information(PI)and the predictive power(PP).(3)An evaluation of the signal-dependent feature of the prediction skill scores suggests the relationship between the“Spring predictability barrier(SPB)”of ENSO prediction and the weak ENSO signal phase during boreal spring and early summer.