Some non-linear height–diameter models performance for mixed stand in forests in Northwest Iran

This study evaluated the total height of trees based on diameter at breast height by using 23 widely used height-diameter non-linear regression models for mixed-species forest stands consisting of Caucasian oak,field maple,and hornbeam from forests in Northwest Iran.1920 trees were measured in 6 sampling plots(every sampling plot has 1 ha area).The fit of the best height–diameter models for each species were compared based on R2,Root Mean Square Error(RMSE),Akaike information criterion(AIC),standard error,and relative ranking performance criteria.In the final step,verification of results was performed by paired sample t-test to compare the observed height and estimated height.Results showed that among 23 height-diameter models,the best models were obtained from the top five ones including Modified-logistic,Prodan,Sibbesen,Burkhart,and Exponential.Comparison between the actual observed height and estimated height for Caucasian oak showed that Modified–Logistic,Prodan,Sibbesen,Burkhart,and Exponential performed better than the others,respectively(There were no statistically significant differences between observed heights and predicted height(p≥0.05)).Prodan,Modified-Logistic,Burkhart,and Loetch evaluated field maple tree height correctly,and Modified-Logistic,Burkhart,and Loetch had better fitness compared to the others for hornbeam,respectively.Although other models were introduced as appropriate criteria,they could not reliably predict the height of trees.Using the Rank analysis,the Modified-Logistic model for the Caucasian oak and Prodan model for field maple and hornbeam had the best performance.Finally,to complement the results of this study,it is suggested to assess how environmental factors such as elevation,climate parameters,forest protection policy and forest structure will modify height-diameter allometry models and will enhance the prediction accuracy of tree heights prediction in mixed stands.

A novel collapse prediction index for voltage stability analysis and contingency ranking in power systems

Voltage instability is a serious phenomenon that can occur in a power system because of critical or stressed condi-tions.To prevent voltage collapse caused by such instability,accurate voltage collapse prediction is necessary for power system planning and operation.This paper proposes a novel collapse prediction index(NCPI)to assess the volt-age stability conditions of the power system and the critical conditions of lines.The effectiveness and applicability of the proposed index are investigated on the IEEE 30-bus and IEEE 118-bus systems and compared with the well-known existing indices(Lmn,FVSI,LQP,NLSI,and VSLI)under several power system operations to validate its practicability and versatility.The study also presents the sensitivity assumptions of existing indices and analyzes their impact on voltage collapse prediction.The application results under intensive case studies prove that the proposed index NCPI adapts to several operating power conditions.The results show the superiority of the proposed index in accurately estimating the maximum load-ability and predicting the critical lines,weak buses,and weak areas in medium and large networks during various power load operations and contingencies.A line interruption or generation unit outage in a power system can also lead to voltage collapse,and this is a contingency in the power system.Line and generation unit outage contingencies are examined to identify the lines and generators that significantly impact system stability in the event of an outage.The contingencies are also ranked to identify the most severe outages that significantly cause voltage collapse because of the outage of line or generator.

IMPULSE NOISE REMOVAL BY L1 WEIGHTED NUCLEAR NORM MINIMIZATION

In recent years,the nuclear norm minimization(NNM)as a convex relaxation of the rank minimization has attracted great research interest.By assigning different weights to singular values,the weighted nuclear norm minimization(WNNM)has been utilized in many applications.However,most of the work on WNNM is combined with the l 2-data-fidelity term,which is under additive Gaussian noise assumption.In this paper,we introduce the L1-WNNM model,which incorporates the l 1-data-fidelity term and the regularization from WNNM.We apply the alternating direction method of multipliers(ADMM)to solve the non-convex minimization problem in this model.We exploit the low rank prior on the patch matrices extracted based on the image non-local self-similarity and apply the L1-WNNM model on patch matrices to restore the image corrupted by impulse noise.Numerical results show that our method can effectively remove impulse noise.

Comprehensive evaluation method for tidal current power generation device

There are many types of tidal current power generation devices,and it is necessary to make comprehensive evaluation of tidal current power generation devices in order to provide valuable reference for the improvement of their performance indexes.On the basis of the analysis of the tidal current power generation device performance indexes,the hierarchical model for comprehensive evaluation of device performance is given in this paper.By normalizing the membership matrix elements based on fuzzy comprehensive evaluation model,all the values of the matrix elements are restrained in the range of0.0 to 1.0,hence the complexity of the calculations is reduced.Vector similarity is used to determine the expert weights which reflect the knowledge and experience of the experts.This paper presents an improved method for rank correlation analysis,and calculates the comprehensive weight value and the final evaluation results of tidal current power generation devices.The presented method improves the credibility of the evaluation.In the end,measured data of two units of tidal current power generation devices are evaluated in the paper,and the effectiveness of the presented method is verified.

Ensemble unit and Al techniques for prediction of rock strain

The behavior of rock masses is influenced by a variety of forces,with measurement of stress and strain playing the most critical roles in assessing deformation.The laboratory test for determining strain at each location within rock samples is expensive and difficult but rock strain data are important for predicting failure of rock material.Many researchers employ AI technology in order to solve these difficulties.AI algorithms such as gradient boosting machine(GBM),support vector regression(SVR),random forest(RF),and group method of data handling(GMDH)are used to efficiently estimate the strain at every point within a rock sample.Additionally,the ensemble unit(EnU)may be utilized to evaluate rock strain.In this study,3000 experimental data are used for the purpose of prediction.The obtained strain values are then evaluated using various statistical parameters and compared to each other using EnU.Ranking analysis,stress-strain curve,Young’s modulus,Poisson’s ratio,actual vs.predicted curve,error matrix and the Akaike’s information criterion(AIC)values are used for comparing models.The GBM model achieved 98.16%and 99.98%prediction accuracy(in terms of values of R2)in the longitudinal and lateral dimensions,respectively,during the testing phase.The GBM model,based on the experimental data,has the potential to be a new option for engineers to use when assessing rock strain.