Reliability-based Robust Optimization Design of Automobile Components with Non-normal Distribution Parameters

In the reliability designing procedure of the vehicle components, when the distribution styles of the random variables are unknown or non-normal distribution, the result evaluated contains great error or even is wrong if the reliability value R is larger than 1 by using the existent method, in which case the formula is necessary to be revised. This is obviously inconvenient for programming. Combining reliability-based optimization theory, robust designing method and reliability based sensitivity analysis, a new method for reliability robust designing is proposed. Therefore the influence level of the designing parameters’ changing to the reliability of vehicle components can be obtained. The reliability sensitivity with respect to design parameters is viewed as a sub-objective function in the multi-objective optimization problem satisfying reliability constraints. Given the first four moments of basic random variables, a fourth-moment technique and the proposed optimization procedure can obtain reliability-based robust design of automobile components with non-normal distribution parameters accurately and quickly. By using the proposed method, the distribution style of the random parameters is relaxed. Therefore it is much closer to the actual reliability problems. The numerical examples indicate the following: (1) The reliability value obtained by the robust method proposed increases (】0.04%) comparing to the value obtained by the ordinary optimization algorithm; (2) The absolute value of reliability-based sensitivity decreases (】0.01%), and the robustness of the products’ quality is improved accordingly. Utilizing the reliability-based optimization and robust design method in the reliability designing procedure reduces the manufacture cost and provides the theoretical basis for the reliability and robust design of the vehicle components.

Observer-based dynamic event-triggered control for distributed parameter systems over mobile sensor-plus-actuator networks

We develop a policy of observer-based dynamic event-triggered state feedback control for distributed parameter systems over a mobile sensor-plus-actuator network.It is assumed that the mobile sensing devices that provide spatially averaged state measurements can be used to improve state estimation in the network.For the purpose of decreasing the update frequency of controller and unnecessary sampled data transmission, an efficient dynamic event-triggered control policy is constructed.In an event-triggered system, when an error signal exceeds a specified time-varying threshold, it indicates the occurrence of a typical event.The global asymptotic stability of the event-triggered closed-loop system and the boundedness of the minimum inter-event time can be guaranteed.Based on the linear quadratic optimal regulator, the actuator selects the optimal displacement only when an event occurs.A simulation example is finally used to verify that the effectiveness of such a control strategy can enhance the system performance.

Ensemble Bayesian method for parameter distribution inference:application to reactor physics

The estimation of model parameters is an important subject in engineering.In this area of work,the prevailing approach is to estimate or calculate these as deterministic parameters.In this study,we consider the model parameters from the perspective of random variables and describe the general form of the parameter distribution inference problem.Under this framework,we propose an ensemble Bayesian method by introducing Bayesian inference and the Markov chain Monte Carlo(MCMC)method.Experiments on a finite cylindrical reactor and a 2D IAEA benchmark problem show that the proposed method converges quickly and can estimate parameters effectively,even for several correlated parameters simultaneously.Our experiments include cases of engineering software calls,demonstrating that the method can be applied to engineering,such as nuclear reactor engineering.

Influence of explosion parameters on wavelet packet frequency band energy distribution of blast vibration

Blast vibration analysis is one of the important foundations for studying the control technology of blast vibration damage. According to blast vibration live data that have been collected and the characteristics of short-time non-stationary random signals, the wavelet packet energy spectrum analysis for blast vibration signal has made by wavelet packet analysis technology and the signals were measured under different explosion parameters （the maximal section dose, the distance of blast source to measuring point and the section number of millisecond detonator）. The results show that more than 95% frequency band energy of the signals sl-s8 concentrates at 0-200 Hz and the main vibration frequency bands of the signals sl-s8 are 70.313-125, 46.875-93.75, 15.625-93.75, 0-62.5, 42.969-125, 15.625-82.031, 7.813-62.5 and 0-62.5 Hz. Energy distributions for different frequency bands of blast vibration signal are obtained and the characteristics of energy distributions for blast vibration signal measured under different explosion parameters are analyzed. From blast vibration signal energy, the decreasing law of blast seismic waves measured under different explosion parameters was studied and the wavelet packet analysis is an effective means for studying seismic effect induced by blast.

A new early warning method for dam displacement behavior based on non-normal distribution function

Traditional methods for early warning of dam displacements usually assume that residual displacements follow a normal distribution.This assumption deviates from the reality,thereby affecting the reliability of early warning results and leading to misjudgments of dam displacement behavior.To solve this problem,this study proposed an early warning method using a non-normal distribution function.A new early warning index was developed using cumulative distribution function(CDF)values.The method of kernel density estimation was used to calculate the CDF values of residual displacements at a single point.The copula function was used to compute the CDF values of residual displacements at multiple points.Numerical results showed that,with residual displacements in a non-normal distribution,the early warning method proposed in this study accurately reflected the dam displacement behavior and effectively reduced the frequency of false alarms.This method is expected to aid in the safe operation of dams.

Fitness of Four-Parameter Beta Distribution Function for Forecasting Gold Reserve and Its Production Lifespan in Ghana

Ghana, renowned for its abundant gold reserves, plays a significant role in the global mining industry. Effective management and accurate forecasting of these reserves are vital for sustainable resource utilization and economic planning. Forecasting gold reserves and estimating their production lifespan are complex tasks that require robust statistical models capable of capturing the underlying dynamics of gold deposit accumulation and extraction. To this end, the four-parameter Beta distribution function emerges as a promising candidate due to its flexibility and ability to handle non-negative data. This research aims to investigate the fitness and applicability of the four-parameter Beta distribution function for forecasting Ghana’s gold reserves and estimating the production lifespan of this precious resource. The empirical paper relied mainly on quarterly secondary datasets on gold reserve between the years 2009 and 2022 secured from the Minerals Commission of Ghana, Accra. Several known statistical distributions including Beta, Weibull, Normal, Logistic and Gamma were explored with Maximum Likelihood Estimation (MLE) and evaluated using model selection criteria as AIC and BIC. Goodness of Fits were evaluated using Kolmogorov-Smirnov Test (K-S), Cramer-Von Mises Statistic and Anderson-Darling Statistic. Based on the analysis conducted, the four-parameter Beta distribution provided the best fit for gold reserve in Ghana. At a 99.9% confidence level and considering the current annual average gold production estimate of 3,700,031.248 to 4,302,647.888 ounces, the projected lifespan of gold production in Ghana extends to the year 1,953,765. This astounding estimate suggests that the country’s gold reserves are expected to sustain production for an extended period, providing a critical resource for economic development and supporting the mining industry well into the distant future.

Fitness of Four-Parameter Beta Distribution Function for Forecasting Gold Reserve and Its Production Lifespan in Ghana

Ghana, renowned for its abundant gold reserves, plays a significant role in the global mining industry. Effective management and accurate forecasting of these reserves are vital for sustainable resource utilization and economic planning. Forecasting gold reserves and estimating their production lifespan are complex tasks that require robust statistical models capable of capturing the underlying dynamics of gold deposit accumulation and extraction. To this end, the four-parameter Beta distribution function emerges as a promising candidate due to its flexibility and ability to handle non-negative data. This research aims to investigate the fitness and applicability of the four-parameter Beta distribution function for forecasting Ghana’s gold reserves and estimating the production lifespan of this precious resource. The empirical paper relied mainly on quarterly secondary datasets on gold reserve between the years 2009 and 2022 secured from the Minerals Commission of Ghana, Accra. Several known statistical distributions including Beta, Weibull, Normal, Logistic and Gamma were explored with Maximum Likelihood Estimation (MLE) and evaluated using model selection criteria as AIC and BIC. Goodness of Fits were evaluated using Kolmogorov-Smirnov Test (K-S), Cramer-Von Mises Statistic and Anderson-Darling Statistic. Based on the analysis conducted, the four-parameter Beta distribution provided the best fit for gold reserve in Ghana. At a 99.9% confidence level and considering the current annual average gold production estimate of 3,700,031.248 to 4,302,647.888 ounces, the projected lifespan of gold production in Ghana extends to the year 1,953,765. This astounding estimate suggests that the country’s gold reserves are expected to sustain production for an extended period, providing a critical resource for economic development and supporting the mining industry well into the distant future.

Adjustment of Model Parameters to Estimate Distribution Transformers Remaining Lifespan

Currently, the electrical system in Argentina is working at its maximum capacity, decreasing the margin between the installed power and demanded consumption, and drastically reducing the service life of transformer substations due to overload (since the margin for summer peaks is small). The advent of the Smart Grids allows electricity distribution companies to apply data analysis techniques to manage resources more efficiently at different levels (avoiding damages, better contingency management, maintenance planning, etc.). The Smart Grids in Argentina progresses slowly due to the high costs involved. In this context, the estimation of the lifespan reduction of distribution transformers is a key tool to efficiently manage human and material resources, maximizing the lifetime of this equipment. Despite the current state of the smart grids, the electricity distribution companies can implement it using the available data. Thermal models provide guidelines for lifespan estimation, but the adjustment to particular conditions, brands, or material quality is done by adjusting parameters. In this work we propose a method to adjust the parameters of a thermal model using Genetic Algorithms, comparing the estimation values of top-oil temperature with measurements from 315 kVA distribution transformers, located in the province of Tucumán, Argentina. The results show that, despite limited data availability, the adjusted model is suitable to implement a transformer monitoring system.

Fisher’s Fiducial Inference for Parameters of Uniform Distribution

Fisher’s Fiducial Inference for the parameters of a totality uniformly distributed on [α, β] is discussed. The corresponding fiducial distributions are derived. The maximum fiducial estimators, fiducial median estimators and fiducial expect estimators of α and β are got. The problems about the fiducial interval, fiducial region and hypothesis testing are discussed. An example which showed that Neyman-Pearson’s confidence interval has some place to be improved is illustrated. An idea about deriving fiducial distribution is proposed.

PARAMETERS FOR ARRIVAL TIME DISTRIBUTION OF AIR SHOWER PARTICLES

Several parameters which could be used to describe the arrival time distribution of extensive air shower(EAS)particles have been discussed according to their relations with core distance and the fluctuations.Among these parameters,τis considered to be better than the others when applied to Linsley Effect.

High-Accuracy Confidence Regions for Distribution Parameters

With the help of today’s computers, it is always relatively easy to find maximum-likelihood estimators of one or more parameters of any specific statistical distribution, and use these to construct the corresponding approximate confidence interval/region, facilitated by the well-known asymptotic properties of the likelihood function. The purpose of this article is to make this approximation substantially more accurate by extending the Taylor expansion of the corresponding probability density function to include quadratic and cubic terms in several centralized sample means, and thus finding the corresponding -proportional correction to the original algorithm. We then demonstrate the new procedure’s usage, both for constructing confidence regions and for testing hypotheses, emphasizing that incorporating this correction carries minimal computational and programming cost. In our final chapter, we present two examples to indicate how significantly the new approximation improves the procedure’s accuracy.

Relationship between Physico-Chemical Parameters and the Population Distribution of Fresh Water Snails in Amassoma Community and Niger Delta University Campuses,Bayelsa State,Nigeria

Evaluating the presence of fresh water snails in a location is germane in establishing a snail-borne disease control program.The purpose of the study was to see how physicochemical parameters influenced the population distribution of four fresh water snails(Lymnaea natalensis,Bulinus globosus,Biomphalaria pfeiffer,and Melanoides spp.)in the Amassoma community and Niger Delta University between March,2021 and May 2021.Snails were gathered by plucking and scooping them by hand.Snail was identified using standard pictorial keys.Physicochemical of the water of the snail habitat were measured using standard in-situ apparatus across eight sites Physico chemical measured were temperature,pH,conductivity,BOD,turbidity,salinity,and alkalinity.A total of 258 snails were gathered from the eight(8)different sites.In all sites,Lymnaea natalensis was more abundant.Bulinus globosus,Biomphalaria pfeiffer,and Melanoides spp.were among the other snails discovered.Lymnaea had negative correlation with pH,conductivity,BOD,and alkalinity and a positive correlation with salinity and temperature.Bulinus had a positive relationship with pH,salinity,and conductivity and a negative correlation with temperature,turbidity,BOD,and alkalinity.Biomphalaria had a positive correlation with temperature,pH,salinity,turbidity,and alkalinity Melanoides had positive correlations with Temperature,pH,salinity,BOD,and alkalinity and a negative correlation with turbidity and concentration.The was correlation between snails and snails.The presence of these snails suggests that the Amassoma village and Niger Delta University campuses are potential hotspots for a variety of snail-borne diseases.

Developing Weibull-based diameter distributions for the major coniferous species in Heilongjiang Province,China

Diameter distribution models play an important role in forest inventories,growth prediction,and management.The Weibull probability density function is widely used in forestry.Although a number of methods have been proposed to predict or recover the Weibull distribution,their applicability and predictive performance for the major tree species of China remain to be determined.Trees in sample plots of three even-aged coniferous species(Larix olgensis,Pinus sylvestris and Pinus koraiensis)were measured both in un-thinned and thinned stands to develop parameter prediction models for the Weibull probability density function.Ordinary least squares(OLS)and maximum likelihood regression(MLER),as well as cumulative distribution function regression(CDFR)were used,and their performance compared.The results show that MLER and CDFR were better than OLS in predicting diameter distributions of tree plantations.CDFR produced the best results in terms of fitting statistics.Based on the error statistics calculated for different age groups,CDFR was considered the most suitable method for developing prediction models for Weibull parameters in coniferous plantations.

Statistical Characteristics of Raindrop Size Distribution in the South China Monsoon Region(Guangdong Province)

While heavy rainfall frequently takes place in southern China during summer monsoon seasons,quantitative precipitation forecast skills are relatively poor.Therefore,detailed knowledge about the raindrop size distribution(DSD)is useful in improving the quantitative precipitation estimation and forecast.Based on the data during 2018-2022 from 86stations in a ground-based optical disdrometer measurement network,the characteristics of the DSD in Guangdong province are investigated in terms of the particle size distribution(N(D)),mass-weighted mean diameter(Dm) and other integral DSD parameters such as radar reflectivity(Z),rainfall rate(R) and liquid water content(LWC).In addition,the effects of geographical locations,weather systems(tropical cyclones,frontal systems and the summer monsoon) and precipitation types on DSD characteristics are also considered.The results are shown as follows.1) Convective precipitation has a broader N(D) and larger mean particle diameter than the stratiform precipitation,and the DSD observations in Guangdong are consistent with the three-parameter gamma distribution.The relationships between the Z and R for stratiform and convective precipitation are also derived for the province,i.e.,Z=332.34 R1.32and Z=366.26R1.42which is distinctly different from that of the Next-generation Weather Radar(NEXRAD) Z-R relationship in United States.2) In the rainy season(April-September),the Dm, R and LWC are larger than those in the dry season(OctoberMarch).Moreover the above parameters are larger,especially in mid-May,which is the onset of the South China Sea summer monsoon.3) The spatial analysis of DSD shows that the coastal station observations indicate a smaller Dmand a larger normalized intercept parameter(log10Nw),suggestive of maritime-like rainfall.Dmis larger and log10Nwis smaller in the inland area,suggestive of continental-like rainfall.4) Affected by such weather systems as the tropical cyclone,frontal system and summer monsoon,the DSD shows characteristics with distinct differences.Furthermore,frontal system rainfall tends to present a continental-like rainfall,tropical cyclone rainfall tends to have a maritime-like rainfall,and summer monsoon rainfall characteristic are between maritime-and continental-like cluster(raindrop concentration and diameter are higher than continental cluster and maritime cluster,respectively.)

Adaptive Load Balancing for Parameter Servers in Distributed Machine Learning over Heterogeneous Networks

In distributed machine learning(DML)based on the parameter server(PS)architecture,unbalanced communication load distribution of PSs will lead to a significant slowdown of model synchronization in heterogeneous networks due to low utilization of bandwidth.To address this problem,a network-aware adaptive PS load distribution scheme is proposed,which accelerates model synchronization by proactively adjusting the communication load on PSs according to network states.We evaluate the proposed scheme on MXNet,known as a realworld distributed training platform,and results show that our scheme achieves up to 2.68 times speed-up of model training in the dynamic and heterogeneous network environment.

Cauchy prior distribution-based AVO elastic parameter estimation via weakly nonlinear waveform inversion

Cauchy priori distribution-based Bayesian AVO reflectivity inversion may lead to sparse estimates that are sensitive to large reflectivities. For the inversion, the computation of the covariance matrix and regularized terms requires prior estimation of model parameters, which makes the iterative inversion weakly nonlinear. At the same time, the relations among the model parameters are assumed linear. Furthermore, the reflectivities, the results of the inversion, or the elastic parameters with cumulative error recovered by integrating reflectivities are not well suited for detecting hydrocarbons and fuids. In contrast, in Bayesian linear AVO inversion, the elastic parameters can be directly extracted from prestack seismic data without linear assumptions for the model parameters. Considering the advantages of the abovementioned methods, the Bayesian AVO reflectivity inversion process is modified and Cauchy distribution is explored as a prior probability distribution and the time-variant covariance is also considered. Finally, we propose a new method for the weakly nonlinear AVO waveform inversion. Furthermore, the linear assumptions are abandoned and elastic parameters, such as P-wave velocity, S-wave velocity, and density, can be directly recovered from seismic data especially for interfaces with large reflectivities. Numerical analysis demonstrates that all the elastic parameters can be estimated from prestack seismic data even when the signal-to-noise ratio of the seismic data is low.

SPECTRUM DISTRIBUTION OF THE SEDOND ORDER GENERALIZED DISTRIBUTED PARAMETER SYSTEMS

Spectrum distribution of the second order generalized distributed parameter system was discussed via the functional analysis and operator theory in Hilbert space. The solutions of the problem and the constructive expression of the solutions are given by the generalized inverse one of bounded linear operator. This is theoretically important for studying the stabilization and asymptotic stability of the second order generalized distributed parameter system.

A Two-Parameter Lindley Distribution for Modeling Waiting and Survival Times Data

In this paper, a two-parameter Lindley distribution, of which the one parameter Lindley distribution (LD) is a particular case, for modeling waiting and survival times data has been introduced. Its moments, failure rate function, mean residual life function, and stochastic orderings have been discussed. It is found that the expressions for failure rate function mean residual life function and stochastic orderings of the two-parameter LD shows flexibility over one-parameter LD and exponential distribution. The maximum likelihood method and the method of moments have been discussed for estimating its parameters. The distribution has been fitted to some data-sets relating to waiting times and survival times to test its goodness of fit to which earlier the one parameter LD has been fitted by others and it is found that to almost all these data-sets the two parameter LD distribution provides closer fits than those by the one parameter LD.

A modified lumped parameter model of distribution transformer winding

The modelling of the distribution transformer winding is the starting point and serves as important basis for the transformer characteristics analysis and the lightning pulse response prediction.A distributed parameters model can depict the winding characteristics accurately,but it requires complex calculations.Lumped parameter model requires less calculations,but its applicable frequency range is not wide.This paper studies the amplitude-frequency characteristics of the lightning wave,compares the transformer modelling methods and finally proposes a modified lumped parameter model,based on the above comparison.The proposed model minimizes the errors provoked by the lumped parameter approximation,and the hyperbolic functions of the distributed parameter model.By this modification it becomes possible to accurately describe the winding characteristics and rapidly obtain the node voltage response.The proposed model can provide theoretical and experimental support to lightning protection of the distribution transformer.

Asymmetry parameters in single ionization of He,Ne by XUV pulses

Fully differential cross-sections of single ionization of He and Ne atoms are studied by linearly polarized extreme ultraviolet(XUV)photons in the energy range of 22.1 e V–43.7 e V,using a reaction microscope.Photoelectron angular distributions and theβasymmetry parameters for He 1s^(2) electrons prove the reliability of our experiment,and the β asymmetry parameters extracted from the angular distributions of Ne 2p^(6) electrons are obtained.By comparing with different theoretical calculations,it is found that the contribution of the electron correlation effect in Ne 2p^(6) single ionization becomes increasingly important as the incident photon energy increases,while the relativistic effect is relatively low in the whole incident energy range.Our experimentalβasymmetry parameters may serve as a significant reference to test the most elaborated theories in the field.The datasets presented in this paper,including the photoelectron angular distributions andβasymmetry parameters,are openly available at https://doi.org/10.57760/sciencedb.j00113.00073.