Designing Adaptive Multiple Dependent State Sampling Plan for Accelerated Life Tests

A novel adaptive multiple dependent state sampling plan(AMDSSP)was designed to inspect products from a continuous manufacturing process under the accelerated life test(ALT)using both double sampling plan(DSP)and multiple dependent state sampling plan(MDSSP)concepts.Under accelerated conditions,the lifetime of a product follows the Weibull distribution with a known shape parameter,while the scale parameter can be determined using the acceleration factor(AF).The Arrhenius model is used to estimate AF when the damaging process is temperature-sensitive.An economic design of the proposed sampling plan was also considered for the ALT.A genetic algorithm with nonlinear optimization was used to estimate optimal plan parameters to minimize the average sample number(ASN)and total cost of inspection(TC)under both producer’s and consumer’s risks.Numerical results are presented to support the AMDSSP for the ALT,while performance comparisons between the AMDSSP,the MDSSP and a single sampling plan(SSP)for the ALT are discussed.Results indicated that the AMDSSP was more flexible and efficient for ASN and TC than the MDSSP and SSP plans under accelerated conditions.The AMDSSP also had a higher operating characteristic(OC)curve than both the existing sampling plans.Two real datasets of electronic devices for the ALT at high temperatures demonstrated the practicality and usefulness of the proposed sampling plan.

A Novel Multiple Dependent State Sampling Plan Based on Time Truncated Life Tests Using Mean Lifetime

The design of a new adaptive version of the multiple dependent state(AMDS)sampling plan is presented based on the time truncated life test under the Weibull distribution.We achieved the proposed sampling plan by applying the concept of the double sampling plan and existing multiple dependent state sampling plans.A warning sign for acceptance number was proposed to increase the probability of current lot acceptance.The optimal plan parameters were determined simultaneously with nonlinear optimization problems under the producer’s risk and consumer’s risk.A simulation study was presented to support the proposed sampling plan.A comparison between the proposed and existing sampling plans,namely multiple dependent state(MDS)sampling plans and a modified multiple dependent state(MMDS)sampling plan,was considered under the average sampling number and operating characteristic curve values.In addition,the use of two real datasets demonstrated the practicality and usefulness of the proposed sampling plan.The results indicated that the proposed plan is more flexible and efficient in terms of the average sample number compared to the existing MDS and MMDS sampling plans.

A computational framework for improving genetic variants identification from 5,061 sheep sequencing data

Background Pan-genomics is a recently emerging strategy that can be utilized to provide a more comprehensive characterization of genetic variation.Joint calling is routinely used to combine identified variants across multiple related samples.However,the improvement of variants identification using the mutual support information from mul-tiple samples remains quite limited for population-scale genotyping.Results In this study,we developed a computational framework for joint calling genetic variants from 5,061 sheep by incorporating the sequencing error and optimizing mutual support information from multiple samples’data.The variants were accurately identified from multiple samples by using four steps:(1)Probabilities of variants from two widely used algorithms,GATK and Freebayes,were calculated by Poisson model incorporating base sequencing error potential;(2)The variants with high mapping quality or consistently identified from at least two samples by GATK and Freebayes were used to construct the raw high-confidence identification(rHID)variants database;(3)The high confidence variants identified in single sample were ordered by probability value and controlled by false discovery rate(FDR)using rHID database;(4)To avoid the elimination of potentially true variants from rHID database,the vari-ants that failed FDR were reexamined to rescued potential true variants and ensured high accurate identification variants.The results indicated that the percent of concordant SNPs and Indels from Freebayes and GATK after our new method were significantly improved 12%-32%compared with raw variants and advantageously found low frequency variants of individual sheep involved several traits including nipples number(GPC5),scrapie pathology(PAPSS2),sea-sonal reproduction and litter size(GRM1),coat color(RAB27A),and lentivirus susceptibility(TMEM154).Conclusion The new method used the computational strategy to reduce the number of false positives,and simulta-neously improve the identification of genetic variants.This strategy did not incur any extra cost by using any addi-tional samples or sequencing data information and advantageously identified rare variants which can be important for practical applications of animal breeding.

A K-Means Clustering-Based Multiple Importance Sampling Algorithm for Integral Global Optimization

In this paper, we propose a K-means clustering-based integral level-value estimation algorithm to solve a kind of box-constrained global optimization problem. For this purpose, we introduce the generalized variance function associated with the level-value of the objective function to be minimized. The variance function has a good property when Newton’s method is used to solve a variance equation resulting by setting the variance function to zero. We prove that the largest root of the variance equation is equal to the global minimum value of the corresponding optimization problem. Based on the K-means clustering algorithm, the multiple importance sampling technique is proposed in the implementable algorithm. The main idea of the cross-entropy method is used to update the parameters of sampling density function. The asymptotic convergence of the algorithm is proved, and the validity of the algorithm is verified by numerical experiments.

Power distribution analysis for multiple modulation formats in an all-optical sampling wavelength division multiplexing system

An optimal power distribution analysis for an all-optical sampling orthagonal frequency division multiplexing(OFDM) scheme with multiple modulation formats including diferential phase shift keyed(DPSK), diferential quadrature phase shift keyed(DQPSK), and non-return-to-zero(NRZ) is proposed. The noise tolerances of different modulation formats are analyzed, and the optimal input power ratio between phase and intensity modulation formats for the best overall receiving performance is investigated under unchanged total input power. Moreover, this scheme can seamlessly coexist with the traditional WDM channel.

Constrained circular-patch-based texture synthesis

Texture synthesis has been developed for several years.The traditional technique can generate a larger image from a small image while avoid feeling of repetition or uncontinuity.Some constrained synthesis methods which can synthesize image according to special location demand or other demands have been also proposed in recent years.However,in general,these constrained texture synthesis methods are simple and have few controllable factors to meet user＇s diverse needs.To control multiple-sample texture synthesis more flexibly in various aspects such as synthesis location,proportion and semantic objects,we present an interactive texture synthesis approach based on circular patches and constrained by objects according to a certain ratio.With this approach,source exemplars and the target image are firstly divided into several regions with different characters.Users can click the blocks in the source exemplars and the want-to-be-synthesized region in the target image,and then texture in the target image is synthesized with the corresponding regions in the source exemplars.In the process of texture synthesis,circular patch instead of square patch is used to eliminate the aliasing phenomena.Images are synthesized from multiple sample images with ratio constraint and experiments on images show that our approach can get effective results of ratio-constrained multi-sample synthesis.

Recent developments in two-dimensional (2D) correlation spectroscopy

Recent noteworthy developments in the field of two-dimensional（2D） correlation spectroscopy are reviewed.2D correlation spectroscopy has become a very popular tool due to its versatility and relative ease of use.The technique utilizes a spectroscopic or other analytical probe from a number of selections for a broad range of sample systems by employing different types of external perturbations to induce systematic variations in intensities of spectra.Such spectral intensity variations are then converted into2 D spectra by a form of correlation analysis for subsequent interpretation.Many different types of 2D correlation approaches have been proposed.In particular,2D hetero-correlation and multiple perturbation correlation analyses,including orthogonal sample design scheme,are discussed in this review.Additional references to other important developments in the field of 2D correlation spectroscopy,such as projection correlation and codistribution analysis,were also provided.