A wavelet-based procedure for process fault detection

To detect faults in a time-dependent process, we apply a discrete wavelet transform (DWT) to several independently replicated data sets generated by that process. The DWT can capture irregular data patterns such as sharp "jumps" better than the Fourier transform and standard statistical procedures without adding much computational complexity. Our wavelet coefficient selection method effectively balances model parsimony against data reconstruction error. The few selected wavelet coefficients serve as the "reduced-size" data set to facilitate an efficient decision-making method in situations with potentially large-volume data sets. We develop a general procedure to detect process faults based on differences between the reduced-size data sets obtained from the nominal (in-control) process and from a new instance of the target process that must be tested for an out-of-control condition. The distribution of the test statistic is constructed first using normal distribution theory and then with a new resampling procedure called "reversed jackknifing" that does not require any restrictive distributional assumptions. A Monte Carlo study demonstrates the effectiveness of these procedures. Our methods successfully detect process faults for quadrupole mass spectrometry samples collected from a rapid thermal chemical vapor deposition process     

Simulation of programmable logic controller

The development and test of a PLC control program takes time, increases equipment down-time, and might damage hardware due to program errors. All of these problems can be eliminated if there is a computer simulation system for testing control programs off line. This paper presented a new method, called Direct Sequential Method, for simulating PLCs. This method resolves a ladder logic sequentially by applying a series of logic deductions. This simulation system has been implemented in C and tested successfully.     

Improved Poisson intensity estimation: denoising application using Poisson data

Recently, Timmermann and Nowak developed algorithms for estimating the means of independent Poisson random variables. The algorithms are based on a multiscale model where certain random variables are assumed to obey a beta-mixture density function. Timmermann and Nowak simplify the density estimation problem by assuming the beta parameters are known and only one mixture parameter is unknown. They use the observed data and the method of moments to estimate the unknown mixture parameter. Taking a different approach, we generate training data from the observed data and compute maximum likelihood estimates of all of the beta-mixture parameters. To assess the improved performance obtained by the proposed modification, we consider a denoising application using Poisson data.     

An investigation of bulk recycling planning for an electronics recycling facility receiving industrial returns versus residential returns

Since most end-of-life electronics equipment contain hazardous materials such as lead solder alloys or lead-impregnated glass, it is important to divert them from landfills. For end-of-life products that are not repairable and do not contain reusable parts, bulk recycling is an alternative to recover base materials. In this paper, we contrast production and recycling planning and distribution decisions, activities, and costs. We reveal that while a traditional production facility connects suppliers and customers, a recycling facility connects both "input" and "output" customers. As a result, decisions in short-term bulk recycling planning include what products to accept, what products to process and reprocess, and what products to carry in inventory. Many recyclers set prices to receive "input" based on experience. For various prices to receive "input", we use a new analytical model to investigate the sensitivity of the short-term bulk recycling planning decisions in products from two different sources: industrial returns versus residential returns. The results of the case study show that different decisions are recommended for the industrial returns versus the residential returns when the total quantity of the incoming products is equal.     

A vectorial boundary element method analysis of integrated optical waveguides

In this paper, a new vectorial boundary element method is introduced and applied to the modal analysis of dielectric waveguides with piecewise homogeneous refractive indexes. The procedure, which is free of spurious modes, determines the full field distribution from the longitudinal fields at the refractive index boundaries. Singular kernels are evaluated through series solutions while the electric field discontinuity at corners is accommodated through either a grid refinement technique or a semianalytic approach. Our formalism generates propagation constants and modal field distributions for several representative refractive index profiles with far higher accuracy than standard finite-difference or finite-element procedures.     

热分析在PVA纤维预处理中的应用

用差热、热重法对原丝的热性能及由不同预处理方法所引起的PVA纤维的热行为变化进行了研究;并通过模拟脱水条件,用不同升温速率,求出了经不同预处理纤维的脱水反应活化能。结果表明,PVA原丝在230℃有一软化点,三种预处理都不同程度地影响了原丝的热效应变化;纤维在180～340℃主要为脱水反应;原丝及经脱水剂、O_3和O_3综合脱水剂处理的纤维,其脱水反应的表观活化能分别为:164.3kJ/mol,92.9kJ/mol,130.5kJ/mol和117.6kJ/mol。     

Optical scattering in beef steak to predict tenderness using hyperspectral imaging in the VIS-NIR region

The objective of this research is to develop a non-destructive method for predicting cooked beef tenderness using optical scattering of light on fresh beef muscle tissue. A hyperspectral imaging system (λ = 496–1,036 nm) that consists of a CCD camera and an imaging spectrograph, was used to acquire beef steak images. The hyperspectral image consisted of 120 bands with spectral intervals of 4.54 nm. Sixty-one fresh beef steaks, including 44 strip loin and 17 tenderloin cuts, were collected. After imaging, the steaks were cooked and Warner-Bratzler shear (WBS) force values were collected as tenderness references. The optical scattering profiles were derived from the hyperspectral images and fitted to the modified Lorentzian function. Parameters, such as the peak height, full scattering width at half maximum (FWHM), and the slope around the FWHM were determined at each wavelength. Stepwise regression was used to identify 7 key wavelengths and parameters. The parameters were then used to predict the WBS scores. The model was able to predict WBS scores with an R = 0.67. Optical scattering implemented with hyperspectral imaging shows limited success for predicting current status of tenderness in beef steak.