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This article relates to the investigation of innovative power distribution components based on microelectromechanical technologies integrated with advanced optimization methods to improve the performance of electric power distribution systems. A novel medium voltage class circuit breaker using two switching string assemblies has been designed and implemented in a laboratory prototype. The building block is a switching unit, which includes microelectromechanical switch shunted by a diode. Several switching units are connected in series to form the switching string, and several strings are connected in parallel to form the switching string assembly. A magnetic coil activates the switches. A circuit breaker is comprised of two switching string assemblies connected in series. One string interrupts the current in the positive half cycle, and the other interrupts current in the negative half cycle. The opening of the mechanical switches transfers the current to the diodes, without arcing. In this way, ...

Under the assumption of normality, the distribution of estimators of a class of capability indices, containing the indices , , and , is derived when the process parameters are estimated from subsamples. The process mean is estimated using the grand average and the process variance is estimated using the pooled variance from subsamples collected over time for an in-control process. The derived theory is then applied to study the use of hypothesis testing to assess process capability. Numerical investigations are made to explore the effect of the size and number of subsamples on the efficiency of the hypothesis test for some indices in the studied class. The results for and indicate that, even when the total number of sampled observations remains constant, the power of the test decreases as the subsample size decreases. It is shown how the power of the test is dependent not only on the subsample size and the number of subsamples, but also on the relative location of the process mean from the target value. As part of this investigation, a simple form of the cumulative distribution function for the non-central -distribution is also provided. Copyright © 2003 John Wiley & Sons, Ltd.

We present statistical evidence that when students voluntarily write reflective “journal entry” essays on assigned reading, their performance on multiple-choice quizzes on the reading is improved compared to students who do not complete such essays. A Smirnov non-parametric test and a pair-wise comparison analysis were used to establish the validity of the hypotheses.

For many quality characteristics, such as circularity, cylindricity, straightness and flatness, positive skewness in the inspection data is the norm, and, in fact, is desirable. Summarizing the process performance using such data in conjunction with capability indices has recently received a considerable amount of attention, with new indices being proposed and compared for usefulness and accuracy. This paper is intended to contribute to this growing discussion, and to add a unique focus. In particular, this investigation concentrates on one form of a neoclassical index, the Cs index, originally proposed to be sensitive to skewness and to decrease in value as the skewness increased in the underlying distribution of the data. In other words, ‘skewness is badness’. Looking at this index from an altered perspective, the possibility that this index could serve a useful purpose in summarizing process performance for such non-normal processes by merely changing its interpretation or slightly changing its form is considered. Hence, actual data from circularity measurements are used to identify a relevant group of distributions, and then the accuracy of Cs is investigated along with its modified version for this group of distributions. In particular, this investigation includes several Rayleigh and gamma distributions for various sample sizes and reports on the bias of the proposed estimators. These findings indicate that such a modified index has some useful attributes in reflecting process performance, with respect to the percentage of non-conformance and the accuracy for relatively large samples. Copyright © 2001 John Wiley & Sons, Ltd.

Point estimates of the capability index Cpm are generated from sample observations from manufacturing processes and are used to make comparisons of processes. Decisions based on these quantities should necessarily include information about the variabili..

Back-propagation pattern recognizers (BPPR) are proposed to identify unnatural patterns exhibited on Shewhart control charts. These unnatural patterns, such as cycles and trends, can provide valuable information for real-time process control. In a computer-integrated manufacturing environment, the operator need not routinely monitor the control chart but, rather, can be alerted to patterns by a computer signal generated by the proposed algorithm. In this paper, an off-line analysis is performed to investigate the training and learning speed of these BPPRs on simulated X¯ data. The best configuration of the network is further tested to demonstrate the classification capability of the proposed BPPR.

Boltzmann machines (BM), a type of neural networking algorithm, have been proven to be useful in pattern recognition. Patterns on quality control charts have long been recognized as providing useful information for correcting process performance problems. In computer-integrated manufacturing environments, where the control charts are monitored by computer algorithms, the potential for using pattern-recognition algorithms is considerable. The main purpose of this paper is to formulate a Boltzmann machine pattern recognizer (BMPR) and demonstrate its utility in control chart pattern recognition. It is not the intent of this paper to make comparisons between existing related algorithms. A factorial design of experiments was conducted to study the effects of numerous factors on the convergence behavior and performance of these BMPRs. These factors include the number of hidden nodes used in the network and the annealing schedule. Simulations indicate that the temperature level of the annealing schedule significantly affects the convergence behavior of the training process and that, to achieve a balanced performance of these BMPRs, a medium to high level of annealing temperatures is recommended. Numerical results for cyclical and stratification patterns illustrate that the classification capability of these BMPRs is quite powerful.

An improved neural network approach to produce short-term electric load forecasts is proposed. A strategy suitable for selecting the training cases for the neural network is presented. This strategy has the advantage of circumventing the problem of holidays and drastic changes in weather patterns, which make the most recent observations unlikely candidates for training the network. In addition, an improved neural network algorithm is proposed. This algorithm includes a combination of linear and nonlinear terms which map past load and temperature inputs to the load forecast output. The search strategy and algorithm demonstrate improved accuracy over other methods when tested using two years of utility data. In addition to reporting the summary statistics of average and standard deviation of absolute percentage error, an alternate method using a cumulative distribution plot for presenting load forecasting results is demonstrated. >

[This abstract is based on the author's abstract.] Jet-turbine component manufacturing involves complex manufacturing operations. The manufacture of airfoil or blades and the subsequent profile inspection through coordinate measuring machines is es..

A hierarchical classification algorithm is applied to hourly temperature readings to divide the historical database into seasonal subsets. These subsets are used to statistically identify and fit a response function for each season. These functional models constitute a library of models useful to the power scheduler. For a particular day, the appropriate model is selected by performing discriminant analysis. This approach is illustrated using data from a summer peaking utility. This application demonstrates that an entire procedure for specifying forecasting models can be formed with currently available statistical software. Furthermore, the models can be implemented on a microcomputer spreadsheet. >