THE DESIGN AND ANALYSIS OF EXPERIMENTS. By Oscar Kempthorne. New York, John Wiley and Sons, Inc., 1952. 631 pp. $8.50. This book by a teacher of statistics (as well as a consultant for \"experimenters\") is a comprehensive study of the philosophical background for the statistical design of experiment. It is necessary to have some facility with algebraic notation and manipulation to be able to use the volume intelligently. The problems are presented from the theoretical point of view, without such practical examples as would be helpful for those not acquainted with mathematics. The mathematical justification for the techniques is given. As a somewhat advanced treatment of the design and analysis of experiments, this volume will be interesting and helpful for many who approach statistics theoretically as well as practically. With emphasis on the \"why,\" and with description given broadly, the author relates the subject matter to the general theory of statistics and to the general problem of experimental inference. MARGARET J. ROBERTSON

The Design and Analysis of Experiments

Economic Control of Quality of Manufactured Product.

Stable non-Gaussian random processes , by G. Samorodnitsky and M. S. Taqqu. Pp. 632. £49.50. 1994. ISBN 0-412-05171-0 (Chapman and Hall).

Review of photovoltaic power forecasting

To mitigate the impact of climate change and global warming, the use of renewable energies is increasing day by day significantly. A considerable amount of electricity is generated from renewable energy sources since the last decade. Among the potential renewable energies, photovoltaic (PV) has experienced enormous growth in electricity generation. A large number of PV systems have been installed in on-grid and off-grid systems in the last few years. The number of PV systems will increase rapidly in the future due to the policies of the government and international organizations, and the advantages of PV technology. However, the variability of PV power generation creates different negative impacts on the electric grid system, such as the stability, reliability, and planning of the operation, aside from the economic benefits. Therefore, accurate forecasting of PV power generation is significantly important to stabilize and secure grid operation and promote large-scale PV power integration. A good number of research has been conducted to forecast PV power generation in different perspectives. This paper made a comprehensive and systematic review of the direct forecasting of PV power generation. The importance of the correlation of the input-output data and the preprocessing of model input data are discussed. This review covers the performance analysis of several PV power forecasting models based on different classifications. The critical analysis of recent works, including statistical and machine-learning models based on historical data, is also presented. Moreover, the strengths and weaknesses of the different forecasting models, including hybrid models, and performance matrices in evaluating the forecasting model, are considered in this research. In addition, the potential benefits of model optimization are also discussed.

Forecasting of photovoltaic power generation and model optimization: A review

An ARMAX model for forecasting the power output of a grid connected photovoltaic system

Forecasting the daily power output of a grid-connected photovoltaic system based on multivariate adaptive regression splines

Manufacturing and service processes today usually involve more than one process stages and operations. With an emphasis on achieving satisfactory product and service quality, multistage processes surveillance and fault diagnosis has become a necessity. Statistical Process Control (SPC) methods have been widely recognised as effective approaches for process monitoring and diagnosis. However, most conventional SPC methods focus on single-stage process without considering the multistage scenario. In this paper, we attempt to offer comprehensive references of statistical control methods for multistage manufacturing and service operations. Existing methods are compared and some future research topics are discussed.

Statistical process control for multistage manufacturing and service operations: A review and some extensions

Wind turbine fault diagnosis based on transfer learning and convolutional autoencoder with small-scale data

Most statistical process control research focuses on single-stage processes. This article considers the problem of multistage process monitoring and fault identification. This problem is formulated as a multiple hypotheses testing problem; however, as the number of stages increases, the detection power of multiple hypotheses testing methods that seek to control the type I error rate decreases dramatically. To maintain the detection power, we use a false discovery rate (FDR) control approach, which is widely used in microarray research. Two multistage process monitoring and fault identification schemes—an FDR-adjusted Shewhart chart and an FDR-adjusted cumulative sum (CUSUM) chart—are established. To apply the FDR approach, the distribution of the CUSUM statistics are obtained based on Markov chain theory and Brownian motion with drift models. The detection and fault identification power of the new schemes are evaluated by the Monte Carlo method. The results indicate that the novel FDR-adjusted approaches ...

Yanting Li

Papers

An ARMAX model for forecasting the power output of a grid connected photovoltaic system

Forecasting the daily power output of a grid-connected photovoltaic system based on multivariate adaptive regression splines

Statistical process control for multistage manufacturing and service operations: A review and some extensions

Wind turbine fault diagnosis based on transfer learning and convolutional autoencoder with small-scale data

False Discovery Rate-Adjusted Charting Schemes for Multistage Process Monitoring and Fault Identification