There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

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

Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

According to W. Edwards Deming, American companies require nothing less than a transformation of management style and of governmental relations with industry. In Out of the Crisis, originally published in 1982, Deming offers a theory of management based on his famous 14 Points for Management. Management's failure to plan for the future, he claims, brings about loss of market, which brings about loss of jobs. Management must be judged not only by the quarterly dividend, but by innovative plans to stay in business, protect investment, ensure future dividends, and provide more jobs through improved product and service. In simple, direct language, he explains the principles of management transformation and how to apply them.

Out of the Crisis

Statistics for Spatial Data.

We propose a statistical process control scheme that can be implemented in industrial practice, in which the quality of a process can be characterized by a general linear profile. We start by reviewing the general linear profile model and the existing monitoring methods. Based on this, we propose a novel multivariate exponentially weighted moving average monitoring scheme for such a profile. We introduce two other enhancement features, the variable sampling interval and the parametric diagnostic approach, to further improve the performance of the proposed scheme. Throughout the article, we use a deep reactive ion etching example from semiconductor manufacturing, which has a profile that fits a quadratic polynomial regression model well, to illustrate the implementation of the proposed approach.

Monitoring General Linear Profiles Using Multivariate Exponentially Weighted Moving Average Schemes

In this paper, we consider a buyer who designs a product and owns the brand, yet outsources the production to a supplier. Both the buyer and the supplier incur quality-related costs, e.g., costs of customer goodwill and future market share loss by the buyer and warranty-related costs shared by both the buyer and the supplier whenever a nonconforming item is sold to a customer. Therefore, both parties have an incentive to invest in quality-improvement efforts. This paper explores the roles of different parties in a supply chain in quality improvement. We show that the buyer's involvement can have a significant impact on the profits of both parties and of the supply chain as a whole, and he cannot cede the responsibility of quality improvement to the supplier in many cases. We also investigate how quality-improvement decisions interact with operational decisions such as the buyer's order quantity and the supplier's production lot size.

Pushing Quality Improvement Along Supply Chains

The modern manufacturing industry is investing in new technologies such as the Internet of Things (IoT), big data analytics, cloud computing and cybersecurity to cope with system complexity, increa...

The Internet of Things for Smart Manufacturing: A Review

This paper focuses on the monitoring techniques applied in multivariate processes when the underlying distribution of the quality characteristics departs from normality. For most conventional control charts, such as Hotelling's T 2 charts, the design of the control limits is commonly based on the assumption that the quality characteristics follow a multivariate normal distribution. However, this may not be reasonable in many real-world problems. This paper addresses this issue and proposes a monitoring approach motivated by statistical learning theory, which has been applied successfully in the field of pattern recognition. The developed multivariate control chart is based on the kernel distance, which is a measure of the distance between the 'kernel centre' and the incoming new sample to be monitored. The kernel distance can be calculated using support vector methods. This chart makes use of information extracted from in-control preliminary samples. A case study demonstrates that the kernel-distance-base...

A kernel-distance-based multivariate control chart using support vector methods

The use statistical process control (SPC) in monitoring and diagnosis of process and product quality profiles remains an important problem in various manufacturing industries. The SPC problem with a nonlinear profile is particularly challenging. This article proposes a novel scheme to monitor changes in both the regression relationship and the variation of the profile online. It integrates the multivariate exponentially weighted moving average procedure with the generalized likelihood ratio test based on nonparametric regression. The proposed scheme not only provides an effective SPC solution to handle nonlinear profiles, which are common in industrial practice, but it also resolves the latent problem in popular parametric monitoring methods of being unable to detect certain types of changes due to a misspecified, out-of-control model. Our simulation results demonstrate the effectiveness and efficiency of the proposed monitoring scheme. In addition, a systematic diagnostic approach is provided to locate t...

Fugee Tsung

Papers

Monitoring General Linear Profiles Using Multivariate Exponentially Weighted Moving Average Schemes

Pushing Quality Improvement Along Supply Chains

The Internet of Things for Smart Manufacturing: A Review

A kernel-distance-based multivariate control chart using support vector methods

Monitoring Profiles Based on Nonparametric Regression Methods