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

다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

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

Additive manufacturing of metallic components – Process, structure and properties

Mathematical model for stainless steelmaking - 2. application to aod heats.

Spiking, rippling and humping seriously reduce the strength of welds. The effects of beam focusing, volatile alloying element concentration and welding velocity on spiking, coarse rippling and humping in keyhole mode electron-beam welding are examined through scale analysis. Although these defects have been studied in the past, the mechanisms for their formation are not fully understood. This work relates the average amplitudes of spikes to fusion zone depth for the welding of Al 6061, SS 304 and carbon steel, and Al 5083. The scale analysis introduces welding and melting efficiencies and an appropriate power distribution to account for the focusing effects, and the energy which is reflected and escapes through the keyhole opening to the surroundings. The frequency of humping and spiking can also be predicted from the scale analysis. The analysis also reveals the interrelation between coarse rippling and humping. The data and the mechanistic findings reported in this study are useful for understanding and preventing spiking and humping during keyhole mode electron and laser beam welding.

https://iopscience.iop.org/article/10.1088/0022-3727/44/24/245501/pdf

Scaling of spiking and humping in keyhole welding

Synthesis of nanocomposite thin films with self-assembled structures by pulsed ion beam ablation of MoS2 target

During plasma enhanced chemical vapor deposition of silicon oxide films, plasma diagnostics is important for process control and reproducibility of film quality. In this study the film growth rate has been investigated with special emphasis on plasma properties to seek a better understanding of the deposition process. Light emissions from the plasma were monitored using an optical multichannel analyzer for various deposition conditions. The electron temperature and density were determined from the spectral data. With increasing silane concentration, the number density of the electrons, the concentrations of active species and the deposition rate increased. The growth rate also increased with nitrous oxide concentration. However, the emission intensities did not show a concomitant increase because of the radiation quenching effect. Analysis of the results emphasizes the crucial importance of electron density to generate sufficient concentration of active species responsible for determining the film growth ...

Optical emission investigation of the plasma enhanced chemical vapor deposition of silicon oxide films

In recent years, numerical heat and fluid flow models have provided significant insight into welding processes and welded materials that could not have been achieved otherwise. However, these calculations are complex and time consuming, and are unsuitable in situations where rapid calculations are desired. A practical solution to this problem is to develop a neural network that is trained with the data generated by a numerical heat and fluid flow model. Apart from providing high computational speed, the results of this neural network conform to the basic laws of conservation of mass, momentum, and energy. In the present study, six feed-forward neural networks have been developed for the gas tungsten arc (GTA) welding of low-carbon steel. Each network provides one of the six output parameters of GTA welds, i.e., depth, width, and length of the weld pool, peak temperature, cooling time from 800° to 500°C, and maximum liquid velocity. The networks require values of 17 input parameters including the welding variables like current, voltage, welding speed, arc efficiency, arc radius, and power distribution factor, and material properties like thermal conductivity and specific heat. The weights of the neural networks were calculated using two optimization schemes, first using the gradient descent (GD) method with various sets of randomized initial weights, and then applying a hybrid optimization scheme where a genetic algorithm (GA) is used in combination with the GD method. The neural networks produced by the hybrid optimization approach gave better results than all the networks based on only the GD method. Unlike the GD method alone, the hybrid optimization scheme could find the significantly better weights, which is illustrated by the good agreement between all the outputs from the neural networks and the corresponding results from the heat and fluid flow model.

Tarasankar Debroy

Papers

Mathematical model for stainless steelmaking - 2. application to aod heats.

Scaling of spiking and humping in keyhole welding

Synthesis of nanocomposite thin films with self-assembled structures by pulsed ion beam ablation of MoS2 target

Optical emission investigation of the plasma enhanced chemical vapor deposition of silicon oxide films

A genetic algorithm and gradient-descent-based neural network with the predictive power of a heat and fluid flow model for welding