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

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

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Thank you very much for downloading modern applied statistics with s. As you may know, people have search hundreds times for their favorite readings like this modern applied statistics with s, but end up in harmful downloads. Rather than reading a good book with a cup of coffee in the afternoon, instead they cope with some harmful virus inside their laptop. modern applied statistics with s is available in our digital library an online access to it is set as public so you can download it instantly. Our digital library saves in multiple countries, allowing you to get the most less latency time to download any of our books like this one. Kindly say, the modern applied statistics with s is universally compatible with any devices to read.

Modern Applied Statistics With S

Strategies to the Prediction, Mitigation and Management of Product Obsolescence: Bartels/Prediction

Electronic device encapsulation using red phosphorus flame retardants

Printed circuit board (PCB) specimens containing three different IPC-B-25 test structures were exposed to temperature-humidity-bias conditions in order to evaluate the effects of no-clean flux chemistry, conductor spacing, voltage bias, and test environment on surface insulation resistance (SIR). Comb patterns on the PCBs were coated with a eutectic (63Sn/37Pb) solder applied by a hot air solder leveling and processed by using no-clean aqueous-based and rosin-based fluxes. The SIR failure rate with rosin-based no-clean flux was observed to be greater than that with aqueous-based no-clean flux. This was explained by the more corrosive nature of the flux residues and the larger concentration of hygroscopic weak organic acids in the rosin-based flux residues. A characteristic of the SIR failures for PCBs processed with rosin-based flux was a series of intermittent SIR drops, which could severely affect the reliability of electronic assemblies. It was hypothesized that flux residues combined with adsorbed moisture from the environment form an acidic medium, occasionally breaking through the tin oxide passivation layer on the electrodes. Penetration of the passivation layer combined with conductive flux residues bridging the electrodes caused the resistance to decrease, and rehealing of the passivation layer resulted in the intermittent behavior. Conductor spacing was observed to represent a factor in the electrochemical migration process that is independent of electric field. Since conductor spacings in electronic products continue to decrease, the experimental results support recommendations to replace 25-mil (0.64-mm) comb structures on industry standard test boards with those having smaller spacings, below 12.5 mil (0.32 mm), that accurately reflect the greater risk for SIR drops of today's higher density assemblies.

Reliability of Printed Circuit Boards Processed Using No-Clean Flux Technology in Temperature–Humidity–Bias Conditions

Li-ion batteries experience mechanical stress evolution due in part to Li intercalation into and de-intercalation out of the electrodes, ultimately resulting in performance degradation. In situ measurements of electrode stress can be used to analyze stress generation factors, verify mechanical deformation models, and validate degradation mechanisms. They can also be embedded in Li-ion battery management systems when stress sensors are either implanted in electrodes or attached on battery surfaces. This paper reviews in situ measurement methods of electrode stress based on optical principles, including digital image correlation, curvature measurement, and fiber optical sensors. Their experimental setups, principles, and applications are described and contrasted. This literature review summarizes the current status of these stress measurement methods for battery electrodes and discusses recent developments and trends.

In Situ Stress Measurement Techniques on Li-ion Battery Electrodes: A Review

The statistical bases for current models of RAID reliability are reviewed and a highly accurate alternative is provided and justified. This new model corrects statistical errors associated with the pervasive assumption that system (RAID group) times to failure follow a homogeneous Poisson process, and corrects errors associated with assuming the time-to-failure and time-to-restore distributions are exponentially distributed. Statistical justification for the new model uses theory for reliability of repairable systems. Four critical component distributions are developed from field data. These distributions are for times to catastrophic failure, reconstruction and restoration, read errors, and disk data scrubs. Model results have been verified and predict between 2 to 1,500 times as many double disk failures as estimates made using the mean time to data loss method. Model results are compared to system level field data for RAID group of 14 drives and show excellent correlation and greater accuracy than either MTTDL.

Michael Pecht

Papers

Strategies to the Prediction, Mitigation and Management of Product Obsolescence: Bartels/Prediction

Electronic device encapsulation using red phosphorus flame retardants

Reliability of Printed Circuit Boards Processed Using No-Clean Flux Technology in Temperature–Humidity–Bias Conditions

In Situ Stress Measurement Techniques on Li-ion Battery Electrodes: A Review

A Highly Accurate Method for Assessing Reliability of Redundant Arrays of Inexpensive Disks (RAID)