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

Data Mining - Concepts and Techniques.

Data Mining Practical Machine Learning Tools and Techniques

Hundreds of different types of coatings are used to protect a variety of structural engineering materials from corrosion, wear, and erosion, and to provide lubrication and thermal insulation. Of all these, thermal barrier coatings (TBCs) have the most complex structure and must operate in the most demanding high-temperature environment of aircraft and industrial gas-turbine engines. TBCs, which comprise metal and ceramic multilayers, insulate turbine and combustor engine components from the hot gas stream, and improve the durability and energy efficiency of these engines. Improvements in TBCs will require a better understanding of the complex changes in their structure and properties that occur under operating conditions that lead to their failure. The structure, properties, and failure mechanisms of TBCs are herein reviewed, together with a discussion of current limitations and future opportunities.

http://science.sciencemag.org/content/sci/296/5566/280.full.pdf

Thermal Barrier Coatings for Gas-Turbine Engine Applications

QuenchMinerTM: Decision Support for Optimization of Heat Treating Processes.

This paper describes our proposed technique AutoDomainMine that performs data mining guided by fundamental knowledge of the domain. The data being mined consists of input conditions from quenching experiments and the resulting heat transfer curves, i.e., plots of heat transfer coefficients versus part temperature. Since heat transfer coefficients characterize quenching, the estimation assists decision-making. This avoids running laboratory experiments which consume considerable time and resources. AutoDomainMine integrates two data mining techniques, clustering and classification, into a learning strategy. It clusters curves resulting from existing experiments and uses decision tree classifiers to learn the clustering criteria, i.e., input conditions characterizing the clusters. The learned criteria are used to design a representative pair of input conditions and heat transfer curve per cluster. The decision trees and representatives serve as the basis for estimation. When input conditions of an unperformed experiment are submitted, the decision tree path is traced to estimate its cluster and hence the corresponding heat transfer curve. Also when a desired heat transfer curve is submitted, it is compared with the representative curves. The input conditions of the closest matching curve are the estimated conditions to achieve the desired curve. AutoDomainMine on evaluation gives accuracy higher than state-of-the-art estimation techniques.

Estimating heat transfer coefficients as a function of temperature by data mining

Robust methodology for determination of heat transfer coefficient distribution in convection

Cooling rates of work pieces in heat treating processes heavily depend on the interfacial heat transfer coefficient (HTC) between work pieces and quenchants. Because the HTC plays an important role in the resulted distortion, residual stress, and mechanical property distributions, understanding how the HTC varies with respect to various influencing factors is of importance for an optimal quenching process. In this paper, the influencing factors such as air/gas velocity, pressure, temperature, gas mixture and part orientation, air/gas temperature, air humidity, surface roughness, oxide, and material to the variation of HTC are studied experimentally by using the Center for Heat Treating Excellence quenching system and theoretically by using empirical equations.

Influencing Factors of Heat Transfer Coefficient in Air and Gas Quenching

Scientific experimental results are often depicted as plots of functions to aid their visual analysis and comparison. In computationally comparing these plots using techniques such as similarity search and clustering, the notion of similarity is typically distance. However, it is seldom known which distance metric(s) best preserve(s) semantics in the respective domain. It is thus desirable to learn such domain-specific distance metrics for the comparison of plots. This paper describes a technique called LearnMet proposed to learn such metrics. The input to LearnMet is a training set with actual clusters of plots. These are iteratively compared with clusters over the same plots predicted using an arbitrary but fixed clustering algorithm. Using a guessed initial metric for clustering, adjustments are made to the metric in each epoch based on the error between the predicted and actual clusters until the error is minimal or below a given threshold. The metric giving the lowest error is output as the learned metric. The proposed LearnMet technique and its enhancements are discussed in detail in this paper. The primary application of LearnMet is clustering plots in the Heat Treating domain. Hence it is rigorously evaluated using Heat Treating data. Given distinct test sets for evaluation, clusters of plots predicted using the learned metrics are compared with given actual clusters over the same plots. The extent to which the predicted and actual clusters match each other denotes the accuracy of the learned metrics.

Richard D. Sisson

Papers

QuenchMinerTM: Decision Support for Optimization of Heat Treating Processes.

Estimating heat transfer coefficients as a function of temperature by data mining

Robust methodology for determination of heat transfer coefficient distribution in convection

Influencing Factors of Heat Transfer Coefficient in Air and Gas Quenching

LearnMet: learning domain-specific distance metrics for plots of scientific functions