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

[신간의 별자리x] 우리/미술, 그리고 ‘슬픔의 박물관’

Graphene is a wonder material with many superlatives to its name. It is the thinnest known material in the universe and the strongest ever measured. Its charge carriers exhibit giant intrinsic mobility, have zero effective mass, and can travel for micrometers without scattering at room temperature. Graphene can sustain current densities six orders of magnitude higher than that of copper, shows record thermal conductivity and stiffness, is impermeable to gases, and reconciles such conflicting qualities as brittleness and ductility. Electron transport in graphene is described by a Dirac-like equation, which allows the investigation of relativistic quantum phenomena in a benchtop experiment. This review analyzes recent trends in graphene research and applications, and attempts to identify future directions in which the field is likely to develop.

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Graphene: Status and Prospects

There is intense interest in graphene in fields such as physics, chemistry, and materials science, among others. Interest in graphene's exceptional physical properties, chemical tunability, and potential for applications has generated thousands of publications and an accelerating pace of research, making review of such research timely. Here is an overview of the synthesis, properties, and applications of graphene and related materials (primarily, graphite oxide and its colloidal suspensions and materials made from them), from a materials science perspective.

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Graphene and Graphene Oxide: Synthesis, Properties, and Applications

Photovoltaic technology is a direct and effective way to utilize solar energy. The mismatch between the absorption band of solar cells and the solar light band restricts solar energy's efficient use. Full-spectrum conversion of solar energy with spectral modification and coupling solar thermal application are reviewed. Additionally, implementing machine learning (ML) methods to improve solar energy utilization is also examined. With the utilization of up-conversion materials for solar cells, the solar-energy-utilization efficiency is enhanced. Combining PV and thermal applications have been validated to be promising for improving efficiency. With the development of computer science and the urgent need to design solar energy materials and forecast PV performances, ML methods in solar energy utilization have been successfully implemented into solar energy utilization.

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Recent Advances in Solar Energy Full Spectrum Conversion and Utilization

Quasiballistic heat conduction from nanoscale heat sources of size comparable to phonon mean free paths has recently become of intense interest both scientifically and for its applications. Prior work has established that, in the quasiballistic regime, the apparent thermal properties of materials depend both on intrinsic mechanisms and the characteristics of the applied thermal gradient. However, many aspects of this regime remain poorly understood. Here, we experimentally study the thermal response of crystals to large thermal gradients generated by optical heating of nanoline arrays. Our experiments reveal the key role of the spatial frequencies and Fourier series amplitudes of the heating profile for thermal transport in the quasiballistic regime, in contrast to the conventional picture that focuses on the geometric dimensions of the individual heaters. Our work provides the insight needed to rationally mitigate local hot spots in modern applications by manipulating the spatial frequencies of the heater patterns.

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Quasiballistic Thermal Transport from Nanoscale Heaters and the Role of the Spatial Frequency

High-throughput computational and experimental design of materials aided by machine learning have become an increasingly important field in material science. This area of research has emerged in leaps and bounds in the thermal sciences, in part due to the advances in computational and experimental methods in obtaining thermal properties of materials. In this paper, we provide a current overview of some of the recent work and highlight the challenges and opportunities that are ahead of us in this field. In particular, we focus on the use of machine learning and high-throughput methods for screening of thermal conductivity for compounds, composites and alloys as well as interfacial thermal conductance. These new tools have brought about a feedback mechanism for understanding new correlations and identifying new descriptors, speeding up the discovery of novel thermal functional materials.

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Hang Zhang

Papers

Recent Advances in Solar Energy Full Spectrum Conversion and Utilization

Quasiballistic Thermal Transport from Nanoscale Heaters and the Role of the Spatial Frequency

Machine Learning for Novel Thermal-Materials Discovery: Early Successes, Opportunities, and Challenges

Technical and economic feasibility of the Isopropanol-Acetone-Hydrogen chemical heat pump based on a lab-scale prototype

Exergy analysis of LBE-helium heat exchanger in the experimental cooling loop based on accelerator driven sub-critical power system