L.A. Zadeh and E.H. Mamdani have proposed methods for a fuzzy reasoning in which the antecedent involves a fuzzy conditional proposition "If x is A then y is B", with A and B being fuzzy concepts. This paper...
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L.A. Zadeh and E.H. Mamdani have proposed methods for a fuzzy reasoning in which the antecedent involves a fuzzy conditional proposition "If x is A then y is B", with A and B being fuzzy concepts. This paper points out that the consequences inferred by their methods do not always fit our intuitions, and suggests several new methods which fit our intuitions under several criteria such as modus ponens and modus tollens.
This communication contains a proof of the fact that the coefficient of variation of the contents of a compartment of a stochastic compartmental model with deterministic rate parameters is small for large populations....
This communication contains a proof of the fact that the coefficient of variation of the contents of a compartment of a stochastic compartmental model with deterministic rate parameters is small for large populations. We can therefore conclude that the use of stochastic compartmental models is not of great consequence in the case of systems involving large populations when only the randomness of the transfer mechanism is considered.
There has been an upsurge of discussion recently about the status of engineering as a profession, the obligations of the engineer toward the public, and the relationship of the engineer to his employer. Some very impo...
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There has been an upsurge of discussion recently about the status of engineering as a profession, the obligations of the engineer toward the public, and the relationship of the engineer to his employer. Some very important facets of these questions are illuminated by the fate of three engineers employed by BART. (Bay Area Rapid Transit).
作者：
BECKER, LOUIS A.SIEGRIST, FRANKLIN I.Louis A. Becker was born in New Rochelle
N.Y. in 1930 receiving his earlier education in the New Rochelle Public Schools. He completed his undergraduate studies at Manhattan College in 1952 receiving his BCE degree during which time he was also engaged in land surveying. Following this he did postgraduate study at Virginia Polytechnic Institute obtaining his MS in 1954. He joined Naval Ship Research and Development Center in 1953 as a Junior Engineer and is currently the Head of the Engineering & Facilities Division Structures Department. His field of specialization is Structural Research and Development. Franklin I. Siegrist was born in Knoxville
Tenn. in 1937 receiving his earlier education in the Public Schools of Erie Pa. He attended Pennsylvania State University graduating in 1962 with a Bachelor of Science degree in Electrical Engineering having prior to that time served four years in the U. S. Navy. He was a Junior Engineer in the AC Spark Plug Division of General Motors from 1962 until 1964 at which time he came to the David Taylor Model Basin as an Electrical Engineer in the Industrial Department. He is currently Supervisory Engineer for Electrical and Electronics Engineering Structures Department Naval Ship Research and Development Center. His field of specialization is Electrical Engineering Control Systems Data Collection Systems Computer Applications to Structural Research and Hydraulic System Design. In the last of these he holds Patent Rights on a “Hydraulic Supercharge and Cooling Circuit” granted in 1970.
In recent years, machine learning has proven to be a powerful tool for deriving insights from data. In this review, we describe ways in which machine learning has been leveraged to facilitate the development and opera...
In recent years, machine learning has proven to be a powerful tool for deriving insights from data. In this review, we describe ways in which machine learning has been leveraged to facilitate the development and operation of sustainable energy systems. We first provide a taxonomy of machine learning paradigms and techniques, along with a discussion of their strengths and limitations. We then provide an overview of existing research using machine learning for sustainable energy production, delivery, and storage. Finally, we identify gaps in this literature, propose future research directions, and discuss important considerations for deployment.
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