103,954 research outputs found
Semantics, Modelling, and the Problem of Representation of Meaning -- a Brief Survey of Recent Literature
Over the past 50 years many have debated what representation should be used
to capture the meaning of natural language utterances. Recently new needs of
such representations have been raised in research. Here I survey some of the
interesting representations suggested to answer for these new needs.Comment: 15 pages, no figure
Fuzzy Inference System for VOLT/VAR control in distribution substations in isolated power systems
This paper presents a fuzzy inference system for voltage/reactive power
control in distribution substations. The purpose is go forward to automation
distribution and its implementation in isolated power systems where control
capabilities are limited and it is common using the same applications as in
continental power systems. This means that lot of functionalities do not apply
and computational burden generates high response times. A fuzzy controller,
with logic guidelines embedded based upon heuristic rules resulting from
operators at dispatch control center past experience, has been designed.
Working as an on-line tool, it has been tested under real conditions and it has
managed the operation during a whole day in a distribution substation. Within
the limits of control capabilities of the system, the controller maintained
successfully an acceptable voltage profile, power factor values over 0,98 and
it has ostensibly improved the performance given by an optimal power flow based
automation system
Probabilistic Programming Concepts
A multitude of different probabilistic programming languages exists today,
all extending a traditional programming language with primitives to support
modeling of complex, structured probability distributions. Each of these
languages employs its own probabilistic primitives, and comes with a particular
syntax, semantics and inference procedure. This makes it hard to understand the
underlying programming concepts and appreciate the differences between the
different languages. To obtain a better understanding of probabilistic
programming, we identify a number of core programming concepts underlying the
primitives used by various probabilistic languages, discuss the execution
mechanisms that they require and use these to position state-of-the-art
probabilistic languages and their implementation. While doing so, we focus on
probabilistic extensions of logic programming languages such as Prolog, which
have been developed since more than 20 years
Issues in Statistical Inference
The APA Task Force’s treatment of research methods is critically examined. The present defense of the experiment rests on showing that (a) the control group cannot be replaced by the contrast group, (b) experimental psychologists have valid reasons to use non-randomly selected subjects, (c) there is no evidential support for the experimenter expectancy effect, (d) the Task Force had misrepresented the role of inductive and deductive logic, and (e) the validity of experimental data does not require appealing to the effect size or statistical power
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