5 research outputs found
EVMDD-Based Analysis and Diagnosis Methods of Multi-State Systems with Multi-State Components *
A multi-state system with multi-state components is a model of systems, where performance, capacity, or reliability levels of the systems are represented as states. It usually has more than two states, and thus can be considered as a multi-valued function, called a structure function. Since many structure functions are monotone increasing, their multi-state systems can be represented compactly by edge-valued multivalued decision diagrams (EVMDDs). This paper presents an analysis method of multi-state systems with multi-state components using EVMDDs. Experimental results show that, by using EVMDDs, structure functions can be represented more compactly than existing methods using ordinary MDDs. Further, EVMDDs yield comparable computation time for system analysis. This paper also proposes a new diagnosis method using EVMDDs, and shows that the proposed method can infer the most probable causes for system failures more efficiently than conventional methods based on Bayesian networks
Computação de funções elementares em FPGA
Mestrado em Engenharia Electrónica e TelecomunicaçõesSince C.Y.Lee first proposed the idea of representing switching circuits as
decision diagrams, there has been some interest in developing these diagrams
in order to make them more compact and effective. One of the main applications
of this technique is to represent circuits that perform elementary
functions, such as cosine, sine, square root, etc. In this thesis, we try to
prove that by choosing the right polarity for an Arithmetic Decision Diagram
we can compactly and effectively represent a switching function and
implement it in hardware. This thesis proposes algorithms that can compactly
implement a given elementary function in hardware by finding the
best possible polarity for the respective Arithmetic Decision Diagram.Desde que C.Y.Lee propôs a ideia de representar funções de comutação sob a
forma de diagramas de decisão, tem havido algum interesse em desenvolver
estes diagramas de modo a torná-los mais compactos e eficientes. Uma
das principais aplicações desta técnica é representar circuitos que realizem
funções elementares, como é o caso do seno, coseno, raÃz quadrada, etc.
Nesta tese tentamos provar que escolhendo a polaridade certa para um Diagrama
de Decisão Aritmético é possÃvel representar compacta e eficazmente
uma função de comutação e implementá-la em hardware. Esta tese propõe
algoritmos que conseguem implementar compactamente uma dada função
elementar em hardware encontrando a melhor polaridade possÃvel para o
respetivo Diagrama de Decisão Aritmético
Optimal Planning with State Constraints
In the classical planning model, state variables are assigned
values in the initial state and remain unchanged unless
explicitly affected by action effects. However, some properties
of states are more naturally modelled not as direct effects of
actions but instead as derived, in each state, from the primary
variables via a set of rules. We refer to those rules as state
constraints. The two types of state constraints that will be
discussed here are numeric state constraints and logical rules
that we will refer to as axioms.
When using state constraints we make a distinction between
primary variables, whose values are directly affected by action
effects, and secondary variables, whose values are determined by
state constraints. While primary variables have finite and
discrete domains, as in classical planning, there is no such
requirement for secondary variables. For example, using numeric
state constraints allows us to have secondary variables whose
values are real numbers. We show that state constraints are a
construct that lets us combine classical planning methods with
specialised solvers developed for other types of problems. For
example, introducing numeric state constraints enables us to
apply planning techniques in domains involving interconnected
physical systems, such as power networks.
To solve these types of problems optimally, we adapt commonly
used methods from optimal classical planning, namely state-space
search guided by admissible heuristics. In heuristics based on
monotonic relaxation, the idea is that in a relaxed state each
variable assumes a set of values instead of just a single value.
With state constraints, the challenge becomes to evaluate the
conditions, such as goals and action preconditions, that involve
secondary variables. We employ consistency checking tools to
evaluate whether these conditions are satisfied in the relaxed
state. In our work with numerical constraints we use linear
programming, while with axioms we use answer set programming and
three value semantics. This allows us to build a relaxed planning
graph and compute constraint-aware version of heuristics based on
monotonic relaxation.
We also adapt pattern database heuristics. We notice that an
abstract state can be thought of as a state in the monotonic
relaxation in which the variables in the pattern hold only one
value, while the variables not in the pattern simultaneously hold
all the values in their domains. This means that we can apply the
same technique for evaluating conditions on secondary variables
as we did for the monotonic relaxation and build pattern
databases similarly as it is done in classical planning.
To make better use of our heuristics, we modify the A* algorithm
by combining two techniques that were previously used
independently – partial expansion and preferred operators. Our
modified algorithm, which we call PrefPEA, is most beneficial in
cases where heuristic is expensive to compute, but accurate, and
states have many successors
Universal Smart Grid Agent for Distributed Power Generation Management
"Somewhere, there is always wind blowing or the sun shining." This maxim could lead the global shift from fossil to renewable energy sources, suggesting that there is enough energy available to be turned into electricity. But the already impressive numbers that are available today, along with the European Union's 20-20-20 goal – to power 20% of the EU energy consumption from renewables until 2020 –, might mislead us over the problem that the go-to renewables readily available rely on a primary energy source mankind cannot control: the weather. At the same time, the notion of the smart grid introduces a vast array of new data coming from sensors in the power grid, at wind farms, power plants, transformers, and consumers. The new wealth of information might seem overwhelming, but can help to manage the different actors in the power grid. This book proposes to view the problem of power generation and distribution in the face of increased volatility as a problem of information distribution and processing. It enhances the power grid by turning its nodes into agents that forecast their local power balance from historical data, using artificial neural networks and the multi-part evolutionary training algorithm described in this book. They pro-actively communicate power demand and supply, adhering to a set of behavioral rules this book defines, and finally solve the 0-1 knapsack problem of choosing offers in such a way that not only solves the disequilibrium, but also minimizes line loss, by elegant modeling in the Boolean domain. The book shows that the Divide-et-Impera approach of a distributed grid control can lead to an efficient, reliable integration of volatile renewable energy sources into the power grid