3,879 research outputs found
A Survey of the Probability Density Function Control for Stochastic Dynamic Systems
Probability density function (PDF) control strategy investigates the controller design approaches in order to to realise a desirable distributions shape control of the random variables for the stochastic processes. Different from the existing stochastic optimisation and control methods, the most important problem of PDF control is to establish the evolution of the PDF expressions of the system variables. Once the relationship between the control input and the output PDF is formulated, the control objective can be described as obtaining the control input signals which would adjust the system output PDFs to follow the pre-specified target PDFs. This paper summarises the recent research results of the PDF control while the controller design approaches can be categorised into three groups: 1) system model-based direct evolution PDF control; 2) model-based distribution-transformation PDF control methods and 3) databased PDF control. In addition, minimum entropy control, PDF-based filter design, fault diagnosis and probabilistic decoupling design are also introduced briefly as extended applications in theory sense
Geodesic Density Tracking with Applications to Data Driven Modeling
Many problems in dynamic data driven modeling deals with distributed rather
than lumped observations. In this paper, we show that the Monge-Kantorovich
optimal transport theory provides a unifying framework to tackle such problems
in the systems-control parlance. Specifically, given distributional
measurements at arbitrary instances of measurement availability, we show how to
derive dynamical systems that interpolate the observed distributions along the
geodesics. We demonstrate the framework in the context of three specific
problems: (i) \emph{finding a feedback control} to track observed ensembles
over finite-horizon, (ii) \emph{finding a model} whose prediction matches the
observed distributional data, and (iii) \emph{refining a baseline model} that
results a distribution-level prediction-observation mismatch. We emphasize how
the three problems can be posed as variants of the optimal transport problem,
but lead to different types of numerical methods depending on the problem
context. Several examples are given to elucidate the ideas.Comment: 8 pages, 7 figure
Communication skills: what do employers' expect? (workplace communication skills for engineering graduates)
A brilliant engineer who cannot communicate is a matter to be taken seriously. What will happen to Malaysia if we keep churning out thousands of brilliant engineers but when it comes to employability skills, they are retarded? Malaysian engineering graduates especially are handicapped when it comes to communicating in English. English language is the international language used for education, business and technology. Therefore, it is crucial that an engineer masters the art of communicating in English as well as engineering knowledge. Thus, this research is conducted to find out what communication skills that the employers in the industry deem that their employees should have? However, this research only focuses on engineering graduates and the manufacturing industry. The researcher intends to learn the importance that is given to communication skills by the industry and whether it helps an engineering graduate to be promoted and be successful in their jobs. After obtaining the results from the employers, the researcher will suggest recommendation to improve the course content of KUiTTHO's Communication Skills course to be parallel with the demands of the industry
Learning and Management for Internet-of-Things: Accounting for Adaptivity and Scalability
Internet-of-Things (IoT) envisions an intelligent infrastructure of networked
smart devices offering task-specific monitoring and control services. The
unique features of IoT include extreme heterogeneity, massive number of
devices, and unpredictable dynamics partially due to human interaction. These
call for foundational innovations in network design and management. Ideally, it
should allow efficient adaptation to changing environments, and low-cost
implementation scalable to massive number of devices, subject to stringent
latency constraints. To this end, the overarching goal of this paper is to
outline a unified framework for online learning and management policies in IoT
through joint advances in communication, networking, learning, and
optimization. From the network architecture vantage point, the unified
framework leverages a promising fog architecture that enables smart devices to
have proximity access to cloud functionalities at the network edge, along the
cloud-to-things continuum. From the algorithmic perspective, key innovations
target online approaches adaptive to different degrees of nonstationarity in
IoT dynamics, and their scalable model-free implementation under limited
feedback that motivates blind or bandit approaches. The proposed framework
aspires to offer a stepping stone that leads to systematic designs and analysis
of task-specific learning and management schemes for IoT, along with a host of
new research directions to build on.Comment: Submitted on June 15 to Proceeding of IEEE Special Issue on Adaptive
and Scalable Communication Network
An introductory survey of probability density function control
YesProbability density function (PDF) control strategy investigates the controller design approaches where the random variables for the stochastic processes were adjusted to follow the desirable distributions. In other words, the shape of the system PDF can be regulated by controller design.Different from the existing stochastic optimization and control methods, the most important problem of PDF control is to establish the evolution of the PDF expressions of the system variables. Once the relationship between the control input and the output PDF is formulated, the control objective can be described as obtaining the control input signals which would adjust the system output PDFs to follow the pre-specified target PDFs. Motivated by the development of data-driven control and the state of the art PDF-based applications, this paper summarizes the recent research results of the PDF control while the controller design approaches can be categorized into three groups: (1) system model-based direct evolution PDF control; (2) model-based distribution-transformation PDF control methods and (3) data-based PDF control. In addition, minimum entropy control, PDF-based filter design, fault diagnosis and probabilistic decoupling design are also introduced briefly as extended applications in theory sense.De Montfort University - DMU HEIF’18 project, Natural Science Foundation of Shanxi Province [grant number 201701D221112], National Natural Science Foundation of China [grant numbers 61503271 and 61603136
Theory and Application of Dynamic Spatial Time Series Models
Stochastic economic processes are often characterized by dynamic interactions between variables that are dependent in both space and time. Analyzing these processes raises a number of questions about the econometric methods used that are both practically and theoretically interesting. This work studies econometric approaches to analyze spatial data that evolves dynamically over time. The book provides a background on least squares and maximum likelihood estimators, and discusses some of the limits of basic econometric theory. It then discusses the importance of addressing spatial heterogeneity in policies. The next chapters cover parametric modeling of linear and nonlinear spatial time series, non-parametric modeling of nonlinearities in panel data, modeling of multiple spatial time series variables that exhibit long and short memory, and probabilistic causality in spatial time series settings
The Dynamic Brain: From Spiking Neurons to Neural Masses and Cortical Fields
The cortex is a complex system, characterized by its dynamics and architecture,
which underlie many functions such as action, perception, learning, language,
and cognition. Its structural architecture has been studied for more than a
hundred years; however, its dynamics have been addressed much less thoroughly.
In this paper, we review and integrate, in a unifying framework, a variety of
computational approaches that have been used to characterize the dynamics of the
cortex, as evidenced at different levels of measurement. Computational models at
different space–time scales help us understand the fundamental
mechanisms that underpin neural processes and relate these processes to
neuroscience data. Modeling at the single neuron level is necessary because this
is the level at which information is exchanged between the computing elements of
the brain; the neurons. Mesoscopic models tell us how neural elements interact
to yield emergent behavior at the level of microcolumns and cortical columns.
Macroscopic models can inform us about whole brain dynamics and interactions
between large-scale neural systems such as cortical regions, the thalamus, and
brain stem. Each level of description relates uniquely to neuroscience data,
from single-unit recordings, through local field potentials to functional
magnetic resonance imaging (fMRI), electroencephalogram (EEG), and
magnetoencephalogram (MEG). Models of the cortex can establish which types of
large-scale neuronal networks can perform computations and characterize their
emergent properties. Mean-field and related formulations of dynamics also play
an essential and complementary role as forward models that can be inverted given
empirical data. This makes dynamic models critical in integrating theory and
experiments. We argue that elaborating principled and informed models is a
prerequisite for grounding empirical neuroscience in a cogent theoretical
framework, commensurate with the achievements in the physical sciences
Characterizing optimal hierarchical policy inference on graphs via non-equilibrium thermodynamics
Hierarchies are of fundamental interest in both stochastic optimal control
and biological control due to their facilitation of a range of desirable
computational traits in a control algorithm and the possibility that they may
form a core principle of sensorimotor and cognitive control systems. However, a
theoretically justified construction of state-space hierarchies over all
spatial resolutions and their evolution through a policy inference process
remains elusive. Here, a formalism for deriving such normative representations
of discrete Markov decision processes is introduced in the context of graphs.
The resulting hierarchies correspond to a hierarchical policy inference
algorithm approximating a discrete gradient flow between state-space trajectory
densities generated by the prior and optimal policies.Comment: NIPS 2017 Workshop on Hierarchical Reinforcement Learning. 8 pages, 1
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