8,218 research outputs found
Some notions of decentralization and coordination in large-scale dynamic systems
Some notions of decentralization and coordination in the control of large-scale dynamic systems are discussed. Decentralization and coordination have always been important concepts in the study of large systems. Roughly speaking decentralization is the process of dividing a large problem into subproblems so that it can be handled more easily. Coordination is the manipulation of the subproblem so that the original problem is solved. The various types of decentralization and coordination that have been used to control dynamic systems are discussed. The emphasis was to distinguish between on-line and off-line operations to understand the results available by indicating the aspects of the problem which are decentralized
Coherent perfect absorption and reflection in slow-light waveguides
We identify a family of unusual slow-light modes occurring in lossy
multi-mode grating waveguides, for which either the forward or backward mode
components, or both, become degenerate. In the fully-degenerate case, by
varying the wave amplitudes in a uniform input waveguide, one can modulate
between coherent perfect absorption (zero reflection) and perfect reflection.
The perfectly-absorbed wave has anomalously short absorption length, scaling as
the inverse 1/3 power of the absorptivity
Estimating the timing of stillbirths in countries worldwide using a Bayesian hierarchical penalized splines regression model
Reducing the global burden of stillbirths is important to improving child and
maternal health. Of interest is understanding patterns in the timing of
stillbirths -- that is, whether they occur in the intra- or antepartum period
-- because stillbirths that occur intrapartum are largely preventable. However,
data availability on the timing of stillbirths is highly variable across the
world, with low- and middle-income countries generally having few reliable
observations. In this paper we develop a Bayesian penalized splines regression
framework to estimate the proportion of stillbirths that are intrapartum for
all countries worldwide. The model accounts for known relationships with
neonatal mortality, pools information across geographic regions, incorporates
different errors based on data attributes, and allows for data-driven temporal
trends. A weighting procedure is proposed to account for unrepresentative
subnational data. Results suggest that the intrapartum proportion is generally
decreasing over time, but progress is slower in some regions, particularly
Sub-Saharan Africa
Artificial Intelligence and Machine Learning: A Perspective on Integrated Systems Opportunities and Challenges for Multi-Domain Operations
This paper provides a perspective on historical background, innovation and applications of Artificial Intelligence (AI)
and Machine Learning (ML), data successes and systems challenges, national security interests, and mission
opportunities for system problems. AI and ML today are used interchangeably, or together as AI/ML, and are ubiquitous
among many industries and applications. The recent explosion, based on a confluence of new ML algorithms, large data
sets, and fast and cheap computing, has demonstrated impressive results in classification and regression and used for
prediction, and decision-making. Yet, AI/ML today lacks a precise definition, and as a technical discipline, it has grown
beyond its origins in computer science. Even though there are impressive feats, primarily of ML, there still is much work
needed in order to see the systems benefits of AI, such as perception, reasoning, planning, acting, learning,
communicating, and abstraction. Recent national security interests in AI/ML have focused on problems including multidomain operations (MDO), and this has renewed the focus on a systems view of AI/ML. This paper will address the
solutions for systems from an AI/ML perspective and that these solutions will draw from methods in AI and ML, as well
as computational methods in control, estimation, communication, and information theory, as in the early days of
cybernetics. Along with the focus on developing technology, this paper will also address the challenges of integrating
these AI/ML systems for warfare
Inferring gene regulatory networks from gene expression data by a dynamic Bayesian network-based model
Enabled by recent advances in bioinformatics, the inference of gene regulatory networks (GRNs) from gene expression data has garnered much interest from researchers. This is due to the need of researchers to understand the dynamic behavior and uncover the vast information lay hidden within the networks. In this regard, dynamic Bayesian network (DBN) is extensively used to infer GRNs due to its ability to handle time-series microarray data and modeling feedback loops. However, the efficiency of DBN in inferring GRNs is often hampered by missing values in expression data, and excessive computation time due to the large search space whereby DBN treats all genes as potential regulators for a target gene. In this paper, we proposed a DBN-based model with missing values imputation to improve inference efficiency, and potential regulators detection which aims to lessen computation time by limiting potential regulators based on expression changes. The performance of the proposed model is assessed by using time-series expression data of yeast cell cycle. The experimental results showed reduced computation time and improved efficiency in detecting gene-gene relationships
CAutoCSD-evolutionary search and optimisation enabled computer automated control system design
This paper attempts to set a unified scene for various linear time-invariant (LTI) control system design schemes, by transforming the existing concept of 'Computer-Aided Control System Design' (CACSD) to the novel 'Computer-Automated Control System Design' (CAutoCSD). The first step towards this goal is to accommodate, under practical constraints, various design objectives that are desirable in both time and frequency-domains. Such performance-prioritised unification is aimed to relieve practising engineers from having to select a particular control scheme and from sacrificing certain performance goals resulting from pre-committing to the adopted scheme. With the recent progress in evolutionary computing based extra-numeric, multi-criterion search and optimisation techniques, such unification of LTI control schemes becomes feasible, analytically and practically, and the resultant designs can be creative. The techniques developed are applied to, and illustrated by, three design problems. The unified approach automatically provides an integrator for zero-steady state error in velocity control of a DC motor, meets multiple objectives in designing an LTI controller for a non-minimum phase plant and offers a high-performing LTI controller network for a nonlinear chemical process
Inferring gene regulatory networks from gene expression data by a dynamic Bayesian network-based model
Enabled by recent advances in bioinformatics, the inference of gene regulatory networks (GRNs) from gene expression data has garnered much interest from researchers. This is due to the need of researchers to understand the dynamic behavior and uncover the vast information lay hidden within the networks. In this regard, dynamic Bayesian network (DBN) is extensively used to infer GRNs due to its ability to handle time-series microarray data and modeling feedback loops. However, the efficiency of DBN in inferring GRNs is often hampered by missing values in expression data, and excessive computation time due to the large search space whereby DBN treats all genes as potential regulators for a target gene. In this paper, we proposed a DBN-based model with missing values imputation to improve inference efficiency, and potential regulators detection which aims to lessen computation time by limiting potential regulators based on expression changes. The performance of the proposed model is assessed by using time-series expression data of yeast cell cycle. The experimental results showed reduced computation time and improved efficiency in detecting gene-gene relationships
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