71,317 research outputs found
Robust filtering for bilinear uncertain stochastic discrete-time systems
Copyright [2002] IEEE. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Brunel University's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to [email protected]. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.This paper deals with the robust filtering problem for uncertain bilinear stochastic discrete-time systems with estimation error variance constraints. The uncertainties are allowed to be norm-bounded and enter into both the state and measurement matrices. We focus on the design of linear filters, such that for all admissible parameter uncertainties, the error state of the bilinear stochastic system is mean square bounded, and the steady-state variance of the estimation error of each state is not more than the individual prespecified value. It is shown that the design of the robust filters can be carried out by solving some algebraic quadratic matrix inequalities. In particular, we establish both the existence conditions and the explicit expression of desired robust filters. A numerical example is included to show the applicability of the present method
ACCAMS: Additive Co-Clustering to Approximate Matrices Succinctly
Matrix completion and approximation are popular tools to capture a user's
preferences for recommendation and to approximate missing data. Instead of
using low-rank factorization we take a drastically different approach, based on
the simple insight that an additive model of co-clusterings allows one to
approximate matrices efficiently. This allows us to build a concise model that,
per bit of model learned, significantly beats all factorization approaches to
matrix approximation. Even more surprisingly, we find that summing over small
co-clusterings is more effective in modeling matrices than classic
co-clustering, which uses just one large partitioning of the matrix.
Following Occam's razor principle suggests that the simple structure induced
by our model better captures the latent preferences and decision making
processes present in the real world than classic co-clustering or matrix
factorization. We provide an iterative minimization algorithm, a collapsed
Gibbs sampler, theoretical guarantees for matrix approximation, and excellent
empirical evidence for the efficacy of our approach. We achieve
state-of-the-art results on the Netflix problem with a fraction of the model
complexity.Comment: 22 pages, under review for conference publicatio
CASP-DM: Context Aware Standard Process for Data Mining
We propose an extension of the Cross Industry Standard Process for Data
Mining (CRISPDM) which addresses specific challenges of machine learning and
data mining for context and model reuse handling. This new general
context-aware process model is mapped with CRISP-DM reference model proposing
some new or enhanced outputs
Transport geography: perspectives upon entering an accomplished research sub-discipline
No abstract available
Lessons learned in effective community-university-industry collaboration models for smart and connected communities research
In 2017, the Boston University Hariri Institute for Computing and the Initiative on Cities co-hosted two workshops on âEffective Community-University-Industry Collaboration Models for Smart and Connected Communities Research,â with the support of the National Science Foundation (NSF). These efforts brought together over one hundred principal investigators and research directors from universities across the country, as well as city officials, community partners, NSF program managers and other federal agency representatives, MetroLab Network representatives and industry experts. The focus was on transdisciplinary âsmart cityâ projects that bring technical fields such as engineering and computer science together with social scientists and community stakeholders to tackle community-sourced problems. Presentations, panel discussions, working sessions and participant white papers surfaced operational models as well as barriers and levers to enabling effective research partnerships. To capture the perspectives and beliefs of all participants, in addition to the presenters, attendees were asked to synthesize lessons on each panel topic. This white paper summarizes the opportunities and recommendations that emerged from these sessions, and provides guidance to communities and researchers interested in engaging in these types of partnerships as well as universities and funders that endeavor to nurture them. It draws on the collective wisdom of the assembled participants and the authors. While many of the examples noted are drawn from medium and large cities, the lessons may still be applicable to communities of various sizes.National Science Foundatio
A neural network for mining large volumes of time series data
Efficiently mining large volumes of time series data is amongst the most challenging problems that are fundamental in many fields such as industrial process monitoring, medical data analysis and business forecasting. This paper discusses a high-performance neural network for mining large time series data set and some practical issues on time series data mining. Examples of how this technology is used to search the engine data within a major UK eScience Grid project (DAME) for supporting the maintenance of Rolls-Royce aero-engine are presented
Delineating Intra-Urban Spatial Connectivity Patterns by Travel-Activities: A Case Study of Beijing, China
Travel activities have been widely applied to quantify spatial interactions
between places, regions and nations. In this paper, we model the spatial
connectivities between 652 Traffic Analysis Zones (TAZs) in Beijing by a taxi
OD dataset. First, we unveil the gravitational structure of intra-urban spatial
connectivities of Beijing. On overall, the inter-TAZ interactions are well
governed by the Gravity Model , where
, are degrees of TAZ , and the distance between
them, with a goodness-of-fit around 0.8. Second, the network based analysis
well reveals the polycentric form of Beijing. Last, we detect the semantics of
inter-TAZ connectivities based on their spatiotemporal patterns. We further
find that inter-TAZ connections deviating from the Gravity Model can be well
explained by link semantics.Comment: 6 pages, 4 figure
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