Results 1  10
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15
Nearoptimal sensor placements in gaussian processes
 In ICML
, 2005
"... When monitoring spatial phenomena, which can often be modeled as Gaussian processes (GPs), choosing sensor locations is a fundamental task. There are several common strategies to address this task, for example, geometry or disk models, placing sensors at the points of highest entropy (variance) in t ..."
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Cited by 333 (34 self)
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When monitoring spatial phenomena, which can often be modeled as Gaussian processes (GPs), choosing sensor locations is a fundamental task. There are several common strategies to address this task, for example, geometry or disk models, placing sensors at the points of highest entropy (variance) in the GP model, and A, D, or Eoptimal design. In this paper, we tackle the combinatorial optimization problem of maximizing the mutual information between the chosen locations and the locations which are not selected. We prove that the problem of finding the configuration that maximizes mutual information is NPcomplete. To address this issue, we describe a polynomialtime approximation that is within (1 − 1/e) of the optimum by exploiting the submodularity of mutual information. We also show how submodularity can be used to obtain online bounds, and design branch and bound search procedures. We then extend our algorithm to exploit lazy evaluations and local structure in the GP, yielding significant speedups. We also extend our approach to find placements which are robust against node failures and uncertainties in the model. These extensions are again associated with rigorous theoretical approximation guarantees, exploiting the submodularity of the objective function. We demonstrate the advantages of our approach towards optimizing mutual information in a very extensive empirical study on two realworld data sets.
Sensor Selection via Convex Optimization
 IEEE Transactions on Signal Processing
, 2009
"... We consider the problem of choosing a set of k sensor measurements, from a set of m possible or potential sensor measurements, that minimizes the error in estimating some parameters. Solving this problem by evaluating the performance for each of the(m k possible choices of sensor measurements is not ..."
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Cited by 89 (2 self)
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We consider the problem of choosing a set of k sensor measurements, from a set of m possible or potential sensor measurements, that minimizes the error in estimating some parameters. Solving this problem by evaluating the performance for each of the(m k possible choices of sensor measurements is not practical unless m and k are small. In this paper we describe a heuristic, based on convex optimization, for approximately solving this problem. Our heuristic gives a subset selection as well as a bound on the best performance that can be achieved by any selection of k sensor measurements. There is no guarantee that the gap between the performance of the chosen subset and the performance bound is always small; but numerical experiments suggest that the gap is small in many cases. Our heuristic method requires on the order of m3 operations; for m = 1000 possible sensors, we can carry out sensor selection in a few seconds on a 2 GHz personal computer. 1
An algorithm for constructing orthogonal and nearlyorthogonal arrays with mixed levels and small runs
 Technometrics
, 2002
"... Orthogonal arrays are used widely in manufacturing and hightechnology industries for quality and productivity improvement experiments. For reasons of run size economy or � exibility, nearlyorthogonal arrays are also used. The construction of orthogonal or nearlyorthogonal arrays can be quite chal ..."
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Cited by 17 (4 self)
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Orthogonal arrays are used widely in manufacturing and hightechnology industries for quality and productivity improvement experiments. For reasons of run size economy or � exibility, nearlyorthogonal arrays are also used. The construction of orthogonal or nearlyorthogonal arrays can be quite challenging. Most existing methods are complex and produce limited types of arrays. This article describes a simple and effective algorithm for constructing mixedlevel orthogonal and nearlyorthogonal arrays that can construct a variety of smallrun designs with good statistical properties ef � ciently. KEY WORDS: Doptimality; Exchange algorithm; Interchange algorithm; J 2optimality. 1.
Design Of Spatial Experiments: Model Fitting And Prediction
"... The main objective of the paper is to describe and develop model oriented methods and algorithms for the design of spatial experiments. Unlike many other publications in this area, the approach proposed here is essentially based on the ideas of convex design theory. 1. ..."
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Cited by 5 (1 self)
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The main objective of the paper is to describe and develop model oriented methods and algorithms for the design of spatial experiments. Unlike many other publications in this area, the approach proposed here is essentially based on the ideas of convex design theory. 1.
Computergenerated experimental designs for irregularshaped regions, Quality
 Technology & Quantitative Management 2, 147160 Nguyen, NK & Cheng, CS (2005) New E(s2)optimal
, 2005
"... Abstract: This paper focuses on the construction of computergenerated designs on irregularlyshaped, constrained regions. Overviews of the Fedorov exchange algorithm (FEA) and other exchange algorithms for the construction of Doptimal designs are given. A faster implementation of the FEA is presen ..."
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Cited by 3 (1 self)
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Abstract: This paper focuses on the construction of computergenerated designs on irregularlyshaped, constrained regions. Overviews of the Fedorov exchange algorithm (FEA) and other exchange algorithms for the construction of Doptimal designs are given. A faster implementation of the FEA is presented, which is referred to as fastFEA (denoted FFEA). The FFEA was applied to construct Doptimal designs for several published examples with constrained experimental regions. Designs resulting from the FFEA are more Defficient than published designs, and provide benchmarks for future comparisons of design construction algorithms. The construction of Goptimal designs for constrained regions is also discussed and illustrated with a published example.
Optimal data augmentation for the estimation of a linear parametric function in linear models’, Sankhyā Ser
 B
, 2001
"... SUMMARY. In the setup of a linear regression model, the problem of augmenting a given set of observations is investigated, when the inference problem is the estimation of a linear parametric function of the mean vector. For this problem, the optimal selection of additional observations is studied. T ..."
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Cited by 3 (0 self)
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SUMMARY. In the setup of a linear regression model, the problem of augmenting a given set of observations is investigated, when the inference problem is the estimation of a linear parametric function of the mean vector. For this problem, the optimal selection of additional observations is studied. The optimal design matrix is constructed following the A, D and Eoptimality criteria. The results are illustrated with an example. 1.
Testing of Analog Systems Using Behavioral Models and Optimal Experimental Design Techniques
 Proc. IEEE ICCAD
, 1994
"... This paper describesa new CAD algorithm which performsautomatic test pattern generation (ATPG) for a general class of analog systems, namely those circuits which can be efficiently modeled as an additive combination of userdefined basis functions. The algorithm is based on the statistical technique ..."
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Cited by 2 (0 self)
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This paper describesa new CAD algorithm which performsautomatic test pattern generation (ATPG) for a general class of analog systems, namely those circuits which can be efficiently modeled as an additive combination of userdefined basis functions. The algorithm is based on the statistical technique of Ioptimal experimental design, in which test vectors are chosen to be maximally independent so that circuit performance will be characterized as accurately as possible in the presence of measurement noise and model inaccuracies. This technique allows analog systems to be characterized more accurately and more efficiently, thereby significantly reducing system test time and hence total manufacturing cost. 1 Introduction The complexity of electronic systems being designed today is increasing in many dimensions: on one hand, the number of components is growing constantly; on the other, several radically different functions must be integrated. For example, in the exploding personal communi...
Interactive Cloud Data Farming Environment for Military Mission Planning Support
 Computer Science Journal
"... SUPPORT Abstract In a modern globalised world, military and peace keeping forces often face situations which require very subtle and well planned operations taking into account cultural and social aspects of a given region and its population as well as dynamic psychological awareness related to re ..."
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SUPPORT Abstract In a modern globalised world, military and peace keeping forces often face situations which require very subtle and well planned operations taking into account cultural and social aspects of a given region and its population as well as dynamic psychological awareness related to recent events which can have impact on the attitude of the civilians. The goal of the EUSAS project is to develop a prototype of a system enabling mission planning support and training capabilities for soldiers and police forces dealing with asymmetric threat situations, such as crowd control in urban territory. In this paper, we discuss the datafarming infrastructure developed for this project, allowing generation of large amount of data from agent based simulations for further analysis allowing soldier training and evaluation of possible outcomes of different rules of engagement.
SUBSET SELECTION AND OPTIMIZATION FOR SELECTING BINOMIAL SYSTEMS APPLIED TO SUPERSATURATED DESIGN GENERATION
"... The problem of finding the binomial population with the highest success probability is considered when the number of binomial populations is large. A new rigorous indifference zone subset selection procedure for binomial populations is proposed with the proof of the corresponding least favorable con ..."
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The problem of finding the binomial population with the highest success probability is considered when the number of binomial populations is large. A new rigorous indifference zone subset selection procedure for binomial populations is proposed with the proof of the corresponding least favorable configuration. For cases involving large numbers of binomial populations, a simulation optimization method combining the proposed subset selection procedure with an elitist Genetic Algorithm (GA) is proposed to find the highestmean solution. Convergence of the proposed GA frame work are established under general assumptions. The problem of deriving supersaturated screening designs is described and used to illustrate the application of all methods. Computational comparisons are also presented for the problem of generating supersaturated experimental designs. 1
unknown title
, 2005
"... www.elsevier.com/locate/apthermeng Multiperiod steam turbine network optimisation. Part I: Simulation based regression models and an evolutionary algorithm for finding Doptimal designs ..."
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www.elsevier.com/locate/apthermeng Multiperiod steam turbine network optimisation. Part I: Simulation based regression models and an evolutionary algorithm for finding Doptimal designs