Estimation of time-varying regression model constrained at each time moment by linear inequalities is a natural statistical formulation of a wide class of nonstationary signal processing problems. the presence of line...
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ISBN:
(纸本)9780889868236
Estimation of time-varying regression model constrained at each time moment by linear inequalities is a natural statistical formulation of a wide class of nonstationary signal processing problems. the presence of linear constraints turns the originally quadratic three-diagonal problem of minimizing the residual squares sum, which is solvable by the linear Kalman-Bucy filtration-smoothing procedure, into that of quadratic programming, which inevitably leads to the necessity of applying much more complicated nonlinear signal processing techniques. However, the three-diagonal kind of the quadratic objective function, on one hand, and the specificity of inequality constraints imposed individually upon each vector variable in the sequence of unknown regression coefficients, on the other, essentially simplify the resulting quadratic programming problem in comparison with its standard form. We call problems of such a kind pair-wise separable quadratic programming problem. Two algorithms of nonstationary regression estimation considered in this paper are built as those of pair-wise separable quadratic programming and have linear computational complexity in contrast to polynomial complexity of the quadratic programming problem of general kind. the asymptotically strict iterative algorithm is based on the traditional steepest descent method of quadratic programming, whereas the fast approximate algorithm consists in a single run of a special version of the Kalman-Bucy filter-smoother.
As shown by Hurricane Katrina, disposing of disaster-generated debris can be quite challenging. Extraordinary amounts of debris far exceeding typical annual amounts of solid waste are almost instantaneously deposited ...
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As shown by Hurricane Katrina, disposing of disaster-generated debris can be quite challenging. Extraordinary amounts of debris far exceeding typical annual amounts of solid waste are almost instantaneously deposited across a widespread area. Although the locations and amounts of debris can be easily summarized looking back after recovery activities have been completed, they are uncertain and difficult at best to estimate as debris operations begin to unfold. Further complicating matters is that the capacity of cleanup resources, which is dependent upon available equipment, labor, and subcontractors, can fluctuate during on-going cleanup operations. As a result, debris coordinators often modify initial resource assignments as more accurate debris estimates and more stable resource capacities become known. In this research, we develop a computer-based decision support system that incorporates a multi-stage programming model to assist decision makers with allocating debris cleanup resources immediately following a crisis event and during ongoing operations as debris volumes and resource capacities become known with increasing certainty.
Considering the uncertainty of emergency item demand in emergency logistics, the transportation paths from item supply spots to disaster spots and emergency item allocation to the selected paths are simultaneously det...
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the binary-to-C translation becomes more and more important due to large amount of legacy binaries, since many different architectures including multicores become available to markets. In this paper, we propose the x8...
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Building up an understanding of aspects of quality, and how to critically assess them, is a complex problem. this paper provides an overview of research on student conceptions of what constitutes quality in different ...
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ISBN:
(纸本)9780769542416
Building up an understanding of aspects of quality, and how to critically assess them, is a complex problem. this paper provides an overview of research on student conceptions of what constitutes quality in different programming domains. these conceptions are linked to tertiary education and computing education research results. Using this literature as a background we discuss how to develop and use instructional approaches that might assist students in developing a better understanding of software quality.
Cryogenic air separation, while widely used in industry, is an energy intensive process. Effective design can improve efficiency and reduce energy consumption, however, uncertainties can make determination of the opti...
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Cryogenic air separation, while widely used in industry, is an energy intensive process. Effective design can improve efficiency and reduce energy consumption, however, uncertainties can make determination of the optimal design difficult. this paper addresses the conceptual design of cryogenic air separation process under uncertainty. A rigorous, highly nonlinear model of three integrated columns is developed to capture the coupled nature of the process. the multi-scenario approach is used to incorporate the uncertainty, giving rise to a nonlinear programming problem with over half a million variables. Nevertheless, this problem is solved efficiently using IPOPT, demonstrating the effectiveness of interior-point methods on complex, large-scale nonlinear programming problems. the optimal design from the multi-scenario approach is compared against the optimal design using nominal parameter values. As expected, the results using the multi-scenario approach are more conservative than the nominal case;however, they may be less conservative than traditional overdesign factors. (C) 2010 Elsevier Ltd. All rights reserved.
this paper presents and evaluates an original approach to automatically align bitexts at the word level. It relies on a syntactic dependency analysis of the source and target texts and uses a machine-learning techniqu...
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ISBN:
(纸本)9782951740860
this paper presents and evaluates an original approach to automatically align bitexts at the word level. It relies on a syntactic dependency analysis of the source and target texts and uses a machine-learning technique, namely inductive logic programming, to automatically infer rules called syntactic alignment rules. these rules make the most of the syntactic information to align words. this machine learning approach is entirely automatic, requires a very small amount of training data, and its performance rivals some of the best existing alignment systems. Moreover, syntactic isomorphisms between the source language and the target language are easily identified through the inferred rules.
Sustainability has recently emerged as a key issue in process systems engineering (PSE). Mathematical programming techniques offer a general modeling framework for including environmental concerns in the synthesis and...
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Sustainability has recently emerged as a key issue in process systems engineering (PSE). Mathematical programming techniques offer a general modeling framework for including environmental concerns in the synthesis and planning of chemical processes. In this paper, we review major contributions in process synthesis and supply chain management, highlighting the main optimization approaches that are available, including the handling of uncertainty and the multi-objective optimization of economic and environmental objectives. Finally, we discuss challenges and opportunities identified in the area. (C) 2009 Elsevier Ltd. All rights reserved.
It has been an increasing trend to develop multicriteria decision-making models with imprecise, uncertain, or vague decision data. An effective way to express a decision maker's (DM's) uncertain preference inf...
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A Generation Company (GenCo) can participate in the Iberian Electricity Market (MIBEL) through different mechanisms and pools: the bilateral contracts, the physical derivatives products at the Derivatives Market, the ...
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