Contemporary strategic forest management goals have become increasingly complex in spatial definition and scale. For example, the Canadian Council of Forest Ministers Criteria and Indicators (CCFM C&I) includes me...
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Contemporary strategic forest management goals have become increasingly complex in spatial definition and scale. For example, the Canadian Council of Forest Ministers Criteria and Indicators (CCFM C&I) includes metrics that are expressed at multiple levels of spatial resolution such as ecodistricts, watersheds, and vegetative communities. Supporting these criteria with aspatial models is sometimes difficult, and results are often not transferable to the actual forest. We describe a spatial Model I stand and prescription-based strategic forest planning model that includes spatial metrics in a realistic sized problem. We compare its formulation, capabilities, and computational efficiency with a Model II formulation using a case study on Nova Scotia's Crown Central Forest. We demonstrate that the spatial Model I is better suited to support strategic forest management when spatial criteria are included.
We present a method for 3D shape reconstruction of inextensible deformable surfaces from a single image. The key of our approach is to represent the surface as a 3D triangulated mesh and formulate the reconstruction p...
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We present a method for 3D shape reconstruction of inextensible deformable surfaces from a single image. The key of our approach is to represent the surface as a 3D triangulated mesh and formulate the reconstruction problem as a sequence of linear programming (LP) problems. The LP problem consists of data constraints which are 3D-to-2D keypoint correspondences and shape constraints which are designed to retain original lengths of mesh edges. We use a closed-form method to generate an initial structure, then refine this structure by solving the LP problem iteratively. Compared with previous methods, ours neither involves smoothness constraints nor temporal consistency, which enables us to recover shapes of surfaces with various deformations from a single image. The robustness and accuracy of our approach are evaluated quantitatively on synthetic data and qualitatively on real data.
European incentive policies led to a faster development of renewable energy sources during these last years. These new energy sources highly impact the planning and operation of our electrical system. More flexibility...
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ISBN:
(纸本)9781509017980
European incentive policies led to a faster development of renewable energy sources during these last years. These new energy sources highly impact the planning and operation of our electrical system. More flexibility is now required for electrical system operators to be able to counterpace the increase of power variability and unwished energy flow in their networks. Energy storage systems are a possible flexibility and thus open new challenges about research works and studies for grid applications. The task of this paper is to study the use of a lithium-ion battery to remove constraints in electrical grids and to increase the penetration of renewable energy sources in the liberalized electricity market. A linearized model of a lithium-ion battery is presented and is used in an optimization framework. This one is then used to calculate the optimal schedule of storage load and discharge actions in order to produce the highest possible benefits while taking into account hardware and grid constraints. An application in an industrial microgrid is presented to reduce the operation energy cost.
By providing substantial gains in terms of both spectral and energy-efficiency, Massive MIMO is expected to be the promising enabler for the fifth generation (5G) communications. However the performance of massive MIM...
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ISBN:
(纸本)9781509016983
By providing substantial gains in terms of both spectral and energy-efficiency, Massive MIMO is expected to be the promising enabler for the fifth generation (5G) communications. However the performance of massive MIMO is greatly affected by pilot contamination due to the insufficiency of pilot sequences. To overcome this, we propose a linear programming based pilot allocation with the purpose of alleviating the effect of pilot contamination and maximizing the system throughput. We first formulate the pilot allocation as a user clustering problem, which can be converted to a linear programming one by introducing the integer factor and constraint relaxation. An efficient linear programming algorithm is proposed to solve the problem. Simulation results demonstrate that the proposed scheme outperforms the other candidates in the presence of pilot contamination.
In this paper, we develop a new decoding algorithm of binary linear codes for symbol-pair read channel. The Symbol-pair read channel has recently been introduced by Cassuto and Blaum to model channel whose write resol...
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ISBN:
(纸本)9781509018062
In this paper, we develop a new decoding algorithm of binary linear codes for symbol-pair read channel. The Symbol-pair read channel has recently been introduced by Cassuto and Blaum to model channel whose write resolution is higher than read resolution. The proposed decoding algorithm is based on the linear programming (LP). It is proved that the proposed LP decoder has the maximum-likelihood (ML) certificate property, i.e., the output of the decoder is guaranteed to be the ML codeword when it is integral. We also introduce the fractional pair distance d(fp) of the code which is a lower bound on the minimum pair distance. It is proved that the proposed LP decoder corrects up to [d(fp)/2] - 1 errors.
In real estate appraisal, research has long been addressed to the experimentation of multi-parametric models able to reduce the margin of error of the estimate and to overcome or to limit, as far as possible, the prob...
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ISBN:
(纸本)9783319420851
In real estate appraisal, research has long been addressed to the experimentation of multi-parametric models able to reduce the margin of error of the estimate and to overcome or to limit, as far as possible, the problems and difficulties that the use of these models often involves. On the one hand, researchers are trying to overcome the essentially deductive approach that has characterized the traditional discipline, and on the other, to minimize the problems arising from a merely inductive approach. The real estate market is characterized by an inelastic supply and by properties whose complexity and differentiation often involve, also and especially on the demand side, subjective and psychological elements that could distort the results of an inductive investigation. This problem can be overcome by increasing the size of the survey sample, and by using statistical analysis. Statistical analyses, however, are often based on very strong assumptions. A multi-criteria valuation model that uses linear programming is applied to the real estate market. The model, integrated with the inductive and deductive approach, exceeds many of the assumptions of the best known statistical approaches.
Software-defined networks have been proposed as a viable solution to decrease the power consumption of the networking component in data center networks. Still the question remains on which scheduling algorithms are mo...
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ISBN:
(纸本)9781467394864
Software-defined networks have been proposed as a viable solution to decrease the power consumption of the networking component in data center networks. Still the question remains on which scheduling algorithms are most suited to achieve this goal. We propose 4 different linear programming approaches that schedule requested traffic flows on SDN switches according to different objectives. Depending on pre-defined software quality requirements such as delay and performance, a single variation or a combination of variations can be selected to optimize the power saving and the performance metrics. Our simulation results demonstrate that all our algorithm variations outperform the shortest path scheduling algorithm, our baseline on power savings, less or more strongly depending on the power model chosen. We show that in FatTree networks, where switches can save up to 60% of power in sleeping mode, we can achieve 15% minimum improvement assuming a one-to-one traffic scenario. Two of our algorithm variations privilege performance over power saving and still provide around 45% of the maximum achievable savings.
A market allocation decision is related to the choice of media effectiveness, media budget etc. especially when advertising is required in a market. Usually in real decision making problems related to advertising, the...
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A market allocation decision is related to the choice of media effectiveness, media budget etc. especially when advertising is required in a market. Usually in real decision making problems related to advertising, the goals, the constraints and the outcomes of actions are uncertain. In this paper we investigate the problem of choice of suitable media options and allocation of the available advertising budget amongst them. The problem is formulated as interval linear programming problem where uncertain environment is described by interval numbers. Sensitivity analysis of the proposed decision model is performed.
Managing an invasive species is particularly challenging as little is generally known about the species' biological characteristics in its new habitat. In practice, removal of individuals often starts before the s...
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Managing an invasive species is particularly challenging as little is generally known about the species' biological characteristics in its new habitat. In practice, removal of individuals often starts before the species is studied to provide the information that will later improve control. Therefore, the locations and the amount of control have to be determined in the face of great uncertainty about the species characteristics and with a limited amount of resources. We propose framing spatial control as a linear programming optimization problem. This formulation, paired with a discrete reaction-diffusion model, permits calculation of an optimal control strategy that minimizes the remaining number of invaders for a fixed cost or that minimizes the control cost for containment or protecting specific areas from invasion. We propose computing the optimal strategy for a range of possible model parameters, representing current uncertainty on the possible invasion scenarios. Then, a best strategy can be identified depending on the risk attitude of the decision-maker. We use this framework to study the spatial control of the Argentine black and white tegus (Salvator merianae) in South Florida. There is uncertainty about tegu demography and we considered several combinations of model parameters, exhibiting various dynamics of invasion. For a fixed one-year budget, we show that the risk-averse strategy, which optimizes the worst-case scenario of tegus' dynamics, and the risk-neutral strategy, which optimizes the expected scenario, both concentrated control close to the point of introduction. A risk-seeking strategy, which optimizes the best-case scenario, focuses more on models where eradication of the species in a cell is possible and consists of spreading control as much as possible. For the establishment of a containment area, assuming an exponential growth we show that with current control methods it might not be possible to implement such a strategy for some of the model
This paper presents the convergence proof and complexity analysis of an interior-point framework that solves linear programming problems by dynamically selecting and adding relevant inequalities. First, we formulate a...
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This paper presents the convergence proof and complexity analysis of an interior-point framework that solves linear programming problems by dynamically selecting and adding relevant inequalities. First, we formulate a new primal-dual interior-point algorithm for solving linear programmes in non-standard form with equality and inequality constraints. The algorithm uses a primal-dual path-following predictor-corrector short-step interior-point method that starts with a reduced problem without any inequalities and selectively adds a given inequality only if it becomes active on the way to optimality. Second, we prove convergence of this algorithm to an optimal solution at which all inequalities are satisfied regardless of whether they have been added by the algorithm or not. We thus provide a theoretical foundation for similar schemes already used in practice. We also establish conditions under which the complexity of such algorithm is polynomial in the problem dimension and address remaining limitations without these conditions for possible further research.
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