A large data set is used to infer the fraction of immune cells using gene expression data. A model should be developed to infer the fraction of immune cells from a small set of genes used in cell surface marker experi...
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ISBN:
(纸本)9781728118680
A large data set is used to infer the fraction of immune cells using gene expression data. A model should be developed to infer the fraction of immune cells from a small set of genes used in cell surface marker experiments. We present a model created by linear programming (LP) by simplex method. To evaluate the accuracy of the algorithm, we created a simulated data set and evaluated its performance against the digital cell quantization (DCQ) algorithm. Finally, we applied our LP method to the systemic lupus erythematosus (SLE) patient dataset and examined the differences from healthy controls.
In this paper, Sparse layer inversion using linear programming (LP) approach is introduced as an improvement to the Basis Pursuit Decomposition (BPD) method. In the BPD method, the seismic trace is represented as a su...
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In this paper, Sparse layer inversion using linear programming (LP) approach is introduced as an improvement to the Basis Pursuit Decomposition (BPD) method. In the BPD method, the seismic trace is represented as a superposition of dictionary patterns. A dictionary is built using the functions of odd and even reflection coefficients which represent a reflectivity series. The seismic data is reconstructed as the convolution of a seismic wavelet and the reflectivity series. For this convolution based model, the model parameters are estimated by minimizing a loss function which consists of the L2 norm of error and an L1 norm of model parameters. However, in this paper, it is suggested to replace the L2 norm of error with that of L1 norm. These L1 norms of error and the model parameters are rewritten as the inequality constrained optimization problems and this is solved using linear programming approach. Application of this method on a real data set from onshore of the Western part of India shows improved resolution of thin layers in seismic sections.
Attitude control of the lunar lander is achieved through reaction control system (RCS). RCS works in pulsing mode and needs control allocation system which takes care of attitude tracking with minimum fuel consumption...
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Attitude control of the lunar lander is achieved through reaction control system (RCS). RCS works in pulsing mode and needs control allocation system which takes care of attitude tracking with minimum fuel consumption. Most of the control allocation techniques are based on pulse modulation technique, which are difficult to implement for RCS, where torques generated by RCS are generally coupled with each other. In this paper, eight unilateral pulsing mode attitude thruster for attitude correction of lunar module targeted for soft landing is presented. linear programming based control allocation technique for RCS has been discussed, which gives fuel optimal solution with accurate tracking as a constraint. Landing specific simulation is done to validate the control system. (C) 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.
The paper addresses the problem of multi-agent distributed solutions for a class of linear programming (LP) problems which include box constraints on the decision variables and inequality constraints. The major differ...
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ISBN:
(纸本)9783952426982
The paper addresses the problem of multi-agent distributed solutions for a class of linear programming (LP) problems which include box constraints on the decision variables and inequality constraints. The major difference with existing literature on distributed solution of LP problems is that each agent is expected to compute only a single or few entries of the global minimizer vector, often referred as a partition-based optimization. This class of LP problems is relevant in different applications such as optimal power transfer in remotely powered battery-less wireless sensor networks, minimum energy LED luminaries control in smart offices, and optimal temperature control in start buildings. Via a suitable approximation of the original LP problem, we propose three different primal-dual distributed algorithms based on dual gradient ascent, on the methods of multipliers and on the Alternating Direction Methods of Multipliers. We discuss the computational and communication requirements of these methods and we provide numerical comparisons.
Smart grid attacks can be applied on a single component or multiple components. The corresponding defense strategies are totally different. In this paper, we investigate the solutions (e.g., linear programming and rei...
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ISBN:
(纸本)9781509060146
Smart grid attacks can be applied on a single component or multiple components. The corresponding defense strategies are totally different. In this paper, we investigate the solutions (e.g., linear programming and reinforcement learning) for one-shot game between the attacker and defender in smart power systems. We designed one-shot game with multi-line-switching attack and solved it using linear programming. We also designed the game with single-line-switching attack and solved it using reinforcement learning. The pay-off and utility/reward of the game is calculated based on the generation loss due to initiated attack by the attacker. Defender's defense action is considered while evaluating the pay-off from attacker's and defender's action. The linear programming based solution gives the probability of choosing best attack actions against different defense actions. The reinforcement learning based solution gives the optimal action to take under selected defense action. The proposed game is demonstrated on 6 bus system and IEEE 30 bus system and optimal solutions are analyzed.
We formulate the optimal energy arbitrage problem for a piecewise linear cost function for energy storage devices using linear programming (LP). The LP formulation is based on the equivalent minimization of the epigra...
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ISBN:
(纸本)9781538681008
We formulate the optimal energy arbitrage problem for a piecewise linear cost function for energy storage devices using linear programming (LP). The LP formulation is based on the equivalent minimization of the epigraph. This formulation considers ramping and capacity constraints, charging and discharging efficiency losses of the storage, inelastic consumer load and local renewable generation in presence of net-metering which facilitates selling of energy to the grid and incentivizes consumers to install renewable generation and energy storage. We consider the case where the consumer loads, electricity prices, and renewable generations at different instances are uncertain. These uncertain quantities are predicted using an Auto-Regressive Moving Average (ARMA) model and used in a model predictive control (MPC) framework to obtain the arbitrage decision at each instance. In numerical results we present the sensitivity analysis of storage performing arbitrage with varying ramping batteries and different ratio of selling and buying price of electricity.
We present a new improvement in the linear programming technique to derive lower bounds on the information ratio of secret sharing schemes. We obtain non-Shannon-type bounds without using information inequalities expl...
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ISBN:
(纸本)9783319783819;9783319783802
We present a new improvement in the linear programming technique to derive lower bounds on the information ratio of secret sharing schemes. We obtain non-Shannon-type bounds without using information inequalities explicitly. Our new technique makes it possible to determine the optimal information ratio of linear secret sharing schemes for all access structures on 5 participants and all graph-based access structures on 6 participants. In addition, new lower bounds are presented also for some small matroid ports and, in particular, the optimal information ratios of the linear secret sharing schemes for the ports of the Vamos matroid are determined.
With the advances of mobile technologies, users must enjoy digital environment using smart mobile interfaces without any kind of restrictions. In addition, different users want to easily communicate and use media cont...
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ISBN:
(纸本)9781450365079
With the advances of mobile technologies, users must enjoy digital environment using smart mobile interfaces without any kind of restrictions. In addition, different users want to easily communicate and use media contents through the network platform. Recently, Mobile Ad hoc Networks (MANETs) where Optimized Link State Routing (OLSR) used as routing protocol, can be used to ensure communications in digital environment. In such networks, each node broadcasts the Topology Control (TC) messages to forward the network topology with other devices using a mechanism called MultiPoint Relays (MPR). However, the control traffic broadcasting method can influence different performances, and duplicate TC messages transmission will always exist. In this paper, we propose a minimization-based approach considering both the network over-head and energy consumption. The main goal of this solution is to provide users with smart mobile networking in order to optimize communications between different devices. Moreover, the proposal which is based on the linear programming approach, permits to control the duplicate TC transmission to prolong the network lifetime by saving the energy consumption in order to offer a smart digital environment.
We obtain new restrictions on the linear programming bound for sphere packing, by optimizing over spaces of modular forms to produce feasible points in the dual linear program. In contrast to the situation in dimensio...
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In Bayesian Multi-Objective optimisation, expected hypervolume improvement is often used to measure the goodness of candidate solutions. However when there are many objectives the calculation of expected hypervolume i...
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ISBN:
(纸本)9780996643139
In Bayesian Multi-Objective optimisation, expected hypervolume improvement is often used to measure the goodness of candidate solutions. However when there are many objectives the calculation of expected hypervolume improvement can become computationally prohibitive. An alternative approach measures the goodness of a candidate based on the distance of that candidate from the Pareto front in objective space. In this paper we present a novel distance-based Bayesian Many-Objective optimisation algorithm. We demonstrate the efficacy of our algorithm on three problems, namely the DTLZ2 benchmark problem, a hyper-parameter selection problem, and high-temperature creep-resistant alloy design.
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