Efficient data communication protocols for wireless networks

NASA Astrophysics Data System (ADS)

Zeydan, Engin

In this dissertation, efficient decentralized algorithms are investigated for cost minimization problems in wireless networks. For wireless sensor networks, we investigate both the reduction in the energy consumption and throughput maximization problems separately using multi-hop data aggregation for correlated data in wireless sensor networks. The proposed algorithms exploit data redundancy using a game theoretic framework. For energy minimization, routes are chosen to minimize the total energy expended by the network using best response dynamics to local data. The cost function used in routing takes into account distance, interference and in-network data aggregation. The proposed energy-efficient correlation-aware routing algorithm significantly reduces the energy consumption in the network and converges in a finite number of steps iteratively. For throughput maximization, we consider both the interference distribution across the network and correlation between forwarded data when establishing routes. Nodes along each route are chosen to minimize the interference impact in their neighborhood and to maximize the in-network data aggregation. The resulting network topology maximizes the global network throughput and the algorithm is guaranteed to converge with a finite number of steps using best response dynamics. For multiple antenna wireless ad-hoc networks, we present distributed cooperative and regret-matching based learning schemes for joint transmit beanformer and power level selection problem for nodes operating in multi-user interference environment. Total network transmit power is minimized while ensuring a constant received signal-to-interference and noise ratio at each receiver. In cooperative and regret-matching based power minimization algorithms, transmit beanformers are selected from a predefined codebook to minimize the total power. By selecting transmit beamformers judiciously and performing power adaptation, the cooperative algorithm is shown to converge to pure strategy Nash equilibrium with high probability throughout the iterations in the interference impaired network. On the other hand, the regret-matching learning algorithm is noncooperative and requires minimum amount of overhead. The proposed cooperative and regret-matching based distributed algorithms are also compared with centralized solutions through simulation results.

Minimum energy information fusion in sensor networks

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chapline, G

1999-05-11

In this paper we consider how to organize the sharing of information in a distributed network of sensors and data processors so as to provide explanations for sensor readings with minimal expenditure of energy. We point out that the Minimum Description Length principle provides an approach to information fusion that is more naturally suited to energy minimization than traditional Bayesian approaches. In addition we show that for networks consisting of a large number of identical sensors Kohonen self-organization provides an exact solution to the problem of combing the sensor outputs into minimal description length explanations.

On the Minimal Length Uncertainty Relation and the Foundations of String Theory

DOE PAGES

Chang, Lay Nam; Lewis, Zachary; Minic, Djordje; ...

2011-01-01

We review our work on the minimal length uncertainty relation as suggested by perturbative string theory. We discuss simple phenomenological implications of the minimal length uncertainty relation and then argue that the combination of the principles of quantum theory and general relativity allow for a dynamical energy-momentum space. We discuss the implication of this for the problem of vacuum energy and the foundations of nonperturbative string theory.

Stochastic Control of Energy Efficient Buildings: A Semidefinite Programming Approach

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ma, Xiao; Dong, Jin; Djouadi, Seddik M

2015-01-01

The key goal in energy efficient buildings is to reduce energy consumption of Heating, Ventilation, and Air- Conditioning (HVAC) systems while maintaining a comfortable temperature and humidity in the building. This paper proposes a novel stochastic control approach for achieving joint performance and power control of HVAC. We employ a constrained Stochastic Linear Quadratic Control (cSLQC) by minimizing a quadratic cost function with a disturbance assumed to be Gaussian. The problem is formulated to minimize the expected cost subject to a linear constraint and a probabilistic constraint. By using cSLQC, the problem is reduced to a semidefinite optimization problem, wheremore » the optimal control can be computed efficiently by Semidefinite programming (SDP). Simulation results are provided to demonstrate the effectiveness and power efficiency by utilizing the proposed control approach.« less

Collective intelligence for control of distributed dynamical systems

NASA Astrophysics Data System (ADS)

Wolpert, D. H.; Wheeler, K. R.; Tumer, K.

2000-03-01

We consider the El Farol bar problem, also known as the minority game (W. B. Arthur, The American Economic Review, 84 (1994) 406; D. Challet and Y. C. Zhang, Physica A, 256 (1998) 514). We view it as an instance of the general problem of how to configure the nodal elements of a distributed dynamical system so that they do not "work at cross purposes", in that their collective dynamics avoids frustration and thereby achieves a provided global goal. We summarize a mathematical theory for such configuration applicable when (as in the bar problem) the global goal can be expressed as minimizing a global energy function and the nodes can be expressed as minimizers of local free energy functions. We show that a system designed with that theory performs nearly optimally for the bar problem.

The Multiple-Minima Problem in Protein Folding

NASA Astrophysics Data System (ADS)

Scheraga, Harold A.

1991-10-01

The conformational energy surface of a polypeptide or protein has many local minima, and conventional energy minimization procedures reach only a local minimum (near the starting point of the optimization algorithm) instead of the global minimum (the multiple-minima problem). Several procedures have been developed to surmount this problem, the most promising of which are: (a) build up procedure, (b) optimization of electrostatics, (c) Monte Carlo-plus-energy minimization, (d) electrostatically-driven Monte Carlo, (e) inclusion of distance restraints, (f) adaptive importance-sampling Monte Carlo, (g) relaxation of dimensionality, (h) pattern-recognition, and (i) diffusion equation method. These procedures have been applied to a variety of polypeptide structural problems, and the results of such computations are presented. These include the computation of the structures of open-chain and cyclic peptides, fibrous proteins and globular proteins. Present efforts are being devoted to scaling up these procedures from small polypeptides to proteins, to try to compute the three-dimensional structure of a protein from its amino sequence.

Free energy minimization to predict RNA secondary structures and computational RNA design.

PubMed

Churkin, Alexander; Weinbrand, Lina; Barash, Danny

2015-01-01

Determining the RNA secondary structure from sequence data by computational predictions is a long-standing problem. Its solution has been approached in two distinctive ways. If a multiple sequence alignment of a collection of homologous sequences is available, the comparative method uses phylogeny to determine conserved base pairs that are more likely to form as a result of billions of years of evolution than by chance. In the case of single sequences, recursive algorithms that compute free energy structures by using empirically derived energy parameters have been developed. This latter approach of RNA folding prediction by energy minimization is widely used to predict RNA secondary structure from sequence. For a significant number of RNA molecules, the secondary structure of the RNA molecule is indicative of its function and its computational prediction by minimizing its free energy is important for its functional analysis. A general method for free energy minimization to predict RNA secondary structures is dynamic programming, although other optimization methods have been developed as well along with empirically derived energy parameters. In this chapter, we introduce and illustrate by examples the approach of free energy minimization to predict RNA secondary structures.

Optimal RTP Based Power Scheduling for Residential Load in Smart Grid

NASA Astrophysics Data System (ADS)

Joshi, Hemant I.; Pandya, Vivek J.

2015-12-01

To match supply and demand, shifting of load from peak period to off-peak period is one of the effective solutions. Presently flat rate tariff is used in major part of the world. This type of tariff doesn't give incentives to the customers if they use electrical energy during off-peak period. If real time pricing (RTP) tariff is used, consumers can be encouraged to use energy during off-peak period. Due to advancement in information and communication technology, two-way communications is possible between consumers and utility. To implement this technique in smart grid, home energy controller (HEC), smart meters, home area network (HAN) and communication link between consumers and utility are required. HEC interacts automatically by running an algorithm to find optimal energy consumption schedule for each consumer. However, all the consumers are not allowed to shift their load simultaneously during off-peak period to avoid rebound peak condition. Peak to average ratio (PAR) is considered while carrying out minimization problem. Linear programming problem (LPP) method is used for minimization. The simulation results of this work show the effectiveness of the minimization method adopted. The hardware work is in progress and the program based on the method described here will be made to solve real problem.

POWERING AIRPOWER: IS THE AIR FORCES ENERGY SECURE

DTIC Science & Technology

2016-02-01

needs. More on-site renewable energy generation increases AF readiness in crisis times by minimizing the AF’s dependency on fossil fuels. Financing...reducing the need for traditional fossil fuels, and the high investment cost of onsite renewable energy sources is still a serious roadblock in this...help installations better plan holistically. This research will take the form of problem/solution framework. With any complex problem, rarely does a

Concurrent optimization of material spatial distribution and material anisotropy repartition for two-dimensional structures

NASA Astrophysics Data System (ADS)

Ranaivomiarana, Narindra; Irisarri, François-Xavier; Bettebghor, Dimitri; Desmorat, Boris

2018-04-01

An optimization methodology to find concurrently material spatial distribution and material anisotropy repartition is proposed for orthotropic, linear and elastic two-dimensional membrane structures. The shape of the structure is parameterized by a density variable that determines the presence or absence of material. The polar method is used to parameterize a general orthotropic material by its elasticity tensor invariants by change of frame. A global structural stiffness maximization problem written as a compliance minimization problem is treated, and a volume constraint is applied. The compliance minimization can be put into a double minimization of complementary energy. An extension of the alternate directions algorithm is proposed to solve the double minimization problem. The algorithm iterates between local minimizations in each element of the structure and global minimizations. Thanks to the polar method, the local minimizations are solved explicitly providing analytical solutions. The global minimizations are performed with finite element calculations. The method is shown to be straightforward and efficient. Concurrent optimization of density and anisotropy distribution of a cantilever beam and a bridge are presented.

Free-energy minimization and the dark-room problem.

PubMed

Friston, Karl; Thornton, Christopher; Clark, Andy

2012-01-01

Recent years have seen the emergence of an important new fundamental theory of brain function. This theory brings information-theoretic, Bayesian, neuroscientific, and machine learning approaches into a single framework whose overarching principle is the minimization of surprise (or, equivalently, the maximization of expectation). The most comprehensive such treatment is the "free-energy minimization" formulation due to Karl Friston (see e.g., Friston and Stephan, 2007; Friston, 2010a,b - see also Fiorillo, 2010; Thornton, 2010). A recurrent puzzle raised by critics of these models is that biological systems do not seem to avoid surprises. We do not simply seek a dark, unchanging chamber, and stay there. This is the "Dark-Room Problem." Here, we describe the problem and further unpack the issues to which it speaks. Using the same format as the prolog of Eddington's Space, Time, and Gravitation (Eddington, 1920) we present our discussion as a conversation between: an information theorist (Thornton), a physicist (Friston), and a philosopher (Clark).

Energy-Efficient Deadline-Aware Data-Gathering Scheme Using Multiple Mobile Data Collectors.

PubMed

Dasgupta, Rumpa; Yoon, Seokhoon

2017-04-01

In wireless sensor networks, the data collected by sensors are usually forwarded to the sink through multi-hop forwarding. However, multi-hop forwarding can be inefficient due to the energy hole problem and high communications overhead. Moreover, when the monitored area is large and the number of sensors is small, sensors cannot send the data via multi-hop forwarding due to the lack of network connectivity. In order to address those problems of multi-hop forwarding, in this paper, we consider a data collection scheme that uses mobile data collectors (MDCs), which visit sensors and collect data from them. Due to the recent breakthroughs in wireless power transfer technology, MDCs can also be used to recharge the sensors to keep them from draining their energy. In MDC-based data-gathering schemes, a big challenge is how to find the MDCs' traveling paths in a balanced way, such that their energy consumption is minimized and the packet-delay constraint is satisfied. Therefore, in this paper, we aim at finding the MDCs' paths, taking energy efficiency and delay constraints into account. We first define an optimization problem, named the delay-constrained energy minimization (DCEM) problem, to find the paths for MDCs. An integer linear programming problem is formulated to find the optimal solution. We also propose a two-phase path-selection algorithm to efficiently solve the DCEM problem. Simulations are performed to compare the performance of the proposed algorithms with two heuristics algorithms for the vehicle routing problem under various scenarios. The simulation results show that the proposed algorithms can outperform existing algorithms in terms of energy efficiency and packet delay.

Energy-Efficient Deadline-Aware Data-Gathering Scheme Using Multiple Mobile Data Collectors

PubMed Central

Dasgupta, Rumpa; Yoon, Seokhoon

2017-01-01

In wireless sensor networks, the data collected by sensors are usually forwarded to the sink through multi-hop forwarding. However, multi-hop forwarding can be inefficient due to the energy hole problem and high communications overhead. Moreover, when the monitored area is large and the number of sensors is small, sensors cannot send the data via multi-hop forwarding due to the lack of network connectivity. In order to address those problems of multi-hop forwarding, in this paper, we consider a data collection scheme that uses mobile data collectors (MDCs), which visit sensors and collect data from them. Due to the recent breakthroughs in wireless power transfer technology, MDCs can also be used to recharge the sensors to keep them from draining their energy. In MDC-based data-gathering schemes, a big challenge is how to find the MDCs’ traveling paths in a balanced way, such that their energy consumption is minimized and the packet-delay constraint is satisfied. Therefore, in this paper, we aim at finding the MDCs’ paths, taking energy efficiency and delay constraints into account. We first define an optimization problem, named the delay-constrained energy minimization (DCEM) problem, to find the paths for MDCs. An integer linear programming problem is formulated to find the optimal solution. We also propose a two-phase path-selection algorithm to efficiently solve the DCEM problem. Simulations are performed to compare the performance of the proposed algorithms with two heuristics algorithms for the vehicle routing problem under various scenarios. The simulation results show that the proposed algorithms can outperform existing algorithms in terms of energy efficiency and packet delay. PMID:28368300

Energy efficient sensor scheduling with a mobile sink node for the target tracking application.

PubMed

Maheswararajah, Suhinthan; Halgamuge, Saman; Premaratne, Malin

2009-01-01

Measurement losses adversely affect the performance of target tracking. The sensor network's life span depends on how efficiently the sensor nodes consume energy. In this paper, we focus on minimizing the total energy consumed by the sensor nodes whilst avoiding measurement losses. Since transmitting data over a long distance consumes a significant amount of energy, a mobile sink node collects the measurements and transmits them to the base station. We assume that the default transmission range of the activated sensor node is limited and it can be increased to maximum range only if the mobile sink node is out-side the default transmission range. Moreover, the active sensor node can be changed after a certain time period. The problem is to select an optimal sensor sequence which minimizes the total energy consumed by the sensor nodes. In this paper, we consider two different problems depend on the mobile sink node's path. First, we assume that the mobile sink node's position is known for the entire time horizon and use the dynamic programming technique to solve the problem. Second, the position of the sink node is varied over time according to a known Markov chain, and the problem is solved by stochastic dynamic programming. We also present sub-optimal methods to solve our problem. A numerical example is presented in order to discuss the proposed methods' performance.

Energy Efficient Sensor Scheduling with a Mobile Sink Node for the Target Tracking Application

PubMed Central

Maheswararajah, Suhinthan; Halgamuge, Saman; Premaratne, Malin

2009-01-01

Measurement losses adversely affect the performance of target tracking. The sensor network's life span depends on how efficiently the sensor nodes consume energy. In this paper, we focus on minimizing the total energy consumed by the sensor nodes whilst avoiding measurement losses. Since transmitting data over a long distance consumes a significant amount of energy, a mobile sink node collects the measurements and transmits them to the base station. We assume that the default transmission range of the activated sensor node is limited and it can be increased to maximum range only if the mobile sink node is out-side the default transmission range. Moreover, the active sensor node can be changed after a certain time period. The problem is to select an optimal sensor sequence which minimizes the total energy consumed by the sensor nodes. In this paper, we consider two different problems depend on the mobile sink node's path. First, we assume that the mobile sink node's position is known for the entire time horizon and use the dynamic programming technique to solve the problem. Second, the position of the sink node is varied over time according to a known Markov chain, and the problem is solved by stochastic dynamic programming. We also present sub-optimal methods to solve our problem. A numerical example is presented in order to discuss the proposed methods' performance PMID:22399934

Amber Plug-In for Protein Shop

DOE Office of Scientific and Technical Information (OSTI.GOV)

Oliva, Ricardo

2004-05-10

The Amber Plug-in for ProteinShop has two main components: an AmberEngine library to compute the protein energy models, and a module to solve the energy minimization problem using an optimization algorithm in the OPTI-+ library. Together, these components allow the visualization of the protein folding process in ProteinShop. AmberEngine is a object-oriented library to compute molecular energies based on the Amber model. The main class is called ProteinEnergy. Its main interface methods are (1) "init" to initialize internal variables needed to compute the energy. (2) "eval" to evaluate the total energy given a vector of coordinates. Additional methods allow themore » user to evaluate the individual components of the energy model (bond, angle, dihedral, non-bonded-1-4, and non-bonded energies) and to obtain the energy of each individual atom. The Amber Engine library source code includes examples and test routines that illustrate the use of the library in stand alone programs. The energy minimization module uses the AmberEngine library and the nonlinear optimization library OPT++. OPT++ is open source software available under the GNU Lesser General Public License. The minimization module currently makes use of the LBFGS optimization algorithm in OPT++ to perform the energy minimization. Future releases may give the user a choice of other algorithms available in OPT++.« less

Finite Element Analysis in Concurrent Processing: Computational Issues

NASA Technical Reports Server (NTRS)

Sobieszczanski-Sobieski, Jaroslaw; Watson, Brian; Vanderplaats, Garrett

2004-01-01

The purpose of this research is to investigate the potential application of new methods for solving large-scale static structural problems on concurrent computers. It is well known that traditional single-processor computational speed will be limited by inherent physical limits. The only path to achieve higher computational speeds lies through concurrent processing. Traditional factorization solution methods for sparse matrices are ill suited for concurrent processing because the null entries get filled, leading to high communication and memory requirements. The research reported herein investigates alternatives to factorization that promise a greater potential to achieve high concurrent computing efficiency. Two methods, and their variants, based on direct energy minimization are studied: a) minimization of the strain energy using the displacement method formulation; b) constrained minimization of the complementary strain energy using the force method formulation. Initial results indicated that in the context of the direct energy minimization the displacement formulation experienced convergence and accuracy difficulties while the force formulation showed promising potential.

Classical Optimal Control for Energy Minimization Based On Diffeomorphic Modulation under Observable-Response-Preserving Homotopy.

PubMed

Soley, Micheline B; Markmann, Andreas; Batista, Victor S

2018-06-12

We introduce the so-called "Classical Optimal Control Optimization" (COCO) method for global energy minimization based on the implementation of the diffeomorphic modulation under observable-response-preserving homotopy (DMORPH) gradient algorithm. A probe particle with time-dependent mass m( t;β) and dipole μ( r, t;β) is evolved classically on the potential energy surface V( r) coupled to an electric field E( t;β), as described by the time-dependent density of states represented on a grid, or otherwise as a linear combination of Gaussians generated by the k-means clustering algorithm. Control parameters β defining m( t;β), μ( r, t;β), and E( t;β) are optimized by following the gradients of the energy with respect to β, adapting them to steer the particle toward the global minimum energy configuration. We find that the resulting COCO algorithm is capable of resolving near-degenerate states separated by large energy barriers and successfully locates the global minima of golf potentials on flat and rugged surfaces, previously explored for testing quantum annealing methodologies and the quantum optimal control optimization (QuOCO) method. Preliminary results show successful energy minimization of multidimensional Lennard-Jones clusters. Beyond the analysis of energy minimization in the specific model systems investigated, we anticipate COCO should be valuable for solving minimization problems in general, including optimization of parameters in applications to machine learning and molecular structure determination.

Higher Integrability for Minimizers of the Mumford-Shah Functional

NASA Astrophysics Data System (ADS)

De Philippis, Guido; Figalli, Alessio

2014-08-01

We prove higher integrability for the gradient of local minimizers of the Mumford-Shah energy functional, providing a positive answer to a conjecture of De Giorgi (Free discontinuity problems in calculus of variations. Frontiers in pure and applied mathematics, North-Holland, Amsterdam, pp 55-62, 1991).

Hybrid Microgrid Configuration Optimization with Evolutionary Algorithms

NASA Astrophysics Data System (ADS)

Lopez, Nicolas

This dissertation explores the Renewable Energy Integration Problem, and proposes a Genetic Algorithm embedded with a Monte Carlo simulation to solve large instances of the problem that are impractical to solve via full enumeration. The Renewable Energy Integration Problem is defined as finding the optimum set of components to supply the electric demand to a hybrid microgrid. The components considered are solar panels, wind turbines, diesel generators, electric batteries, connections to the power grid and converters, which can be inverters and/or rectifiers. The methodology developed is explained as well as the combinatorial formulation. In addition, 2 case studies of a single objective optimization version of the problem are presented, in order to minimize cost and to minimize global warming potential (GWP) followed by a multi-objective implementation of the offered methodology, by utilizing a non-sorting Genetic Algorithm embedded with a monte Carlo Simulation. The method is validated by solving a small instance of the problem with known solution via a full enumeration algorithm developed by NREL in their software HOMER. The dissertation concludes that the evolutionary algorithms embedded with Monte Carlo simulation namely modified Genetic Algorithms are an efficient form of solving the problem, by finding approximate solutions in the case of single objective optimization, and by approximating the true Pareto front in the case of multiple objective optimization of the Renewable Energy Integration Problem.

On a Minimum Problem in Smectic Elastomers

NASA Astrophysics Data System (ADS)

Buonsanti, Michele; Giovine, Pasquale

2008-07-01

Smectic elastomers are layered materials exhibiting a solid-like elastic response along the layer normal and a rubbery one in the plane. Balance equations for smectic elastomers are derived from the general theory of continua with constrained microstructure. In this work we investigate a very simple minimum problem based on multi-well potentials where the microstructure is taken into account. The set of polymeric strains minimizing the elastic energy contains a one-parameter family of simple strain associated with a micro-variation of the degree of freedom. We develop the energy functional through two terms, the first one nematic and the second one considering the tilting phenomenon; after, by developing in the rubber elasticity framework, we minimize over the tilt rotation angle and extract the engineering stress.

The application of nonlinear programming and collocation to optimal aeroassisted orbital transfers

NASA Astrophysics Data System (ADS)

Shi, Y. Y.; Nelson, R. L.; Young, D. H.; Gill, P. E.; Murray, W.; Saunders, M. A.

1992-01-01

Sequential quadratic programming (SQP) and collocation of the differential equations of motion were applied to optimal aeroassisted orbital transfers. The Optimal Trajectory by Implicit Simulation (OTIS) computer program codes with updated nonlinear programming code (NZSOL) were used as a testbed for the SQP nonlinear programming (NLP) algorithms. The state-of-the-art sparse SQP method is considered to be effective for solving large problems with a sparse matrix. Sparse optimizers are characterized in terms of memory requirements and computational efficiency. For the OTIS problems, less than 10 percent of the Jacobian matrix elements are nonzero. The SQP method encompasses two phases: finding an initial feasible point by minimizing the sum of infeasibilities and minimizing the quadratic objective function within the feasible region. The orbital transfer problem under consideration involves the transfer from a high energy orbit to a low energy orbit.

Energy-efficient approach to minimizing the energy consumption in an extended job-shop scheduling problem

NASA Astrophysics Data System (ADS)

Tang, Dunbing; Dai, Min

2015-09-01

The traditional production planning and scheduling problems consider performance indicators like time, cost and quality as optimization objectives in manufacturing processes. However, environmentally-friendly factors like energy consumption of production have not been completely taken into consideration. Against this background, this paper addresses an approach to modify a given schedule generated by a production planning and scheduling system in a job shop floor, where machine tools can work at different cutting speeds. It can adjust the cutting speeds of the operations while keeping the original assignment and processing sequence of operations of each job fixed in order to obtain energy savings. First, the proposed approach, based on a mixed integer programming mathematical model, changes the total idle time of the given schedule to minimize energy consumption in the job shop floor while accepting the optimal solution of the scheduling objective, makespan. Then, a genetic-simulated annealing algorithm is used to explore the optimal solution due to the fact that the problem is strongly NP-hard. Finally, the effectiveness of the approach is performed smalland large-size instances, respectively. The experimental results show that the approach can save 5%-10% of the average energy consumption while accepting the optimal solution of the makespan in small-size instances. In addition, the average maximum energy saving ratio can reach to 13%. And it can save approximately 1%-4% of the average energy consumption and approximately 2.4% of the average maximum energy while accepting the near-optimal solution of the makespan in large-size instances. The proposed research provides an interesting point to explore an energy-aware schedule optimization for a traditional production planning and scheduling problem.

Direct Density Functional Energy Minimization using an Tetrahedral Finite Element Grid

NASA Astrophysics Data System (ADS)

Vaught, A.; Schmidt, K. E.; Chizmeshya, A. V. G.

1998-03-01

We describe an O(N) (N proportional to volume) technique for solving electronic structure problems using the finite element method (FEM). A real--space tetrahedral grid is used as a basis to represent the electronic density, of a free or periodic system and Poisson's equation is solved as a boundary value problem. Nuclear cusps are treated using a local grid consisting of radial elements. These features facilitate the implementation of complicated energy functionals and permit a direct (constrained) energy minimization with respect to the density. We demonstrate the usefulness of the scheme by calculating the binding trends and polarizabilities of a number of atoms and molecules using a number of recently proposed non--local, orbital--free kinetic energy functionals^1,2. Scaling behavior, computational efficiency and the generalization to band--structure will also be discussed. indent 0 pt øbeylines øbeyspaces skip 0 pt ^1 P. Garcia-Gonzalez, J.E. Alvarellos and E. Chacon, Phys. Rev. B 54, 1897 (1996). ^2 A. J. Thakkar, Phys.Rev.B 46, 6920 (1992).

Dimension Reduction for the Landau-de Gennes Model on Curved Nematic Thin Films

NASA Astrophysics Data System (ADS)

Golovaty, Dmitry; Montero, José Alberto; Sternberg, Peter

2017-12-01

We use the method of Γ -convergence to study the behavior of the Landau-de Gennes model for a nematic liquid crystalline film attached to a general fixed surface in the limit of vanishing thickness. This paper generalizes the approach in Golovaty et al. (J Nonlinear Sci 25(6):1431-1451, 2015) where we considered a similar problem for a planar surface. Since the anchoring energy dominates when the thickness of the film is small, it is essential to understand its influence on the structure of the minimizers of the limiting energy. In particular, the anchoring energy dictates the class of admissible competitors and the structure of the limiting problem. We assume general weak anchoring conditions on the top and the bottom surfaces of the film and strong Dirichlet boundary conditions on the lateral boundary of the film when the surface is not closed. We establish a general convergence result to an energy defined on the surface that involves a somewhat surprising remnant of the normal component of the tensor gradient. Then we exhibit one effect of curvature through an analysis of the behavior of minimizers to the limiting problem when the substrate is a frustum.

Energy efficient LED layout optimization for near-uniform illumination

NASA Astrophysics Data System (ADS)

Ali, Ramy E.; Elgala, Hany

2016-09-01

In this paper, we consider the problem of designing energy efficient light emitting diodes (LEDs) layout while satisfying the illumination constraints. Towards this objective, we present a simple approach to the illumination design problem based on the concept of the virtual LED. We formulate a constrained optimization problem for minimizing the power consumption while maintaining a near-uniform illumination throughout the room. By solving the resulting constrained linear program, we obtain the number of required LEDs and the optimal output luminous intensities that achieve the desired illumination constraints.

Computers and the Environment: Minimizing the Carbon Footprint

ERIC Educational Resources Information Center

Kaestner, Rich

2009-01-01

Computers can be good and bad for the environment; one can maximize the good and minimize the bad. When dealing with environmental issues, it's difficult to ignore the computing infrastructure. With an operations carbon footprint equal to the airline industry's, computer energy use is only part of the problem; everyone is also dealing with the use…

A convex optimization method for self-organization in dynamic (FSO/RF) wireless networks

NASA Astrophysics Data System (ADS)

Llorca, Jaime; Davis, Christopher C.; Milner, Stuart D.

2008-08-01

Next generation communication networks are becoming increasingly complex systems. Previously, we presented a novel physics-based approach to model dynamic wireless networks as physical systems which react to local forces exerted on network nodes. We showed that under clear atmospheric conditions the network communication energy can be modeled as the potential energy of an analogous spring system and presented a distributed mobility control algorithm where nodes react to local forces driving the network to energy minimizing configurations. This paper extends our previous work by including the effects of atmospheric attenuation and transmitted power constraints in the optimization problem. We show how our new formulation still results in a convex energy minimization problem. Accordingly, an updated force-driven mobility control algorithm is presented. Forces on mobile backbone nodes are computed as the negative gradient of the new energy function. Results show how in the presence of atmospheric obscuration stronger forces are exerted on network nodes that make them move closer to each other, avoiding loss of connectivity. We show results in terms of network coverage and backbone connectivity and compare the developed algorithms for different scenarios.

Consolidation of hydrophobic transition criteria by using an approximate energy minimization approach.

PubMed

Patankar, Neelesh A

2010-06-01

Recent experimental work has successfully revealed pressure induced transition from Cassie to Wenzel state on rough hydrophobic substrates. Formulas, based on geometric considerations and imposed pressure, have been developed as transition criteria. In the past, transition has also been considered as a process of overcoming the energy barrier between the Cassie and Wenzel states. A unified understanding of the various considerations of transition has not been apparent. To address this issue, in this work, we consolidate the transition criteria with a homogenized energy minimization approach. This approach decouples the problem of minimizing the energy to wet the rough substrate, from the energy of the macroscopic drop. It is seen that the transition from Cassie to Wenzel state, due to depinning of the liquid-air interface, emerges from the approximate energy minimization approach if the pressure-volume energy associated with the impaled liquid in the roughness is included. This transition can be viewed as a process in which the work done by the pressure force is greater than the barrier due to the surface energy associated with wetting the roughness. It is argued that another transition mechanism, due to a sagging liquid-air interface that touches the bottom of the roughness grooves, is not typically relevant if the substrate roughness is designed such that the Cassie state is at lower energy compared to the Wenzel state.

Model Predictive Control-based Optimal Coordination of Distributed Energy Resources

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mayhorn, Ebony T.; Kalsi, Karanjit; Lian, Jianming

2013-01-07

Distributed energy resources, such as renewable energy resources (wind, solar), energy storage and demand response, can be used to complement conventional generators. The uncertainty and variability due to high penetration of wind makes reliable system operations and controls challenging, especially in isolated systems. In this paper, an optimal control strategy is proposed to coordinate energy storage and diesel generators to maximize wind penetration while maintaining system economics and normal operation performance. The goals of the optimization problem are to minimize fuel costs and maximize the utilization of wind while considering equipment life of generators and energy storage. Model predictive controlmore » (MPC) is used to solve a look-ahead dispatch optimization problem and the performance is compared to an open loop look-ahead dispatch problem. Simulation studies are performed to demonstrate the efficacy of the closed loop MPC in compensating for uncertainties and variability caused in the system.« less

Model Predictive Control-based Optimal Coordination of Distributed Energy Resources

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mayhorn, Ebony T.; Kalsi, Karanjit; Lian, Jianming

2013-04-03

Distributed energy resources, such as renewable energy resources (wind, solar), energy storage and demand response, can be used to complement conventional generators. The uncertainty and variability due to high penetration of wind makes reliable system operations and controls challenging, especially in isolated systems. In this paper, an optimal control strategy is proposed to coordinate energy storage and diesel generators to maximize wind penetration while maintaining system economics and normal operation performance. The goals of the optimization problem are to minimize fuel costs and maximize the utilization of wind while considering equipment life of generators and energy storage. Model predictive controlmore » (MPC) is used to solve a look-ahead dispatch optimization problem and the performance is compared to an open loop look-ahead dispatch problem. Simulation studies are performed to demonstrate the efficacy of the closed loop MPC in compensating for uncertainties and variability caused in the system.« less

Sparse RNA folding revisited: space-efficient minimum free energy structure prediction.

PubMed

Will, Sebastian; Jabbari, Hosna

2016-01-01

RNA secondary structure prediction by energy minimization is the central computational tool for the analysis of structural non-coding RNAs and their interactions. Sparsification has been successfully applied to improve the time efficiency of various structure prediction algorithms while guaranteeing the same result; however, for many such folding problems, space efficiency is of even greater concern, particularly for long RNA sequences. So far, space-efficient sparsified RNA folding with fold reconstruction was solved only for simple base-pair-based pseudo-energy models. Here, we revisit the problem of space-efficient free energy minimization. Whereas the space-efficient minimization of the free energy has been sketched before, the reconstruction of the optimum structure has not even been discussed. We show that this reconstruction is not possible in trivial extension of the method for simple energy models. Then, we present the time- and space-efficient sparsified free energy minimization algorithm SparseMFEFold that guarantees MFE structure prediction. In particular, this novel algorithm provides efficient fold reconstruction based on dynamically garbage-collected trace arrows. The complexity of our algorithm depends on two parameters, the number of candidates Z and the number of trace arrows T; both are bounded by [Formula: see text], but are typically much smaller. The time complexity of RNA folding is reduced from [Formula: see text] to [Formula: see text]; the space complexity, from [Formula: see text] to [Formula: see text]. Our empirical results show more than 80 % space savings over RNAfold [Vienna RNA package] on the long RNAs from the RNA STRAND database (≥2500 bases). The presented technique is intentionally generalizable to complex prediction algorithms; due to their high space demands, algorithms like pseudoknot prediction and RNA-RNA-interaction prediction are expected to profit even stronger than "standard" MFE folding. SparseMFEFold is free software, available at http://www.bioinf.uni-leipzig.de/~will/Software/SparseMFEFold.

Inelastic scattering with Chebyshev polynomials and preconditioned conjugate gradient minimization.

PubMed

Temel, Burcin; Mills, Greg; Metiu, Horia

2008-03-27

We describe and test an implementation, using a basis set of Chebyshev polynomials, of a variational method for solving scattering problems in quantum mechanics. This minimum error method (MEM) determines the wave function Psi by minimizing the least-squares error in the function (H Psi - E Psi), where E is the desired scattering energy. We compare the MEM to an alternative, the Kohn variational principle (KVP), by solving the Secrest-Johnson model of two-dimensional inelastic scattering, which has been studied previously using the KVP and for which other numerical solutions are available. We use a conjugate gradient (CG) method to minimize the error, and by preconditioning the CG search, we are able to greatly reduce the number of iterations necessary; the method is thus faster and more stable than a matrix inversion, as is required in the KVP. Also, we avoid errors due to scattering off of the boundaries, which presents substantial problems for other methods, by matching the wave function in the interaction region to the correct asymptotic states at the specified energy; the use of Chebyshev polynomials allows this boundary condition to be implemented accurately. The use of Chebyshev polynomials allows for a rapid and accurate evaluation of the kinetic energy. This basis set is as efficient as plane waves but does not impose an artificial periodicity on the system. There are problems in surface science and molecular electronics which cannot be solved if periodicity is imposed, and the Chebyshev basis set is a good alternative in such situations.

A new mathematical modeling approach for the energy of threonine molecule

NASA Astrophysics Data System (ADS)

Sahiner, Ahmet; Kapusuz, Gulden; Yilmaz, Nurullah

2017-07-01

In this paper, we propose an improved new methodology in energy conformation problems for finding optimum energy values. First, we construct the Bezier surfaces near local minimizers based on the data obtained from Density Functional Theory (DFT) calculations. Second, we blend the constructed surfaces in order to obtain a single smooth model. Finally, we apply the global optimization algorithm to find two torsion angles those make the energy of the molecule minimum.

Observational constraints on tachyonic chameleon dark energy model

NASA Astrophysics Data System (ADS)

Banijamali, A.; Bellucci, S.; Fazlpour, B.; Solbi, M.

2018-03-01

It has been recently shown that tachyonic chameleon model of dark energy in which tachyon scalar field non-minimally coupled to the matter admits stable scaling attractor solution that could give rise to the late-time accelerated expansion of the universe and hence alleviate the coincidence problem. In the present work, we use data from Type Ia supernova (SN Ia) and Baryon Acoustic oscillations to place constraints on the model parameters. In our analysis we consider in general exponential and non-exponential forms for the non-minimal coupling function and tachyonic potential and show that the scenario is compatible with observations.

A control problem for Burgers' equation with bounded input/output

NASA Technical Reports Server (NTRS)

Burns, John A.; Kang, Sungkwon

1990-01-01

A stabilization problem for Burgers' equation is considered. Using linearization, various controllers are constructed which minimize certain weighted energy functionals. These controllers produce the desired degree of stability for the closed-loop nonlinear system. A numerical scheme for computing the feedback gain functional is developed and several numerical experiments are performed to show the theoretical results.

Energy-aware virtual network embedding in flexi-grid optical networks

NASA Astrophysics Data System (ADS)

Lin, Rongping; Luo, Shan; Wang, Haoran; Wang, Sheng; Chen, Bin

2018-01-01

Virtual network embedding (VNE) problem is to map multiple heterogeneous virtual networks (VN) on a shared substrate network, which mitigate the ossification of the substrate network. Meanwhile, energy efficiency has been widely considered in the network design. In this paper, we aim to solve the energy-aware VNE problem in flexi-grid optical networks. We provide an integer linear programming (ILP) formulation to minimize the power increment of each arriving VN request. We also propose a polynomial-time heuristic algorithm where virtual links are embedded sequentially to keep a reasonable acceptance ratio and maintain a low energy consumption. Numerical results show the functionality of the heuristic algorithm in a 24-node network.

Life cycle optimization model for integrated cogeneration and energy systems applications in buildings

NASA Astrophysics Data System (ADS)

Osman, Ayat E.

Energy use in commercial buildings constitutes a major proportion of the energy consumption and anthropogenic emissions in the USA. Cogeneration systems offer an opportunity to meet a building's electrical and thermal demands from a single energy source. To answer the question of what is the most beneficial and cost effective energy source(s) that can be used to meet the energy demands of the building, optimizations techniques have been implemented in some studies to find the optimum energy system based on reducing cost and maximizing revenues. Due to the significant environmental impacts that can result from meeting the energy demands in buildings, building design should incorporate environmental criteria in the decision making criteria. The objective of this research is to develop a framework and model to optimize a building's operation by integrating congregation systems and utility systems in order to meet the electrical, heating, and cooling demand by considering the potential life cycle environmental impact that might result from meeting those demands as well as the economical implications. Two LCA Optimization models have been developed within a framework that uses hourly building energy data, life cycle assessment (LCA), and mixed-integer linear programming (MILP). The objective functions that are used in the formulation of the problems include: (1) Minimizing life cycle primary energy consumption, (2) Minimizing global warming potential, (3) Minimizing tropospheric ozone precursor potential, (4) Minimizing acidification potential, (5) Minimizing NOx, SO 2 and CO2, and (6) Minimizing life cycle costs, considering a study period of ten years and the lifetime of equipment. The two LCA optimization models can be used for: (a) long term planning and operational analysis in buildings by analyzing the hourly energy use of a building during a day and (b) design and quick analysis of building operation based on periodic analysis of energy use of a building in a year. A Pareto-optimal frontier is also derived, which defines the minimum cost required to achieve any level of environmental emission or primary energy usage value or inversely the minimum environmental indicator and primary energy usage value that can be achieved and the cost required to achieve that value.

A minimization principle for the description of modes associated with finite-time instabilities

PubMed Central

Babaee, H.

2016-01-01

We introduce a minimization formulation for the determination of a finite-dimensional, time-dependent, orthonormal basis that captures directions of the phase space associated with transient instabilities. While these instabilities have finite lifetime, they can play a crucial role either by altering the system dynamics through the activation of other instabilities or by creating sudden nonlinear energy transfers that lead to extreme responses. However, their essentially transient character makes their description a particularly challenging task. We develop a minimization framework that focuses on the optimal approximation of the system dynamics in the neighbourhood of the system state. This minimization formulation results in differential equations that evolve a time-dependent basis so that it optimally approximates the most unstable directions. We demonstrate the capability of the method for two families of problems: (i) linear systems, including the advection–diffusion operator in a strongly non-normal regime as well as the Orr–Sommerfeld/Squire operator, and (ii) nonlinear problems, including a low-dimensional system with transient instabilities and the vertical jet in cross-flow. We demonstrate that the time-dependent subspace captures the strongly transient non-normal energy growth (in the short-time regime), while for longer times the modes capture the expected asymptotic behaviour. PMID:27118900

Validation of molecular crystal structures from powder diffraction data with dispersion-corrected density functional theory (DFT-D).

PubMed

van de Streek, Jacco; Neumann, Marcus A

2014-12-01

In 2010 we energy-minimized 225 high-quality single-crystal (SX) structures with dispersion-corrected density functional theory (DFT-D) to establish a quantitative benchmark. For the current paper, 215 organic crystal structures determined from X-ray powder diffraction (XRPD) data and published in an IUCr journal were energy-minimized with DFT-D and compared to the SX benchmark. The on average slightly less accurate atomic coordinates of XRPD structures do lead to systematically higher root mean square Cartesian displacement (RMSCD) values upon energy minimization than for SX structures, but the RMSCD value is still a good indicator for the detection of structures that deserve a closer look. The upper RMSCD limit for a correct structure must be increased from 0.25 Å for SX structures to 0.35 Å for XRPD structures; the grey area must be extended from 0.30 to 0.40 Å. Based on the energy minimizations, three structures are re-refined to give more precise atomic coordinates. For six structures our calculations provide the missing positions for the H atoms, for five structures they provide corrected positions for some H atoms. Seven crystal structures showed a minor error for a non-H atom. For five structures the energy minimizations suggest a higher space-group symmetry. For the 225 SX structures, the only deviations observed upon energy minimization were three minor H-atom related issues. Preferred orientation is the most important cause of problems. A preferred-orientation correction is the only correction where the experimental data are modified to fit the model. We conclude that molecular crystal structures determined from powder diffraction data that are published in IUCr journals are of high quality, with less than 4% containing an error in a non-H atom.

Validation of molecular crystal structures from powder diffraction data with dispersion-corrected density functional theory (DFT-D)

PubMed Central

van de Streek, Jacco; Neumann, Marcus A.

2014-01-01

In 2010 we energy-minimized 225 high-quality single-crystal (SX) structures with dispersion-corrected density functional theory (DFT-D) to establish a quantitative benchmark. For the current paper, 215 organic crystal structures determined from X-ray powder diffraction (XRPD) data and published in an IUCr journal were energy-minimized with DFT-D and compared to the SX benchmark. The on average slightly less accurate atomic coordinates of XRPD structures do lead to systematically higher root mean square Cartesian displacement (RMSCD) values upon energy minimization than for SX structures, but the RMSCD value is still a good indicator for the detection of structures that deserve a closer look. The upper RMSCD limit for a correct structure must be increased from 0.25 Å for SX structures to 0.35 Å for XRPD structures; the grey area must be extended from 0.30 to 0.40 Å. Based on the energy minimizations, three structures are re-refined to give more precise atomic coordinates. For six structures our calculations provide the missing positions for the H atoms, for five structures they provide corrected positions for some H atoms. Seven crystal structures showed a minor error for a non-H atom. For five structures the energy minimizations suggest a higher space-group symmetry. For the 225 SX structures, the only deviations observed upon energy minimization were three minor H-atom related issues. Preferred orientation is the most important cause of problems. A preferred-orientation correction is the only correction where the experimental data are modified to fit the model. We conclude that molecular crystal structures determined from powder diffraction data that are published in IUCr journals are of high quality, with less than 4% containing an error in a non-H atom. PMID:25449625

Optimal trajectories for aeroassisted orbital transfer

NASA Technical Reports Server (NTRS)

Miele, A.; Venkataraman, P.

1983-01-01

Consideration is given to classical and minimax problems involved in aeroassisted transfer from high earth orbit (HEO) to low earth orbit (LEO). The transfer is restricted to coplanar operation, with trajectory control effected by means of lift modulation. The performance of the maneuver is indexed to the energy expenditure or, alternatively, the time integral of the heating rate. Firist-order optimality conditions are defined for the classical approach, as are a sequential gradient-restoration algorithm and a combined gradient-restoration algorithm. Minimization techniques are presented for the aeroassisted transfer energy consumption and time-delay integral of the heating rate, as well as minimization of the pressure. It is shown that the eigenvalues of the Jacobian matrix of the differential system is both stiff and unstable, implying that the sequential gradient restoration algorithm in its present version is unsuitable. A new method, involving a multipoint approach to the two-poing boundary value problem, is recommended.

Sequential and parallel image restoration: neural network implementations.

PubMed

Figueiredo, M T; Leitao, J N

1994-01-01

Sequential and parallel image restoration algorithms and their implementations on neural networks are proposed. For images degraded by linear blur and contaminated by additive white Gaussian noise, maximum a posteriori (MAP) estimation and regularization theory lead to the same high dimension convex optimization problem. The commonly adopted strategy (in using neural networks for image restoration) is to map the objective function of the optimization problem into the energy of a predefined network, taking advantage of its energy minimization properties. Departing from this approach, we propose neural implementations of iterative minimization algorithms which are first proved to converge. The developed schemes are based on modified Hopfield (1985) networks of graded elements, with both sequential and parallel updating schedules. An algorithm supported on a fully standard Hopfield network (binary elements and zero autoconnections) is also considered. Robustness with respect to finite numerical precision is studied, and examples with real images are presented.

Cosmological Constant as a Manifestation of the Hierarchy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chen, Pisin; Gu, Je-An

2007-12-21

There has been the suggestion that the cosmological constant as implied by the dark energy is related to the well-known hierarchy between the Planck scale, M{sub PI}, and the Standard Model scale, M{sub SM}. Here we further propose that the same framework that addresses this hierarchy problem must also address the smallness problem of the cosmological constant. Specifically, we investigate the minimal supersymmetric (SUSY) extension of the Randall-Sundrum model where SUSY-breaking is induced on the TeV brane and transmitted into the bulk. We show that the Casimir energy density of the system indeed conforms with the observed dark energy scale.

Optimal trajectories of aircraft and spacecraft

NASA Technical Reports Server (NTRS)

Miele, A.

1990-01-01

Work done on algorithms for the numerical solutions of optimal control problems and their application to the computation of optimal flight trajectories of aircraft and spacecraft is summarized. General considerations on calculus of variations, optimal control, numerical algorithms, and applications of these algorithms to real-world problems are presented. The sequential gradient-restoration algorithm (SGRA) is examined for the numerical solution of optimal control problems of the Bolza type. Both the primal formulation and the dual formulation are discussed. Aircraft trajectories, in particular, the application of the dual sequential gradient-restoration algorithm (DSGRA) to the determination of optimal flight trajectories in the presence of windshear are described. Both take-off trajectories and abort landing trajectories are discussed. Take-off trajectories are optimized by minimizing the peak deviation of the absolute path inclination from a reference value. Abort landing trajectories are optimized by minimizing the peak drop of altitude from a reference value. Abort landing trajectories are optimized by minimizing the peak drop of altitude from a reference value. The survival capability of an aircraft in a severe windshear is discussed, and the optimal trajectories are found to be superior to both constant pitch trajectories and maximum angle of attack trajectories. Spacecraft trajectories, in particular, the application of the primal sequential gradient-restoration algorithm (PSGRA) to the determination of optimal flight trajectories for aeroassisted orbital transfer are examined. Both the coplanar case and the noncoplanar case are discussed within the frame of three problems: minimization of the total characteristic velocity; minimization of the time integral of the square of the path inclination; and minimization of the peak heating rate. The solution of the second problem is called nearly-grazing solution, and its merits are pointed out as a useful engineering compromise between energy requirements and aerodynamics heating requirements.

Energy aware path planning in complex four dimensional environments

NASA Astrophysics Data System (ADS)

Chakrabarty, Anjan

This dissertation addresses the problem of energy-aware path planning for small autonomous vehicles. While small autonomous vehicles can perform missions that are too risky (or infeasible) for larger vehicles, the missions are limited by the amount of energy that can be carried on board the vehicle. Path planning techniques that either minimize energy consumption or exploit energy available in the environment can thus increase range and endurance. Path planning is complicated by significant spatial (and potentially temporal) variations in the environment. While the main focus is on autonomous aircraft, this research also addresses autonomous ground vehicles. Range and endurance of small unmanned aerial vehicles (UAVs) can be greatly improved by utilizing energy from the atmosphere. Wind can be exploited to minimize energy consumption of a small UAV. But wind, like any other atmospheric component , is a space and time varying phenomenon. To effectively use wind for long range missions, both exploration and exploitation of wind is critical. This research presents a kinematics based tree algorithm which efficiently handles the four dimensional (three spatial and time) path planning problem. The Kinematic Tree algorithm provides a sequence of waypoints, airspeeds, heading and bank angle commands for each segment of the path. The planner is shown to be resolution complete and computationally efficient. Global optimality of the cost function cannot be claimed, as energy is gained from the atmosphere, making the cost function inadmissible. However the Kinematic Tree is shown to be optimal up to resolution if the cost function is admissible. Simulation results show the efficacy of this planning method for a glider in complex real wind data. Simulation results verify that the planner is able to extract energy from the atmosphere enabling long range missions. The Kinematic Tree planning framework, developed to minimize energy consumption of UAVs, is applied for path planning in ground robots. In traditional path planning problem the focus is on obstacle avoidance and navigation. The optimal Kinematic Tree algorithm named Kinematic Tree* is shown to find optimal paths to reach the destination while avoiding obstacles. A more challenging path planning scenario arises for planning in complex terrain. This research shows how the Kinematic Tree* algorithm can be extended to find minimum energy paths for a ground vehicle in difficult mountainous terrain.

Colorado's energy boom: impact on crime and criminal justice

DOE Office of Scientific and Technical Information (OSTI.GOV)

Not Available

1981-02-01

Information is reported on the impact of rapid energy development on western slope criminal justice agencies. The focus is on crime rates, law enforcement, the courts, and juvenile justice problems. The problems that are likely to develop and what might be done to minimize the negative consequences are analyzed. The social characteristics of boom towns and the changes resulting from rapid growth, the changes in crime rates, the impact experienced by law enforcement agencies and the courts, and information on planning and funding in impact areas are described. (MCW)

Energy Harvesting Based Body Area Networks for Smart Health.

PubMed

Hao, Yixue; Peng, Limei; Lu, Huimin; Hassan, Mohammad Mehedi; Alamri, Atif

2017-07-10

Body area networks (BANs) are configured with a great number of ultra-low power consumption wearable devices, which constantly monitor physiological signals of the human body and thus realize intelligent monitoring. However, the collection and transfer of human body signals consume energy, and considering the comfort demand of wearable devices, both the size and the capacity of a wearable device's battery are limited. Thus, minimizing the energy consumption of wearable devices and optimizing the BAN energy efficiency is still a challenging problem. Therefore, in this paper, we propose an energy harvesting-based BAN for smart health and discuss an optimal resource allocation scheme to improve BAN energy efficiency. Specifically, firstly, considering energy harvesting in a BAN and the time limits of human body signal transfer, we formulate the energy efficiency optimization problem of time division for wireless energy transfer and wireless information transfer. Secondly, we convert the optimization problem into a convex optimization problem under a linear constraint and propose a closed-form solution to the problem. Finally, simulation results proved that when the size of data acquired by the wearable devices is small, the proportion of energy consumed by the circuit and signal acquisition of the wearable devices is big, and when the size of data acquired by the wearable devices is big, the energy consumed by the signal transfer of the wearable device is decisive.

Energy Harvesting Based Body Area Networks for Smart Health

PubMed Central

Hao, Yixue; Peng, Limei; Alamri, Atif

2017-01-01

Body area networks (BANs) are configured with a great number of ultra-low power consumption wearable devices, which constantly monitor physiological signals of the human body and thus realize intelligent monitoring. However, the collection and transfer of human body signals consume energy, and considering the comfort demand of wearable devices, both the size and the capacity of a wearable device’s battery are limited. Thus, minimizing the energy consumption of wearable devices and optimizing the BAN energy efficiency is still a challenging problem. Therefore, in this paper, we propose an energy harvesting-based BAN for smart health and discuss an optimal resource allocation scheme to improve BAN energy efficiency. Specifically, firstly, considering energy harvesting in a BAN and the time limits of human body signal transfer, we formulate the energy efficiency optimization problem of time division for wireless energy transfer and wireless information transfer. Secondly, we convert the optimization problem into a convex optimization problem under a linear constraint and propose a closed-form solution to the problem. Finally, simulation results proved that when the size of data acquired by the wearable devices is small, the proportion of energy consumed by the circuit and signal acquisition of the wearable devices is big, and when the size of data acquired by the wearable devices is big, the energy consumed by the signal transfer of the wearable device is decisive. PMID:28698501

Optimal Control of Distributed Energy Resources using Model Predictive Control

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mayhorn, Ebony T.; Kalsi, Karanjit; Elizondo, Marcelo A.

2012-07-22

In an isolated power system (rural microgrid), Distributed Energy Resources (DERs) such as renewable energy resources (wind, solar), energy storage and demand response can be used to complement fossil fueled generators. The uncertainty and variability due to high penetration of wind makes reliable system operations and controls challenging. In this paper, an optimal control strategy is proposed to coordinate energy storage and diesel generators to maximize wind penetration while maintaining system economics and normal operation. The problem is formulated as a multi-objective optimization problem with the goals of minimizing fuel costs and changes in power output of diesel generators, minimizingmore » costs associated with low battery life of energy storage and maintaining system frequency at the nominal operating value. Two control modes are considered for controlling the energy storage to compensate either net load variability or wind variability. Model predictive control (MPC) is used to solve the aforementioned problem and the performance is compared to an open-loop look-ahead dispatch problem. Simulation studies using high and low wind profiles, as well as, different MPC prediction horizons demonstrate the efficacy of the closed-loop MPC in compensating for uncertainties in wind and demand.« less

Primal-dual methods of shape sensitivity analysis for curvilinear cracks with nonpenetration

NASA Astrophysics Data System (ADS)

Kovtunenko, V. A.

2006-10-01

Based on a level-set description of a crack moving with a given velocity, the problem of shape perturb-ation of the crack is considered. Nonpenetration conditions are imposed between opposite crack surfaces which result in a constrained minimization problem describing equilibrium of a solid with the crack. We suggest a minimax formulation of the state problem thus allowing curvilinear (nonplanar) cracks for the consideration. Utilizing primal-dual methods of shape sensitivity analysis we obtain the general formula for a shape derivative of the potential energy, which describes an energy-release rate for the curvilinear cracks. The conditions sufficient to rewrite it in the form of a path-independent integral (J-integral) are derived.

Shape optimization of self-avoiding curves

NASA Astrophysics Data System (ADS)

Walker, Shawn W.

2016-04-01

This paper presents a softened notion of proximity (or self-avoidance) for curves. We then derive a sensitivity result, based on shape differential calculus, for the proximity. This is combined with a gradient-based optimization approach to compute three-dimensional, parameterized curves that minimize the sum of an elastic (bending) energy and a proximity energy that maintains self-avoidance by a penalization technique. Minimizers are computed by a sequential-quadratic-programming (SQP) method where the bending energy and proximity energy are approximated by a finite element method. We then apply this method to two problems. First, we simulate adsorbed polymer strands that are constrained to be bound to a surface and be (locally) inextensible. This is a basic model of semi-flexible polymers adsorbed onto a surface (a current topic in material science). Several examples of minimizing curve shapes on a variety of surfaces are shown. An advantage of the method is that it can be much faster than using molecular dynamics for simulating polymer strands on surfaces. Second, we apply our proximity penalization to the computation of ideal knots. We present a heuristic scheme, utilizing the SQP method above, for minimizing rope-length and apply it in the case of the trefoil knot. Applications of this method could be for generating good initial guesses to a more accurate (but expensive) knot-tightening algorithm.

Limit behavior of mass critical Hartree minimization problems with steep potential wells

NASA Astrophysics Data System (ADS)

Guo, Yujin; Luo, Yong; Wang, Zhi-Qiang

2018-06-01

We consider minimizers of the following mass critical Hartree minimization problem: eλ(N ) ≔inf {u ∈H1(Rd ) , ‖u‖2 2=N } Eλ(u ) , where d ≥ 3, λ > 0, and the Hartree energy functional Eλ(u) is defined by Eλ(u ) ≔∫Rd|∇u (x ) |2d x +λ ∫Rdg (x ) u2(x ) d x -1/2 ∫Rd∫Rdu/2(x ) u2(y ) |x -y |2 d x d y . Here the steep potential g(x) satisfies 0 =g (0 ) =infRdg (x ) ≤g (x ) ≤1 and 1 -g (x ) ∈Ld/2(Rd ) . We prove that there exists a constant N* > 0, independent of λg(x), such that if N ≥ N*, then eλ(N) does not admit minimizers for any λ > 0; if 0 < N < N*, then there exists a constant λ*(N) > 0 such that eλ(N) admits minimizers for any λ > λ*(N) and eλ(N) does not admit minimizers for 0 < λ < λ*(N). For any given 0 < N < N*, the limit behavior of positive minimizers for eλ(N) is also studied as λ → ∞, where the mass concentrates at the bottom of g(x).

Single product lot-sizing on unrelated parallel machines with non-decreasing processing times

NASA Astrophysics Data System (ADS)

Eremeev, A.; Kovalyov, M.; Kuznetsov, P.

2018-01-01

We consider a problem in which at least a given quantity of a single product has to be partitioned into lots, and lots have to be assigned to unrelated parallel machines for processing. In one version of the problem, the maximum machine completion time should be minimized, in another version of the problem, the sum of machine completion times is to be minimized. Machine-dependent lower and upper bounds on the lot size are given. The product is either assumed to be continuously divisible or discrete. The processing time of each machine is defined by an increasing function of the lot volume, given as an oracle. Setup times and costs are assumed to be negligibly small, and therefore, they are not considered. We derive optimal polynomial time algorithms for several special cases of the problem. An NP-hard case is shown to admit a fully polynomial time approximation scheme. An application of the problem in energy efficient processors scheduling is considered.

SXTF Instrumentation Definition.

DTIC Science & Technology

1978-05-31

Reference 53. 3.3.2 Multipactor Jectron Sowrces I The multipactor source (Ref 54) has attractive features for application to space - craft charging ...The effect of high 9 energy electrons impinging on the cables can be minimized by shielding them with a sufficient thickness of dielectric . Of the...encountered because of dielectric charging of insulating surfaces, dose and dose rate problems caused by the high energy electron component penetrating

Variational Implicit Solvation with Solute Molecular Mechanics: From Diffuse-Interface to Sharp-Interface Models.

PubMed

Li, Bo; Zhao, Yanxiang

2013-01-01

Central in a variational implicit-solvent description of biomolecular solvation is an effective free-energy functional of the solute atomic positions and the solute-solvent interface (i.e., the dielectric boundary). The free-energy functional couples together the solute molecular mechanical interaction energy, the solute-solvent interfacial energy, the solute-solvent van der Waals interaction energy, and the electrostatic energy. In recent years, the sharp-interface version of the variational implicit-solvent model has been developed and used for numerical computations of molecular solvation. In this work, we propose a diffuse-interface version of the variational implicit-solvent model with solute molecular mechanics. We also analyze both the sharp-interface and diffuse-interface models. We prove the existence of free-energy minimizers and obtain their bounds. We also prove the convergence of the diffuse-interface model to the sharp-interface model in the sense of Γ-convergence. We further discuss properties of sharp-interface free-energy minimizers, the boundary conditions and the coupling of the Poisson-Boltzmann equation in the diffuse-interface model, and the convergence of forces from diffuse-interface to sharp-interface descriptions. Our analysis relies on the previous works on the problem of minimizing surface areas and on our observations on the coupling between solute molecular mechanical interactions with the continuum solvent. Our studies justify rigorously the self consistency of the proposed diffuse-interface variational models of implicit solvation.

Mature red blood cells: from optical model to inverse light-scattering problem.

PubMed

Gilev, Konstantin V; Yurkin, Maxim A; Chernyshova, Ekaterina S; Strokotov, Dmitry I; Chernyshev, Andrei V; Maltsev, Valeri P

2016-04-01

We propose a method for characterization of mature red blood cells (RBCs) morphology, based on measurement of light-scattering patterns (LSPs) of individual RBCs with the scanning flow cytometer and on solution of the inverse light-scattering (ILS) problem for each LSP. We considered a RBC shape model, corresponding to the minimal bending energy of the membrane with isotropic elasticity, and constructed an analytical approximation, which allows rapid simulation of the shape, given the diameter and minimal and maximal thicknesses. The ILS problem was solved by the nearest-neighbor interpolation using a preliminary calculated database of 250,000 theoretical LSPs. For each RBC in blood sample we determined three abovementioned shape characteristics and refractive index, which also allows us to calculate volume, surface area, sphericity index, spontaneous curvature, hemoglobin concentration and content.

Mature red blood cells: from optical model to inverse light-scattering problem

PubMed Central

Gilev, Konstantin V.; Yurkin, Maxim A.; Chernyshova, Ekaterina S.; Strokotov, Dmitry I.; Chernyshev, Andrei V.; Maltsev, Valeri P.

2016-01-01

We propose a method for characterization of mature red blood cells (RBCs) morphology, based on measurement of light-scattering patterns (LSPs) of individual RBCs with the scanning flow cytometer and on solution of the inverse light-scattering (ILS) problem for each LSP. We considered a RBC shape model, corresponding to the minimal bending energy of the membrane with isotropic elasticity, and constructed an analytical approximation, which allows rapid simulation of the shape, given the diameter and minimal and maximal thicknesses. The ILS problem was solved by the nearest-neighbor interpolation using a preliminary calculated database of 250,000 theoretical LSPs. For each RBC in blood sample we determined three abovementioned shape characteristics and refractive index, which also allows us to calculate volume, surface area, sphericity index, spontaneous curvature, hemoglobin concentration and content. PMID:27446656

Energy-Efficient Routing and Spectrum Assignment Algorithm with Physical-Layer Impairments Constraint in Flexible Optical Networks

NASA Astrophysics Data System (ADS)

Zhao, Jijun; Zhang, Nawa; Ren, Danping; Hu, Jinhua

2017-12-01

The recently proposed flexible optical network can provide more efficient accommodation of multiple data rates than the current wavelength-routed optical networks. Meanwhile, the energy efficiency has also been a hot topic because of the serious energy consumption problem. In this paper, the energy efficiency problem of flexible optical networks with physical-layer impairments constraint is studied. We propose a combined impairment-aware and energy-efficient routing and spectrum assignment (RSA) algorithm based on the link availability, in which the impact of power consumption minimization on signal quality is considered. By applying the proposed algorithm, the connection requests are established on a subset of network topology, reducing the number of transitions from sleep to active state. The simulation results demonstrate that our proposed algorithm can improve the energy efficiency and spectrum resources utilization with the acceptable blocking probability and average delay.

Computational methods for reactive transport modeling: An extended law of mass-action, xLMA, method for multiphase equilibrium calculations

NASA Astrophysics Data System (ADS)

Leal, Allan M. M.; Kulik, Dmitrii A.; Kosakowski, Georg; Saar, Martin O.

2016-10-01

We present an extended law of mass-action (xLMA) method for multiphase equilibrium calculations and apply it in the context of reactive transport modeling. This extended LMA formulation differs from its conventional counterpart in that (i) it is directly derived from the Gibbs energy minimization (GEM) problem (i.e., the fundamental problem that describes the state of equilibrium of a chemical system under constant temperature and pressure); and (ii) it extends the conventional mass-action equations with Lagrange multipliers from the Gibbs energy minimization problem, which can be interpreted as stability indices of the chemical species. Accounting for these multipliers enables the method to determine all stable phases without presuming their types (e.g., aqueous, gaseous) or their presence in the equilibrium state. Therefore, the here proposed xLMA method inherits traits of Gibbs energy minimization algorithms that allow it to naturally detect the phases present in equilibrium, which can be single-component phases (e.g., pure solids or liquids) or non-ideal multi-component phases (e.g., aqueous, melts, gaseous, solid solutions, adsorption, or ion exchange). Moreover, our xLMA method requires no technique that tentatively adds or removes reactions based on phase stability indices (e.g., saturation indices for minerals), since the extended mass-action equations are valid even when their corresponding reactions involve unstable species. We successfully apply the proposed method to a reactive transport modeling problem in which we use PHREEQC and GEMS as alternative backends for the calculation of thermodynamic properties such as equilibrium constants of reactions, standard chemical potentials of species, and activity coefficients. Our tests show that our algorithm is efficient and robust for demanding applications, such as reactive transport modeling, where it converges within 1-3 iterations in most cases. The proposed xLMA method is implemented in Reaktoro, a unified open-source framework for modeling chemically reactive systems.

Analysis of the Energy Performance of the Chesapeake Bay Foundation's Philip Merrill Environmental Center

DOE Office of Scientific and Technical Information (OSTI.GOV)

Griffith, B.; Deru M.; Torcellini, P.

2005-04-01

The Chesapeake Bay Foundation designed their new headquarters building to minimize its environmental impact on the already highly polluted Chesapeake Bay by incorporating numerous high-performance energy saving features into the building design. CBF then contacted NREL to perform a nonbiased energy evaluation of the building. Because their building attracted much attention in the sustainable design community, an unbiased evaluation was necessary to help designers replicate successes and identify and correct problem areas. This report focuses on NREL's monitoring and analysis of the overall energy performance of the building.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Suryanarayana, Phanish, E-mail: phanish.suryanarayana@ce.gatech.edu; Phanish, Deepa

We present an Augmented Lagrangian formulation and its real-space implementation for non-periodic Orbital-Free Density Functional Theory (OF-DFT) calculations. In particular, we rewrite the constrained minimization problem of OF-DFT as a sequence of minimization problems without any constraint, thereby making it amenable to powerful unconstrained optimization algorithms. Further, we develop a parallel implementation of this approach for the Thomas–Fermi–von Weizsacker (TFW) kinetic energy functional in the framework of higher-order finite-differences and the conjugate gradient method. With this implementation, we establish that the Augmented Lagrangian approach is highly competitive compared to the penalty and Lagrange multiplier methods. Additionally, we show that higher-ordermore » finite-differences represent a computationally efficient discretization for performing OF-DFT simulations. Overall, we demonstrate that the proposed formulation and implementation are both efficient and robust by studying selected examples, including systems consisting of thousands of atoms. We validate the accuracy of the computed energies and forces by comparing them with those obtained by existing plane-wave methods.« less

Approximation of a Brittle Fracture Energy with a Constraint of Non-interpenetration

NASA Astrophysics Data System (ADS)

Chambolle, Antonin; Conti, Sergio; Francfort, Gilles A.

2018-06-01

Linear fracture mechanics (or at least the initiation part of that theory) can be framed in a variational context as a minimization problem over an SBD type space. The corresponding functional can in turn be approximated in the sense of {Γ}-convergence by a sequence of functionals involving a phase field as well as the displacement field. We show that a similar approximation persists if additionally imposing a non-interpenetration constraint in the minimization, namely that only nonnegative normal jumps should be permissible.

When the lowest energy does not induce native structures: parallel minimization of multi-energy values by hybridizing searching intelligences.

PubMed

Lü, Qiang; Xia, Xiao-Yan; Chen, Rong; Miao, Da-Jun; Chen, Sha-Sha; Quan, Li-Jun; Li, Hai-Ou

2012-01-01

Protein structure prediction (PSP), which is usually modeled as a computational optimization problem, remains one of the biggest challenges in computational biology. PSP encounters two difficult obstacles: the inaccurate energy function problem and the searching problem. Even if the lowest energy has been luckily found by the searching procedure, the correct protein structures are not guaranteed to obtain. A general parallel metaheuristic approach is presented to tackle the above two problems. Multi-energy functions are employed to simultaneously guide the parallel searching threads. Searching trajectories are in fact controlled by the parameters of heuristic algorithms. The parallel approach allows the parameters to be perturbed during the searching threads are running in parallel, while each thread is searching the lowest energy value determined by an individual energy function. By hybridizing the intelligences of parallel ant colonies and Monte Carlo Metropolis search, this paper demonstrates an implementation of our parallel approach for PSP. 16 classical instances were tested to show that the parallel approach is competitive for solving PSP problem. This parallel approach combines various sources of both searching intelligences and energy functions, and thus predicts protein conformations with good quality jointly determined by all the parallel searching threads and energy functions. It provides a framework to combine different searching intelligence embedded in heuristic algorithms. It also constructs a container to hybridize different not-so-accurate objective functions which are usually derived from the domain expertise.

When the Lowest Energy Does Not Induce Native Structures: Parallel Minimization of Multi-Energy Values by Hybridizing Searching Intelligences

PubMed Central

Lü, Qiang; Xia, Xiao-Yan; Chen, Rong; Miao, Da-Jun; Chen, Sha-Sha; Quan, Li-Jun; Li, Hai-Ou

2012-01-01

Background Protein structure prediction (PSP), which is usually modeled as a computational optimization problem, remains one of the biggest challenges in computational biology. PSP encounters two difficult obstacles: the inaccurate energy function problem and the searching problem. Even if the lowest energy has been luckily found by the searching procedure, the correct protein structures are not guaranteed to obtain. Results A general parallel metaheuristic approach is presented to tackle the above two problems. Multi-energy functions are employed to simultaneously guide the parallel searching threads. Searching trajectories are in fact controlled by the parameters of heuristic algorithms. The parallel approach allows the parameters to be perturbed during the searching threads are running in parallel, while each thread is searching the lowest energy value determined by an individual energy function. By hybridizing the intelligences of parallel ant colonies and Monte Carlo Metropolis search, this paper demonstrates an implementation of our parallel approach for PSP. 16 classical instances were tested to show that the parallel approach is competitive for solving PSP problem. Conclusions This parallel approach combines various sources of both searching intelligences and energy functions, and thus predicts protein conformations with good quality jointly determined by all the parallel searching threads and energy functions. It provides a framework to combine different searching intelligence embedded in heuristic algorithms. It also constructs a container to hybridize different not-so-accurate objective functions which are usually derived from the domain expertise. PMID:23028708

Application of GA, PSO, and ACO algorithms to path planning of autonomous underwater vehicles

NASA Astrophysics Data System (ADS)

Aghababa, Mohammad Pourmahmood; Amrollahi, Mohammad Hossein; Borjkhani, Mehdi

2012-09-01

In this paper, an underwater vehicle was modeled with six dimensional nonlinear equations of motion, controlled by DC motors in all degrees of freedom. Near-optimal trajectories in an energetic environment for underwater vehicles were computed using a numerical solution of a nonlinear optimal control problem (NOCP). An energy performance index as a cost function, which should be minimized, was defined. The resulting problem was a two-point boundary value problem (TPBVP). A genetic algorithm (GA), particle swarm optimization (PSO), and ant colony optimization (ACO) algorithms were applied to solve the resulting TPBVP. Applying an Euler-Lagrange equation to the NOCP, a conjugate gradient penalty method was also adopted to solve the TPBVP. The problem of energetic environments, involving some energy sources, was discussed. Some near-optimal paths were found using a GA, PSO, and ACO algorithms. Finally, the problem of collision avoidance in an energetic environment was also taken into account.

An energy-optimal solution for transportation control of cranes with double pendulum dynamics: Design and experiments

NASA Astrophysics Data System (ADS)

Sun, Ning; Wu, Yiming; Chen, He; Fang, Yongchun

2018-03-01

Underactuated cranes play an important role in modern industry. Specifically, in most situations of practical applications, crane systems exhibit significant double pendulum characteristics, which makes the control problem quite challenging. Moreover, most existing planners/controllers obtained with standard methods/techniques for double pendulum cranes cannot minimize the energy consumption when fulfilling the transportation tasks. Therefore, from a practical perspective, this paper proposes an energy-optimal solution for transportation control of double pendulum cranes. By applying the presented approach, the transportation objective, including fast trolley positioning and swing elimination, is achieved with minimized energy consumption, and the residual oscillations are suppressed effectively with all the state constrains being satisfied during the entire transportation process. As far as we know, this is the first energy-optimal solution for transportation control of underactuated double pendulum cranes with various state and control constraints. Hardware experimental results are included to verify the effectiveness of the proposed approach, whose superior performance is reflected by being experimentally compared with some comparative controllers.

Dimension Reduction for the Landau-de Gennes Model in Planar Nematic Thin Films

NASA Astrophysics Data System (ADS)

Golovaty, Dmitry; Montero, José Alberto; Sternberg, Peter

2015-12-01

We use the method of Γ -convergence to study the behavior of the Landau-de Gennes model for a nematic liquid crystalline film in the limit of vanishing thickness. In this asymptotic regime, surface energy plays a greater role, and we take particular care in understanding its influence on the structure of the minimizers of the derived two-dimensional energy. We assume general weak anchoring conditions on the top and the bottom surfaces of the film and the strong Dirichlet boundary conditions on the lateral boundary of the film. The constants in the weak anchoring conditions are chosen so as to enforce that a surface-energy-minimizing nematic Q-tensor has the normal to the film as one of its eigenvectors. We establish a general convergence result and then discuss the limiting problem in several parameter regimes.

FEM Modeling of a Magnetoelectric Transducer for Autonomous Micro Sensors in Medical Application

NASA Astrophysics Data System (ADS)

Yang, Gang; Talleb, Hakeim; Gensbittel, Aurélie; Ren, Zhuoxiang

2015-11-01

In the context of wireless and autonomous sensors, this paper presents the multiphysics modeling of an energy transducer based on magnetoelectric (ME) composite for biomedical applications. The study considers the power requirement of an implanted sensor, the communication distance, the size limit of the device for minimal invasive insertion as well as the electromagnetic exposure restriction of the human body. To minimize the electromagnetic absorption by the human body, the energy source is provided by an external reader emitting low frequency magnetic field. The modeling is carried out with the finite element method by solving simultaneously the multiple physics problems including the electric load of the conditioning circuit. The simulation results show that with the T-L mode of a trilayer laminated ME composite, the transducer can deliver the required energy in respecting different constraints.

Optimal aeroassisted coplanar orbital transfer using an energy model

NASA Technical Reports Server (NTRS)

Halyo, Nesim; Taylor, Deborah B.

1989-01-01

The atmospheric portion of the trajectories for the aeroassisted coplanar orbit transfer was investigated. The equations of motion for the problem are expressed using reduced order model and total vehicle energy, kinetic plus potential, as the independent variable rather than time. The order reduction is achieved analytically without an approximation of the vehicle dynamics. In this model, the problem of coplanar orbit transfer is seen as one in which a given amount of energy must be transferred from the vehicle to the atmosphere during the trajectory without overheating the vehicle. An optimal control problem is posed where a linear combination of the integrated square of the heating rate and the vehicle drag is the cost function to be minimized. The necessary conditions for optimality are obtained. These result in a 4th order two-point-boundary-value problem. A parametric study of the optimal guidance trajectory in which the proportion of the heating rate term versus the drag varies is made. Simulations of the guidance trajectories are presented.

The anatomy of choice: active inference and agency.

PubMed

Friston, Karl; Schwartenbeck, Philipp; Fitzgerald, Thomas; Moutoussis, Michael; Behrens, Timothy; Dolan, Raymond J

2013-01-01

This paper considers agency in the setting of embodied or active inference. In brief, we associate a sense of agency with prior beliefs about action and ask what sorts of beliefs underlie optimal behavior. In particular, we consider prior beliefs that action minimizes the Kullback-Leibler (KL) divergence between desired states and attainable states in the future. This allows one to formulate bounded rationality as approximate Bayesian inference that optimizes a free energy bound on model evidence. We show that constructs like expected utility, exploration bonuses, softmax choice rules and optimism bias emerge as natural consequences of this formulation. Previous accounts of active inference have focused on predictive coding and Bayesian filtering schemes for minimizing free energy. Here, we consider variational Bayes as an alternative scheme that provides formal constraints on the computational anatomy of inference and action-constraints that are remarkably consistent with neuroanatomy. Furthermore, this scheme contextualizes optimal decision theory and economic (utilitarian) formulations as pure inference problems. For example, expected utility theory emerges as a special case of free energy minimization, where the sensitivity or inverse temperature (of softmax functions and quantal response equilibria) has a unique and Bayes-optimal solution-that minimizes free energy. This sensitivity corresponds to the precision of beliefs about behavior, such that attainable goals are afforded a higher precision or confidence. In turn, this means that optimal behavior entails a representation of confidence about outcomes that are under an agent's control.

Discretized energy minimization in a wave guide with point sources

NASA Technical Reports Server (NTRS)

Propst, G.

1994-01-01

An anti-noise problem on a finite time interval is solved by minimization of a quadratic functional on the Hilbert space of square integrable controls. To this end, the one-dimensional wave equation with point sources and pointwise reflecting boundary conditions is decomposed into a system for the two propagating components of waves. Wellposedness of this system is proved for a class of data that includes piecewise linear initial conditions and piecewise constant forcing functions. It is shown that for such data the optimal piecewise constant control is the solution of a sparse linear system. Methods for its computational treatment are presented as well as examples of their applicability. The convergence of discrete approximations to the general optimization problem is demonstrated by finite element methods.

Switching neuronal state: optimal stimuli revealed using a stochastically-seeded gradient algorithm.

PubMed

Chang, Joshua; Paydarfar, David

2014-12-01

Inducing a switch in neuronal state using energy optimal stimuli is relevant to a variety of problems in neuroscience. Analytical techniques from optimal control theory can identify such stimuli; however, solutions to the optimization problem using indirect variational approaches can be elusive in models that describe neuronal behavior. Here we develop and apply a direct gradient-based optimization algorithm to find stimulus waveforms that elicit a change in neuronal state while minimizing energy usage. We analyze standard models of neuronal behavior, the Hodgkin-Huxley and FitzHugh-Nagumo models, to show that the gradient-based algorithm: (1) enables automated exploration of a wide solution space, using stochastically generated initial waveforms that converge to multiple locally optimal solutions; and (2) finds optimal stimulus waveforms that achieve a physiological outcome condition, without a priori knowledge of the optimal terminal condition of all state variables. Analysis of biological systems using stochastically-seeded gradient methods can reveal salient dynamical mechanisms underlying the optimal control of system behavior. The gradient algorithm may also have practical applications in future work, for example, finding energy optimal waveforms for therapeutic neural stimulation that minimizes power usage and diminishes off-target effects and damage to neighboring tissue.

Power allocation strategies to minimize energy consumption in wireless body area networks.

PubMed

Kailas, Aravind

2011-01-01

The wide scale deployment of wireless body area networks (WBANs) hinges on designing energy efficient communication protocols to support the reliable communication as well as to prolong the network lifetime. Cooperative communications, a relatively new idea in wireless communications, offers the benefits of multi-antenna systems, thereby improving the link reliability and boosting energy efficiency. In this short paper, the advantages of resorting to cooperative communications for WBANs in terms of minimized energy consumption are investigated. Adopting an energy model that encompasses energy consumptions in the transmitter and receiver circuits, and transmitting energy per bit, it is seen that cooperative transmission can improve energy efficiency of the wireless network. In particular, the problem of optimal power allocation is studied with the constraint of targeted outage probability. Two strategies of power allocation are considered: power allocation with and without posture state information. Using analysis and simulation-based results, two key points are demonstrated: (i) allocating power to the on-body sensors making use of the posture information can reduce the total energy consumption of the WBAN; and (ii) when the channel condition is good, it is better to recruit less relays for cooperation to enhance energy efficiency.

Evolution, Energy Landscapes and the Paradoxes of Protein Folding

PubMed Central

Wolynes, Peter G.

2014-01-01

Protein folding has been viewed as a difficult problem of molecular self-organization. The search problem involved in folding however has been simplified through the evolution of folding energy landscapes that are funneled. The funnel hypothesis can be quantified using energy landscape theory based on the minimal frustration principle. Strong quantitative predictions that follow from energy landscape theory have been widely confirmed both through laboratory folding experiments and from detailed simulations. Energy landscape ideas also have allowed successful protein structure prediction algorithms to be developed. The selection constraint of having funneled folding landscapes has left its imprint on the sequences of existing protein structural families. Quantitative analysis of co-evolution patterns allows us to infer the statistical characteristics of the folding landscape. These turn out to be consistent with what has been obtained from laboratory physicochemical folding experiments signalling a beautiful confluence of genomics and chemical physics. PMID:25530262

Numerical solution of the nonlinear Schrodinger equation by feedforward neural networks

NASA Astrophysics Data System (ADS)

Shirvany, Yazdan; Hayati, Mohsen; Moradian, Rostam

2008-12-01

We present a method to solve boundary value problems using artificial neural networks (ANN). A trial solution of the differential equation is written as a feed-forward neural network containing adjustable parameters (the weights and biases). From the differential equation and its boundary conditions we prepare the energy function which is used in the back-propagation method with momentum term to update the network parameters. We improved energy function of ANN which is derived from Schrodinger equation and the boundary conditions. With this improvement of energy function we can use unsupervised training method in the ANN for solving the equation. Unsupervised training aims to minimize a non-negative energy function. We used the ANN method to solve Schrodinger equation for few quantum systems. Eigenfunctions and energy eigenvalues are calculated. Our numerical results are in agreement with their corresponding analytical solution and show the efficiency of ANN method for solving eigenvalue problems.

Minimization of the energy loss of nuclear power plants in case of partial in-core monitoring system failure

NASA Astrophysics Data System (ADS)

Zagrebaev, A. M.; Ramazanov, R. N.; Lunegova, E. A.

2017-01-01

In this paper we consider the optimization problem minimize of the energy loss of nuclear power plants in case of partial in-core monitoring system failure. It is possible to continuation of reactor operation at reduced power or total replacement of the channel neutron measurements, requiring shutdown of the reactor and the stock of detectors. This article examines the reconstruction of the energy release in the core of a nuclear reactor on the basis of the indications of height sensors. The missing measurement information can be reconstructed by mathematical methods, and replacement of the failed sensors can be avoided. It is suggested that a set of ‘natural’ functions determined by means of statistical estimates obtained from archival data be constructed. The procedure proposed makes it possible to reconstruct the field even with a significant loss of measurement information. Improving the accuracy of the restoration of the neutron flux density in partial loss of measurement information to minimize the stock of necessary components and the associated losses.

Inference with minimal Gibbs free energy in information field theory.

PubMed

Ensslin, Torsten A; Weig, Cornelius

2010-11-01

Non-linear and non-gaussian signal inference problems are difficult to tackle. Renormalization techniques permit us to construct good estimators for the posterior signal mean within information field theory (IFT), but the approximations and assumptions made are not very obvious. Here we introduce the simple concept of minimal Gibbs free energy to IFT, and show that previous renormalization results emerge naturally. They can be understood as being the gaussian approximation to the full posterior probability, which has maximal cross information with it. We derive optimized estimators for three applications, to illustrate the usage of the framework: (i) reconstruction of a log-normal signal from poissonian data with background counts and point spread function, as it is needed for gamma ray astronomy and for cosmography using photometric galaxy redshifts, (ii) inference of a gaussian signal with unknown spectrum, and (iii) inference of a poissonian log-normal signal with unknown spectrum, the combination of (i) and (ii). Finally we explain how gaussian knowledge states constructed by the minimal Gibbs free energy principle at different temperatures can be combined into a more accurate surrogate of the non-gaussian posterior.

Scheduling Non-Preemptible Jobs to Minimize Peak Demand

DOE PAGES

Yaw, Sean; Mumey, Brendan

2017-10-28

Our paper examines an important problem in smart grid energy scheduling; peaks in power demand are proportionally more expensive to generate and provision for. The issue is exacerbated in local microgrids that do not benefit from the aggregate smoothing experienced by large grids. Demand-side scheduling can reduce these peaks by taking advantage of the fact that there is often flexibility in job start times. We then focus attention on the case where the jobs are non-preemptible, meaning once started, they run to completion. The associated optimization problem is called the peak demand minimization problem, and has been previously shown tomore » be NP-hard. These results include an optimal fixed-parameter tractable algorithm, a polynomial-time approximation algorithm, as well as an effective heuristic that can also be used in an online setting of the problem. Simulation results show that these methods can reduce peak demand by up to 50% versus on-demand scheduling for household power jobs.« less

Scheduling Non-Preemptible Jobs to Minimize Peak Demand

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yaw, Sean; Mumey, Brendan

Our paper examines an important problem in smart grid energy scheduling; peaks in power demand are proportionally more expensive to generate and provision for. The issue is exacerbated in local microgrids that do not benefit from the aggregate smoothing experienced by large grids. Demand-side scheduling can reduce these peaks by taking advantage of the fact that there is often flexibility in job start times. We then focus attention on the case where the jobs are non-preemptible, meaning once started, they run to completion. The associated optimization problem is called the peak demand minimization problem, and has been previously shown tomore » be NP-hard. These results include an optimal fixed-parameter tractable algorithm, a polynomial-time approximation algorithm, as well as an effective heuristic that can also be used in an online setting of the problem. Simulation results show that these methods can reduce peak demand by up to 50% versus on-demand scheduling for household power jobs.« less

Neural networks for vertical microcode compaction

NASA Astrophysics Data System (ADS)

Chu, Pong P.

1992-09-01

Neural networks provide an alternative way to solve complex optimization problems. Instead of performing a program of instructions sequentially as in a traditional computer, neural network model explores many competing hypotheses simultaneously using its massively parallel net. The paper shows how to use the neural network approach to perform vertical micro-code compaction for a micro-programmed control unit. The compaction procedure includes two basic steps. The first step determines the compatibility classes and the second step selects a minimal subset to cover the control signals. Since the selection process is an NP- complete problem, to find an optimal solution is impractical. In this study, we employ a customized neural network to obtain the minimal subset. We first formalize this problem, and then define an `energy function' and map it to a two-layer fully connected neural network. The modified network has two types of neurons and can always obtain a valid solution.

Minimal entropy reconstructions of thermal images for emissivity correction

NASA Astrophysics Data System (ADS)

Allred, Lloyd G.

1999-03-01

Low emissivity with corresponding low thermal emission is a problem which has long afflicted infrared thermography. The problem is aggravated by reflected thermal energy which increases as the emissivity decreases, thus reducing the net signal-to-noise ratio, which degrades the resulting temperature reconstructions. Additional errors are introduced from the traditional emissivity-correction approaches, wherein one attempts to correct for emissivity either using thermocouples or using one or more baseline images, collected at known temperatures. These corrections are numerically equivalent to image differencing. Errors in the baseline images are therefore additive, causing the resulting measurement error to either double or triple. The practical application of thermal imagery usually entails coating the objective surface to increase the emissivity to a uniform and repeatable value. While the author recommends that the thermographer still adhere to this practice, he has devised a minimal entropy reconstructions which not only correct for emissivity variations, but also corrects for variations in sensor response, using the baseline images at known temperatures to correct for these values. The minimal energy reconstruction is actually based on a modified Hopfield neural network which finds the resulting image which best explains the observed data and baseline data, having minimal entropy change between adjacent pixels. The autocorrelation of temperatures between adjacent pixels is a feature of most close-up thermal images. A surprising result from transient heating data indicates that the resulting corrected thermal images have less measurement error and are closer to the situational truth than the original data.

Gauss Seidel-type methods for energy states of a multi-component Bose Einstein condensate

NASA Astrophysics Data System (ADS)

Chang, Shu-Ming; Lin, Wen-Wei; Shieh, Shih-Feng

2005-01-01

In this paper, we propose two iterative methods, a Jacobi-type iteration (JI) and a Gauss-Seidel-type iteration (GSI), for the computation of energy states of the time-independent vector Gross-Pitaevskii equation (VGPE) which describes a multi-component Bose-Einstein condensate (BEC). A discretization of the VGPE leads to a nonlinear algebraic eigenvalue problem (NAEP). We prove that the GSI method converges locally and linearly to a solution of the NAEP if and only if the associated minimized energy functional problem has a strictly local minimum. The GSI method can thus be used to compute ground states and positive bound states, as well as the corresponding energies of a multi-component BEC. Numerical experience shows that the GSI converges much faster than JI and converges globally within 10-20 steps.

Single and multiple objective biomass-to-biofuel supply chain optimization considering environmental impacts

NASA Astrophysics Data System (ADS)

Valles Sosa, Claudia Evangelina

Bioenergy has become an important alternative source of energy to alleviate the reliance on petroleum energy. Bioenergy offers diminishing climate change by reducing Green House Gas Emissions, as well as providing energy security and enhancing rural development. The Energy Independence and Security Act mandate the use of 21 billion gallons of advanced biofuels including 16 billion gallons of cellulosic biofuels by the year 2022. It is clear that Biomass can make a substantial contribution to supply future energy demand in a sustainable way. However, the supply of sustainable energy is one of the main challenges that mankind will face over the coming decades. For instance, many logistical challenges will be faced in order to provide an efficient and reliable supply of quality feedstock to biorefineries. 700 million tons of biomass will be required to be sustainably delivered to biorefineries annually to meet the projected use of biofuels by the year of 2022. Approaching this complex logistic problem as a multi-commodity network flow structure, the present work proposes the use of a genetic algorithm as a single objective optimization problem that considers the maximization of profit and the present work also proposes the use of a Multiple Objective Evolutionary Algorithm to simultaneously maximize profit while minimizing global warming potential. Most transportation optimization problems available in the literature have mostly considered the maximization of profit or the minimization of total travel time as potential objectives to be optimized. However, on this research work, we take a more conscious and sustainable approach for this logistic problem. Planners are increasingly expected to adopt a multi-disciplinary approach, especially due to the rising importance of environmental stewardship. The role of a transportation planner and designer is shifting from simple economic analysis to promoting sustainability through the integration of environmental objectives. To respond to these new challenges, the Modified Multiple Objective Evolutionary Algorithm for the design optimization of a biomass to bio-refinery logistic system that considers the simultaneous maximization of the total profit and the minimization of three environmental impacts is presented. Sustainability balances economic, social and environmental goals and objectives. There exist several works in the literature that have considered economic and environmental objectives for the presented supply chain problem. However, there is a lack of research performed in the social aspect of a sustainable logistics system. This work proposes a methodology to integrate social aspect assessment, based on employment creation. Finally, most of the assessment methodologies considered in the literature only contemplate deterministic values, when in realistic situations uncertainties in the supply chain are present. In this work, Value-at-Risk, an advanced risk measure commonly used in portfolio optimization is included to consider the uncertainties in biofuel prices, among the others.

Integrated Building Energy Systems Design Considering Storage Technologies

DOE Office of Scientific and Technical Information (OSTI.GOV)

Stadler, Michael; Marnay, Chris; Siddiqui, Afzal

The addition of storage technologies such as flow batteries, conventional batteries, and heat storage can improve the economic, as well as environmental attraction of micro-generation systems (e.g., PV or fuel cells with or without CHP) and contribute to enhanced demand response. The interactions among PV, solar thermal, and storage systems can be complex, depending on the tariff structure, load profile, etc. In order to examine the impact of storage technologies on demand response and CO2 emissions, a microgrid's distributed energy resources (DER) adoption problem is formulated as a mixed-integer linear program that can pursue two strategies as its objective function.more » These two strategies are minimization of its annual energy costs or of its CO2 emissions. The problem is solved for a given test year at representative customer sites, e.g., nursing homes, to obtain not only the optimal investment portfolio, but also the optimal hourly operating schedules for the selected technologies. This paper focuses on analysis of storage technologies in micro-generation optimization on a building level, with example applications in New York State and California. It shows results from a two-year research projectperformed for the U.S. Department of Energy and ongoing work. Contrary to established expectations, our results indicate that PV and electric storage adoption compete rather than supplement each other considering the tariff structure and costs of electricity supply. The work shows that high electricity tariffs during on-peak hours are a significant driver for the adoption of electric storage technologies. To satisfy the site's objective of minimizing energy costs, the batteries have to be charged by grid power during off-peak hours instead of PV during on-peak hours. In contrast, we also show a CO2 minimization strategy where the common assumption that batteries can be charged by PV can be fulfilled at extraordinarily high energy costs for the site.« less

Reliable Adaptive Data Aggregation Route Strategy for a Trade-off between Energy and Lifetime in WSNs

PubMed Central

Guo, Wenzhong; Hong, Wei; Zhang, Bin; Chen, Yuzhong; Xiong, Naixue

2014-01-01

Mobile security is one of the most fundamental problems in Wireless Sensor Networks (WSNs). The data transmission path will be compromised for some disabled nodes. To construct a secure and reliable network, designing an adaptive route strategy which optimizes energy consumption and network lifetime of the aggregation cost is of great importance. In this paper, we address the reliable data aggregation route problem for WSNs. Firstly, to ensure nodes work properly, we propose a data aggregation route algorithm which improves the energy efficiency in the WSN. The construction process achieved through discrete particle swarm optimization (DPSO) saves node energy costs. Then, to balance the network load and establish a reliable network, an adaptive route algorithm with the minimal energy and the maximum lifetime is proposed. Since it is a non-linear constrained multi-objective optimization problem, in this paper we propose a DPSO with the multi-objective fitness function combined with the phenotype sharing function and penalty function to find available routes. Experimental results show that compared with other tree routing algorithms our algorithm can effectively reduce energy consumption and trade off energy consumption and network lifetime. PMID:25215944

Computer program determines chemical composition of physical system at equilibrium

NASA Technical Reports Server (NTRS)

Kwong, S. S.

1966-01-01

FORTRAN 4 digital computer program calculates equilibrium composition of complex, multiphase chemical systems. This is a free energy minimization method with solution of the problem reduced to mathematical operations, without concern for the chemistry involved. Also certain thermodynamic properties are determined as byproducts of the main calculations.

Energy latency tradeoffs for medium access and sleep scheduling in wireless sensor networks

NASA Astrophysics Data System (ADS)

Gang, Lu

Wireless sensor networks are expected to be used in a wide range of applications from environment monitoring to event detection. The key challenge is to provide energy efficient communication; however, latency remains an important concern for many applications that require fast response. The central thesis of this work is that energy efficient medium access and sleep scheduling mechanisms can be designed without necessarily sacrificing application-specific latency performance. We validate this thesis through results from four case studies that cover various aspects of medium access and sleep scheduling design in wireless sensor networks. Our first effort, DMAC, is to design an adaptive low latency and energy efficient MAC for data gathering to reduce the sleep latency. We propose staggered schedule, duty cycle adaptation, data prediction and the use of more-to-send packets to enable seamless packet forwarding under varying traffic load and channel contentions. Simulation and experimental results show significant energy savings and latency reduction while ensuring high data reliability. The second research effort, DESS, investigates the problem of designing sleep schedules in arbitrary network communication topologies to minimize the worst case end-to-end latency (referred to as delay diameter). We develop a novel graph-theoretical formulation, derive and analyze optimal solutions for the tree and ring topologies and heuristics for arbitrary topologies. The third study addresses the problem of minimum latency joint scheduling and routing (MLSR). By constructing a novel delay graph, the optimal joint scheduling and routing can be solved by M node-disjoint paths algorithm under multiple channel model. We further extended the algorithm to handle dynamic traffic changes and topology changes. A heuristic solution is proposed for MLSR under single channel interference. In the fourth study, EEJSPC, we first formulate a fundamental optimization problem that provides tunable energy-latency-throughput tradeoffs with joint scheduling and power control and present both exponential and polynomial complexity solutions. Then we investigate the problem of minimizing total transmission energy while satisfying transmission requests within a latency bound, and present an iterative approach which converges rapidly to the optimal parameter settings.

Influence maximization in complex networks through optimal percolation

NASA Astrophysics Data System (ADS)

Morone, Flaviano; Makse, Hernán A.

2015-08-01

The whole frame of interconnections in complex networks hinges on a specific set of structural nodes, much smaller than the total size, which, if activated, would cause the spread of information to the whole network, or, if immunized, would prevent the diffusion of a large scale epidemic. Localizing this optimal, that is, minimal, set of structural nodes, called influencers, is one of the most important problems in network science. Despite the vast use of heuristic strategies to identify influential spreaders, the problem remains unsolved. Here we map the problem onto optimal percolation in random networks to identify the minimal set of influencers, which arises by minimizing the energy of a many-body system, where the form of the interactions is fixed by the non-backtracking matrix of the network. Big data analyses reveal that the set of optimal influencers is much smaller than the one predicted by previous heuristic centralities. Remarkably, a large number of previously neglected weakly connected nodes emerges among the optimal influencers. These are topologically tagged as low-degree nodes surrounded by hierarchical coronas of hubs, and are uncovered only through the optimal collective interplay of all the influencers in the network. The present theoretical framework may hold a larger degree of universality, being applicable to other hard optimization problems exhibiting a continuous transition from a known phase.

Influence maximization in complex networks through optimal percolation.

PubMed

Morone, Flaviano; Makse, Hernán A

2015-08-06

The whole frame of interconnections in complex networks hinges on a specific set of structural nodes, much smaller than the total size, which, if activated, would cause the spread of information to the whole network, or, if immunized, would prevent the diffusion of a large scale epidemic. Localizing this optimal, that is, minimal, set of structural nodes, called influencers, is one of the most important problems in network science. Despite the vast use of heuristic strategies to identify influential spreaders, the problem remains unsolved. Here we map the problem onto optimal percolation in random networks to identify the minimal set of influencers, which arises by minimizing the energy of a many-body system, where the form of the interactions is fixed by the non-backtracking matrix of the network. Big data analyses reveal that the set of optimal influencers is much smaller than the one predicted by previous heuristic centralities. Remarkably, a large number of previously neglected weakly connected nodes emerges among the optimal influencers. These are topologically tagged as low-degree nodes surrounded by hierarchical coronas of hubs, and are uncovered only through the optimal collective interplay of all the influencers in the network. The present theoretical framework may hold a larger degree of universality, being applicable to other hard optimization problems exhibiting a continuous transition from a known phase.

Unconditionally energy stable numerical schemes for phase-field vesicle membrane model

NASA Astrophysics Data System (ADS)

Guillén-González, F.; Tierra, G.

2018-02-01

Numerical schemes to simulate the deformation of vesicles membranes via minimizing the bending energy have been widely studied in recent times due to its connection with many biological motivated problems. In this work we propose a new unconditionally energy stable numerical scheme for a vesicle membrane model that satisfies exactly the conservation of volume constraint and penalizes the surface area constraint. Moreover, we extend these ideas to present an unconditionally energy stable splitting scheme decoupling the interaction of the vesicle with a surrounding fluid. Finally, the well behavior of the proposed schemes are illustrated through several computational experiments.

Influence maximization in complex networks through optimal percolation

NASA Astrophysics Data System (ADS)

Morone, Flaviano; Makse, Hernan; CUNY Collaboration; CUNY Collaboration

The whole frame of interconnections in complex networks hinges on a specific set of structural nodes, much smaller than the total size, which, if activated, would cause the spread of information to the whole network, or, if immunized, would prevent the diffusion of a large scale epidemic. Localizing this optimal, that is, minimal, set of structural nodes, called influencers, is one of the most important problems in network science. Here we map the problem onto optimal percolation in random networks to identify the minimal set of influencers, which arises by minimizing the energy of a many-body system, where the form of the interactions is fixed by the non-backtracking matrix of the network. Big data analyses reveal that the set of optimal influencers is much smaller than the one predicted by previous heuristic centralities. Remarkably, a large number of previously neglected weakly connected nodes emerges among the optimal influencers. Reference: F. Morone, H. A. Makse, Nature 524,65-68 (2015)

Minimization of energy and surface roughness of the products machined by milling

NASA Astrophysics Data System (ADS)

Belloufi, A.; Abdelkrim, M.; Bouakba, M.; Rezgui, I.

2017-08-01

Metal cutting represents a large portion in the manufacturing industries, which makes this process the largest consumer of energy. Energy consumption is an indirect source of carbon footprint, we know that CO2 emissions come from the production of energy. Therefore high energy consumption requires a large production, which leads to high cost and a large amount of CO2 emissions. At this day, a lot of researches done on the Metal cutting, but the environmental problems of the processes are rarely discussed. The right selection of cutting parameters is an effective method to reduce energy consumption because of the direct relationship between energy consumption and cutting parameters in machining processes. Therefore, one of the objectives of this research is to propose an optimization strategy suitable for machining processes (milling) to achieve the optimum cutting conditions based on the criterion of the energy consumed during the milling. In this paper the problem of energy consumed in milling is solved by an optimization method chosen. The optimization is done according to the different requirements in the process of roughing and finishing under various technological constraints.

The anatomy of choice: active inference and agency

PubMed Central

Friston, Karl; Schwartenbeck, Philipp; FitzGerald, Thomas; Moutoussis, Michael; Behrens, Timothy; Dolan, Raymond J.

2013-01-01

This paper considers agency in the setting of embodied or active inference. In brief, we associate a sense of agency with prior beliefs about action and ask what sorts of beliefs underlie optimal behavior. In particular, we consider prior beliefs that action minimizes the Kullback–Leibler (KL) divergence between desired states and attainable states in the future. This allows one to formulate bounded rationality as approximate Bayesian inference that optimizes a free energy bound on model evidence. We show that constructs like expected utility, exploration bonuses, softmax choice rules and optimism bias emerge as natural consequences of this formulation. Previous accounts of active inference have focused on predictive coding and Bayesian filtering schemes for minimizing free energy. Here, we consider variational Bayes as an alternative scheme that provides formal constraints on the computational anatomy of inference and action—constraints that are remarkably consistent with neuroanatomy. Furthermore, this scheme contextualizes optimal decision theory and economic (utilitarian) formulations as pure inference problems. For example, expected utility theory emerges as a special case of free energy minimization, where the sensitivity or inverse temperature (of softmax functions and quantal response equilibria) has a unique and Bayes-optimal solution—that minimizes free energy. This sensitivity corresponds to the precision of beliefs about behavior, such that attainable goals are afforded a higher precision or confidence. In turn, this means that optimal behavior entails a representation of confidence about outcomes that are under an agent's control. PMID:24093015

Variational Approach to Enhanced Sampling and Free Energy Calculations

NASA Astrophysics Data System (ADS)

Valsson, Omar; Parrinello, Michele

2014-08-01

The ability of widely used sampling methods, such as molecular dynamics or Monte Carlo simulations, to explore complex free energy landscapes is severely hampered by the presence of kinetic bottlenecks. A large number of solutions have been proposed to alleviate this problem. Many are based on the introduction of a bias potential which is a function of a small number of collective variables. However constructing such a bias is not simple. Here we introduce a functional of the bias potential and an associated variational principle. The bias that minimizes the functional relates in a simple way to the free energy surface. This variational principle can be turned into a practical, efficient, and flexible sampling method. A number of numerical examples are presented which include the determination of a three-dimensional free energy surface. We argue that, beside being numerically advantageous, our variational approach provides a convenient and novel standpoint for looking at the sampling problem.

Multi-objective generation scheduling with hybrid energy resources

NASA Astrophysics Data System (ADS)

Trivedi, Manas

In economic dispatch (ED) of electric power generation, the committed generating units are scheduled to meet the load demand at minimum operating cost with satisfying all unit and system equality and inequality constraints. Generation of electricity from the fossil fuel releases several contaminants into the atmosphere. So the economic dispatch objective can no longer be considered alone due to the environmental concerns that arise from the emissions produced by fossil fueled electric power plants. This research is proposing the concept of environmental/economic generation scheduling with traditional and renewable energy sources. Environmental/economic dispatch (EED) is a multi-objective problem with conflicting objectives since emission minimization is conflicting with fuel cost minimization. Production and consumption of fossil fuel and nuclear energy are closely related to environmental degradation. This causes negative effects to human health and the quality of life. Depletion of the fossil fuel resources will also be challenging for the presently employed energy systems to cope with future energy requirements. On the other hand, renewable energy sources such as hydro and wind are abundant, inexhaustible and widely available. These sources use native resources and have the capacity to meet the present and the future energy demands of the world with almost nil emissions of air pollutants and greenhouse gases. The costs of fossil fuel and renewable energy are also heading in opposite directions. The economic policies needed to support the widespread and sustainable markets for renewable energy sources are rapidly evolving. The contribution of this research centers on solving the economic dispatch problem of a system with hybrid energy resources under environmental restrictions. It suggests an effective solution of renewable energy to the existing fossil fueled and nuclear electric utilities for the cheaper and cleaner production of electricity with hourly emission targets. Since minimizing the emissions and fuel cost are conflicting objectives, a practical approach based on multi-objective optimization is applied to obtain compromised solutions in a single simulation run using genetic algorithm. These solutions are known as non-inferior or Pareto-optimal solutions, graphically illustrated by the trade-off curves between criterions fuel cost and pollutant emission. The efficacy of the proposed approach is illustrated with the help of different sample test cases. This research would be useful for society, electric utilities, consultants, regulatory bodies, policy makers and planners.

78 FR 8705 - Approval and Promulgation of Air Quality Implementation Plans; States of Minnesota and Michigan...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-02-06

... pollutants can lead to secondary environmental problems, as well as increased energy consumption. The Band... companies had demonstrated that burner designs that lower flame temperature can reduce NO X formation in... temperature, which is the opposite strategy needed for minimizing PIC (i.e., increasing combustion temperature...

Wetwood in trees: a timber resource problem.

Treesearch

J.C. Ward; W.Y. Pong

1980-01-01

Available information on wetwood is presented. Wetwood is a type of heartwood which has been internally infused with water. Wetwood is responsible for substantial losses of wood, energy and production expenditures in the forest products industry.The need to evaluate these losses and to find ways to eliminate or minimize them is emphasized....

An Approach to Quad Meshing Based On Cross Valued Maps and the Ginzburg-Landau Theory

DOE Office of Scientific and Technical Information (OSTI.GOV)

Viertel, Ryan; Osting, Braxton

2017-08-01

A generalization of vector fields, referred to as N-direction fields or cross fields when N=4, has been recently introduced and studied for geometry processing, with applications in quadrilateral (quad) meshing, texture mapping, and parameterization. We make the observation that cross field design for two-dimensional quad meshing is related to the well-known Ginzburg-Landau problem from mathematical physics. This identification yields a variety of theoretical tools for efficiently computing boundary-aligned quad meshes, with provable guarantees on the resulting mesh, for example, the number of mesh defects and bounds on the defect locations. The procedure for generating the quad mesh is to (i)more » find a complex-valued "representation" field that minimizes the Dirichlet energy subject to a boundary constraint, (ii) convert the representation field into a boundary-aligned, smooth cross field, (iii) use separatrices of the cross field to partition the domain into four sided regions, and (iv) mesh each of these four-sided regions using standard techniques. Under certain assumptions on the geometry of the domain, we prove that this procedure can be used to produce a cross field whose separatrices partition the domain into four sided regions. To solve the energy minimization problem for the representation field, we use an extension of the Merriman-Bence-Osher (MBO) threshold dynamics method, originally conceived as an algorithm to simulate motion by mean curvature, to minimize the Ginzburg-Landau energy for the optimal representation field. Lastly, we demonstrate the method on a variety of test domains.« less

A New Path-Constrained Rendezvous Planning Approach for Large-Scale Event-Driven Wireless Sensor Networks

PubMed Central

Zhang, Gongxuan; Wang, Yongli; Wang, Tianshu

2018-01-01

We study the problem of employing a mobile-sink into a large-scale Event-Driven Wireless Sensor Networks (EWSNs) for the purpose of data harvesting from sensor-nodes. Generally, this employment improves the main weakness of WSNs that is about energy-consumption in battery-driven sensor-nodes. The main motivation of our work is to address challenges which are related to a network’s topology by adopting a mobile-sink that moves in a predefined trajectory in the environment. Since, in this fashion, it is not possible to gather data from sensor-nodes individually, we adopt the approach of defining some of the sensor-nodes as Rendezvous Points (RPs) in the network. We argue that RP-planning in this case is a tradeoff between minimizing the number of RPs while decreasing the number of hops for a sensor-node that needs data transformation to the related RP which leads to minimizing average energy consumption in the network. We address the problem by formulating the challenges and expectations as a Mixed Integer Linear Programming (MILP). Henceforth, by proving the NP-hardness of the problem, we propose three effective and distributed heuristics for RP-planning, identifying sojourn locations, and constructing routing trees. Finally, experimental results prove the effectiveness of our approach. PMID:29734718

Wireless Power Transfer for Distributed Estimation in Sensor Networks

NASA Astrophysics Data System (ADS)

Mai, Vien V.; Shin, Won-Yong; Ishibashi, Koji

2017-04-01

This paper studies power allocation for distributed estimation of an unknown scalar random source in sensor networks with a multiple-antenna fusion center (FC), where wireless sensors are equipped with radio-frequency based energy harvesting technology. The sensors' observation is locally processed by using an uncoded amplify-and-forward scheme. The processed signals are then sent to the FC, and are coherently combined at the FC, at which the best linear unbiased estimator (BLUE) is adopted for reliable estimation. We aim to solve the following two power allocation problems: 1) minimizing distortion under various power constraints; and 2) minimizing total transmit power under distortion constraints, where the distortion is measured in terms of mean-squared error of the BLUE. Two iterative algorithms are developed to solve the non-convex problems, which converge at least to a local optimum. In particular, the above algorithms are designed to jointly optimize the amplification coefficients, energy beamforming, and receive filtering. For each problem, a suboptimal design, a single-antenna FC scenario, and a common harvester deployment for colocated sensors, are also studied. Using the powerful semidefinite relaxation framework, our result is shown to be valid for any number of sensors, each with different noise power, and for an arbitrarily number of antennas at the FC.

A New Path-Constrained Rendezvous Planning Approach for Large-Scale Event-Driven Wireless Sensor Networks.

PubMed

Vajdi, Ahmadreza; Zhang, Gongxuan; Zhou, Junlong; Wei, Tongquan; Wang, Yongli; Wang, Tianshu

2018-05-04

We study the problem of employing a mobile-sink into a large-scale Event-Driven Wireless Sensor Networks (EWSNs) for the purpose of data harvesting from sensor-nodes. Generally, this employment improves the main weakness of WSNs that is about energy-consumption in battery-driven sensor-nodes. The main motivation of our work is to address challenges which are related to a network’s topology by adopting a mobile-sink that moves in a predefined trajectory in the environment. Since, in this fashion, it is not possible to gather data from sensor-nodes individually, we adopt the approach of defining some of the sensor-nodes as Rendezvous Points (RPs) in the network. We argue that RP-planning in this case is a tradeoff between minimizing the number of RPs while decreasing the number of hops for a sensor-node that needs data transformation to the related RP which leads to minimizing average energy consumption in the network. We address the problem by formulating the challenges and expectations as a Mixed Integer Linear Programming (MILP). Henceforth, by proving the NP-hardness of the problem, we propose three effective and distributed heuristics for RP-planning, identifying sojourn locations, and constructing routing trees. Finally, experimental results prove the effectiveness of our approach.

Non-minimally coupled tachyon field in teleparallel gravity

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fazlpour, Behnaz; Banijamali, Ali, E-mail: b.fazlpour@umz.ac.ir, E-mail: a.banijamali@nit.ac.ir

2015-04-01

We perform a full investigation on dynamics of a new dark energy model in which the four-derivative of a non-canonical scalar field (tachyon) is non-minimally coupled to the vector torsion. Our analysis is done in the framework of teleparallel equivalent of general relativity which is based on torsion instead of curvature. We show that in our model there exists a late-time scaling attractor (point P{sub 4}), corresponding to an accelerating universe with the property that dark energy and dark matter densities are of the same order. Such a point can help to alleviate the cosmological coincidence problem. Existence of thismore » point is the most significant difference between our model and another model in which a canonical scalar field (quintessence) is used instead of tachyon field.« less

Some applications of the most general form of the higher-order GUP with minimal length uncertainty and maximal momentum

NASA Astrophysics Data System (ADS)

Shababi, Homa; Chung, Won Sang

2018-04-01

In this paper, using the new type of D-dimensional nonperturbative Generalized Uncertainty Principle (GUP) which has predicted both a minimal length uncertainty and a maximal observable momentum,1 first, we obtain the maximally localized states and express their connections to [P. Pedram, Phys. Lett. B 714, 317 (2012)]. Then, in the context of our proposed GUP and using the generalized Schrödinger equation, we solve some important problems including particle in a box and one-dimensional hydrogen atom. Next, implying modified Bohr-Sommerfeld quantization, we obtain energy spectra of quantum harmonic oscillator and quantum bouncer. Finally, as an example, we investigate some statistical properties of a free particle, including partition function and internal energy, in the presence of the mentioned GUP.

Alternative fuels

NASA Technical Reports Server (NTRS)

Grobman, J. S.; Butze, H. F.; Friedman, R.; Antoine, A. C.; Reynolds, T. W.

1977-01-01

Potential problems related to the use of alternative aviation turbine fuels are discussed and both ongoing and required research into these fuels is described. This discussion is limited to aviation turbine fuels composed of liquid hydrocarbons. The advantages and disadvantages of the various solutions to the problems are summarized. The first solution is to continue to develop the necessary technology at the refinery to produce specification jet fuels regardless of the crude source. The second solution is to minimize energy consumption at the refinery and keep fuel costs down by relaxing specifications.

A Dynamic Programming Approach for Base Station Sleeping in Cellular Networks

NASA Astrophysics Data System (ADS)

Gong, Jie; Zhou, Sheng; Niu, Zhisheng

The energy consumption of the information and communication technology (ICT) industry, which has become a serious problem, is mostly due to the network infrastructure rather than the mobile terminals. In this paper, we focus on reducing the energy consumption of base stations (BSs) by adjusting their working modes (active or sleep). Specifically, the objective is to minimize the energy consumption while satisfying quality of service (QoS, e.g., blocking probability) requirement and, at the same time, avoiding frequent mode switching to reduce signaling and delay overhead. The problem is modeled as a dynamic programming (DP) problem, which is NP-hard in general. Based on cooperation among neighboring BSs, a low-complexity algorithm is proposed to reduce the size of state space as well as that of action space. Simulations demonstrate that, with the proposed algorithm, the active BS pattern well meets the time variation and the non-uniform spatial distribution of system traffic. Moreover, the tradeoff between the energy saving from BS sleeping and the cost of switching is well balanced by the proposed scheme.

Linear Matrix Inequality Method for a Quadratic Performance Index Minimization Problem with a class of Bilinear Matrix Inequality Conditions

NASA Astrophysics Data System (ADS)

Tanemura, M.; Chida, Y.

2016-09-01

There are a lot of design problems of control system which are expressed as a performance index minimization under BMI conditions. However, a minimization problem expressed as LMIs can be easily solved because of the convex property of LMIs. Therefore, many researchers have been studying transforming a variety of control design problems into convex minimization problems expressed as LMIs. This paper proposes an LMI method for a quadratic performance index minimization problem with a class of BMI conditions. The minimization problem treated in this paper includes design problems of state-feedback gain for switched system and so on. The effectiveness of the proposed method is verified through a state-feedback gain design for switched systems and a numerical simulation using the designed feedback gains.

A random-key encoded harmony search approach for energy-efficient production scheduling with shared resources

NASA Astrophysics Data System (ADS)

Garcia-Santiago, C. A.; Del Ser, J.; Upton, C.; Quilligan, F.; Gil-Lopez, S.; Salcedo-Sanz, S.

2015-11-01

When seeking near-optimal solutions for complex scheduling problems, meta-heuristics demonstrate good performance with affordable computational effort. This has resulted in a gravitation towards these approaches when researching industrial use-cases such as energy-efficient production planning. However, much of the previous research makes assumptions about softer constraints that affect planning strategies and about how human planners interact with the algorithm in a live production environment. This article describes a job-shop problem that focuses on minimizing energy consumption across a production facility of shared resources. The application scenario is based on real facilities made available by the Irish Center for Manufacturing Research. The formulated problem is tackled via harmony search heuristics with random keys encoding. Simulation results are compared to a genetic algorithm, a simulated annealing approach and a first-come-first-served scheduling. The superior performance obtained by the proposed scheduler paves the way towards its practical implementation over industrial production chains.

Unitarity problems in 3D gravity theories

NASA Astrophysics Data System (ADS)

Alkac, Gokhan; Basanisi, Luca; Kilicarslan, Ercan; Tekin, Bayram

2017-07-01

We revisit the problem of the bulk-boundary unitarity clash in 2 +1 -dimensional gravity theories, which has been an obstacle in providing a viable dual two-dimensional conformal field theory for bulk gravity in anti-de Sitter (AdS) spacetime. Chiral gravity, which is a particular limit of cosmological topologically massive gravity (TMG), suffers from perturbative log-modes with negative energies inducing a nonunitary logarithmic boundary field theory. We show here that any f (R ) extension of TMG does not improve the situation. We also study the perturbative modes in the metric formulation of minimal massive gravity—originally constructed in a first-order formulation—and find that the massive mode has again negative energy except in the chiral limit. We comment on this issue and also discuss a possible solution to the problem of negative-energy modes. In any of these theories, the infinitesimal dangerous deformations might not be integrable to full solutions; this suggests a linearization instability of AdS spacetime in the direction of the perturbative log-modes.

Optimal operation management of fuel cell/wind/photovoltaic power sources connected to distribution networks

NASA Astrophysics Data System (ADS)

Niknam, Taher; Kavousifard, Abdollah; Tabatabaei, Sajad; Aghaei, Jamshid

2011-10-01

In this paper a new multiobjective modified honey bee mating optimization (MHBMO) algorithm is presented to investigate the distribution feeder reconfiguration (DFR) problem considering renewable energy sources (RESs) (photovoltaics, fuel cell and wind energy) connected to the distribution network. The objective functions of the problem to be minimized are the electrical active power losses, the voltage deviations, the total electrical energy costs and the total emissions of RESs and substations. During the optimization process, the proposed algorithm finds a set of non-dominated (Pareto) optimal solutions which are stored in an external memory called repository. Since the objective functions investigated are not the same, a fuzzy clustering algorithm is utilized to handle the size of the repository in the specified limits. Moreover, a fuzzy-based decision maker is adopted to select the 'best' compromised solution among the non-dominated optimal solutions of multiobjective optimization problem. In order to see the feasibility and effectiveness of the proposed algorithm, two standard distribution test systems are used as case studies.

Deflation of the cosmological constant associated with inflation and dark energy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Geng, Chao-Qiang; Lee, Chung-Chi, E-mail: geng@phys.nthu.edu.tw, E-mail: chungchi@mx.nthu.edu.tw

2016-06-01

In order to solve the fine-tuning problem of the cosmological constant, we propose a simple model with the vacuum energy non-minimally coupled to the inflaton field. In this model, the vacuum energy decays to the inflaton during pre-inflation and inflation eras, so that the cosmological constant effectively deflates from the Planck mass scale to a much smaller one after inflation and plays the role of dark energy in the late-time of the universe. We show that our deflationary scenario is applicable to arbitrary slow-roll inflation models. We also take two specific inflation potentials to illustrate our results.

The high energy astronomy observatories

NASA Technical Reports Server (NTRS)

Neighbors, A. K.; Doolittle, R. F.; Halpers, R. E.

1977-01-01

The forthcoming NASA project of orbiting High Energy Astronomy Observatories (HEAO's) designed to probe the universe by tracing celestial radiations and particles is outlined. Solutions to engineering problems concerning HEAO's which are integrated, yet built to function independently are discussed, including the onboard digital processor, mirror assembly and the thermal shield. The principle of maximal efficiency with minimal cost and the potential capability of the project to provide explanations to black holes, pulsars and gamma-ray bursts are also stressed. The first satellite is scheduled for launch in April 1977.

Simulation of short-term electric load using an artificial neural network

NASA Astrophysics Data System (ADS)

Ivanin, O. A.

2018-01-01

While solving the task of optimizing operation modes and equipment composition of small energy complexes or other tasks connected with energy planning, it is necessary to have data on energy loads of a consumer. Usually, there is a problem with obtaining real load charts and detailed information about the consumer, because a method of load-charts simulation on the basis of minimal information should be developed. The analysis of work devoted to short-term loads prediction allows choosing artificial neural networks as a most suitable mathematical instrument for solving this problem. The article provides an overview of applied short-term load simulation methods; it describes the advantages of artificial neural networks and offers a neural network structure for electric loads of residential buildings simulation. The results of modeling loads with proposed method and the estimation of its error are presented.

A constrained registration problem based on Ciarlet-Geymonat stored energy

NASA Astrophysics Data System (ADS)

Derfoul, Ratiba; Le Guyader, Carole

2014-03-01

In this paper, we address the issue of designing a theoretically well-motivated registration model capable of handling large deformations and including geometrical constraints, namely landmark points to be matched, in a variational framework. The theory of linear elasticity being unsuitable in this case, since assuming small strains and the validity of Hooke's law, the introduced functional is based on nonlinear elasticity principles. More precisely, the shapes to be matched are viewed as Ciarlet-Geymonat materials. We demonstrate the existence of minimizers of the related functional minimization problem and prove a convergence result when the number of geometric constraints increases. We then describe and analyze a numerical method of resolution based on the introduction of an associated decoupled problem under inequality constraint in which an auxiliary variable simulates the Jacobian matrix of the deformation field. A theoretical result of 􀀀-convergence is established. We then provide preliminary 2D results of the proposed matching model for the registration of mouse brain gene expression data to a neuroanatomical mouse atlas.

Optimum rocket propulsion for energy-limited transfer

NASA Technical Reports Server (NTRS)

Zuppero, Anthony; Landis, Geoffrey A.

1991-01-01

In order to effect large-scale return of extraterrestrial resources to Earth orbit, it is desirable to optimize the propulsion system to maximize the mass of payload returned per unit energy expended. This optimization problem is different from the conventional rocket propulsion optimization. A rocket propulsion system consists of an energy source plus reaction mass. In a conventional chemical rocket, the energy source and the reaction mass are the same. For the transportation system required, however, the best system performance is achieved if the reaction mass used is from a locally available source. In general, the energy source and the reaction mass will be separate. One such rocket system is the nuclear thermal rocket, in which the energy source is a reactor and the reaction mass a fluid which is heated by the reactor and exhausted. Another energy-limited rocket system is the hydrogen/oxygen rocket where H2/O2 fuel is produced by electrolysis of water using a solar array or a nuclear reactor. The problem is to choose the optimum specific impulse (or equivalently exhaust velocity) to minimize the amount of energy required to produce a given mission delta-v in the payload. The somewhat surprising result is that the optimum specific impulse is not the maximum possible value, but is proportional to the mission delta-v. In general terms, at the beginning of the mission it is optimum to use a very low specific impulse and expend a lot of reaction mass, since this is the most energy efficient way to transfer momentum. However, as the mission progresses, it becomes important to minimize the amount of reaction mass expelled, since energy is wasted moving the reaction mass. Thus, the optimum specific impulse will increase with the mission delta-v. Optimum I(sub sp) is derived for maximum payload return per energy expended for both the case of fixed and variable I(sub sp) engines. Sample missions analyzed include return of water payloads from the moons of Mars and of Saturn.

Gradient gravitational search: An efficient metaheuristic algorithm for global optimization.

PubMed

Dash, Tirtharaj; Sahu, Prabhat K

2015-05-30

The adaptation of novel techniques developed in the field of computational chemistry to solve the concerned problems for large and flexible molecules is taking the center stage with regard to efficient algorithm, computational cost and accuracy. In this article, the gradient-based gravitational search (GGS) algorithm, using analytical gradients for a fast minimization to the next local minimum has been reported. Its efficiency as metaheuristic approach has also been compared with Gradient Tabu Search and others like: Gravitational Search, Cuckoo Search, and Back Tracking Search algorithms for global optimization. Moreover, the GGS approach has also been applied to computational chemistry problems for finding the minimal value potential energy of two-dimensional and three-dimensional off-lattice protein models. The simulation results reveal the relative stability and physical accuracy of protein models with efficient computational cost. © 2015 Wiley Periodicals, Inc.

Lumbar load attenuation for rotorcraft occupants using a design methodology for the seat impact energy-absorbing system

NASA Astrophysics Data System (ADS)

Moradi, Rasoul; Beheshti, Hamid K.; Lankarani, Hamid M.

2012-12-01

Aircraft occupant crash-safety considerations require a minimum cushion thickness to limit the relative vertical motion of the seat-pelvis during high vertical impact loadings in crash landings or accidents. In military aircraft and helicopter seat design, due to the potential for high vertical accelerations in crash scenarios, the seat system must be provided with an energy absorber to attenuate the acceleration level sustained by the occupants. Because of the limited stroke available for the seat structure, the design of the energy absorber becomes a trade-off problem between minimizing the stroke and maximizing the energy absorption. The available stroke must be used to prevent bottoming out of the seat as well as to absorb maximum impact energy to protect the occupant. In this study, the energy-absorbing system in a rotorcraft seat design is investigated using a mathematical model of the occupant/seat system. Impact theories between interconnected bodies in multibody mechanical systems are utilized to study the impact between the seat pan and the occupant. Experimental responses of the seat system and the occupant are utilized to validate the results from this study for civil and military helicopters according to FAR 23 and 25 and MIL-S-58095 requirements. A model for the load limiter is proposed to minimize the lumbar load for the occupant by minimizing the relative velocity between the seat pan and the occupant's pelvis. The modified energy absorber/load limiter is then implemented for the seat structure so that it absorbs the energy of impact in an effective manner and below the tolerable limit for the occupant in a minimum stroke. Results show that for a designed stroke, the level of occupant lumbar spine injury would be significantly attenuated using this modified energy-absorber system.

Development of an integrated model for energy systems planning and carbon dioxide mitigation under uncertainty - Tradeoffs between two-level decision makers.

PubMed

Jin, S W; Li, Y P; Xu, L P

2018-07-01

A bi-level fuzzy programming (BFLP) method was developed for energy systems planning (ESP) and carbon dioxide (CO 2 ) mitigation under uncertainty. BFLP could handle fuzzy information and leader-follower problem in decision-making processes. It could also address the tradeoffs among different decision makers in two decision-making levels through prioritizing the most important goal. Then, a BFLP-ESP model was formulated for planning energy system of Beijing, in which the upper-level objective is to minimize CO 2 emission and the lower-level objective is to minimize the system cost. Results provided a range of decision alternatives that corresponded to a tradeoff between system optimality and reliability under uncertainty. Compared to the single-level model with a target to minimize system cost, the amounts of pollutant/CO 2 emissions from BFLP-ESP were reduced since the study system would prefer more clean energies (i.e. natural gas, LPG and electricity) to replace coal fuel. Decision alternatives from BFLP were more beneficial for supporting Beijing to adjust its energy mix and enact its emission-abatement policy. Results also revealed that the low-carbon policy for power plants (e.g., shutting down all coal-fired power plants) could lead to a potentially increment of imported energy for Beijing, which would increase the risk of energy shortage. The findings could help decision makers analyze the interactions between different stakeholders in ESP and provide useful information for policy design under uncertainty. Copyright © 2018 Elsevier Inc. All rights reserved.

Optimal Control of Induction Machines to Minimize Transient Energy Losses

NASA Astrophysics Data System (ADS)

Plathottam, Siby Jose

Induction machines are electromechanical energy conversion devices comprised of a stator and a rotor. Torque is generated due to the interaction between the rotating magnetic field from the stator, and the current induced in the rotor conductors. Their speed and torque output can be precisely controlled by manipulating the magnitude, frequency, and phase of the three input sinusoidal voltage waveforms. Their ruggedness, low cost, and high efficiency have made them ubiquitous component of nearly every industrial application. Thus, even a small improvement in their energy efficient tend to give a large amount of electrical energy savings over the lifetime of the machine. Hence, increasing energy efficiency (reducing energy losses) in induction machines is a constrained optimization problem that has attracted attention from researchers. The energy conversion efficiency of induction machines depends on both the speed-torque operating point, as well as the input voltage waveform. It also depends on whether the machine is in the transient or steady state. Maximizing energy efficiency during steady state is a Static Optimization problem, that has been extensively studied, with commercial solutions available. On the other hand, improving energy efficiency during transients is a Dynamic Optimization problem that is sparsely studied. This dissertation exclusively focuses on improving energy efficiency during transients. This dissertation treats the transient energy loss minimization problem as an optimal control problem which consists of a dynamic model of the machine, and a cost functional. The rotor field oriented current fed model of the induction machine is selected as the dynamic model. The rotor speed and rotor d-axis flux are the state variables in the dynamic model. The stator currents referred to as d-and q-axis currents are the control inputs. A cost functional is proposed that assigns a cost to both the energy losses in the induction machine, as well as the deviations from desired speed-torque-magnetic flux setpoints. Using Pontryagin's minimum principle, a set of necessary conditions that must be satisfied by the optimal control trajectories are derived. The conditions are in the form a two-point boundary value problem, that can be solved numerically. The conjugate gradient method that was modified using the Hestenes-Stiefel formula was used to obtain the numerical solution of both the control and state trajectories. Using the distinctive shape of the numerical trajectories as inspiration, analytical expressions were derived for the state, and control trajectories. It was shown that the trajectory could be fully described by finding the solution of a one-dimensional optimization problem. The sensitivity of both the optimal trajectory and the optimal energy efficiency to different induction machine parameters were analyzed. A non-iterative solution that can use feedback for generating optimal control trajectories in real time was explored. It was found that an artificial neural network could be trained using the numerical solutions and made to emulate the optimal control trajectories with a high degree of accuracy. Hence a neural network along with a supervisory logic was implemented and used in a real-time simulation to control the Finite Element Method model of the induction machine. The results were compared with three other control regimes and the optimal control system was found to have the highest energy efficiency for the same drive cycle.

High-frequency modes in a two-dimensional rectangular room with windows

NASA Astrophysics Data System (ADS)

Shabalina, E. D.; Shirgina, N. V.; Shanin, A. V.

2010-07-01

We examine a two-dimensional model problem of architectural acoustics on sound propagation in a rectangular room with windows. It is supposed that the walls are ideally flat and hard; the windows absorb all energy that falls upon them. We search for the modes of such a room having minimal attenuation indices, which have the expressed structure of billiard trajectories. The main attenuation mechanism for such modes is diffraction at the edges of the windows. We construct estimates for the attenuation indices of the given modes based on the solution to the Weinstein problem. We formulate diffraction problems similar to the statement of the Weinstein problem that describe the attenuation of billiard modes in complex situations.

Prediction-based Dynamic Energy Management in Wireless Sensor Networks

PubMed Central

Wang, Xue; Ma, Jun-Jie; Wang, Sheng; Bi, Dao-Wei

2007-01-01

Energy consumption is a critical constraint in wireless sensor networks. Focusing on the energy efficiency problem of wireless sensor networks, this paper proposes a method of prediction-based dynamic energy management. A particle filter was introduced to predict a target state, which was adopted to awaken wireless sensor nodes so that their sleep time was prolonged. With the distributed computing capability of nodes, an optimization approach of distributed genetic algorithm and simulated annealing was proposed to minimize the energy consumption of measurement. Considering the application of target tracking, we implemented target position prediction, node sleep scheduling and optimal sensing node selection. Moreover, a routing scheme of forwarding nodes was presented to achieve extra energy conservation. Experimental results of target tracking verified that energy-efficiency is enhanced by prediction-based dynamic energy management.

Determining the Optimal Solution for Quadratically Constrained Quadratic Programming (QCQP) on Energy-Saving Generation Dispatch Problem

NASA Astrophysics Data System (ADS)

Lesmana, E.; Chaerani, D.; Khansa, H. N.

2018-03-01

Energy-Saving Generation Dispatch (ESGD) is a scheme made by Chinese Government in attempt to minimize CO2 emission produced by power plant. This scheme is made related to global warming which is primarily caused by too much CO2 in earth’s atmosphere, and while the need of electricity is something absolute, the power plants producing it are mostly thermal-power plant which produced many CO2. Many approach to fulfill this scheme has been made, one of them came through Minimum Cost Flow in which resulted in a Quadratically Constrained Quadratic Programming (QCQP) form. In this paper, ESGD problem with Minimum Cost Flow in QCQP form will be solved using Lagrange’s Multiplier Method

Adaptive Particle Swarm Optimizer with Varying Acceleration Coefficients for Finding the Most Stable Conformer of Small Molecules.

PubMed

Agrawal, Shikha; Silakari, Sanjay; Agrawal, Jitendra

2015-11-01

A novel parameter automation strategy for Particle Swarm Optimization called APSO (Adaptive PSO) is proposed. The algorithm is designed to efficiently control the local search and convergence to the global optimum solution. Parameters c1 controls the impact of the cognitive component on the particle trajectory and c2 controls the impact of the social component. Instead of fixing the value of c1 and c2 , this paper updates the value of these acceleration coefficients by considering time variation of evaluation function along with varying inertia weight factor in PSO. Here the maximum and minimum value of evaluation function is use to gradually decrease and increase the value of c1 and c2 respectively. Molecular energy minimization is one of the most challenging unsolved problems and it can be formulated as a global optimization problem. The aim of the present paper is to investigate the effect of newly developed APSO on the highly complex molecular potential energy function and to check the efficiency of the proposed algorithm to find the global minimum of the function under consideration. The proposed algorithm APSO is therefore applied in two cases: Firstly, for the minimization of a potential energy of small molecules with up to 100 degrees of freedom and finally for finding the global minimum energy conformation of 1,2,3-trichloro-1-flouro-propane molecule based on a realistic potential energy function. The computational results of all the cases show that the proposed method performs significantly better than the other algorithms. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Worst-Case Energy Efficiency Maximization in a 5G Massive MIMO-NOMA System.

PubMed

Chinnadurai, Sunil; Selvaprabhu, Poongundran; Jeong, Yongchae; Jiang, Xueqin; Lee, Moon Ho

2017-09-18

In this paper, we examine the robust beamforming design to tackle the energy efficiency (EE) maximization problem in a 5G massive multiple-input multiple-output (MIMO)-non-orthogonal multiple access (NOMA) downlink system with imperfect channel state information (CSI) at the base station. A novel joint user pairing and dynamic power allocation (JUPDPA) algorithm is proposed to minimize the inter user interference and also to enhance the fairness between the users. This work assumes imperfect CSI by adding uncertainties to channel matrices with worst-case model, i.e., ellipsoidal uncertainty model (EUM). A fractional non-convex optimization problem is formulated to maximize the EE subject to the transmit power constraints and the minimum rate requirement for the cell edge user. The designed problem is difficult to solve due to its nonlinear fractional objective function. We firstly employ the properties of fractional programming to transform the non-convex problem into its equivalent parametric form. Then, an efficient iterative algorithm is proposed established on the constrained concave-convex procedure (CCCP) that solves and achieves convergence to a stationary point of the above problem. Finally, Dinkelbach's algorithm is employed to determine the maximum energy efficiency. Comprehensive numerical results illustrate that the proposed scheme attains higher worst-case energy efficiency as compared with the existing NOMA schemes and the conventional orthogonal multiple access (OMA) scheme.

Worst-Case Energy Efficiency Maximization in a 5G Massive MIMO-NOMA System

PubMed Central

Jeong, Yongchae; Jiang, Xueqin; Lee, Moon Ho

2017-01-01

In this paper, we examine the robust beamforming design to tackle the energy efficiency (EE) maximization problem in a 5G massive multiple-input multiple-output (MIMO)-non-orthogonal multiple access (NOMA) downlink system with imperfect channel state information (CSI) at the base station. A novel joint user pairing and dynamic power allocation (JUPDPA) algorithm is proposed to minimize the inter user interference and also to enhance the fairness between the users. This work assumes imperfect CSI by adding uncertainties to channel matrices with worst-case model, i.e., ellipsoidal uncertainty model (EUM). A fractional non-convex optimization problem is formulated to maximize the EE subject to the transmit power constraints and the minimum rate requirement for the cell edge user. The designed problem is difficult to solve due to its nonlinear fractional objective function. We firstly employ the properties of fractional programming to transform the non-convex problem into its equivalent parametric form. Then, an efficient iterative algorithm is proposed established on the constrained concave-convex procedure (CCCP) that solves and achieves convergence to a stationary point of the above problem. Finally, Dinkelbach’s algorithm is employed to determine the maximum energy efficiency. Comprehensive numerical results illustrate that the proposed scheme attains higher worst-case energy efficiency as compared with the existing NOMA schemes and the conventional orthogonal multiple access (OMA) scheme. PMID:28927019

Application-oriented offloading in heterogeneous networks for mobile cloud computing

NASA Astrophysics Data System (ADS)

Tseng, Fan-Hsun; Cho, Hsin-Hung; Chang, Kai-Di; Li, Jheng-Cong; Shih, Timothy K.

2018-04-01

Nowadays Internet applications have become more complicated that mobile device needs more computing resources for shorter execution time but it is restricted to limited battery capacity. Mobile cloud computing (MCC) is emerged to tackle the finite resource problem of mobile device. MCC offloads the tasks and jobs of mobile devices to cloud and fog environments by using offloading scheme. It is vital to MCC that which task should be offloaded and how to offload efficiently. In the paper, we formulate the offloading problem between mobile device and cloud data center and propose two algorithms based on application-oriented for minimum execution time, i.e. the Minimum Offloading Time for Mobile device (MOTM) algorithm and the Minimum Execution Time for Cloud data center (METC) algorithm. The MOTM algorithm minimizes offloading time by selecting appropriate offloading links based on application categories. The METC algorithm minimizes execution time in cloud data center by selecting virtual and physical machines with corresponding resource requirements of applications. Simulation results show that the proposed mechanism not only minimizes total execution time for mobile devices but also decreases their energy consumption.

Optimal design and control of an electromechanical transfemoral prosthesis with energy regeneration.

PubMed

Rohani, Farbod; Richter, Hanz; van den Bogert, Antonie J

2017-01-01

In this paper, we present the design of an electromechanical above-knee active prosthesis with energy storage and regeneration. The system consists of geared knee and ankle motors, parallel springs for each motor, an ultracapacitor, and controllable four-quadrant power converters. The goal is to maximize the performance of the system by finding optimal controls and design parameters. A model of the system dynamics was developed, and used to solve a combined trajectory and design optimization problem. The objectives of the optimization were to minimize tracking error relative to human joint motions, as well as energy use. The optimization problem was solved by the method of direct collocation, based on joint torque and joint angle data from ten subjects walking at three speeds. After optimization of controls and design parameters, the simulated system could operate at zero energy cost while still closely emulating able-bodied gait. This was achieved by controlled energy transfer between knee and ankle, and by controlled storage and release of energy throughout the gait cycle. Optimal gear ratios and spring parameters were similar across subjects and walking speeds.

Trinification, the hierarchy problem, and inverse seesaw neutrino masses

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cauet, Christophe; Paes, Heinrich; Wiesenfeldt, Soeren

2011-05-01

In minimal trinification models light neutrino masses can be generated via a radiative seesaw mechanism, where the masses of the right-handed neutrinos originate from loops involving Higgs and fermion fields at the unification scale. This mechanism is absent in models aiming at solving or ameliorating the hierarchy problem, such as low-energy supersymmetry, since the large seesaw scale disappears. In this case, neutrino masses need to be generated via a TeV-scale mechanism. In this paper, we investigate an inverse seesaw mechanism and discuss some phenomenological consequences.

Energy-aware virtual network embedding in flexi-grid networks.

PubMed

Lin, Rongping; Luo, Shan; Wang, Haoran; Wang, Sheng

2017-11-27

Network virtualization technology has been proposed to allow multiple heterogeneous virtual networks (VNs) to coexist on a shared substrate network, which increases the utilization of the substrate network. Efficiently mapping VNs on the substrate network is a major challenge on account of the VN embedding (VNE) problem. Meanwhile, energy efficiency has been widely considered in the network design in terms of operation expenses and the ecological awareness. In this paper, we aim to solve the energy-aware VNE problem in flexi-grid optical networks. We provide an integer linear programming (ILP) formulation to minimize the electricity cost of each arriving VN request. We also propose a polynomial-time heuristic algorithm where virtual links are embedded sequentially to keep a reasonable acceptance ratio and maintain a low electricity cost. Numerical results show that the heuristic algorithm performs closely to the ILP for a small size network, and we also demonstrate its applicability to larger networks.

Approximate solution of the p-median minimization problem

NASA Astrophysics Data System (ADS)

Il'ev, V. P.; Il'eva, S. D.; Navrotskaya, A. A.

2016-09-01

A version of the facility location problem (the well-known p-median minimization problem) and its generalization—the problem of minimizing a supermodular set function—is studied. These problems are NP-hard, and they are approximately solved by a gradient algorithm that is a discrete analog of the steepest descent algorithm. A priori bounds on the worst-case behavior of the gradient algorithm for the problems under consideration are obtained. As a consequence, a bound on the performance guarantee of the gradient algorithm for the p-median minimization problem in terms of the production and transportation cost matrix is obtained.

Reformulation of the covering and quantizer problems as ground states of interacting particles.

PubMed

Torquato, S

2010-11-01

It is known that the sphere-packing problem and the number-variance problem (closely related to an optimization problem in number theory) can be posed as energy minimizations associated with an infinite number of point particles in d-dimensional Euclidean space R(d) interacting via certain repulsive pair potentials. We reformulate the covering and quantizer problems as the determination of the ground states of interacting particles in R(d) that generally involve single-body, two-body, three-body, and higher-body interactions. This is done by linking the covering and quantizer problems to certain optimization problems involving the "void" nearest-neighbor functions that arise in the theory of random media and statistical mechanics. These reformulations, which again exemplify the deep interplay between geometry and physics, allow one now to employ theoretical and numerical optimization techniques to analyze and solve these energy minimization problems. The covering and quantizer problems have relevance in numerous applications, including wireless communication network layouts, the search of high-dimensional data parameter spaces, stereotactic radiation therapy, data compression, digital communications, meshing of space for numerical analysis, and coding and cryptography, among other examples. In the first three space dimensions, the best known solutions of the sphere-packing and number-variance problems (or their "dual" solutions) are directly related to those of the covering and quantizer problems, but such relationships may or may not exist for d≥4 , depending on the peculiarities of the dimensions involved. Our reformulation sheds light on the reasons for these similarities and differences. We also show that disordered saturated sphere packings provide relatively thin (economical) coverings and may yield thinner coverings than the best known lattice coverings in sufficiently large dimensions. In the case of the quantizer problem, we derive improved upper bounds on the quantizer error using sphere-packing solutions, which are generally substantially sharper than an existing upper bound in low to moderately large dimensions. We also demonstrate that disordered saturated sphere packings yield relatively good quantizers. Finally, we remark on possible applications of our results for the detection of gravitational waves.

Reformulation of the covering and quantizer problems as ground states of interacting particles

NASA Astrophysics Data System (ADS)

Torquato, S.

2010-11-01

It is known that the sphere-packing problem and the number-variance problem (closely related to an optimization problem in number theory) can be posed as energy minimizations associated with an infinite number of point particles in d -dimensional Euclidean space Rd interacting via certain repulsive pair potentials. We reformulate the covering and quantizer problems as the determination of the ground states of interacting particles in Rd that generally involve single-body, two-body, three-body, and higher-body interactions. This is done by linking the covering and quantizer problems to certain optimization problems involving the “void” nearest-neighbor functions that arise in the theory of random media and statistical mechanics. These reformulations, which again exemplify the deep interplay between geometry and physics, allow one now to employ theoretical and numerical optimization techniques to analyze and solve these energy minimization problems. The covering and quantizer problems have relevance in numerous applications, including wireless communication network layouts, the search of high-dimensional data parameter spaces, stereotactic radiation therapy, data compression, digital communications, meshing of space for numerical analysis, and coding and cryptography, among other examples. In the first three space dimensions, the best known solutions of the sphere-packing and number-variance problems (or their “dual” solutions) are directly related to those of the covering and quantizer problems, but such relationships may or may not exist for d≥4 , depending on the peculiarities of the dimensions involved. Our reformulation sheds light on the reasons for these similarities and differences. We also show that disordered saturated sphere packings provide relatively thin (economical) coverings and may yield thinner coverings than the best known lattice coverings in sufficiently large dimensions. In the case of the quantizer problem, we derive improved upper bounds on the quantizer error using sphere-packing solutions, which are generally substantially sharper than an existing upper bound in low to moderately large dimensions. We also demonstrate that disordered saturated sphere packings yield relatively good quantizers. Finally, we remark on possible applications of our results for the detection of gravitational waves.

Integrating atlas and graph cut methods for right ventricle blood-pool segmentation from cardiac cine MRI

NASA Astrophysics Data System (ADS)

Dangi, Shusil; Linte, Cristian A.

2017-03-01

Segmentation of right ventricle from cardiac MRI images can be used to build pre-operative anatomical heart models to precisely identify regions of interest during minimally invasive therapy. Furthermore, many functional parameters of right heart such as right ventricular volume, ejection fraction, myocardial mass and thickness can also be assessed from the segmented images. To obtain an accurate and computationally efficient segmentation of right ventricle from cardiac cine MRI, we propose a segmentation algorithm formulated as an energy minimization problem in a graph. Shape prior obtained by propagating label from an average atlas using affine registration is incorporated into the graph framework to overcome problems in ill-defined image regions. The optimal segmentation corresponding to the labeling with minimum energy configuration of the graph is obtained via graph-cuts and is iteratively refined to produce the final right ventricle blood pool segmentation. We quantitatively compare the segmentation results obtained from our algorithm to the provided gold-standard expert manual segmentation for 16 cine-MRI datasets available through the MICCAI 2012 Cardiac MR Right Ventricle Segmentation Challenge according to several similarity metrics, including Dice coefficient, Jaccard coefficient, Hausdorff distance, and Mean absolute distance error.

The Thermal Equilibrium Solution of a Generic Bipolar Quantum Hydrodynamic Model

NASA Astrophysics Data System (ADS)

Unterreiter, Andreas

The thermal equilibrium state of a bipolar, isothermic quantum fluid confined to a bounded domain ,d = 1,2 or d = 3 is entirely described by the particle densities n, p, minimizing the energy where G1,2 are strictly convex real valued functions, . It is shown that this variational problem has a unique minimizer in and some regularity results are proven. The semi-classical limit is carried out recovering the minimizer of the limiting functional. The subsequent zero space charge limit leads to extensions of the classical boundary conditions. Due to the lack of regularity the asymptotics can not be settled on Sobolev embedding arguments. The limit is carried out by means of a compactness-by-convexity principle.

Multi-Objective Approach for Energy-Aware Workflow Scheduling in Cloud Computing Environments

PubMed Central

Kadima, Hubert; Granado, Bertrand

2013-01-01

We address the problem of scheduling workflow applications on heterogeneous computing systems like cloud computing infrastructures. In general, the cloud workflow scheduling is a complex optimization problem which requires considering different criteria so as to meet a large number of QoS (Quality of Service) requirements. Traditional research in workflow scheduling mainly focuses on the optimization constrained by time or cost without paying attention to energy consumption. The main contribution of this study is to propose a new approach for multi-objective workflow scheduling in clouds, and present the hybrid PSO algorithm to optimize the scheduling performance. Our method is based on the Dynamic Voltage and Frequency Scaling (DVFS) technique to minimize energy consumption. This technique allows processors to operate in different voltage supply levels by sacrificing clock frequencies. This multiple voltage involves a compromise between the quality of schedules and energy. Simulation results on synthetic and real-world scientific applications highlight the robust performance of the proposed approach. PMID:24319361

Reinforcement learning or active inference?

PubMed

Friston, Karl J; Daunizeau, Jean; Kiebel, Stefan J

2009-07-29

This paper questions the need for reinforcement learning or control theory when optimising behaviour. We show that it is fairly simple to teach an agent complicated and adaptive behaviours using a free-energy formulation of perception. In this formulation, agents adjust their internal states and sampling of the environment to minimize their free-energy. Such agents learn causal structure in the environment and sample it in an adaptive and self-supervised fashion. This results in behavioural policies that reproduce those optimised by reinforcement learning and dynamic programming. Critically, we do not need to invoke the notion of reward, value or utility. We illustrate these points by solving a benchmark problem in dynamic programming; namely the mountain-car problem, using active perception or inference under the free-energy principle. The ensuing proof-of-concept may be important because the free-energy formulation furnishes a unified account of both action and perception and may speak to a reappraisal of the role of dopamine in the brain.

Multi-objective approach for energy-aware workflow scheduling in cloud computing environments.

PubMed

Yassa, Sonia; Chelouah, Rachid; Kadima, Hubert; Granado, Bertrand

2013-01-01

We address the problem of scheduling workflow applications on heterogeneous computing systems like cloud computing infrastructures. In general, the cloud workflow scheduling is a complex optimization problem which requires considering different criteria so as to meet a large number of QoS (Quality of Service) requirements. Traditional research in workflow scheduling mainly focuses on the optimization constrained by time or cost without paying attention to energy consumption. The main contribution of this study is to propose a new approach for multi-objective workflow scheduling in clouds, and present the hybrid PSO algorithm to optimize the scheduling performance. Our method is based on the Dynamic Voltage and Frequency Scaling (DVFS) technique to minimize energy consumption. This technique allows processors to operate in different voltage supply levels by sacrificing clock frequencies. This multiple voltage involves a compromise between the quality of schedules and energy. Simulation results on synthetic and real-world scientific applications highlight the robust performance of the proposed approach.

Template-Based 3D Reconstruction of Non-rigid Deformable Object from Monocular Video

NASA Astrophysics Data System (ADS)

Liu, Yang; Peng, Xiaodong; Zhou, Wugen; Liu, Bo; Gerndt, Andreas

2018-06-01

In this paper, we propose a template-based 3D surface reconstruction system of non-rigid deformable objects from monocular video sequence. Firstly, we generate a semi-dense template of the target object with structure from motion method using a subsequence video. This video can be captured by rigid moving camera orienting the static target object or by a static camera observing the rigid moving target object. Then, with the reference template mesh as input and based on the framework of classical template-based methods, we solve an energy minimization problem to get the correspondence between the template and every frame to get the time-varying mesh to present the deformation of objects. The energy terms combine photometric cost, temporal and spatial smoothness cost as well as as-rigid-as-possible cost which can enable elastic deformation. In this paper, an easy and controllable solution to generate the semi-dense template for complex objects is presented. Besides, we use an effective iterative Schur based linear solver for the energy minimization problem. The experimental evaluation presents qualitative deformation objects reconstruction results with real sequences. Compare against the results with other templates as input, the reconstructions based on our template have more accurate and detailed results for certain regions. The experimental results show that the linear solver we used performs better efficiency compared to traditional conjugate gradient based solver.

A decomposition approach to the design of a multiferroic memory bit

NASA Astrophysics Data System (ADS)

Acevedo, Ruben; Liang, Cheng-Yen; Carman, Gregory P.; Sepulveda, Abdon E.

2017-06-01

The objective of this paper is to present a methodology for the design of a memory bit to minimize the energy required to write data at the bit level. By straining a ferromagnetic nickel nano-dot by means of a piezoelectric substrate, its magnetization vector rotates between two stable states defined as a 1 and 0 for digital memory. The memory bit geometry, actuation mechanism and voltage control law were used as design variables. The approach used was to decompose the overall design process into simpler sub-problems whose structure can be exploited for a more efficient solution. This method minimizes the number of fully dynamic coupled finite element analyses required to converge to a near optimal design, thus decreasing the computational time for the design process. An in-plane sample design problem is presented to illustrate the advantages and flexibility of the procedure.

The maximum work principle regarded as a consequence of an optimization problem based on mechanical virtual power principle and application of constructal theory

NASA Astrophysics Data System (ADS)

Gavrus, Adinel

2017-10-01

This scientific paper proposes to prove that the maximum work principle used by theory of continuum media plasticity can be regarded as a consequence of an optimization problem based on constructal theory (prof. Adrian BEJAN). It is known that the thermodynamics define the conservation of energy and the irreversibility of natural systems evolution. From mechanical point of view the first one permits to define the momentum balance equation, respectively the virtual power principle while the second one explains the tendency of all currents to flow from high to low values. According to the constructal law all finite-size system searches to evolve in such configurations that flow more and more easily over time distributing the imperfections in order to maximize entropy and to minimize the losses or dissipations. During a material forming process the application of constructal theory principles leads to the conclusion that under external loads the material flow is that which all dissipated mechanical power (deformation and friction) become minimal. On a mechanical point of view it is then possible to formulate the real state of all mechanical variables (stress, strain, strain rate) as those that minimize the total dissipated power. So between all other virtual non-equilibrium states, the real state minimizes the total dissipated power. It can be then obtained a variational minimization problem and this paper proof in a mathematical sense that starting from this formulation can be finding in a more general form the maximum work principle together with an equivalent form for the friction term. An application in the case of a plane compression of a plastic material shows the feasibility of the proposed minimization problem formulation to find analytical solution for both cases: one without friction influence and a second which take into account Tresca friction law. To valid the proposed formulation, a comparison with a classical analytical analysis based on slices, upper/lower bound methods and numerical Finite Element simulation is also presented.

Complexes of a Zn-metalloenzyme binding site with hydroxamate-containing ligands. A case for detailed benchmarkings of polarizable molecular mechanics/dynamics potentials when the experimental binding structure is unknown.

PubMed

Gresh, Nohad; Perahia, David; de Courcy, Benoit; Foret, Johanna; Roux, Céline; El-Khoury, Lea; Piquemal, Jean-Philip; Salmon, Laurent

2016-12-15

Zn-metalloproteins are a major class of targets for drug design. They constitute a demanding testing ground for polarizable molecular mechanics/dynamics aimed at extending the realm of quantum chemistry (QC) to very long-duration molecular dynamics (MD). The reliability of such procedures needs to be demonstrated upon comparing the relative stabilities of competing candidate complexes of inhibitors with the recognition site stabilized in the course of MD. This could be necessary when no information is available regarding the experimental structure of the inhibitor-protein complex. Thus, this study bears on the phosphomannose isomerase (PMI) enzyme, considered as a potential therapeutic target for the treatment of several bacterial and parasitic diseases. We consider its complexes with 5-phospho-d-arabinonohydroxamate and three analog ligands differing by the number and location of their hydroxyl groups. We evaluate the energy accuracy expectable from a polarizable molecular mechanics procedure, SIBFA. This is done by comparisons with ab initio quantum-chemistry (QC) calculations in the following cases: (a) the complexes of the four ligands in three distinct structures extracted from the entire PMI-ligand energy-minimized structures, and totaling up to 264 atoms; (b) the solvation energies of several energy-minimized complexes of each ligand with a shell of 64 water molecules; (c) the conformational energy differences of each ligand in different conformations characterized in the course of energy-minimizations; and (d) the continuum solvation energies of the ligands in different conformations. The agreements with the QC results appear convincing. On these bases, we discuss the prospects of applying the procedure to ligand-macromolecule recognition problems. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

One-dimensional Gromov minimal filling problem

NASA Astrophysics Data System (ADS)

Ivanov, Alexandr O.; Tuzhilin, Alexey A.

2012-05-01

The paper is devoted to a new branch in the theory of one-dimensional variational problems with branching extremals, the investigation of one-dimensional minimal fillings introduced by the authors. On the one hand, this problem is a one-dimensional version of a generalization of Gromov's minimal fillings problem to the case of stratified manifolds. On the other hand, this problem is interesting in itself and also can be considered as a generalization of another classical problem, the Steiner problem on the construction of a shortest network connecting a given set of terminals. Besides the statement of the problem, we discuss several properties of the minimal fillings and state several conjectures. Bibliography: 38 titles.

Graph-cut based discrete-valued image reconstruction.

PubMed

Tuysuzoglu, Ahmet; Karl, W Clem; Stojanovic, Ivana; Castañòn, David; Ünlü, M Selim

2015-05-01

Efficient graph-cut methods have been used with great success for labeling and denoising problems occurring in computer vision. Unfortunately, the presence of linear image mappings has prevented the use of these techniques in most discrete-amplitude image reconstruction problems. In this paper, we develop a graph-cut based framework for the direct solution of discrete amplitude linear image reconstruction problems cast as regularized energy function minimizations. We first analyze the structure of discrete linear inverse problem cost functions to show that the obstacle to the application of graph-cut methods to their solution is the variable mixing caused by the presence of the linear sensing operator. We then propose to use a surrogate energy functional that overcomes the challenges imposed by the sensing operator yet can be utilized efficiently in existing graph-cut frameworks. We use this surrogate energy functional to devise a monotonic iterative algorithm for the solution of discrete valued inverse problems. We first provide experiments using local convolutional operators and show the robustness of the proposed technique to noise and stability to changes in regularization parameter. Then we focus on nonlocal, tomographic examples where we consider limited-angle data problems. We compare our technique with state-of-the-art discrete and continuous image reconstruction techniques. Experiments show that the proposed method outperforms state-of-the-art techniques in challenging scenarios involving discrete valued unknowns.

Equivalency principle for magnetoelectroelastic multiferroics with arbitrary microstructure: The phase field approach

NASA Astrophysics Data System (ADS)

Ni, Yong; He, Linghui; Khachaturyan, Armen G.

2010-07-01

A phase field method is proposed to determine the equilibrium fields of a magnetoelectroelastic multiferroic with arbitrarily distributed constitutive constants under applied loadings. This method is based on a developed generalized Eshelby's equivalency principle, in which the elastic strain, electrostatic, and magnetostatic fields at the equilibrium in the original heterogeneous system are exactly the same as those in an equivalent homogeneous magnetoelectroelastic coupled or uncoupled system with properly chosen distributed effective eigenstrain, polarization, and magnetization fields. Finding these effective fields fully solves the equilibrium elasticity, electrostatics, and magnetostatics in the original heterogeneous multiferroic. The paper formulates a variational principle proving that the effective fields are minimizers of appropriate close-form energy functional. The proposed phase field approach produces the energy minimizing effective fields (and thus solving the general multiferroic problem) as a result of artificial relaxation process described by the Ginzburg-Landau-Khalatnikov kinetic equations.

Optimality Principles for Model-Based Prediction of Human Gait

PubMed Central

Ackermann, Marko; van den Bogert, Antonie J.

2010-01-01

Although humans have a large repertoire of potential movements, gait patterns tend to be stereotypical and appear to be selected according to optimality principles such as minimal energy. When applied to dynamic musculoskeletal models such optimality principles might be used to predict how a patient’s gait adapts to mechanical interventions such as prosthetic devices or surgery. In this paper we study the effects of different performance criteria on predicted gait patterns using a 2D musculoskeletal model. The associated optimal control problem for a family of different cost functions was solved utilizing the direct collocation method. It was found that fatigue-like cost functions produced realistic gait, with stance phase knee flexion, as opposed to energy-related cost functions which avoided knee flexion during the stance phase. We conclude that fatigue minimization may be one of the primary optimality principles governing human gait. PMID:20074736

A projection gradient method for computing ground state of spin-2 Bose–Einstein condensates

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, Hanquan, E-mail: hanquan.wang@gmail.com; Yunnan Tongchang Scientific Computing and Data Mining Research Center, Kunming, Yunnan Province, 650221

In this paper, a projection gradient method is presented for computing ground state of spin-2 Bose–Einstein condensates (BEC). We first propose the general projection gradient method for solving energy functional minimization problem under multiple constraints, in which the energy functional takes real functions as independent variables. We next extend the method to solve a similar problem, where the energy functional now takes complex functions as independent variables. We finally employ the method into finding the ground state of spin-2 BEC. The key of our method is: by constructing continuous gradient flows (CGFs), the ground state of spin-2 BEC can bemore » computed as the steady state solution of such CGFs. We discretized the CGFs by a conservative finite difference method along with a proper way to deal with the nonlinear terms. We show that the numerical discretization is normalization and magnetization conservative and energy diminishing. Numerical results of the ground state and their energy of spin-2 BEC are reported to demonstrate the effectiveness of the numerical method.« less

Multi-objective optimal dispatch of distributed energy resources

NASA Astrophysics Data System (ADS)

Longe, Ayomide

This thesis is composed of two papers which investigate the optimal dispatch for distributed energy resources. In the first paper, an economic dispatch problem for a community microgrid is studied. In this microgrid, each agent pursues an economic dispatch for its personal resources. In addition, each agent is capable of trading electricity with other agents through a local energy market. In this paper, a simple market structure is introduced as a framework for energy trades in a small community microgrid such as the Solar Village. It was found that both sellers and buyers benefited by participating in this market. In the second paper, Semidefinite Programming (SDP) for convex relaxation of power flow equations is used for optimal active and reactive dispatch for Distributed Energy Resources (DER). Various objective functions including voltage regulation, reduced transmission line power losses, and minimized reactive power charges for a microgrid are introduced. Combinations of these goals are attained by solving a multiobjective optimization for the proposed ORPD problem. Also, both centralized and distributed versions of this optimal dispatch are investigated. It was found that SDP made the optimal dispatch faster and distributed solution allowed for scalability.

Wing flapping with minimum energy

NASA Technical Reports Server (NTRS)

Jones, R. T.

1980-01-01

A technique employed by Prandtl and Munk is adapted for the case of a wing in flapping motion to determine its lift distribution. The problem may be reduced to one of minimizing induced drag for a specified and periodically varying bending moment at the wing root. It is concluded that two wings in close tandem arrangement, moving in opposite phase, would eliminate the induced aerodynamic losses calculated

Discrete diffraction managed solitons: Threshold phenomena and rapid decay for general nonlinearities

NASA Astrophysics Data System (ADS)

Choi, Mi-Ran; Hundertmark, Dirk; Lee, Young-Ran

2017-10-01

We prove a threshold phenomenon for the existence/non-existence of energy minimizing solitary solutions of the diffraction management equation for strictly positive and zero average diffraction. Our methods allow for a large class of nonlinearities; they are, for example, allowed to change sign, and the weakest possible condition, it only has to be locally integrable, on the local diffraction profile. The solutions are found as minimizers of a nonlinear and nonlocal variational problem which is translation invariant. There exists a critical threshold λcr such that minimizers for this variational problem exist if their power is bigger than λcr and no minimizers exist with power less than the critical threshold. We also give simple criteria for the finiteness and strict positivity of the critical threshold. Our proof of existence of minimizers is rather direct and avoids the use of Lions' concentration compactness argument. Furthermore, we give precise quantitative lower bounds on the exponential decay rate of the diffraction management solitons, which confirm the physical heuristic prediction for the asymptotic decay rate. Moreover, for ground state solutions, these bounds give a quantitative lower bound for the divergence of the exponential decay rate in the limit of vanishing average diffraction. For zero average diffraction, we prove quantitative bounds which show that the solitons decay much faster than exponentially. Our results considerably extend and strengthen the results of Hundertmark and Lee [J. Nonlinear Sci. 22, 1-38 (2012) and Commun. Math. Phys. 309(1), 1-21 (2012)].

Managing risks of market price uncertainty for a microgrid operation

NASA Astrophysics Data System (ADS)

Raghavan, Sriram

After deregulation of electricity in the United States, the day-ahead and real-time markets allow load serving entities and generation companies to bid and purchase/sell energy under the supervision of the independent system operator (ISO). The electricity market prices are inherently uncertain, and can be highly volatile. The main objective of this thesis is to hedge against the risk from the uncertainty of the market prices when purchasing/selling energy from/to the market. The energy manager can also schedule distributed generators (DGs) and storage of the microgrid to meet the demand, in addition to energy transactions from the market. The risk measure used in this work is the variance of the uncertain market purchase/sale cost/revenue, assuming the price following a Gaussian distribution. Using Markowitz optimization, the risk is minimized to find the optimal mix of purchase from the markets. The problem is formulated as a mixed integer quadratic program. The microgrid at Illinois Institute of Technology (IIT) in Chicago, IL was used as a case study. The result of this work reveals the tradeoff faced by the microgrid energy manager between minimizing the risk and minimizing the mean of the total operating cost (TOC) of the microgrid. With this information, the microgrid energy manager can make decisions in the day-ahead and real-time markets according to their risk aversion preference. The assumption of market prices following Gaussian distribution is also verified to be reasonable for the purpose of hedging against their risks. This is done by comparing the result of the proposed formulation with that obtained from the sample market prices randomly generated using the distribution of actual historic market price data.

Correction method for stripe nonuniformity.

PubMed

Qian, Weixian; Chen, Qian; Gu, Guohua; Guan, Zhiqiang

2010-04-01

Stripe nonuniformity is very typical in line infrared focal plane arrays (IR-FPA) and uncooled staring IR-FPA. In this paper, the mechanism of the stripe nonuniformity is analyzed, and the gray-scale co-occurrence matrix theory and optimization theory are studied. Through these efforts, the stripe nonuniformity correction problem is translated into the optimization problem. The goal of the optimization is to find the minimal energy of the image's line gradient. After solving the constrained nonlinear optimization equation, the parameters of the stripe nonuniformity correction are obtained and the stripe nonuniformity correction is achieved. The experiments indicate that this algorithm is effective and efficient.

Integration of prebend optimization in a holistic wind turbine design tool

NASA Astrophysics Data System (ADS)

Sartori, L.; Bortolotti, P.; Croce, A.; Bottasso, C. L.

2016-09-01

This paper considers the problem of identifying the optimal combination of blade prebend, rotor cone angle and nacelle uptilt, within an integrated aero-structural design environment. Prebend is designed to reach maximum rotor area at rated conditions, while cone and uptilt are computed together with all other design variables to minimize the cost of energy. Constraints are added to the problem formulation in order to translate various design requirements. The proposed optimization approach is applied to a conceptual 10 MW offshore wind turbine, highlighting the benefits of an optimal combination of blade curvature, cone and uptilt angles.

Algorithm For Hypersonic Flow In Chemical Equilibrium

NASA Technical Reports Server (NTRS)

Palmer, Grant

1989-01-01

Implicit, finite-difference, shock-capturing algorithm calculates inviscid, hypersonic flows in chemical equilibrium. Implicit formulation chosen because overcomes limitation on mathematical stability encountered in explicit formulations. For dynamical portion of problem, Euler equations written in conservation-law form in Cartesian coordinate system for two-dimensional or axisymmetric flow. For chemical portion of problem, equilibrium state of gas at each point in computational grid determined by minimizing local Gibbs free energy, subject to local conservation of molecules, atoms, ions, and total enthalpy. Major advantage: resulting algorithm naturally stable and captures strong shocks without help of artificial-dissipation terms to damp out spurious numerical oscillations.

Simultaneous optical flow and source estimation: Space–time discretization and preconditioning

PubMed Central

Andreev, R.; Scherzer, O.; Zulehner, W.

2015-01-01

We consider the simultaneous estimation of an optical flow field and an illumination source term in a movie sequence. The particular optical flow equation is obtained by assuming that the image intensity is a conserved quantity up to possible sources and sinks which represent varying illumination. We formulate this problem as an energy minimization problem and propose a space–time simultaneous discretization for the optimality system in saddle-point form. We investigate a preconditioning strategy that renders the discrete system well-conditioned uniformly in the discretization resolution. Numerical experiments complement the theory. PMID:26435561

On well-posedness of variational models of charged drops.

PubMed

Muratov, Cyrill B; Novaga, Matteo

2016-03-01

Electrified liquids are well known to be prone to a variety of interfacial instabilities that result in the onset of apparent interfacial singularities and liquid fragmentation. In the case of electrically conducting liquids, one of the basic models describing the equilibrium interfacial configurations and the onset of instability assumes the liquid to be equipotential and interprets those configurations as local minimizers of the energy consisting of the sum of the surface energy and the electrostatic energy. Here we show that, surprisingly, this classical geometric variational model is mathematically ill-posed irrespective of the degree to which the liquid is electrified. Specifically, we demonstrate that an isolated spherical droplet is never a local minimizer, no matter how small is the total charge on the droplet, as the energy can always be lowered by a smooth, arbitrarily small distortion of the droplet's surface. This is in sharp contrast to the experimental observations that a critical amount of charge is needed in order to destabilize a spherical droplet. We discuss several possible regularization mechanisms for the considered free boundary problem and argue that well-posedness can be restored by the inclusion of the entropic effects resulting in finite screening of free charges.

On well-posedness of variational models of charged drops

PubMed Central

Novaga, Matteo

2016-01-01

Electrified liquids are well known to be prone to a variety of interfacial instabilities that result in the onset of apparent interfacial singularities and liquid fragmentation. In the case of electrically conducting liquids, one of the basic models describing the equilibrium interfacial configurations and the onset of instability assumes the liquid to be equipotential and interprets those configurations as local minimizers of the energy consisting of the sum of the surface energy and the electrostatic energy. Here we show that, surprisingly, this classical geometric variational model is mathematically ill-posed irrespective of the degree to which the liquid is electrified. Specifically, we demonstrate that an isolated spherical droplet is never a local minimizer, no matter how small is the total charge on the droplet, as the energy can always be lowered by a smooth, arbitrarily small distortion of the droplet's surface. This is in sharp contrast to the experimental observations that a critical amount of charge is needed in order to destabilize a spherical droplet. We discuss several possible regularization mechanisms for the considered free boundary problem and argue that well-posedness can be restored by the inclusion of the entropic effects resulting in finite screening of free charges. PMID:27118921

Beyond the Standard Model: The pragmatic approach to the gauge hierarchy problem

NASA Astrophysics Data System (ADS)

Mahbubani, Rakhi

The current favorite solution to the gauge hierarchy problem, the Minimal Supersymmetric Standard Model (MSSM), is looking increasingly fine tuned as recent results from LEP-II have pushed it to regions of its parameter space where a light higgs seems unnatural. Given this fact it seems sensible to explore other approaches to this problem; we study three alternatives here. The first is a Little Higgs theory, in which the Higgs particle is realized as the pseudo-Goldstone boson of an approximate global chiral symmetry and so is naturally light. We analyze precision electroweak observables in the Minimal Moose model, one example of such a theory, and look for regions in its parameter space that are consistent with current limits on these. It is also possible to find a solution within a supersymmetric framework by adding to the MSSM superpotential a lambdaSHuH d term and UV completing with new strong dynamics under which S is a composite before lambda becomes non-perturbative. This allows us to increase the MSSM tree level higgs mass bound to a value that alleviates the supersymmetric fine-tuning problem with elementary higgs fields, maintaining gauge coupling unification in a natural way. Finally we try an entirely different tack, in which we do not attempt to solve the hierarchy problem, but rather assume that the tuning of the higgs can be explained in some unnatural way, from environmental considerations for instance. With this philosophy in mind we study in detail the low-energy phenomenology of the minimal extension to the Standard Model with a dark matter candidate and gauge coupling unification, consisting of additional fermions with the quantum numbers of SUSY higgsinos, and a singlet.

Minimal investment risk of a portfolio optimization problem with budget and investment concentration constraints

NASA Astrophysics Data System (ADS)

Shinzato, Takashi

2017-02-01

In the present paper, the minimal investment risk for a portfolio optimization problem with imposed budget and investment concentration constraints is considered using replica analysis. Since the minimal investment risk is influenced by the investment concentration constraint (as well as the budget constraint), it is intuitive that the minimal investment risk for the problem with an investment concentration constraint can be larger than that without the constraint (that is, with only the budget constraint). Moreover, a numerical experiment shows the effectiveness of our proposed analysis. In contrast, the standard operations research approach failed to identify accurately the minimal investment risk of the portfolio optimization problem.

Viscous Corrections of the Time Incremental Minimization Scheme and Visco-Energetic Solutions to Rate-Independent Evolution Problems

NASA Astrophysics Data System (ADS)

Minotti, Luca; Savaré, Giuseppe

2018-02-01

We propose the new notion of Visco-Energetic solutions to rate-independent systems {(X, E,} d) driven by a time dependent energy E and a dissipation quasi-distance d in a general metric-topological space X. As for the classic Energetic approach, solutions can be obtained by solving a modified time Incremental Minimization Scheme, where at each step the dissipation quasi-distance d is incremented by a viscous correction {δ} (for example proportional to the square of the distance d), which penalizes far distance jumps by inducing a localized version of the stability condition. We prove a general convergence result and a typical characterization by Stability and Energy Balance in a setting comparable to the standard energetic one, thus capable of covering a wide range of applications. The new refined Energy Balance condition compensates for the localized stability and provides a careful description of the jump behavior: at every jump the solution follows an optimal transition, which resembles in a suitable variational sense the discrete scheme that has been implemented for the whole construction.

Outage Probability Minimization for Energy Harvesting Cognitive Radio Sensor Networks

PubMed Central

Zhang, Fan; Jing, Tao; Huo, Yan; Jiang, Kaiwei

2017-01-01

The incorporation of cognitive radio (CR) capability in wireless sensor networks yields a promising network paradigm known as CR sensor networks (CRSNs), which is able to provide spectrum efficient data communication. However, due to the high energy consumption results from spectrum sensing, as well as subsequent data transmission, the energy supply for the conventional sensor nodes powered by batteries is regarded as a severe bottleneck for sustainable operation. The energy harvesting technique, which gathers energy from the ambient environment, is regarded as a promising solution to perpetually power-up energy-limited devices with a continual source of energy. Therefore, applying the energy harvesting (EH) technique in CRSNs is able to facilitate the self-sustainability of the energy-limited sensors. The primary concern of this study is to design sensing-transmission policies to minimize the long-term outage probability of EH-powered CR sensor nodes. We formulate this problem as an infinite-horizon discounted Markov decision process and propose an ϵ-optimal sensing-transmission (ST) policy through using the value iteration algorithm. ϵ is the error bound between the ST policy and the optimal policy, which can be pre-defined according to the actual need. Moreover, for a special case that the signal-to-noise (SNR) power ratio is sufficiently high, we present an efficient transmission (ET) policy and prove that the ET policy achieves the same performance with the ST policy. Finally, extensive simulations are conducted to evaluate the performance of the proposed policies and the impaction of various network parameters. PMID:28125023

Outage Probability Minimization for Energy Harvesting Cognitive Radio Sensor Networks.

PubMed

Zhang, Fan; Jing, Tao; Huo, Yan; Jiang, Kaiwei

2017-01-24

The incorporation of cognitive radio (CR) capability in wireless sensor networks yields a promising network paradigm known as CR sensor networks (CRSNs), which is able to provide spectrum efficient data communication. However, due to the high energy consumption results from spectrum sensing, as well as subsequent data transmission, the energy supply for the conventional sensor nodes powered by batteries is regarded as a severe bottleneck for sustainable operation. The energy harvesting technique, which gathers energy from the ambient environment, is regarded as a promising solution to perpetually power-up energy-limited devices with a continual source of energy. Therefore, applying the energy harvesting (EH) technique in CRSNs is able to facilitate the self-sustainability of the energy-limited sensors. The primary concern of this study is to design sensing-transmission policies to minimize the long-term outage probability of EH-powered CR sensor nodes. We formulate this problem as an infinite-horizon discounted Markov decision process and propose an ϵ -optimal sensing-transmission (ST) policy through using the value iteration algorithm. ϵ is the error bound between the ST policy and the optimal policy, which can be pre-defined according to the actual need. Moreover, for a special case that the signal-to-noise (SNR) power ratio is sufficiently high, we present an efficient transmission (ET) policy and prove that the ET policy achieves the same performance with the ST policy. Finally, extensive simulations are conducted to evaluate the performance of the proposed policies and the impaction of various network parameters.

Combining energy and Laplacian regularization to accurately retrieve the depth of brain activity of diffuse optical tomographic data

NASA Astrophysics Data System (ADS)

Chiarelli, Antonio M.; Maclin, Edward L.; Low, Kathy A.; Mathewson, Kyle E.; Fabiani, Monica; Gratton, Gabriele

2016-03-01

Diffuse optical tomography (DOT) provides data about brain function using surface recordings. Despite recent advancements, an unbiased method for estimating the depth of absorption changes and for providing an accurate three-dimensional (3-D) reconstruction remains elusive. DOT involves solving an ill-posed inverse problem, requiring additional criteria for finding unique solutions. The most commonly used criterion is energy minimization (energy constraint). However, as measurements are taken from only one side of the medium (the scalp) and sensitivity is greater at shallow depths, the energy constraint leads to solutions that tend to be small and superficial. To correct for this bias, we combine the energy constraint with another criterion, minimization of spatial derivatives (Laplacian constraint, also used in low resolution electromagnetic tomography, LORETA). Used in isolation, the Laplacian constraint leads to solutions that tend to be large and deep. Using simulated, phantom, and actual brain activation data, we show that combining these two criteria results in accurate (error <2 mm) absorption depth estimates, while maintaining a two-point spatial resolution of <24 mm up to a depth of 30 mm. This indicates that accurate 3-D reconstruction of brain activity up to 30 mm from the scalp can be obtained with DOT.

Chemotaxis can provide biological organisms with good solutions to the travelling salesman problem.

PubMed

Reynolds, A M

2011-05-01

The ability to find good solutions to the traveling salesman problem can benefit some biological organisms. Bacterial infection would, for instance, be eradicated most promptly if cells of the immune system minimized the total distance they traveled when moving between bacteria. Similarly, foragers would maximize their net energy gain if the distance that they traveled between multiple dispersed prey items was minimized. The traveling salesman problem is one of the most intensively studied problems in combinatorial optimization. There are no efficient algorithms for even solving the problem approximately (within a guaranteed constant factor from the optimum) because the problem is nondeterministic polynomial time complete. The best approximate algorithms can typically find solutions within 1%-2% of the optimal, but these are computationally intensive and can not be implemented by biological organisms. Biological organisms could, in principle, implement the less efficient greedy nearest-neighbor algorithm, i.e., always move to the nearest surviving target. Implementation of this strategy does, however, require quite sophisticated cognitive abilities and prior knowledge of the target locations. Here, with the aid of numerical simulations, it is shown that biological organisms can simply use chemotaxis to solve, or at worst provide good solutions (comparable to those found by the greedy algorithm) to, the traveling salesman problem when the targets are sources of a chemoattractant and are modest in number (n < 10). This applies to neutrophils and macrophages in microbial defense and to some predators.

Determination of optimum allocation and pricing of distributed generation using genetic algorithm methodology

NASA Astrophysics Data System (ADS)

Mwakabuta, Ndaga Stanslaus

Electric power distribution systems play a significant role in providing continuous and "quality" electrical energy to different classes of customers. In the context of the present restrictions on transmission system expansions and the new paradigm of "open and shared" infrastructure, new approaches to distribution system analyses, economic and operational decision-making need investigation. This dissertation includes three layers of distribution system investigations. In the basic level, improved linear models are shown to offer significant advantages over previous models for advanced analysis. In the intermediate level, the improved model is applied to solve the traditional problem of operating cost minimization using capacitors and voltage regulators. In the advanced level, an artificial intelligence technique is applied to minimize cost under Distributed Generation injection from private vendors. Soft computing techniques are finding increasing applications in solving optimization problems in large and complex practical systems. The dissertation focuses on Genetic Algorithm for investigating the economic aspects of distributed generation penetration without compromising the operational security of the distribution system. The work presents a methodology for determining the optimal pricing of distributed generation that would help utilities make a decision on how to operate their system economically. This would enable modular and flexible investments that have real benefits to the electric distribution system. Improved reliability for both customers and the distribution system in general, reduced environmental impacts, increased efficiency of energy use, and reduced costs of energy services are some advantages.

Method for protein structure alignment

DOEpatents

Blankenbecler, Richard; Ohlsson, Mattias; Peterson, Carsten; Ringner, Markus

2005-02-22

This invention provides a method for protein structure alignment. More particularly, the present invention provides a method for identification, classification and prediction of protein structures. The present invention involves two key ingredients. First, an energy or cost function formulation of the problem simultaneously in terms of binary (Potts) assignment variables and real-valued atomic coordinates. Second, a minimization of the energy or cost function by an iterative method, where in each iteration (1) a mean field method is employed for the assignment variables and (2) exact rotation and/or translation of atomic coordinates is performed, weighted with the corresponding assignment variables.

Resource Control in Large-Scale Mobile-Agents Systems

DTIC Science & Technology

2005-07-01

wakeup node schedule , much energy can be conserved. We also designed several protocols for global clock synchronization. The most interesting one is...choice as to which remote hosts to visit and in which order. Scheduling mobile-agent migration in a way that minimizes bandwidth and other resource...use, therefore, is both feasible and attractive. Dartmouth considered several variations of the scheduling problem, and devel- oped an algorithm for

The Lewis Chemical Equilibrium Program with parametric study capability

NASA Technical Reports Server (NTRS)

Sevigny, R.

1981-01-01

The program was developed to determine chemical equilibrium in complex systems. Using a free energy minimization technique, the program permits calculations such as: chemical equilibrium for assigned thermodynamic states; theoretical rocket performance for both equilibrium and frozen compositions during expansion; incident and reflected shock properties; and Chapman-Jouget detonation properties. It is shown that the same program can handle solid coal in an entrained flow coal gasification problem.

Optimization of fuel-cell tram operation based on two dimension dynamic programming

NASA Astrophysics Data System (ADS)

Zhang, Wenbin; Lu, Xuecheng; Zhao, Jingsong; Li, Jianqiu

2018-02-01

This paper proposes an optimal control strategy based on the two-dimension dynamic programming (2DDP) algorithm targeting at minimizing operation energy consumption for a fuel-cell tram. The energy consumption model with the tram dynamics is firstly deduced. Optimal control problem are analyzed and the 2DDP strategy is applied to solve the problem. The optimal tram speed profiles are obtained for each interstation which consist of three stages: accelerate to the set speed with the maximum traction power, dynamically adjust to maintain a uniform speed and decelerate to zero speed with the maximum braking power at a suitable timing. The optimal control curves of all the interstations are connected with the parking time to form the optimal control method of the whole line. The optimized speed profiles are also simplified for drivers to follow.

Testing Method of Degrading Heavy Oil Pollution by Microorganisms

NASA Astrophysics Data System (ADS)

Wu, Qi; Zhao, Lin; Ma, Aijin

2018-01-01

With the development of human society, we are more and more relying on the petrochemical energy. The use of petrochemical energy not only brings us great convenience, but is also accompanied by a series of environmental pollution problems, especially oil pollution. Since it is impractical to restore all pollution problems, the proper use of some remedial measures, under the guidance of functional orientation, may be sufficient to minimize the risk of persistent and diffusing pollutants. In recent years, bioremediation technology has been gradually developed into a promising stage and has played a crucial role in the degradation of heavy oil pollution. Specially, microbes in the degradation of heavy oil have made a great contribution. This paper mainly summarizes the different kinds of microorganisms for degrading heavy oil and the detection method for degradation efficiency of heavy oil pollution.

Optimization-based channel constrained data aggregation routing algorithms in multi-radio wireless sensor networks.

PubMed

Yen, Hong-Hsu

2009-01-01

In wireless sensor networks, data aggregation routing could reduce the number of data transmissions so as to achieve energy efficient transmission. However, data aggregation introduces data retransmission that is caused by co-channel interference from neighboring sensor nodes. This kind of co-channel interference could result in extra energy consumption and significant latency from retransmission. This will jeopardize the benefits of data aggregation. One possible solution to circumvent data retransmission caused by co-channel interference is to assign different channels to every sensor node that is within each other's interference range on the data aggregation tree. By associating each radio with a different channel, a sensor node could receive data from all the children nodes on the data aggregation tree simultaneously. This could reduce the latency from the data source nodes back to the sink so as to meet the user's delay QoS. Since the number of radios on each sensor node and the number of non-overlapping channels are all limited resources in wireless sensor networks, a challenging question here is to minimize the total transmission cost under limited number of non-overlapping channels in multi-radio wireless sensor networks. This channel constrained data aggregation routing problem in multi-radio wireless sensor networks is an NP-hard problem. I first model this problem as a mixed integer and linear programming problem where the objective is to minimize the total transmission subject to the data aggregation routing, channel and radio resources constraints. The solution approach is based on the Lagrangean relaxation technique to relax some constraints into the objective function and then to derive a set of independent subproblems. By optimally solving these subproblems, it can not only calculate the lower bound of the original primal problem but also provide useful information to get the primal feasible solutions. By incorporating these Lagrangean multipliers as the link arc weight, the optimization-based heuristics are proposed to get energy-efficient data aggregation tree with better resource (channel and radio) utilization. From the computational experiments, the proposed optimization-based approach is superior to existing heuristics under all tested cases.

Worst-Case Cooperative Jamming for Secure Communications in CIoT Networks.

PubMed

Li, Zhen; Jing, Tao; Ma, Liran; Huo, Yan; Qian, Jin

2016-03-07

The Internet of Things (IoT) is a significant branch of the ongoing advances in the Internet and mobile communications. The use of a large number of IoT devices makes the spectrum scarcity problem even more serious. The usable spectrum resources are almost entirely occupied, and thus, the increasing radio access demands of IoT devices cannot be met. To tackle this problem, the Cognitive Internet of Things (CIoT) has been proposed. In a CIoT network, secondary users, i.e., sensors and actuators, can access the licensed spectrum bands provided by licensed primary users (such as telephones). Security is a major concern in CIoT networks. However, the traditional encryption method at upper layers (such as symmetric cryptography and asymmetric cryptography) may be compromised in CIoT networks, since these types of networks are heterogeneous. In this paper, we address the security issue in spectrum-leasing-based CIoT networks using physical layer methods. Considering that the CIoT networks are cooperative networks, we propose to employ cooperative jamming to achieve secrecy transmission. In the cooperative jamming scheme, a certain secondary user is employed as the helper to harvest energy transmitted by the source and then uses the harvested energy to generate an artificial noise that jams the eavesdropper without interfering with the legitimate receivers. The goal is to minimize the signal to interference plus noise ratio (SINR) at the eavesdropper subject to the quality of service (QoS) constraints of the primary traffic and the secondary traffic. We formulate the considered minimization problem into a two-stage robust optimization problem based on the worst-case Channel State Information of the Eavesdropper. By using semi-definite programming (SDP), the optimal solutions of the transmit covariance matrices can be obtained. Moreover, in order to build an incentive mechanism for the secondary users, we propose an auction framework based on the cooperative jamming scheme. The proposed auction framework jointly formulates the helper selection and the corresponding energy allocation problems under the constraint of the eavesdropper's SINR. By adopting the Vickrey auction, truthfulness and individual rationality can be guaranteed. Simulation results demonstrate the good performance of the cooperative jamming scheme and the auction framework.

On the energy efficiency of cyclic mechanisms

NASA Astrophysics Data System (ADS)

Briskin, E. S.; Kalinin, Ya. V.; Maloletov, A. V.; Chernyshev, V. V.

2014-01-01

We consider cyclic mechanisms with one degree of freedom driven by engines of various types such as alternating and direct current motors, internal combustion engines, etc. We pose the problem of modifying the mechanism structure by joining additional links or by varying the parameters or operation mode of the original mechanism so as to minimize the thermal losses in the driving motor. The solution is based on the minimization of the functional determining the irreversible power losses. We show that, for the engines considered, all cyclic mechanisms with one degree of freedom should satisfy a fundamental condition ensuring the minimum of losses. We consider two examples, one of which corresponds to actually existing mechanisms.

A mathematical model for the deformation of the eyeball by an elastic band.

PubMed

Keeling, Stephen L; Propst, Georg; Stadler, Georg; Wackernagel, Werner

2009-06-01

In a certain kind of eye surgery, the human eyeball is deformed sustainably by the application of an elastic band. This article presents a mathematical model for the mechanics of the combined eye/band structure along with an algorithm to compute the model solutions. These predict the immediate and the lasting indentation of the eyeball. The model is derived from basic physical principles by minimizing a potential energy subject to a volume constraint. Assuming spherical symmetry, this leads to a two-point boundary-value problem for a non-linear second-order ordinary differential equation that describes the minimizing static equilibrium. By comparison with laboratory data, a preliminary validation of the model is given.

Optimal Electrical Energy Slewing for Reaction Wheel Spacecraft

NASA Astrophysics Data System (ADS)

Marsh, Harleigh Christian

The results contained in this dissertation contribute to a deeper level of understanding to the energy required to slew a spacecraft using reaction wheels. This work addresses the fundamental manner in which spacecrafts are slewed (eigenaxis maneuvering), and demonstrates that this conventional maneuver can be dramatically improved upon in regards to reduction of energy, dissipative losses, as well as peak power. Energy is a fundamental resource that effects every asset, system, and subsystem upon a spacecraft, from the attitude control system which orients the spacecraft, to the communication subsystem to link with ground stations, to the payloads which collect scientific data. For a reaction wheel spacecraft, the attitude control system is a particularly heavy load on the power and energy resources on a spacecraft. The central focus of this dissertation is reducing the burden which the attitude control system places upon the spacecraft in regards to electrical energy, which is shown in this dissertation to be a challenging problem to computationally solve and analyze. Reducing power and energy demands can have a multitude of benefits, spanning from the initial design phase, to in-flight operations, to potentially extending the mission life of the spacecraft. This goal is approached from a practical standpoint apropos to an industry-flight setting. Metrics to measure electrical energy and power are developed which are in-line with the cost associated to operating reaction wheel based attitude control systems. These metrics are incorporated into multiple families of practical high-dimensional constrained nonlinear optimal control problems to reduce the electrical energy, as well as the instantaneous power burdens imposed by the attitude control system upon the spacecraft. Minimizing electrical energy is shown to be a problem in L1 optimal control which is nonsmooth in regards to state variables as well as the control. To overcome the challenge of nonsmoothness, a method is adopted in this dissertation to transform the nonsmooth minimum electrical energy problem into an equivalent smooth formulation, which then allows standard techniques in optimal control to solve and analyze the problem. Through numerically solving families of optimal control problems, the relationship between electrical energy and transfer time is identified and explored for both off-and on-eigenaxis maneuvering, under minimum dissipative losses as well as under minimum electrical energy. A trade space between on-and off-eigenaxis maneuvering is identified, from which is shown that agile near time optimal maneuvers exist within the energy budget associated with conventional eigenaxis maneuvering. Moreover, even for conventional eigenaxis maneuvering, energy requirements can be dramatically reduced by maneuvering off-eigenaxis. These results address one of the fundamental assumptions in the field of optimal path design verses conventional maneuver design. Two practical flight situations are addressed in this dissertation in regards to reducing energy and power: The case when the attitude of the spacecraft is predetermined, and the case where reaction wheels can not be directly controlled. For the setting where the attitude of spacecraft is on a predefined trajectory, it is demonstrated that reduced energy maneuvers are only attainable though the application of null-motions, which requires control of the reaction wheels. A computationally light formulation is developed minimizing the dissipative losses through the application of null motions. In the situation where the reaction wheels can not be directly controlled, it is demonstrated that energy consumption, dissipative losses, and peak-power loads, of the reaction-wheel array can each be reduced substantially by controlling the input to the attitude control system through attitude steering. It is demonstrated that the open loop trajectories correctly predict the closed loop response when tracked by an attitude control system which does not allow direct command of the reaction wheels.

[Can the local energy minimization refine the PDB structures of different resolution universally?].

PubMed

Godzi, M G; Gromova, A P; Oferkin, I V; Mironov, P V

2009-01-01

The local energy minimization was statistically validated as the refinement strategy for PDB structure pairs of different resolution. Thirteen pairs of structures with the only difference in resolution were extracted from PDB, and the structures of 11 identical proteins obtained by different X-ray diffraction techniques were represented. The distribution of RMSD value was calculated for these pairs before and after the local energy minimization of each structure. The MMFF94 field was used for energy calculations, and the quasi-Newton method was used for local energy minimization. By comparison of these two RMSD distributions, the local energy minimization was proved to statistically increase the structural differences in pairs so that it cannot be used for refinement purposes. To explore the prospects of complex refinement strategies based on energy minimization, randomized structures were obtained by moving the initial PDB structures as far as the minimized structures had been moved in a multidimensional space of atomic coordinates. For these randomized structures, the RMSD distribution was calculated and compared with that for minimized structures. The significant differences in their mean values proved the energy surface of the protein to have only few minima near the conformations of different resolution obtained by X-ray diffraction for PDB. Some other results obtained by exploring the energy surface near these conformations are also presented. These results are expected to be very useful for the development of new protein refinement strategies based on energy minimization.

Sensitivity of inelastic response to numerical integration of strain energy. [for cantilever beam

NASA Technical Reports Server (NTRS)

Kamat, M. P.

1976-01-01

The exact solution to the quasi-static, inelastic response of a cantilever beam of rectangular cross section subjected to a bending moment at the tip is obtained. The material of the beam is assumed to be linearly elastic-linearly strain-hardening. This solution is then compared with three different numerical solutions of the same problem obtained by minimizing the total potential energy using Gaussian quadratures of two different orders and a Newton-Cotes scheme for integrating the strain energy of deformation. Significant differences between the exact dissipative strain energy and its numerical counterpart are emphasized. The consequence of this on the nonlinear transient responses of a beam with solid cross section and that of a thin-walled beam on elastic supports under impulsive loads are examined.

Harnessing surface plasmons for solar energy conversion

NASA Technical Reports Server (NTRS)

Anderson, L. M.

1983-01-01

NASA research on the feasibility of solar-energy conversion using surface plasmons is reviewed, with a focus on inelastic-tunnel-diode techniques for power extraction. The need for more efficient solar converters for planned space missions is indicated, and it is shown that a device with 50-percent efficiency could cost up to 40 times as much per sq cm as current Si cells and still be competitive. The parallel-processing approach using broadband carriers and tunable diodes is explained, and the physics of surface plasmons on metal surfaces is outlined. Technical problems being addressed include phase-matching sunlight to surface plasmons, minimizing ohmic losses and reradiation in energy transport, coupling into the tunnels by mode conversion, and gaining an understanding of the tunnel-diode energy-conversion process. Diagrams illustrating the design concepts are provided.

Design and evaluation of a microgrid for PEV charging with flexible distribution of energy sources and storage

NASA Astrophysics Data System (ADS)

Pyne, Moinak

This thesis aspires to model and control, the flow of power in a DC microgrid. Specifically, the energy sources are a photovoltaic system and the utility grid, a lead acid battery based energy storage system and twenty PEV charging stations as the loads. Theoretical principles of large scale state space modeling are applied to model the considerable number of power electronic converters needed for controlling voltage and current thresholds. The energy storage system is developed using principles of neural networks to facilitate a stable and uncomplicated model of the lead acid battery. Power flow control is structured as a hierarchical problem with multiple interactions between individual components of the microgrid. The implementation is done using fuzzy logic with scheduling the maximum use of available solar energy and compensating demand or excess power with the energy storage system, and minimizing utility grid use, while providing multiple speeds of charging the PEVs.

A new procedure to analyze the effect of air changes in building energy consumption

PubMed Central

2014-01-01

Background Today, the International Energy Agency is working under good practice guides that integrate appropriate and cost effective technologies. In this paper a new procedure to define building energy consumption in accordance with the ISO 13790 standard was performed and tested based on real data from a Spanish region. Results Results showed that the effect of air changes on building energy consumption can be defined using the Weibull peak function model. Furthermore, the effect of climate change on building energy consumption under several different air changes was nearly nil during the summer season. Conclusions The procedure obtained could be the much sought-after solution to the problem stated by researchers in the past and future research works relating to this new methodology could help us define the optimal improvement in real buildings to reduce energy consumption, and its related carbon dioxide emissions, at minimal economical cost. PMID:24456655

Energy-efficient algorithm for broadcasting in ad hoc wireless sensor networks.

PubMed

Xiong, Naixue; Huang, Xingbo; Cheng, Hongju; Wan, Zheng

2013-04-12

Broadcasting is a common and basic operation used to support various network protocols in wireless networks. To achieve energy-efficient broadcasting is especially important for ad hoc wireless sensor networks because sensors are generally powered by batteries with limited lifetimes. Energy consumption for broadcast operations can be reduced by minimizing the number of relay nodes based on the observation that data transmission processes consume more energy than data reception processes in the sensor nodes, and how to improve the network lifetime is always an interesting issue in sensor network research. The minimum-energy broadcast problem is then equivalent to the problem of finding the minimum Connected Dominating Set (CDS) for a connected graph that is proved NP-complete. In this paper, we introduce an Efficient Minimum CDS algorithm (EMCDS) with help of a proposed ordered sequence list. EMCDS does not concern itself with node energy and broadcast operations might fail if relay nodes are out of energy. Next we have proposed a Minimum Energy-consumption Broadcast Scheme (MEBS) with a modified version of EMCDS, and aimed at providing an efficient scheduling scheme with maximized network lifetime. The simulation results show that the proposed EMCDS algorithm can find smaller CDS compared with related works, and the MEBS can help to increase the network lifetime by efficiently balancing energy among nodes in the networks.

Toward Affordable Systems III: Portfolio Management for Army Engineering and Manufacturing Development Programs

DTIC Science & Technology

2012-02-01

a public service of the RAND Corporation. CHILDREN AND FAMILIES EDUCATION AND THE ARTS ENERGY AND ENVIRONMENT HEALTH AND HEALTH CARE INFRASTRUCTURE...agencies use capability portfolio management to optimize capa- bility investments and minimize risk in meeting the DoD needs across the defense...broadly expose potential problem areas—that is, which requirements (or areas of demand) are at risk of not being met by that particular portfolio

Optimal Management and Design of Energy Systems under Atmospheric Uncertainty

NASA Astrophysics Data System (ADS)

Anitescu, M.; Constantinescu, E. M.; Zavala, V.

2010-12-01

The generation and distpatch of electricity while maintaining high reliability levels are two of the most daunting engineering problems of the modern era. This was demonstrated by the Northeast blackout of August 2003, which resulted in the loss of 6.2 gigawatts that served more than 50 million people and which resulted in economic losses on the order of $10 billion. In addition, there exist strong socioeconomic pressures to improve the efficiency of the grid. The most prominent solution to this problem is a substantial increase in the use of renewable energy such as wind and solar. In turn, its uncertain availability—which is due to the intrinsic weather variability—will increase the likelihood of disruptions. In this endeavors of current and next-generation power systems, forecasting atmospheric conditions with uncertainty can and will play a central role, at both the demand and the generation ends. User demands are strongly correlated to physical conditions such as temperature, humidity, and solar radiation. The reason is that the ambient temperature and solar radiation dictate the amount of air conditioning and lighting needed in residential and commercial buildings. But these potential benefits would come at the expense of increased variability in the dynamics of both production and demand, which would become even more dependent on weather state and its uncertainty. One of the important challenges for energy in our time is how to harness these benefits while “keeping the lights on”—ensuring that the demand is satisfied at all times and that no blackout occurs while all energy sources are optimally used. If we are to meet this challenge, accounting for uncertainty in the atmospheric conditions is essential, since this will allow minimizing the effects of false positives: committing too little baseline power in anticipation of demand that is underestimated or renewable energy levels that fail to materialize. In this work we describe a framework for the optimal management and design of energy systems, such as the power grid or building systems, under atmospheric conditions uncertainty. The framework is defined in terms of a mathematical paradigm called stochastic programming: minimization of the expected value of the decision-makers objective function subject to physical and operational constraints, such as low blackout porbability, that are enforced on each scenario. We report results on testing the framework on the optimal management of power grid systems under high wind penetration scenarios, a problem whose time horizon is in the order of days. We discuss the computational effort of scenario generation which involves running WRF at high spatio-temporal resolution dictated by the operational constraints as well as solving the optimal dispatch problem. We demonstrate that accounting for uncertainty in atmospheric conditions results in blackout prevention, whereas decisions using only mean forecast does not. We discuss issues in using the framework for planning problems, whose time horizon is of several decades and what requirements this problem would entail from climate simulation systems.

Analysis of an optimization-based atomistic-to-continuum coupling method for point defects

DOE PAGES

Olson, Derek; Shapeev, Alexander V.; Bochev, Pavel B.; ...

2015-11-16

Here, we formulate and analyze an optimization-based Atomistic-to-Continuum (AtC) coupling method for problems with point defects. Application of a potential-based atomistic model near the defect core enables accurate simulation of the defect. Away from the core, where site energies become nearly independent of the lattice position, the method switches to a more efficient continuum model. The two models are merged by minimizing the mismatch of their states on an overlap region, subject to the atomistic and continuum force balance equations acting independently in their domains. We prove that the optimization problem is well-posed and establish error estimates.

Optimal fixed-finite-dimensional compensator for Burgers' equation with unbounded input/output operators

NASA Technical Reports Server (NTRS)

Burns, John A.; Marrekchi, Hamadi

1993-01-01

The problem of using reduced order dynamic compensators to control a class of nonlinear parabolic distributed parameter systems was considered. Concentration was on a system with unbounded input and output operators governed by Burgers' equation. A linearized model was used to compute low-order-finite-dimensional control laws by minimizing certain energy functionals. Then these laws were applied to the nonlinear model. Standard approaches to this problem employ model/controller reduction techniques in conjunction with linear quadratic Gaussian (LQG) theory. The approach used is based on the finite dimensional Bernstein/Hyland optimal projection theory which yields a fixed-finite-order controller.

CAD of control systems: Application of nonlinear programming to a linear quadratic formulation

NASA Technical Reports Server (NTRS)

Fleming, P.

1983-01-01

The familiar suboptimal regulator design approach is recast as a constrained optimization problem and incorporated in a Computer Aided Design (CAD) package where both design objective and constraints are quadratic cost functions. This formulation permits the separate consideration of, for example, model following errors, sensitivity measures and control energy as objectives to be minimized or limits to be observed. Efficient techniques for computing the interrelated cost functions and their gradients are utilized in conjunction with a nonlinear programming algorithm. The effectiveness of the approach and the degree of insight into the problem which it affords is illustrated in a helicopter regulation design example.

Microalgae biorefineries: The Brazilian scenario in perspective.

PubMed

Brasil, B S A F; Silva, F C P; Siqueira, F G

2017-10-25

Biorefineries have the potential to meet a significant part of the growing demand for energy, fuels, chemicals and materials worldwide. Indeed, the bio-based industry is expected to play a major role in energy security and climate change mitigation during the 21th century. Despite this, there are challenges related to resource consumption, processing optimization and waste minimization that still need to be overcome. In this context, microalgae appear as a promising non-edible feedstock with advantages over traditional land crops, such as high productivity, continuous harvesting throughout the year and minimal problems regarding land use. Importantly, both cultivation and microalgae processing can take place at the same site, which increases the possibilities for process integration and a reduction in logistic costs at biorefinery facilities. This review describes the actual scenario for microalgae biorefineries integration to the biofuels and petrochemical industries in Brazil, while highlighting the major challenges and recent advances in microalgae large-scale production. Copyright © 2016 Elsevier B.V. All rights reserved.

Saptio-temporal complementarity of wind and solar power in India

NASA Astrophysics Data System (ADS)

Lolla, Savita; Baidya Roy, Somnath; Chowdhury, Sourangshu

2015-04-01

Wind and solar power are likely to be a part of the solution to the climate change problem. That is why they feature prominently in the energy policies of all industrial economies including India. One of the major hindrances that is preventing an explosive growth of wind and solar energy is the issue of intermittency. This is a major problem because in a rapidly moving economy, energy production must match the patterns of energy demand. Moreover, sudden increase and decrease in energy supply may destabilize the power grids leading to disruptions in power supply. In this work we explore if the patterns of variability in wind and solar energy availability can offset each other so that a constant supply can be guaranteed. As a first step, this work focuses on seasonal-scale variability for each of the 5 regional power transmission grids in India. Communication within each grid is better than communication between grids. Hence, it is assumed that the grids can switch sources relatively easily. Wind and solar resources are estimated using the MERRA Reanalysis data for the 1979-2013 period. Solar resources are calculated with a 20% conversion efficiency. Wind resources are estimated using a 2 MW turbine power curve. Total resources are obtained by optimizing location and number of wind/solar energy farms. Preliminary results show that the southern and western grids are more appropriate for cogeneration than the other grids. Many studies on wind-solar cogeneration have focused on temporal complementarity at local scale. However, this is one of the first studies to explore spatial complementarity over regional scales. This project may help accelerate renewable energy penetration in India by identifying regional grid(s) where the renewable energy intermittency problem can be minimized.

Combining gait optimization with passive system to increase the energy efficiency of a humanoid robot walking movement

DOE Office of Scientific and Technical Information (OSTI.GOV)

Pereira, Ana I.; ALGORITMI,University of Minho; Lima, José

There are several approaches to create the Humanoid robot gait planning. This problem presents a large number of unknown parameters that should be found to make the humanoid robot to walk. Optimization in simulation models can be used to find the gait based on several criteria such as energy minimization, acceleration, step length among the others. The energy consumption can also be reduced with elastic elements coupled to each joint. The presented paper addresses an optimization method, the Stretched Simulated Annealing, that runs in an accurate and stable simulation model to find the optimal gait combined with elastic elements. Finalmore » results demonstrate that optimization is a valid gait planning technique.« less

An Optimization Framework for Dynamic Hybrid Energy Systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wenbo Du; Humberto E Garcia; Christiaan J.J. Paredis

A computational framework for the efficient analysis and optimization of dynamic hybrid energy systems (HES) is developed. A microgrid system with multiple inputs and multiple outputs (MIMO) is modeled using the Modelica language in the Dymola environment. The optimization loop is implemented in MATLAB, with the FMI Toolbox serving as the interface between the computational platforms. Two characteristic optimization problems are selected to demonstrate the methodology and gain insight into the system performance. The first is an unconstrained optimization problem that optimizes the dynamic properties of the battery, reactor and generator to minimize variability in the HES. The second problemmore » takes operating and capital costs into consideration by imposing linear and nonlinear constraints on the design variables. The preliminary optimization results obtained in this study provide an essential step towards the development of a comprehensive framework for designing HES.« less

Stochastic DG Placement for Conservation Voltage Reduction Based on Multiple Replications Procedure

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, Zhaoyu; Chen, Bokan; Wang, Jianhui

2015-06-01

Conservation voltage reduction (CVR) and distributed-generation (DG) integration are popular strategies implemented by utilities to improve energy efficiency. This paper investigates the interactions between CVR and DG placement to minimize load consumption in distribution networks, while keeping the lowest voltage level within the predefined range. The optimal placement of DG units is formulated as a stochastic optimization problem considering the uncertainty of DG outputs and load consumptions. A sample average approximation algorithm-based technique is developed to solve the formulated problem effectively. A multiple replications procedure is developed to test the stability of the solution and calculate the confidence interval ofmore » the gap between the candidate solution and optimal solution. The proposed method has been applied to the IEEE 37-bus distribution test system with different scenarios. The numerical results indicate that the implementations of CVR and DG, if combined, can achieve significant energy savings.« less

Inverse problems with nonnegative and sparse solutions: algorithms and application to the phase retrieval problem

NASA Astrophysics Data System (ADS)

Quy Muoi, Pham; Nho Hào, Dinh; Sahoo, Sujit Kumar; Tang, Dongliang; Cong, Nguyen Huu; Dang, Cuong

2018-05-01

In this paper, we study a gradient-type method and a semismooth Newton method for minimization problems in regularizing inverse problems with nonnegative and sparse solutions. We propose a special penalty functional forcing the minimizers of regularized minimization problems to be nonnegative and sparse, and then we apply the proposed algorithms in a practical the problem. The strong convergence of the gradient-type method and the local superlinear convergence of the semismooth Newton method are proven. Then, we use these algorithms for the phase retrieval problem and illustrate their efficiency in numerical examples, particularly in the practical problem of optical imaging through scattering media where all the noises from experiment are presented.

Mesh refinement in finite element analysis by minimization of the stiffness matrix trace

NASA Technical Reports Server (NTRS)

Kittur, Madan G.; Huston, Ronald L.

1989-01-01

Most finite element packages provide means to generate meshes automatically. However, the user is usually confronted with the problem of not knowing whether the mesh generated is appropriate for the problem at hand. Since the accuracy of the finite element results is mesh dependent, mesh selection forms a very important step in the analysis. Indeed, in accurate analyses, meshes need to be refined or rezoned until the solution converges to a value so that the error is below a predetermined tolerance. A-posteriori methods use error indicators, developed by using the theory of interpolation and approximation theory, for mesh refinements. Some use other criterions, such as strain energy density variation and stress contours for example, to obtain near optimal meshes. Although these methods are adaptive, they are expensive. Alternatively, a priori methods, until now available, use geometrical parameters, for example, element aspect ratio. Therefore, they are not adaptive by nature. An adaptive a-priori method is developed. The criterion is that the minimization of the trace of the stiffness matrix with respect to the nodal coordinates, leads to a minimization of the potential energy, and as a consequence provide a good starting mesh. In a few examples the method is shown to provide the optimal mesh. The method is also shown to be relatively simple and amenable to development of computer algorithms. When the procedure is used in conjunction with a-posteriori methods of grid refinement, it is shown that fewer refinement iterations and fewer degrees of freedom are required for convergence as opposed to when the procedure is not used. The mesh obtained is shown to have uniform distribution of stiffness among the nodes and elements which, as a consequence, leads to uniform error distribution. Thus the mesh obtained meets the optimality criterion of uniform error distribution.

An optimization formulation for characterization of pulsatile cortisol secretion.

PubMed

Faghih, Rose T; Dahleh, Munther A; Brown, Emery N

2015-01-01

Cortisol is released to relay information to cells to regulate metabolism and reaction to stress and inflammation. In particular, cortisol is released in the form of pulsatile signals. This low-energy method of signaling seems to be more efficient than continuous signaling. We hypothesize that there is a controller in the anterior pituitary that leads to pulsatile release of cortisol, and propose a mathematical formulation for such controller, which leads to impulse control as opposed to continuous control. We postulate that this controller is minimizing the number of secretory events that result in cortisol secretion, which is a way of minimizing the energy required for cortisol secretion; this controller maintains the blood cortisol levels within a specific circadian range while complying with the first order dynamics underlying cortisol secretion. We use an ℓ0-norm cost function for this controller, and solve a reweighed ℓ1-norm minimization algorithm for obtaining the solution to this optimization problem. We use four examples to illustrate the performance of this approach: (i) a toy problem that achieves impulse control, (ii) two examples that achieve physiologically plausible pulsatile cortisol release, (iii) an example where the number of pulses is not within the physiologically plausible range for healthy subjects while the cortisol levels are within the desired range. This novel approach results in impulse control where the impulses and the obtained blood cortisol levels have a circadian rhythm and an ultradian rhythm that are in agreement with the known physiology of cortisol secretion. The proposed formulation is a first step in developing intermittent controllers for curing cortisol deficiency. This type of bio-inspired pulse controllers can be employed for designing non-continuous controllers in brain-machine interface design for neuroscience applications.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dall'Arno, Michele; ICFO-Institut de Ciencies Fotoniques, E-08860 Castelldefels; Quit Group, Dipartimento di Fisica, via Bassi 6, I-27100 Pavia

We address the problem of quantum reading of optical memories, namely the retrieving of classical information stored in the optical properties of a media with minimum energy. We present optimal strategies for ambiguous and unambiguous quantum reading of unitary optical memories, namely when one's task is to minimize the probability of errors in the retrieved information and when perfect retrieving of information is achieved probabilistically, respectively. A comparison of the optimal strategy with coherent probes and homodyne detection shows that the former saves orders of magnitude of energy when achieving the same performances. Experimental proposals for quantum reading which aremore » feasible with present quantum optical technology are reported.« less

Optimizing Wellfield Operation in a Variable Power Price Regime.

PubMed

Bauer-Gottwein, Peter; Schneider, Raphael; Davidsen, Claus

2016-01-01

Wellfield management is a multiobjective optimization problem. One important objective has been energy efficiency in terms of minimizing the energy footprint (EFP) of delivered water (MWh/m(3) ). However, power systems in most countries are moving in the direction of deregulated markets and price variability is increasing in many markets because of increased penetration of intermittent renewable power sources. In this context the relevant management objective becomes minimizing the cost of electric energy used for pumping and distribution of groundwater from wells rather than minimizing energy use itself. We estimated EFP of pumped water as a function of wellfield pumping rate (EFP-Q relationship) for a wellfield in Denmark using a coupled well and pipe network model. This EFP-Q relationship was subsequently used in a Stochastic Dynamic Programming (SDP) framework to minimize total cost of operating the combined wellfield-storage-demand system over the course of a 2-year planning period based on a time series of observed price on the Danish power market and a deterministic, time-varying hourly water demand. In the SDP setup, hourly pumping rates are the decision variables. Constraints include storage capacity and hourly water demand fulfilment. The SDP was solved for a baseline situation and for five scenario runs representing different EFP-Q relationships and different maximum wellfield pumping rates. Savings were quantified as differences in total cost between the scenario and a constant-rate pumping benchmark. Minor savings up to 10% were found in the baseline scenario, while the scenario with constant EFP and unlimited pumping rate resulted in savings up to 40%. Key factors determining potential cost savings obtained by flexible wellfield operation under a variable power price regime are the shape of the EFP-Q relationship, the maximum feasible pumping rate and the capacity of available storage facilities. © 2015 The Authors. Groundwater published by Wiley Periodicals, Inc. on behalf of National Ground Water Association.

Image denoising by a direct variational minimization

NASA Astrophysics Data System (ADS)

Janev, Marko; Atanacković, Teodor; Pilipović, Stevan; Obradović, Radovan

2011-12-01

In this article we introduce a novel method for the image de-noising which combines a mathematically well-posdenes of the variational modeling with the efficiency of a patch-based approach in the field of image processing. It based on a direct minimization of an energy functional containing a minimal surface regularizer that uses fractional gradient. The minimization is obtained on every predefined patch of the image, independently. By doing so, we avoid the use of an artificial time PDE model with its inherent problems of finding optimal stopping time, as well as the optimal time step. Moreover, we control the level of image smoothing on each patch (and thus on the whole image) by adapting the Lagrange multiplier using the information on the level of discontinuities on a particular patch, which we obtain by pre-processing. In order to reduce the average number of vectors in the approximation generator and still to obtain the minimal degradation, we combine a Ritz variational method for the actual minimization on a patch, and a complementary fractional variational principle. Thus, the proposed method becomes computationally feasible and applicable for practical purposes. We confirm our claims with experimental results, by comparing the proposed method with a couple of PDE-based methods, where we get significantly better denoising results specially on the oscillatory regions.

Development of optimized segmentation map in dual energy computed tomography

NASA Astrophysics Data System (ADS)

Yamakawa, Keisuke; Ueki, Hironori

2012-03-01

Dual energy computed tomography (DECT) has been widely used in clinical practice and has been particularly effective for tissue diagnosis. In DECT the difference of two attenuation coefficients acquired by two kinds of X-ray energy enables tissue segmentation. One problem in conventional DECT is that the segmentation deteriorates in some cases, such as bone removal. This is due to two reasons. Firstly, the segmentation map is optimized without considering the Xray condition (tube voltage and current). If we consider the tube voltage, it is possible to create an optimized map, but unfortunately we cannot consider the tube current. Secondly, the X-ray condition is not optimized. The condition can be set empirically, but this means that the optimized condition is not used correctly. To solve these problems, we have developed methods for optimizing the map (Method-1) and the condition (Method-2). In Method-1, the map is optimized to minimize segmentation errors. The distribution of the attenuation coefficient is modeled by considering the tube current. In Method-2, the optimized condition is decided to minimize segmentation errors depending on tube voltagecurrent combinations while keeping the total exposure constant. We evaluated the effectiveness of Method-1 by performing a phantom experiment under the fixed condition and of Method-2 by performing a phantom experiment under different combinations calculated from the total exposure constant. When Method-1 was followed with Method-2, the segmentation error was reduced from 37.8 to 13.5 %. These results demonstrate that our developed methods can achieve highly accurate segmentation while keeping the total exposure constant.

Action-minimizing solutions of the one-dimensional N-body problem

NASA Astrophysics Data System (ADS)

Yu, Xiang; Zhang, Shiqing

2018-05-01

We supplement the following result of C. Marchal on the Newtonian N-body problem: A path minimizing the Lagrangian action functional between two given configurations is always a true (collision-free) solution when the dimension d of the physical space R^d satisfies d≥2. The focus of this paper is on the fixed-ends problem for the one-dimensional Newtonian N-body problem. We prove that a path minimizing the action functional in the set of paths joining two given configurations and having all the time the same order is always a true (collision-free) solution. Considering the one-dimensional N-body problem with equal masses, we prove that (i) collision instants are isolated for a path minimizing the action functional between two given configurations, (ii) if the particles at two endpoints have the same order, then the path minimizing the action functional is always a true (collision-free) solution and (iii) when the particles at two endpoints have different order, although there must be collisions for any path, we can prove that there are at most N! - 1 collisions for any action-minimizing path.

An Efficient Offloading Scheme For MEC System Considering Delay and Energy Consumption

NASA Astrophysics Data System (ADS)

Sun, Yanhua; Hao, Zhe; Zhang, Yanhua

2018-01-01

With the increasing numbers of mobile devices, mobile edge computing (MEC) which provides cloud computing capabilities proximate to mobile devices in 5G networks has been envisioned as a promising paradigm to enhance users experience. In this paper, we investigate a joint consideration of delay and energy consumption offloading scheme (JCDE) for MEC system in 5G heterogeneous networks. An optimization is formulated to minimize the delay as well as energy consumption of the offloading system, which the delay and energy consumption of transmitting and calculating tasks are taken into account. We adopt an iterative greedy algorithm to solve the optimization problem. Furthermore, simulations were carried out to validate the utility and effectiveness of our proposed scheme. The effect of parameter variations on the system is analysed as well. Numerical results demonstrate delay and energy efficiency promotion of our proposed scheme compared with another paper’s scheme.

Energy conservation in solid waste management in Bangladesh

DOE Office of Scientific and Technical Information (OSTI.GOV)

Rahman, M.H.

1994-12-31

Recycling of solid wastes has a characteristic pattern in Bangladesh in the context of the general habits and socio-economic status of the population. Extensive resource recovery from solid wastes is being carried out at various stages of disposal. The characteristics of solid wastes at the final disposal site indicate that they contain more than 90% of organic wastes. Hence, anaerobic digestion of these wastes serves a dual purpose in the conservation of energy and of valuable crop nutrients for efficient recycling especially in an agriculture-based economy. This also improves overall environmental sanitation and reduces environmental degradation. In this paper, differentmore » recycling and reuse options for solid wastes are critically discussed from the energy recovery and energy conservation point of view. It has been shown that the resource recovery from solid wastes would minimize the energy problem and would lead to a net reduction of greenhouse gases, particularly in the developing world.« less

Active inference and epistemic value.

PubMed

Friston, Karl; Rigoli, Francesco; Ognibene, Dimitri; Mathys, Christoph; Fitzgerald, Thomas; Pezzulo, Giovanni

2015-01-01

We offer a formal treatment of choice behavior based on the premise that agents minimize the expected free energy of future outcomes. Crucially, the negative free energy or quality of a policy can be decomposed into extrinsic and epistemic (or intrinsic) value. Minimizing expected free energy is therefore equivalent to maximizing extrinsic value or expected utility (defined in terms of prior preferences or goals), while maximizing information gain or intrinsic value (or reducing uncertainty about the causes of valuable outcomes). The resulting scheme resolves the exploration-exploitation dilemma: Epistemic value is maximized until there is no further information gain, after which exploitation is assured through maximization of extrinsic value. This is formally consistent with the Infomax principle, generalizing formulations of active vision based upon salience (Bayesian surprise) and optimal decisions based on expected utility and risk-sensitive (Kullback-Leibler) control. Furthermore, as with previous active inference formulations of discrete (Markovian) problems, ad hoc softmax parameters become the expected (Bayes-optimal) precision of beliefs about, or confidence in, policies. This article focuses on the basic theory, illustrating the ideas with simulations. A key aspect of these simulations is the similarity between precision updates and dopaminergic discharges observed in conditioning paradigms.

Optimizing Cluster Heads for Energy Efficiency in Large-Scale Heterogeneous Wireless Sensor Networks

DOE PAGES

Gu, Yi; Wu, Qishi; Rao, Nageswara S. V.

2010-01-01

Many complex sensor network applications require deploying a large number of inexpensive and small sensors in a vast geographical region to achieve quality through quantity. Hierarchical clustering is generally considered as an efficient and scalable way to facilitate the management and operation of such large-scale networks and minimize the total energy consumption for prolonged lifetime. Judicious selection of cluster heads for data integration and communication is critical to the success of applications based on hierarchical sensor networks organized as layered clusters. We investigate the problem of selecting sensor nodes in a predeployed sensor network to be the cluster heads tomore » minimize the total energy needed for data gathering. We rigorously derive an analytical formula to optimize the number of cluster heads in sensor networks under uniform node distribution, and propose a Distance-based Crowdedness Clustering algorithm to determine the cluster heads in sensor networks under general node distribution. The results from an extensive set of experiments on a large number of simulated sensor networks illustrate the performance superiority of the proposed solution over the clustering schemes based on k -means algorithm.« less

Useful lower limits to polarization contributions to intermolecular interactions using a minimal basis of localized orthogonal orbitals: theory and analysis of the water dimer.

PubMed

Azar, R Julian; Horn, Paul Richard; Sundstrom, Eric Jon; Head-Gordon, Martin

2013-02-28

The problem of describing the energy-lowering associated with polarization of interacting molecules is considered in the overlapping regime for self-consistent field wavefunctions. The existing approach of solving for absolutely localized molecular orbital (ALMO) coefficients that are block-diagonal in the fragments is shown based on formal grounds and practical calculations to often overestimate the strength of polarization effects. A new approach using a minimal basis of polarized orthogonal local MOs (polMOs) is developed as an alternative. The polMO basis is minimal in the sense that one polarization function is provided for each unpolarized orbital that is occupied; such an approach is exact in second-order perturbation theory. Based on formal grounds and practical calculations, the polMO approach is shown to underestimate the strength of polarization effects. In contrast to the ALMO method, however, the polMO approach yields results that are very stable to improvements in the underlying AO basis expansion. Combining the ALMO and polMO approaches allows an estimate of the range of energy-lowering due to polarization. Extensive numerical calculations on the water dimer using a large range of basis sets with Hartree-Fock theory and a variety of different density functionals illustrate the key considerations. Results are also presented for the polarization-dominated Na(+)CH4 complex. Implications for energy decomposition analysis of intermolecular interactions are discussed.

Split Bregman's optimization method for image construction in compressive sensing

NASA Astrophysics Data System (ADS)

Skinner, D.; Foo, S.; Meyer-Bäse, A.

2014-05-01

The theory of compressive sampling (CS) was reintroduced by Candes, Romberg and Tao, and D. Donoho in 2006. Using a priori knowledge that a signal is sparse, it has been mathematically proven that CS can defY Nyquist sampling theorem. Theoretically, reconstruction of a CS image relies on the minimization and optimization techniques to solve this complex almost NP-complete problem. There are many paths to consider when compressing and reconstructing an image but these methods have remained untested and unclear on natural images, such as underwater sonar images. The goal of this research is to perfectly reconstruct the original sonar image from a sparse signal while maintaining pertinent information, such as mine-like object, in Side-scan sonar (SSS) images. Goldstein and Osher have shown how to use an iterative method to reconstruct the original image through a method called Split Bregman's iteration. This method "decouples" the energies using portions of the energy from both the !1 and !2 norm. Once the energies are split, Bregman iteration is used to solve the unconstrained optimization problem by recursively solving the problems simultaneously. The faster these two steps or energies can be solved then the faster the overall method becomes. While the majority of CS research is still focused on the medical field, this paper will demonstrate the effectiveness of the Split Bregman's methods on sonar images.

Non-Rocket Earth-Moon Transport System

NASA Technical Reports Server (NTRS)

Bolonkin, Alexander

2002-01-01

This paper proposes a new method and transportation system to travel to the Moon. This transportation system uses a mechanical energy transfer and requires only minimal energy so that it provides a 'Free Trip' into space. The method uses the rotary and kinetic energy of the Moon. This paper presents the theory and results of computations for the project provided Free Trips (without rockets and spend a big energy) to the Moon for six thousand people annually. The project uses artificial materials like nanotubes and whiskers that have a ratio of tensile strength to density equal 4 million meters. In the future, nanotubes will be produced that can reach a specific stress up 100 millions meter and will significantly improve the parameters of suggested project. The author is prepared to discuss the problems with serious organizations that want to research and develop these innovations.

Wind farm optimization using evolutionary algorithms

NASA Astrophysics Data System (ADS)

Ituarte-Villarreal, Carlos M.

In recent years, the wind power industry has focused its efforts on solving the Wind Farm Layout Optimization (WFLO) problem. Wind resource assessment is a pivotal step in optimizing the wind-farm design and siting and, in determining whether a project is economically feasible or not. In the present work, three (3) different optimization methods are proposed for the solution of the WFLO: (i) A modified Viral System Algorithm applied to the optimization of the proper location of the components in a wind-farm to maximize the energy output given a stated wind environment of the site. The optimization problem is formulated as the minimization of energy cost per unit produced and applies a penalization for the lack of system reliability. The viral system algorithm utilized in this research solves three (3) well-known problems in the wind-energy literature; (ii) a new multiple objective evolutionary algorithm to obtain optimal placement of wind turbines while considering the power output, cost, and reliability of the system. The algorithm presented is based on evolutionary computation and the objective functions considered are the maximization of power output, the minimization of wind farm cost and the maximization of system reliability. The final solution to this multiple objective problem is presented as a set of Pareto solutions and, (iii) A hybrid viral-based optimization algorithm adapted to find the proper component configuration for a wind farm with the introduction of the universal generating function (UGF) analytical approach to discretize the different operating or mechanical levels of the wind turbines in addition to the various wind speed states. The proposed methodology considers the specific probability functions of the wind resource to describe their proper behaviors to account for the stochastic comportment of the renewable energy components, aiming to increase their power output and the reliability of these systems. The developed heuristic considers a variable number of system components and wind turbines with different operating characteristics and sizes, to have a more heterogeneous model that can deal with changes in the layout and in the power generation requirements over the time. Moreover, the approach evaluates the impact of the wind-wake effect of the wind turbines upon one another to describe and evaluate the power production capacity reduction of the system depending on the layout distribution of the wind turbines.

Prediction-based manufacturing center self-adaptive demand side energy optimization in cyber physical systems

NASA Astrophysics Data System (ADS)

Sun, Xinyao; Wang, Xue; Wu, Jiangwei; Liu, Youda

2014-05-01

Cyber physical systems(CPS) recently emerge as a new technology which can provide promising approaches to demand side management(DSM), an important capability in industrial power systems. Meanwhile, the manufacturing center is a typical industrial power subsystem with dozens of high energy consumption devices which have complex physical dynamics. DSM, integrated with CPS, is an effective methodology for solving energy optimization problems in manufacturing center. This paper presents a prediction-based manufacturing center self-adaptive energy optimization method for demand side management in cyber physical systems. To gain prior knowledge of DSM operating results, a sparse Bayesian learning based componential forecasting method is introduced to predict 24-hour electric load levels for specific industrial areas in China. From this data, a pricing strategy is designed based on short-term load forecasting results. To minimize total energy costs while guaranteeing manufacturing center service quality, an adaptive demand side energy optimization algorithm is presented. The proposed scheme is tested in a machining center energy optimization experiment. An AMI sensing system is then used to measure the demand side energy consumption of the manufacturing center. Based on the data collected from the sensing system, the load prediction-based energy optimization scheme is implemented. By employing both the PSO and the CPSO method, the problem of DSM in the manufacturing center is solved. The results of the experiment show the self-adaptive CPSO energy optimization method enhances optimization by 5% compared with the traditional PSO optimization method.

Solving Variational Problems and Partial Differential Equations Mapping into General Target Manifolds

DTIC Science & Technology

2002-01-01

1998. [36] T. Sakai, Riemannian Geometry, AMS Translations of Mathematical Monographs, vol 149. [37] N. Sochen, R . Kimmel, and R , Malladi , “A general...matical Physics 107, pp. 649-705, 1986. [5] V. Caselles, R . Kimmel, G. Sapiro, and C. Sbert, “Minimal surfaces based object segmentation,” IEEE- PAMI...June 2000 [9] R . Cohen, R . M. Hardt, D. Kinderlehrer, S. Y. Lin, and M. Luskin, “Minimum energy configurations for liquid crystals: Computational

An efficient annealing in Boltzmann machine in Hopfield neural network

NASA Astrophysics Data System (ADS)

Kin, Teoh Yeong; Hasan, Suzanawati Abu; Bulot, Norhisam; Ismail, Mohammad Hafiz

2012-09-01

This paper proposes and implements Boltzmann machine in Hopfield neural network doing logic programming based on the energy minimization system. The temperature scheduling in Boltzmann machine enhancing the performance of doing logic programming in Hopfield neural network. The finest temperature is determined by observing the ratio of global solution and final hamming distance using computer simulations. The study shows that Boltzmann Machine model is more stable and competent in term of representing and solving difficult combinatory problems.

Power Allocation Based on Data Classification in Wireless Sensor Networks

PubMed Central

Wang, Houlian; Zhou, Gongbo

2017-01-01

Limited node energy in wireless sensor networks is a crucial factor which affects the monitoring of equipment operation and working conditions in coal mines. In addition, due to heterogeneous nodes and different data acquisition rates, the number of arriving packets in a queue network can differ, which may lead to some queue lengths reaching the maximum value earlier compared with others. In order to tackle these two problems, an optimal power allocation strategy based on classified data is proposed in this paper. Arriving data is classified into dissimilar classes depending on the number of arriving packets. The problem is formulated as a Lyapunov drift optimization with the objective of minimizing the weight sum of average power consumption and average data class. As a result, a suboptimal distributed algorithm without any knowledge of system statistics is presented. The simulations, conducted in the perfect channel state information (CSI) case and the imperfect CSI case, reveal that the utility can be pushed arbitrarily close to optimal by increasing the parameter V, but with a corresponding growth in the average delay, and that other tunable parameters W and the classification method in the interior of utility function can trade power optimality for increased average data class. The above results show that data in a high class has priorities to be processed than data in a low class, and energy consumption can be minimized in this resource allocation strategy. PMID:28498346

Analysis of Carbon Policies for Electricity Networks with High Penetration of Green Generation

NASA Astrophysics Data System (ADS)

Feijoo, Felipe A.

In recent decades, climate change has become one of the most crucial challenges for humanity. Climate change has a direct correlation with global warming, caused mainly by the green house gas emissions (GHG). The Environmental Protection Agency in the U.S. (EPA) attributes carbon dioxide to account for approximately 82% of the GHG emissions. Unfortunately, the energy sector is the main producer of carbon dioxide, with China and the U.S. as the highest emitters. Therefore, there is a strong (positive) correlation between energy production, global warming, and climate change. Stringent carbon emissions reduction targets have been established in order to reduce the impacts of GHG. Achieving these emissions reduction goals will require implementation of policies like as cap-and-trade and carbon taxes, together with transformation of the electricity grid into a smarter system with high green energy penetration. However, the consideration of policies solely in view of carbon emissions reduction may adversely impact other market outcomes such as electricity prices and consumption. In this dissertation, a two-layer mathematical-statistical framework is presented, that serves to develop carbon policies to reduce emissions level while minimizing the negative impacts on other market outcomes. The bottom layer of the two layer model comprises a bi-level optimization problem. The top layer comprises a statistical model and a Pareto analysis. Two related but different problems are studied under this methodology. The first problem looks into the design of cap-and-trade policies for deregulated electricity markets that satisfy the interest of different market constituents. Via the second problem, it is demonstrated how the framework can be used to obtain levels of carbon emissions reduction while minimizing the negative impact on electricity demand and maximizing green penetration from microgrids. In the aforementioned studies, forecasts for electricity prices and production cost are considered. This, this dissertation also presents anew forecast model that can be easily integrated in the two-layer framework. It is demonstrated in this dissertation that the proposed framework can be utilized by policy-makers, power companies, consumers, and market regulators in developing emissions policy decisions, bidding strategies, market regulations, and electricity dispatch strategies.

Utilization of biocatalysts in cellulose waste minimization

DOE Office of Scientific and Technical Information (OSTI.GOV)

Woodward, J.; Evans, B.R.

1996-09-01

Cellulose, a polymer of glucose, is the principal component of biomass and, therefore, a major source of waste that is either buried or burned. Examples of biomass waste include agricultural crop residues, forestry products, and municipal wastes. Recycling of this waste is important for energy conservation as well as waste minimization and there is some probability that in the future biomass could become a major energy source and replace fossil fuels that are currently used for fuels and chemicals production. It has been estimated that in the United States, between 100-450 million dry tons of agricultural waste are produced annually,more » approximately 6 million dry tons of animal waste, and of the 190 million tons of municipal solid waste (MSW) generated annually, approximately two-thirds is cellulosic in nature and over one-third is paper waste. Interestingly, more than 70% of MSW is landfilled or burned, however landfill space is becoming increasingly scarce. On a smaller scale, important cellulosic products such as cellulose acetate also present waste problems; an estimated 43 thousand tons of cellulose ester waste are generated annually in the United States. Biocatalysts could be used in cellulose waste minimization and this chapter describes their characteristics and potential in bioconversion and bioremediation processes.« less

A minimal model for the structural energetics of VO2

NASA Astrophysics Data System (ADS)

Kim, Chanul; Marianetti, Chris; The Marianetti Group Team

Resolving the structural, magnetic, and electronic structure of VO2 from the first-principles of quantum mechanics is still a forefront problem despite decades of attention. Hybrid functionals have been shown to qualitatively ruin the structural energetics. While density functional theory (DFT) combined with cluster extensions of dynamical mean-field theory (DMFT) have demonstrated promising results in terms of the electronic properties, structural phase stability has not yet been addressed. In order to capture the basic physics of the structural transition, we propose a minimal model of VO2 based on the one dimensional Peierls-Hubbard model and parameterize this based on DFT calculations of VO2. The total energy versus dimerization in the minimal mode is then solved numerically exactly using density matrix renormalization group (DMRG) and compared to the Hartree-Fock solution. We demonstrate that the Hartree-Fock solution exhibits the same pathologies as DFT+U, and spin density functional theory for that matter, while the DMRG solution is consistent with experimental observation. Our results demonstrate the critical role of non-locality in the total energy, and this will need to be accounted for to obtain a complete description of VO2 from first-principles. The authors acknowledge support from FAME, one of six centers of STARnet, a Semiconductor Research Corporation program sponsored by MARCO and DARPA.

An information geometric approach to least squares minimization

NASA Astrophysics Data System (ADS)

Transtrum, Mark; Machta, Benjamin; Sethna, James

2009-03-01

Parameter estimation by nonlinear least squares minimization is a ubiquitous problem that has an elegant geometric interpretation: all possible parameter values induce a manifold embedded within the space of data. The minimization problem is then to find the point on the manifold closest to the origin. The standard algorithm for minimizing sums of squares, the Levenberg-Marquardt algorithm, also has geometric meaning. When the standard algorithm fails to efficiently find accurate fits to the data, geometric considerations suggest improvements. Problems involving large numbers of parameters, such as often arise in biological contexts, are notoriously difficult. We suggest an algorithm based on geodesic motion that may offer improvements over the standard algorithm for a certain class of problems.

Semismooth Newton method for gradient constrained minimization problem

NASA Astrophysics Data System (ADS)

Anyyeva, Serbiniyaz; Kunisch, Karl

2012-08-01

In this paper we treat a gradient constrained minimization problem, particular case of which is the elasto-plastic torsion problem. In order to get the numerical approximation to the solution we have developed an algorithm in an infinite dimensional space framework using the concept of the generalized (Newton) differentiation. Regularization was done in order to approximate the problem with the unconstrained minimization problem and to make the pointwise maximum function Newton differentiable. Using semismooth Newton method, continuation method was developed in function space. For the numerical implementation the variational equations at Newton steps are discretized using finite elements method.

Replica analysis for the duality of the portfolio optimization problem

NASA Astrophysics Data System (ADS)

Shinzato, Takashi

2016-11-01

In the present paper, the primal-dual problem consisting of the investment risk minimization problem and the expected return maximization problem in the mean-variance model is discussed using replica analysis. As a natural extension of the investment risk minimization problem under only a budget constraint that we analyzed in a previous study, we herein consider a primal-dual problem in which the investment risk minimization problem with budget and expected return constraints is regarded as the primal problem, and the expected return maximization problem with budget and investment risk constraints is regarded as the dual problem. With respect to these optimal problems, we analyze a quenched disordered system involving both of these optimization problems using the approach developed in statistical mechanical informatics and confirm that both optimal portfolios can possess the primal-dual structure. Finally, the results of numerical simulations are shown to validate the effectiveness of the proposed method.

Replica analysis for the duality of the portfolio optimization problem.

PubMed

Shinzato, Takashi

2016-11-01

In the present paper, the primal-dual problem consisting of the investment risk minimization problem and the expected return maximization problem in the mean-variance model is discussed using replica analysis. As a natural extension of the investment risk minimization problem under only a budget constraint that we analyzed in a previous study, we herein consider a primal-dual problem in which the investment risk minimization problem with budget and expected return constraints is regarded as the primal problem, and the expected return maximization problem with budget and investment risk constraints is regarded as the dual problem. With respect to these optimal problems, we analyze a quenched disordered system involving both of these optimization problems using the approach developed in statistical mechanical informatics and confirm that both optimal portfolios can possess the primal-dual structure. Finally, the results of numerical simulations are shown to validate the effectiveness of the proposed method.

Learning free energy landscapes using artificial neural networks.

PubMed

Sidky, Hythem; Whitmer, Jonathan K

2018-03-14

Existing adaptive bias techniques, which seek to estimate free energies and physical properties from molecular simulations, are limited by their reliance on fixed kernels or basis sets which hinder their ability to efficiently conform to varied free energy landscapes. Further, user-specified parameters are in general non-intuitive yet significantly affect the convergence rate and accuracy of the free energy estimate. Here we propose a novel method, wherein artificial neural networks (ANNs) are used to develop an adaptive biasing potential which learns free energy landscapes. We demonstrate that this method is capable of rapidly adapting to complex free energy landscapes and is not prone to boundary or oscillation problems. The method is made robust to hyperparameters and overfitting through Bayesian regularization which penalizes network weights and auto-regulates the number of effective parameters in the network. ANN sampling represents a promising innovative approach which can resolve complex free energy landscapes in less time than conventional approaches while requiring minimal user input.

Stochastic Routing and Scheduling Policies for Energy Harvesting Communication Networks

NASA Astrophysics Data System (ADS)

Calvo-Fullana, Miguel; Anton-Haro, Carles; Matamoros, Javier; Ribeiro, Alejandro

2018-07-01

In this paper, we study the joint routing-scheduling problem in energy harvesting communication networks. Our policies, which are based on stochastic subgradient methods on the dual domain, act as an energy harvesting variant of the stochastic family of backpresure algorithms. Specifically, we propose two policies: (i) the Stochastic Backpressure with Energy Harvesting (SBP-EH), in which a node's routing-scheduling decisions are determined by the difference between the Lagrange multipliers associated to their queue stability constraints and their neighbors'; and (ii) the Stochastic Soft Backpressure with Energy Harvesting (SSBP-EH), an improved algorithm where the routing-scheduling decision is of a probabilistic nature. For both policies, we show that given sustainable data and energy arrival rates, the stability of the data queues over all network nodes is guaranteed. Numerical results corroborate the stability guarantees and illustrate the minimal gap in performance that our policies offer with respect to classical ones which work with an unlimited energy supply.

Learning free energy landscapes using artificial neural networks

NASA Astrophysics Data System (ADS)

Sidky, Hythem; Whitmer, Jonathan K.

2018-03-01

Existing adaptive bias techniques, which seek to estimate free energies and physical properties from molecular simulations, are limited by their reliance on fixed kernels or basis sets which hinder their ability to efficiently conform to varied free energy landscapes. Further, user-specified parameters are in general non-intuitive yet significantly affect the convergence rate and accuracy of the free energy estimate. Here we propose a novel method, wherein artificial neural networks (ANNs) are used to develop an adaptive biasing potential which learns free energy landscapes. We demonstrate that this method is capable of rapidly adapting to complex free energy landscapes and is not prone to boundary or oscillation problems. The method is made robust to hyperparameters and overfitting through Bayesian regularization which penalizes network weights and auto-regulates the number of effective parameters in the network. ANN sampling represents a promising innovative approach which can resolve complex free energy landscapes in less time than conventional approaches while requiring minimal user input.

National education program for energy efficient illumination engineering

NASA Astrophysics Data System (ADS)

Walker, Constance E.; Pompea, Stephen M.

2011-05-01

About one-third of outdoor lighting escapes unused into the sky, wasting energy and causing sky glow. Because of excessive sky glow around astronomical facilities, the National Optical Astronomy Observatory has a strong motivation to lead light pollution education efforts. While our original motivation of preserving the dark skies near observatories is still important, energy conservation is a critical problem that needs to be addressed nationwide. To address this problem we have created an extensive educational program on understanding and measuring light pollution. A set of four learning experiences introduces school students at all grade levels to basic energy-responsive illumination engineering design principles that can minimize light pollution. We created and utilize the GLOBE at Night citizen science light pollution assessment campaign as a cornerstone activity. We also utilize educational activities on light shielding that are introduced through a teaching kit. These two components provide vocabulary, concepts, and visual illustrations of the causes of light pollution. The third, more advanced component is the school outdoor lighting audit, which has students perform an audit and produce a revised master plan for compliant lighting. These learning experiences provide an integrated learning unit that is highly adaptable for U.S. and international education efforts in this area.

Correlation between the norm and the geometry of minimal networks

NASA Astrophysics Data System (ADS)

Laut, I. L.

2017-05-01

The paper is concerned with the inverse problem of the minimal Steiner network problem in a normed linear space. Namely, given a normed space in which all minimal networks are known for any finite point set, the problem is to describe all the norms on this space for which the minimal networks are the same as for the original norm. We survey the available results and prove that in the plane a rotund differentiable norm determines a distinctive set of minimal Steiner networks. In a two-dimensional space with rotund differentiable norm the coordinates of interior vertices of a nondegenerate minimal parametric network are shown to vary continuously under small deformations of the boundary set, and the turn direction of the network is determined. Bibliography: 15 titles.

Systematic optimization model and algorithm for binding sequence selection in computational enzyme design

PubMed Central

Huang, Xiaoqiang; Han, Kehang; Zhu, Yushan

2013-01-01

A systematic optimization model for binding sequence selection in computational enzyme design was developed based on the transition state theory of enzyme catalysis and graph-theoretical modeling. The saddle point on the free energy surface of the reaction system was represented by catalytic geometrical constraints, and the binding energy between the active site and transition state was minimized to reduce the activation energy barrier. The resulting hyperscale combinatorial optimization problem was tackled using a novel heuristic global optimization algorithm, which was inspired and tested by the protein core sequence selection problem. The sequence recapitulation tests on native active sites for two enzyme catalyzed hydrolytic reactions were applied to evaluate the predictive power of the design methodology. The results of the calculation show that most of the native binding sites can be successfully identified if the catalytic geometrical constraints and the structural motifs of the substrate are taken into account. Reliably predicting active site sequences may have significant implications for the creation of novel enzymes that are capable of catalyzing targeted chemical reactions. PMID:23649589

Energy Efficiency Optimization in Relay-Assisted MIMO Systems With Perfect and Statistical CSI

NASA Astrophysics Data System (ADS)

Zappone, Alessio; Cao, Pan; Jorswieck, Eduard A.

2014-01-01

A framework for energy-efficient resource allocation in a single-user, amplify-and-forward relay-assisted MIMO system is devised in this paper. Previous results in this area have focused on rate maximization or sum power minimization problems, whereas fewer results are available when bits/Joule energy efficiency (EE) optimization is the goal. The performance metric to optimize is the ratio between the system's achievable rate and the total consumed power. The optimization is carried out with respect to the source and relay precoding matrices, subject to QoS and power constraints. Such a challenging non-convex problem is tackled by means of fractional programming and and alternating maximization algorithms, for various CSI assumptions at the source and relay. In particular the scenarios of perfect CSI and those of statistical CSI for either the source-relay or the relay-destination channel are addressed. Moreover, sufficient conditions for beamforming optimality are derived, which is useful in simplifying the system design. Numerical results are provided to corroborate the validity of the theoretical findings.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Carrington, David Bradley; Waters, Jiajia

Research and development of KIVA-hpFE for turbulent reactive and multiphase flow particularly as related to engine modeling program has relevance to National energy security and climate change. Climate change is a source problem, and energy national security is consumption of petroleum products problem. Accurately predicting engine processes leads to, lower greenhouse gas (GHG) emission, where engines in the transportation sector currently account for 26% of the U.S. GHG emissions. Less dependence on petroleum products leads to greater energy security. By Environmental Protection Agency standards, some vehicles are now reaching 42 to the 50 mpg mark. These are conventional gasoline engines.more » Continued investment and research into new technical innovations, the potential exists to save more than 4 million barrels of oil per day or approximately $200 to $400 million per day. This would be a significant decrease in emission and use of petroleum and a very large economic stimulus too! It is estimated with further advancements in combustion, the current emissions can be reduced up to 40%. Enabling better understanding of fuel injection and fuel-air mixing, thermodynamic combustion losses, and combustion/emission formation processes enhances our ability to help solve both problems. To provide adequate capability for accurately simulating these processes, minimize time and labor for development of engine technology, are the goals of our KIVA development program.« less

COMPARISONS OF THE FINITE-ELEMENT-WITH-DISCONTIGUOUS-SUPPORT METHOD TO CONTINUOUS-ENERGY MONTE CARLO FOR PIN-CELL PROBLEMS

DOE Office of Scientific and Technical Information (OSTI.GOV)

A. T. Till; M. Hanuš; J. Lou

The standard multigroup (MG) method for energy discretization of the transport equation can be sensitive to approximations in the weighting spectrum chosen for cross-section averaging. As a result, MG often inaccurately treats important phenomena such as self-shielding variations across a material. From a finite-element viewpoint, MG uses a single fixed basis function (the pre-selected spectrum) within each group, with no mechanism to adapt to local solution behavior. In this work, we introduce the Finite-Element-with-Discontiguous-Support (FEDS) method, whose only approximation with respect to energy is that the angular flux is a linear combination of unknowns multiplied by basis functions. A basismore » function is non-zero only in the discontiguous set of energy intervals associated with its energy element. Discontiguous energy elements are generalizations of bands and are determined by minimizing a norm of the difference between snapshot spectra and their averages over the energy elements. We begin by presenting the theory of the FEDS method. We then compare to continuous-energy Monte Carlo for one-dimensional slab and two-dimensional pin-cell problem. We find FEDS to be accurate and efficient at producing quantities of interest such as reaction rates and eigenvalues. Results show that FEDS converges at a rate that is approximately first-order in the number of energy elements and that FEDS is less sensitive to weighting spectrum than standard MG.« less

Cuckoo Search with Lévy Flights for Weighted Bayesian Energy Functional Optimization in Global-Support Curve Data Fitting

PubMed Central

Gálvez, Akemi; Iglesias, Andrés; Cabellos, Luis

2014-01-01

The problem of data fitting is very important in many theoretical and applied fields. In this paper, we consider the problem of optimizing a weighted Bayesian energy functional for data fitting by using global-support approximating curves. By global-support curves we mean curves expressed as a linear combination of basis functions whose support is the whole domain of the problem, as opposed to other common approaches in CAD/CAM and computer graphics driven by piecewise functions (such as B-splines and NURBS) that provide local control of the shape of the curve. Our method applies a powerful nature-inspired metaheuristic algorithm called cuckoo search, introduced recently to solve optimization problems. A major advantage of this method is its simplicity: cuckoo search requires only two parameters, many fewer than other metaheuristic approaches, so the parameter tuning becomes a very simple task. The paper shows that this new approach can be successfully used to solve our optimization problem. To check the performance of our approach, it has been applied to five illustrative examples of different types, including open and closed 2D and 3D curves that exhibit challenging features, such as cusps and self-intersections. Our results show that the method performs pretty well, being able to solve our minimization problem in an astonishingly straightforward way. PMID:24977175

Cuckoo search with Lévy flights for weighted Bayesian energy functional optimization in global-support curve data fitting.

PubMed

Gálvez, Akemi; Iglesias, Andrés; Cabellos, Luis

2014-01-01

The problem of data fitting is very important in many theoretical and applied fields. In this paper, we consider the problem of optimizing a weighted Bayesian energy functional for data fitting by using global-support approximating curves. By global-support curves we mean curves expressed as a linear combination of basis functions whose support is the whole domain of the problem, as opposed to other common approaches in CAD/CAM and computer graphics driven by piecewise functions (such as B-splines and NURBS) that provide local control of the shape of the curve. Our method applies a powerful nature-inspired metaheuristic algorithm called cuckoo search, introduced recently to solve optimization problems. A major advantage of this method is its simplicity: cuckoo search requires only two parameters, many fewer than other metaheuristic approaches, so the parameter tuning becomes a very simple task. The paper shows that this new approach can be successfully used to solve our optimization problem. To check the performance of our approach, it has been applied to five illustrative examples of different types, including open and closed 2D and 3D curves that exhibit challenging features, such as cusps and self-intersections. Our results show that the method performs pretty well, being able to solve our minimization problem in an astonishingly straightforward way.

Network-Cognizant Design of Decentralized Volt/VAR Controllers

DOE Office of Scientific and Technical Information (OSTI.GOV)

Baker, Kyri A; Bernstein, Andrey; Zhao, Changhong

This paper considers the problem of designing decentralized Volt/VAR controllers for distributed energy resources (DERs). The voltage-reactive power characteristics of individual DERs are obtained by solving a convex optimization problem, where given performance objectives (e.g., minimization of the voltage deviations from a given profile) are specified and stability constraints are enforced. The resultant Volt/VAR characteristics are network-cognizant, in the sense that they embed information on the location of the DERs and, consequently, on the effect of reactive-power adjustments on the voltages throughout the feeder. Bounds on the maximum voltage deviation incurred by the controllers are analytically established. Numerical results aremore » reported to corroborate the technical findings.« less

Optimal speeds for walking and running, and walking on a moving walkway.

PubMed

Srinivasan, Manoj

2009-06-01

Many aspects of steady human locomotion are thought to be constrained by a tendency to minimize the expenditure of metabolic cost. This paper has three parts related to the theme of energetic optimality: (1) a brief review of energetic optimality in legged locomotion, (2) an examination of the notion of optimal locomotion speed, and (3) an analysis of walking on moving walkways, such as those found in some airports. First, I describe two possible connotations of the term "optimal locomotion speed:" that which minimizes the total metabolic cost per unit distance and that which minimizes the net cost per unit distance (total minus resting cost). Minimizing the total cost per distance gives the maximum range speed and is a much better predictor of the speeds at which people and horses prefer to walk naturally. Minimizing the net cost per distance is equivalent to minimizing the total daily energy intake given an idealized modern lifestyle that requires one to walk a given distance every day--but it is not a good predictor of animals' walking speeds. Next, I critique the notion that there is no energy-optimal speed for running, making use of some recent experiments and a review of past literature. Finally, I consider the problem of predicting the speeds at which people walk on moving walkways--such as those found in some airports. I present two substantially different theories to make predictions. The first theory, minimizing total energy per distance, predicts that for a range of low walkway speeds, the optimal absolute speed of travel will be greater--but the speed relative to the walkway smaller--than the optimal walking speed on stationary ground. At higher walkway speeds, this theory predicts that the person will stand still. The second theory is based on the assumption that the human optimally reconciles the sensory conflict between the forward speed that the eye sees and the walking speed that the legs feel and tries to equate the best estimate of the forward speed to the naturally preferred speed. This sensory conflict theory also predicts that people would walk slower than usual relative to the walkway yet move faster than usual relative to the ground. These predictions agree qualitatively with available experimental observations, but there are quantitative differences.

Stochastic dark energy from inflationary quantum fluctuations

NASA Astrophysics Data System (ADS)

Glavan, Dražen; Prokopec, Tomislav; Starobinsky, Alexei A.

2018-05-01

We study the quantum backreaction from inflationary fluctuations of a very light, non-minimally coupled spectator scalar and show that it is a viable candidate for dark energy. The problem is solved by suitably adapting the formalism of stochastic inflation. This allows us to self-consistently account for the backreaction on the background expansion rate of the Universe where its effects are large. This framework is equivalent to that of semiclassical gravity in which matter vacuum fluctuations are included at the one loop level, but purely quantum gravitational fluctuations are neglected. Our results show that dark energy in our model can be characterized by a distinct effective equation of state parameter (as a function of redshift) which allows for testing of the model at the level of the background.

A minimal dissipation type-based classification in irreversible thermodynamics and microeconomics

NASA Astrophysics Data System (ADS)

Tsirlin, A. M.; Kazakov, V.; Kolinko, N. A.

2003-10-01

We formulate the problem of finding classes of kinetic dependencies in irreversible thermodynamic and microeconomic systems for which minimal dissipation processes belong to the same type. We show that this problem is an inverse optimal control problem and solve it. The commonality of this problem in irreversible thermodynamics and microeconomics is emphasized.

Minimization In Digital Design As A Meta-Planning Problem

NASA Astrophysics Data System (ADS)

Ho, William P. C.; Wu, Jung-Gen

1987-05-01

In our model-based expert system for automatic digital system design, we formalize the design process into three sub-processes - compiling high-level behavioral specifications into primitive behavioral operations, grouping primitive operations into behavioral functions, and grouping functions into modules. Consideration of design minimization explicitly controls decision-making in the last two subprocesses. Design minimization, a key task in the automatic design of digital systems, is complicated by the high degree of interaction among the time sequence and content of design decisions. In this paper, we present an AI approach which directly addresses these interactions and their consequences by modeling the minimization prob-lem as a planning problem, and the management of design decision-making as a meta-planning problem.

Aeroelasticity of morphing wings using neural networks

NASA Astrophysics Data System (ADS)

Natarajan, Anand

In this dissertation, neural networks are designed to effectively model static non-linear aeroelastic problems in adaptive structures and linear dynamic aeroelastic systems with time varying stiffness. The use of adaptive materials in aircraft wings allows for the change of the contour or the configuration of a wing (morphing) in flight. The use of smart materials, to accomplish these deformations, can imply that the stiffness of the wing with a morphing contour changes as the contour changes. For a rapidly oscillating body in a fluid field, continuously adapting structural parameters may render the wing to behave as a time variant system. Even the internal spars/ribs of the aircraft wing which define the wing stiffness can be made adaptive, that is, their stiffness can be made to vary with time. The immediate effect on the structural dynamics of the wing, is that, the wing motion is governed by a differential equation with time varying coefficients. The study of this concept of a time varying torsional stiffness, made possible by the use of active materials and adaptive spars, in the dynamic aeroelastic behavior of an adaptable airfoil is performed here. Another type of aeroelastic problem of an adaptive structure that is investigated here, is the shape control of an adaptive bump situated on the leading edge of an airfoil. Such a bump is useful in achieving flow separation control for lateral directional maneuverability of the aircraft. Since actuators are being used to create this bump on the wing surface, the energy required to do so needs to be minimized. The adverse pressure drag as a result of this bump needs to be controlled so that the loss in lift over the wing is made minimal. The design of such a "spoiler bump" on the surface of the airfoil is an optimization problem of maximizing pressure drag due to flow separation while minimizing the loss in lift and energy required to deform the bump. One neural network is trained using the CFD code FLUENT to represent the aerodynamic loading over the bump. A second neural network is trained for calculating the actuator loads, bump displacement and lift, drag forces over the airfoil using the finite element solver, ANSYS and the previously trained neural network. This non-linear aeroelastic model of the deforming bump on an airfoil surface using neural networks can serve as a fore-runner for other non-linear aeroelastic problems.

TTSA: An Effective Scheduling Approach for Delay Bounded Tasks in Hybrid Clouds.

PubMed

Yuan, Haitao; Bi, Jing; Tan, Wei; Zhou, MengChu; Li, Bo Hu; Li, Jianqiang

2017-11-01

The economy of scale provided by cloud attracts a growing number of organizations and industrial companies to deploy their applications in cloud data centers (CDCs) and to provide services to users around the world. The uncertainty of arriving tasks makes it a big challenge for private CDC to cost-effectively schedule delay bounded tasks without exceeding their delay bounds. Unlike previous studies, this paper takes into account the cost minimization problem for private CDC in hybrid clouds, where the energy price of private CDC and execution price of public clouds both show the temporal diversity. Then, this paper proposes a temporal task scheduling algorithm (TTSA) to effectively dispatch all arriving tasks to private CDC and public clouds. In each iteration of TTSA, the cost minimization problem is modeled as a mixed integer linear program and solved by a hybrid simulated-annealing particle-swarm-optimization. The experimental results demonstrate that compared with the existing methods, the optimal or suboptimal scheduling strategy produced by TTSA can efficiently increase the throughput and reduce the cost of private CDC while meeting the delay bounds of all the tasks.

A Model of Controlled Growth

NASA Astrophysics Data System (ADS)

Bressan, Alberto; Lewicka, Marta

2018-03-01

We consider a free boundary problem for a system of PDEs, modeling the growth of a biological tissue. A morphogen, controlling volume growth, is produced by specific cells and then diffused and absorbed throughout the domain. The geometric shape of the growing tissue is determined by the instantaneous minimization of an elastic deformation energy, subject to a constraint on the volumetric growth. For an initial domain with C}^{2,α boundary, our main result establishes the local existence and uniqueness of a classical solution, up to a rigid motion.

Energy optimization in mobile sensor networks

NASA Astrophysics Data System (ADS)

Yu, Shengwei

Mobile sensor networks are considered to consist of a network of mobile robots, each of which has computation, communication and sensing capabilities. Energy efficiency is a critical issue in mobile sensor networks, especially when mobility (i.e., locomotion control), routing (i.e., communications) and sensing are unique characteristics of mobile robots for energy optimization. This thesis focuses on the problem of energy optimization of mobile robotic sensor networks, and the research results can be extended to energy optimization of a network of mobile robots that monitors the environment, or a team of mobile robots that transports materials from stations to stations in a manufacturing environment. On the energy optimization of mobile robotic sensor networks, our research focuses on the investigation and development of distributed optimization algorithms to exploit the mobility of robotic sensor nodes for network lifetime maximization. In particular, the thesis studies these five problems: 1. Network-lifetime maximization by controlling positions of networked mobile sensor robots based on local information with distributed optimization algorithms; 2. Lifetime maximization of mobile sensor networks with energy harvesting modules; 3. Lifetime maximization using joint design of mobility and routing; 4. Optimal control for network energy minimization; 5. Network lifetime maximization in mobile visual sensor networks. In addressing the first problem, we consider only the mobility strategies of the robotic relay nodes in a mobile sensor network in order to maximize its network lifetime. By using variable substitutions, the original problem is converted into a convex problem, and a variant of the sub-gradient method for saddle-point computation is developed for solving this problem. An optimal solution is obtained by the method. Computer simulations show that mobility of robotic sensors can significantly prolong the lifetime of the whole robotic sensor network while consuming negligible amount of energy for mobility cost. For the second problem, the problem is extended to accommodate mobile robotic nodes with energy harvesting capability, which makes it a non-convex optimization problem. The non-convexity issue is tackled by using the existing sequential convex approximation method, based on which we propose a novel procedure of modified sequential convex approximation that has fast convergence speed. For the third problem, the proposed procedure is used to solve another challenging non-convex problem, which results in utilizing mobility and routing simultaneously in mobile robotic sensor networks to prolong the network lifetime. The results indicate that joint design of mobility and routing has an edge over other methods in prolonging network lifetime, which is also the justification for the use of mobility in mobile sensor networks for energy efficiency purpose. For the fourth problem, we include the dynamics of the robotic nodes in the problem by modeling the networked robotic system using hybrid systems theory. A novel distributed method for the networked hybrid system is used to solve the optimal moving trajectories for robotic nodes and optimal network links, which are not answered by previous approaches. Finally, the fact that mobility is more effective in prolonging network lifetime for a data-intensive network leads us to apply our methods to study mobile visual sensor networks, which are useful in many applications. We investigate the joint design of mobility, data routing, and encoding power to help improving the video quality while maximizing the network lifetime. This study leads to a better understanding of the role mobility can play in data-intensive surveillance sensor networks.

Construction of optimum controls and trajectories of motion of the center of masses of a spacecraft equipped with the solar sail and low-thrust engine, using quaternions and Kustaanheimo-Stiefel variables

NASA Astrophysics Data System (ADS)

Sapunkov, Ya. G.; Chelnokov, Yu. N.

2014-11-01

The problem of optimum rendezvous of a controllable spacecraft (SC) with an uncontrollable spacecraft, moving over a Keplerian elliptic orbit in the gravitational field of the Sun, is considered. Control of the SC is performed using a solar sail and low-thrust engine. For solving the problem, the regular quaternion equations of the two-body problem with the Kustaanheimo-Stiefel variables and the Pontryagin maximum principle are used. The combined integral quality functional, which characterizes energy consumption for controllable SC transition from an initial to final state and the time spent for this transition, is used as a minimized functional. The differential boundary-value optimization problems are formulated, and their first integrals are found. Examples of numerical solution of problems are presented. The paper develops the application [1-6] of quaternion regular equations with the Kustaanheimo-Stiefel variables in the space flight mechanics.

Active control of panel vibrations induced by boundary-layer flow

NASA Technical Reports Server (NTRS)

Chow, Pao-Liu

1991-01-01

Some problems in active control of panel vibration excited by a boundary layer flow over a flat plate are studied. In the first phase of the study, the optimal control problem of vibrating elastic panel induced by a fluid dynamical loading was studied. For a simply supported rectangular plate, the vibration control problem can be analyzed by a modal analysis. The control objective is to minimize the total cost functional, which is the sum of a vibrational energy and the control cost. By means of the modal expansion, the dynamical equation for the plate and the cost functional are reduced to a system of ordinary differential equations and the cost functions for the modes. For the linear elastic plate, the modes become uncoupled. The control of each modal amplitude reduces to the so-called linear regulator problem in control theory. Such problems can then be solved by the method of adjoint state. The optimality system of equations was solved numerically by a shooting method. The results are summarized.

A distributed algorithm for demand-side management: Selling back to the grid.

PubMed

Latifi, Milad; Khalili, Azam; Rastegarnia, Amir; Zandi, Sajad; Bazzi, Wael M

2017-11-01

Demand side energy consumption scheduling is a well-known issue in the smart grid research area. However, there is lack of a comprehensive method to manage the demand side and consumer behavior in order to obtain an optimum solution. The method needs to address several aspects, including the scale-free requirement and distributed nature of the problem, consideration of renewable resources, allowing consumers to sell electricity back to the main grid, and adaptivity to a local change in the solution point. In addition, the model should allow compensation to consumers and ensurance of certain satisfaction levels. To tackle these issues, this paper proposes a novel autonomous demand side management technique which minimizes consumer utility costs and maximizes consumer comfort levels in a fully distributed manner. The technique uses a new logarithmic cost function and allows consumers to sell excess electricity (e.g. from renewable resources) back to the grid in order to reduce their electric utility bill. To develop the proposed scheme, we first formulate the problem as a constrained convex minimization problem. Then, it is converted to an unconstrained version using the segmentation-based penalty method. At each consumer location, we deploy an adaptive diffusion approach to obtain the solution in a distributed fashion. The use of adaptive diffusion makes it possible for consumers to find the optimum energy consumption schedule with a small number of information exchanges. Moreover, the proposed method is able to track drifts resulting from changes in the price parameters and consumer preferences. Simulations and numerical results show that our framework can reduce the total load demand peaks, lower the consumer utility bill, and improve the consumer comfort level.

The Wrinkling of a Twisted Ribbon

NASA Astrophysics Data System (ADS)

Kohn, Robert V.; O'Brien, Ethan

2018-02-01

Recent experiments by Chopin and Kudrolli (Phys Rev Lett 111:174302, 2013) showed that a thin elastic ribbon, when twisted into a helicoid, may wrinkle in the center. We study this from the perspective of elastic energy minimization, building on recent work by Chopin et al. (J Elast 119(1-2):137-189, 2015) in which they derive a modified von Kármán functional and solve the relaxed problem. Our main contribution is to show matching upper and lower bounds for the minimum energy in the small-thickness limit. Along the way, we show that the displacements must be small where we expect that the ribbon is helicoidal, and we estimate the wavelength of the wrinkles.

Radar signal categorization using a neural network

NASA Technical Reports Server (NTRS)

Anderson, James A.; Gately, Michael T.; Penz, P. Andrew; Collins, Dean R.

1991-01-01

Neural networks were used to analyze a complex simulated radar environment which contains noisy radar pulses generated by many different emitters. The neural network used is an energy minimizing network (the BSB model) which forms energy minima - attractors in the network dynamical system - based on learned input data. The system first determines how many emitters are present (the deinterleaving problem). Pulses from individual simulated emitters give rise to separate stable attractors in the network. Once individual emitters are characterized, it is possible to make tentative identifications of them based on their observed parameters. As a test of this idea, a neural network was used to form a small data base that potentially could make emitter identifications.

Naturally Occurring Radioactive Materials (NORM)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gray, P.

1997-02-01

This paper discusses the broad problems presented by Naturally Occuring Radioactive Materials (NORM). Technologically Enhanced naturally occuring radioactive material includes any radionuclides whose physical, chemical, radiological properties or radionuclide concentration have been altered from their natural state. With regard to NORM in particular, radioactive contamination is radioactive material in an undesired location. This is a concern in a range of industries: petroleum; uranium mining; phosphorus and phosphates; fertilizers; fossil fuels; forestry products; water treatment; metal mining and processing; geothermal energy. The author discusses in more detail the problem in the petroleum industry, including the isotopes of concern, the hazards theymore » present, the contamination which they cause, ways to dispose of contaminated materials, and regulatory issues. He points out there are three key programs to reduce legal exposure and problems due to these contaminants: waste minimization; NORM assesment (surveys); NORM compliance (training).« less

Vistas in applied mathematics: Numerical analysis, atmospheric sciences, immunology

DOE Office of Scientific and Technical Information (OSTI.GOV)

Balakrishnan, A.V.; Dorodnitsyn, A.A.; Lions, J.L.

1986-01-01

Advances in the theory and application of numerical modeling techniques are discussed in papers contributed, primarily by Soviet scientists, on the occasion of the 60th birthday of Gurii I. Marchuk. Topics examined include splitting techniques for computations of industrial flows, the mathematical foundations of the k-epsilon turbulence model, splitting methods for the solution of the incompressible Navier-Stokes equations, the approximation of inhomogeneous hyperbolic boundary-value problems, multigrid methods, and the finite-element approximation of minimal surfaces. Consideration is given to dynamic modeling of moist atmospheres, satellite observations of the earth radiation budget and the problem of energy-active ocean regions, a numerical modelmore » of the biosphere for use with GCMs, and large-scale modeling of ocean circulation. Also included are several papers on modeling problems in immunology.« less

Minimal energy configurations of gravitationally interacting rigid bodies

NASA Astrophysics Data System (ADS)

Moeckel, Richard

2017-05-01

Consider a collection of n rigid, massive bodies interacting according to their mutual gravitational attraction. A relative equilibrium motion is one where the entire configuration rotates rigidly and uniformly about a fixed axis in R^3. Such a motion is possible only for special positions and orientations of the bodies. A minimal energy motion is one which has the minimum possible energy in its fixed angular momentum level. While every minimal energy motion is a relative equilibrium motion, the main result here is that a relative equilibrium motion of n≥3 disjoint rigid bodies is never an energy minimizer. This generalizes a known result about point masses to the case of rigid bodies.

Robust penalty method for structural synthesis

NASA Technical Reports Server (NTRS)

Kamat, M. P.

1983-01-01

The Sequential Unconstrained Minimization Technique (SUMT) offers an easy way of solving nonlinearly constrained problems. However, this algorithm frequently suffers from the need to minimize an ill-conditioned penalty function. An ill-conditioned minimization problem can be solved very effectively by posing the problem as one of integrating a system of stiff differential equations utilizing concepts from singular perturbation theory. This paper evaluates the robustness and the reliability of such a singular perturbation based SUMT algorithm on two different problems of structural optimization of widely separated scales. The report concludes that whereas conventional SUMT can be bogged down by frequent ill-conditioning, especially in large scale problems, the singular perturbation SUMT has no such difficulty in converging to very accurate solutions.

L{sup {infinity}} Variational Problems with Running Costs and Constraints

DOE Office of Scientific and Technical Information (OSTI.GOV)

Aronsson, G., E-mail: gunnar.aronsson@liu.se; Barron, E. N., E-mail: enbarron@math.luc.edu

2012-02-15

Various approaches are used to derive the Aronsson-Euler equations for L{sup {infinity}} calculus of variations problems with constraints. The problems considered involve holonomic, nonholonomic, isoperimetric, and isosupremic constraints on the minimizer. In addition, we derive the Aronsson-Euler equation for the basic L{sup {infinity}} problem with a running cost and then consider properties of an absolute minimizer. Many open problems are introduced for further study.

Blow-up behavior of ground states for a nonlinear Schrödinger system with attractive and repulsive interactions

NASA Astrophysics Data System (ADS)

Guo, Yujin; Zeng, Xiaoyu; Zhou, Huan-Song

2018-01-01

We consider a nonlinear Schrödinger system arising in a two-component Bose-Einstein condensate (BEC) with attractive intraspecies interactions and repulsive interspecies interactions in R2. We get ground states of this system by solving a constrained minimization problem. For some kinds of trapping potentials, we prove that the minimization problem has a minimizer if and only if the attractive interaction strength ai (i = 1 , 2) of each component of the BEC system is strictly less than a threshold a*. Furthermore, as (a1 ,a2) ↗ (a* ,a*), the asymptotical behavior for the minimizers of the minimization problem is discussed. Our results show that each component of the BEC system concentrates at a global minimum of the associated trapping potential.

Planning energy-efficient bipedal locomotion on patterned terrain

NASA Astrophysics Data System (ADS)

Zamani, Ali; Bhounsule, Pranav A.; Taha, Ahmad

2016-05-01

Energy-efficient bipedal walking is essential in realizing practical bipedal systems. However, current energy-efficient bipedal robots (e.g., passive-dynamics-inspired robots) are limited to walking at a single speed and step length. The objective of this work is to address this gap by developing a method of synthesizing energy-efficient bipedal locomotion on patterned terrain consisting of stepping stones using energy-efficient primitives. A model of Cornell Ranger (a passive-dynamics inspired robot) is utilized to illustrate our technique. First, an energy-optimal trajectory control problem for a single step is formulated and solved. The solution minimizes the Total Cost Of Transport (TCOT is defined as the energy used per unit weight per unit distance travelled) subject to various constraints such as actuator limits, foot scuffing, joint kinematic limits, ground reaction forces. The outcome of the optimization scheme is a table of TCOT values as a function of step length and step velocity. Next, we parameterize the terrain to identify the location of the stepping stones. Finally, the TCOT table is used in conjunction with the parameterized terrain to plan an energy-efficient stepping strategy.

Quantum scattering in one-dimensional systems satisfying the minimal length uncertainty relation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bernardo, Reginald Christian S., E-mail: rcbernardo@nip.upd.edu.ph; Esguerra, Jose Perico H., E-mail: jesguerra@nip.upd.edu.ph

In quantum gravity theories, when the scattering energy is comparable to the Planck energy the Heisenberg uncertainty principle breaks down and is replaced by the minimal length uncertainty relation. In this paper, the consequences of the minimal length uncertainty relation on one-dimensional quantum scattering are studied using an approach involving a recently proposed second-order differential equation. An exact analytical expression for the tunneling probability through a locally-periodic rectangular potential barrier system is obtained. Results show that the existence of a non-zero minimal length uncertainty tends to shift the resonant tunneling energies to the positive direction. Scattering through a locally-periodic potentialmore » composed of double-rectangular potential barriers shows that the first band of resonant tunneling energies widens for minimal length cases when the double-rectangular potential barrier is symmetric but narrows down when the double-rectangular potential barrier is asymmetric. A numerical solution which exploits the use of Wronskians is used to calculate the transmission probabilities through the Pöschl–Teller well, Gaussian barrier, and double-Gaussian barrier. Results show that the probability of passage through the Pöschl–Teller well and Gaussian barrier is smaller in the minimal length cases compared to the non-minimal length case. For the double-Gaussian barrier, the probability of passage for energies that are more positive than the resonant tunneling energy is larger in the minimal length cases compared to the non-minimal length case. The approach is exact and applicable to many types of scattering potential.« less

Exact solution for the optimal neuronal layout problem.

PubMed

Chklovskii, Dmitri B

2004-10-01

Evolution perfected brain design by maximizing its functionality while minimizing costs associated with building and maintaining it. Assumption that brain functionality is specified by neuronal connectivity, implemented by costly biological wiring, leads to the following optimal design problem. For a given neuronal connectivity, find a spatial layout of neurons that minimizes the wiring cost. Unfortunately, this problem is difficult to solve because the number of possible layouts is often astronomically large. We argue that the wiring cost may scale as wire length squared, reducing the optimal layout problem to a constrained minimization of a quadratic form. For biologically plausible constraints, this problem has exact analytical solutions, which give reasonable approximations to actual layouts in the brain. These solutions make the inverse problem of inferring neuronal connectivity from neuronal layout more tractable.

Minimizing the Total Service Time of Discrete Dynamic Berth Allocation Problem by an Iterated Greedy Heuristic

PubMed Central

2014-01-01

Berth allocation is the forefront operation performed when ships arrive at a port and is a critical task in container port optimization. Minimizing the time ships spend at berths constitutes an important objective of berth allocation problems. This study focuses on the discrete dynamic berth allocation problem (discrete DBAP), which aims to minimize total service time, and proposes an iterated greedy (IG) algorithm to solve it. The proposed IG algorithm is tested on three benchmark problem sets. Experimental results show that the proposed IG algorithm can obtain optimal solutions for all test instances of the first and second problem sets and outperforms the best-known solutions for 35 out of 90 test instances of the third problem set. PMID:25295295

Cluster Size Optimization in Sensor Networks with Decentralized Cluster-Based Protocols

PubMed Central

Amini, Navid; Vahdatpour, Alireza; Xu, Wenyao; Gerla, Mario; Sarrafzadeh, Majid

2011-01-01

Network lifetime and energy-efficiency are viewed as the dominating considerations in designing cluster-based communication protocols for wireless sensor networks. This paper analytically provides the optimal cluster size that minimizes the total energy expenditure in such networks, where all sensors communicate data through their elected cluster heads to the base station in a decentralized fashion. LEACH, LEACH-Coverage, and DBS comprise three cluster-based protocols investigated in this paper that do not require any centralized support from a certain node. The analytical outcomes are given in the form of closed-form expressions for various widely-used network configurations. Extensive simulations on different networks are used to confirm the expectations based on the analytical results. To obtain a thorough understanding of the results, cluster number variability problem is identified and inspected from the energy consumption point of view. PMID:22267882

Computational study of a calcium release-activated calcium channel

NASA Astrophysics Data System (ADS)

Talukdar, Keka; Shantappa, Anil

2016-05-01

The naturally occurring proteins that form hole in membrane are commonly known as ion channels. They play multiple roles in many important biological processes. Deletion or alteration of these channels often leads to serious problems in the physiological processes as it controls the flow of ions through it. The proper maintenance of the flow of ions, in turn, is required for normal health. Here we have investigated the behavior of a calcium release-activated calcium ion channel with pdb entry 4HKR in Drosophila Melanogaster. The equilibrium energy as well as molecular dynamics simulation is performed first. The protein is subjected to molecular dynamics simulation to find their energy minimized value. Simulation of the protein in the environment of water and ions has given us important results too. The solvation energy is also found using Charmm potential.

Early experience in using and 18 Me V linear accelerator for mycosis fungoides at Howard University Hospital.

PubMed

Kumar, P P; Henschke, K; Mandal, K P; Nibhanupudy, J R; Patel, I S

1977-04-01

This paper describes the problems and solutions in using 18 MeV linear accelerator, with minimum 6 MeV electron capability, for total skin irradiation for mycosis fungoides. The 6 MeV electron energy can be degraded to acceptable electron energy of 3.2 MeV by interposing a plexiglass sheet of 9.6 mm in the beam. To minimize the bremsstrahlung, the degrading plexiglass should be kept away from the machine head. A wide area with uniform dose distribution over single plane can be achieved by using dual fields but homogenous dose distribution over irregular body surface cannot be achieved mainly because of self-shielding. The nails and the ocular lens can be easily shielded from the low energy electrons with 1.5 mm lead shield.

An Optimal CDS Construction Algorithm with Activity Scheduling in Ad Hoc Networks

PubMed Central

Penumalli, Chakradhar; Palanichamy, Yogesh

2015-01-01

A new energy efficient optimal Connected Dominating Set (CDS) algorithm with activity scheduling for mobile ad hoc networks (MANETs) is proposed. This algorithm achieves energy efficiency by minimizing the Broadcast Storm Problem [BSP] and at the same time considering the node's remaining energy. The Connected Dominating Set is widely used as a virtual backbone or spine in mobile ad hoc networks [MANETs] or Wireless Sensor Networks [WSN]. The CDS of a graph representing a network has a significant impact on an efficient design of routing protocol in wireless networks. Here the CDS is a distributed algorithm with activity scheduling based on unit disk graph [UDG]. The node's mobility and residual energy (RE) are considered as parameters in the construction of stable optimal energy efficient CDS. The performance is evaluated at various node densities, various transmission ranges, and mobility rates. The theoretical analysis and simulation results of this algorithm are also presented which yield better results. PMID:26221627

Spectral CT Reconstruction with Image Sparsity and Spectral Mean

PubMed Central

Zhang, Yi; Xi, Yan; Yang, Qingsong; Cong, Wenxiang; Zhou, Jiliu

2017-01-01

Photon-counting detectors can acquire x-ray intensity data in different energy bins. The signal to noise ratio of resultant raw data in each energy bin is generally low due to the narrow bin width and quantum noise. To address this problem, here we propose an image reconstruction approach for spectral CT to simultaneously reconstructs x-ray attenuation coefficients in all the energy bins. Because the measured spectral data are highly correlated among the x-ray energy bins, the intra-image sparsity and inter-image similarity are important prior acknowledge for image reconstruction. Inspired by this observation, the total variation (TV) and spectral mean (SM) measures are combined to improve the quality of reconstructed images. For this purpose, a linear mapping function is used to minimalize image differences between energy bins. The split Bregman technique is applied to perform image reconstruction. Our numerical and experimental results show that the proposed algorithms outperform competing iterative algorithms in this context. PMID:29034267

Text-line extraction in handwritten Chinese documents based on an energy minimization framework.

PubMed

Koo, Hyung Il; Cho, Nam Ik

2012-03-01

Text-line extraction in unconstrained handwritten documents remains a challenging problem due to nonuniform character scale, spatially varying text orientation, and the interference between text lines. In order to address these problems, we propose a new cost function that considers the interactions between text lines and the curvilinearity of each text line. Precisely, we achieve this goal by introducing normalized measures for them, which are based on an estimated line spacing. We also present an optimization method that exploits the properties of our cost function. Experimental results on a database consisting of 853 handwritten Chinese document images have shown that our method achieves a detection rate of 99.52% and an error rate of 0.32%, which outperforms conventional methods.

The inverse problem of brain energetics: ketone bodies as alternative substrates

NASA Astrophysics Data System (ADS)

Calvetti, D.; Occhipinti, R.; Somersalo, E.

2008-07-01

Little is known about brain energy metabolism under ketosis, although there is evidence that ketone bodies have a neuroprotective role in several neurological disorders. We investigate the inverse problem of estimating reaction fluxes and transport rates in the different cellular compartments of the brain, when the data amounts to a few measured arterial venous concentration differences. By using a recently developed methodology to perform Bayesian Flux Balance Analysis and a new five compartment model of the astrocyte-glutamatergic neuron cellular complex, we are able to identify the preferred biochemical pathways during shortage of glucose and in the presence of ketone bodies in the arterial blood. The analysis is performed in a minimally biased way, therefore revealing the potential of this methodology for hypothesis testing.

[Possible changes in energy-minimizer mechanisms of locomotion due to chronic low back pain - a literature review].

PubMed

de Carvalho, Alberito Rodrigo; Andrade, Alexandro; Peyré-Tartaruga, Leonardo Alexandre

2015-01-01

One goal of the locomotion is to move the body in the space at the most economical way possible. However, little is known about the mechanical and energetic aspects of locomotion that are affected by low back pain. And in case of occurring some damage, little is known about how the mechanical and energetic characteristics of the locomotion are manifested in functional activities, especially with respect to the energy-minimizer mechanisms during locomotion. This study aimed: a) to describe the main energy-minimizer mechanisms of locomotion; b) to check if there are signs of damage on the mechanical and energetic characteristics of the locomotion due to chronic low back pain (CLBP) which may endanger the energy-minimizer mechanisms. This study is characterized as a narrative literature review. The main theory that explains the minimization of energy expenditure during the locomotion is the inverted pendulum mechanism, by which the energy-minimizer mechanism converts kinetic energy into potential energy of the center of mass and vice-versa during the step. This mechanism is strongly influenced by spatio-temporal gait (locomotion) parameters such as step length and preferred walking speed, which, in turn, may be severely altered in patients with chronic low back pain. However, much remains to be understood about the effects of chronic low back pain on the individual's ability to practice an economic locomotion, because functional impairment may compromise the mechanical and energetic characteristics of this type of gait, making it more costly. Thus, there are indications that such changes may compromise the functional energy-minimizer mechanisms. Copyright © 2014 Elsevier Editora Ltda. All rights reserved.

RNAslider: a faster engine for consecutive windows folding and its application to the analysis of genomic folding asymmetry.

PubMed

Horesh, Yair; Wexler, Ydo; Lebenthal, Ilana; Ziv-Ukelson, Michal; Unger, Ron

2009-03-04

Scanning large genomes with a sliding window in search of locally stable RNA structures is a well motivated problem in bioinformatics. Given a predefined window size L and an RNA sequence S of size N (L < N), the consecutive windows folding problem is to compute the minimal free energy (MFE) for the folding of each of the L-sized substrings of S. The consecutive windows folding problem can be naively solved in O(NL3) by applying any of the classical cubic-time RNA folding algorithms to each of the N-L windows of size L. Recently an O(NL2) solution for this problem has been described. Here, we describe and implement an O(NLpsi(L)) engine for the consecutive windows folding problem, where psi(L) is shown to converge to O(1) under the assumption of a standard probabilistic polymer folding model, yielding an O(L) speedup which is experimentally confirmed. Using this tool, we note an intriguing directionality (5'-3' vs. 3'-5') folding bias, i.e. that the minimal free energy (MFE) of folding is higher in the native direction of the DNA than in the reverse direction of various genomic regions in several organisms including regions of the genomes that do not encode proteins or ncRNA. This bias largely emerges from the genomic dinucleotide bias which affects the MFE, however we see some variations in the folding bias in the different genomic regions when normalized to the dinucleotide bias. We also present results from calculating the MFE landscape of a mouse chromosome 1, characterizing the MFE of the long ncRNA molecules that reside in this chromosome. The efficient consecutive windows folding engine described in this paper allows for genome wide scans for ncRNA molecules as well as large-scale statistics. This is implemented here as a software tool, called RNAslider, and applied to the scanning of long chromosomes, leading to the observation of features that are visible only on a large scale.

Energy management and cooperation in microgrids

NASA Astrophysics Data System (ADS)

Rahbar, Katayoun

Microgrids are key components of future smart power grids, which integrate distributed renewable energy generators to efficiently serve the load demand locally. However, random and intermittent characteristics of renewable energy generations may hinder the reliable operation of microgrids. This thesis is thus devoted to investigating new strategies for microgrids to optimally manage their energy consumption, energy storage system (ESS) and cooperation in real time to achieve the reliable and cost-effective operation. This thesis starts with a single microgrid system. The optimal energy scheduling and ESS management policy is derived to minimize the energy cost of the microgrid resulting from drawing conventional energy from the main grid under both the off-line and online setups, where the renewable energy generation/load demand are assumed to be non-causally known and causally known at the microgrid, respectively. The proposed online algorithm is designed based on the optimal off-line solution and works under arbitrary (even unknown) realizations of future renewable energy generation/load demand. Therefore, it is more practically applicable as compared to solutions based on conventional techniques such as dynamic programming and stochastic programming that require the prior knowledge of renewable energy generation and load demand realizations/distributions. Next, for a group of microgrids that cooperate in energy management, we study efficient methods for sharing energy among them for both fully and partially cooperative scenarios, where microgrids are of common interests and self-interested, respectively. For the fully cooperative energy management, the off-line optimization problem is first formulated and optimally solved, where a distributed algorithm is proposed to minimize the total (sum) energy cost of microgrids. Inspired by the results obtained from the off-line optimization, efficient online algorithms are proposed for the real-time energy management, which are of low complexity and work given arbitrary realizations of renewable energy generation/load demand. On the other hand, for self-interested microgrids, the partially cooperative energy management is formulated and a distributed algorithm is proposed to optimize the energy cooperation such that energy costs of individual microgrids reduce simultaneously over the case without energy cooperation while limited information is shared among the microgrids and the central controller.

Meeting China's electricity needs through clean energy sources: A 2030 low-carbon energy roadmap

NASA Astrophysics Data System (ADS)

Hu, Zheng

China is undergoing rapid economic development that generates significant increase in energy demand, primarily for electricity. Energy supply in China is heavily relying on coal, which leads to high carbon emissions. This dissertation explores opportunities for meeting China's growing power demand through clean energy sources. The utilization of China's clean energy sources as well as demand-side management is still at the initial phase. Therefore, development of clean energy sources would require substantial government support in order to be competitive in the market. One of the widely used means to consider clean energy in power sector supplying is Integrated Resource Strategic Planning, which aims to minimize the long term electricity costs while screening various power supply options for the power supply and demand analysis. The IRSP tool tackles the energy problem from the perspective of power sector regulators, and provides different policy scenarios to quantify the impacts of combined incentives. Through three scenario studies, Business as Usual, High Renewable, and Renewable and Demand Side Management, this dissertation identifies the optimized scenario for China to achieve the clean energy target of 2030. The scenarios are assessed through energy, economics, environment, and equity dimensions.

Hydrothermal carbonization of rice husk for fuel upgrading

NASA Astrophysics Data System (ADS)

Suteerawattananonda, N.; Kongkaew, N.; Patumsawad, S.

2018-01-01

The biomass is popularly used as renewable energy. In Thailand rice is the most consume agricultural products. Agricultural residues from rice husk can be an energy resource. However, alkali and alkali earth materials (AAEMs) in biomass ash are the causes of corrosion and erosion problem in the heat exchanger equipment, while the acidity of ash affects the slagging agglomeration problem. Reduction of alkali and alkali earth materials can minimize the problem. In order to challenge the reduction of alkali and alkali earth materials in biomass ash, hydrothermal carbonization process was selected. Thai rice husk was used as sample to compare the result of treatment. The rice husk was heated under the condition of different temperature ranged from 180°C to 250°C, at operate pressure ranges from 12 bar to 42 bar with residence holding reaction time 1 hour. The results of proximate analysis show that the percentage by mass of fixed carbon are increased 2 times, but volatile matter is decreased by 40% and ash content is decreased by 11% due to the increment of temperature. Meanwhile, the X-Ray fluorescence (XRF) analysis results show the decreasing of alkali and alkali earth materials are reduced.

A novel multireceiver communications system configuration based on optimal estimation theory

NASA Technical Reports Server (NTRS)

Kumar, R.

1990-01-01

A multireceiver configuration for the purpose of carrier arraying and/or signal arraying is presented. Such a problem arises for example, in the NASA Deep Space Network where the same data-modulated signal from a spacecraft is received by a number of geographically separated antennas and the data detection must be efficiently performed on the basis of the various received signals. The proposed configuration is arrived at by formulating the carrier and/or signal arraying problem as an optimal estimation problem. Two specific solutions are proposed. The first solution is to simultaneously and optimally estimate the various phase processes received at different receivers with coupled phase locked loops (PLLs) wherein the individual PLLs acquire and track their respective receivers' phase processes, but are aided by each other in an optimal manner. However, when the phase processes are relatively weakly correlated, and for the case of relatively high values of symbol energy-to-noise spectral density ratio, a novel configuration for combining the data modulated, loop-output signals is proposed. The scheme can be extended to the case of low symbol energy-to-noise case by performing the combining/detection process over a multisymbol period. Such a configuration results in the minimization of the effective radio loss at the combiner output, and thus a maximization of energy per bit to noise-power spectral density ration is achieved.

KIVA-hpFE. Predictive turbulent reactive and multiphase flow in engines - An Overview

DOE Office of Scientific and Technical Information (OSTI.GOV)

Carrington, David Bradley

2016-05-23

Research and development of KIVA-hpFE for turbulent reactive and multiphase flow particularly as related to engine modeling program has relevance to National energy security and climate change. Climate change is a source problem, and energy national security is consumption of petroleum products problem. Accurately predicting engine processes leads to, lower greenhouse gas (GHG) emission, where engines in the transportation sector currently account for 26% of the U.S. GHG emissions. Less dependence on petroleum products leads to greater energy security. By Environmental Protection Agency standards, some vehicles are now reaching 42 to the 50 mpg mark. These are conventional gasoline engines.more » Continued investment and research into new technical innovations, the potential exists to save more than 4 million barrels of oil per day or approximately $200 to $400 million per day. This would be a significant decrease in emission and use of petroleum and a very large economic stimulus too! It is estimated with further advancements in combustion, the current emissions can be reduced up to 40%. Enabling better understanding of fuel injection and fuel-air mixing, thermodynamic combustion losses, and combustion/emission formation processes enhances our ability to help solve both problems. To provide adequate capability for accurately simulating these processes, minimize time and labor for development of engine technology, are the goals of our KIVA development program.« less

Analytical solution of Schrödinger equation in minimal length formalism for trigonometric potential using hypergeometry method

NASA Astrophysics Data System (ADS)

Nurhidayati, I.; Suparmi, A.; Cari, C.

2018-03-01

The Schrödinger equation has been extended by applying the minimal length formalism for trigonometric potential. The wave function and energy spectra were used to describe the behavior of subatomic particle. The wave function and energy spectra were obtained by using hypergeometry method. The result showed that the energy increased by the increasing both of minimal length parameter and the potential parameter. The energy were calculated numerically using MatLab.

Joint optimization of regional water-power systems

NASA Astrophysics Data System (ADS)

Pereira-Cardenal, Silvio J.; Mo, Birger; Gjelsvik, Anders; Riegels, Niels D.; Arnbjerg-Nielsen, Karsten; Bauer-Gottwein, Peter

2016-06-01

Energy and water resources systems are tightly coupled; energy is needed to deliver water and water is needed to extract or produce energy. Growing pressure on these resources has raised concerns about their long-term management and highlights the need to develop integrated solutions. A method for joint optimization of water and electric power systems was developed in order to identify methodologies to assess the broader interactions between water and energy systems. The proposed method is to include water users and power producers into an economic optimization problem that minimizes the cost of power production and maximizes the benefits of water allocation, subject to constraints from the power and hydrological systems. The method was tested on the Iberian Peninsula using simplified models of the seven major river basins and the power market. The optimization problem was successfully solved using stochastic dual dynamic programming. The results showed that current water allocation to hydropower producers in basins with high irrigation productivity, and to irrigation users in basins with high hydropower productivity was sub-optimal. Optimal allocation was achieved by managing reservoirs in very distinct ways, according to the local inflow, storage capacity, hydropower productivity, and irrigation demand and productivity. This highlights the importance of appropriately representing the water users' spatial distribution and marginal benefits and costs when allocating water resources optimally. The method can handle further spatial disaggregation and can be extended to include other aspects of the water-energy nexus.

Decomposing Large Inverse Problems with an Augmented Lagrangian Approach: Application to Joint Inversion of Body-Wave Travel Times and Surface-Wave Dispersion Measurements

NASA Astrophysics Data System (ADS)

Reiter, D. T.; Rodi, W. L.

2015-12-01

Constructing 3D Earth models through the joint inversion of large geophysical data sets presents numerous theoretical and practical challenges, especially when diverse types of data and model parameters are involved. Among the challenges are the computational complexity associated with large data and model vectors and the need to unify differing model parameterizations, forward modeling methods and regularization schemes within a common inversion framework. The challenges can be addressed in part by decomposing the inverse problem into smaller, simpler inverse problems that can be solved separately, providing one knows how to merge the separate inversion results into an optimal solution of the full problem. We have formulated an approach to the decomposition of large inverse problems based on the augmented Lagrangian technique from optimization theory. As commonly done, we define a solution to the full inverse problem as the Earth model minimizing an objective function motivated, for example, by a Bayesian inference formulation. Our decomposition approach recasts the minimization problem equivalently as the minimization of component objective functions, corresponding to specified data subsets, subject to the constraints that the minimizing models be equal. A standard optimization algorithm solves the resulting constrained minimization problems by alternating between the separate solution of the component problems and the updating of Lagrange multipliers that serve to steer the individual solution models toward a common model solving the full problem. We are applying our inversion method to the reconstruction of the·crust and upper-mantle seismic velocity structure across Eurasia.· Data for the inversion comprise a large set of P and S body-wave travel times·and fundamental and first-higher mode Rayleigh-wave group velocities.

Problem of quality assurance during metal constructions welding via robotic technological complexes

NASA Astrophysics Data System (ADS)

Fominykh, D. S.; Rezchikov, A. F.; Kushnikov, V. A.; Ivashchenko, V. A.; Bogomolov, A. S.; Filimonyuk, L. Yu; Dolinina, O. N.; Kushnikov, O. V.; Shulga, T. E.; Tverdokhlebov, V. A.

2018-05-01

The problem of minimizing the probability for critical combinations of events that lead to a loss in welding quality via robotic process automation is examined. The problem is formulated, models and algorithms for its solution are developed. The problem is solved by minimizing the criterion characterizing the losses caused by defective products. Solving the problem may enhance the quality and accuracy of operations performed and reduce the losses caused by defective product

Trading strategies for distribution company with stochastic distributed energy resources

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhang, Chunyu; Wang, Qi; Wang, Jianhui

2016-09-01

This paper proposes a methodology to address the trading strategies of a proactive distribution company (PDISCO) engaged in the transmission-level (TL) markets. A one-leader multi-follower bilevel model is presented to formulate the gaming framework between the PDISCO and markets. The lower-level (LL) problems include the TL day-ahead market and scenario-based real-time markets, respectively with the objectives of maximizing social welfare and minimizing operation cost. The upper-level (UL) problem is to maximize the PDISCO’s profit across these markets. The PDISCO’s strategic offers/bids interactively influence the outcomes of each market. Since the LL problems are linear and convex, while the UL problemmore » is non-linear and non-convex, an equivalent primal–dual approach is used to reformulate this bilevel model to a solvable mathematical program with equilibrium constraints (MPEC). The effectiveness of the proposed model is verified by case studies.« less

Replicator equations, maximal cliques, and graph isomorphism.

PubMed

Pelillo, M

1999-11-15

We present a new energy-minimization framework for the graph isomorphism problem that is based on an equivalent maximum clique formulation. The approach is centered around a fundamental result proved by Motzkin and Straus in the mid-1960s, and recently expanded in various ways, which allows us to formulate the maximum clique problem in terms of a standard quadratic program. The attractive feature of this formulation is that a clear one-to-one correspondence exists between the solutions of the quadratic program and those in the original, combinatorial problem. To solve the program we use the so-called replicator equations--a class of straightforward continuous- and discrete-time dynamical systems developed in various branches of theoretical biology. We show how, despite their inherent inability to escape from local solutions, they nevertheless provide experimental results that are competitive with those obtained using more elaborate mean-field annealing heuristics.

A Generalized Formulation of Demand Response under Market Environments

NASA Astrophysics Data System (ADS)

Nguyen, Minh Y.; Nguyen, Duc M.

2015-06-01

This paper presents a generalized formulation of Demand Response (DR) under deregulated electricity markets. The problem is scheduling and controls the consumption of electrical loads according to the market price to minimize the energy cost over a day. Taking into account the modeling of customers' comfort (i.e., preference), the formulation can be applied to various types of loads including what was traditionally classified as critical loads (e.g., air conditioning, lights). The proposed DR scheme is based on Dynamic Programming (DP) framework and solved by DP backward algorithm in which the stochastic optimization is used to treat the uncertainty, if any occurred in the problem. The proposed formulation is examined with the DR problem of different loads, including Heat Ventilation and Air Conditioning (HVAC), Electric Vehicles (EVs) and a newly DR on the water supply systems of commercial buildings. The result of simulation shows significant saving can be achieved in comparison with their traditional (On/Off) scheme.

Rate and power efficient image compressed sensing and transmission

NASA Astrophysics Data System (ADS)

Olanigan, Saheed; Cao, Lei; Viswanathan, Ramanarayanan

2016-01-01

This paper presents a suboptimal quantization and transmission scheme for multiscale block-based compressed sensing images over wireless channels. The proposed method includes two stages: dealing with quantization distortion and transmission errors. First, given the total transmission bit rate, the optimal number of quantization bits is assigned to the sensed measurements in different wavelet sub-bands so that the total quantization distortion is minimized. Second, given the total transmission power, the energy is allocated to different quantization bit layers based on their different error sensitivities. The method of Lagrange multipliers with Karush-Kuhn-Tucker conditions is used to solve both optimization problems, for which the first problem can be solved with relaxation and the second problem can be solved completely. The effectiveness of the scheme is illustrated through simulation results, which have shown up to 10 dB improvement over the method without the rate and power optimization in medium and low signal-to-noise ratio cases.

Quadratic Optimization in the Problems of Active Control of Sound

NASA Technical Reports Server (NTRS)

Loncaric, J.; Tsynkov, S. V.; Bushnell, Dennis M. (Technical Monitor)

2002-01-01

We analyze the problem of suppressing the unwanted component of a time-harmonic acoustic field (noise) on a predetermined region of interest. The suppression is rendered by active means, i.e., by introducing the additional acoustic sources called controls that generate the appropriate anti-sound. Previously, we have obtained general solutions for active controls in both continuous and discrete formulations of the problem. We have also obtained optimal solutions that minimize the overall absolute acoustic source strength of active control sources. These optimal solutions happen to be particular layers of monopoles on the perimeter of the protected region. Mathematically, minimization of acoustic source strength is equivalent to minimization in the sense of L(sub 1). By contrast. in the current paper we formulate and study optimization problems that involve quadratic functions of merit. Specifically, we minimize the L(sub 2) norm of the control sources, and we consider both the unconstrained and constrained minimization. The unconstrained L(sub 2) minimization is certainly the easiest problem to address numerically. On the other hand, the constrained approach allows one to analyze sophisticated geometries. In a special case, we call compare our finite-difference optimal solutions to the continuous optimal solutions obtained previously using a semi-analytic technique. We also show that the optima obtained in the sense of L(sub 2) differ drastically from those obtained in the sense of L(sub 1).

Development of Advanced Methods of Structural and Trajectory Analysis for Transport Aircraft

NASA Technical Reports Server (NTRS)

Ardema, Mark D.; Windhorst, Robert; Phillips, James

1998-01-01

This paper develops a near-optimal guidance law for generating minimum fuel, time, or cost fixed-range trajectories for supersonic transport aircraft. The approach uses a choice of new state variables along with singular perturbation techniques to time-scale decouple the dynamic equations into multiple equations of single order (second order for the fast dynamics). Application of the maximum principle to each of the decoupled equations, as opposed to application to the original coupled equations, avoids the two point boundary value problem and transforms the problem from one of a functional optimization to one of multiple function optimizations. It is shown that such an approach produces well known aircraft performance results such as minimizing the Brequet factor for minimum fuel consumption and the energy climb path. Furthermore, the new state variables produce a consistent calculation of flight path angle along the trajectory, eliminating one of the deficiencies in the traditional energy state approximation. In addition, jumps in the energy climb path are smoothed out by integration of the original dynamic equations at constant load factor. Numerical results performed for a supersonic transport design show that a pushover dive followed by a pullout at nominal load factors are sufficient maneuvers to smooth the jump.

Optimization of Supersonic Transport Trajectories

NASA Technical Reports Server (NTRS)

Ardema, Mark D.; Windhorst, Robert; Phillips, James

1998-01-01

This paper develops a near-optimal guidance law for generating minimum fuel, time, or cost fixed-range trajectories for supersonic transport aircraft. The approach uses a choice of new state variables along with singular perturbation techniques to time-scale decouple the dynamic equations into multiple equations of single order (second order for the fast dynamics). Application of the maximum principle to each of the decoupled equations, as opposed to application to the original coupled equations, avoids the two point boundary value problem and transforms the problem from one of a functional optimization to one of multiple function optimizations. It is shown that such an approach produces well known aircraft performance results such as minimizing the Brequet factor for minimum fuel consumption and the energy climb path. Furthermore, the new state variables produce a consistent calculation of flight path angle along the trajectory, eliminating one of the deficiencies in the traditional energy state approximation. In addition, jumps in the energy climb path are smoothed out by integration of the original dynamic equations at constant load factor. Numerical results performed for a supersonic transport design show that a pushover dive followed by a pullout at nominal load factors are sufficient maneuvers to smooth the jump.

Energy-efficient ECG compression on wireless biosensors via minimal coherence sensing and weighted ℓ₁ minimization reconstruction.

PubMed

Zhang, Jun; Gu, Zhenghui; Yu, Zhu Liang; Li, Yuanqing

2015-03-01

Low energy consumption is crucial for body area networks (BANs). In BAN-enabled ECG monitoring, the continuous monitoring entails the need of the sensor nodes to transmit a huge data to the sink node, which leads to excessive energy consumption. To reduce airtime over energy-hungry wireless links, this paper presents an energy-efficient compressed sensing (CS)-based approach for on-node ECG compression. At first, an algorithm called minimal mutual coherence pursuit is proposed to construct sparse binary measurement matrices, which can be used to encode the ECG signals with superior performance and extremely low complexity. Second, in order to minimize the data rate required for faithful reconstruction, a weighted ℓ1 minimization model is derived by exploring the multisource prior knowledge in wavelet domain. Experimental results on MIT-BIH arrhythmia database reveals that the proposed approach can obtain higher compression ratio than the state-of-the-art CS-based methods. Together with its low encoding complexity, our approach can achieve significant energy saving in both encoding process and wireless transmission.

10 CFR 20.1406 - Minimization of contamination.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 10 Energy 1 2010-01-01 2010-01-01 false Minimization of contamination. 20.1406 Section 20.1406 Energy NUCLEAR REGULATORY COMMISSION STANDARDS FOR PROTECTION AGAINST RADIATION Radiological Criteria for License Termination § 20.1406 Minimization of contamination. (a) Applicants for licenses, other than early...

10 CFR 20.1406 - Minimization of contamination.

Code of Federal Regulations, 2011 CFR

2011-01-01

... 10 Energy 1 2011-01-01 2011-01-01 false Minimization of contamination. 20.1406 Section 20.1406 Energy NUCLEAR REGULATORY COMMISSION STANDARDS FOR PROTECTION AGAINST RADIATION Radiological Criteria for License Termination § 20.1406 Minimization of contamination. (a) Applicants for licenses, other than early...

A volumetric conformal mapping approach for clustering white matter fibers in the brain

PubMed Central

Gupta, Vikash; Prasad, Gautam; Thompson, Paul

2017-01-01

The human brain may be considered as a genus-0 shape, topologically equivalent to a sphere. Various methods have been used in the past to transform the brain surface to that of a sphere using harmonic energy minimization methods used for cortical surface matching. However, very few methods have studied volumetric parameterization of the brain using a spherical embedding. Volumetric parameterization is typically used for complicated geometric problems like shape matching, morphing and isogeometric analysis. Using conformal mapping techniques, we can establish a bijective mapping between the brain and the topologically equivalent sphere. Our hypothesis is that shape analysis problems are simplified when the shape is defined in an intrinsic coordinate system. Our goal is to establish such a coordinate system for the brain. The efficacy of the method is demonstrated with a white matter clustering problem. Initial results show promise for future investigation in these parameterization technique and its application to other problems related to computational anatomy like registration and segmentation. PMID:29177252

Functional and Radiological Outcome of Schatzker type V and VI Tibial Plateau Fracture Treatment with Dual Plates with Minimum 3 years follow-up: A Prospective Study

PubMed Central

Suri, Harpreet Singh; Gangrade, Kewal

2016-01-01

Introduction High energy intra-articular fractures involving the tibial plateau causes various problems related to management like wound dehiscence, severe comminution leading to malalignment and delayed complications like varus collapse, implant failure and arthritis of knee joint. Aim This study was done to determine functional, radiological outcome and the complications of Schatzker V and VI tibial plateau fractures treated with bipillar plating with dual plates with a regular follow-up of atleast 3 years. Materials and Methods Total 34 cases of tibial plateau fracture type V and VI treated with dual plating were studied from January 2011 to December 2013 in KIMS Hospital were followed for minimum of 3 years. The patients were operated through an anterolateral approach for lateral plate and a medial column plate was put through a minimally invasive medial approach or an open posteromedial approach. Results Total 34 patients were evaluated postoperatively thoroughly for functional outcome using The Knee Society Score and radiological outcomes by Modified Rasmussen Assessment criteria which showed 29 patients (85.29%) had excellent and 5 patients (14.71%) had good objective knee society score. 24 patients (70.59%) had excellent, 8 patients (23.53%) had good and 1patient (2.94%) were each of poor and fair functional knee society score. Eleven patients (32.35%) had excellent, 21patients (61.76%) had good and 2 patients (5.88%) had fair radiological outcome. Conclusion We conclude that open reduction and internal fixation of high-energy tibial plateau fractures with dual plates via 2 incisions gives excellent to good functional outcome with minimal soft tissue complications. Thus, a minimally invasive approach should be used which helps in preventing soft tissue problems and helps in early wound healing. Fixation done by bipillar plating is important for early mobilization of knee joint. Early mobilization leads to better range of movements and thereby better functional outcome. PMID:27437315

The Lagrangian Multiplier Method of Finding Upper and Lower Limits to Critical Stresses of Clamped Plates

DTIC Science & Technology

1946-01-01

geometrica ~ boundary condi- tions of the problem. (2) The energy of the load-plate system is computed for this deflection surface and is then minimized...and interpolating to find the k that makes the seriw vanish. The correct value of m is that which gives the lowest value of k. For two half waves (m=2...the square plate, the present rekdively simple upper- and lower-limit calcula- tions show that his est,imatd limit of error is correct for this case

Computing motion using resistive networks

NASA Technical Reports Server (NTRS)

Koch, Christof; Luo, Jin; Mead, Carver; Hutchinson, James

1988-01-01

Recent developments in the theory of early vision are described which lead from the formulation of the motion problem as an ill-posed one to its solution by minimizing certain 'cost' functions. These cost or energy functions can be mapped onto simple analog and digital resistive networks. It is shown how the optical flow can be computed by injecting currents into resistive networks and recording the resulting stationary voltage distribution at each node. These networks can be implemented in cMOS VLSI circuits and represent plausible candidates for biological vision systems.

Fast Algorithms for Earth Mover’s Distance Based on Optimal Transport and L1 Type Regularization I

DTIC Science & Technology

2016-09-01

which EMD can be reformulated as a familiar homogeneous degree 1 regularized minimization. The new minimization problem is very similar to problems which...which is also named the Monge problem or the Wasserstein metric, plays a central role in many applications, including image processing, computer vision

Exploiting node mobility for energy optimization in wireless sensor networks

NASA Astrophysics Data System (ADS)

El-Moukaddem, Fatme Mohammad

Wireless Sensor Networks (WSNs) have become increasingly available for data-intensive applications such as micro-climate monitoring, precision agriculture, and audio/video surveillance. A key challenge faced by data-intensive WSNs is to transmit the sheer amount of data generated within an application's lifetime to the base station despite the fact that sensor nodes have limited power supplies such as batteries or small solar panels. The availability of numerous low-cost robotic units (e.g. Robomote and Khepera) has made it possible to construct sensor networks consisting of mobile sensor nodes. It has been shown that the controlled mobility offered by mobile sensors can be exploited to improve the energy efficiency of a network. In this thesis, we propose schemes that use mobile sensor nodes to reduce the energy consumption of data-intensive WSNs. Our approaches differ from previous work in two main aspects. First, our approaches do not require complex motion planning of mobile nodes, and hence can be implemented on a number of low-cost mobile sensor platforms. Second, we integrate the energy consumption due to both mobility and wireless communications into a holistic optimization framework. We consider three problems arising from the limited energy in the sensor nodes. In the first problem, the network consists of mostly static nodes and contains only a few mobile nodes. In the second and third problems, we assume essentially that all nodes in the WSN are mobile. We first study a new problem called max-data mobile relay configuration (MMRC ) that finds the positions of a set of mobile sensors, referred to as relays, that maximize the total amount of data gathered by the network during its lifetime. We show that the MMRC problem is surprisingly complex even for a trivial network topology due to the joint consideration of the energy consumption of both wireless communication and mechanical locomotion. We present optimal MMRC algorithms and practical distributed implementations for several important network topologies and applications. Second, we consider the problem of minimizing the total energy consumption of a network. We design an iterative algorithm that improves a given configuration by relocating nodes to new positions. We show that this algorithm converges to the optimal configuration for the given transmission routes. Moreover, we propose an efficient distributed implementation that does not require explicit synchronization. Finally, we consider the problem of maximizing the lifetime of the network. We propose an approach that exploits the mobility of the nodes to balance the energy consumption throughout the network. We develop efficient algorithms for single and multiple round approaches. For all three problems, we evaluate the efficiency of our algorithms through simulations. Our simulation results based on realistic energy models obtained from existing mobile and static sensor platforms show that our approaches significantly improve the network's performance and outperform existing approaches.

Smoothed low rank and sparse matrix recovery by iteratively reweighted least squares minimization.

PubMed

Lu, Canyi; Lin, Zhouchen; Yan, Shuicheng

2015-02-01

This paper presents a general framework for solving the low-rank and/or sparse matrix minimization problems, which may involve multiple nonsmooth terms. The iteratively reweighted least squares (IRLSs) method is a fast solver, which smooths the objective function and minimizes it by alternately updating the variables and their weights. However, the traditional IRLS can only solve a sparse only or low rank only minimization problem with squared loss or an affine constraint. This paper generalizes IRLS to solve joint/mixed low-rank and sparse minimization problems, which are essential formulations for many tasks. As a concrete example, we solve the Schatten-p norm and l2,q-norm regularized low-rank representation problem by IRLS, and theoretically prove that the derived solution is a stationary point (globally optimal if p,q ≥ 1). Our convergence proof of IRLS is more general than previous one that depends on the special properties of the Schatten-p norm and l2,q-norm. Extensive experiments on both synthetic and real data sets demonstrate that our IRLS is much more efficient.

Energy, Society, and Education, with Emphasis on Educational Technology Policy for K-12

NASA Astrophysics Data System (ADS)

Chedid, Loutfallah Georges

2005-03-01

This paper begins by examining the profound impact of energy usage on our lives, and on every major sector of the economy. Then, the anticipated US energy needs by the year 2025 are presented based on the Department of Energy's projections. The paper considers the much-touted National Energy Policy Report, and identifies a major flaw where the policy report neglects education as a contributor to solving future energy problems. The inextricable interaction between energy solutions and education is described, with emphasis on education policy as a potential vehicle for developing economically and commercially sustainable energy systems that have a minimal impact on the environment. With that said, an earnest argument is made as to the need to educate science, technology, engineering, and mathematics (STEM) proficient individuals for the energy technology development workforce, starting with the K-12 level. A framework for the aforementioned STEM education policies is presented that includes a sustained national awareness campaign, address the teacher's salary issues, and addresses teacher quality issues. Moreover, the framework suggests a John Dewey-style "learning-by-doing" shift in pedagogy. Finally, the framework presents specific changes to the current national standards that would be valuable to the 21st century student.

Diagrammatic expansion for positive spectral functions beyond GW: Application to vertex corrections in the electron gas

NASA Astrophysics Data System (ADS)

Stefanucci, G.; Pavlyukh, Y.; Uimonen, A.-M.; van Leeuwen, R.

2014-09-01

We present a diagrammatic approach to construct self-energy approximations within many-body perturbation theory with positive spectral properties. The method cures the problem of negative spectral functions which arises from a straightforward inclusion of vertex diagrams beyond the GW approximation. Our approach consists of a two-step procedure: We first express the approximate many-body self-energy as a product of half-diagrams and then identify the minimal number of half-diagrams to add in order to form a perfect square. The resulting self-energy is an unconventional sum of self-energy diagrams in which the internal lines of half a diagram are time-ordered Green's functions, whereas those of the other half are anti-time-ordered Green's functions, and the lines joining the two halves are either lesser or greater Green's functions. The theory is developed using noninteracting Green's functions and subsequently extended to self-consistent Green's functions. Issues related to the conserving properties of diagrammatic approximations with positive spectral functions are also addressed. As a major application of the formalism we derive the minimal set of additional diagrams to make positive the spectral function of the GW approximation with lowest-order vertex corrections and screened interactions. The method is then applied to vertex corrections in the three-dimensional homogeneous electron gas by using a combination of analytical frequency integrations and numerical Monte Carlo momentum integrations to evaluate the diagrams.

A minimal scale invariant axion solution to the strong CP-problem

NASA Astrophysics Data System (ADS)

Tokareva, Anna

2018-05-01

We present a scale-invariant extension of the Standard model allowing for the Kim-Shifman-Vainstein-Zakharov (KSVZ) axion solution of the strong CP problem in QCD. We add the minimal number of new particles and show that the Peccei-Quinn scalar might be identified with the complex dilaton field. Scale invariance, together with the Peccei-Quinn symmetry, is broken spontaneously near the Planck scale before inflation, which is driven by the Standard Model Higgs field. We present a set of general conditions which makes this scenario viable and an explicit example of an effective theory possessing spontaneous breaking of scale invariance. We show that this description works both for inflation and low-energy physics in the electroweak vacuum. This scenario can provide a self-consistent inflationary stage and, at the same time, successfully avoid the cosmological bounds on the axion. Our general predictions are the existence of colored TeV mass fermion and the QCD axion. The latter has all the properties of the KSVZ axion but does not contribute to dark matter. This axion can be searched via its mixing to a photon in an external magnetic field.

Unified anomaly suppression and boundary extraction in laser radar range imagery based on a joint curve-evolution and expectation-maximization algorithm.

PubMed

Feng, Haihua; Karl, William Clem; Castañon, David A

2008-05-01

In this paper, we develop a new unified approach for laser radar range anomaly suppression, range profiling, and segmentation. This approach combines an object-based hybrid scene model for representing the range distribution of the field and a statistical mixture model for the range data measurement noise. The image segmentation problem is formulated as a minimization problem which jointly estimates the target boundary together with the target region range variation and background range variation directly from the noisy and anomaly-filled range data. This formulation allows direct incorporation of prior information concerning the target boundary, target ranges, and background ranges into an optimal reconstruction process. Curve evolution techniques and a generalized expectation-maximization algorithm are jointly employed as an efficient solver for minimizing the objective energy, resulting in a coupled pair of object and intensity optimization tasks. The method directly and optimally extracts the target boundary, avoiding a suboptimal two-step process involving image smoothing followed by boundary extraction. Experiments are presented demonstrating that the proposed approach is robust to anomalous pixels (missing data) and capable of producing accurate estimation of the target boundary and range values from noisy data.

Energy minimization for self-organized structure formation and actuation

NASA Astrophysics Data System (ADS)

Kofod, Guggi; Wirges, Werner; Paajanen, Mika; Bauer, Siegfried

2007-02-01

An approach for creating complex structures with embedded actuation in planar manufacturing steps is presented. Self-organization and energy minimization are central to this approach, illustrated with a model based on minimization of the hyperelastic free energy strain function of a stretched elastomer and the bending elastic energy of a plastic frame. A tulip-shaped gripper structure illustrates the technological potential of the approach. Advantages are simplicity of manufacture, complexity of final structures, and the ease with which any electroactive material can be exploited as means of actuation.

Analog "neuronal" networks in early vision.

PubMed Central

Koch, C; Marroquin, J; Yuille, A

1986-01-01

Many problems in early vision can be formulated in terms of minimizing a cost function. Examples are shape from shading, edge detection, motion analysis, structure from motion, and surface interpolation. As shown by Poggio and Koch [Poggio, T. & Koch, C. (1985) Proc. R. Soc. London, Ser. B 226, 303-323], quadratic variational problems, an important subset of early vision tasks, can be "solved" by linear, analog electrical, or chemical networks. However, in the presence of discontinuities, the cost function is nonquadratic, raising the question of designing efficient algorithms for computing the optimal solution. Recently, Hopfield and Tank [Hopfield, J. J. & Tank, D. W. (1985) Biol. Cybern. 52, 141-152] have shown that networks of nonlinear analog "neurons" can be effective in computing the solution of optimization problems. We show how these networks can be generalized to solve the nonconvex energy functionals of early vision. We illustrate this approach by implementing a specific analog network, solving the problem of reconstructing a smooth surface from sparse data while preserving its discontinuities. These results suggest a novel computational strategy for solving early vision problems in both biological and real-time artificial vision systems. PMID:3459172

Network switching strategy for energy conservation in heterogeneous networks.

PubMed

Song, Yujae; Choi, Wooyeol; Baek, Seungjae

2017-01-01

In heterogeneous networks (HetNets), the large-scale deployment of small base stations (BSs) together with traditional macro BSs is an economical and efficient solution that is employed to address the exponential growth in mobile data traffic. In dense HetNets, network switching, i.e., handovers, plays a critical role in connecting a mobile terminal (MT) to the best of all accessible networks. In the existing literature, a handover decision is made using various handover metrics such as the signal-to-noise ratio, data rate, and movement speed. However, there are few studies on handovers that focus on energy efficiency in HetNets. In this paper, we propose a handover strategy that helps to minimize energy consumption at BSs in HetNets without compromising the quality of service (QoS) of each MT. The proposed handover strategy aims to capture the effect of the stochastic behavior of handover parameters and the expected energy consumption due to handover execution when making a handover decision. To identify the validity of the proposed handover strategy, we formulate a handover problem as a constrained Markov decision process (CMDP), by which the effects of the stochastic behaviors of handover parameters and consequential handover energy consumption can be accurately reflected when making a handover decision. In the CMDP, the aim is to minimize the energy consumption to service an MT over the lifetime of its connection, and the constraint is to guarantee the QoS requirements of the MT given in terms of the transmission delay and call-dropping probability. We find an optimal policy for the CMDP using a combination of the Lagrangian method and value iteration. Simulation results verify the validity of the proposed handover strategy.

Ubiquitous Green Computing Techniques for High Demand Applications in Smart Environments

PubMed Central

Zapater, Marina; Sanchez, Cesar; Ayala, Jose L.; Moya, Jose M.; Risco-Martín, José L.

2012-01-01

Ubiquitous sensor network deployments, such as the ones found in Smart cities and Ambient intelligence applications, require constantly increasing high computational demands in order to process data and offer services to users. The nature of these applications imply the usage of data centers. Research has paid much attention to the energy consumption of the sensor nodes in WSNs infrastructures. However, supercomputing facilities are the ones presenting a higher economic and environmental impact due to their very high power consumption. The latter problem, however, has been disregarded in the field of smart environment services. This paper proposes an energy-minimization workload assignment technique, based on heterogeneity and application-awareness, that redistributes low-demand computational tasks from high-performance facilities to idle nodes with low and medium resources in the WSN infrastructure. These non-optimal allocation policies reduce the energy consumed by the whole infrastructure and the total execution time. PMID:23112621

Mathematical Model and Artificial Intelligent Techniques Applied to a Milk Industry through DSM

NASA Astrophysics Data System (ADS)

Babu, P. Ravi; Divya, V. P. Sree

2011-08-01

The resources for electrical energy are depleting and hence the gap between the supply and the demand is continuously increasing. Under such circumstances, the option left is optimal utilization of available energy resources. The main objective of this chapter is to discuss about the Peak load management and overcome the problems associated with it in processing industries such as Milk industry with the help of DSM techniques. The chapter presents a generalized mathematical model for minimizing the total operating cost of the industry subject to the constraints. The work presented in this chapter also deals with the results of application of Neural Network, Fuzzy Logic and Demand Side Management (DSM) techniques applied to a medium scale milk industrial consumer in India to achieve the improvement in load factor, reduction in Maximum Demand (MD) and also the consumer gets saving in the energy bill.

Photovoltaic design optimization for terrestrial applications

NASA Technical Reports Server (NTRS)

Ross, R. G., Jr.

1978-01-01

As part of the Jet Propulsion Laboratory's Low-Cost Solar Array Project, a comprehensive program of module cost-optimization has been carried out. The objective of these studies has been to define means of reducing the cost and improving the utility and reliability of photovoltaic modules for the broad spectrum of terrestrial applications. This paper describes one of the methods being used for module optimization, including the derivation of specific equations which allow the optimization of various module design features. The method is based on minimizing the life-cycle cost of energy for the complete system. Comparison of the life-cycle energy cost with the marginal cost of energy each year allows the logical plant lifetime to be determined. The equations derived allow the explicit inclusion of design parameters such as tracking, site variability, and module degradation with time. An example problem involving the selection of an optimum module glass substrate is presented.

Research on Power System Scheduling Improving Wind Power Accommodation Considering Thermal Energy Storage and Flexible Load

NASA Astrophysics Data System (ADS)

Zou, Chenlu; Cui, Xue; Wang, Heng; Zhou, Bin; Liu, Yang

2018-01-01

In the case of rapid development of wind power and heavy wind curtailment, the study of wind power accommodation of combined heat and power system has become the focus of attention. A two-stage scheduling model contains of wind power, thermal energy storage, CHP unit and flexible load were constructed. This model with the objective function of minimizing wind curtailment and the operation cost of units while taking into account of the total coal consumption of units, constraint of thermal energy storage and electricity-heat characteristic of CHP. This paper uses MICA to solve the problem of too many constraints and make the solution more feasible. A numerical example showed that the two stage decision scheduling model can consume more wind power, and it could provide a reference for combined heat and power system short-term operation

High Energy Vibration for Gas Piping

NASA Astrophysics Data System (ADS)

Lee, Gary Y. H.; Chan, K. B.; Lee, Aylwin Y. S.; Jia, ShengXiang

2017-07-01

In September 2016, a gas compressor in offshore Sarawak has its rotor changed out. Prior to this change-out, pipe vibration study was carried-out by the project team to evaluate any potential high energy pipe vibration problems at the compressor’s existing relief valve downstream pipes due to process condition changes after rotor change out. This paper covers high frequency acoustic excitation (HFAE) vibration also known as acoustic induced vibration (AIV) study and discusses detailed methodologies as a companion to the Energy Institute Guidelines for the avoidance of vibration induced fatigue failure, which is a common industry practice to assess and mitigate for AIV induced fatigue failure. Such detailed theoretical studies can help to minimize or totally avoid physical pipe modification, leading to reduce offshore plant shutdown days to plant shutdowns only being required to accommodate gas compressor upgrades, reducing cost without compromising process safety.

From metadynamics to dynamics.

PubMed

Tiwary, Pratyush; Parrinello, Michele

2013-12-06

Metadynamics is a commonly used and successful enhanced sampling method. By the introduction of a history dependent bias which depends on a restricted number of collective variables it can explore complex free energy surfaces characterized by several metastable states separated by large free energy barriers. Here we extend its scope by introducing a simple yet powerful method for calculating the rates of transition between different metastable states. The method does not rely on a previous knowledge of the transition states or reaction coordinates, as long as collective variables are known that can distinguish between the various stable minima in free energy space. We demonstrate that our method recovers the correct escape rates out of these stable states and also preserves the correct sequence of state-to-state transitions, with minimal extra computational effort needed over ordinary metadynamics. We apply the formalism to three different problems and in each case find excellent agreement with the results of long unbiased molecular dynamics runs.

From Metadynamics to Dynamics

NASA Astrophysics Data System (ADS)

Tiwary, Pratyush; Parrinello, Michele

2013-12-01

Metadynamics is a commonly used and successful enhanced sampling method. By the introduction of a history dependent bias which depends on a restricted number of collective variables it can explore complex free energy surfaces characterized by several metastable states separated by large free energy barriers. Here we extend its scope by introducing a simple yet powerful method for calculating the rates of transition between different metastable states. The method does not rely on a previous knowledge of the transition states or reaction coordinates, as long as collective variables are known that can distinguish between the various stable minima in free energy space. We demonstrate that our method recovers the correct escape rates out of these stable states and also preserves the correct sequence of state-to-state transitions, with minimal extra computational effort needed over ordinary metadynamics. We apply the formalism to three different problems and in each case find excellent agreement with the results of long unbiased molecular dynamics runs.

Ubiquitous green computing techniques for high demand applications in Smart environments.

PubMed

Zapater, Marina; Sanchez, Cesar; Ayala, Jose L; Moya, Jose M; Risco-Martín, José L

2012-01-01

Ubiquitous sensor network deployments, such as the ones found in Smart cities and Ambient intelligence applications, require constantly increasing high computational demands in order to process data and offer services to users. The nature of these applications imply the usage of data centers. Research has paid much attention to the energy consumption of the sensor nodes in WSNs infrastructures. However, supercomputing facilities are the ones presenting a higher economic and environmental impact due to their very high power consumption. The latter problem, however, has been disregarded in the field of smart environment services. This paper proposes an energy-minimization workload assignment technique, based on heterogeneity and application-awareness, that redistributes low-demand computational tasks from high-performance facilities to idle nodes with low and medium resources in the WSN infrastructure. These non-optimal allocation policies reduce the energy consumed by the whole infrastructure and the total execution time.

Reflections on fifteen years of energy policy

NASA Astrophysics Data System (ADS)

Gibbons, John H.

1985-11-01

The events of the 1970's—both the ``energy crises'' and the measures taken to alleviate them—changed our ways of thinking about energy. We now look at energy consumption as a largely substitutable means to various ends, not a goal or a measure of progress in and of itself. Energy demand growth has dropped markedly, even as the economy has grown. But there are many issues yet to be resolved if the United States is to have a comprehensive, rotational energy strategy. This paper tackles four of them: Is there a place for continued government economic and regulatory intervention in the energy marketplace? What should be the federal role in energy research and development? What are our prospects for new discoveries in domestic oil and gas? What is the future of nuclear power in the United States? The author believes that the best way to solve our energy problems is to gauge, and then reflect in our energy policy, the true costs and benefits of energy production and consumption. He concludes that conservation investments have proven to be so rewarding that energy efficiency should be receiving a major amount of attention from energy policy makers for reasons of economic efficiency and in order to minimize the impact of future crises.

The analytic solution of the firm's cost-minimization problem with box constraints and the Cobb-Douglas model

NASA Astrophysics Data System (ADS)

Bayón, L.; Grau, J. M.; Ruiz, M. M.; Suárez, P. M.

2012-12-01

One of the most well-known problems in the field of Microeconomics is the Firm's Cost-Minimization Problem. In this paper we establish the analytical expression for the cost function using the Cobb-Douglas model and considering maximum constraints for the inputs. Moreover we prove that it belongs to the class C1.

Intelligent and robust optimization frameworks for smart grids

NASA Astrophysics Data System (ADS)

Dhansri, Naren Reddy

A smart grid implies a cyberspace real-time distributed power control system to optimally deliver electricity based on varying consumer characteristics. Although smart grids solve many of the contemporary problems, they give rise to new control and optimization problems with the growing role of renewable energy sources such as wind or solar energy. Under highly dynamic nature of distributed power generation and the varying consumer demand and cost requirements, the total power output of the grid should be controlled such that the load demand is met by giving a higher priority to renewable energy sources. Hence, the power generated from renewable energy sources should be optimized while minimizing the generation from non renewable energy sources. This research develops a demand-based automatic generation control and optimization framework for real-time smart grid operations by integrating conventional and renewable energy sources under varying consumer demand and cost requirements. Focusing on the renewable energy sources, the intelligent and robust control frameworks optimize the power generation by tracking the consumer demand in a closed-loop control framework, yielding superior economic and ecological benefits and circumvent nonlinear model complexities and handles uncertainties for superior real-time operations. The proposed intelligent system framework optimizes the smart grid power generation for maximum economical and ecological benefits under an uncertain renewable wind energy source. The numerical results demonstrate that the proposed framework is a viable approach to integrate various energy sources for real-time smart grid implementations. The robust optimization framework results demonstrate the effectiveness of the robust controllers under bounded power plant model uncertainties and exogenous wind input excitation while maximizing economical and ecological performance objectives. Therefore, the proposed framework offers a new worst-case deterministic optimization algorithm for smart grid automatic generation control.

Optimization in Radiation Therapy: Applications in Brachytherapy and Intensity Modulated Radiation Therapy

NASA Astrophysics Data System (ADS)

McGeachy, Philip David

Over 50% of cancer patients require radiation therapy (RT). RT is an optimization problem requiring maximization of the radiation damage to the tumor while minimizing the harm to the healthy tissues. This dissertation focuses on two main RT optimization problems: 1) brachytherapy and 2) intensity modulated radiation therapy (IMRT). The brachytherapy research involved solving a non-convex optimization problem by creating an open-source genetic algorithm optimizer to determine the optimal radioactive seed distribution for a given set of patient volumes and constraints, both dosimetric- and implant-based. The optimizer was tested for a set of 45 prostate brachytherapy patients. While all solutions met the clinical standards, they also benchmarked favorably with those generated by a standard commercial solver. Compared to its compatriot, the salient features of the generated solutions were: slightly reduced prostate coverage, lower dose to the urethra and rectum, and a smaller number of needles required for an implant. Historically, IMRT requires modulation of fluence while keeping the photon beam energy fixed. The IMRT-related investigation in this thesis aimed at broadening the solution space by varying photon energy. The problem therefore involved simultaneous optimization of photon beamlet energy and fluence, denoted by XMRT. Formulating the problem as convex, linear programming was applied to obtain solutions for optimal energy-dependent fluences, while achieving all clinical objectives and constraints imposed. Dosimetric advantages of XMRT over single-energy IMRT in the improved sparing of organs at risk (OARs) was demonstrated in simplified phantom studies. The XMRT algorithm was improved to include clinical dose-volume constraints and clinical studies for prostate and head and neck cancer patients were investigated. Compared to IMRT, XMRT provided improved dosimetric benefit in the prostate case, particularly within intermediate- to low-dose regions (≤ 40 Gy) for OARs. For head and neck cases, XMRT solutions showed no significant disadvantage or advantage over IMRT. The deliverability concerns for the fluence maps generated from XMRT were addressed by incorporating smoothing constraints during the optimization and through successful generation of treatment machine files. Further research is needed to explore the full potential of the XMRT approach to RT.

Free Energy Minimization Calculation of Complex Chemical Equilibria. Reduction of Silicon Dioxide with Carbon at High Temperature.

ERIC Educational Resources Information Center

Wai, C. M.; Hutchinson, S. G.

1989-01-01

Discusses the calculation of free energy in reactions between silicon dioxide and carbon. Describes several computer programs for calculating the free energy minimization and their uses in chemistry classrooms. Lists 16 references. (YP)

Biological countermeasures in space radiation health.

PubMed

Kennedy, Ann R; Todd, Paul

2003-06-01

Exposure to the types of ionizing radiation encountered during space travel may cause a number of health-related problems, but the primary concern is related to the increased risk of cancer induction in astronauts. The major types of radiation considered to be of importance during space travel are protons and particles of high atomic number and high energy (HZE particles). It is now clear that biological countermeasures can be used to prevent or reduce the levels of biological consequences resulting from exposure to protons or HZE particles, including the induction of cancer, immunosuppression and neurological defects caused by these types of ionizing radiation. Research related to the dietary additions of agents to minimize the risks of developing health-related problems which can result from exposure to space radiations is reviewed.

Biological countermeasures in space radiation health

NASA Technical Reports Server (NTRS)

Kennedy, Ann R.; Todd, Paul

2003-01-01

Exposure to the types of ionizing radiation encountered during space travel may cause a number of health-related problems, but the primary concern is related to the increased risk of cancer induction in astronauts. The major types of radiation considered to be of importance during space travel are protons and particles of high atomic number and high energy (HZE particles). It is now clear that biological countermeasures can be used to prevent or reduce the levels of biological consequences resulting from exposure to protons or HZE particles, including the induction of cancer, immunosuppression and neurological defects caused by these types of ionizing radiation. Research related to the dietary additions of agents to minimize the risks of developing health-related problems which can result from exposure to space radiations is reviewed.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Elliott, G.R.B.; Vanderborgh, N.E.

Experimental and theoretical analyses show that uncontrolled water invasion during underground coal conversion (UCC) is harmful at all stages of UCC. By contrast, if water invasion is prevented, coal porosity can be created for further processing, pyrolysis can yield uniform hydrocarbon products, gasification can produce a uniform product, coal is fully consumed (not bypassed) during combustion, and environmental problems are minimized. In all cases the experimental results are supportive of the theory of underground coal processing presented. We see no insurmountable technical problems existing for a staged underground coal conversion process, but we emphasize that all concepts in underground coalmore » processing depend critically upon control of water influx. It is important that techniques for measuring and controlling water flow be developed if this technology is to make a contribution to the Nation's energy supply.« less

Free Energy Minimization by Simulated Annealing with Applications to Lithospheric Slabs and Mantle Plumes

NASA Astrophysics Data System (ADS)

Bina, C. R.

An optimization algorithm based upon the method of simulated annealing is of utility in calculating equilibrium phase assemblages as functions of pressure, temperature, and chemical composi tion. Operating by analogy to the statistical mechanics of the chemical system, it is applicable both to problems of strict chemical equilibrium and to problems involving metastability. The method reproduces known phase diagrams and illustrates the expected thermal deflection of phase transitions in thermal models of subducting lithospheric slabs and buoyant mantle plumes. It reveals temperature-induced changes in phase transition sharpness and the stability of Fe-rich γ phase within an α+γ field in cold slab thermal models, and it suggests that transitions such as the possible breakdown of silicate perovskite to mixed oxides can amplify velocity anomalies.

Optimal Solar PV Arrays Integration for Distributed Generation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Omitaomu, Olufemi A; Li, Xueping

2012-01-01

Solar photovoltaic (PV) systems hold great potential for distributed energy generation by installing PV panels on rooftops of residential and commercial buildings. Yet challenges arise along with the variability and non-dispatchability of the PV systems that affect the stability of the grid and the economics of the PV system. This paper investigates the integration of PV arrays for distributed generation applications by identifying a combination of buildings that will maximize solar energy output and minimize system variability. Particularly, we propose mean-variance optimization models to choose suitable rooftops for PV integration based on Markowitz mean-variance portfolio selection model. We further introducemore » quantity and cardinality constraints to result in a mixed integer quadratic programming problem. Case studies based on real data are presented. An efficient frontier is obtained for sample data that allows decision makers to choose a desired solar energy generation level with a comfortable variability tolerance level. Sensitivity analysis is conducted to show the tradeoffs between solar PV energy generation potential and variability.« less

Direct dynamics trajectory study of the reaction of formaldehyde cation with D2: vibrational and zero-point energy effects on quasiclassical trajectories.

PubMed

Liu, Jianbo; Song, Kihyung; Hase, William L; Anderson, Scott L

2005-12-22

Quasiclassical, direct dynamics trajectories have been used to study the reaction of formaldehyde cation with molecular hydrogen, simulating the conditions in an experimental study of H2CO+ vibrational effects on this reaction. Effects of five different H2CO+ modes were probed, and we also examined different approaches to treating zero-point energy in quasiclassical trajectories. The calculated absolute cross-sections are in excellent agreement with experiments, and the results provide insight into the reaction mechanism, product scattering behavior, and energy disposal, and how they vary with impact parameter and reactant state. The reaction is sharply orientation-dependent, even at high collision energies, and both trajectories and experiment find that H2CO+ vibration inhibits reaction. On the other hand, the trajectories do not reproduce the anomalously strong effect of nu2(+) (the CO stretch). The origin of the discrepancy and approaches for minimizing such problems in quasiclassical trajectories are discussed.

Technical and Economic Assessment of the Implementation of Measures for Reducing Energy Losses in Distribution Systems

NASA Astrophysics Data System (ADS)

Aguila, Alexander; Wilson, Jorge

2017-07-01

This paper develops a methodology to assess a group of measures of electrical improvements in distribution systems, starting from the complementation of technical and economic criteria. In order to solve the problem of energy losses in distribution systems, technical and economic analysis was performed based on a mathematical model to establish a direct relationship between the energy saved by way of minimized losses and the costs of implementing the proposed measures. This paper aims at analysing the feasibility of reducing energy losses in distribution systems, by changing existing network conductors by larger crosssection conductors and distribution voltage change at higher levels. The impact of this methodology provides a highly efficient mathematical tool for analysing the feasibility of implementing improvement projects based on their costs which is a very useful tool for the distribution companies that will serve as a starting point to the analysis for this type of projects in distribution systems.

Environment and air pollution: health services bequeath to grotesque menace.

PubMed

Qureshi, Muhammad Imran; Rasli, Amran Md; Awan, Usama; Ma, Jian; Ali, Ghulam; Faridullah; Alam, Arif; Sajjad, Faiza; Zaman, Khalid

2015-03-01

The objective of the study is to establish the link between air pollution, fossil fuel energy consumption, industrialization, alternative and nuclear energy, combustible renewable and wastes, urbanization, and resulting impact on health services in Malaysia. The study employed two-stage least square regression technique on the time series data from 1975 to 2012 to possibly minimize the problem of endogeniety in the health services model. The results in general show that air pollution and environmental indicators act as a strong contributor to influence Malaysian health services. Urbanization and nuclear energy consumption both significantly increases the life expectancy in Malaysia, while fertility rate decreases along with the increasing urbanization in a country. Fossil fuel energy consumption and industrialization both have an indirect relationship with the infant mortality rate, whereas, carbon dioxide emissions have a direct relationship with the sanitation facility in a country. The results conclude that balancing the air pollution, environment, and health services needs strong policy vistas on the end of the government officials.

Conformational Sampling of a Biomolecular Rugged Energy Landscape.

PubMed

Rydzewski, Jakub; Jakubowski, Rafal; Nicosia, Giuseppe; Nowak, Wieslaw

2018-01-01

The protein structure refinement using conformational sampling is important in hitherto protein studies. In this paper, we examined the protein structure refinement by means of potential energy minimization using immune computing as a method of sampling conformations. The method was tested on the x-ray structure and 30 decoys of the mutant of [Leu]Enkephalin, a paradigmatic example of the biomolecular multiple-minima problem. In order to score the refined conformations, we used a standard potential energy function with the OPLSAA force field. The effectiveness of the search was assessed using a variety of methods. The robustness of sampling was checked by the energy yield function which measures quantitatively the number of the peptide decoys residing in an energetic funnel. Furthermore, the potential energy-dependent Pareto fronts were calculated to elucidate dissimilarities between peptide conformations and the native state as observed by x-ray crystallography. Our results showed that the probed potential energy landscape of [Leu]Enkephalin is self-similar on different metric scales and that the local potential energy minima of the peptide decoys are metastable, thus they can be refined to conformations whose potential energy is decreased by approximately 250 kJ/mol.

Improved Equivalent Linearization Implementations Using Nonlinear Stiffness Evaluation

NASA Technical Reports Server (NTRS)

Rizzi, Stephen A.; Muravyov, Alexander A.

2001-01-01

This report documents two new implementations of equivalent linearization for solving geometrically nonlinear random vibration problems of complicated structures. The implementations are given the acronym ELSTEP, for "Equivalent Linearization using a STiffness Evaluation Procedure." Both implementations of ELSTEP are fundamentally the same in that they use a novel nonlinear stiffness evaluation procedure to numerically compute otherwise inaccessible nonlinear stiffness terms from commercial finite element programs. The commercial finite element program MSC/NASTRAN (NASTRAN) was chosen as the core of ELSTEP. The FORTRAN implementation calculates the nonlinear stiffness terms and performs the equivalent linearization analysis outside of NASTRAN. The Direct Matrix Abstraction Program (DMAP) implementation performs these operations within NASTRAN. Both provide nearly identical results. Within each implementation, two error minimization approaches for the equivalent linearization procedure are available - force and strain energy error minimization. Sample results for a simply supported rectangular plate are included to illustrate the analysis procedure.

Minimization of the root of a quadratic functional under a system of affine equality constraints with application to portfolio management

NASA Astrophysics Data System (ADS)

Landsman, Zinoviy

2008-10-01

We present an explicit closed form solution of the problem of minimizing the root of a quadratic functional subject to a system of affine constraints. The result generalizes Z. Landsman, Minimization of the root of a quadratic functional under an affine equality constraint, J. Comput. Appl. Math. 2007, to appear, see , articles in press, where the optimization problem was solved under only one linear constraint. This is of interest for solving significant problems pertaining to financial economics as well as some classes of feasibility and optimization problems which frequently occur in tomography and other fields. The results are illustrated in the problem of optimal portfolio selection and the particular case when the expected return of finance portfolio is certain is discussed.

Common origin of 3.55 keV x-ray line and gauge coupling unification with left-right dark matter

NASA Astrophysics Data System (ADS)

Borah, Debasish; Dasgupta, Arnab; Patra, Sudhanwa

2017-12-01

We present a minimal left-right dark matter framework that can simultaneously explain the recently observed 3.55 keV x-ray line from several galaxy clusters and gauge coupling unification at high energy scale. Adopting a minimal dark matter strategy, we consider both left and right handed triplet fermionic dark matter candidates which are stable by virtue of a remnant Z2≃(-1 )B -L symmetry arising after the spontaneous symmetry breaking of left-right gauge symmetry to that of the standard model. A scalar bitriplet field is incorporated whose first role is to allow radiative decay of right handed triplet dark matter into the left handed one and a photon with energy 3.55 keV. The other role this bitriplet field at TeV scale plays is to assist in achieving gauge coupling unification at a high energy scale within a nonsupersymmetric S O (10 ) model while keeping the scale of left-right gauge symmetry around the TeV corner. Apart from solving the neutrino mass problem and giving verifiable new contributions to neutrinoless double beta decay and charged lepton flavor violation, the model with TeV scale gauge bosons can also give rise to interesting collider signatures like diboson excess, dilepton plus two jets excess reported recently in the large hadron collider data.

DQM: Decentralized Quadratically Approximated Alternating Direction Method of Multipliers

NASA Astrophysics Data System (ADS)

Mokhtari, Aryan; Shi, Wei; Ling, Qing; Ribeiro, Alejandro

2016-10-01

This paper considers decentralized consensus optimization problems where nodes of a network have access to different summands of a global objective function. Nodes cooperate to minimize the global objective by exchanging information with neighbors only. A decentralized version of the alternating directions method of multipliers (DADMM) is a common method for solving this category of problems. DADMM exhibits linear convergence rate to the optimal objective but its implementation requires solving a convex optimization problem at each iteration. This can be computationally costly and may result in large overall convergence times. The decentralized quadratically approximated ADMM algorithm (DQM), which minimizes a quadratic approximation of the objective function that DADMM minimizes at each iteration, is proposed here. The consequent reduction in computational time is shown to have minimal effect on convergence properties. Convergence still proceeds at a linear rate with a guaranteed constant that is asymptotically equivalent to the DADMM linear convergence rate constant. Numerical results demonstrate advantages of DQM relative to DADMM and other alternatives in a logistic regression problem.

Stochastic control of smart home energy management with plug-in electric vehicle battery energy storage and photovoltaic array

NASA Astrophysics Data System (ADS)

Wu, Xiaohua; Hu, Xiaosong; Moura, Scott; Yin, Xiaofeng; Pickert, Volker

2016-11-01

Energy management strategies are instrumental in the performance and economy of smart homes integrating renewable energy and energy storage. This article focuses on stochastic energy management of a smart home with PEV (plug-in electric vehicle) energy storage and photovoltaic (PV) array. It is motivated by the challenges associated with sustainable energy supplies and the local energy storage opportunity provided by vehicle electrification. This paper seeks to minimize a consumer's energy charges under a time-of-use tariff, while satisfying home power demand and PEV charging requirements, and accommodating the variability of solar power. First, the random-variable models are developed, including Markov Chain model of PEV mobility, as well as predictive models of home power demand and PV power supply. Second, a stochastic optimal control problem is mathematically formulated for managing the power flow among energy sources in the smart home. Finally, based on time-varying electricity price, we systematically examine the performance of the proposed control strategy. As a result, the electric cost is 493.6% less for a Tesla Model S with optimal stochastic dynamic programming (SDP) control relative to the no optimal control case, and it is by 175.89% for a Nissan Leaf.

A two-stage approach to the depot shunting driver assignment problem with workload balance considerations.

PubMed

Wang, Jiaxi; Gronalt, Manfred; Sun, Yan

2017-01-01

Due to its environmentally sustainable and energy-saving characteristics, railway transportation nowadays plays a fundamental role in delivering passengers and goods. Emerged in the area of transportation planning, the crew (workforce) sizing problem and the crew scheduling problem have been attached great importance by the railway industry and the scientific community. In this paper, we aim to solve the two problems by proposing a novel two-stage optimization approach in the context of the electric multiple units (EMU) depot shunting driver assignment problem. Given a predefined depot shunting schedule, the first stage of the approach focuses on determining an optimal size of shunting drivers. While the second stage is formulated as a bi-objective optimization model, in which we comprehensively consider the objectives of minimizing the total walking distance and maximizing the workload balance. Then we combine the normalized normal constraint method with a modified Pareto filter algorithm to obtain Pareto solutions for the bi-objective optimization problem. Furthermore, we conduct a series of numerical experiments to demonstrate the proposed approach. Based on the computational results, the regression analysis yield a driver size predictor and the sensitivity analysis give some interesting insights that are useful for decision makers.

A two-stage approach to the depot shunting driver assignment problem with workload balance considerations

PubMed Central

Gronalt, Manfred; Sun, Yan

2017-01-01

Due to its environmentally sustainable and energy-saving characteristics, railway transportation nowadays plays a fundamental role in delivering passengers and goods. Emerged in the area of transportation planning, the crew (workforce) sizing problem and the crew scheduling problem have been attached great importance by the railway industry and the scientific community. In this paper, we aim to solve the two problems by proposing a novel two-stage optimization approach in the context of the electric multiple units (EMU) depot shunting driver assignment problem. Given a predefined depot shunting schedule, the first stage of the approach focuses on determining an optimal size of shunting drivers. While the second stage is formulated as a bi-objective optimization model, in which we comprehensively consider the objectives of minimizing the total walking distance and maximizing the workload balance. Then we combine the normalized normal constraint method with a modified Pareto filter algorithm to obtain Pareto solutions for the bi-objective optimization problem. Furthermore, we conduct a series of numerical experiments to demonstrate the proposed approach. Based on the computational results, the regression analysis yield a driver size predictor and the sensitivity analysis give some interesting insights that are useful for decision makers. PMID:28704489

Excitation spectrum of a mixture of two Bose gases confined in a ring potential with interaction asymmetry

NASA Astrophysics Data System (ADS)

Roussou, A.; Smyrnakis, J.; Magiropoulos, M.; Efremidis, N. K.; Kavoulakis, G. M.; Sandin, P.; Ögren, M.; Gulliksson, M.

2018-04-01

We study the rotational properties of a two-component Bose–Einstein condensed gas of distinguishable atoms which are confined in a ring potential using both the mean-field approximation, as well as the method of diagonalization of the many-body Hamiltonian. We demonstrate that the angular momentum may be given to the system either via single-particle, or ‘collective’ excitation. Furthermore, despite the complexity of this problem, under rather typical conditions the dispersion relation takes a remarkably simple and regular form. Finally, we argue that under certain conditions the dispersion relation is determined via collective excitation. The corresponding many-body state, which, in addition to the interaction energy minimizes also the kinetic energy, is dictated by elementary number theory.

A survey of gas-side fouling in industrial heat-transfer equipment

NASA Astrophysics Data System (ADS)

Marner, W. J.; Suitor, J. W.

1983-11-01

Gas-side fouling and corrosion problems occur in all of the energy intensive industries including the chemical, petroleum, primary metals, pulp and paper, glass, cement, foodstuffs, and textile industries. Topics of major interest include: (1) heat exchanger design procedures for gas-side fouling service; (2) gas-side fouling factors which are presently available; (3) startup and shutdown procedures used to minimize the effects of gas-side fouling; (4) gas-side fouling prevention, mitigation, and accommodation techniques; (5) economic impact of gas-side fouling on capital costs, maintenance costs, loss of production, and energy losses; and (6) miscellaneous considerations related to gas-side fouling. The present state-of-the-art for industrial gas-side fouling is summarized by a list of recommendations for further work in this area.

A survey of gas-side fouling in industrial heat-transfer equipment

NASA Technical Reports Server (NTRS)

Marner, W. J.; Suitor, J. W.

1983-01-01

Gas-side fouling and corrosion problems occur in all of the energy intensive industries including the chemical, petroleum, primary metals, pulp and paper, glass, cement, foodstuffs, and textile industries. Topics of major interest include: (1) heat exchanger design procedures for gas-side fouling service; (2) gas-side fouling factors which are presently available; (3) startup and shutdown procedures used to minimize the effects of gas-side fouling; (4) gas-side fouling prevention, mitigation, and accommodation techniques; (5) economic impact of gas-side fouling on capital costs, maintenance costs, loss of production, and energy losses; and (6) miscellaneous considerations related to gas-side fouling. The present state-of-the-art for industrial gas-side fouling is summarized by a list of recommendations for further work in this area.

Minimal norm constrained interpolation. Ph.D. Thesis

NASA Technical Reports Server (NTRS)

Irvine, L. D.

1985-01-01

In computational fluid dynamics and in CAD/CAM, a physical boundary is usually known only discreetly and most often must be approximated. An acceptable approximation preserves the salient features of the data such as convexity and concavity. In this dissertation, a smooth interpolant which is locally concave where the data are concave and is locally convex where the data are convex is described. The interpolant is found by posing and solving a minimization problem whose solution is a piecewise cubic polynomial. The problem is solved indirectly by using the Peano Kernal theorem to recast it into an equivalent minimization problem having the second derivative of the interpolant as the solution. This approach leads to the solution of a nonlinear system of equations. It is shown that Newton's method is an exceptionally attractive and efficient method for solving the nonlinear system of equations. Examples of shape-preserving interpolants, as well as convergence results obtained by using Newton's method are also shown. A FORTRAN program to compute these interpolants is listed. The problem of computing the interpolant of minimal norm from a convex cone in a normal dual space is also discussed. An extension of de Boor's work on minimal norm unconstrained interpolation is presented.

Analysis of Drop Shapes during Electrowetting on a Dielectric

NASA Astrophysics Data System (ADS)

Daneshbod, Yousef

2005-03-01

Electrowetting refers to the electrostatic control of the interfacial energy of a liquid on a solid, primarily used for the transport of micro-liter volumes of drops on surfaces with embedded electrode arrays. In the present work, the drop is modeled as a two-dimensional lens-like conductor immersed in an infinite dielectric medium slightly above a planar conductor. A matched asymptotic expansion is used to approximate the electrostatic field surrounding the drop. The outer problem models the drop as a conducting circular segment resting on the conducting plane, each maintained at a separate constant potential. The inner problem corrects the region near the edge of the drop by modeling it as an infinite planar conducting wedge lying slightly above the conducting plane. By matching the inner and outer solutions, the charge density along the entire surface of the drop can be approximated, enabling the calculation of the total capacitance of the system. An energy minimization method similar to that of Shapiro et al. [J. Appl. Phys., 93, 5794 (2003)] is applied to the total energy consisting of the liquid/gas, liquid/solid and solid/gas surface energies, together with the electrostatic contribution, subject to the constraint that the drop volume remains constant. A modified form of the Young-Lippmann equation is thus derived that includes the contribution from the extra capacitance of the drop obtained via matched asymptotics.

Pseudo-spectral control of a novel oscillating surge wave energy converter in regular waves for power optimization including load reduction

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tom, Nathan M.; Yu, Yi -Hsiang; Wright, Alan D.

The aim of this study is to describe a procedure to maximize the power-to-load ratio of a novel wave energy converter (WEC) that combines an oscillating surge wave energy converter with variable structural components. The control of the power-take-off torque will be on a wave-to-wave timescale, whereas the structure will be controlled statically such that the geometry remains the same throughout the wave period. Linear hydrodynamic theory is used to calculate the upper and lower bounds for the time-averaged absorbed power and surge foundation loads while assuming that the WEC motion remains sinusoidal. Previous work using pseudo-spectral techniques to solvemore » the optimal control problem focused solely on maximizing absorbed energy. This work extends the optimal control problem to include a measure of the surge foundation force in the optimization. The objective function includes two competing terms that force the optimizer to maximize power capture while minimizing structural loads. A penalty weight was included with the surge foundation force that allows control of the optimizer performance based on whether emphasis should be placed on power absorption or load shedding. Results from pseudo-spectral optimal control indicate that a unit reduction in time-averaged power can be accompanied by a greater reduction in surge-foundation force.« less

Pseudo-spectral control of a novel oscillating surge wave energy converter in regular waves for power optimization including load reduction

DOE PAGES

Tom, Nathan M.; Yu, Yi -Hsiang; Wright, Alan D.; ...

2017-04-18

The aim of this study is to describe a procedure to maximize the power-to-load ratio of a novel wave energy converter (WEC) that combines an oscillating surge wave energy converter with variable structural components. The control of the power-take-off torque will be on a wave-to-wave timescale, whereas the structure will be controlled statically such that the geometry remains the same throughout the wave period. Linear hydrodynamic theory is used to calculate the upper and lower bounds for the time-averaged absorbed power and surge foundation loads while assuming that the WEC motion remains sinusoidal. Previous work using pseudo-spectral techniques to solvemore » the optimal control problem focused solely on maximizing absorbed energy. This work extends the optimal control problem to include a measure of the surge foundation force in the optimization. The objective function includes two competing terms that force the optimizer to maximize power capture while minimizing structural loads. A penalty weight was included with the surge foundation force that allows control of the optimizer performance based on whether emphasis should be placed on power absorption or load shedding. Results from pseudo-spectral optimal control indicate that a unit reduction in time-averaged power can be accompanied by a greater reduction in surge-foundation force.« less

Rapid sampling of local minima in protein energy surface and effective reduction through a multi-objective filter

PubMed Central

2013-01-01

Background Many problems in protein modeling require obtaining a discrete representation of the protein conformational space as an ensemble of conformations. In ab-initio structure prediction, in particular, where the goal is to predict the native structure of a protein chain given its amino-acid sequence, the ensemble needs to satisfy energetic constraints. Given the thermodynamic hypothesis, an effective ensemble contains low-energy conformations which are similar to the native structure. The high-dimensionality of the conformational space and the ruggedness of the underlying energy surface currently make it very difficult to obtain such an ensemble. Recent studies have proposed that Basin Hopping is a promising probabilistic search framework to obtain a discrete representation of the protein energy surface in terms of local minima. Basin Hopping performs a series of structural perturbations followed by energy minimizations with the goal of hopping between nearby energy minima. This approach has been shown to be effective in obtaining conformations near the native structure for small systems. Recent work by us has extended this framework to larger systems through employment of the molecular fragment replacement technique, resulting in rapid sampling of large ensembles. Methods This paper investigates the algorithmic components in Basin Hopping to both understand and control their effect on the sampling of near-native minima. Realizing that such an ensemble is reduced before further refinement in full ab-initio protocols, we take an additional step and analyze the quality of the ensemble retained by ensemble reduction techniques. We propose a novel multi-objective technique based on the Pareto front to filter the ensemble of sampled local minima. Results and conclusions We show that controlling the magnitude of the perturbation allows directly controlling the distance between consecutively-sampled local minima and, in turn, steering the exploration towards conformations near the native structure. For the minimization step, we show that the addition of Metropolis Monte Carlo-based minimization is no more effective than a simple greedy search. Finally, we show that the size of the ensemble of sampled local minima can be effectively and efficiently reduced by a multi-objective filter to obtain a simpler representation of the probed energy surface. PMID:24564970

Optimal dynamic voltage scaling for wireless sensor nodes with real-time constraints

NASA Astrophysics Data System (ADS)

Cassandras, Christos G.; Zhuang, Shixin

2005-11-01

Sensors are increasingly embedded in manufacturing systems and wirelessly networked to monitor and manage operations ranging from process and inventory control to tracking equipment and even post-manufacturing product monitoring. In building such sensor networks, a critical issue is the limited and hard to replenish energy in the devices involved. Dynamic voltage scaling is a technique that controls the operating voltage of a processor to provide desired performance while conserving energy and prolonging the overall network's lifetime. We consider such power-limited devices processing time-critical tasks which are non-preemptive, aperiodic and have uncertain arrival times. We treat voltage scaling as a dynamic optimization problem whose objective is to minimize energy consumption subject to hard or soft real-time execution constraints. In the case of hard constraints, we build on prior work (which engages a voltage scaling controller at task completion times) by developing an intra-task controller that acts at all arrival times of incoming tasks. We show that this optimization problem can be decomposed into two simpler ones whose solution leads to an algorithm that does not actually require solving any nonlinear programming problems. In the case of soft constraints, this decomposition must be partly relaxed, but it still leads to a scalable (linear in the number of tasks) algorithm. Simulation results are provided to illustrate performance improvements in systems with intra-task controllers compared to uncontrolled systems or those using inter-task control.

On the Minimal Accuracy Required for Simulating Self-gravitating Systems by Means of Direct N-body Methods

NASA Astrophysics Data System (ADS)

Portegies Zwart, Simon; Boekholt, Tjarda

2014-04-01

The conservation of energy, linear momentum, and angular momentum are important drivers of our physical understanding of the evolution of the universe. These quantities are also conserved in Newton's laws of motion under gravity. Numerical integration of the associated equations of motion is extremely challenging, in particular due to the steady growth of numerical errors (by round-off and discrete time-stepping and the exponential divergence between two nearby solutions. As a result, numerical solutions to the general N-body problem are intrinsically questionable. Using brute force integrations to arbitrary numerical precision we demonstrate empirically that ensembles of different realizations of resonant three-body interactions produce statistically indistinguishable results. Although individual solutions using common integration methods are notoriously unreliable, we conjecture that an ensemble of approximate three-body solutions accurately represents an ensemble of true solutions, so long as the energy during integration is conserved to better than 1/10. We therefore provide an independent confirmation that previous work on self-gravitating systems can actually be trusted, irrespective of the intrinsically chaotic nature of the N-body problem.

Wavelets in electronic structure calculations

NASA Astrophysics Data System (ADS)

Modisette, Jason Perry

1997-09-01

Ab initio calculations of the electronic structure of bulk materials and large clusters are not possible on today's computers using current techniques. The storage and diagonalization of the Hamiltonian matrix are the limiting factors in both memory and execution time. The scaling of both quantities with problem size can be reduced by using approximate diagonalization or direct minimization of the total energy with respect to the density matrix in conjunction with a localized basis. Wavelet basis members are much more localized than conventional bases such as Gaussians or numerical atomic orbitals. This localization leads to sparse matrices of the operators that arise in SCF multi-electron calculations. We have investigated the construction of the one-electron Hamiltonian, and also the effective one- electron Hamiltonians that appear in density-functional and Hartree-Fock theories. We develop efficient methods for the generation of the kinetic energy and potential matrices, the Hartree and exchange potentials, and the local exchange-correlation potential of the LDA. Test calculations are performed on one-electron problems with a variety of potentials in one and three dimensions.

Conversion of laser energy to gas kinetic energy

NASA Technical Reports Server (NTRS)

Caledonia, G. E.

1976-01-01

Techniques for the gas phase absorption of laser radiation for ultimate conversion to gas kinetic energy are discussed. Particular emphasis is placed on absorption by the vibration rotation bands of diatomic molecules at high pressures. This high pressure absorption appears to offer efficient conversion of laser energy to gas translational energy. Bleaching and chemical effects are minimized and the variation of the total absorption coefficient with temperature is minimal.

Energy Minimization of Discrete Protein Titration State Models Using Graph Theory.

PubMed

Purvine, Emilie; Monson, Kyle; Jurrus, Elizabeth; Star, Keith; Baker, Nathan A

2016-08-25

There are several applications in computational biophysics that require the optimization of discrete interacting states, for example, amino acid titration states, ligand oxidation states, or discrete rotamer angles. Such optimization can be very time-consuming as it scales exponentially in the number of sites to be optimized. In this paper, we describe a new polynomial time algorithm for optimization of discrete states in macromolecular systems. This algorithm was adapted from image processing and uses techniques from discrete mathematics and graph theory to restate the optimization problem in terms of "maximum flow-minimum cut" graph analysis. The interaction energy graph, a graph in which vertices (amino acids) and edges (interactions) are weighted with their respective energies, is transformed into a flow network in which the value of the minimum cut in the network equals the minimum free energy of the protein and the cut itself encodes the state that achieves the minimum free energy. Because of its deterministic nature and polynomial time performance, this algorithm has the potential to allow for the ionization state of larger proteins to be discovered.

Energy Minimization of Discrete Protein Titration State Models Using Graph Theory

DOE Office of Scientific and Technical Information (OSTI.GOV)

Purvine, Emilie AH; Monson, Kyle E.; Jurrus, Elizabeth R.

There are several applications in computational biophysics which require the optimization of discrete interacting states; e.g., amino acid titration states, ligand oxidation states, or discrete rotamer angles. Such optimization can be very time-consuming as it scales exponentially in the number of sites to be optimized. In this paper, we describe a new polynomial-time algorithm for optimization of discrete states in macromolecular systems. This algorithm was adapted from image processing and uses techniques from discrete mathematics and graph theory to restate the optimization problem in terms of maximum flow-minimum cut graph analysis. The interaction energy graph, a graph in which verticesmore » (amino acids) and edges (interactions) are weighted with their respective energies, is transformed into a flow network in which the value of the minimum cut in the network equals the minimum free energy of the protein, and the cut itself encodes the state that achieves the minimum free energy. Because of its deterministic nature and polynomial-time performance, this algorithm has the potential to allow for the ionization state of larger proteins to be discovered.« less

Minimum energy control for a two-compartment neuron to extracellular electric fields

NASA Astrophysics Data System (ADS)

Yi, Guo-Sheng; Wang, Jiang; Li, Hui-Yan; Wei, Xi-Le; Deng, Bin

2016-11-01

The energy optimization of extracellular electric field (EF) stimulus for a neuron is considered in this paper. We employ the optimal control theory to design a low energy EF input for a reduced two-compartment model. It works by driving the neuron to closely track a prescriptive spike train. A cost function is introduced to balance the contradictory objectives, i.e., tracking errors and EF stimulus energy. By using the calculus of variations, we transform the minimization of cost function to a six-dimensional two-point boundary value problem (BVP). Through solving the obtained BVP in the cases of three fundamental bifurcations, it is shown that the control method is able to provide an optimal EF stimulus of reduced energy for the neuron to effectively track a prescriptive spike train. Further, the feasibility of the adopted method is interpreted from the point of view of the biophysical basis of spike initiation. These investigations are conducive to designing stimulating dose for extracellular neural stimulation, which are also helpful to interpret the effects of extracellular field on neural activity.

Energy Minimization of Discrete Protein Titration State Models Using Graph Theory

PubMed Central

Purvine, Emilie; Monson, Kyle; Jurrus, Elizabeth; Star, Keith; Baker, Nathan A.

2016-01-01

There are several applications in computational biophysics which require the optimization of discrete interacting states; e.g., amino acid titration states, ligand oxidation states, or discrete rotamer angles. Such optimization can be very time-consuming as it scales exponentially in the number of sites to be optimized. In this paper, we describe a new polynomial-time algorithm for optimization of discrete states in macromolecular systems. This algorithm was adapted from image processing and uses techniques from discrete mathematics and graph theory to restate the optimization problem in terms of “maximum flow-minimum cut” graph analysis. The interaction energy graph, a graph in which vertices (amino acids) and edges (interactions) are weighted with their respective energies, is transformed into a flow network in which the value of the minimum cut in the network equals the minimum free energy of the protein, and the cut itself encodes the state that achieves the minimum free energy. Because of its deterministic nature and polynomial-time performance, this algorithm has the potential to allow for the ionization state of larger proteins to be discovered. PMID:27089174

Renormalized Energy Concentration in Random Matrices

NASA Astrophysics Data System (ADS)

Borodin, Alexei; Serfaty, Sylvia

2013-05-01

We define a "renormalized energy" as an explicit functional on arbitrary point configurations of constant average density in the plane and on the real line. The definition is inspired by ideas of Sandier and Serfaty (From the Ginzburg-Landau model to vortex lattice problems, 2012; 1D log-gases and the renormalized energy, 2013). Roughly speaking, it is obtained by subtracting two leading terms from the Coulomb potential on a growing number of charges. The functional is expected to be a good measure of disorder of a configuration of points. We give certain formulas for its expectation for general stationary random point processes. For the random matrix β-sine processes on the real line ( β = 1,2,4), and Ginibre point process and zeros of Gaussian analytic functions process in the plane, we compute the expectation explicitly. Moreover, we prove that for these processes the variance of the renormalized energy vanishes, which shows concentration near the expected value. We also prove that the β = 2 sine process minimizes the renormalized energy in the class of determinantal point processes with translation invariant correlation kernels.

Novel characteristics of energy spectrum for 3D Dirac oscillator analyzed via Lorentz covariant deformed algebra

PubMed Central

Betrouche, Malika; Maamache, Mustapha; Choi, Jeong Ryeol

2013-01-01

We investigate the Lorentz-covariant deformed algebra for Dirac oscillator problem, which is a generalization of Kempf deformed algebra in 3 + 1 dimension of space-time, where Lorentz symmetry are preserved. The energy spectrum of the system is analyzed by taking advantage of the corresponding wave functions with explicit spin state. We obtained entirely new results from our development based on Kempf algebra in comparison to the studies carried out with the non-Lorentz-covariant deformed one. A novel result of this research is that the quantized relativistic energy of the system in the presence of minimal length cannot grow indefinitely as quantum number n increases, but converges to a finite value, where c is the speed of light and β is a parameter that determines the scale of noncommutativity in space. If we consider the fact that the energy levels of ordinary oscillator is equally spaced, which leads to monotonic growth of quantized energy with the increment of n, this result is very interesting. The physical meaning of this consequence is discussed in detail. PMID:24225900

Novel characteristics of energy spectrum for 3D Dirac oscillator analyzed via Lorentz covariant deformed algebra.

PubMed

Betrouche, Malika; Maamache, Mustapha; Choi, Jeong Ryeol

2013-11-14

We investigate the Lorentz-covariant deformed algebra for Dirac oscillator problem, which is a generalization of Kempf deformed algebra in 3 + 1 dimension of space-time, where Lorentz symmetry are preserved. The energy spectrum of the system is analyzed by taking advantage of the corresponding wave functions with explicit spin state. We obtained entirely new results from our development based on Kempf algebra in comparison to the studies carried out with the non-Lorentz-covariant deformed one. A novel result of this research is that the quantized relativistic energy of the system in the presence of minimal length cannot grow indefinitely as quantum number n increases, but converges to a finite value, where c is the speed of light and β is a parameter that determines the scale of noncommutativity in space. If we consider the fact that the energy levels of ordinary oscillator is equally spaced, which leads to monotonic growth of quantized energy with the increment of n, this result is very interesting. The physical meaning of this consequence is discussed in detail.

Performance simulation of a grid connected photovoltaic power system using TRNSYS 17

NASA Astrophysics Data System (ADS)

Raja Sekhar, Y.; Ganesh, D.; Kumar, A. Suresh; Abraham, Raju; Padmanathan, P.

2017-11-01

Energy plays an important role in a country’s economic growth in the current energy scenario, the major problem is depletion of energy sources (non-renewable) are more than being formed. One of the prominent solutions is minimizing the use of fossil fuels by utilization of renewable energy resources. A photovoltaic system is an efficient option in terms of utilizing the solar energy resource. The electricity output produced by the photovoltaic systems depends upon the incident solar radiation. This paper examines the performance simulation of 200KW photovoltaic power system at VIT University, Vellore. The main objective of this paper is to correlate the results between the predicted simulation data and the experimental data. The simulation tool used here is TRNSYS. Using TRNSYS modelling prediction of electricity produced throughout the year can be calculated with the help of TRNSYS weather station. The deviation of the simulated results with the experimented results varies due to the choice of weather station. Results from the field test and simulation results are to be correlated to attain the maximum performance of the system.

Geodesic active fields--a geometric framework for image registration.

PubMed

Zosso, Dominique; Bresson, Xavier; Thiran, Jean-Philippe

2011-05-01

In this paper we present a novel geometric framework called geodesic active fields for general image registration. In image registration, one looks for the underlying deformation field that best maps one image onto another. This is a classic ill-posed inverse problem, which is usually solved by adding a regularization term. Here, we propose a multiplicative coupling between the registration term and the regularization term, which turns out to be equivalent to embed the deformation field in a weighted minimal surface problem. Then, the deformation field is driven by a minimization flow toward a harmonic map corresponding to the solution of the registration problem. This proposed approach for registration shares close similarities with the well-known geodesic active contours model in image segmentation, where the segmentation term (the edge detector function) is coupled with the regularization term (the length functional) via multiplication as well. As a matter of fact, our proposed geometric model is actually the exact mathematical generalization to vector fields of the weighted length problem for curves and surfaces introduced by Caselles-Kimmel-Sapiro. The energy of the deformation field is measured with the Polyakov energy weighted by a suitable image distance, borrowed from standard registration models. We investigate three different weighting functions, the squared error and the approximated absolute error for monomodal images, and the local joint entropy for multimodal images. As compared to specialized state-of-the-art methods tailored for specific applications, our geometric framework involves important contributions. Firstly, our general formulation for registration works on any parametrizable, smooth and differentiable surface, including nonflat and multiscale images. In the latter case, multiscale images are registered at all scales simultaneously, and the relations between space and scale are intrinsically being accounted for. Second, this method is, to the best of our knowledge, the first reparametrization invariant registration method introduced in the literature. Thirdly, the multiplicative coupling between the registration term, i.e. local image discrepancy, and the regularization term naturally results in a data-dependent tuning of the regularization strength. Finally, by choosing the metric on the deformation field one can freely interpolate between classic Gaussian and more interesting anisotropic, TV-like regularization.

Nonlocal low-rank and sparse matrix decomposition for spectral CT reconstruction

NASA Astrophysics Data System (ADS)

Niu, Shanzhou; Yu, Gaohang; Ma, Jianhua; Wang, Jing

2018-02-01

Spectral computed tomography (CT) has been a promising technique in research and clinics because of its ability to produce improved energy resolution images with narrow energy bins. However, the narrow energy bin image is often affected by serious quantum noise because of the limited number of photons used in the corresponding energy bin. To address this problem, we present an iterative reconstruction method for spectral CT using nonlocal low-rank and sparse matrix decomposition (NLSMD), which exploits the self-similarity of patches that are collected in multi-energy images. Specifically, each set of patches can be decomposed into a low-rank component and a sparse component, and the low-rank component represents the stationary background over different energy bins, while the sparse component represents the rest of the different spectral features in individual energy bins. Subsequently, an effective alternating optimization algorithm was developed to minimize the associated objective function. To validate and evaluate the NLSMD method, qualitative and quantitative studies were conducted by using simulated and real spectral CT data. Experimental results show that the NLSMD method improves spectral CT images in terms of noise reduction, artifact suppression and resolution preservation.

Approximate error conjugation gradient minimization methods

DOEpatents

Kallman, Jeffrey S

2013-05-21

In one embodiment, a method includes selecting a subset of rays from a set of all rays to use in an error calculation for a constrained conjugate gradient minimization problem, calculating an approximate error using the subset of rays, and calculating a minimum in a conjugate gradient direction based on the approximate error. In another embodiment, a system includes a processor for executing logic, logic for selecting a subset of rays from a set of all rays to use in an error calculation for a constrained conjugate gradient minimization problem, logic for calculating an approximate error using the subset of rays, and logic for calculating a minimum in a conjugate gradient direction based on the approximate error. In other embodiments, computer program products, methods, and systems are described capable of using approximate error in constrained conjugate gradient minimization problems.

Assessment of the potential of halophytes as energy crops for the electric utility industry. Final report

DOE Office of Scientific and Technical Information (OSTI.GOV)

Goodin, J.R.

1984-09-01

This technical report assesses and estimates the potential of selected halophytes as future renewable energy resources, especially by US electric utilities, and familiarizes nonspecialists with research and development problems that must be resolved before these energy sources can become dependable supplies of energy. A literature search related to both indigenous and exotic species of halophytes has been done and appropriate terrestrial species have been selected. Selection criteria include: total biomass potential, genetic constraints, establishment and cultivation requirements, regions of suitability, secondary credits, and a number of other factors. Based on these selection criteria, for the arid western states with highmore » levels of salinity in water and/or soils, there is little potential for energy feedstocks derived from grasses and herbaceous forbs. Likewise, coastal marshes, estuaries, and mangrove swamps, although excellent biomass producers, are too limited by region and have too many ecological and environmental problems for consideration. The deep-rooted, perennial woody shrubs indigenous to many saline regions of the west provide the best potential. The number of species in this group is limited, and Atriplex canescens, Sarcobatus vermiculatus, and Chrysothamnus nauseosus are the three species with the greatest biological potential. These shrubs would receive minimal energy inputs in cultivation, would not compete with agricultural land, and would restore productivity to severely disturbed sites. One might logically expect to achieve biomass feedstock yields of three to five tons/acre/yr on a long-term sustainable basis. The possibility also exists that exotic species might be introduced. 67 references, 1 figure, 5 tables.« less

On multiple crack identification by ultrasonic scanning

NASA Astrophysics Data System (ADS)

Brigante, M.; Sumbatyan, M. A.

2018-04-01

The present work develops an approach which reduces operator equations arising in the engineering problems to the problem of minimizing the discrepancy functional. For this minimization, an algorithm of random global search is proposed, which is allied to some genetic algorithms. The efficiency of the method is demonstrated by the solving problem of simultaneous identification of several linear cracks forming an array in an elastic medium by using the circular Ultrasonic scanning.

Optimal Force Control of Vibro-Impact Systems for Autonomous Drilling Applications

NASA Technical Reports Server (NTRS)

Aldrich, Jack B.; Okon, Avi B.

2012-01-01

The need to maintain optimal energy efficiency is critical during the drilling operations performed on future and current planetary rover missions (see figure). Specifically, this innovation seeks to solve the following problem. Given a spring-loaded percussive drill driven by a voice-coil motor, one needs to determine the optimal input voltage waveform (periodic function) and the optimal hammering period that minimizes the dissipated energy, while ensuring that the hammer-to-rock impacts are made with sufficient (user-defined) impact velocity (or impact energy). To solve this problem, it was first observed that when voice-coil-actuated percussive drills are driven at high power, it is of paramount importance to ensure that the electrical current of the device remains in phase with the velocity of the hammer. Otherwise, negative work is performed and the drill experiences a loss of performance (i.e., reduced impact energy) and an increase in Joule heating (i.e., reduction in energy efficiency). This observation has motivated many drilling products to incorporate the standard bang-bang control approach for driving their percussive drills. However, the bang-bang control approach is significantly less efficient than the optimal energy-efficient control approach solved herein. To obtain this solution, the standard tools of classical optimal control theory were applied. It is worth noting that these tools inherently require the solution of a two-point boundary value problem (TPBVP), i.e., a system of differential equations where half the equations have unknown boundary conditions. Typically, the TPBVP is impossible to solve analytically for high-dimensional dynamic systems. However, for the case of the spring-loaded vibro-impactor, this approach yields the exact optimal control solution as the sum of four analytic functions whose coefficients are determined using a simple, easy-to-implement algorithm. Once the optimal control waveform is determined, it can be used optimally in the context of both open-loop and closed-loop control modes (using standard realtime control hardware).

Reproducing the Ensemble Average Polar Solvation Energy of a Protein from a Single Structure: Gaussian-Based Smooth Dielectric Function for Macromolecular Modeling.

PubMed

Chakravorty, Arghya; Jia, Zhe; Li, Lin; Zhao, Shan; Alexov, Emil

2018-02-13

Typically, the ensemble average polar component of solvation energy (ΔG polar solv ) of a macromolecule is computed using molecular dynamics (MD) or Monte Carlo (MC) simulations to generate conformational ensemble and then single/rigid conformation solvation energy calculation is performed on each snapshot. The primary objective of this work is to demonstrate that Poisson-Boltzmann (PB)-based approach using a Gaussian-based smooth dielectric function for macromolecular modeling previously developed by us (Li et al. J. Chem. Theory Comput. 2013, 9 (4), 2126-2136) can reproduce that ensemble average (ΔG polar solv ) of a protein from a single structure. We show that the Gaussian-based dielectric model reproduces the ensemble average ΔG polar solv (⟨ΔG polar solv ⟩) from an energy-minimized structure of a protein regardless of the minimization environment (structure minimized in vacuo, implicit or explicit waters, or crystal structure); the best case, however, is when it is paired with an in vacuo-minimized structure. In other minimization environments (implicit or explicit waters or crystal structure), the traditional two-dielectric model can still be selected with which the model produces correct solvation energies. Our observations from this work reflect how the ability to appropriately mimic the motion of residues, especially the salt bridge residues, influences a dielectric model's ability to reproduce the ensemble average value of polar solvation free energy from a single in vacuo-minimized structure.

Flow Past a Descending Balloon

NASA Technical Reports Server (NTRS)

Baginski, Frank

2001-01-01

In this report, we present our findings related to aerodynamic loading of partially inflated balloon shapes. This report will consider aerodynamic loading of partially inflated inextensible natural shape balloons and some relevant problems in potential flow. For the axisymmetric modeling, we modified our Balloon Design Shape Program (BDSP) to handle axisymmetric inextensible ascent shapes with aerodynamic loading. For a few simple examples of two dimensional potential flows, we used the Matlab PDE Toolbox. In addition, we propose a model for aerodynamic loading of strained energy minimizing balloon shapes with lobes. Numerical solutions are presented for partially inflated strained balloon shapes with lobes and no aerodynamic loading.

Development of sinkholes resulting from man's activities in the Eastern United States

USGS Publications Warehouse

Newton, John G.

1987-01-01

Alternatives that allow avoiding or minimizing sinkhole hazards are most numerous when a problem or potential problem is recognized during site evaluation. The number of alternatives declines after the beginning of site development. Where sinkhole development is predictable, zoning of land use can minimize hazards.

Decentralized Energy Management System for Networked Microgrids in Grid-connected and Islanded Modes

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, Zhaoyu; Chen, Bokan; Wang, Jianhui

This paper proposes a decentralized energy management system (EMS) for the coordinated operation of networked Microgirds (MGs) in a distribution system. In the grid-connected mode, the distribution network operator (DNO) and each MG are considered as distinct entities with individual objectives to minimize their own operation costs. It is assumed that both dispatchable and renewable energy source (RES)-based distributed generators (DGs) exist in the distribution network and the networked MGs. In order to coordinate the operation of all entities, we apply a decentralized bi-level algorithm to solve the problem with the first level to conduct negotiations among all entities andmore » the second level to update the non-converging penalties. In the islanded mode, the objective of each MG is to maintain a reliable power supply to its customers. In order to take into account the uncertainties of DG outputs and load consumption, we formulate the problems as two-stage stochastic programs. The first stage is to determine base generation setpoints based on the forecasts and the second stage is to adjust the generation outputs based on the realized scenarios. Case studies of a distribution system with networked MGs demonstrate the effectiveness of the proposed methodology in both grid-connected and islanded modes.« less

New Additions to the ClusPro Server Motivated by CAPRI

PubMed Central

Vajda, Sandor; Yueh, Christine; Beglov, Dmitri; Bohnuud, Tanggis; Mottarella, Scott E.; Xia, Bing; Hall, David R.; Kozakov, Dima

2016-01-01

The heavily used protein-protein docking server ClusPro performs three computational steps as follows: (1) rigid body docking, (2) RMSD based clustering of the 1000 lowest energy structures, and (3) the removal of steric clashes by energy minimization. In response to challenges encountered in recent CAPRI targets, we added three new options to ClusPro. These are (1) accounting for Small Angle X-ray Scattering (SAXS) data in docking; (2) considering pairwise interaction data as restraints; and (3) enabling discrimination between biological and crystallographic dimers. In addition, we have developed an extremely fast docking algorithm based on 5D rotational manifold FFT, and an algorithm for docking flexible peptides that include known sequence motifs. We feel that these developments will further improve the utility of ClusPro. However, CAPRI emphasized several shortcomings of the current server, including the problem of selecting the right energy parameters among the five options provided, and the problem of selecting the best models among the 10 generated for each parameter set. In addition, results convinced us that further development is needed for docking homology models. Finally we discuss the difficulties we have encountered when attempting to develop a refinement algorithm that would be computationally efficient enough for inclusion in a heavily used server. PMID:27936493

Radial Symmetry of p-Harmonic Minimizers

NASA Astrophysics Data System (ADS)

Koski, Aleksis; Onninen, Jani

2018-03-01

"It is still not known if the radial cavitating minimizers obtained by uc(Ball) (Philos Trans R Soc Lond A 306:557-611, 1982) (and subsequently by many others) are global minimizers of any physically reasonable nonlinearly elastic energy". This quotation is from uc(Sivaloganathan) and uc(Spector) (Ann Inst Henri Poincaré Anal Non Linéaire 25(1):201-213, 2008) and seems to be still accurate. The model case of the p-harmonic energy is considered here. We prove that the planar radial minimizers are indeed the global minimizers provided we prescribe the admissible deformations on the boundary. In the traction free setting, however, even the identity map need not be a global minimizer.

Intelligent emission-sensitive routing for plugin hybrid electric vehicles.

PubMed

Sun, Zhonghao; Zhou, Xingshe

2016-01-01

The existing transportation sector creates heavily environmental impacts and is a prime cause for the current climate change. The need to reduce emissions from this sector has stimulated efforts to speed up the application of electric vehicles (EVs). A subset of EVs, called plug-in hybrid electric vehicles (PHEVs), backup batteries with combustion engine, which makes PHEVs have a comparable driving range to conventional vehicles. However, this hybridization comes at a cost of higher emissions than all-electric vehicles. This paper studies the routing problem for PHEVs to minimize emissions. The existing shortest-path based algorithms cannot be applied to solving this problem, because of the several new challenges: (1) an optimal route may contain circles caused by detour for recharging; (2) emissions of PHEVs not only depend on the driving distance, but also depend on the terrain and the state of charge (SOC) of batteries; (3) batteries can harvest energy by regenerative braking, which makes some road segments have negative energy consumption. To address these challenges, this paper proposes a green navigation algorithm (GNA) which finds the optimal strategies: where to go and where to recharge. GNA discretizes the SOC, then makes the PHEV routing problem to satisfy the principle of optimality. Finally, GNA adopts dynamic programming to solve the problem. We evaluate GNA using synthetic maps generated by the delaunay triangulation. The results show that GNA can save more than 10 % energy and reduce 10 % emissions when compared to the shortest path algorithm. We also observe that PHEVs with the battery capacity of 10-15 KWh detour most and nearly no detour when larger than 30 KWh. This observation gives some insights when developing PHEVs.

Minimization principles for the coupled problem of Darcy-Biot-type fluid transport in porous media linked to phase field modeling of fracture

NASA Astrophysics Data System (ADS)

Miehe, Christian; Mauthe, Steffen; Teichtmeister, Stephan

2015-09-01

This work develops new minimization and saddle point principles for the coupled problem of Darcy-Biot-type fluid transport in porous media at fracture. It shows that the quasi-static problem of elastically deforming, fluid-saturated porous media is related to a minimization principle for the evolution problem. This two-field principle determines the rate of deformation and the fluid mass flux vector. It provides a canonically compact model structure, where the stress equilibrium and the inverse Darcy's law appear as the Euler equations of a variational statement. A Legendre transformation of the dissipation potential relates the minimization principle to a characteristic three field saddle point principle, whose Euler equations determine the evolutions of deformation and fluid content as well as Darcy's law. A further geometric assumption results in modified variational principles for a simplified theory, where the fluid content is linked to the volumetric deformation. The existence of these variational principles underlines inherent symmetries of Darcy-Biot theories of porous media. This can be exploited in the numerical implementation by the construction of time- and space-discrete variational principles, which fully determine the update problems of typical time stepping schemes. Here, the proposed minimization principle for the coupled problem is advantageous with regard to a new unconstrained stable finite element design, while space discretizations of the saddle point principles are constrained by the LBB condition. The variational principles developed provide the most fundamental approach to the discretization of nonlinear fluid-structure interactions, showing symmetric systems in algebraic update procedures. They also provide an excellent starting point for extensions towards more complex problems. This is demonstrated by developing a minimization principle for a phase field description of fracture in fluid-saturated porous media. It is designed for an incorporation of alternative crack driving forces, such as a convenient criterion in terms of the effective stress. The proposed setting provides a modeling framework for the analysis of complex problems such as hydraulic fracture. This is demonstrated by a spectrum of model simulations.

Naturalness of unknown physics: Theoretical models and experimental signatures

NASA Astrophysics Data System (ADS)

Kilic, Can

In the last few decades collider experiments have not only spectacularly confirmed the predictions of the Standard Model but also have not revealed any direct evidence for new physics beyond the SM, which has led theorists to devise numerous models where the new physics couples weakly to the SM or is simply beyond the reach of past experiments. While phenomenologically viable, many such models appear finely tuned, even contrived. This work illustrates three attempts at coming up with explanations to fine-tunings we observe in the world around us, such as the gauge hierarchy problem or the cosmological constant problem, emphasizing both the theoretical aspects of model building as well as possible experimental signatures. First we investigate the "Little Higgs" mechanism and work on a specifical model, the "Minimal Moose" to highlight its impact on precision observables in the SM, and illustrate that it does not require implausible fine-tuning. Next we build a supersymmetric model, the "Fat Higgs", with an extended gauge structure which becomes confining. This model, aside from naturally preserving the unification of the SM gauge couplings at high energies, also makes it possible to evade the bounds on the lightest Higgs boson mass which are quite restrictive in minimal SUSY scenarios. Lastly we take a look at a possible resolution of the cosmological constant problem through the mechanism of "Ghost Condensation" and dwell on astrophysical observables from the Lorentz Violating sector in this model. We use current experimental data to constrain the coupling of this sector to the SM.

BWM*: A Novel, Provable, Ensemble-based Dynamic Programming Algorithm for Sparse Approximations of Computational Protein Design.

PubMed

Jou, Jonathan D; Jain, Swati; Georgiev, Ivelin S; Donald, Bruce R

2016-06-01

Sparse energy functions that ignore long range interactions between residue pairs are frequently used by protein design algorithms to reduce computational cost. Current dynamic programming algorithms that fully exploit the optimal substructure produced by these energy functions only compute the GMEC. This disproportionately favors the sequence of a single, static conformation and overlooks better binding sequences with multiple low-energy conformations. Provable, ensemble-based algorithms such as A* avoid this problem, but A* cannot guarantee better performance than exhaustive enumeration. We propose a novel, provable, dynamic programming algorithm called Branch-Width Minimization* (BWM*) to enumerate a gap-free ensemble of conformations in order of increasing energy. Given a branch-decomposition of branch-width w for an n-residue protein design with at most q discrete side-chain conformations per residue, BWM* returns the sparse GMEC in O([Formula: see text]) time and enumerates each additional conformation in merely O([Formula: see text]) time. We define a new measure, Total Effective Search Space (TESS), which can be computed efficiently a priori before BWM* or A* is run. We ran BWM* on 67 protein design problems and found that TESS discriminated between BWM*-efficient and A*-efficient cases with 100% accuracy. As predicted by TESS and validated experimentally, BWM* outperforms A* in 73% of the cases and computes the full ensemble or a close approximation faster than A*, enumerating each additional conformation in milliseconds. Unlike A*, the performance of BWM* can be predicted in polynomial time before running the algorithm, which gives protein designers the power to choose the most efficient algorithm for their particular design problem.

Ising formulation of associative memory models and quantum annealing recall

NASA Astrophysics Data System (ADS)

Santra, Siddhartha; Shehab, Omar; Balu, Radhakrishnan

2017-12-01

Associative memory models, in theoretical neuro- and computer sciences, can generally store at most a linear number of memories. Recalling memories in these models can be understood as retrieval of the energy minimizing configuration of classical Ising spins, closest in Hamming distance to an imperfect input memory, where the energy landscape is determined by the set of stored memories. We present an Ising formulation for associative memory models and consider the problem of memory recall using quantum annealing. We show that allowing for input-dependent energy landscapes allows storage of up to an exponential number of memories (in terms of the number of neurons). Further, we show how quantum annealing may naturally be used for recall tasks in such input-dependent energy landscapes, although the recall time may increase with the number of stored memories. Theoretically, we obtain the radius of attractor basins R (N ) and the capacity C (N ) of such a scheme and their tradeoffs. Our calculations establish that for randomly chosen memories the capacity of our model using the Hebbian learning rule as a function of problem size can be expressed as C (N ) =O (eC1N) , C1≥0 , and succeeds on randomly chosen memory sets with a probability of (1 -e-C2N) , C2≥0 with C1+C2=(0.5-f ) 2/(1 -f ) , where f =R (N )/N , 0 ≤f ≤0.5 , is the radius of attraction in terms of the Hamming distance of an input probe from a stored memory as a fraction of the problem size. We demonstrate the application of this scheme on a programmable quantum annealing device, the D-wave processor.

Energy stability of droplets and dry spots in a thin film model of hanging drops

NASA Astrophysics Data System (ADS)

Cheung, Ka-Luen; Chou, Kai-Seng

2017-10-01

The 2-D thin film equation describing the evolution of hang drops is studied. All radially symmetric steady states are classified, and their energy stability is determined. It is shown that the droplet with zero contact angle is the only global energy minimizer and the dry spot with zero contact angle is a strict local energy minimizer.

Minimizing distortion and internal forces in truss structures by simulated annealing

NASA Technical Reports Server (NTRS)

Kincaid, Rex K.; Padula, Sharon L.

1990-01-01

Inaccuracies in the length of members and the diameters of joints of large space structures may produce unacceptable levels of surface distortion and internal forces. Here, two discrete optimization problems are formulated, one to minimize surface distortion (DSQRMS) and the other to minimize internal forces (FSQRMS). Both of these problems are based on the influence matrices generated by a small-deformation linear analysis. Good solutions are obtained for DSQRMS and FSQRMS through the use of a simulated annealing heuristic.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kolda, Christopher

In this talk, I review recent work on using a generalization of the Next-to-Minimal Supersymmetric Standard Model (NMSSM), called the Singlet-extended Minimal Supersymmetric Standard Model (SMSSM), to raise the mass of the Standard Model-like Higgs boson without requiring extremely heavy top squarks or large stop mixing. In so doing, this model solves the little hierarchy problem of the minimal model (MSSM), at the expense of leaving the {mu}-problem of the MSSM unresolved. This talk is based on work published in Refs. [1, 2, 3].

Electromagnetic interference-aware transmission scheduling and power control for dynamic wireless access in hospital environments.

PubMed

Phunchongharn, Phond; Hossain, Ekram; Camorlinga, Sergio

2011-11-01

We study the multiple access problem for e-Health applications (referred to as secondary users) coexisting with medical devices (referred to as primary or protected users) in a hospital environment. In particular, we focus on transmission scheduling and power control of secondary users in multiple spatial reuse time-division multiple access (STDMA) networks. The objective is to maximize the spectrum utilization of secondary users and minimize their power consumption subject to the electromagnetic interference (EMI) constraints for active and passive medical devices and minimum throughput guarantee for secondary users. The multiple access problem is formulated as a dual objective optimization problem which is shown to be NP-complete. We propose a joint scheduling and power control algorithm based on a greedy approach to solve the problem with much lower computational complexity. To this end, an enhanced greedy algorithm is proposed to improve the performance of the greedy algorithm by finding the optimal sequence of secondary users for scheduling. Using extensive simulations, the tradeoff in performance in terms of spectrum utilization, energy consumption, and computational complexity is evaluated for both the algorithms.

Teleparallel dark energy in a system of D0-branes

NASA Astrophysics Data System (ADS)

Sharma, Umesh Kumar; Sepehri, Alireza; Pradhan, Anirudh

A new model which allows a non-minimal coupling between gravity and quintessence in the configuration of teleparallel gravity was recently proposed by Geng et al. [“Teleparallel” dark energy, Phys. Lett. B 704 (2011) 384-387] and they named it teleparallel dark energy. Now the main problem which arises is to know what is the source of this dark energy? The answer of this question is given by us in M-theory. This type of dark energy may be produced at three stages in our model. First, one six-dimensional universe is formed by combining and expanding D0-branes. We know that this universe-brane is polarized on two circles and our four-dimensional cosmos and two D1-branes are yielded. At third stage, two D1-branes glued to each other and one D2-brane is formed. This D2 connects our universe with another universe, gives its energy to them and causes the production of dark energy. Thus, the D2-brane is unstable and dissolves in our four-dimensional universes and supplies the needed teleparallel dark energy for expansion. These calculations are extended to M-theory and shown that the amount of teleparallel dark energy which is produced by compactification of universe-branes in M-theory is more than string theory.

Novel multireceiver communication systems configurations based on optimal estimation theory

NASA Technical Reports Server (NTRS)

Kumar, Rajendra

1992-01-01

A novel multireceiver configuration for carrier arraying and/or signal arraying is presented. The proposed configuration is obtained by formulating the carrier and/or signal arraying problem as an optimal estimation problem, and it consists of two stages. The first stage optimally estimates various phase processes received at different receivers with coupled phase-locked loops wherein the individual loops acquire and track their respective receivers' phase processes but are aided by each other in an optimal manner via LF error signals. The proposed configuration results in the minimization of the the effective radio loss at the combiner output, and thus maximization of energy per bit to noise power spectral density ratio is achieved. A novel adaptive algorithm for the estimator of the signal model parameters when these are not known a priori is also presented.

A non-linear optimization programming model for air quality planning including co-benefits for GHG emissions.

PubMed

Turrini, Enrico; Carnevale, Claudio; Finzi, Giovanna; Volta, Marialuisa

2018-04-15

This paper introduces the MAQ (Multi-dimensional Air Quality) model aimed at defining cost-effective air quality plans at different scales (urban to national) and assessing the co-benefits for GHG emissions. The model implements and solves a non-linear multi-objective, multi-pollutant decision problem where the decision variables are the application levels of emission abatement measures allowing the reduction of energy consumption, end-of pipe technologies and fuel switch options. The objectives of the decision problem are the minimization of tropospheric secondary pollution exposure and of internal costs. The model assesses CO 2 equivalent emissions in order to support decision makers in the selection of win-win policies. The methodology is tested on Lombardy region, a heavily polluted area in northern Italy. Copyright © 2017 Elsevier B.V. All rights reserved.

State dependent model predictive control for orbital rendezvous using pulse-width pulse-frequency modulated thrusters

NASA Astrophysics Data System (ADS)

Li, Peng; Zhu, Zheng H.; Meguid, S. A.

2016-07-01

This paper studies the pulse-width pulse-frequency modulation based trajectory planning for orbital rendezvous and proximity maneuvering near a non-cooperative spacecraft in an elliptical orbit. The problem is formulated by converting the continuous control input, output from the state dependent model predictive control, into a sequence of pulses of constant magnitude by controlling firing frequency and duration of constant-magnitude thrusters. The state dependent model predictive control is derived by minimizing the control error of states and control roughness of control input for a safe, smooth and fuel efficient approaching trajectory. The resulting nonlinear programming problem is converted into a series of quadratic programming problem and solved by numerical iteration using the receding horizon strategy. The numerical results show that the proposed state dependent model predictive control with the pulse-width pulse-frequency modulation is able to effectively generate optimized trajectories using equivalent control pulses for the proximity maneuvering with less energy consumption.

Numerical benchmarking of a Coarse-Mesh Transport (COMET) Method for medical physics applications

NASA Astrophysics Data System (ADS)

Blackburn, Megan Satterfield

2009-12-01

Radiation therapy has become a very import method for treating cancer patients. Thus, it is extremely important to accurately determine the location of energy deposition during these treatments, maximizing dose to the tumor region and minimizing it to healthy tissue. A Coarse-Mesh Transport Method (COMET) has been developed at the Georgia Institute of Technology in the Computational Reactor and Medical Physics Group for use very successfully with neutron transport to analyze whole-core criticality. COMET works by decomposing a large, heterogeneous system into a set of smaller fixed source problems. For each unique local problem that exists, a solution is obtained that we call a response function. These response functions are pre-computed and stored in a library for future use. The overall solution to the global problem can then be found by a linear superposition of these local problems. This method has now been extended to the transport of photons and electrons for use in medical physics problems to determine energy deposition from radiation therapy treatments. The main goal of this work was to develop benchmarks for testing in order to evaluate the COMET code to determine its strengths and weaknesses for these medical physics applications. For response function calculations, legendre polynomial expansions are necessary for space, angle, polar angle, and azimuthal angle. An initial sensitivity study was done to determine the best orders for future testing. After the expansion orders were found, three simple benchmarks were tested: a water phantom, a simplified lung phantom, and a non-clinical slab phantom. Each of these benchmarks was decomposed into 1cm x 1cm and 0.5cm x 0.5cm coarse meshes. Three more clinically relevant problems were developed from patient CT scans. These benchmarks modeled a lung patient, a prostate patient, and a beam re-entry situation. As before, the problems were divided into 1cm x 1cm, 0.5cm x 0.5cm, and 0.25cm x 0.25cm coarse mesh cases. Multiple beam energies were also tested for each case. The COMET solutions for each case were compared to a reference solution obtained by pure Monte Carlo results from EGSnrc. When comparing the COMET results to the reference cases, a pattern of differences appeared in each phantom case. It was found that better results were obtained for lower energy incident photon beams as well as for larger mesh sizes. Possible changes may need to be made with the expansion orders used for energy and angle to better model high energy secondary electrons. Heterogeneity also did not pose a problem for the COMET methodology. Heterogeneous results were found in a comparable amount of time to the homogeneous water phantom. The COMET results were typically found in minutes to hours of computational time, whereas the reference cases typically required hundreds or thousands of hours. A second sensitivity study was also performed on a more stringent problem and with smaller coarse meshes. Previously, the same expansion order was used for each incident photon beam energy so better comparisons could be made. From this second study, it was found that it is optimal to have different expansion orders based on the incident beam energy. Recommendations for future work with this method include more testing on higher expansion orders or possible code modification to better handle secondary electrons. The method also needs to handle more clinically relevant beam descriptions with an energy and angular distribution associated with it.

Distance majorization and its applications.

PubMed

Chi, Eric C; Zhou, Hua; Lange, Kenneth

2014-08-01

The problem of minimizing a continuously differentiable convex function over an intersection of closed convex sets is ubiquitous in applied mathematics. It is particularly interesting when it is easy to project onto each separate set, but nontrivial to project onto their intersection. Algorithms based on Newton's method such as the interior point method are viable for small to medium-scale problems. However, modern applications in statistics, engineering, and machine learning are posing problems with potentially tens of thousands of parameters or more. We revisit this convex programming problem and propose an algorithm that scales well with dimensionality. Our proposal is an instance of a sequential unconstrained minimization technique and revolves around three ideas: the majorization-minimization principle, the classical penalty method for constrained optimization, and quasi-Newton acceleration of fixed-point algorithms. The performance of our distance majorization algorithms is illustrated in several applications.

Cognitive radio adaptation for power consumption minimization using biogeography-based optimization

NASA Astrophysics Data System (ADS)

Qi, Pei-Han; Zheng, Shi-Lian; Yang, Xiao-Niu; Zhao, Zhi-Jin

2016-12-01

Adaptation is one of the key capabilities of cognitive radio, which focuses on how to adjust the radio parameters to optimize the system performance based on the knowledge of the radio environment and its capability and characteristics. In this paper, we consider the cognitive radio adaptation problem for power consumption minimization. The problem is formulated as a constrained power consumption minimization problem, and the biogeography-based optimization (BBO) is introduced to solve this optimization problem. A novel habitat suitability index (HSI) evaluation mechanism is proposed, in which both the power consumption minimization objective and the quality of services (QoS) constraints are taken into account. The results show that under different QoS requirement settings corresponding to different types of services, the algorithm can minimize power consumption while still maintaining the QoS requirements. Comparison with particle swarm optimization (PSO) and cat swarm optimization (CSO) reveals that BBO works better, especially at the early stage of the search, which means that the BBO is a better choice for real-time applications. Project supported by the National Natural Science Foundation of China (Grant No. 61501356), the Fundamental Research Funds of the Ministry of Education, China (Grant No. JB160101), and the Postdoctoral Fund of Shaanxi Province, China.

Ideas That Work! The Midnight Audit

DOE Office of Scientific and Technical Information (OSTI.GOV)

Parker, Steven A.

The midnight audit provides valuable insight toward identifying opportunities to reduce energy consumption—insight that can be easily overlooked during the normal (daytime) energy auditing process. The purpose of the midnight audit is to observe after-hour operation with the mindset of seeking ways to further minimize energy consumption during the unoccupied mode and minimize energy waste by reducing unnecessary operation. The midnight audit should be used to verify that equipment is off when it is supposed to be, or operating in set-back mode when applicable. Even a facility that operates 2 shifts per day, 5 days per week experiences fewer annualmore » hours in occupied mode than it does during unoccupied mode. Minimizing energy loads during unoccupied hours can save significant energy, which is why the midnight audit is an Idea That Works.« less

Quad-rotor flight path energy optimization

NASA Astrophysics Data System (ADS)

Kemper, Edward

Quad-Rotor unmanned areal vehicles (UAVs) have been a popular area of research and development in the last decade, especially with the advent of affordable microcontrollers like the MSP 430 and the Raspberry Pi. Path-Energy Optimization is an area that is well developed for linear systems. In this thesis, this idea of path-energy optimization is extended to the nonlinear model of the Quad-rotor UAV. The classical optimization technique is adapted to the nonlinear model that is derived for the problem at hand, coming up with a set of partial differential equations and boundary value conditions to solve these equations. Then, different techniques to implement energy optimization algorithms are tested using simulations in Python. First, a purely nonlinear approach is used. This method is shown to be computationally intensive, with no practical solution available in a reasonable amount of time. Second, heuristic techniques to minimize the energy of the flight path are tested, using Ziegler-Nichols' proportional integral derivative (PID) controller tuning technique. Finally, a brute force look-up table based PID controller is used. Simulation results of the heuristic method show that both reliable control of the system and path-energy optimization are achieved in a reasonable amount of time.

Quantum annealing correction with minor embedding

NASA Astrophysics Data System (ADS)

Vinci, Walter; Albash, Tameem; Paz-Silva, Gerardo; Hen, Itay; Lidar, Daniel A.

2015-10-01

Quantum annealing provides a promising route for the development of quantum optimization devices, but the usefulness of such devices will be limited in part by the range of implementable problems as dictated by hardware constraints. To overcome constraints imposed by restricted connectivity between qubits, a larger set of interactions can be approximated using minor embedding techniques whereby several physical qubits are used to represent a single logical qubit. However, minor embedding introduces new types of errors due to its approximate nature. We introduce and study quantum annealing correction schemes designed to improve the performance of quantum annealers in conjunction with minor embedding, thus leading to a hybrid scheme defined over an encoded graph. We argue that this scheme can be efficiently decoded using an energy minimization technique provided the density of errors does not exceed the per-site percolation threshold of the encoded graph. We test the hybrid scheme using a D-Wave Two processor on problems for which the encoded graph is a two-level grid and the Ising model is known to be NP-hard. The problems we consider are frustrated Ising model problem instances with "planted" (a priori known) solutions. Applied in conjunction with optimized energy penalties and decoding techniques, we find that this approach enables the quantum annealer to solve minor embedded instances with significantly higher success probability than it would without error correction. Our work demonstrates that quantum annealing correction can and should be used to improve the robustness of quantum annealing not only for natively embeddable problems but also when minor embedding is used to extend the connectivity of physical devices.

High-Order Model and Dynamic Filtering for Frame Rate Up-Conversion.

PubMed

Bao, Wenbo; Zhang, Xiaoyun; Chen, Li; Ding, Lianghui; Gao, Zhiyong

2018-08-01

This paper proposes a novel frame rate up-conversion method through high-order model and dynamic filtering (HOMDF) for video pixels. Unlike the constant brightness and linear motion assumptions in traditional methods, the intensity and position of the video pixels are both modeled with high-order polynomials in terms of time. Then, the key problem of our method is to estimate the polynomial coefficients that represent the pixel's intensity variation, velocity, and acceleration. We propose to solve it with two energy objectives: one minimizes the auto-regressive prediction error of intensity variation by its past samples, and the other minimizes video frame's reconstruction error along the motion trajectory. To efficiently address the optimization problem for these coefficients, we propose the dynamic filtering solution inspired by video's temporal coherence. The optimal estimation of these coefficients is reformulated into a dynamic fusion of the prior estimate from pixel's temporal predecessor and the maximum likelihood estimate from current new observation. Finally, frame rate up-conversion is implemented using motion-compensated interpolation by pixel-wise intensity variation and motion trajectory. Benefited from the advanced model and dynamic filtering, the interpolated frame has much better visual quality. Extensive experiments on the natural and synthesized videos demonstrate the superiority of HOMDF over the state-of-the-art methods in both subjective and objective comparisons.

Energy neutral and low power wireless communications

NASA Astrophysics Data System (ADS)

Orhan, Oner

Wireless sensor nodes are typically designed to have low cost and small size. These design objectives impose restrictions on the capacity and efficiency of the transceiver components and energy storage units that can be used. As a result, energy becomes a bottleneck and continuous operation of the sensor network requires frequent battery replacements, increasing the maintenance cost. Energy harvesting and energy efficient transceiver architectures are able to overcome these challenges by collecting energy from the environment and utilizing the energy in an intelligent manner. However, due to the nature of the ambient energy sources, the amount of useful energy that can be harvested is limited and unreliable. Consequently, optimal management of the harvested energy and design of low power transceivers pose new challenges for wireless network design and operation. The first part of this dissertation is on energy neutral wireless networking, where optimal transmission schemes under different system setups and objectives are investigated. First, throughput maximization for energy harvesting two-hop networks with decode-and-forward half-duplex relays is studied. For a system with two parallel relays, various combinations of the following four transmission modes are considered: Broadcast from the source, multi-access from the relays, and successive relaying phases I and II. Next, the energy cost of the processing circuitry as well as the transmission energy are taken into account for communication over a broadband fading channel powered by an energy harvesting transmitter. Under this setup, throughput maximization, energy maximization, and transmission completion time minimization problems are studied. Finally, source and channel coding for an energy-limited wireless sensor node is investigated under various energy constraints including energy harvesting, processing and sampling costs. For each objective, optimal transmission policies are formulated as the solutions of a convex optimization problem, and the properties of these optimal policies are identified. In the second part of this thesis, low power transceiver design is considered for millimeter wave communication systems. In particular, using an additive quantization noise model, the effect of analog-digital conversion (ADC) resolution and bandwidth on the achievable rate is investigated for a multi-antenna system under a receiver power constraint. Two receiver architectures, analog and digital combining, are compared in terms of performance.

Nonlinear transient analysis by energy minimization: A theoretical basis for the ACTION computer code. [predicting the response of a lightweight aircraft during a crash

NASA Technical Reports Server (NTRS)

Kamat, M. P.

1980-01-01

The formulation basis for establishing the static or dynamic equilibrium configurations of finite element models of structures which may behave in the nonlinear range are provided. With both geometric and time independent material nonlinearities included, the development is restricted to simple one and two dimensional finite elements which are regarded as being the basic elements for modeling full aircraft-like structures under crash conditions. Representations of a rigid link and an impenetrable contact plane are added to the deformation model so that any number of nodes of the finite element model may be connected by a rigid link or may contact the plane. Equilibrium configurations are derived as the stationary conditions of a potential function of the generalized nodal variables of the model. Minimization of the nonlinear potential function is achieved by using the best current variable metric update formula for use in unconstrained minimization. Powell's conjugate gradient algorithm, which offers very low storage requirements at some slight increase in the total number of calculations, is the other alternative algorithm to be used for extremely large scale problems.

An optimization-based approach for facility energy management with uncertainties, and, Power portfolio optimization in deregulated electricity markets with risk management

NASA Astrophysics Data System (ADS)

Xu, Jun

Topic 1. An Optimization-Based Approach for Facility Energy Management with Uncertainties. Effective energy management for facilities is becoming increasingly important in view of the rising energy costs, the government mandate on the reduction of energy consumption, and the human comfort requirements. This part of dissertation presents a daily energy management formulation and the corresponding solution methodology for HVAC systems. The problem is to minimize the energy and demand costs through the control of HVAC units while satisfying human comfort, system dynamics, load limit constraints, and other requirements. The problem is difficult in view of the fact that the system is nonlinear, time-varying, building-dependent, and uncertain; and that the direct control of a large number of HVAC components is difficult. In this work, HVAC setpoints are the control variables developed on top of a Direct Digital Control (DDC) system. A method that combines Lagrangian relaxation, neural networks, stochastic dynamic programming, and heuristics is developed to predict the system dynamics and uncontrollable load, and to optimize the setpoints. Numerical testing and prototype implementation results show that our method can effectively reduce total costs, manage uncertainties, and shed the load, is computationally efficient. Furthermore, it is significantly better than existing methods. Topic 2. Power Portfolio Optimization in Deregulated Electricity Markets with Risk Management. In a deregulated electric power system, multiple markets of different time scales exist with various power supply instruments. A load serving entity (LSE) has multiple choices from these instruments to meet its load obligations. In view of the large amount of power involved, the complex market structure, risks in such volatile markets, stringent constraints to be satisfied, and the long time horizon, a power portfolio optimization problem is of critical importance but difficulty for an LSE to serve the load, maximize its profit, and manage risks. In this topic, a mid-term power portfolio optimization problem with risk management is presented. Key instruments are considered, risk terms based on semi-variances of spot market transactions are introduced, and penalties on load obligation violations are added to the objective function to improve algorithm convergence and constraint satisfaction. To overcome the inseparability of the resulting problem, a surrogate optimization framework is developed enabling a decomposition and coordination approach. Numerical testing results show that our method effectively provides decisions for various instruments to maximize profit, manage risks, and is computationally efficient.

Minimizing center of mass vertical movement increases metabolic cost in walking.

PubMed

Ortega, Justus D; Farley, Claire T

2005-12-01

A human walker vaults up and over each stance limb like an inverted pendulum. This similarity suggests that the vertical motion of a walker's center of mass reduces metabolic cost by providing a mechanism for pendulum-like mechanical energy exchange. Alternatively, some researchers have hypothesized that minimizing vertical movements of the center of mass during walking minimizes the metabolic cost, and this view remains prevalent in clinical gait analysis. We examined the relationship between vertical movement and metabolic cost by having human subjects walk normally and with minimal center of mass vertical movement ("flat-trajectory walking"). In flat-trajectory walking, subjects reduced center of mass vertical displacement by an average of 69% (P = 0.0001) but consumed approximately twice as much metabolic energy over a range of speeds (0.7-1.8 m/s) (P = 0.0001). In flat-trajectory walking, passive pendulum-like mechanical energy exchange provided only a small portion of the energy required to accelerate the center of mass because gravitational potential energy fluctuated minimally. Thus, despite the smaller vertical movements in flat-trajectory walking, the net external mechanical work needed to move the center of mass was similar in both types of walking (P = 0.73). Subjects walked with more flexed stance limbs in flat-trajectory walking (P < 0.001), and the resultant increase in stance limb force generation likely helped cause the doubling in metabolic cost compared with normal walking. Regardless of the cause, these findings clearly demonstrate that human walkers consume substantially more metabolic energy when they minimize vertical motion.

Energy minimization strategies and renewable energy utilization for desalination: a review.

PubMed

Subramani, Arun; Badruzzaman, Mohammad; Oppenheimer, Joan; Jacangelo, Joseph G

2011-02-01

Energy is a significant cost in the economics of desalinating waters, but water scarcity is driving the rapid expansion in global installed capacity of desalination facilities. Conventional fossil fuels have been utilized as their main energy source, but recent concerns over greenhouse gas (GHG) emissions have promoted global development and implementation of energy minimization strategies and cleaner energy supplies. In this paper, a comprehensive review of energy minimization strategies for membrane-based desalination processes and utilization of lower GHG emission renewable energy resources is presented. The review covers the utilization of energy efficient design, high efficiency pumping, energy recovery devices, advanced membrane materials (nanocomposite, nanotube, and biomimetic), innovative technologies (forward osmosis, ion concentration polarization, and capacitive deionization), and renewable energy resources (solar, wind, and geothermal). Utilization of energy efficient design combined with high efficiency pumping and energy recovery devices have proven effective in full-scale applications. Integration of advanced membrane materials and innovative technologies for desalination show promise but lack long-term operational data. Implementation of renewable energy resources depends upon geography-specific abundance, a feasible means of handling renewable energy power intermittency, and solving technological and economic scale-up and permitting issues. Copyright © 2011 Elsevier Ltd. All rights reserved.

A Reserve-based Method for Mitigating the Impact of Renewable Energy

NASA Astrophysics Data System (ADS)

Krad, Ibrahim

The fundamental operating paradigm of today's power systems is undergoing a significant shift. This is partially motivated by the increased desire for incorporating variable renewable energy resources into generation portfolios. While these generating technologies offer clean energy at zero marginal cost, i.e. no fuel costs, they also offer unique operating challenges for system operators. Perhaps the biggest operating challenge these resources introduce is accommodating their intermittent fuel source availability. For this reason, these generators increase the system-wide variability and uncertainty. As a result, system operators are revisiting traditional operating strategies to more efficiently incorporate these generation resources to maximize the benefit they provide while minimizing the challenges they introduce. One way system operators have accounted for system variability and uncertainty is through the use of operating reserves. Operating reserves can be simplified as excess capacity kept online during real time operations to help accommodate unforeseen fluctuations in demand. With new generation resources, a new class of operating reserves has emerged that is generally known as flexibility, or ramping, reserves. This new reserve class is meant to better position systems to mitigate severe ramping in the net load profile. The best way to define this new requirement is still under investigation. Typical requirement definitions focus on the additional uncertainty introduced by variable generation and there is room for improvement regarding explicit consideration for the variability they introduce. An exogenous reserve modification method is introduced in this report that can improve system reliability with minimal impacts on total system wide production costs. Another potential solution to this problem is to formulate the problem as a stochastic programming problem. The unit commitment and economic dispatch problems are typically formulated as deterministic problems due to fast solution times and the solutions being sufficient for operations. Improvements in technical computing hardware have reignited interest in stochastic modeling. The variability of wind and solar naturally lends itself to stochastic modeling. The use of explicit reserve requirements in stochastic models is an area of interest for power system researchers. This report introduces a new reserve modification implementation based on previous results to be used in a stochastic modeling framework. With technological improvements in distributed generation technologies, microgrids are currently being researched and implemented. Microgrids are small power systems that have the ability to serve their demand with their own generation resources and may have a connection to a larger power system. As battery technologies improve, they are becoming a more viable option in these distributed power systems and research is necessary to determine the most efficient way to utilize them. This report will investigate several unique operating strategies for batteries in small power systems and analyze their benefits. These new operating strategies will help reduce operating costs and improve system reliability.

Number Partitioning via Quantum Adiabatic Computation

NASA Technical Reports Server (NTRS)

Smelyanskiy, Vadim N.; Toussaint, Udo

2002-01-01

We study both analytically and numerically the complexity of the adiabatic quantum evolution algorithm applied to random instances of combinatorial optimization problems. We use as an example the NP-complete set partition problem and obtain an asymptotic expression for the minimal gap separating the ground and exited states of a system during the execution of the algorithm. We show that for computationally hard problem instances the size of the minimal gap scales exponentially with the problem size. This result is in qualitative agreement with the direct numerical simulation of the algorithm for small instances of the set partition problem. We describe the statistical properties of the optimization problem that are responsible for the exponential behavior of the algorithm.

Exact recovery of sparse multiple measurement vectors by [Formula: see text]-minimization.

PubMed

Wang, Changlong; Peng, Jigen

2018-01-01

The joint sparse recovery problem is a generalization of the single measurement vector problem widely studied in compressed sensing. It aims to recover a set of jointly sparse vectors, i.e., those that have nonzero entries concentrated at a common location. Meanwhile [Formula: see text]-minimization subject to matrixes is widely used in a large number of algorithms designed for this problem, i.e., [Formula: see text]-minimization [Formula: see text] Therefore the main contribution in this paper is two theoretical results about this technique. The first one is proving that in every multiple system of linear equations there exists a constant [Formula: see text] such that the original unique sparse solution also can be recovered from a minimization in [Formula: see text] quasi-norm subject to matrixes whenever [Formula: see text]. The other one is showing an analytic expression of such [Formula: see text]. Finally, we display the results of one example to confirm the validity of our conclusions, and we use some numerical experiments to show that we increase the efficiency of these algorithms designed for [Formula: see text]-minimization by using our results.

Distributed Optimal Dispatch of Distributed Energy Resources Over Lossy Communication Networks

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wu, Junfeng; Yang, Tao; Wu, Di

In this paper, we consider the economic dispatch problem (EDP), where a cost function that is assumed to be strictly convex is assigned to each of distributed energy resources (DERs), over packet dropping networks. The goal of a standard EDP is to minimize the total generation cost while meeting total demand and satisfying individual generator output limit. We propose a distributed algorithm for solving the EDP over networks. The proposed algorithm is resilient against packet drops over communication links. Under the assumption that the underlying communication network is strongly connected with a positive probability and the packet drops are independentmore » and identically distributed (i.i.d.), we show that the proposed algorithm is able to solve the EDP. Numerical simulation results are used to validate and illustrate the main results of the paper.« less

An optimal autonomous microgrid cluster based on distributed generation droop parameter optimization and renewable energy sources using an improved grey wolf optimizer

NASA Astrophysics Data System (ADS)

Moazami Goodarzi, Hamed; Kazemi, Mohammad Hosein

2018-05-01

Microgrid (MG) clustering is regarded as an important driver in improving the robustness of MGs. However, little research has been conducted on providing appropriate MG clustering. This article addresses this shortfall. It proposes a novel multi-objective optimization approach for finding optimal clustering of autonomous MGs by focusing on variables such as distributed generation (DG) droop parameters, the location and capacity of DG units, renewable energy sources, capacitors and powerline transmission. Power losses are minimized and voltage stability is improved while virtual cut-set lines with minimum power transmission for clustering MGs are obtained. A novel chaotic grey wolf optimizer (CGWO) algorithm is applied to solve the proposed multi-objective problem. The performance of the approach is evaluated by utilizing a 69-bus MG in several scenarios.

Calculating intensities using effective Hamiltonians in terms of Coriolis-adapted normal modes.

PubMed

Karthikeyan, S; Krishnan, Mangala Sunder; Carrington, Tucker

2005-01-15

The calculation of rovibrational transition energies and intensities is often hampered by the fact that vibrational states are strongly coupled by Coriolis terms. Because it invalidates the use of perturbation theory for the purpose of decoupling these states, the coupling makes it difficult to analyze spectra and to extract information from them. One either ignores the problem and hopes that the effect of the coupling is minimal or one is forced to diagonalize effective rovibrational matrices (rather than diagonalizing effective rotational matrices). In this paper we apply a procedure, based on a quantum mechanical canonical transformation for deriving decoupled effective rotational Hamiltonians. In previous papers we have used this technique to compute energy levels. In this paper we show that it can also be applied to determine intensities. The ideas are applied to the ethylene molecule.

Co-optimization of Energy and Demand-Side Reserves in Day-Ahead Electricity Markets

NASA Astrophysics Data System (ADS)

Surender Reddy, S.; Abhyankar, A. R.; Bijwe, P. R.

2015-04-01

This paper presents a new multi-objective day-ahead market clearing (DAMC) mechanism with demand-side reserves/demand response (DR) offers, considering realistic voltage-dependent load modeling. The paper proposes objectives such as social welfare maximization (SWM) including demand-side reserves, and load served error (LSE) minimization. In this paper, energy and demand-side reserves are cleared simultaneously through co-optimization process. The paper clearly brings out the unsuitability of conventional SWM for DAMC in the presence of voltage-dependent loads, due to reduction of load served (LS). Under such circumstances multi-objective DAMC with DR offers is essential. Multi-objective Strength Pareto Evolutionary Algorithm 2+ (SPEA 2+) has been used to solve the optimization problem. The effectiveness of the proposed scheme is confirmed with results obtained from IEEE 30 bus system.

High Performance Magnetic Bearings for Aero Applications

NASA Technical Reports Server (NTRS)

Allaire, P. E.; Knospe, C. R.; Williams, R. D.; Lewis, D. W.; Barrett, L. E.; Maslen, E. H.; Humphris, R. R.

1997-01-01

Several previous annual reports were written and numerous papers published on the topics for this grant. That work is not repeated here in this final report. Only the work completed in the final year of the grant is presented in this final report. This final year effort concentrated on power loss measurements in magnetic bearing rotors. The effect of rotor power losses in magnetic bearings are very important for many applications. In some cases, these losses must be minimized to maximize the length of time the rotating machine can operate on a fixed energy or power supply. Examples include aircraft gas turbine engines, space devices, or energy storage flywheels. In other applications, the heating caused by the magnetic bearing must be removed. Excessive heating can be a significant problem in machines as diverse as large compressors, electric motors, textile spindles, and artificial heart pumps.

Carbon Nanotube Embedded Nanostructure for Biometrics.

PubMed

Park, Juhyuk; Youn, Jae Ryoun; Song, Young Seok

2017-12-27

Low electric energy loss is a very important problem to minimize the decay of transferred energy intensity due to impedance mismatch. This issue has been dealt with by adding an impedance matching layer at the interface between two media. A strategy was proposed to improve the charge transfer from the human body to a biometric device by using an impedance matching nanostructure. Nanocomposite pattern arrays were fabricated with shape memory polymer and carbon nanotubes. The shape recovery ability of the nanopatterns enhanced durability and sustainability of the structure. It was found that the composite nanopatterns improved the current transfer by two times compared with the nonpatterned composite sample. The underlying mechanism of the enhanced charge transport was understood by carrying out a numerical simulation. We anticipate that this study can provide a new pathway for developing advanced biometric devices with high sensitivity to biological information.

Pacific Northwest National Laboratory institutional plan FY 1997--2002

DOE Office of Scientific and Technical Information (OSTI.GOV)

NONE

1996-10-01

Pacific Northwest National Laboratory`s core mission is to deliver environmental science and technology in the service of the nation and humanity. Through basic research fundamental knowledge is created of natural, engineered, and social systems that is the basis for both effective environmental technology and sound public policy. Legacy environmental problems are solved by delivering technologies that remedy existing environmental hazards, today`s environmental needs are addressed with technologies that prevent pollution and minimize waste, and the technical foundation is being laid for tomorrow`s inherently clean energy and industrial processes. Pacific Northwest National Laboratory also applies its capabilities to meet selected nationalmore » security, energy, and human health needs; strengthen the US economy; and support the education of future scientists and engineers. Brief summaries are given of the various tasks being carried out under these broad categories.« less

Numerical Optimization Using Computer Experiments

NASA Technical Reports Server (NTRS)

Trosset, Michael W.; Torczon, Virginia

1997-01-01

Engineering design optimization often gives rise to problems in which expensive objective functions are minimized by derivative-free methods. We propose a method for solving such problems that synthesizes ideas from the numerical optimization and computer experiment literatures. Our approach relies on kriging known function values to construct a sequence of surrogate models of the objective function that are used to guide a grid search for a minimizer. Results from numerical experiments on a standard test problem are presented.

Scheduling with non-decreasing deterioration jobs and variable maintenance activities on a single machine

NASA Astrophysics Data System (ADS)

Zhang, Xingong; Yin, Yunqiang; Wu, Chin-Chia

2017-01-01

There is a situation found in many manufacturing systems, such as steel rolling mills, fire fighting or single-server cycle-queues, where a job that is processed later consumes more time than that same job when processed earlier. The research finds that machine maintenance can improve the worsening of processing conditions. After maintenance activity, the machine will be restored. The maintenance duration is a positive and non-decreasing differentiable convex function of the total processing times of the jobs between maintenance activities. Motivated by this observation, the makespan and the total completion time minimization problems in the scheduling of jobs with non-decreasing rates of job processing time on a single machine are considered in this article. It is shown that both the makespan and the total completion time minimization problems are NP-hard in the strong sense when the number of maintenance activities is arbitrary, while the makespan minimization problem is NP-hard in the ordinary sense when the number of maintenance activities is fixed. If the deterioration rates of the jobs are identical and the maintenance duration is a linear function of the total processing times of the jobs between maintenance activities, then this article shows that the group balance principle is satisfied for the makespan minimization problem. Furthermore, two polynomial-time algorithms are presented for solving the makespan problem and the total completion time problem under identical deterioration rates, respectively.

Design optimization of transmitting antennas for weakly coupled magnetic induction communication systems

PubMed Central

2017-01-01

This work focuses on the design of transmitting coils in weakly coupled magnetic induction communication systems. We propose several optimization methods that reduce the active, reactive and apparent power consumption of the coil. These problems are formulated as minimization problems, in which the power consumed by the transmitting coil is minimized, under the constraint of providing a required magnetic field at the receiver location. We develop efficient numeric and analytic methods to solve the resulting problems, which are of high dimension, and in certain cases non-convex. For the objective of minimal reactive power an analytic solution for the optimal current distribution in flat disc transmitting coils is provided. This problem is extended to general three-dimensional coils, for which we develop an expression for the optimal current distribution. Considering the objective of minimal apparent power, a method is developed to reduce the computational complexity of the problem by transforming it to an equivalent problem of lower dimension, allowing a quick and accurate numeric solution. These results are verified experimentally by testing a number of coil geometries. The results obtained allow reduced power consumption and increased performances in magnetic induction communication systems. Specifically, for wideband systems, an optimal design of the transmitter coil reduces the peak instantaneous power provided by the transmitter circuitry, and thus reduces its size, complexity and cost. PMID:28192463

NEWSUMT: A FORTRAN program for inequality constrained function minimization, users guide

NASA Technical Reports Server (NTRS)

Miura, H.; Schmit, L. A., Jr.

1979-01-01

A computer program written in FORTRAN subroutine form for the solution of linear and nonlinear constrained and unconstrained function minimization problems is presented. The algorithm is the sequence of unconstrained minimizations using the Newton's method for unconstrained function minimizations. The use of NEWSUMT and the definition of all parameters are described.

Minimizing the Diameter of a Network Using Shortcut Edges

NASA Astrophysics Data System (ADS)

Demaine, Erik D.; Zadimoghaddam, Morteza

We study the problem of minimizing the diameter of a graph by adding k shortcut edges, for speeding up communication in an existing network design. We develop constant-factor approximation algorithms for different variations of this problem. We also show how to improve the approximation ratios using resource augmentation to allow more than k shortcut edges. We observe a close relation between the single-source version of the problem, where we want to minimize the largest distance from a given source vertex, and the well-known k-median problem. First we show that our constant-factor approximation algorithms for the general case solve the single-source problem within a constant factor. Then, using a linear-programming formulation for the single-source version, we find a (1 + ɛ)-approximation using O(klogn) shortcut edges. To show the tightness of our result, we prove that any ({3 over 2}-ɛ)-approximation for the single-source version must use Ω(klogn) shortcut edges assuming P ≠ NP.

Dirac δ -function potential in quasiposition representation of a minimal-length scenario

NASA Astrophysics Data System (ADS)

Gusson, M. F.; Gonçalves, A. Oakes O.; Francisco, R. O.; Furtado, R. G.; Fabris, J. C.; Nogueira, J. A.

2018-03-01

A minimal-length scenario can be considered as an effective description of quantum gravity effects. In quantum mechanics the introduction of a minimal length can be accomplished through a generalization of Heisenberg's uncertainty principle. In this scenario, state eigenvectors of the position operator are no longer physical states and the representation in momentum space or a representation in a quasiposition space must be used. In this work, we solve the Schroedinger equation with a Dirac δ -function potential in quasiposition space. We calculate the bound state energy and the coefficients of reflection and transmission for the scattering states. We show that leading corrections are of order of the minimal length ({ O}(√{β })) and the coefficients of reflection and transmission are no longer the same for the Dirac delta well and barrier as in ordinary quantum mechanics. Furthermore, assuming that the equivalence of the 1s state energy of the hydrogen atom and the bound state energy of the Dirac {{δ }}-function potential in the one-dimensional case is kept in a minimal-length scenario, we also find that the leading correction term for the ground state energy of the hydrogen atom is of the order of the minimal length and Δx_{\\min } ≤ 10^{-25} m.

Associations between social vulnerabilities and psychosocial problems in European children. Results from the IDEFICS study.

PubMed

Iguacel, Isabel; Michels, Nathalie; Fernández-Alvira, Juan M; Bammann, Karin; De Henauw, Stefaan; Felső, Regina; Gwozdz, Wencke; Hunsberger, Monica; Reisch, Lucia; Russo, Paola; Tornaritis, Michael; Thumann, Barbara Franziska; Veidebaum, Toomas; Börnhorst, Claudia; Moreno, Luis A

2017-09-01

The effect of socioeconomic inequalities on children's mental health remains unclear. This study aims to explore the cross-sectional and longitudinal associations between social vulnerabilities and psychosocial problems, and the association between accumulation of vulnerabilities and psychosocial problems. 5987 children aged 2-9 years from eight European countries were assessed at baseline and 2-year follow-up. Two different instruments were employed to assess children's psychosocial problems: the KINDL (Questionnaire for Measuring Health-Related Quality of Life in Children and Adolescents) was used to evaluate children's well-being and the Strengths and Difficulties Questionnaire (SDQ) was used to evaluate children's internalising problems. Vulnerable groups were defined as follows: children whose parents had minimal social networks, children from non-traditional families, children of migrant origin or children with unemployed parents. Logistic mixed-effects models were used to assess the associations between social vulnerabilities and psychosocial problems. After adjusting for classical socioeconomic and lifestyle indicators, children whose parents had minimal social networks were at greater risk of presenting internalising problems at baseline and follow-up (OR 1.53, 99% CI 1.11-2.11). The highest risk for psychosocial problems was found in children whose status changed from traditional families at T0 to non-traditional families at T1 (OR 1.60, 99% CI 1.07-2.39) and whose parents had minimal social networks at both time points (OR 1.97, 99% CI 1.26-3.08). Children with one or more vulnerabilities accumulated were at a higher risk of developing psychosocial problems at baseline and follow-up. Therefore, policy makers should implement measures to strengthen the social support for parents with a minimal social network.

FPGA design for constrained energy minimization

NASA Astrophysics Data System (ADS)

Wang, Jianwei; Chang, Chein-I.; Cao, Mang

2004-02-01

The Constrained Energy Minimization (CEM) has been widely used for hyperspectral detection and classification. The feasibility of implementing the CEM as a real-time processing algorithm in systolic arrays has been also demonstrated. The main challenge of realizing the CEM in hardware architecture in the computation of the inverse of the data correlation matrix performed in the CEM, which requires a complete set of data samples. In order to cope with this problem, the data correlation matrix must be calculated in a causal manner which only needs data samples up to the sample at the time it is processed. This paper presents a Field Programmable Gate Arrays (FPGA) design of such a causal CEM. The main feature of the proposed FPGA design is to use the Coordinate Rotation DIgital Computer (CORDIC) algorithm that can convert a Givens rotation of a vector to a set of shift-add operations. As a result, the CORDIC algorithm can be easily implemented in hardware architecture, therefore in FPGA. Since the computation of the inverse of the data correlction involves a series of Givens rotations, the utility of the CORDIC algorithm allows the causal CEM to perform real-time processing in FPGA. In this paper, an FPGA implementation of the causal CEM will be studied and its detailed architecture will be also described.

Matrix Interdiction Problem

NASA Astrophysics Data System (ADS)

Kasiviswanathan, Shiva Prasad; Pan, Feng

In the matrix interdiction problem, a real-valued matrix and an integer k is given. The objective is to remove a set of k matrix columns that minimizes in the residual matrix the sum of the row values, where the value of a row is defined to be the largest entry in that row. This combinatorial problem is closely related to bipartite network interdiction problem that can be applied to minimize the probability that an adversary can successfully smuggle weapons. After introducing the matrix interdiction problem, we study the computational complexity of this problem. We show that the matrix interdiction problem is NP-hard and that there exists a constant γ such that it is even NP-hard to approximate this problem within an n γ additive factor. We also present an algorithm for this problem that achieves an (n - k) multiplicative approximation ratio.

On Deployment of Multiple Base Stations for Energy-Efficient Communication in Wireless Sensor Networks

DOE PAGES

Lin, Yunyue; Wu, Qishi; Cai, Xiaoshan; ...

2010-01-01

Data transmission from sensor nodes to a base station or a sink node often incurs significant energy consumption, which critically affects network lifetime. We generalize and solve the problem of deploying multiple base stations to maximize network lifetime in terms of two different metrics under one-hop and multihop communication models. In the one-hop communication model, the sensors far away from base stations always deplete their energy much faster than others. We propose an optimal solution and a heuristic approach based on the minimal enclosing circle algorithm to deploy a base station at the geometric center of each cluster. In themore » multihop communication model, both base station location and data routing mechanism need to be considered in maximizing network lifetime. We propose an iterative algorithm based on rigorous mathematical derivations and use linear programming to compute the optimal routing paths for data transmission. Simulation results show the distinguished performance of the proposed deployment algorithms in maximizing network lifetime.« less

Effects of human running cadence and experimental validation of the bouncing ball model

NASA Astrophysics Data System (ADS)

Bencsik, László; Zelei, Ambrus

2017-05-01

The biomechanical analysis of human running is a complex problem, because of the large number of parameters and degrees of freedom. However, simplified models can be constructed, which are usually characterized by some fundamental parameters, like step length, foot strike pattern and cadence. The bouncing ball model of human running is analysed theoretically and experimentally in this work. It is a minimally complex dynamic model when the aim is to estimate the energy cost of running and the tendency of ground-foot impact intensity as a function of cadence. The model shows that cadence has a direct effect on energy efficiency of running and ground-foot impact intensity. Furthermore, it shows that higher cadence implies lower risk of injury and better energy efficiency. An experimental data collection of 121 amateur runners is presented. The experimental results validate the model and provides information about the walk-to-run transition speed and the typical development of cadence and grounded phase ratio in different running speed ranges.

Exotic superconductivity with enhanced energy scales in materials with three band crossings

NASA Astrophysics Data System (ADS)

Lin, Yu-Ping; Nandkishore, Rahul M.

2018-04-01

Three band crossings can arise in three-dimensional quantum materials with certain space group symmetries. The low energy Hamiltonian supports spin one fermions and a flat band. We study the pairing problem in this setting. We write down a minimal BCS Hamiltonian and decompose it into spin-orbit coupled irreducible pairing channels. We then solve the resulting gap equations in channels with zero total angular momentum. We find that in the s-wave spin singlet channel (and also in an unusual d-wave `spin quintet' channel), superconductivity is enormously enhanced, with a possibility for the critical temperature to be linear in interaction strength. Meanwhile, in the p-wave spin triplet channel, the superconductivity exhibits features of conventional BCS theory due to the absence of flat band pairing. Three band crossings thus represent an exciting new platform for realizing exotic superconducting states with enhanced energy scales. We also discuss the effects of doping, nonzero temperature, and of retaining additional terms in the k .p expansion of the Hamiltonian.

Designing train-speed trajectory with energy efficiency and service quality

NASA Astrophysics Data System (ADS)

Jia, Jiannan; Yang, Kai; Yang, Lixing; Gao, Yuan; Li, Shukai

2018-05-01

With the development of automatic train operations, optimal trajectory design is significant to the performance of train operations in railway transportation systems. Considering energy efficiency and service quality, this article formulates a bi-objective train-speed trajectory optimization model to minimize simultaneously the energy consumption and travel time in an inter-station section. This article is distinct from previous studies in that more sophisticated train driving strategies characterized by the acceleration/deceleration gear, the cruising speed, and the speed-shift site are specifically considered. For obtaining an optimal train-speed trajectory which has equal satisfactory degree on both objectives, a fuzzy linear programming approach is applied to reformulate the objectives. In addition, a genetic algorithm is developed to solve the proposed train-speed trajectory optimization problem. Finally, a series of numerical experiments based on a real-world instance of Beijing-Tianjin Intercity Railway are implemented to illustrate the practicability of the proposed model as well as the effectiveness of the solution methodology.

The impact of long-range electron-hole interaction on the charge separation yield of molecular photocells

NASA Astrophysics Data System (ADS)

Nemati Aram, Tahereh; Ernzerhof, Matthias; Asgari, Asghar; Mayou, Didier

2017-01-01

We discuss the effects of charge carrier interaction and recombination on the operation of molecular photocells. Molecular photocells are devices where the energy conversion process takes place in a single molecular donor-acceptor complex attached to electrodes. Our investigation is based on the quantum scattering theory, in particular on the Lippmann-Schwinger equation; this minimizes the complexity of the problem while providing useful and non-trivial insight into the mechanism governing photocell operation. In this study, both exciton pair creation and dissociation are treated in the energy domain, and therefore there is access to detailed spectral information, which can be used as a framework to interpret the charge separation yield. We demonstrate that the charge carrier separation is a complex process that is affected by different parameters, such as the strength of the electron-hole interaction and the non-radiative recombination rate. Our analysis helps to optimize the charge separation process and the energy transfer in organic solar cells and in molecular photocells.

Design optimization under uncertainty and speed variability for a piezoelectric energy harvester powering a tire pressure monitoring sensor

NASA Astrophysics Data System (ADS)

Toghi Eshghi, Amin; Lee, Soobum; Kazem Sadoughi, Mohammad; Hu, Chao; Kim, Young-Cheol; Seo, Jong-Ho

2017-10-01

Energy harvesting (EH) technologies to power small sized electronic devices are attracting great attention. Wasted energy in a vehicle’s rotating tire has a great potential to enable self-powered tire pressure monitoring sensors (TPMS). Piezoelectric type energy harvesters can be used to collect vibrational energy and power such systems. Due to the presence of harsh acceleration in a rotating tire, a design tradeoff needs to be studied to prolong the harvester’s fatigue life as well as to ensure sufficient power generation. However, the design by traditional deterministic design optimization (DDO) does not show reliable performance due to the lack of consideration of various uncertainty factors (e.g., manufacturing tolerances, material properties, and loading conditions). In this study, we address a new EH design formulation that considers the uncertainty in car speed, dimensional tolerances and material properties, and solve this design problem using reliability-based design optimization (RBDO). The RBDO problem is formulated to maximize compactness and minimize weight of a TPMS harvester while satisfying power and durability requirements. A transient analysis has been done to measure the time varying response of EH such as power generation, dynamic strain, and stress. A conservative design formulation is proposed to consider the expected power from varied speed and stress at higher speed. When compared to the DDO, the RBDO results show that the reliability of EH is increased significantly by scarifying the objective function. Finally, experimental test has been conducted to demonstrate the merits of RBDO design over DDO.

A two-stage stochastic optimization model for scheduling electric vehicle charging loads to relieve distribution-system constraints

DOE PAGES

Wu, Fei; Sioshansi, Ramteen

2017-05-25

Electric vehicles (EVs) hold promise to improve the energy efficiency and environmental impacts of transportation. However, widespread EV use can impose significant stress on electricity-distribution systems due to their added charging loads. This paper proposes a centralized EV charging-control model, which schedules the charging of EVs that have flexibility. This flexibility stems from EVs that are parked at the charging station for a longer duration of time than is needed to fully recharge the battery. The model is formulated as a two-stage stochastic optimization problem. The model captures the use of distributed energy resources and uncertainties around EV arrival timesmore » and charging demands upon arrival, non-EV loads on the distribution system, energy prices, and availability of energy from the distributed energy resources. We use a Monte Carlo-based sample-average approximation technique and an L-shaped method to solve the resulting optimization problem efficiently. We also apply a sequential sampling technique to dynamically determine the optimal size of the randomly sampled scenario tree to give a solution with a desired quality at minimal computational cost. Here, we demonstrate the use of our model on a Central-Ohio-based case study. We show the benefits of the model in reducing charging costs, negative impacts on the distribution system, and unserved EV-charging demand compared to simpler heuristics. Lastly, we also conduct sensitivity analyses, to show how the model performs and the resulting costs and load profiles when the design of the station or EV-usage parameters are changed.« less

On the nullspace of TLS multi-station adjustment

NASA Astrophysics Data System (ADS)

Sterle, Oskar; Kogoj, Dušan; Stopar, Bojan; Kregar, Klemen

2018-07-01

In the article we present an analytic aspect of TLS multi-station least-squares adjustment with the main focus on the datum problem. The datum problem is, compared to previously published researches, theoretically analyzed and solved, where the solution is based on nullspace derivation of the mathematical model. The importance of datum problem solution is seen in a complete description of TLS multi-station adjustment solutions from a set of all minimally constrained least-squares solutions. On a basis of known nullspace, estimable parameters are described and the geometric interpretation of all minimally constrained least squares solutions is presented. At the end a simulated example is used to analyze the results of TLS multi-station minimally constrained and inner constrained least-squares adjustment solutions.

Method of grid generation

DOEpatents

Barnette, Daniel W.

2002-01-01

The present invention provides a method of grid generation that uses the geometry of the problem space and the governing relations to generate a grid. The method can generate a grid with minimized discretization errors, and with minimal user interaction. The method of the present invention comprises assigning grid cell locations so that, when the governing relations are discretized using the grid, at least some of the discretization errors are substantially zero. Conventional grid generation is driven by the problem space geometry; grid generation according to the present invention is driven by problem space geometry and by governing relations. The present invention accordingly can provide two significant benefits: more efficient and accurate modeling since discretization errors are minimized, and reduced cost grid generation since less human interaction is required.

Minimum Bayes risk image correlation

NASA Technical Reports Server (NTRS)

Minter, T. C., Jr.

1980-01-01

In this paper, the problem of designing a matched filter for image correlation will be treated as a statistical pattern recognition problem. It is shown that, by minimizing a suitable criterion, a matched filter can be estimated which approximates the optimum Bayes discriminant function in a least-squares sense. It is well known that the use of the Bayes discriminant function in target classification minimizes the Bayes risk, which in turn directly minimizes the probability of a false fix. A fast Fourier implementation of the minimum Bayes risk correlation procedure is described.

Thermodynamic stability of biomolecules and evolution.

PubMed

Chakravarty, Ashim K

2017-08-01

The thermodynamic stability of biomolecules in the perspective of evolution is a complex issue and needs discussion. Intra molecular bonds maintain the structure and the state of internal energy (E) of a biomolecule at "local minima". In this communication, possibility of loss in internal energy level of a biomolecule through the changes in the bonds has been discussed, that might earn more thermodynamic stability for the molecule. In the process variations in structure and functions of the molecule could occur. Thus, E of a biomolecule is likely to have energy stature for minimization. Such change in energy status is an intrinsic factor for evolving biomolecules buying more stability and generating variations in the structure and function of DNA molecules undergoing natural selection. Thus, the variations might very well contribute towards the process of evolution. A brief discussion on conserved sequence in the light of proposition in this communication has been made at the end. Extension of the idea may resolve certain standing problems in evolution, such as maintenance of conserved sequences in genome of diverse species, pre- versus post adaptive mutations, 'orthogenesis', etc. Copyright © 2017 Elsevier Ltd. All rights reserved.

Decomposability and convex structure of thermal processes

NASA Astrophysics Data System (ADS)

Mazurek, Paweł; Horodecki, Michał

2018-05-01

We present an example of a thermal process (TP) for a system of d energy levels, which cannot be performed without an instant access to the whole energy space. This TP is uniquely connected with a transition between some states of the system, that cannot be performed without access to the whole energy space even when approximate transitions are allowed. Pursuing the question about the decomposability of TPs into convex combinations of compositions of processes acting non-trivially on smaller subspaces, we investigate transitions within the subspace of states diagonal in the energy basis. For three level systems, we determine the set of extremal points of these operations, as well as the minimal set of operations needed to perform an arbitrary TP, and connect the set of TPs with thermomajorization criterion. We show that the structure of the set depends on temperature, which is associated with the fact that TPs cannot increase deterministically extractable work from a state—the conclusion that holds for arbitrary d level system. We also connect the decomposability problem with detailed balance symmetry of an extremal TPs.

Growth failure and nutrition considerations in chronic childhood wasting diseases.

PubMed

Kyle, Ursula G; Shekerdemian, Lara S; Coss-Bu, Jorge A

2015-04-01

Growth failure is a common problem in many children with chronic diseases. This article is an overview of the most common causes of growth failure/growth retardation that affect children with a number of chronic diseases. We also briefly review the nutrition considerations and treatment goals. Growth failure is multifactorial in children with chronic conditions, including patients with cystic fibrosis, chronic kidney disease, chronic liver disease, congenital heart disease, human immunodeficiency virus, inflammatory bowel disease, short bowel syndrome, and muscular dystrophies. Important contributory factors to growth failure include increased energy needs, increased energy loss, malabsorption, decreased energy intake, anorexia, pain, vomiting, intestinal obstruction, and inflammatory cytokines. Various metabolic and pathologic abnormalities that are characteristic of chronic diseases further lead to significant malnutrition and growth failure. In addition to treating disease-specific abnormalities, treatment should address the energy and protein deficits, including vitamin and mineral supplements to correct deficiencies, correct metabolic and endocrinologic abnormalities, and include long-term monitoring of weight and growth. Individualized, age-appropriate nutrition intervention will minimize the malnutrition and growth failure seen in children with chronic diseases. © 2014 American Society for Parenteral and Enteral Nutrition.

Distance majorization and its applications

PubMed Central

Chi, Eric C.; Zhou, Hua; Lange, Kenneth

2014-01-01

The problem of minimizing a continuously differentiable convex function over an intersection of closed convex sets is ubiquitous in applied mathematics. It is particularly interesting when it is easy to project onto each separate set, but nontrivial to project onto their intersection. Algorithms based on Newton’s method such as the interior point method are viable for small to medium-scale problems. However, modern applications in statistics, engineering, and machine learning are posing problems with potentially tens of thousands of parameters or more. We revisit this convex programming problem and propose an algorithm that scales well with dimensionality. Our proposal is an instance of a sequential unconstrained minimization technique and revolves around three ideas: the majorization-minimization principle, the classical penalty method for constrained optimization, and quasi-Newton acceleration of fixed-point algorithms. The performance of our distance majorization algorithms is illustrated in several applications. PMID:25392563

Service-Oriented Node Scheduling Scheme for Wireless Sensor Networks Using Markov Random Field Model

PubMed Central

Cheng, Hongju; Su, Zhihuang; Lloret, Jaime; Chen, Guolong

2014-01-01

Future wireless sensor networks are expected to provide various sensing services and energy efficiency is one of the most important criterions. The node scheduling strategy aims to increase network lifetime by selecting a set of sensor nodes to provide the required sensing services in a periodic manner. In this paper, we are concerned with the service-oriented node scheduling problem to provide multiple sensing services while maximizing the network lifetime. We firstly introduce how to model the data correlation for different services by using Markov Random Field (MRF) model. Secondly, we formulate the service-oriented node scheduling issue into three different problems, namely, the multi-service data denoising problem which aims at minimizing the noise level of sensed data, the representative node selection problem concerning with selecting a number of active nodes while determining the services they provide, and the multi-service node scheduling problem which aims at maximizing the network lifetime. Thirdly, we propose a Multi-service Data Denoising (MDD) algorithm, a novel multi-service Representative node Selection and service Determination (RSD) algorithm, and a novel MRF-based Multi-service Node Scheduling (MMNS) scheme to solve the above three problems respectively. Finally, extensive experiments demonstrate that the proposed scheme efficiently extends the network lifetime. PMID:25384005

10 CFR 20.1406 - Minimization of contamination.

Code of Federal Regulations, 2013 CFR

2013-01-01

... 10 Energy 1 2013-01-01 2013-01-01 false Minimization of contamination. 20.1406 Section 20.1406 Energy NUCLEAR REGULATORY COMMISSION STANDARDS FOR PROTECTION AGAINST RADIATION Radiological Criteria for... subsurface, in accordance with the existing radiation protection requirements in subpart B and radiological...

10 CFR 20.1406 - Minimization of contamination.

Code of Federal Regulations, 2014 CFR

2014-01-01

... 10 Energy 1 2014-01-01 2014-01-01 false Minimization of contamination. 20.1406 Section 20.1406 Energy NUCLEAR REGULATORY COMMISSION STANDARDS FOR PROTECTION AGAINST RADIATION Radiological Criteria for... subsurface, in accordance with the existing radiation protection requirements in subpart B and radiological...

Minimizing energy dissipation of matrix multiplication kernel on Virtex-II

NASA Astrophysics Data System (ADS)

Choi, Seonil; Prasanna, Viktor K.; Jang, Ju-wook

2002-07-01

In this paper, we develop energy-efficient designs for matrix multiplication on FPGAs. To analyze the energy dissipation, we develop a high-level model using domain-specific modeling techniques. In this model, we identify architecture parameters that significantly affect the total energy (system-wide energy) dissipation. Then, we explore design trade-offs by varying these parameters to minimize the system-wide energy. For matrix multiplication, we consider a uniprocessor architecture and a linear array architecture to develop energy-efficient designs. For the uniprocessor architecture, the cache size is a parameter that affects the I/O complexity and the system-wide energy. For the linear array architecture, the amount of storage per processing element is a parameter affecting the system-wide energy. By using maximum amount of storage per processing element and minimum number of multipliers, we obtain a design that minimizes the system-wide energy. We develop several energy-efficient designs for matrix multiplication. For example, for 6×6 matrix multiplication, energy savings of upto 52% for the uniprocessor architecture and 36% for the linear arrary architecture is achieved over an optimized library for Virtex-II FPGA from Xilinx.

Random Matrix Approach for Primal-Dual Portfolio Optimization Problems

NASA Astrophysics Data System (ADS)

Tada, Daichi; Yamamoto, Hisashi; Shinzato, Takashi

2017-12-01

In this paper, we revisit the portfolio optimization problems of the minimization/maximization of investment risk under constraints of budget and investment concentration (primal problem) and the maximization/minimization of investment concentration under constraints of budget and investment risk (dual problem) for the case that the variances of the return rates of the assets are identical. We analyze both optimization problems by the Lagrange multiplier method and the random matrix approach. Thereafter, we compare the results obtained from our proposed approach with the results obtained in previous work. Moreover, we use numerical experiments to validate the results obtained from the replica approach and the random matrix approach as methods for analyzing both the primal and dual portfolio optimization problems.

Modeling of tool path for the CNC sheet cutting machines

NASA Astrophysics Data System (ADS)

Petunin, Aleksandr A.

2015-11-01

In the paper the problem of tool path optimization for CNC (Computer Numerical Control) cutting machines is considered. The classification of the cutting techniques is offered. We also propose a new classification of toll path problems. The tasks of cost minimization and time minimization for standard cutting technique (Continuous Cutting Problem, CCP) and for one of non-standard cutting techniques (Segment Continuous Cutting Problem, SCCP) are formalized. We show that the optimization tasks can be interpreted as discrete optimization problem (generalized travel salesman problem with additional constraints, GTSP). Formalization of some constraints for these tasks is described. For the solution GTSP we offer to use mathematical model of Prof. Chentsov based on concept of a megalopolis and dynamic programming.

Greedy algorithms in disordered systems

NASA Astrophysics Data System (ADS)

Duxbury, P. M.; Dobrin, R.

1999-08-01

We discuss search, minimal path and minimal spanning tree algorithms and their applications to disordered systems. Greedy algorithms solve these problems exactly, and are related to extremal dynamics in physics. Minimal cost path (Dijkstra) and minimal cost spanning tree (Prim) algorithms provide extremal dynamics for a polymer in a random medium (the KPZ universality class) and invasion percolation (without trapping) respectively.

An Energy-Aware Runtime Management of Multi-Core Sensory Swarms.

PubMed

Kim, Sungchan; Yang, Hoeseok

2017-08-24

In sensory swarms, minimizing energy consumption under performance constraint is one of the key objectives. One possible approach to this problem is to monitor application workload that is subject to change at runtime, and to adjust system configuration adaptively to satisfy the performance goal. As today's sensory swarms are usually implemented using multi-core processors with adjustable clock frequency, we propose to monitor the CPU workload periodically and adjust the task-to-core allocation or clock frequency in an energy-efficient way in response to the workload variations. In doing so, we present an online heuristic that determines the most energy-efficient adjustment that satisfies the performance requirement. The proposed method is based on a simple yet effective energy model that is built upon performance prediction using IPC (instructions per cycle) measured online and power equation derived empirically. The use of IPC accounts for memory intensities of a given workload, enabling the accurate prediction of execution time. Hence, the model allows us to rapidly and accurately estimate the effect of the two control knobs, clock frequency adjustment and core allocation. The experiments show that the proposed technique delivers considerable energy saving of up to 45%compared to the state-of-the-art multi-core energy management technique.

An Energy-Aware Runtime Management of Multi-Core Sensory Swarms

PubMed Central

Kim, Sungchan

2017-01-01

In sensory swarms, minimizing energy consumption under performance constraint is one of the key objectives. One possible approach to this problem is to monitor application workload that is subject to change at runtime, and to adjust system configuration adaptively to satisfy the performance goal. As today’s sensory swarms are usually implemented using multi-core processors with adjustable clock frequency, we propose to monitor the CPU workload periodically and adjust the task-to-core allocation or clock frequency in an energy-efficient way in response to the workload variations. In doing so, we present an online heuristic that determines the most energy-efficient adjustment that satisfies the performance requirement. The proposed method is based on a simple yet effective energy model that is built upon performance prediction using IPC (instructions per cycle) measured online and power equation derived empirically. The use of IPC accounts for memory intensities of a given workload, enabling the accurate prediction of execution time. Hence, the model allows us to rapidly and accurately estimate the effect of the two control knobs, clock frequency adjustment and core allocation. The experiments show that the proposed technique delivers considerable energy saving of up to 45%compared to the state-of-the-art multi-core energy management technique. PMID:28837094

Dynamic implicit 3D adaptive mesh refinement for non-equilibrium radiation diffusion

NASA Astrophysics Data System (ADS)

Philip, B.; Wang, Z.; Berrill, M. A.; Birke, M.; Pernice, M.

2014-04-01

The time dependent non-equilibrium radiation diffusion equations are important for solving the transport of energy through radiation in optically thick regimes and find applications in several fields including astrophysics and inertial confinement fusion. The associated initial boundary value problems that are encountered often exhibit a wide range of scales in space and time and are extremely challenging to solve. To efficiently and accurately simulate these systems we describe our research on combining techniques that will also find use more broadly for long term time integration of nonlinear multi-physics systems: implicit time integration for efficient long term time integration of stiff multi-physics systems, local control theory based step size control to minimize the required global number of time steps while controlling accuracy, dynamic 3D adaptive mesh refinement (AMR) to minimize memory and computational costs, Jacobian Free Newton-Krylov methods on AMR grids for efficient nonlinear solution, and optimal multilevel preconditioner components that provide level independent solver convergence.

Spacecraft Environmental Testing SMAP (Soil, Moisture, Active, Passive)

NASA Technical Reports Server (NTRS)

Fields, Keith

2014-01-01

Testing a complete full up spacecraft to verify it will survive the environment, in which it will be exposed to during its mission, is a formidable task in itself. However, the ''test like you fly'' philosophy sometimes gets compromised because of cost, design and or time. This paper describes the thermal-vacuum and mass properties testing of the Soil Moisture Active Passive (SMAP) earth orbiting satellite. SMAP will provide global observations of soil moisture and freeze/thaw state (the hydrosphere state). SMAP hydrosphere state measurements will be used to enhance understanding of processes that link the water, energy, and carbon cycles, and to extend the capabilities of weather and climate prediction models. It will explain the problems encountered, and the solutions developed, which minimized the risk typically associated with such an arduous process. Also discussed, the future of testing on expensive long lead-time spacecraft. Will we ever reach the ''build and shoot" scenario with minimal or no verification testing?

Theoretical study of symmetry of flux onto a capsule

NASA Astrophysics Data System (ADS)

Duan, Hao; Wu, Changshu; Pei, Wenbing; Zou, Shiyang

2015-09-01

An analytic model to describe the flux asymmetry onto a capsule based on the viewfactor approximation is developed and verified with numerical simulations. By using a nested spheres technique to represent the various sources of flux asymmetry, the model can treat spherically and cylindrically symmetric hohlraums, e.g., cylinder, elliptic, and rugby. This approach includes the more realistic case of frequency-dependent flux asymmetry compared with the more standard frequency-integrated or single-frequency approaches [D. W. Phillion and S. M. Pollaine, Phys. Plasmas 1, 2963 (1994)]. Correspondingly, the approach can be used to assess x-ray preheat asymmetry generated from localized laser absorption in the high-Z hohlraum wall. For spherical hohlraums with 4, 6, or 8 laser entrance holes (LEHs), an optimal configuration of LEHs, laser spot placement, and angle-of-incidence of the single-ringed laser beams is defined. An analogy between minimizing the flux asymmetry onto a capsule and the Thomson problem of point charge placement on a sphere for minimized energy is shown.

Theoretical study of symmetry of flux onto a capsule

DOE Office of Scientific and Technical Information (OSTI.GOV)

Duan, Hao; Wu, Changshu; Zou, Shiyang, E-mail: duan-hao@iapcm.ac.cn

2015-09-15

An analytic model to describe the flux asymmetry onto a capsule based on the viewfactor approximation is developed and verified with numerical simulations. By using a nested spheres technique to represent the various sources of flux asymmetry, the model can treat spherically and cylindrically symmetric hohlraums, e.g., cylinder, elliptic, and rugby. This approach includes the more realistic case of frequency-dependent flux asymmetry compared with the more standard frequency-integrated or single-frequency approaches [D. W. Phillion and S. M. Pollaine, Phys. Plasmas 1, 2963 (1994)]. Correspondingly, the approach can be used to assess x-ray preheat asymmetry generated from localized laser absorption inmore » the high-Z hohlraum wall. For spherical hohlraums with 4, 6, or 8 laser entrance holes (LEHs), an optimal configuration of LEHs, laser spot placement, and angle-of-incidence of the single-ringed laser beams is defined. An analogy between minimizing the flux asymmetry onto a capsule and the Thomson problem of point charge placement on a sphere for minimized energy is shown.« less

System for decision analysis support on complex waste management issues

DOE Office of Scientific and Technical Information (OSTI.GOV)

Shropshire, D.E.

1997-10-01

A software system called the Waste Flow Analysis has been developed and applied to complex environmental management processes for the United States Department of Energy (US DOE). The system can evaluate proposed methods of waste retrieval, treatment, storage, transportation, and disposal. Analysts can evaluate various scenarios to see the impacts to waste slows and schedules, costs, and health and safety risks. Decision analysis capabilities have been integrated into the system to help identify preferred alternatives based on a specific objectives may be to maximize the waste moved to final disposition during a given time period, minimize health risks, minimize costs,more » or combinations of objectives. The decision analysis capabilities can support evaluation of large and complex problems rapidly, and under conditions of variable uncertainty. The system is being used to evaluate environmental management strategies to safely disposition wastes in the next ten years and reduce the environmental legacy resulting from nuclear material production over the past forty years.« less

Optimal control problem for linear fractional-order systems, described by equations with Hadamard-type derivative

NASA Astrophysics Data System (ADS)

Postnov, Sergey

2017-11-01

Two kinds of optimal control problem are investigated for linear time-invariant fractional-order systems with lumped parameters which dynamics described by equations with Hadamard-type derivative: the problem of control with minimal norm and the problem of control with minimal time at given restriction on control norm. The problem setting with nonlocal initial conditions studied. Admissible controls allowed to be the p-integrable functions (p > 1) at half-interval. The optimal control problem studied by moment method. The correctness and solvability conditions for the corresponding moment problem are derived. For several special cases the optimal control problems stated are solved analytically. Some analogies pointed for results obtained with the results which are known for integer-order systems and fractional-order systems describing by equations with Caputo- and Riemann-Liouville-type derivatives.

Interior design for passive solar homes

NASA Astrophysics Data System (ADS)

Breen, J. C.

1981-07-01

The increasing emphasis on refinement of passive solar systems brought recognition to interior design as an integral part of passive solar architecture. Interior design can be used as a finetuning tool minimizing many of the problems associated with passive solar energy use in residential buildings. In addition, treatment of interior space in solar model homes may be a prime factor in determining sales success. A new style of interior design is evolving in response to changes in building from incorporating passive solar design features. The psychology behind passive solar architecture is reflected in interiors, and selection of interior components increasingly depends on the functional suitably of various interior elements.

Monitoring of the stability of underground workings in Polish copper mines conditions

NASA Astrophysics Data System (ADS)

Fuławka, Krzysztof; Mertuszka, Piotr; Pytel, Witold

2018-01-01

One of the problems associated with the excavation of deposit in underground mines is the local disturbance in a state of unstable equilibrium results in the sudden release of energy, mainly in the form of roof falls. The scale and intensity of this type of events depends on a number of factors. To minimize the risk of instability occurrence, continuous observations of the roof strata condition are recommended. Different roof strata observation methods used in the Polish copper mines have been analysed within the framework of presented paper. In addition, selected prospective methods, which could significantly increase efficiency of rock fall prevention are presented.

A 40 MWe floating OTEC plant at Punta Tuna, Puerto Rico

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dambly, B.W.

1981-01-01

A development project leading to a closed-cycle ocean thermal energy conversion (OTEC) pilot plant is considered. In connection with this project, a plan was submitted for design, construction, deployment, start-up, and operation of a 40 MWe floating electric generating plant at Punta Tuna, Puerto Rico. Attention is given to the OTEC concept, organizational aspects related to the project, the major problems regarding the OTEC program, and the commercialization plan. Questions of design philosophy are examined, taking into account the need for efficient heat exchangers, the minimization of water flow, the importance of achieving maximized efficiency, and requirements for environmental safety.

Synchronization in neural nets

NASA Technical Reports Server (NTRS)

Vidal, Jacques J.; Haggerty, John

1988-01-01

The paper presents an artificial neural network concept (the Synchronizable Oscillator Networks) where the instants of individual firings in the form of point processes constitute the only form of information transmitted between joining neurons. In the model, neurons fire spontaneously and regularly in the absence of perturbation. When interaction is present, the scheduled firings are advanced or delayed by the firing of neighboring neurons. Networks of such neurons become global oscillators which exhibit multiple synchronizing attractors. From arbitrary initial states, energy minimization learning procedures can make the network converge to oscillatory modes that satisfy multi-dimensional constraints. Such networks can directly represent routing and scheduling problems that consist of ordering sequences of events.

Sheath-Based Rollable Lenticular-Shaped and Low-Stiction Composite Boom

NASA Technical Reports Server (NTRS)

Fernandez, Juan M. (Inventor)

2018-01-01

Various embodiments provide rollable and deployable composite booms that may be used in a wide range of applications both for space and terrestrial structural solutions. Various embodiment composite booms may be bistable, i.e. having a stable strain energy minimum in the coiled configuration as well as the in the deployed configuration. In various embodiments, a boom may be fabricated by aligning two independent tape-springs front-to-front encircled by a durable seamless polymer sleeve. The durable seamless polymer sleeve may allow the two tape-springs to slide past each other during the coiling/deployment process so as to reduce, e.g., minimize, shear and its derived problems.

BetaSCPWeb: side-chain prediction for protein structures using Voronoi diagrams and geometry prioritization

PubMed Central

Ryu, Joonghyun; Lee, Mokwon; Cha, Jehyun; Laskowski, Roman A.; Ryu, Seong Eon; Kim, Deok-Soo

2016-01-01

Many applications, such as protein design, homology modeling, flexible docking, etc. require the prediction of a protein's optimal side-chain conformations from just its amino acid sequence and backbone structure. Side-chain prediction (SCP) is an NP-hard energy minimization problem. Here, we present BetaSCPWeb which efficiently computes a conformation close to optimal using a geometry-prioritization method based on the Voronoi diagram of spherical atoms. Its outputs are visual, textual and PDB file format. The web server is free and open to all users at http://voronoi.hanyang.ac.kr/betascpweb with no login requirement. PMID:27151195

From molecule to solid: The prediction of organic crystal structures

NASA Astrophysics Data System (ADS)

Dzyabchenko, A. V.

2008-10-01

A method for predicting the structure of a molecular crystal based on the systematic search for a global potential energy minimum is considered. The method takes into account unequal occurrences of the structural classes of organic crystals and symmetry of the multidimensional configuration space. The programs of global minimization PMC, comparison of crystal structures CRYCOM, and approximation to the distributions of the electrostatic potentials of molecules FitMEP are presented as tools for numerically solving the problem. Examples of predicted structures substantiated experimentally and the experience of author’s participation in international tests of crystal structure prediction organized by the Cambridge Crystallographic Data Center (Cambridge, UK) are considered.

SUSY: Quo Vadis?

NASA Astrophysics Data System (ADS)

Ross, G. G.

2014-05-01

Given that there is currently no direct evidence for supersymmetric particles at the LHC it is timely to re-evaluate the need for low scale supersymmetry and to ask whether it is likely to be discoverable by the LHC running at its full energy. We review the status of simple SUSY extensions of the Standard Model in the light of the Higgs discovery and the non-observation of evidence for SUSY at the LHC. The need for large radiative corrections to drive the Higgs mass up to 126 GeV and for the coloured SUSY states to be heavy to explain their non-observation introduces a little hierarchy problem and we discuss how to quantify the associated fine tuning. The requirement of low fine tuning requires non-minimal SUSY extensions and we discuss the nature and phenomenology of models which still have perfectly acceptable low fine tuning. A brief discussion of SUSY flavour-changing and CP-violation problems and their resolution is presented.

Stringy Gravity: Solving the Dark Problems at `short' distance

NASA Astrophysics Data System (ADS)

Park, Jeong-Hyuck

2018-01-01

Dictated by Symmetry Principle, string theory predicts not General Relativity but its own gravity which assumes the entire closed string massless sector to be geometric and thus gravitational. In terms of R/(MG), i.e. the dimensionless radial variable normalized by mass, Stringy Gravity agrees with General Relativity toward infinity, but modifies it at short distance. At far short distance, gravitational force can be even repulsive. These may solve the dark matter and energy problems, as they arise essentially from small R/(MG) observations: long distance divided by much heavier mass. We address the pertinent differential geometry for Stringy Gravity, stringy Equivalence Principle, stringy geodesics and the minimal coupling to the Standard Model. We highlight the notion of `doubled-yet-gauged' coordinate system, in which a gauge orbit corresponds to a single physical point and proper distance is defined between two gauge orbits by a path integral.

Communication Measures to Bridge Ten Millennia

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sebeok, Thomas A.

1984-04-01

The Department of Energy created the Human Interference Task Force (HITF) in 1980 to investigate the problems connected with the postclosure, final marking of a filled nuclear waste repository. The task of the HITF is to devise a method of warning future generations not to mine or drill at that site unless they are aware of the consequences of their actions. Since the likelihood of human interference should be minimized for 10,000 years, an effective and long-lasting warning system must be designed. This report is a semiotic analysis of the problem, examining it in terms of the science or theorymore » of messages and symbols. Because of the long period of time involved, the report recommends that a relay system of recoding messages be initiated; that the messages contain a mixture of iconic, indexical, and symbolic elements; and that a high degree of redundancy of messages be employed.« less

Exponentially-Biased Ground-State Sampling of Quantum Annealing Machines with Transverse-Field Driving Hamiltonians

NASA Technical Reports Server (NTRS)

Mandra, Salvatore

2017-01-01

We study the performance of the D-Wave 2X quantum annealing machine on systems with well-controlled ground-state degeneracy. While obtaining the ground state of a spin-glass benchmark instance represents a difficult task, the gold standard for any optimization algorithm or machine is to sample all solutions that minimize the Hamiltonian with more or less equal probability. Our results show that while naive transverse-field quantum annealing on the D-Wave 2X device can find the ground-state energy of the problems, it is not well suited in identifying all degenerate ground-state configurations associated to a particular instance. Even worse, some states are exponentially suppressed, in agreement with previous studies on toy model problems [New J. Phys. 11, 073021 (2009)]. These results suggest that more complex driving Hamiltonians are needed in future quantum annealing machines to ensure a fair sampling of the ground-state manifold.

Non-Boolean computing with nanomagnets for computer vision applications

NASA Astrophysics Data System (ADS)

Bhanja, Sanjukta; Karunaratne, D. K.; Panchumarthy, Ravi; Rajaram, Srinath; Sarkar, Sudeep

2016-02-01

The field of nanomagnetism has recently attracted tremendous attention as it can potentially deliver low-power, high-speed and dense non-volatile memories. It is now possible to engineer the size, shape, spacing, orientation and composition of sub-100 nm magnetic structures. This has spurred the exploration of nanomagnets for unconventional computing paradigms. Here, we harness the energy-minimization nature of nanomagnetic systems to solve the quadratic optimization problems that arise in computer vision applications, which are computationally expensive. By exploiting the magnetization states of nanomagnetic disks as state representations of a vortex and single domain, we develop a magnetic Hamiltonian and implement it in a magnetic system that can identify the salient features of a given image with more than 85% true positive rate. These results show the potential of this alternative computing method to develop a magnetic coprocessor that might solve complex problems in fewer clock cycles than traditional processors.

Dynamics and Stability of Rolling Viscoelastic Tires

DOE Office of Scientific and Technical Information (OSTI.GOV)

Potter, Trevor

2013-04-30

Current steady state rolling tire calculations often do not include treads because treads destroy the rotational symmetry of the tire. We describe two methodologies to compute time periodic solutions of a two-dimensional viscoelastic tire with treads: solving a minimization problem and solving a system of equations. We also expand on work by Oden and Lin on free spinning rolling elastic tires in which they disovered a hierachy of N-peak steady state standing wave solutions. In addition to discovering a two-dimensional hierarchy of standing wave solutions that includes their N-peak hiearchy, we consider the eects of viscoelasticity on the standing wavemore » solutions. Finally, a commonplace model of viscoelasticity used in our numerical experiments led to non-physical elastic energy growth for large tire speeds. We show that a viscoelastic model of Govindjee and Reese remedies the problem.« less

Introducing PROFESS 2.0: A parallelized, fully linear scaling program for orbital-free density functional theory calculations

NASA Astrophysics Data System (ADS)

Hung, Linda; Huang, Chen; Shin, Ilgyou; Ho, Gregory S.; Lignères, Vincent L.; Carter, Emily A.

2010-12-01

Orbital-free density functional theory (OFDFT) is a first principles quantum mechanics method to find the ground-state energy of a system by variationally minimizing with respect to the electron density. No orbitals are used in the evaluation of the kinetic energy (unlike Kohn-Sham DFT), and the method scales nearly linearly with the size of the system. The PRinceton Orbital-Free Electronic Structure Software (PROFESS) uses OFDFT to model materials from the atomic scale to the mesoscale. This new version of PROFESS allows the study of larger systems with two significant changes: PROFESS is now parallelized, and the ion-electron and ion-ion terms scale quasilinearly, instead of quadratically as in PROFESS v1 (L. Hung and E.A. Carter, Chem. Phys. Lett. 475 (2009) 163). At the start of a run, PROFESS reads the various input files that describe the geometry of the system (ion positions and cell dimensions), the type of elements (defined by electron-ion pseudopotentials), the actions you want it to perform (minimize with respect to electron density and/or ion positions and/or cell lattice vectors), and the various options for the computation (such as which functionals you want it to use). Based on these inputs, PROFESS sets up a computation and performs the appropriate optimizations. Energies, forces, stresses, material geometries, and electron density configurations are some of the values that can be output throughout the optimization. New version program summaryProgram Title: PROFESS Catalogue identifier: AEBN_v2_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEBN_v2_0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: Standard CPC licence, http://cpc.cs.qub.ac.uk/licence/licence.html No. of lines in distributed program, including test data, etc.: 68 721 No. of bytes in distributed program, including test data, etc.: 1 708 547 Distribution format: tar.gz Programming language: Fortran 90 Computer: Intel with ifort; AMD Opteron with pathf90 Operating system: Linux Has the code been vectorized or parallelized?: Yes. Parallelization is implemented through domain composition using MPI. RAM: Problem dependent, but 2 GB is sufficient for up to 10,000 ions. Classification: 7.3 External routines: FFTW 2.1.5 ( http://www.fftw.org) Catalogue identifier of previous version: AEBN_v1_0 Journal reference of previous version: Comput. Phys. Comm. 179 (2008) 839 Does the new version supersede the previous version?: Yes Nature of problem: Given a set of coordinates describing the initial ion positions under periodic boundary conditions, recovers the ground state energy, electron density, ion positions, and cell lattice vectors predicted by orbital-free density functional theory. The computation of all terms is effectively linear scaling. Parallelization is implemented through domain decomposition, and up to ˜10,000 ions may be included in the calculation on just a single processor, limited by RAM. For example, when optimizing the geometry of ˜50,000 aluminum ions (plus vacuum) on 48 cores, a single iteration of conjugate gradient ion geometry optimization takes ˜40 minutes wall time. However, each CG geometry step requires two or more electron density optimizations, so step times will vary. Solution method: Computes energies as described in text; minimizes this energy with respect to the electron density, ion positions, and cell lattice vectors. Reasons for new version: To allow much larger systems to be simulated using PROFESS. Restrictions: PROFESS cannot use nonlocal (such as ultrasoft) pseudopotentials. A variety of local pseudopotential files are available at the Carter group website ( http://www.princeton.edu/mae/people/faculty/carter/homepage/research/localpseudopotentials/). Also, due to the current state of the kinetic energy functionals, PROFESS is only reliable for main group metals and some properties of semiconductors. Running time: Problem dependent: the test example provided with the code takes less than a second to run. Timing results for large scale problems are given in the PROFESS paper and Ref. [1].

10 CFR 20.1406 - Minimization of contamination.

Code of Federal Regulations, 2012 CFR

2012-01-01

... 10 Energy 1 2012-01-01 2012-01-01 false Minimization of contamination. 20.1406 Section 20.1406 Energy NUCLEAR REGULATORY COMMISSION STANDARDS FOR PROTECTION AGAINST RADIATION Radiological Criteria for..., including the subsurface, in accordance with the existing radiation protection requirements in Subpart B and...

Site-directed protein recombination as a shortest-path problem.

PubMed

Endelman, Jeffrey B; Silberg, Jonathan J; Wang, Zhen-Gang; Arnold, Frances H

2004-07-01

Protein function can be tuned using laboratory evolution, in which one rapidly searches through a library of proteins for the properties of interest. In site-directed recombination, n crossovers are chosen in an alignment of p parents to define a set of p(n + 1) peptide fragments. These fragments are then assembled combinatorially to create a library of p(n+1) proteins. We have developed a computational algorithm to enrich these libraries in folded proteins while maintaining an appropriate level of diversity for evolution. For a given set of parents, our algorithm selects crossovers that minimize the average energy of the library, subject to constraints on the length of each fragment. This problem is equivalent to finding the shortest path between nodes in a network, for which the global minimum can be found efficiently. Our algorithm has a running time of O(N(3)p(2) + N(2)n) for a protein of length N. Adjusting the constraints on fragment length generates a set of optimized libraries with varying degrees of diversity. By comparing these optima for different sets of parents, we rapidly determine which parents yield the lowest energy libraries.

INFO-RNA--a fast approach to inverse RNA folding.

PubMed

Busch, Anke; Backofen, Rolf

2006-08-01

The structure of RNA molecules is often crucial for their function. Therefore, secondary structure prediction has gained much interest. Here, we consider the inverse RNA folding problem, which means designing RNA sequences that fold into a given structure. We introduce a new algorithm for the inverse folding problem (INFO-RNA) that consists of two parts; a dynamic programming method for good initial sequences and a following improved stochastic local search that uses an effective neighbor selection method. During the initialization, we design a sequence that among all sequences adopts the given structure with the lowest possible energy. For the selection of neighbors during the search, we use a kind of look-ahead of one selection step applying an additional energy-based criterion. Afterwards, the pre-ordered neighbors are tested using the actual optimization criterion of minimizing the structure distance between the target structure and the mfe structure of the considered neighbor. We compared our algorithm to RNAinverse and RNA-SSD for artificial and biological test sets. Using INFO-RNA, we performed better than RNAinverse and in most cases, we gained better results than RNA-SSD, the probably best inverse RNA folding tool on the market. www.bioinf.uni-freiburg.de?Subpages/software.html.

How to pass information and deliver energy to a network of implantable devices within the human body.

PubMed

Sun, Mingui; Hackworth, Steven A; Tang, Zhide; Gilbert, Gary; Cardin, Sylvain; Sclabassi, Robert J

2007-01-01

It has been envisioned that a body network can be built to collect data from, and transport information to, implanted miniature devices at multiple sites within the human body. Currently, two problems of utmost importance remain unsolved: 1) how to link information between a pair of implants at a distance? and 2) how to provide electric power to these implants allowing them to function and communicate? In this paper, we present new solutions to these problems by minimizing the intra-body communication distances. We show that, based on a study of human anatomy, the maximum distance from the body surface to the deepest point inside the body is approximately 15 cm. This finding provides an upper bound for the lengths of communication pathways required to reach the body's interior. We also show that these pathways do not have to cross any joins within the body. In order to implement the envisioned body network, we present the design of a new device, called an energy pad. This small-size, light-weight device can easily interface with the skin to perform data communication with, and supply power to, miniature implants.

Solid recovered fuel production from biodegradable waste in grain processing industry.

PubMed

Kliopova, Irina; Staniskis, Jurgis Kazimieras; Petraskiene, Violeta

2013-04-01

Management of biodegradable waste is one of the most important environmental problems in the grain-processing industry since this waste cannot be dumped anymore due to legal requirements. Biodegradable waste is generated in each stage of grain processing, including the waste-water and air emissions treatment processes. Their management causes some environmental and financial problems. The majority of Lithuanian grain-processing enterprises own and operate composting sites, but in Lithuania the demand for compost is not given. This study focused on the analysis of the possibility of using biodegradable waste for the production of solid recovered fuel, as a local renewable fuel with the purpose of increasing environmental performance and decreasing the direct costs of grain processing. Experimental research with regard to a pilot grain-processing plant has proven that alternative fuel production will lead to minimizing of the volume of biodegradable waste by 75% and the volume of natural gas for heat energy production by 62%. Environmental indicators of grain processing, laboratory analysis of the chemical and physical characteristics of biodegradable waste, mass and energy balances of the solid recovered fuel production, environmental and economical benefits of the project are presented and discussed herein.

Health effects from indoor air pollution: case studies.

PubMed

White, L E; Clarkson, J R; Chang, S N

1987-01-01

In recent years there has been a growing awareness of the health effects associated with the presence of contaminants in indoor air. Numerous agents can accumulate in public buildings, homes and automobiles as a result of ongoing activities that normally occur in these closed spaces. Ventilation is a major factor in the control of indoor air pollutants since proper movement of air can prevent or minimize the build up of compounds in buildings. The recent emphasis on energy conservation has lead to measures which economize on energy for heating and air conditioning, but which also trap pollutants within a building. Three cases of indoor air pollution were investigated. A typical investigation of indoor air pollutant problems includes the following: interviews with building occupants; history of the building with regard to maintenance, pesticide treatment, etc.; a survey of the building and ventilation; and when warranted, sampling and analysis of air. Each case presented is unique in that atypical situations caused agents to accumulate in a building or section of a building. The indoor air problems in these cases were solved by identifying and removing the source of the offending agent and/or improving the ventilation in the building.

What's wrong, what's ahead, how do we make it right?

PubMed

1988-03-01

The "Environmental Perspective" of the UN outlines 6 major problem areas and suggests what should be done about them. 1) Overpopulation exceeds the carrying capacity of the environment. Specific attention should be given to the problems of cities and public works projects should be designed to provide employment and improve the environment. 2) Food shortages must be dealt with to ensure security and restore the environment. Governments must adopt policies and institute regulatory measures for land and water use. 3) The available energy resources are being consumed at vastly different rates throughout the world. Policies should be devised to more equitably meet energy demands without further increasing the costs to the environment. 4) Industrial development is damaging the environment, and government policies, especially in developing countries, must be geared to minimizing waste of resources and increasing pollution. 5) Inadequate housing and public health services are causing high morbidity and mortality in many areas. Programs must be developed to deal with tropical diseases and unsanitary conditions. 6) International economic relations often adversely affect development. Aid to developing countries must be increased and trade patterns developed to mutual advantage and to safeguard the environment.

ON THE MINIMAL ACCURACY REQUIRED FOR SIMULATING SELF-GRAVITATING SYSTEMS BY MEANS OF DIRECT N-BODY METHODS

DOE Office of Scientific and Technical Information (OSTI.GOV)

Portegies Zwart, Simon; Boekholt, Tjarda

2014-04-10

The conservation of energy, linear momentum, and angular momentum are important drivers of our physical understanding of the evolution of the universe. These quantities are also conserved in Newton's laws of motion under gravity. Numerical integration of the associated equations of motion is extremely challenging, in particular due to the steady growth of numerical errors (by round-off and discrete time-stepping and the exponential divergence between two nearby solutions. As a result, numerical solutions to the general N-body problem are intrinsically questionable. Using brute force integrations to arbitrary numerical precision we demonstrate empirically that ensembles of different realizations of resonant three-bodymore » interactions produce statistically indistinguishable results. Although individual solutions using common integration methods are notoriously unreliable, we conjecture that an ensemble of approximate three-body solutions accurately represents an ensemble of true solutions, so long as the energy during integration is conserved to better than 1/10. We therefore provide an independent confirmation that previous work on self-gravitating systems can actually be trusted, irrespective of the intrinsically chaotic nature of the N-body problem.« less

Evaluation of Brief Group-Administered Instruction for Parents to Prevent or Minimize Sleep Problems in Young Children with Down Syndrome

ERIC Educational Resources Information Center

Stores, Rebecca; Stores, Gregory

2004-01-01

Background: The study concerns the unknown value of group instruction for mothers of young children with Down syndrome (DS) in preventing or minimizing sleep problems. Method: (1) Children with DS were randomly allocated to an Instruction group (given basic information about children's sleep) and a Control group for later comparison including…

Does Self-Help Increase Rates of Help Seeking for Student Mental Health Problems by Minimizing Stigma as a Barrier?

ERIC Educational Resources Information Center

Levin, Michael E.; Krafft, Jennifer; Levin, Crissa

2018-01-01

Objective: This study examined whether self-help (books, websites, mobile apps) increases help seeking for mental health problems among college students by minimizing stigma as a barrier. Participants and Methods: A survey was conducted with 200 college students reporting elevated distress from February to April 2017. Results: Intentions to use…

Fast Algorithms for Designing Unimodular Waveform(s) With Good Correlation Properties

NASA Astrophysics Data System (ADS)

Li, Yongzhe; Vorobyov, Sergiy A.

2018-03-01

In this paper, we develop new fast and efficient algorithms for designing single/multiple unimodular waveforms/codes with good auto- and cross-correlation or weighted correlation properties, which are highly desired in radar and communication systems. The waveform design is based on the minimization of the integrated sidelobe level (ISL) and weighted ISL (WISL) of waveforms. As the corresponding optimization problems can quickly grow to large scale with increasing the code length and number of waveforms, the main issue turns to be the development of fast large-scale optimization techniques. The difficulty is also that the corresponding optimization problems are non-convex, but the required accuracy is high. Therefore, we formulate the ISL and WISL minimization problems as non-convex quartic optimization problems in frequency domain, and then simplify them into quadratic problems by utilizing the majorization-minimization technique, which is one of the basic techniques for addressing large-scale and/or non-convex optimization problems. While designing our fast algorithms, we find out and use inherent algebraic structures in the objective functions to rewrite them into quartic forms, and in the case of WISL minimization, to derive additionally an alternative quartic form which allows to apply the quartic-quadratic transformation. Our algorithms are applicable to large-scale unimodular waveform design problems as they are proved to have lower or comparable computational burden (analyzed theoretically) and faster convergence speed (confirmed by comprehensive simulations) than the state-of-the-art algorithms. In addition, the waveforms designed by our algorithms demonstrate better correlation properties compared to their counterparts.

Distributed query plan generation using multiobjective genetic algorithm.

PubMed

Panicker, Shina; Kumar, T V Vijay

2014-01-01

A distributed query processing strategy, which is a key performance determinant in accessing distributed databases, aims to minimize the total query processing cost. One way to achieve this is by generating efficient distributed query plans that involve fewer sites for processing a query. In the case of distributed relational databases, the number of possible query plans increases exponentially with respect to the number of relations accessed by the query and the number of sites where these relations reside. Consequently, computing optimal distributed query plans becomes a complex problem. This distributed query plan generation (DQPG) problem has already been addressed using single objective genetic algorithm, where the objective is to minimize the total query processing cost comprising the local processing cost (LPC) and the site-to-site communication cost (CC). In this paper, this DQPG problem is formulated and solved as a biobjective optimization problem with the two objectives being minimize total LPC and minimize total CC. These objectives are simultaneously optimized using a multiobjective genetic algorithm NSGA-II. Experimental comparison of the proposed NSGA-II based DQPG algorithm with the single objective genetic algorithm shows that the former performs comparatively better and converges quickly towards optimal solutions for an observed crossover and mutation probability.

Distributed Query Plan Generation Using Multiobjective Genetic Algorithm

PubMed Central

Panicker, Shina; Vijay Kumar, T. V.

2014-01-01

A distributed query processing strategy, which is a key performance determinant in accessing distributed databases, aims to minimize the total query processing cost. One way to achieve this is by generating efficient distributed query plans that involve fewer sites for processing a query. In the case of distributed relational databases, the number of possible query plans increases exponentially with respect to the number of relations accessed by the query and the number of sites where these relations reside. Consequently, computing optimal distributed query plans becomes a complex problem. This distributed query plan generation (DQPG) problem has already been addressed using single objective genetic algorithm, where the objective is to minimize the total query processing cost comprising the local processing cost (LPC) and the site-to-site communication cost (CC). In this paper, this DQPG problem is formulated and solved as a biobjective optimization problem with the two objectives being minimize total LPC and minimize total CC. These objectives are simultaneously optimized using a multiobjective genetic algorithm NSGA-II. Experimental comparison of the proposed NSGA-II based DQPG algorithm with the single objective genetic algorithm shows that the former performs comparatively better and converges quickly towards optimal solutions for an observed crossover and mutation probability. PMID:24963513

New additions to the ClusPro server motivated by CAPRI.

PubMed

Vajda, Sandor; Yueh, Christine; Beglov, Dmitri; Bohnuud, Tanggis; Mottarella, Scott E; Xia, Bing; Hall, David R; Kozakov, Dima

2017-03-01

The heavily used protein-protein docking server ClusPro performs three computational steps as follows: (1) rigid body docking, (2) RMSD based clustering of the 1000 lowest energy structures, and (3) the removal of steric clashes by energy minimization. In response to challenges encountered in recent CAPRI targets, we added three new options to ClusPro. These are (1) accounting for small angle X-ray scattering data in docking; (2) considering pairwise interaction data as restraints; and (3) enabling discrimination between biological and crystallographic dimers. In addition, we have developed an extremely fast docking algorithm based on 5D rotational manifold FFT, and an algorithm for docking flexible peptides that include known sequence motifs. We feel that these developments will further improve the utility of ClusPro. However, CAPRI emphasized several shortcomings of the current server, including the problem of selecting the right energy parameters among the five options provided, and the problem of selecting the best models among the 10 generated for each parameter set. In addition, results convinced us that further development is needed for docking homology models. Finally, we discuss the difficulties we have encountered when attempting to develop a refinement algorithm that would be computationally efficient enough for inclusion in a heavily used server. Proteins 2017; 85:435-444. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

A TV-constrained decomposition method for spectral CT

NASA Astrophysics Data System (ADS)

Guo, Xiaoyue; Zhang, Li; Xing, Yuxiang

2017-03-01

Spectral CT is attracting more and more attention in medicine, industrial nondestructive testing and security inspection field. Material decomposition is an important issue to a spectral CT to discriminate materials. Because of the spectrum overlap of energy channels, as well as the correlation of basis functions, it is well acknowledged that decomposition step in spectral CT imaging causes noise amplification and artifacts in component coefficient images. In this work, we propose materials decomposition via an optimization method to improve the quality of decomposed coefficient images. On the basis of general optimization problem, total variance minimization is constrained on coefficient images in our overall objective function with adjustable weights. We solve this constrained optimization problem under the framework of ADMM. Validation on both a numerical dental phantom in simulation and a real phantom of pig leg on a practical CT system using dual-energy imaging is executed. Both numerical and physical experiments give visually obvious better reconstructions than a general direct inverse method. SNR and SSIM are adopted to quantitatively evaluate the image quality of decomposed component coefficients. All results demonstrate that the TV-constrained decomposition method performs well in reducing noise without losing spatial resolution so that improving the image quality. The method can be easily incorporated into different types of spectral imaging modalities, as well as for cases with energy channels more than two.

Dissociative conceptual and quantitative problem solving outcomes across interactive engagement and traditional format introductory physics

NASA Astrophysics Data System (ADS)

McDaniel, Mark A.; Stoen, Siera M.; Frey, Regina F.; Markow, Zachary E.; Hynes, K. Mairin; Zhao, Jiuqing; Cahill, Michael J.

2016-12-01

The existing literature indicates that interactive-engagement (IE) based general physics classes improve conceptual learning relative to more traditional lecture-oriented classrooms. Very little research, however, has examined quantitative problem-solving outcomes from IE based relative to traditional lecture-based physics classes. The present study included both pre- and post-course conceptual-learning assessments and a new quantitative physics problem-solving assessment that included three representative conservation of energy problems from a first-semester calculus-based college physics course. Scores for problem translation, plan coherence, solution execution, and evaluation of solution plausibility were extracted for each problem. Over 450 students in three IE-based sections and two traditional lecture sections taught at the same university during the same semester participated. As expected, the IE-based course produced more robust gains on a Force Concept Inventory than did the lecture course. By contrast, when the full sample was considered, gains in quantitative problem solving were significantly greater for lecture than IE-based physics; when students were matched on pre-test scores, there was still no advantage for IE-based physics on gains in quantitative problem solving. Further, the association between performance on the concept inventory and quantitative problem solving was minimal. These results highlight that improved conceptual understanding does not necessarily support improved quantitative physics problem solving, and that the instructional method appears to have less bearing on gains in quantitative problem solving than does the kinds of problems emphasized in the courses and homework and the overlap of these problems to those on the assessment.

A videoscope for use in minimally invasive periodontal surgery.

PubMed

Harrel, Stephen K; Wilson, Thomas G; Rivera-Hidalgo, Francisco

2013-09-01

Minimally invasive periodontal procedures have been reported to produce excellent clinical results. Visualization during minimally invasive procedures has traditionally been obtained by the use of surgical telescopes, surgical microscopes, glass fibre endoscopes or a combination of these devices. All of these methods for visualization are less than fully satisfactory due to problems with access, magnification and blurred imaging. A videoscope for use with minimally invasive periodontal procedures has been developed to overcome some of the difficulties that exist with current visualization approaches. This videoscope incorporates a gas shielding technology that eliminates the problems of fogging and fouling of the optics of the videoscope that has previously prevented the successful application of endoscopic visualization to periodontal surgery. In addition, as part of the gas shielding technology the videoscope also includes a moveable retractor specifically adapted for minimally invasive surgery. The clinical use of the videoscope during minimally invasive periodontal surgery is demonstrated and discussed. The videoscope with gas shielding alleviates many of the difficulties associated with visualization during minimally invasive periodontal surgery. © 2013 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Optimal mistuning for enhanced aeroelastic stability of transonic fans

NASA Technical Reports Server (NTRS)

Hall, K. C.; Crawley, E. F.

1983-01-01

An inverse design procedure was developed for the design of a mistuned rotor. The design requirements are that the stability margin of the eigenvalues of the aeroelastic system be greater than or equal to some minimum stability margin, and that the mass added to each blade be positive. The objective was to achieve these requirements with a minimal amount of mistuning. Hence, the problem was posed as a constrained optimization problem. The constrained minimization problem was solved by the technique of mathematical programming via augmented Lagrangians. The unconstrained minimization phase of this technique was solved by the variable metric method. The bladed disk was modelled as being composed of a rigid disk mounted on a rigid shaft. Each of the blades were modelled with a single tosional degree of freedom.

Minimizing communication cost among distributed controllers in software defined networks

NASA Astrophysics Data System (ADS)

Arlimatti, Shivaleela; Elbreiki, Walid; Hassan, Suhaidi; Habbal, Adib; Elshaikh, Mohamed

2016-08-01

Software Defined Networking (SDN) is a new paradigm to increase the flexibility of today's network by promising for a programmable network. The fundamental idea behind this new architecture is to simplify network complexity by decoupling control plane and data plane of the network devices, and by making the control plane centralized. Recently controllers have distributed to solve the problem of single point of failure, and to increase scalability and flexibility during workload distribution. Even though, controllers are flexible and scalable to accommodate more number of network switches, yet the problem of intercommunication cost between distributed controllers is still challenging issue in the Software Defined Network environment. This paper, aims to fill the gap by proposing a new mechanism, which minimizes intercommunication cost with graph partitioning algorithm, an NP hard problem. The methodology proposed in this paper is, swapping of network elements between controller domains to minimize communication cost by calculating communication gain. The swapping of elements minimizes inter and intra communication cost among network domains. We validate our work with the OMNeT++ simulation environment tool. Simulation results show that the proposed mechanism minimizes the inter domain communication cost among controllers compared to traditional distributed controllers.

The Movable Type Method Applied to Protein-Ligand Binding.

PubMed

Zheng, Zheng; Ucisik, Melek N; Merz, Kenneth M

2013-12-10

Accurately computing the free energy for biological processes like protein folding or protein-ligand association remains a challenging problem. Both describing the complex intermolecular forces involved and sampling the requisite configuration space make understanding these processes innately difficult. Herein, we address the sampling problem using a novel methodology we term "movable type". Conceptually it can be understood by analogy with the evolution of printing and, hence, the name movable type. For example, a common approach to the study of protein-ligand complexation involves taking a database of intact drug-like molecules and exhaustively docking them into a binding pocket. This is reminiscent of early woodblock printing where each page had to be laboriously created prior to printing a book. However, printing evolved to an approach where a database of symbols (letters, numerals, etc.) was created and then assembled using a movable type system, which allowed for the creation of all possible combinations of symbols on a given page, thereby, revolutionizing the dissemination of knowledge. Our movable type (MT) method involves the identification of all atom pairs seen in protein-ligand complexes and then creating two databases: one with their associated pairwise distant dependent energies and another associated with the probability of how these pairs can combine in terms of bonds, angles, dihedrals and non-bonded interactions. Combining these two databases coupled with the principles of statistical mechanics allows us to accurately estimate binding free energies as well as the pose of a ligand in a receptor. This method, by its mathematical construction, samples all of configuration space of a selected region (the protein active site here) in one shot without resorting to brute force sampling schemes involving Monte Carlo, genetic algorithms or molecular dynamics simulations making the methodology extremely efficient. Importantly, this method explores the free energy surface eliminating the need to estimate the enthalpy and entropy components individually. Finally, low free energy structures can be obtained via a free energy minimization procedure yielding all low free energy poses on a given free energy surface. Besides revolutionizing the protein-ligand docking and scoring problem this approach can be utilized in a wide range of applications in computational biology which involve the computation of free energies for systems with extensive phase spaces including protein folding, protein-protein docking and protein design.

Adoption of waste minimization technology to benefit electroplaters

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ching, E.M.K.; Li, C.P.H.; Yu, C.M.K.

Because of increasingly stringent environmental legislation and enhanced environmental awareness, electroplaters in Hong Kong are paying more heed to protect the environment. To comply with the array of environmental controls, electroplaters can no longer rely solely on the end-of-pipe approach as a means for abating their pollution problems under the particular local industrial environment. The preferred approach is to adopt waste minimization measures that yield both economic and environmental benefits. This paper gives an overview of electroplating activities in Hong Kong, highlights their characteristics, and describes the pollution problems associated with conventional electroplating operations. The constraints of using pollution controlmore » measures to achieve regulatory compliance are also discussed. Examples and case studies are given on some low-cost waste minimization techniques readily available to electroplaters, including dragout minimization and water conservation techniques. Recommendations are given as to how electroplaters can adopt and exercise waste minimization techniques in their operations. 1 tab.« less

Adaptive Connectivity Restoration from Node Failure(s) in Wireless Sensor Networks

PubMed Central

Wang, Huaiyuan; Ding, Xu; Huang, Cheng; Wu, Xiaobei

2016-01-01

Recently, there is a growing interest in the applications of wireless sensor networks (WSNs). A set of sensor nodes is deployed in order to collectively survey an area of interest and/or perform specific surveillance tasks in some of the applications, such as battlefield reconnaissance. Due to the harsh deployment environments and limited energy supply, nodes may fail, which impacts the connectivity of the whole network. Since a single node failure (cut-vertex) will destroy the connectivity and divide the network into disjoint blocks, most of the existing studies focus on the problem of single node failure. However, the failure of multiple nodes would be a disaster to the whole network and must be repaired effectively. Only few studies are proposed to handle the problem of multiple cut-vertex failures, which is a special case of multiple node failures. Therefore, this paper proposes a comprehensive solution to address the problems of node failure (single and multiple). Collaborative Single Node Failure Restoration algorithm (CSFR) is presented to solve the problem of single node failure only with cooperative communication, but CSFR-M, which is the extension of CSFR, handles the single node failure problem more effectively with node motion. Moreover, Collaborative Connectivity Restoration Algorithm (CCRA) is proposed on the basis of cooperative communication and node maneuverability to restore network connectivity after multiple nodes fail. CSFR-M and CCRA are reactive methods that initiate the connectivity restoration after detecting the node failure(s). In order to further minimize the energy dissipation, CCRA opts to simplify the recovery process by gridding. Moreover, the distance that an individual node needs to travel during recovery is reduced by choosing the nearest suitable candidates. Finally, extensive simulations validate the performance of CSFR, CSFR-M and CCRA. PMID:27690030

A parallel process growth mixture model of conduct problems and substance use with risky sexual behavior.

PubMed

Wu, Johnny; Witkiewitz, Katie; McMahon, Robert J; Dodge, Kenneth A

2010-10-01

Conduct problems, substance use, and risky sexual behavior have been shown to coexist among adolescents, which may lead to significant health problems. The current study was designed to examine relations among these problem behaviors in a community sample of children at high risk for conduct disorder. A latent growth model of childhood conduct problems showed a decreasing trend from grades K to 5. During adolescence, four concurrent conduct problem and substance use trajectory classes were identified (high conduct problems and high substance use, increasing conduct problems and increasing substance use, minimal conduct problems and increasing substance use, and minimal conduct problems and minimal substance use) using a parallel process growth mixture model. Across all substances (tobacco, binge drinking, and marijuana use), higher levels of childhood conduct problems during kindergarten predicted a greater probability of classification into more problematic adolescent trajectory classes relative to less problematic classes. For tobacco and binge drinking models, increases in childhood conduct problems over time also predicted a greater probability of classification into more problematic classes. For all models, individuals classified into more problematic classes showed higher proportions of early sexual intercourse, infrequent condom use, receiving money for sexual services, and ever contracting an STD. Specifically, tobacco use and binge drinking during early adolescence predicted higher levels of sexual risk taking into late adolescence. Results highlight the importance of studying the conjoint relations among conduct problems, substance use, and risky sexual behavior in a unified model. Copyright © 2010 Elsevier Ireland Ltd. All rights reserved.

Energy conservation through sealing technology

NASA Technical Reports Server (NTRS)

Stair, W. K.; Ludwig, L. P.

1978-01-01

Improvements in fluid film sealing resulting from a proposed research program could lead to an annual energy saving, on a national basis, equivalent to about 37 million bbl of oil or 0.3% of the total U.S. energy consumption. Further, the application of known sealing technology can result in an annual saving of an additional 10 million bbl of oil. The energy saving would be accomplished by reduction in process heat energy loss, reduction of frictional energy generated, and minimization of energy required to operate ancillary equipment associated with the seal system. In addition to energy saving, cost effectiveness is further enhanced by reduction in maintenance and in minimization of equipment for collecting leakage and for meeting environmental pollution standards.

Extrema principles of entrophy production and energy dissipation in fluid mechanics

NASA Technical Reports Server (NTRS)

Horne, W. Clifton; Karamcheti, Krishnamurty

1988-01-01

A survey is presented of several extrema principles of energy dissipation as applied to problems in fluid mechanics. An exact equation is derived for the dissipation function of a homogeneous, isotropic, Newtonian fluid, with terms associated with irreversible compression or expansion, wave radiation, and the square of the vorticity. By using entropy extrema principles, simple flows such as the incompressible channel flow and the cylindrical vortex are identified as minimal dissipative distributions. The principal notions of stability of parallel shear flows appears to be associated with a maximum dissipation condition. These different conditions are consistent with Prigogine's classification of thermodynamic states into categories of equilibrium, linear nonequilibrium, and nonlinear nonequilibrium thermodynamics; vortices and acoustic waves appear as examples of dissipative structures. The measurements of a typical periodic shear flow, the rectangular wall jet, show that direct measurements of the dissipative terms are possible.

Tilted-axis wobbling in odd-mass nuclei

NASA Astrophysics Data System (ADS)

Budaca, R.

2018-02-01

A triaxial rotor Hamiltonian with a rigidly aligned high-j quasiparticle is treated by a time-dependent variational principle, using angular momentum coherent states. The resulting classical energy function has three unique critical points in a space of generalized conjugate coordinates, which can minimize the energy for specific ordering of the inertial parameters and a fixed angular momentum state. Because of the symmetry of the problem, there are only two unique solutions, corresponding to wobbling motion around a principal axis and, respectively, a tilted axis. The wobbling frequencies are obtained after a quantization procedure and then used to calculate E 2 and M 1 transition probabilities. The analytical results are employed in the study of the wobbling excitations of 135Pr nucleus, which is found to undergo a transition from low angular momentum transverse wobbling around a principal axis toward a tilted-axis wobbling at higher angular momentum.

Free-viewpoint video of human actors using multiple handheld Kinects.

PubMed

Ye, Genzhi; Liu, Yebin; Deng, Yue; Hasler, Nils; Ji, Xiangyang; Dai, Qionghai; Theobalt, Christian

2013-10-01

We present an algorithm for creating free-viewpoint video of interacting humans using three handheld Kinect cameras. Our method reconstructs deforming surface geometry and temporal varying texture of humans through estimation of human poses and camera poses for every time step of the RGBZ video. Skeletal configurations and camera poses are found by solving a joint energy minimization problem, which optimizes the alignment of RGBZ data from all cameras, as well as the alignment of human shape templates to the Kinect data. The energy function is based on a combination of geometric correspondence finding, implicit scene segmentation, and correspondence finding using image features. Finally, texture recovery is achieved through jointly optimization on spatio-temporal RGB data using matrix completion. As opposed to previous methods, our algorithm succeeds on free-viewpoint video of human actors under general uncontrolled indoor scenes with potentially dynamic background, and it succeeds even if the cameras are moving.

Assessing CO2 Mitigation Options Utilizing Detailed Electricity Characteristics and Including Renewable Generation

NASA Astrophysics Data System (ADS)

Bensaida, K.; Alie, Colin; Elkamel, A.; Almansoori, A.

2017-08-01

This paper presents a novel techno-economic optimization model for assessing the effectiveness of CO2 mitigation options for the electricity generation sub-sector that includes renewable energy generation. The optimization problem was formulated as a MINLP model using the GAMS modeling system. The model seeks the minimization of the power generation costs under CO2 emission constraints by dispatching power from low CO2 emission-intensity units. The model considers the detailed operation of the electricity system to effectively assess the performance of GHG mitigation strategies and integrates load balancing, carbon capture and carbon taxes as methods for reducing CO2 emissions. Two case studies are discussed to analyze the benefits and challenges of the CO2 reduction methods in the electricity system. The proposed mitigations options would not only benefit the environment, but they will as well improve the marginal cost of producing energy which represents an advantage for stakeholders.

Synthesis of Nano-Crystalline Gamma-TiAl Materials

NASA Technical Reports Server (NTRS)

Hales, Stephen J.; Vasquez, Peter

2003-01-01

One of the principal problems with nano-crystalline materials is producing them in quantities and sizes large enough for valid mechanical property evaluation. The purpose of this study was to explore an innovative method for producing nano-crystalline gamma-TiAl bulk materials using high energy ball milling and brief secondary processes. Nano-crystalline powder feedstock was produced using a Fritsch P4(TM) vario-planetary ball mill recently installed at NASA-LaRC. The high energy ball milling process employed tungsten carbide tooling (vials and balls) and no process control agents to minimize contamination. In a collaborative effort, two approaches were investigated, namely mechanical alloying of elemental powders and attrition milling of pre-alloyed powders. The objective was to subsequently use RF plasma spray deposition and short cycle vacuum hot pressing in order to effect consolidation while retaining nano-crystalline structure in bulk material. Results and discussion of the work performed to date are presented.

Heuristic Approach for Configuration of a Grid-Tied Microgrid in Puerto Rico

NASA Astrophysics Data System (ADS)

Rodriguez, Miguel A.

The high rates of cost of electricity that consumers are being charged by the utility grid in Puerto Rico have created an energy crisis around the island. This situation is due to the island's dependence on imported fossil fuels. In order to aid in the transition from fossil-fuel based electricity into electricity from renewable and alternative sources, this research work focuses on reducing the cost of electricity for Puerto Rico through means of finding the optimal microgrid configuration for a set number of consumers from the residential sector. The Hybrid Optimization Modeling for Energy Renewables (HOMER) software, developed by NREL, is utilized as an aid in determining the optimal microgrid setting. The problem is also approached via convex optimization; specifically, an objective function C(t) is formulated in order to be minimized. The cost function depends on the energy supplied by the grid, the energy supplied by renewable sources, the energy not supplied due to outages, as well as any excess energy sold to the utility in a yearly manner. A term for considering the social cost of carbon is also considered in the cost function. Once the microgrid settings from HOMER are obtained, those are evaluated via the optimized function C( t), which will in turn assess the true optimality of the microgrid configuration. A microgrid to supply 10 consumers is considered; each consumer can possess a different microgrid configuration. The cost function C( t) is minimized, and the Net Present Value and Cost of Electricity are computed for each configuration, in order to assess the true feasibility. Results show that the greater the penetration of components into the microgrid, the greater the energy produced by the renewable sources in the microgrid, the greater the energy not supplied due to outages. The proposed method demonstrates that adding large amounts of renewable components in a microgrid does not necessarily translates into economic benefits for the consumer; in fact, there is a trade back between cost and addition of elements that must be considered. Any configurations which consider further increases in microgrid components will result in increased NPV and increased costs of electricity, which deem the configurations as unfeasible.

NP-hardness of the cluster minimization problem revisited

NASA Astrophysics Data System (ADS)

Adib, Artur B.

2005-10-01

The computational complexity of the 'cluster minimization problem' is revisited (Wille and Vennik 1985 J. Phys. A: Math. Gen. 18 L419). It is argued that the original NP-hardness proof does not apply to pairwise potentials of physical interest, such as those that depend on the geometric distance between the particles. A geometric analogue of the original problem is formulated, and a new proof for such potentials is provided by polynomial time transformation from the independent set problem for unit disk graphs. Limitations of this formulation are pointed out, and new subproblems that bear more direct consequences to the numerical study of clusters are suggested.

Nonconvex Nonsmooth Low Rank Minimization via Iteratively Reweighted Nuclear Norm.

PubMed

Lu, Canyi; Tang, Jinhui; Yan, Shuicheng; Lin, Zhouchen

2016-02-01

The nuclear norm is widely used as a convex surrogate of the rank function in compressive sensing for low rank matrix recovery with its applications in image recovery and signal processing. However, solving the nuclear norm-based relaxed convex problem usually leads to a suboptimal solution of the original rank minimization problem. In this paper, we propose to use a family of nonconvex surrogates of L0-norm on the singular values of a matrix to approximate the rank function. This leads to a nonconvex nonsmooth minimization problem. Then, we propose to solve the problem by an iteratively re-weighted nuclear norm (IRNN) algorithm. IRNN iteratively solves a weighted singular value thresholding problem, which has a closed form solution due to the special properties of the nonconvex surrogate functions. We also extend IRNN to solve the nonconvex problem with two or more blocks of variables. In theory, we prove that the IRNN decreases the objective function value monotonically, and any limit point is a stationary point. Extensive experiments on both synthesized data and real images demonstrate that IRNN enhances the low rank matrix recovery compared with the state-of-the-art convex algorithms.

Trace Norm Regularized CANDECOMP/PARAFAC Decomposition With Missing Data.

PubMed

Liu, Yuanyuan; Shang, Fanhua; Jiao, Licheng; Cheng, James; Cheng, Hong

2015-11-01

In recent years, low-rank tensor completion (LRTC) problems have received a significant amount of attention in computer vision, data mining, and signal processing. The existing trace norm minimization algorithms for iteratively solving LRTC problems involve multiple singular value decompositions of very large matrices at each iteration. Therefore, they suffer from high computational cost. In this paper, we propose a novel trace norm regularized CANDECOMP/PARAFAC decomposition (TNCP) method for simultaneous tensor decomposition and completion. We first formulate a factor matrix rank minimization model by deducing the relation between the rank of each factor matrix and the mode- n rank of a tensor. Then, we introduce a tractable relaxation of our rank function, and then achieve a convex combination problem of much smaller-scale matrix trace norm minimization. Finally, we develop an efficient algorithm based on alternating direction method of multipliers to solve our problem. The promising experimental results on synthetic and real-world data validate the effectiveness of our TNCP method. Moreover, TNCP is significantly faster than the state-of-the-art methods and scales to larger problems.

Knee point search using cascading top-k sorting with minimized time complexity.

PubMed

Wang, Zheng; Tseng, Shian-Shyong

2013-01-01

Anomaly detection systems and many other applications are frequently confronted with the problem of finding the largest knee point in the sorted curve for a set of unsorted points. This paper proposes an efficient knee point search algorithm with minimized time complexity using the cascading top-k sorting when a priori probability distribution of the knee point is known. First, a top-k sort algorithm is proposed based on a quicksort variation. We divide the knee point search problem into multiple steps. And in each step an optimization problem of the selection number k is solved, where the objective function is defined as the expected time cost. Because the expected time cost in one step is dependent on that of the afterwards steps, we simplify the optimization problem by minimizing the maximum expected time cost. The posterior probability of the largest knee point distribution and the other parameters are updated before solving the optimization problem in each step. An example of source detection of DNS DoS flooding attacks is provided to illustrate the applications of the proposed algorithm.

Estimates of the absolute error and a scheme for an approximate solution to scheduling problems

NASA Astrophysics Data System (ADS)

Lazarev, A. A.

2009-02-01

An approach is proposed for estimating absolute errors and finding approximate solutions to classical NP-hard scheduling problems of minimizing the maximum lateness for one or many machines and makespan is minimized. The concept of a metric (distance) between instances of the problem is introduced. The idea behind the approach is, given the problem instance, to construct another instance for which an optimal or approximate solution can be found at the minimum distance from the initial instance in the metric introduced. Instead of solving the original problem (instance), a set of approximating polynomially/pseudopolynomially solvable problems (instances) are considered, an instance at the minimum distance from the given one is chosen, and the resulting schedule is then applied to the original instance.

Naturalness in the Standard Model and beyond

NASA Astrophysics Data System (ADS)

Papaioannou, Anastasios Yiannaki

After an introduction to the Standard Model of particle physics and the unresolved question of naturalness posed by its treatment of electroweak symmetry breaking, we consider several different theoretical approaches that attempt to answer this question. First, we present work in which we consider the possibility that the Higgs boson, the long-sought hypothetical particle intimately associated with electroweak symmetry breaking, has a much larger mass than is usually assumed. Absent direct experimental evidence for a light Higgs boson (m ˜ O (100 GeV)), and precision electroweak data consistent with a light Higgs notwithstanding, we propose a heavier (m ˜ O (500 GeV)), thus more natural, Higgs boson. This heavy Higgs can be made consistent with the precision electroweak data if we also extend the Standard Model via the inclusion of new fermionic states near the weak energy scale. These new states, in addition to bringing the heavy Higgs boson in line with the precision data, also serve as a candidate for the elusive dark matter that pervades the universe. From there we go on to consider the problem of naturalness from the perspective of supersymmetry, one of the most popular candidates for physics beyond the Standard Model. In particular, the theory of the Next-to-Minimal Supersymmetric Standard Model (NMSSM) has found favor in its ability to solve the problem of naturalness posed by the Standard Model, in its hints at unification of the strong, weak, and electromagnetic interactions at high energies, and in its ability to provide supersymmetric particles as dark matter candidates. The NMSSM, however, requires rather large superpartner masses in order to accommodate a Higgs boson heavier than current experimental bounds while still maintaining gauge unification at high energies. We explore the possibility of new supersymmetric states at intermediate energies between the weak scale and the unification scale, which preserve gauge unification and allow a heavier Higgs, with only moderately heavy superpartners. Finally, we explore the possibility that previous attempts to resolve the naturalness problem may be too limited in scope. Perhaps the anthropic principle can instead provide a new way to answer such questions. We consider the intriguing scenario in which our observed universe is but one region of a much larger multi-verse, and the "constants" of nature are not constant after all, but take on a range of values. The anthropic principle's unique answer to the problem of naturalness is that only those regions of the multiverse with unnatural or "fine-tuned" parameters can give rise to the physical processes and structures that are necessary for our very existence; those regions with more natural values are dead universes where life is impossible. In particular, we examine an implementation of a unified physical theory, the Minimal Supersymmetric Standard Model (MSSM), within this multi-verse framework, and its consequences for the naturalness of electroweak symmetry breaking.

Hack's relation and optimal channel networks: The elongation of river basins as a consequence of energy minimization

NASA Astrophysics Data System (ADS)

Ijjasz-Vasquez, Ede J.; Bras, Rafael L.; Rodriguez-Iturbe, Ignacio

1993-08-01

As pointed by Hack (1957), river basins tend to become longer and narrower as their size increases. This work shows that this property may be partially regarded as the consequence of competition and minimization of energy expenditure in river basins.

High energy KrCl electric discharge laser

DOEpatents

Sze, Robert C.; Scott, Peter B.

1981-01-01

A high energy KrCl laser for producing coherent radiation at 222 nm. Output energies on the order of 100 mJ per pulse are produced utilizing a discharge excitation source to minimize formation of molecular ions, thereby minimizing absorption of laser radiation by the active medium. Additionally, HCl is used as a halogen donor which undergoes a harpooning reaction with metastable Kr.sub.M * to form KrCl.