Multipoint to multipoint routing and wavelength assignment in multi-domain optical networks

NASA Astrophysics Data System (ADS)

Qin, Panke; Wu, Jingru; Li, Xudong; Tang, Yongli

2018-01-01

In multi-point to multi-point (MP2MP) routing and wavelength assignment (RWA) problems, researchers usually assume the optical networks to be a single domain. However, the optical networks develop toward to multi-domain and larger scale in practice. In this context, multi-core shared tree (MST)-based MP2MP RWA are introduced problems including optimal multicast domain sequence selection, core nodes belonging in which domains and so on. In this letter, we focus on MST-based MP2MP RWA problems in multi-domain optical networks, mixed integer linear programming (MILP) formulations to optimally construct MP2MP multicast trees is presented. A heuristic algorithm base on network virtualization and weighted clustering algorithm (NV-WCA) is proposed. Simulation results show that, under different traffic patterns, the proposed algorithm achieves significant improvement on network resources occupation and multicast trees setup latency in contrast with the conventional algorithms which were proposed base on a single domain network environment.

A common and optimized age scale for Antarctic ice cores

NASA Astrophysics Data System (ADS)

Parrenin, F.; Veres, D.; Landais, A.; Bazin, L.; Lemieux-Dudon, B.; Toye Mahamadou Kele, H.; Wolff, E.; Martinerie, P.

2012-04-01

Dating ice cores is a complex problem because 1) there is a age shift between the gas bubbles and the surrounding ice 2) there are many different ice cores which can be synchronized with various proxies and 3) there are many methods to date the ice and the gas bubbles, each with advantages and drawbacks. These methods fall into the following categories: 1) Ice flow (for the ice) and firn densification modelling (for the gas bubbles); 2) Comparison of ice core proxies with insolation variations (so-called orbital tuning methods); 3) Comparison of ice core proxies with other well dated archives; 4) Identification of well-dated horizons, such as tephra layers or geomagnetic anomalies. Recently, an new dating tool has been developped (DATICE, Lemieux-Dudon et al., 2010), to take into account all the different dating information into account and produce a common and optimal chronology for ice cores with estimated confidence intervals. In this talk we will review the different dating information for Antarctic ice cores and show how the DATICE tool can be applied.

Fuel management optimization using genetic algorithms and expert knowledge

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

DeChaine, M.D.; Feltus, M.A.

1996-09-01

The CIGARO fuel management optimization code based on genetic algorithms is described and tested. The test problem optimized the core lifetime for a pressurized water reactor with a penalty function constraint on the peak normalized power. A bit-string genotype encoded the loading patterns, and genotype bias was reduced with additional bits. Expert knowledge about fuel management was incorporated into the genetic algorithm. Regional crossover exchanged physically adjacent fuel assemblies and improved the optimization slightly. Biasing the initial population toward a known priority table significantly improved the optimization.

Annealing Ant Colony Optimization with Mutation Operator for Solving TSP.

PubMed

Mohsen, Abdulqader M

2016-01-01

Ant Colony Optimization (ACO) has been successfully applied to solve a wide range of combinatorial optimization problems such as minimum spanning tree, traveling salesman problem, and quadratic assignment problem. Basic ACO has drawbacks of trapping into local minimum and low convergence rate. Simulated annealing (SA) and mutation operator have the jumping ability and global convergence; and local search has the ability to speed up the convergence. Therefore, this paper proposed a hybrid ACO algorithm integrating the advantages of ACO, SA, mutation operator, and local search procedure to solve the traveling salesman problem. The core of algorithm is based on the ACO. SA and mutation operator were used to increase the ants population diversity from time to time and the local search was used to exploit the current search area efficiently. The comparative experiments, using 24 TSP instances from TSPLIB, show that the proposed algorithm outperformed some well-known algorithms in the literature in terms of solution quality.

IceChrono1: a probabilistic model to compute a common and optimal chronology for several ice cores

NASA Astrophysics Data System (ADS)

Parrenin, F.; Bazin, L.; Capron, E.; Landais, A.; Lemieux-Dudon, B.; Masson-Delmotte, V.

2015-05-01

Polar ice cores provide exceptional archives of past environmental conditions. The dating of ice cores and the estimation of the age-scale uncertainty are essential to interpret the climate and environmental records that they contain. It is, however, a complex problem which involves different methods. Here, we present IceChrono1, a new probabilistic model integrating various sources of chronological information to produce a common and optimized chronology for several ice cores, as well as its uncertainty. IceChrono1 is based on the inversion of three quantities: the surface accumulation rate, the lock-in depth (LID) of air bubbles and the thinning function. The chronological information integrated into the model are models of the sedimentation process (accumulation of snow, densification of snow into ice and air trapping, ice flow), ice- and air-dated horizons, ice and air depth intervals with known durations, depth observations (depth shift between synchronous events recorded in the ice and in the air) and finally air and ice stratigraphic links in between ice cores. The optimization is formulated as a least squares problem, implying that all densities of probabilities are assumed to be Gaussian. It is numerically solved using the Levenberg-Marquardt algorithm and a numerical evaluation of the model's Jacobian. IceChrono follows an approach similar to that of the Datice model which was recently used to produce the AICC2012 (Antarctic ice core chronology) for four Antarctic ice cores and one Greenland ice core. IceChrono1 provides improvements and simplifications with respect to Datice from the mathematical, numerical and programming point of views. The capabilities of IceChrono1 are demonstrated on a case study similar to the AICC2012 dating experiment. We find results similar to those of Datice, within a few centuries, which is a confirmation of both IceChrono1 and Datice codes. We also test new functionalities with respect to the original version of Datice: observations as ice intervals with known durations, correlated observations, observations as air intervals with known durations and observations as mixed ice-air stratigraphic links. IceChrono1 is freely available under the General Public License v3 open source license.

Research on the integration of teaching content of core courses in Agro-ecological environmental specialties of higher vocational colleges

NASA Astrophysics Data System (ADS)

Chen, Juan; Ma, Guosheng

2018-02-01

Curriculum is the means to cultivate higher vocational talents. On the basis of analyzing the core curriculum problems of curriculum reform and Agro-ecological environmental specialties in higher vocational colleges, this paper puts forward the optimization and integration measures of 6 core courses, including “Eco-environment Repair Technology”, “Agro-environmental Management Plan”, “Environmental Engineering Design”, “Environmental Pest Management Technology”, “Agro-chemical Pollution Control Technology”, “Agro-environmental Testing and Analysis”. It integrates the vocational qualification certificate education and professional induction certificate training items, and enhances the adaptability, skills and professionalism of professional core curriculum.

Evaluating Multi-core Architectures through Accelerating the Three-Dimensional Lax–Wendroff Correction

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

You, Yang; Fu, Haohuan; Song, Shuaiwen

2014-07-18

Wave propagation forward modeling is a widely used computational method in oil and gas exploration. The iterative stencil loops in such problems have broad applications in scientific computing. However, executing such loops can be highly time time-consuming, which greatly limits application’s performance and power efficiency. In this paper, we accelerate the forward modeling technique on the latest multi-core and many-core architectures such as Intel Sandy Bridge CPUs, NVIDIA Fermi C2070 GPU, NVIDIA Kepler K20x GPU, and the Intel Xeon Phi Co-processor. For the GPU platforms, we propose two parallel strategies to explore the performance optimization opportunities for our stencil kernels.more » For Sandy Bridge CPUs and MIC, we also employ various optimization techniques in order to achieve the best.« less

The Geriatric ICF Core Set reflecting health-related problems in community-living older adults aged 75 years and older without dementia: development and validation.

PubMed

Spoorenberg, Sophie L W; Reijneveld, Sijmen A; Middel, Berrie; Uittenbroek, Ronald J; Kremer, Hubertus P H; Wynia, Klaske

2015-01-01

The aim of the present study was to develop a valid Geriatric ICF Core Set reflecting relevant health-related problems of community-living older adults without dementia. A Delphi study was performed in order to reach consensus (≥70% agreement) on second-level categories from the International Classification of Functioning, Disability and Health (ICF). The Delphi panel comprised 41 older adults, medical and non-medical experts. Content validity of the set was tested in a cross-sectional study including 267 older adults identified as frail or having complex care needs. Consensus was reached for 30 ICF categories in the Delphi study (fourteen Body functions, ten Activities and Participation and six Environmental Factors categories). Content validity of the set was high: the prevalence of all the problems was >10%, except for d530 Toileting. The most frequently reported problems were b710 Mobility of joint functions (70%), b152 Emotional functions (65%) and b455 Exercise tolerance functions (62%). No categories had missing values. The final Geriatric ICF Core Set is a comprehensive and valid set of 29 ICF categories, reflecting the most relevant health-related problems among community-living older adults without dementia. This Core Set may contribute to optimal care provision and support of the older population. Implications for Rehabilitation The Geriatric ICF Core Set may provide a practical tool for gaining an understanding of the relevant health-related problems of community-living older adults without dementia. The Geriatric ICF Core Set may be used in primary care practice as an assessment tool in order to tailor care and support to the needs of older adults. The Geriatric ICF Core Set may be suitable for use in multidisciplinary teams in integrated care settings, since it is based on a broad range of problems in functioning. Professionals should pay special attention to health problems related to mobility and emotional functioning since these are the most prevalent problems in community-living older adults.

Multi-objective optimal design of sandwich panels using a genetic algorithm

NASA Astrophysics Data System (ADS)

Xu, Xiaomei; Jiang, Yiping; Pueh Lee, Heow

2017-10-01

In this study, an optimization problem concerning sandwich panels is investigated by simultaneously considering the two objectives of minimizing the panel mass and maximizing the sound insulation performance. First of all, the acoustic model of sandwich panels is discussed, which provides a foundation to model the acoustic objective function. Then the optimization problem is formulated as a bi-objective programming model, and a solution algorithm based on the non-dominated sorting genetic algorithm II (NSGA-II) is provided to solve the proposed model. Finally, taking an example of a sandwich panel that is expected to be used as an automotive roof panel, numerical experiments are carried out to verify the effectiveness of the proposed model and solution algorithm. Numerical results demonstrate in detail how the core material, geometric constraints and mechanical constraints impact the optimal designs of sandwich panels.

IceChrono1: a probabilistic model to compute a common and optimal chronology for several ice cores

NASA Astrophysics Data System (ADS)

Parrenin, Frédéric; Bazin, Lucie; Capron, Emilie; Landais, Amaëlle; Lemieux-Dudon, Bénédicte; Masson-Delmotte, Valérie

2016-04-01

Polar ice cores provide exceptional archives of past environmental conditions. The dating of ice cores and the estimation of the age scale uncertainty are essential to interpret the climate and environmental records that they contain. It is however a complex problem which involves different methods. Here, we present IceChrono1, a new probabilistic model integrating various sources of chronological information to produce a common and optimized chronology for several ice cores, as well as its uncertainty. IceChrono1 is based on the inversion of three quantities: the surface accumulation rate, the Lock-In Depth (LID) of air bubbles and the thinning function. The chronological information integrated into the model are: models of the sedimentation process (accumulation of snow, densification of snow into ice and air trapping, ice flow), ice and air dated horizons, ice and air depth intervals with known durations, Δdepth observations (depth shift between synchronous events recorded in the ice and in the air) and finally air and ice stratigraphic links in between ice cores. The optimization is formulated as a least squares problem, implying that all densities of probabilities are assumed to be Gaussian. It is numerically solved using the Levenberg-Marquardt algorithm and a numerical evaluation of the model's Jacobian. IceChrono follows an approach similar to that of the Datice model which was recently used to produce the AICC2012 chronology for 4 Antarctic ice cores and 1 Greenland ice core. IceChrono1 provides improvements and simplifications with respect to Datice from the mathematical, numerical and programming point of views. The capabilities of IceChrono is demonstrated on a case study similar to the AICC2012 dating experiment. We find results similar to those of Datice, within a few centuries, which is a confirmation of both IceChrono and Datice codes. We also test new functionalities with respect to the original version of Datice: observations as ice intervals with known durations, correlated observations, observations as gas intervals with known durations and observations as mixed ice-air stratigraphic links. IceChrono1 is freely available under the GPL v3 open source license.

Annealing Ant Colony Optimization with Mutation Operator for Solving TSP

PubMed Central

2016-01-01

Ant Colony Optimization (ACO) has been successfully applied to solve a wide range of combinatorial optimization problems such as minimum spanning tree, traveling salesman problem, and quadratic assignment problem. Basic ACO has drawbacks of trapping into local minimum and low convergence rate. Simulated annealing (SA) and mutation operator have the jumping ability and global convergence; and local search has the ability to speed up the convergence. Therefore, this paper proposed a hybrid ACO algorithm integrating the advantages of ACO, SA, mutation operator, and local search procedure to solve the traveling salesman problem. The core of algorithm is based on the ACO. SA and mutation operator were used to increase the ants population diversity from time to time and the local search was used to exploit the current search area efficiently. The comparative experiments, using 24 TSP instances from TSPLIB, show that the proposed algorithm outperformed some well-known algorithms in the literature in terms of solution quality. PMID:27999590

Optimization design of toroidal core for magnetic energy harvesting near power line by considering saturation effect

NASA Astrophysics Data System (ADS)

Park, Bumjin; Kim, Dongwook; Park, Jaehyoung; Kim, Kibeom; Koo, Jay; Park, HyunHo; Ahn, Seungyoung

2018-05-01

Recently, magnetic energy harvesting technologies have been studied actively for self-sustainable operation of applications around power line. However, magnetic energy harvesting around power lines has the problem of magnetic saturation, which can cause power performance degradation of the harvester. In this paper, optimal design of a toroidal core for magnetic energy harvesters has been proposed with consideration of magnetic saturation near power lines. Using Permeability-H curve and Ampere's circuital law, the optimum dimensional parameters needed to generate induced voltage were analyzed via calculation and simulation. To reflect a real environment, we consider the nonlinear characteristic of the magnetic core material and supply current through a 3-phase distribution panel used in the industry. The effectiveness of the proposed design methodology is verified by experiments in a power distribution panel and takes 60.9 V from power line current of 60 A at 60 Hz.

Adversarial Geospatial Abduction Problems

DTIC Science & Technology

2011-01-01

which is new , shows that #GCD is #P-complete and, moreover, that there is no fully-polynomial random approximation scheme for #GCD unless NP equals the...use L∗ to form a new set of constraints to find a δ-core optimal explanation. We now present these δ-core constraints. Notice that the cardinality...EXBrf (∅, efd), flag1 = true, i = 2 (4) While flag1 (a) new val = cur val + inci (b) If new val > (1 + |L|2 ) · cur val then i. If EXBrf (B ∪ {pi

Memory and Energy Optimization Strategies for Multithreaded Operating System on the Resource-Constrained Wireless Sensor Node

PubMed Central

Liu, Xing; Hou, Kun Mean; de Vaulx, Christophe; Xu, Jun; Yang, Jianfeng; Zhou, Haiying; Shi, Hongling; Zhou, Peng

2015-01-01

Memory and energy optimization strategies are essential for the resource-constrained wireless sensor network (WSN) nodes. In this article, a new memory-optimized and energy-optimized multithreaded WSN operating system (OS) LiveOS is designed and implemented. Memory cost of LiveOS is optimized by using the stack-shifting hybrid scheduling approach. Different from the traditional multithreaded OS in which thread stacks are allocated statically by the pre-reservation, thread stacks in LiveOS are allocated dynamically by using the stack-shifting technique. As a result, memory waste problems caused by the static pre-reservation can be avoided. In addition to the stack-shifting dynamic allocation approach, the hybrid scheduling mechanism which can decrease both the thread scheduling overhead and the thread stack number is also implemented in LiveOS. With these mechanisms, the stack memory cost of LiveOS can be reduced more than 50% if compared to that of a traditional multithreaded OS. Not is memory cost optimized, but also the energy cost is optimized in LiveOS, and this is achieved by using the multi-core “context aware” and multi-core “power-off/wakeup” energy conservation approaches. By using these approaches, energy cost of LiveOS can be reduced more than 30% when compared to the single-core WSN system. Memory and energy optimization strategies in LiveOS not only prolong the lifetime of WSN nodes, but also make the multithreaded OS feasible to run on the memory-constrained WSN nodes. PMID:25545264

Parallel Monotonic Basin Hopping for Low Thrust Trajectory Optimization

NASA Technical Reports Server (NTRS)

McCarty, Steven L.; McGuire, Melissa L.

2018-01-01

Monotonic Basin Hopping has been shown to be an effective method of solving low thrust trajectory optimization problems. This paper outlines an extension to the common serial implementation by parallelizing it over any number of available compute cores. The Parallel Monotonic Basin Hopping algorithm described herein is shown to be an effective way to more quickly locate feasible solutions, and improve locally optimal solutions in an automated way without requiring a feasible initial guess. The increased speed achieved through parallelization enables the algorithm to be applied to more complex problems that would otherwise be impractical for a serial implementation. Low thrust cislunar transfers and a hybrid Mars example case demonstrate the effectiveness of the algorithm. Finally, a preliminary scaling study quantifies the expected decrease in solve time compared to a serial implementation.,

Guaranteed Discrete Energy Optimization on Large Protein Design Problems.

PubMed

Simoncini, David; Allouche, David; de Givry, Simon; Delmas, Céline; Barbe, Sophie; Schiex, Thomas

2015-12-08

In Computational Protein Design (CPD), assuming a rigid backbone and amino-acid rotamer library, the problem of finding a sequence with an optimal conformation is NP-hard. In this paper, using Dunbrack's rotamer library and Talaris2014 decomposable energy function, we use an exact deterministic method combining branch and bound, arc consistency, and tree-decomposition to provenly identify the global minimum energy sequence-conformation on full-redesign problems, defining search spaces of size up to 10(234). This is achieved on a single core of a standard computing server, requiring a maximum of 66GB RAM. A variant of the algorithm is able to exhaustively enumerate all sequence-conformations within an energy threshold of the optimum. These proven optimal solutions are then used to evaluate the frequencies and amplitudes, in energy and sequence, at which an existing CPD-dedicated simulated annealing implementation may miss the optimum on these full redesign problems. The probability of finding an optimum drops close to 0 very quickly. In the worst case, despite 1,000 repeats, the annealing algorithm remained more than 1 Rosetta unit away from the optimum, leading to design sequences that could differ from the optimal sequence by more than 30% of their amino acids.

Highly Efficient Parallel Multigrid Solver For Large-Scale Simulation of Grain Growth Using the Structural Phase Field Crystal Model

NASA Astrophysics Data System (ADS)

Guan, Zhen; Pekurovsky, Dmitry; Luce, Jason; Thornton, Katsuyo; Lowengrub, John

The structural phase field crystal (XPFC) model can be used to model grain growth in polycrystalline materials at diffusive time-scales while maintaining atomic scale resolution. However, the governing equation of the XPFC model is an integral-partial-differential-equation (IPDE), which poses challenges in implementation onto high performance computing (HPC) platforms. In collaboration with the XSEDE Extended Collaborative Support Service, we developed a distributed memory HPC solver for the XPFC model, which combines parallel multigrid and P3DFFT. The performance benchmarking on the Stampede supercomputer indicates near linear strong and weak scaling for both multigrid and transfer time between multigrid and FFT modules up to 1024 cores. Scalability of the FFT module begins to decline at 128 cores, but it is sufficient for the type of problem we will be examining. We have demonstrated simulations using 1024 cores, and we expect to achieve 4096 cores and beyond. Ongoing work involves optimization of MPI/OpenMP-based codes for the Intel KNL Many-Core Architecture. This optimizes the code for coming pre-exascale systems, in particular many-core systems such as Stampede 2.0 and Cori 2 at NERSC, without sacrificing efficiency on other general HPC systems.

Exploring biorthonormal transformations of pair-correlation functions in atomic structure variational calculations

NASA Astrophysics Data System (ADS)

Verdebout, S.; Jönsson, P.; Gaigalas, G.; Godefroid, M.; Froese Fischer, C.

2010-04-01

Multiconfiguration expansions frequently target valence correlation and correlation between valence electrons and the outermost core electrons. Correlation within the core is often neglected. A large orbital basis is needed to saturate both the valence and core-valence correlation effects. This in turn leads to huge numbers of configuration state functions (CSFs), many of which are unimportant. To avoid the problems inherent to the use of a single common orthonormal orbital basis for all correlation effects in the multiconfiguration Hartree-Fock (MCHF) method, we propose to optimize independent MCHF pair-correlation functions (PCFs), bringing their own orthonormal one-electron basis. Each PCF is generated by allowing single- and double-excitations from a multireference (MR) function. This computational scheme has the advantage of using targeted and optimally localized orbital sets for each PCF. These pair-correlation functions are coupled together and with each component of the MR space through a low dimension generalized eigenvalue problem. Nonorthogonal orbital sets being involved, the interaction and overlap matrices are built using biorthonormal transformation of the coupled basis sets followed by a counter-transformation of the PCF expansions. Applied to the ground state of beryllium, the new method gives total energies that are lower than the ones from traditional complete active space (CAS)-MCHF calculations using large orbital active sets. It is fair to say that we now have the possibility to account for, in a balanced way, correlation deep down in the atomic core in variational calculations.

Developing a Shuffled Complex-Self Adaptive Hybrid Evolution (SC-SAHEL) Framework for Water Resources Management and Water-Energy System Optimization

NASA Astrophysics Data System (ADS)

Rahnamay Naeini, M.; Sadegh, M.; AghaKouchak, A.; Hsu, K. L.; Sorooshian, S.; Yang, T.

2017-12-01

Meta-Heuristic optimization algorithms have gained a great deal of attention in a wide variety of fields. Simplicity and flexibility of these algorithms, along with their robustness, make them attractive tools for solving optimization problems. Different optimization methods, however, hold algorithm-specific strengths and limitations. Performance of each individual algorithm obeys the "No-Free-Lunch" theorem, which means a single algorithm cannot consistently outperform all possible optimization problems over a variety of problems. From users' perspective, it is a tedious process to compare, validate, and select the best-performing algorithm for a specific problem or a set of test cases. In this study, we introduce a new hybrid optimization framework, entitled Shuffled Complex-Self Adaptive Hybrid EvoLution (SC-SAHEL), which combines the strengths of different evolutionary algorithms (EAs) in a parallel computing scheme, and allows users to select the most suitable algorithm tailored to the problem at hand. The concept of SC-SAHEL is to execute different EAs as separate parallel search cores, and let all participating EAs to compete during the course of the search. The newly developed SC-SAHEL algorithm is designed to automatically select, the best performing algorithm for the given optimization problem. This algorithm is rigorously effective in finding the global optimum for several strenuous benchmark test functions, and computationally efficient as compared to individual EAs. We benchmark the proposed SC-SAHEL algorithm over 29 conceptual test functions, and two real-world case studies - one hydropower reservoir model and one hydrological model (SAC-SMA). Results show that the proposed framework outperforms individual EAs in an absolute majority of the test problems, and can provide competitive results to the fittest EA algorithm with more comprehensive information during the search. The proposed framework is also flexible for merging additional EAs, boundary-handling techniques, and sampling schemes, and has good potential to be used in Water-Energy system optimal operation and management.

Optimization of the Brillouin operator on the KNL architecture

NASA Astrophysics Data System (ADS)

Dürr, Stephan

2018-03-01

Experiences with optimizing the matrix-times-vector application of the Brillouin operator on the Intel KNL processor are reported. Without adjustments to the memory layout, performance figures of 360 Gflop/s in single and 270 Gflop/s in double precision are observed. This is with Nc = 3 colors, Nv = 12 right-hand-sides, Nthr = 256 threads, on lattices of size 323 × 64, using exclusively OMP pragmas. Interestingly, the same routine performs quite well on Intel Core i7 architectures, too. Some observations on the much harderWilson fermion matrix-times-vector optimization problem are added.

Hybrid Optimization Parallel Search PACKage

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

2009-11-10

HOPSPACK is open source software for solving optimization problems without derivatives. Application problems may have a fully nonlinear objective function, bound constraints, and linear and nonlinear constraints. Problem variables may be continuous, integer-valued, or a mixture of both. The software provides a framework that supports any derivative-free type of solver algorithm. Through the framework, solvers request parallel function evaluation, which may use MPI (multiple machines) or multithreading (multiple processors/cores on one machine). The framework provides a Cache and Pending Cache of saved evaluations that reduces execution time and facilitates restarts. Solvers can dynamically create other algorithms to solve subproblems, amore » useful technique for handling multiple start points and integer-valued variables. HOPSPACK ships with the Generating Set Search (GSS) algorithm, developed at Sandia as part of the APPSPACK open source software project.« less

Optimization of Wireless Power Transfer Systems Enhanced by Passive Elements and Metasurfaces

NASA Astrophysics Data System (ADS)

Lang, Hans-Dieter; Sarris, Costas D.

2017-10-01

This paper presents a rigorous optimization technique for wireless power transfer (WPT) systems enhanced by passive elements, ranging from simple reflectors and intermedi- ate relays all the way to general electromagnetic guiding and focusing structures, such as metasurfaces and metamaterials. At its core is a convex semidefinite relaxation formulation of the otherwise nonconvex optimization problem, of which tightness and optimality can be confirmed by a simple test of its solutions. The resulting method is rigorous, versatile, and general -- it does not rely on any assumptions. As shown in various examples, it is able to efficiently and reliably optimize such WPT systems in order to find their physical limitations on performance, optimal operating parameters and inspect their working principles, even for a large number of active transmitters and passive elements.

A parallel metaheuristic for large mixed-integer dynamic optimization problems, with applications in computational biology

PubMed Central

Henriques, David; González, Patricia; Doallo, Ramón; Saez-Rodriguez, Julio; Banga, Julio R.

2017-01-01

Background We consider a general class of global optimization problems dealing with nonlinear dynamic models. Although this class is relevant to many areas of science and engineering, here we are interested in applying this framework to the reverse engineering problem in computational systems biology, which yields very large mixed-integer dynamic optimization (MIDO) problems. In particular, we consider the framework of logic-based ordinary differential equations (ODEs). Methods We present saCeSS2, a parallel method for the solution of this class of problems. This method is based on an parallel cooperative scatter search metaheuristic, with new mechanisms of self-adaptation and specific extensions to handle large mixed-integer problems. We have paid special attention to the avoidance of convergence stagnation using adaptive cooperation strategies tailored to this class of problems. Results We illustrate its performance with a set of three very challenging case studies from the domain of dynamic modelling of cell signaling. The simpler case study considers a synthetic signaling pathway and has 84 continuous and 34 binary decision variables. A second case study considers the dynamic modeling of signaling in liver cancer using high-throughput data, and has 135 continuous and 109 binaries decision variables. The third case study is an extremely difficult problem related with breast cancer, involving 690 continuous and 138 binary decision variables. We report computational results obtained in different infrastructures, including a local cluster, a large supercomputer and a public cloud platform. Interestingly, the results show how the cooperation of individual parallel searches modifies the systemic properties of the sequential algorithm, achieving superlinear speedups compared to an individual search (e.g. speedups of 15 with 10 cores), and significantly improving (above a 60%) the performance with respect to a non-cooperative parallel scheme. The scalability of the method is also good (tests were performed using up to 300 cores). Conclusions These results demonstrate that saCeSS2 can be used to successfully reverse engineer large dynamic models of complex biological pathways. Further, these results open up new possibilities for other MIDO-based large-scale applications in the life sciences such as metabolic engineering, synthetic biology, drug scheduling. PMID:28813442

A parallel metaheuristic for large mixed-integer dynamic optimization problems, with applications in computational biology.

PubMed

Penas, David R; Henriques, David; González, Patricia; Doallo, Ramón; Saez-Rodriguez, Julio; Banga, Julio R

2017-01-01

We consider a general class of global optimization problems dealing with nonlinear dynamic models. Although this class is relevant to many areas of science and engineering, here we are interested in applying this framework to the reverse engineering problem in computational systems biology, which yields very large mixed-integer dynamic optimization (MIDO) problems. In particular, we consider the framework of logic-based ordinary differential equations (ODEs). We present saCeSS2, a parallel method for the solution of this class of problems. This method is based on an parallel cooperative scatter search metaheuristic, with new mechanisms of self-adaptation and specific extensions to handle large mixed-integer problems. We have paid special attention to the avoidance of convergence stagnation using adaptive cooperation strategies tailored to this class of problems. We illustrate its performance with a set of three very challenging case studies from the domain of dynamic modelling of cell signaling. The simpler case study considers a synthetic signaling pathway and has 84 continuous and 34 binary decision variables. A second case study considers the dynamic modeling of signaling in liver cancer using high-throughput data, and has 135 continuous and 109 binaries decision variables. The third case study is an extremely difficult problem related with breast cancer, involving 690 continuous and 138 binary decision variables. We report computational results obtained in different infrastructures, including a local cluster, a large supercomputer and a public cloud platform. Interestingly, the results show how the cooperation of individual parallel searches modifies the systemic properties of the sequential algorithm, achieving superlinear speedups compared to an individual search (e.g. speedups of 15 with 10 cores), and significantly improving (above a 60%) the performance with respect to a non-cooperative parallel scheme. The scalability of the method is also good (tests were performed using up to 300 cores). These results demonstrate that saCeSS2 can be used to successfully reverse engineer large dynamic models of complex biological pathways. Further, these results open up new possibilities for other MIDO-based large-scale applications in the life sciences such as metabolic engineering, synthetic biology, drug scheduling.

SN-38 loading capacity of hydrophobic polymer blend nanoparticles: formulation, optimization and efficacy evaluation.

PubMed

Dimchevska, Simona; Geskovski, Nikola; Petruševski, Gjorgji; Chacorovska, Marina; Popeski-Dimovski, Riste; Ugarkovic, Sonja; Goracinova, Katerina

2017-03-01

One of the most important problems in nanoencapsulation of extremely hydrophobic drugs is poor drug loading due to rapid drug crystallization outside the polymer core. The effort to use nanoprecipitation, as a simple one-step procedure with good reproducibility and FDA approved polymers like Poly(lactic-co-glycolic acid) (PLGA) and Polycaprolactone (PCL), will only potentiate this issue. Considering that drug loading is one of the key defining characteristics, in this study we attempted to examine whether the nanoparticle (NP) core composed of two hydrophobic polymers will provide increased drug loading for 7-Ethyl-10-hydroxy-camptothecin (SN-38), relative to NPs prepared using individual polymers. D-optimal design was applied to optimize PLGA/PCL ratio in the polymer blend and the mode of addition of the amphiphilic copolymer Lutrol ® F127 in order to maximize SN-38 loading and obtain NPs with acceptable size for passive tumor targeting. Drug/polymer and polymer/polymer interaction analysis pointed to high degree of compatibility and miscibility among both hydrophobic polymers, providing core configuration with higher drug loading capacity. Toxicity studies outlined the biocompatibility of the blank NPs. Increased in vitro efficacy of drug-loaded NPs compared to the free drug was confirmed by growth inhibition studies using SW-480 cell line. Additionally, the optimized NP formulation showed very promising blood circulation profile with elimination half-time of 7.4 h.

Optimization of Land Use Suitability for Agriculture Using Integrated Geospatial Model and Genetic Algorithms

NASA Astrophysics Data System (ADS)

Mansor, S. B.; Pormanafi, S.; Mahmud, A. R. B.; Pirasteh, S.

2012-08-01

In this study, a geospatial model for land use allocation was developed from the view of simulating the biological autonomous adaptability to environment and the infrastructural preference. The model was developed based on multi-agent genetic algorithm. The model was customized to accommodate the constraint set for the study area, namely the resource saving and environmental-friendly. The model was then applied to solve the practical multi-objective spatial optimization allocation problems of land use in the core region of Menderjan Basin in Iran. The first task was to study the dominant crops and economic suitability evaluation of land. Second task was to determine the fitness function for the genetic algorithms. The third objective was to optimize the land use map using economical benefits. The results has indicated that the proposed model has much better performance for solving complex multi-objective spatial optimization allocation problems and it is a promising method for generating land use alternatives for further consideration in spatial decision-making.

Decision theory, reinforcement learning, and the brain.

PubMed

Dayan, Peter; Daw, Nathaniel D

2008-12-01

Decision making is a core competence for animals and humans acting and surviving in environments they only partially comprehend, gaining rewards and punishments for their troubles. Decision-theoretic concepts permeate experiments and computational models in ethology, psychology, and neuroscience. Here, we review a well-known, coherent Bayesian approach to decision making, showing how it unifies issues in Markovian decision problems, signal detection psychophysics, sequential sampling, and optimal exploration and discuss paradigmatic psychological and neural examples of each problem. We discuss computational issues concerning what subjects know about their task and how ambitious they are in seeking optimal solutions; we address algorithmic topics concerning model-based and model-free methods for making choices; and we highlight key aspects of the neural implementation of decision making.

Surgery scheduling optimization considering real life constraints and comprehensive operation cost of operating room.

PubMed

Xiang, Wei; Li, Chong

2015-01-01

Operating Room (OR) is the core sector in hospital expenditure, the operation management of which involves a complete three-stage surgery flow, multiple resources, prioritization of the various surgeries, and several real-life OR constraints. As such reasonable surgery scheduling is crucial to OR management. To optimize OR management and reduce operation cost, a short-term surgery scheduling problem is proposed and defined based on the survey of the OR operation in a typical hospital in China. The comprehensive operation cost is clearly defined considering both under-utilization and overutilization. A nested Ant Colony Optimization (nested-ACO) incorporated with several real-life OR constraints is proposed to solve such a combinatorial optimization problem. The 10-day manual surgery schedules from a hospital in China are compared with the optimized schedules solved by the nested-ACO. Comparison results show the advantage using the nested-ACO in several measurements: OR-related time, nurse-related time, variation in resources' working time, and the end time. The nested-ACO considering real-life operation constraints such as the difference between first and following case, surgeries priority, and fixed nurses in pre/post-operative stage is proposed to solve the surgery scheduling optimization problem. The results clearly show the benefit of using the nested-ACO in enhancing the OR management efficiency and minimizing the comprehensive overall operation cost.

Parallel computation of GA search for the artery shape determinants with CFD

NASA Astrophysics Data System (ADS)

Himeno, M.; Noda, S.; Fukasaku, K.; Himeno, R.

2010-06-01

We studied which factors play important role to determine the shape of arteries at the carotid artery bifurcation by performing multi-objective optimization with computation fluid dynamics (CFD) and the genetic algorithm (GA). To perform it, the most difficult problem is how to reduce turn-around time of the GA optimization with 3D unsteady computation of blood flow. We devised two levels of parallel computation method with the following features: level 1: parallel CFD computation with appropriate number of cores; level 2: parallel jobs generated by "master", which finds quickly available job cue and dispatches jobs, to reduce turn-around time. As a result, the turn-around time of one GA trial, which would have taken 462 days with one core, was reduced to less than two days on RIKEN supercomputer system, RICC, with 8192 cores. We performed a multi-objective optimization to minimize the maximum mean WSS and to minimize the sum of circumference for four different shapes and obtained a set of trade-off solutions for each shape. In addition, we found that the carotid bulb has the feature of the minimum local mean WSS and minimum local radius. We confirmed that our method is effective for examining determinants of artery shapes.

DOMe: A deduplication optimization method for the NewSQL database backups

PubMed Central

Wang, Longxiang; Zhu, Zhengdong; Zhang, Xingjun; Wang, Yinfeng

2017-01-01

Reducing duplicated data of database backups is an important application scenario for data deduplication technology. NewSQL is an emerging database system and is now being used more and more widely. NewSQL systems need to improve data reliability by periodically backing up in-memory data, resulting in a lot of duplicated data. The traditional deduplication method is not optimized for the NewSQL server system and cannot take full advantage of hardware resources to optimize deduplication performance. A recent research pointed out that the future NewSQL server will have thousands of CPU cores, large DRAM and huge NVRAM. Therefore, how to utilize these hardware resources to optimize the performance of data deduplication is an important issue. To solve this problem, we propose a deduplication optimization method (DOMe) for NewSQL system backup. To take advantage of the large number of CPU cores in the NewSQL server to optimize deduplication performance, DOMe parallelizes the deduplication method based on the fork-join framework. The fingerprint index, which is the key data structure in the deduplication process, is implemented as pure in-memory hash table, which makes full use of the large DRAM in NewSQL system, eliminating the performance bottleneck problem of fingerprint index existing in traditional deduplication method. The H-store is used as a typical NewSQL database system to implement DOMe method. DOMe is experimentally analyzed by two representative backup data. The experimental results show that: 1) DOMe can reduce the duplicated NewSQL backup data. 2) DOMe significantly improves deduplication performance by parallelizing CDC algorithms. In the case of the theoretical speedup ratio of the server is 20.8, the speedup ratio of DOMe can achieve up to 18; 3) DOMe improved the deduplication throughput by 1.5 times through the pure in-memory index optimization method. PMID:29049307

On-the-Job Orientation of Unemployed Negro Skill Center Trainees and Their Supervisors. Final Report.

ERIC Educational Resources Information Center

Rosen, Hjalmar

The problems inherent in employing hard-core unemployed Negroes and the optimal locus of on-the-job orientation to integrate such employees into thework force were subjects of this study. It focused on young Negro females who, because of their inability to meet selections minimums for job entry, had a high potential for chronic unemployment. Among…

Essays on variational approximation techniques for stochastic optimization problems

NASA Astrophysics Data System (ADS)

Deride Silva, Julio A.

This dissertation presents five essays on approximation and modeling techniques, based on variational analysis, applied to stochastic optimization problems. It is divided into two parts, where the first is devoted to equilibrium problems and maxinf optimization, and the second corresponds to two essays in statistics and uncertainty modeling. Stochastic optimization lies at the core of this research as we were interested in relevant equilibrium applications that contain an uncertain component, and the design of a solution strategy. In addition, every stochastic optimization problem relies heavily on the underlying probability distribution that models the uncertainty. We studied these distributions, in particular, their design process and theoretical properties such as their convergence. Finally, the last aspect of stochastic optimization that we covered is the scenario creation problem, in which we described a procedure based on a probabilistic model to create scenarios for the applied problem of power estimation of renewable energies. In the first part, Equilibrium problems and maxinf optimization, we considered three Walrasian equilibrium problems: from economics, we studied a stochastic general equilibrium problem in a pure exchange economy, described in Chapter 3, and a stochastic general equilibrium with financial contracts, in Chapter 4; finally from engineering, we studied an infrastructure planning problem in Chapter 5. We stated these problems as belonging to the maxinf optimization class and, in each instance, we provided an approximation scheme based on the notion of lopsided convergence and non-concave duality. This strategy is the foundation of the augmented Walrasian algorithm, whose convergence is guaranteed by lopsided convergence, that was implemented computationally, obtaining numerical results for relevant examples. The second part, Essays about statistics and uncertainty modeling, contains two essays covering a convergence problem for a sequence of estimators, and a problem for creating probabilistic scenarios on renewable energies estimation. In Chapter 7 we re-visited one of the "folk theorems" in statistics, where a family of Bayes estimators under 0-1 loss functions is claimed to converge to the maximum a posteriori estimator. This assertion is studied under the scope of the hypo-convergence theory, and the density functions are included in the class of upper semicontinuous functions. We conclude this chapter with an example in which the convergence does not hold true, and we provided sufficient conditions that guarantee convergence. The last chapter, Chapter 8, addresses the important topic of creating probabilistic scenarios for solar power generation. Scenarios are a fundamental input for the stochastic optimization problem of energy dispatch, especially when incorporating renewables. We proposed a model designed to capture the constraints induced by physical characteristics of the variables based on the application of an epi-spline density estimation along with a copula estimation, in order to account for partial correlations between variables.

Minimax confidence intervals in geomagnetism

NASA Technical Reports Server (NTRS)

Stark, Philip B.

1992-01-01

The present paper uses theory of Donoho (1989) to find lower bounds on the lengths of optimally short fixed-length confidence intervals (minimax confidence intervals) for Gauss coefficients of the field of degree 1-12 using the heat flow constraint. The bounds on optimal minimax intervals are about 40 percent shorter than Backus' intervals: no procedure for producing fixed-length confidence intervals, linear or nonlinear, can give intervals shorter than about 60 percent the length of Backus' in this problem. While both methods rigorously account for the fact that core field models are infinite-dimensional, the application of the techniques to the geomagnetic problem involves approximations and counterfactual assumptions about the data errors, and so these results are likely to be extremely optimistic estimates of the actual uncertainty in Gauss coefficients.

Identification of multi-criteria for supplier selection in IT project outsourcing

NASA Astrophysics Data System (ADS)

Fusiripong, Prashaya; Baharom, Fauziah; Yusof, Yuhanis

2017-10-01

In the increasing global business competitiveness, most organizations have attempted to determine the suitable external parties to support their core and non-core competency, particularly, in IT project outsourcing. The IT supplier selection is required to apply multi-criteria which comprised tangible criteria and intangible criteria in consider optimal IT supplier. Most researches attempted to identify optimal criteria for selecting IT supplier, however, the criteria cannot be the considered common criteria support the variety of IT outsourcing. Therefore, the study aimed to identify a common set of criteria being used in the various types of IT outsourcing. The common criteria are constructed by multi-criteria and success criteria, which were collected by literature review with comprehensive and comparative approach. Consequently, the researchers are able to identify a common set of criteria adopted in the variety of selection problem IT outsourcing supplier.

A mathematical framework for yield (vs. rate) optimization in constraint-based modeling and applications in metabolic engineering.

PubMed

Klamt, Steffen; Müller, Stefan; Regensburger, Georg; Zanghellini, Jürgen

2018-05-01

The optimization of metabolic rates (as linear objective functions) represents the methodical core of flux-balance analysis techniques which have become a standard tool for the study of genome-scale metabolic models. Besides (growth and synthesis) rates, metabolic yields are key parameters for the characterization of biochemical transformation processes, especially in the context of biotechnological applications. However, yields are ratios of rates, and hence the optimization of yields (as nonlinear objective functions) under arbitrary linear constraints is not possible with current flux-balance analysis techniques. Despite the fundamental importance of yields in constraint-based modeling, a comprehensive mathematical framework for yield optimization is still missing. We present a mathematical theory that allows one to systematically compute and analyze yield-optimal solutions of metabolic models under arbitrary linear constraints. In particular, we formulate yield optimization as a linear-fractional program. For practical computations, we transform the linear-fractional yield optimization problem to a (higher-dimensional) linear problem. Its solutions determine the solutions of the original problem and can be used to predict yield-optimal flux distributions in genome-scale metabolic models. For the theoretical analysis, we consider the linear-fractional problem directly. Most importantly, we show that the yield-optimal solution set (like the rate-optimal solution set) is determined by (yield-optimal) elementary flux vectors of the underlying metabolic model. However, yield- and rate-optimal solutions may differ from each other, and hence optimal (biomass or product) yields are not necessarily obtained at solutions with optimal (growth or synthesis) rates. Moreover, we discuss phase planes/production envelopes and yield spaces, in particular, we prove that yield spaces are convex and provide algorithms for their computation. We illustrate our findings by a small example and demonstrate their relevance for metabolic engineering with realistic models of E. coli. We develop a comprehensive mathematical framework for yield optimization in metabolic models. Our theory is particularly useful for the study and rational modification of cell factories designed under given yield and/or rate requirements. Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.

A minimal cost function method for optimizing the age-Depth relation of deep-sea sediment cores

NASA Astrophysics Data System (ADS)

Brüggemann, Wolfgang

1992-08-01

The question of an optimal age-depth relation for deep-sea sediment cores has been raised frequently. The data from such cores (e.g., δ18O values) are used to test the astronomical theory of ice ages as established by Milankovitch in 1938. In this work, we use a minimal cost function approach to find simultaneously an optimal age-depth relation and a linear model that optimally links solar insolation or other model input with global ice volume. Thus a general tool for the calibration of deep-sea cores to arbitrary tuning targets is presented. In this inverse modeling type approach, an objective function is minimized that penalizes: (1) the deviation of the data from the theoretical linear model (whose transfer function can be computed analytically for a given age-depth relation) and (2) the violation of a set of plausible assumptions about the model, the data and the obtained correction of a first guess age-depth function. These assumptions have been suggested before but are now quantified and incorporated explicitly into the objective function as penalty terms. We formulate an optimization problem that is solved numerically by conjugate gradient type methods. Using this direct approach, we obtain high coherences in the Milankovitch frequency bands (over 90%). Not only the data time series but also the the derived correction to a first guess linear age-depth function (and therefore the sedimentation rate) itself contains significant energy in a broad frequency band around 100 kyr. The use of a sedimentation rate which varies continuously on ice age time scales results in a shift of energy from 100 kyr in the original data spectrum to 41, 23, and 19 kyr in the spectrum of the corrected data. However, a large proportion of the data variance remains unexplained, particularly in the 100 kyr frequency band, where there is no significant input by orbital forcing. The presented method is applied to a real sediment core and to the SPECMAP stack, and results are compared with those obtained in earlier investigations.

Adaptive bi-level programming for optimal gene knockouts for targeted overproduction under phenotypic constraints

PubMed Central

2013-01-01

Background Optimization procedures to identify gene knockouts for targeted biochemical overproduction have been widely in use in modern metabolic engineering. Flux balance analysis (FBA) framework has provided conceptual simplifications for genome-scale dynamic analysis at steady states. Based on FBA, many current optimization methods for targeted bio-productions have been developed under the maximum cell growth assumption. The optimization problem to derive gene knockout strategies recently has been formulated as a bi-level programming problem in OptKnock for maximum targeted bio-productions with maximum growth rates. However, it has been shown that knockout mutants in fact reach the steady states with the minimization of metabolic adjustment (MOMA) from the corresponding wild-type strains instead of having maximal growth rates after genetic or metabolic intervention. In this work, we propose a new bi-level computational framework--MOMAKnock--which can derive robust knockout strategies under the MOMA flux distribution approximation. Methods In this new bi-level optimization framework, we aim to maximize the production of targeted chemicals by identifying candidate knockout genes or reactions under phenotypic constraints approximated by the MOMA assumption. Hence, the targeted chemical production is the primary objective of MOMAKnock while the MOMA assumption is formulated as the inner problem of constraining the knockout metabolic flux to be as close as possible to the steady-state phenotypes of wide-type strains. As this new inner problem becomes a quadratic programming problem, a novel adaptive piecewise linearization algorithm is developed in this paper to obtain the exact optimal solution to this new bi-level integer quadratic programming problem for MOMAKnock. Results Our new MOMAKnock model and the adaptive piecewise linearization solution algorithm are tested with a small E. coli core metabolic network and a large-scale iAF1260 E. coli metabolic network. The derived knockout strategies are compared with those from OptKnock. Our preliminary experimental results show that MOMAKnock can provide improved targeted productions with more robust knockout strategies. PMID:23368729

Adaptive bi-level programming for optimal gene knockouts for targeted overproduction under phenotypic constraints.

PubMed

Ren, Shaogang; Zeng, Bo; Qian, Xiaoning

2013-01-01

Optimization procedures to identify gene knockouts for targeted biochemical overproduction have been widely in use in modern metabolic engineering. Flux balance analysis (FBA) framework has provided conceptual simplifications for genome-scale dynamic analysis at steady states. Based on FBA, many current optimization methods for targeted bio-productions have been developed under the maximum cell growth assumption. The optimization problem to derive gene knockout strategies recently has been formulated as a bi-level programming problem in OptKnock for maximum targeted bio-productions with maximum growth rates. However, it has been shown that knockout mutants in fact reach the steady states with the minimization of metabolic adjustment (MOMA) from the corresponding wild-type strains instead of having maximal growth rates after genetic or metabolic intervention. In this work, we propose a new bi-level computational framework--MOMAKnock--which can derive robust knockout strategies under the MOMA flux distribution approximation. In this new bi-level optimization framework, we aim to maximize the production of targeted chemicals by identifying candidate knockout genes or reactions under phenotypic constraints approximated by the MOMA assumption. Hence, the targeted chemical production is the primary objective of MOMAKnock while the MOMA assumption is formulated as the inner problem of constraining the knockout metabolic flux to be as close as possible to the steady-state phenotypes of wide-type strains. As this new inner problem becomes a quadratic programming problem, a novel adaptive piecewise linearization algorithm is developed in this paper to obtain the exact optimal solution to this new bi-level integer quadratic programming problem for MOMAKnock. Our new MOMAKnock model and the adaptive piecewise linearization solution algorithm are tested with a small E. coli core metabolic network and a large-scale iAF1260 E. coli metabolic network. The derived knockout strategies are compared with those from OptKnock. Our preliminary experimental results show that MOMAKnock can provide improved targeted productions with more robust knockout strategies.

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

A Review on Medical Image Registration as an Optimization Problem

PubMed Central

Song, Guoli; Han, Jianda; Zhao, Yiwen; Wang, Zheng; Du, Huibin

2017-01-01

Objective: In the course of clinical treatment, several medical media are required by a phy-sician in order to provide accurate and complete information about a patient. Medical image registra-tion techniques can provide a richer diagnosis and treatment information to doctors and to provide a comprehensive reference source for the researchers involved in image registration as an optimization problem. Methods: The essence of image registration is associating two or more different images spatial asso-ciation, and getting the translation of their spatial relationship. For medical image registration, its pro-cess is not absolute. Its core purpose is finding the conversion relationship between different images. Result: The major step of image registration includes the change of geometrical dimensions, and change of the image of the combination, image similarity measure, iterative optimization and interpo-lation process. Conclusion: The contribution of this review is sort of related image registration research methods, can provide a brief reference for researchers about image registration. PMID:28845149

Hybrid parallel code acceleration methods in full-core reactor physics calculations

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

Courau, T.; Plagne, L.; Ponicot, A.

2012-07-01

When dealing with nuclear reactor calculation schemes, the need for three dimensional (3D) transport-based reference solutions is essential for both validation and optimization purposes. Considering a benchmark problem, this work investigates the potential of discrete ordinates (Sn) transport methods applied to 3D pressurized water reactor (PWR) full-core calculations. First, the benchmark problem is described. It involves a pin-by-pin description of a 3D PWR first core, and uses a 8-group cross-section library prepared with the DRAGON cell code. Then, a convergence analysis is performed using the PENTRAN parallel Sn Cartesian code. It discusses the spatial refinement and the associated angular quadraturemore » required to properly describe the problem physics. It also shows that initializing the Sn solution with the EDF SPN solver COCAGNE reduces the number of iterations required to converge by nearly a factor of 6. Using a best estimate model, PENTRAN results are then compared to multigroup Monte Carlo results obtained with the MCNP5 code. Good consistency is observed between the two methods (Sn and Monte Carlo), with discrepancies that are less than 25 pcm for the k{sub eff}, and less than 2.1% and 1.6% for the flux at the pin-cell level and for the pin-power distribution, respectively. (authors)« less

An Introduction to Kristof's Theorem for Solving Least-Square Optimization Problems Without Calculus.

PubMed

Waller, Niels

2018-01-01

Kristof's Theorem (Kristof, 1970 ) describes a matrix trace inequality that can be used to solve a wide-class of least-square optimization problems without calculus. Considering its generality, it is surprising that Kristof's Theorem is rarely used in statistics and psychometric applications. The underutilization of this method likely stems, in part, from the mathematical complexity of Kristof's ( 1964 , 1970 ) writings. In this article, I describe the underlying logic of Kristof's Theorem in simple terms by reviewing four key mathematical ideas that are used in the theorem's proof. I then show how Kristof's Theorem can be used to provide novel derivations to two cognate models from statistics and psychometrics. This tutorial includes a glossary of technical terms and an online supplement with R (R Core Team, 2017 ) code to perform the calculations described in the text.

Adaptive building skin structures

NASA Astrophysics Data System (ADS)

Del Grosso, A. E.; Basso, P.

2010-12-01

The concept of adaptive and morphing structures has gained considerable attention in the recent years in many fields of engineering. In civil engineering very few practical applications are reported to date however. Non-conventional structural concepts like deployable, inflatable and morphing structures may indeed provide innovative solutions to some of the problems that the construction industry is being called to face. To give some examples, searches for low-energy consumption or even energy-harvesting green buildings are amongst such problems. This paper first presents a review of the above problems and technologies, which shows how the solution to these problems requires a multidisciplinary approach, involving the integration of architectural and engineering disciplines. The discussion continues with the presentation of a possible application of two adaptive and dynamically morphing structures which are proposed for the realization of an acoustic envelope. The core of the two applications is the use of a novel optimization process which leads the search for optimal solutions by means of an evolutionary technique while the compatibility of the resulting configurations of the adaptive envelope is ensured by the virtual force density method.

Scout: high-performance heterogeneous computing made simple

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

Jablin, James; Mc Cormick, Patrick; Herlihy, Maurice

2011-01-26

Researchers must often write their own simulation and analysis software. During this process they simultaneously confront both computational and scientific problems. Current strategies for aiding the generation of performance-oriented programs do not abstract the software development from the science. Furthermore, the problem is becoming increasingly complex and pressing with the continued development of many-core and heterogeneous (CPU-GPU) architectures. To acbieve high performance, scientists must expertly navigate both software and hardware. Co-design between computer scientists and research scientists can alleviate but not solve this problem. The science community requires better tools for developing, optimizing, and future-proofing codes, allowing scientists to focusmore » on their research while still achieving high computational performance. Scout is a parallel programming language and extensible compiler framework targeting heterogeneous architectures. It provides the abstraction required to buffer scientists from the constantly-shifting details of hardware while still realizing higb-performance by encapsulating software and hardware optimization within a compiler framework.« less

Large-scale optimization-based non-negative computational framework for diffusion equations: Parallel implementation and performance studies

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

Chang, Justin; Karra, Satish; Nakshatrala, Kalyana B.

It is well-known that the standard Galerkin formulation, which is often the formulation of choice under the finite element method for solving self-adjoint diffusion equations, does not meet maximum principles and the non-negative constraint for anisotropic diffusion equations. Recently, optimization-based methodologies that satisfy maximum principles and the non-negative constraint for steady-state and transient diffusion-type equations have been proposed. To date, these methodologies have been tested only on small-scale academic problems. The purpose of this paper is to systematically study the performance of the non-negative methodology in the context of high performance computing (HPC). PETSc and TAO libraries are, respectively, usedmore » for the parallel environment and optimization solvers. For large-scale problems, it is important for computational scientists to understand the computational performance of current algorithms available in these scientific libraries. The numerical experiments are conducted on the state-of-the-art HPC systems, and a single-core performance model is used to better characterize the efficiency of the solvers. Furthermore, our studies indicate that the proposed non-negative computational framework for diffusion-type equations exhibits excellent strong scaling for real-world large-scale problems.« less

Large-scale optimization-based non-negative computational framework for diffusion equations: Parallel implementation and performance studies

DOE PAGES

Chang, Justin; Karra, Satish; Nakshatrala, Kalyana B.

2016-07-26

It is well-known that the standard Galerkin formulation, which is often the formulation of choice under the finite element method for solving self-adjoint diffusion equations, does not meet maximum principles and the non-negative constraint for anisotropic diffusion equations. Recently, optimization-based methodologies that satisfy maximum principles and the non-negative constraint for steady-state and transient diffusion-type equations have been proposed. To date, these methodologies have been tested only on small-scale academic problems. The purpose of this paper is to systematically study the performance of the non-negative methodology in the context of high performance computing (HPC). PETSc and TAO libraries are, respectively, usedmore » for the parallel environment and optimization solvers. For large-scale problems, it is important for computational scientists to understand the computational performance of current algorithms available in these scientific libraries. The numerical experiments are conducted on the state-of-the-art HPC systems, and a single-core performance model is used to better characterize the efficiency of the solvers. Furthermore, our studies indicate that the proposed non-negative computational framework for diffusion-type equations exhibits excellent strong scaling for real-world large-scale problems.« less

Safety and core design of large liquid-metal cooled fast breeder reactors

NASA Astrophysics Data System (ADS)

Qvist, Staffan Alexander

In light of the scientific evidence for changes in the climate caused by greenhouse-gas emissions from human activities, the world is in ever more desperate need of new, inexhaustible, safe and clean primary energy sources. A viable solution to this problem is the widespread adoption of nuclear breeder reactor technology. Innovative breeder reactor concepts using liquid-metal coolants such as sodium or lead will be able to utilize the waste produced by the current light water reactor fuel cycle to power the entire world for several centuries to come. Breed & burn (B&B) type fast reactor cores can unlock the energy potential of readily available fertile material such as depleted uranium without the need for chemical reprocessing. Using B&B technology, nuclear waste generation, uranium mining needs and proliferation concerns can be greatly reduced, and after a transitional period, enrichment facilities may no longer be needed. In this dissertation, new passively operating safety systems for fast reactors cores are presented. New analysis and optimization methods for B&B core design have been developed, along with a comprehensive computer code that couples neutronics, thermal-hydraulics and structural mechanics and enables a completely automated and optimized fast reactor core design process. In addition, an experiment that expands the knowledge-base of corrosion issues of lead-based coolants in nuclear reactors was designed and built. The motivation behind the work presented in this thesis is to help facilitate the widespread adoption of safe and efficient fast reactor technology.

A Projection free method for Generalized Eigenvalue Problem with a nonsmooth Regularizer.

PubMed

Hwang, Seong Jae; Collins, Maxwell D; Ravi, Sathya N; Ithapu, Vamsi K; Adluru, Nagesh; Johnson, Sterling C; Singh, Vikas

2015-12-01

Eigenvalue problems are ubiquitous in computer vision, covering a very broad spectrum of applications ranging from estimation problems in multi-view geometry to image segmentation. Few other linear algebra problems have a more mature set of numerical routines available and many computer vision libraries leverage such tools extensively. However, the ability to call the underlying solver only as a "black box" can often become restrictive. Many 'human in the loop' settings in vision frequently exploit supervision from an expert, to the extent that the user can be considered a subroutine in the overall system. In other cases, there is additional domain knowledge, side or even partial information that one may want to incorporate within the formulation. In general, regularizing a (generalized) eigenvalue problem with such side information remains difficult. Motivated by these needs, this paper presents an optimization scheme to solve generalized eigenvalue problems (GEP) involving a (nonsmooth) regularizer. We start from an alternative formulation of GEP where the feasibility set of the model involves the Stiefel manifold. The core of this paper presents an end to end stochastic optimization scheme for the resultant problem. We show how this general algorithm enables improved statistical analysis of brain imaging data where the regularizer is derived from other 'views' of the disease pathology, involving clinical measurements and other image-derived representations.

TOC and TRIZ: using a dual-methodological approach to solve a forest harvesting problem

Treesearch

Ian Conradie

2005-01-01

Although cut-to-length forest harvesting with harvesters and forwarders is hardly used in some parts of the world, it has many advantages over conventional harvesting systems. Research has shown that the core reason for the low adoption of CTL in the southeastern USA is the complexity of the equipment to optimize value recovery. In this paper we delve deeper into this...

Cooperative Scheduling of Imaging Observation Tasks for High-Altitude Airships Based on Propagation Algorithm

PubMed Central

Chuan, He; Dishan, Qiu; Jin, Liu

2012-01-01

The cooperative scheduling problem on high-altitude airships for imaging observation tasks is discussed. A constraint programming model is established by analyzing the main constraints, which takes the maximum task benefit and the minimum cruising distance as two optimization objectives. The cooperative scheduling problem of high-altitude airships is converted into a main problem and a subproblem by adopting hierarchy architecture. The solution to the main problem can construct the preliminary matching between tasks and observation resource in order to reduce the search space of the original problem. Furthermore, the solution to the sub-problem can detect the key nodes that each airship needs to fly through in sequence, so as to get the cruising path. Firstly, the task set is divided by using k-core neighborhood growth cluster algorithm (K-NGCA). Then, a novel swarm intelligence algorithm named propagation algorithm (PA) is combined with the key node search algorithm (KNSA) to optimize the cruising path of each airship and determine the execution time interval of each task. Meanwhile, this paper also provides the realization approach of the above algorithm and especially makes a detailed introduction on the encoding rules, search models, and propagation mechanism of the PA. Finally, the application results and comparison analysis show the proposed models and algorithms are effective and feasible. PMID:23365522

Validation of a coupled core-transport, pedestal-structure, current-profile and equilibrium model

NASA Astrophysics Data System (ADS)

Meneghini, O.

2015-11-01

The first workflow capable of predicting the self-consistent solution to the coupled core-transport, pedestal structure, and equilibrium problems from first-principles and its experimental tests are presented. Validation with DIII-D discharges in high confinement regimes shows that the workflow is capable of robustly predicting the kinetic profiles from on axis to the separatrix and matching the experimental measurements to within their uncertainty, with no prior knowledge of the pedestal height nor of any measurement of the temperature or pressure. Self-consistent coupling has proven to be essential to match the experimental results, and capture the non-linear physics that governs the core and pedestal solutions. In particular, clear stabilization of the pedestal peeling ballooning instabilities by the global Shafranov shift and destabilization by additional edge bootstrap current, and subsequent effect on the core plasma profiles, have been clearly observed and documented. In our model, self-consistency is achieved by iterating between the TGYRO core transport solver (with NEO and TGLF for neoclassical and turbulent flux), and the pedestal structure predicted by the EPED model. A self-consistent equilibrium is calculated by EFIT, while the ONETWO transport package evolves the current profile and calculates the particle and energy sources. The capabilities of such workflow are shown to be critical for the design of future experiments such as ITER and FNSF, which operate in a regime where the equilibrium, the pedestal, and the core transport problems are strongly coupled, and for which none of these quantities can be assumed to be known. Self-consistent core-pedestal predictions for ITER, as well as initial optimizations, will be presented. Supported by the US Department of Energy under DE-FC02-04ER54698, DE-SC0012652.

Construction, classification and parametrization of complex Hadamard matrices

NASA Astrophysics Data System (ADS)

Szöllősi, Ferenc

To improve the design of nuclear systems, high-fidelity neutron fluxes are required. Leadership-class machines provide platforms on which very large problems can be solved. Computing such fluxes efficiently requires numerical methods with good convergence properties and algorithms that can scale to hundreds of thousands of cores. Many 3-D deterministic transport codes are decomposable in space and angle only, limiting them to tens of thousands of cores. Most codes rely on methods such as Gauss Seidel for fixed source problems and power iteration for eigenvalue problems, which can be slow to converge for challenging problems like those with highly scattering materials or high dominance ratios. Three methods have been added to the 3-D SN transport code Denovo that are designed to improve convergence and enable the full use of cutting-edge computers. The first is a multigroup Krylov solver that converges more quickly than Gauss Seidel and parallelizes the code in energy such that Denovo can use hundreds of thousand of cores effectively. The second is Rayleigh quotient iteration (RQI), an old method applied in a new context. This eigenvalue solver finds the dominant eigenvalue in a mathematically optimal way and should converge in fewer iterations than power iteration. RQI creates energy-block-dense equations that the new Krylov solver treats efficiently. However, RQI can have convergence problems because it creates poorly conditioned systems. This can be overcome with preconditioning. The third method is a multigrid-in-energy preconditioner. The preconditioner takes advantage of the new energy decomposition because the grids are in energy rather than space or angle. The preconditioner greatly reduces iteration count for many problem types and scales well in energy. It also allows RQI to be successful for problems it could not solve otherwise. The methods added to Denovo accomplish the goals of this work. They converge in fewer iterations than traditional methods and enable the use of hundreds of thousands of cores. Each method can be used individually, with the multigroup Krylov solver and multigrid-in-energy preconditioner being particularly successful on their own. The largest benefit, though, comes from using these methods in concert.

Applications of Derandomization Theory in Coding

NASA Astrophysics Data System (ADS)

Cheraghchi, Mahdi

2011-07-01

Randomized techniques play a fundamental role in theoretical computer science and discrete mathematics, in particular for the design of efficient algorithms and construction of combinatorial objects. The basic goal in derandomization theory is to eliminate or reduce the need for randomness in such randomized constructions. In this thesis, we explore some applications of the fundamental notions in derandomization theory to problems outside the core of theoretical computer science, and in particular, certain problems related to coding theory. First, we consider the wiretap channel problem which involves a communication system in which an intruder can eavesdrop a limited portion of the transmissions, and construct efficient and information-theoretically optimal communication protocols for this model. Then we consider the combinatorial group testing problem. In this classical problem, one aims to determine a set of defective items within a large population by asking a number of queries, where each query reveals whether a defective item is present within a specified group of items. We use randomness condensers to explicitly construct optimal, or nearly optimal, group testing schemes for a setting where the query outcomes can be highly unreliable, as well as the threshold model where a query returns positive if the number of defectives pass a certain threshold. Finally, we design ensembles of error-correcting codes that achieve the information-theoretic capacity of a large class of communication channels, and then use the obtained ensembles for construction of explicit capacity achieving codes. [This is a shortened version of the actual abstract in the thesis.

Dynamic optimization of metabolic networks coupled with gene expression.

PubMed

Waldherr, Steffen; Oyarzún, Diego A; Bockmayr, Alexander

2015-01-21

The regulation of metabolic activity by tuning enzyme expression levels is crucial to sustain cellular growth in changing environments. Metabolic networks are often studied at steady state using constraint-based models and optimization techniques. However, metabolic adaptations driven by changes in gene expression cannot be analyzed by steady state models, as these do not account for temporal changes in biomass composition. Here we present a dynamic optimization framework that integrates the metabolic network with the dynamics of biomass production and composition. An approximation by a timescale separation leads to a coupled model of quasi-steady state constraints on the metabolic reactions, and differential equations for the substrate concentrations and biomass composition. We propose a dynamic optimization approach to determine reaction fluxes for this model, explicitly taking into account enzyme production costs and enzymatic capacity. In contrast to the established dynamic flux balance analysis, our approach allows predicting dynamic changes in both the metabolic fluxes and the biomass composition during metabolic adaptations. Discretization of the optimization problems leads to a linear program that can be efficiently solved. We applied our algorithm in two case studies: a minimal nutrient uptake network, and an abstraction of core metabolic processes in bacteria. In the minimal model, we show that the optimized uptake rates reproduce the empirical Monod growth for bacterial cultures. For the network of core metabolic processes, the dynamic optimization algorithm predicted commonly observed metabolic adaptations, such as a diauxic switch with a preference ranking for different nutrients, re-utilization of waste products after depletion of the original substrate, and metabolic adaptation to an impending nutrient depletion. These examples illustrate how dynamic adaptations of enzyme expression can be predicted solely from an optimization principle. Copyright © 2014 Elsevier Ltd. All rights reserved.

New approach in the evaluation of a fitness program at a worksite.

PubMed

Shirasaya, K; Miyakawa, M; Yoshida, K; Tanaka, C; Shimada, N; Kondo, T

1999-03-01

The most common methods for the economic evaluation of a fitness program at a worksite are cost-effectiveness, cost-benefit, and cost-utility analyses. In this study, we applied a basic microeconomic theory, "neoclassical firm's problems," as the new approach for it. The optimal number of physical-exercise classes that constitute the core of the fitness program are determined using the cubic health production function. The optimal number is defined as the number that maximizes the profit of the program. The optimal number corresponding to any willingness-to-pay amount of the participants for the effectiveness of the program is presented using a graph. For example, if the willingness-to-pay is $800, the optimal number of classes is 23. Our method can be applied to the evaluation of any health care program if the health production function can be estimated.

Particle-in-Cell laser-plasma simulation on Xeon Phi coprocessors

NASA Astrophysics Data System (ADS)

Surmin, I. A.; Bastrakov, S. I.; Efimenko, E. S.; Gonoskov, A. A.; Korzhimanov, A. V.; Meyerov, I. B.

2016-05-01

This paper concerns the development of a high-performance implementation of the Particle-in-Cell method for plasma simulation on Intel Xeon Phi coprocessors. We discuss the suitability of the method for Xeon Phi architecture and present our experience in the porting and optimization of the existing parallel Particle-in-Cell code PICADOR. Direct porting without code modification gives performance on Xeon Phi close to that of an 8-core CPU on a benchmark problem with 50 particles per cell. We demonstrate step-by-step optimization techniques, such as improving data locality, enhancing parallelization efficiency and vectorization leading to an overall 4.2 × speedup on CPU and 7.5 × on Xeon Phi compared to the baseline version. The optimized version achieves 16.9 ns per particle update on an Intel Xeon E5-2660 CPU and 9.3 ns per particle update on an Intel Xeon Phi 5110P. For a real problem of laser ion acceleration in targets with surface grating, where a large number of macroparticles per cell is required, the speedup of Xeon Phi compared to CPU is 1.6 ×.

Integrated fusion simulation with self-consistent core-pedestal coupling

DOE PAGES

Meneghini, O.; Snyder, P. B.; Smith, S. P.; ...

2016-04-20

In this study, accurate prediction of fusion performance in present and future tokamaks requires taking into account the strong interplay between core transport, pedestal structure, current profile and plasma equilibrium. An integrated modeling workflow capable of calculating the steady-state self- consistent solution to this strongly-coupled problem has been developed. The workflow leverages state-of-the-art components for collisional and turbulent core transport, equilibrium and pedestal stability. Validation against DIII-D discharges shows that the workflow is capable of robustly pre- dicting the kinetic profiles (electron and ion temperature and electron density) from the axis to the separatrix in good agreement with the experiments.more » An example application is presented, showing self-consistent optimization for the fusion performance of the 15 MA D-T ITER baseline scenario as functions of the pedestal density and ion effective charge Z eff.« less

Porous Core-Shell Nanostructures for Catalytic Applications

NASA Astrophysics Data System (ADS)

Ewers, Trevor David

Porous core-shell nanostructures have recently received much attention for their enhanced thermal stability. They show great potential in the field of catalysis, as reactant gases can diffuse in and out of the porous shell while the core particle is protected from sintering, a process in which particles coalesce to form larger particles. Sintering is a large problem in industry and is the primary cause of irreversible deactivation. Despite the obvious advantages of high thermal stability, porous core-shell nanoparticles can be developed to have additional interactive properties from the combination of the core and shell together, rather than just the core particle alone. This dissertation focuses on developing new porous core-shell systems in which both the core and shell take part in catalysis. Two types of systems are explored; (1) yolk-shell nanostructures with reducible oxide shells formed using the Kirkendall effect and (2) ceramic-based porous oxide shells formed using sol-gel chemistry. Of the Kirkendall-based systems, Au FexOy and Cu CoO were synthesized and studied for catalytic applications. Additionally, ZnO was explored as a potential shelling material. Sol-gel work focused on optimizing synthetic methods to allow for coating of small gold particles, which remains a challenge today. Mixed metal oxides were explored as a shelling material to make dual catalysts in which the product of a reaction on the core particle becomes a reactant within the shell.

Competitive repetition suppression (CoRe) clustering: a biologically inspired learning model with application to robust clustering.

PubMed

Bacciu, Davide; Starita, Antonina

2008-11-01

Determining a compact neural coding for a set of input stimuli is an issue that encompasses several biological memory mechanisms as well as various artificial neural network models. In particular, establishing the optimal network structure is still an open problem when dealing with unsupervised learning models. In this paper, we introduce a novel learning algorithm, named competitive repetition-suppression (CoRe) learning, inspired by a cortical memory mechanism called repetition suppression (RS). We show how such a mechanism is used, at various levels of the cerebral cortex, to generate compact neural representations of the visual stimuli. From the general CoRe learning model, we derive a clustering algorithm, named CoRe clustering, that can automatically estimate the unknown cluster number from the data without using a priori information concerning the input distribution. We illustrate how CoRe clustering, besides its biological plausibility, posses strong theoretical properties in terms of robustness to noise and outliers, and we provide an error function describing CoRe learning dynamics. Such a description is used to analyze CoRe relationships with the state-of-the art clustering models and to highlight CoRe similitude with rival penalized competitive learning (RPCL), showing how CoRe extends such a model by strengthening the rival penalization estimation by means of loss functions from robust statistics.

Performance Evaluation of NWChem Ab-Initio Molecular Dynamics (AIMD) Simulations on the Intel® Xeon Phi™ Processor

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

Bylaska, Eric J.; Jacquelin, Mathias; De Jong, Wibe A.

2017-10-20

Ab-initio Molecular Dynamics (AIMD) methods are an important class of algorithms, as they enable scientists to understand the chemistry and dynamics of molecular and condensed phase systems while retaining a first-principles-based description of their interactions. Many-core architectures such as the Intel® Xeon Phi™ processor are an interesting and promising target for these algorithms, as they can provide the computational power that is needed to solve interesting problems in chemistry. In this paper, we describe the efforts of refactoring the existing AIMD plane-wave method of NWChem from an MPI-only implementation to a scalable, hybrid code that employs MPI and OpenMP tomore » exploit the capabilities of current and future many-core architectures. We describe the optimizations required to get close to optimal performance for the multiplication of the tall-and-skinny matrices that form the core of the computational algorithm. We present strong scaling results on the complete AIMD simulation for a test case that simulates 256 water molecules and that strong-scales well on a cluster of 1024 nodes of Intel Xeon Phi processors. We compare the performance obtained with a cluster of dual-socket Intel® Xeon® E5–2698v3 processors.« less

Multi-GPU Jacobian accelerated computing for soft-field tomography.

PubMed

Borsic, A; Attardo, E A; Halter, R J

2012-10-01

Image reconstruction in soft-field tomography is based on an inverse problem formulation, where a forward model is fitted to the data. In medical applications, where the anatomy presents complex shapes, it is common to use finite element models (FEMs) to represent the volume of interest and solve a partial differential equation that models the physics of the system. Over the last decade, there has been a shifting interest from 2D modeling to 3D modeling, as the underlying physics of most problems are 3D. Although the increased computational power of modern computers allows working with much larger FEM models, the computational time required to reconstruct 3D images on a fine 3D FEM model can be significant, on the order of hours. For example, in electrical impedance tomography (EIT) applications using a dense 3D FEM mesh with half a million elements, a single reconstruction iteration takes approximately 15-20 min with optimized routines running on a modern multi-core PC. It is desirable to accelerate image reconstruction to enable researchers to more easily and rapidly explore data and reconstruction parameters. Furthermore, providing high-speed reconstructions is essential for some promising clinical application of EIT. For 3D problems, 70% of the computing time is spent building the Jacobian matrix, and 25% of the time in forward solving. In this work, we focus on accelerating the Jacobian computation by using single and multiple GPUs. First, we discuss an optimized implementation on a modern multi-core PC architecture and show how computing time is bounded by the CPU-to-memory bandwidth; this factor limits the rate at which data can be fetched by the CPU. Gains associated with the use of multiple CPU cores are minimal, since data operands cannot be fetched fast enough to saturate the processing power of even a single CPU core. GPUs have much faster memory bandwidths compared to CPUs and better parallelism. We are able to obtain acceleration factors of 20 times on a single NVIDIA S1070 GPU, and of 50 times on four GPUs, bringing the Jacobian computing time for a fine 3D mesh from 12 min to 14 s. We regard this as an important step toward gaining interactive reconstruction times in 3D imaging, particularly when coupled in the future with acceleration of the forward problem. While we demonstrate results for EIT, these results apply to any soft-field imaging modality where the Jacobian matrix is computed with the adjoint method.

Multi-GPU Jacobian Accelerated Computing for Soft Field Tomography

PubMed Central

Borsic, A.; Attardo, E. A.; Halter, R. J.

2012-01-01

Image reconstruction in soft-field tomography is based on an inverse problem formulation, where a forward model is fitted to the data. In medical applications, where the anatomy presents complex shapes, it is common to use Finite Element Models to represent the volume of interest and to solve a partial differential equation that models the physics of the system. Over the last decade, there has been a shifting interest from 2D modeling to 3D modeling, as the underlying physics of most problems are three-dimensional. Though the increased computational power of modern computers allows working with much larger FEM models, the computational time required to reconstruct 3D images on a fine 3D FEM model can be significant, on the order of hours. For example, in Electrical Impedance Tomography applications using a dense 3D FEM mesh with half a million elements, a single reconstruction iteration takes approximately 15 to 20 minutes with optimized routines running on a modern multi-core PC. It is desirable to accelerate image reconstruction to enable researchers to more easily and rapidly explore data and reconstruction parameters. Further, providing high-speed reconstructions are essential for some promising clinical application of EIT. For 3D problems 70% of the computing time is spent building the Jacobian matrix, and 25% of the time in forward solving. In the present work, we focus on accelerating the Jacobian computation by using single and multiple GPUs. First, we discuss an optimized implementation on a modern multi-core PC architecture and show how computing time is bounded by the CPU-to-memory bandwidth; this factor limits the rate at which data can be fetched by the CPU. Gains associated with use of multiple CPU cores are minimal, since data operands cannot be fetched fast enough to saturate the processing power of even a single CPU core. GPUs have a much faster memory bandwidths compared to CPUs and better parallelism. We are able to obtain acceleration factors of 20 times on a single NVIDIA S1070 GPU, and of 50 times on 4 GPUs, bringing the Jacobian computing time for a fine 3D mesh from 12 minutes to 14 seconds. We regard this as an important step towards gaining interactive reconstruction times in 3D imaging, particularly when coupled in the future with acceleration of the forward problem. While we demonstrate results for Electrical Impedance Tomography, these results apply to any soft-field imaging modality where the Jacobian matrix is computed with the Adjoint Method. PMID:23010857

Using Stochastic Spiking Neural Networks on SpiNNaker to Solve Constraint Satisfaction Problems

PubMed Central

Fonseca Guerra, Gabriel A.; Furber, Steve B.

2017-01-01

Constraint satisfaction problems (CSP) are at the core of numerous scientific and technological applications. However, CSPs belong to the NP-complete complexity class, for which the existence (or not) of efficient algorithms remains a major unsolved question in computational complexity theory. In the face of this fundamental difficulty heuristics and approximation methods are used to approach instances of NP (e.g., decision and hard optimization problems). The human brain efficiently handles CSPs both in perception and behavior using spiking neural networks (SNNs), and recent studies have demonstrated that the noise embedded within an SNN can be used as a computational resource to solve CSPs. Here, we provide a software framework for the implementation of such noisy neural solvers on the SpiNNaker massively parallel neuromorphic hardware, further demonstrating their potential to implement a stochastic search that solves instances of P and NP problems expressed as CSPs. This facilitates the exploration of new optimization strategies and the understanding of the computational abilities of SNNs. We demonstrate the basic principles of the framework by solving difficult instances of the Sudoku puzzle and of the map color problem, and explore its application to spin glasses. The solver works as a stochastic dynamical system, which is attracted by the configuration that solves the CSP. The noise allows an optimal exploration of the space of configurations, looking for the satisfiability of all the constraints; if applied discontinuously, it can also force the system to leap to a new random configuration effectively causing a restart. PMID:29311791

An optimization methodology for heterogeneous minor actinides transmutation

NASA Astrophysics Data System (ADS)

Kooyman, Timothée; Buiron, Laurent; Rimpault, Gérald

2018-04-01

In the case of a closed fuel cycle, minor actinides transmutation can lead to a strong reduction in spent fuel radiotoxicity and decay heat. In the heterogeneous approach, minor actinides are loaded in dedicated targets located at the core periphery so that long-lived minor actinides undergo fission and are turned in shorter-lived fission products. However, such targets require a specific design process due to high helium production in the fuel, high flux gradient at the core periphery and low power production. Additionally, the targets are generally manufactured with a high content in minor actinides in order to compensate for the low flux level at the core periphery. This leads to negative impacts on the fuel cycle in terms of neutron source and decay heat of the irradiated targets, which penalize their handling and reprocessing. In this paper, a simplified methodology for the design of targets is coupled with a method for the optimization of transmutation which takes into account both transmutation performances and fuel cycle impacts. The uncertainties and performances of this methodology are evaluated and shown to be sufficient to carry out scoping studies. An illustration is then made by considering the use of moderating material in the targets, which has a positive impact on the minor actinides consumption but a negative impact both on fuel cycle constraints (higher decay heat and neutron) and on assembly design (higher helium production and lower fuel volume fraction). It is shown that the use of moderating material is an optimal solution of the transmutation problem with regards to consumption and fuel cycle impacts, even when taking geometrical design considerations into account.

Genetic algorithms for protein threading.

PubMed

Yadgari, J; Amir, A; Unger, R

1998-01-01

Despite many years of efforts, a direct prediction of protein structure from sequence is still not possible. As a result, in the last few years researchers have started to address the "inverse folding problem": Identifying and aligning a sequence to the fold with which it is most compatible, a process known as "threading". In two meetings in which protein folding predictions were objectively evaluated, it became clear that threading as a concept promises a real breakthrough, but that much improvement is still needed in the technique itself. Threading is a NP-hard problem, and thus no general polynomial solution can be expected. Still a practical approach with demonstrated ability to find optimal solutions in many cases, and acceptable solutions in other cases, is needed. We applied the technique of Genetic Algorithms in order to significantly improve the ability of threading algorithms to find the optimal alignment of a sequence to a structure, i.e. the alignment with the minimum free energy. A major progress reported here is the design of a representation of the threading alignment as a string of fixed length. With this representation validation of alignments and genetic operators are effectively implemented. Appropriate data structure and parameters have been selected. It is shown that Genetic Algorithm threading is effective and is able to find the optimal alignment in a few test cases. Furthermore, the described algorithm is shown to perform well even without pre-definition of core elements. Existing threading methods are dependent on such constraints to make their calculations feasible. But the concept of core elements is inherently arbitrary and should be avoided if possible. While a rigorous proof is hard to submit yet an, we present indications that indeed Genetic Algorithm threading is capable of finding consistently good solutions of full alignments in search spaces of size up to 10(70).

Energy Efficient Real-Time Scheduling Using DPM on Mobile Sensors with a Uniform Multi-Cores

PubMed Central

Kim, Youngmin; Lee, Chan-Gun

2017-01-01

In wireless sensor networks (WSNs), sensor nodes are deployed for collecting and analyzing data. These nodes use limited energy batteries for easy deployment and low cost. The use of limited energy batteries is closely related to the lifetime of the sensor nodes when using wireless sensor networks. Efficient-energy management is important to extending the lifetime of the sensor nodes. Most effort for improving power efficiency in tiny sensor nodes has focused mainly on reducing the power consumed during data transmission. However, recent emergence of sensor nodes equipped with multi-cores strongly requires attention to be given to the problem of reducing power consumption in multi-cores. In this paper, we propose an energy efficient scheduling method for sensor nodes supporting a uniform multi-cores. We extend the proposed T-Ler plane based scheduling for global optimal scheduling of a uniform multi-cores and multi-processors to enable power management using dynamic power management. In the proposed approach, processor selection for a scheduling and mapping method between the tasks and processors is proposed to efficiently utilize dynamic power management. Experiments show the effectiveness of the proposed approach compared to other existing methods. PMID:29240695

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

Gearhart, Jared Lee; Adair, Kristin Lynn; Durfee, Justin David.

When developing linear programming models, issues such as budget limitations, customer requirements, or licensing may preclude the use of commercial linear programming solvers. In such cases, one option is to use an open-source linear programming solver. A survey of linear programming tools was conducted to identify potential open-source solvers. From this survey, four open-source solvers were tested using a collection of linear programming test problems and the results were compared to IBM ILOG CPLEX Optimizer (CPLEX) [1], an industry standard. The solvers considered were: COIN-OR Linear Programming (CLP) [2], [3], GNU Linear Programming Kit (GLPK) [4], lp_solve [5] and Modularmore » In-core Nonlinear Optimization System (MINOS) [6]. As no open-source solver outperforms CPLEX, this study demonstrates the power of commercial linear programming software. CLP was found to be the top performing open-source solver considered in terms of capability and speed. GLPK also performed well but cannot match the speed of CLP or CPLEX. lp_solve and MINOS were considerably slower and encountered issues when solving several test problems.« less

Design synthesis and optimization of permanent magnet synchronous machines based on computationally-efficient finite element analysis

NASA Astrophysics Data System (ADS)

Sizov, Gennadi Y.

In this dissertation, a model-based multi-objective optimal design of permanent magnet ac machines, supplied by sine-wave current regulated drives, is developed and implemented. The design procedure uses an efficient electromagnetic finite element-based solver to accurately model nonlinear material properties and complex geometric shapes associated with magnetic circuit design. Application of an electromagnetic finite element-based solver allows for accurate computation of intricate performance parameters and characteristics. The first contribution of this dissertation is the development of a rapid computational method that allows accurate and efficient exploration of large multi-dimensional design spaces in search of optimum design(s). The computationally efficient finite element-based approach developed in this work provides a framework of tools that allow rapid analysis of synchronous electric machines operating under steady-state conditions. In the developed modeling approach, major steady-state performance parameters such as, winding flux linkages and voltages, average, cogging and ripple torques, stator core flux densities, core losses, efficiencies and saturated machine winding inductances, are calculated with minimum computational effort. In addition, the method includes means for rapid estimation of distributed stator forces and three-dimensional effects of stator and/or rotor skew on the performance of the machine. The second contribution of this dissertation is the development of the design synthesis and optimization method based on a differential evolution algorithm. The approach relies on the developed finite element-based modeling method for electromagnetic analysis and is able to tackle large-scale multi-objective design problems using modest computational resources. Overall, computational time savings of up to two orders of magnitude are achievable, when compared to current and prevalent state-of-the-art methods. These computational savings allow one to expand the optimization problem to achieve more complex and comprehensive design objectives. The method is used in the design process of several interior permanent magnet industrial motors. The presented case studies demonstrate that the developed finite element-based approach practically eliminates the need for using less accurate analytical and lumped parameter equivalent circuit models for electric machine design optimization. The design process and experimental validation of the case-study machines are detailed in the dissertation.

Optimization benefits analysis in production process of fabrication components

NASA Astrophysics Data System (ADS)

Prasetyani, R.; Rafsanjani, A. Y.; Rimantho, D.

2017-12-01

The determination of an optimal number of product combinations is important. The main problem at part and service department in PT. United Tractors Pandu Engineering (shortened to PT.UTPE) Is the optimization of the combination of fabrication component products (known as Liner Plate) which influence to the profit that will be obtained by the company. Liner Plate is a fabrication component that serves as a protector of core structure for heavy duty attachment, such as HD Vessel, HD Bucket, HD Shovel, and HD Blade. The graph of liner plate sales from January to December 2016 has fluctuated and there is no direct conclusion about the optimization of production of such fabrication components. The optimal product combination can be achieved by calculating and plotting the amount of production output and input appropriately. The method that used in this study is linear programming methods with primal, dual, and sensitivity analysis using QM software for Windows to obtain optimal fabrication components. In the optimal combination of components, PT. UTPE provide the profit increase of Rp. 105,285,000.00 for a total of Rp. 3,046,525,000.00 per month and the production of a total combination of 71 units per unit variance per month.

Synthesis of carbon core–shell pore structures and their performance as supercapacitors

DOE PAGES

Ariyanto, Teguh; Dyatkin, Boris; Zhang, Gui-Rong; ...

2015-07-15

High-power supercapacitors require excellent electrolyte mobility within the pore network and high electrical conductivity for maximum capacitance and efficiency. Achieving high power typically requires sacrificing energy densities, as the latter demands a high specific surface area and narrow porosity that impedes ion transport. Here, we present a novel solution for this optimization problem: a nanostructured core–shell carbonaceous material that exhibits a microporous carbon core surrounded by a mesoporous, graphitic shell. The tunable synthesis parameters yielded a structure that features either a sharp or a gradual transition between the core and shell sections. Electrochemical supercapacitor testing using organic electrolyte revealed thatmore » these novel core–shell materials outperform carbons with homogeneous pore structures. The hybrid core–shell materials showed a combination of good capacitance retention, typical for the carbon present in the shell and high specific capacitance, typical for the core material. These materials achieved power densities in excess of 40 kW kg -1 at energy densities reaching 27 Wh kg -1.« less

Optimization of GRIN lenses coupling system for twin-core fiber interconnection with single core fibers

NASA Astrophysics Data System (ADS)

Chen, Gongdai; Deng, Hongchang; Yuan, Libo

2018-07-01

We aim at a more compact, flexible, and simpler core-to-fiber coupling approach, optimal combinations of two graded refractive index (GRIN) lenses have been demonstrated for the interconnection between a twin-core single-mode fiber and two single-core single-mode fibers. The optimal two-lens combinations achieve an efficient core-to-fiber separating coupling and allow the fibers and lenses to coaxially assemble. Finally, axial deviations and transverse displacements of the components are discussed, and the latter increases the coupling loss more significantly. The gap length between the two lenses is designed to be fine-tuned to compensate for the transverse displacement, and the good linear compensation relationship contributes to the device manufacturing. This approach has potential applications in low coupling loss and low crosstalk devices without sophisticated alignment and adjustment, and enables the channel separating for multicore fibers.

Wavenumber-extended high-order oscillation control finite volume schemes for multi-dimensional aeroacoustic computations

NASA Astrophysics Data System (ADS)

Kim, Sungtae; Lee, Soogab; Kim, Kyu Hong

2008-04-01

A new numerical method toward accurate and efficient aeroacoustic computations of multi-dimensional compressible flows has been developed. The core idea of the developed scheme is to unite the advantages of the wavenumber-extended optimized scheme and M-AUSMPW+/MLP schemes by predicting a physical distribution of flow variables more accurately in multi-space dimensions. The wavenumber-extended optimization procedure for the finite volume approach based on the conservative requirement is newly proposed for accuracy enhancement, which is required to capture the acoustic portion of the solution in the smooth region. Furthermore, the new distinguishing mechanism which is based on the Gibbs phenomenon in discontinuity, between continuous and discontinuous regions is introduced to eliminate the excessive numerical dissipation in the continuous region by the restricted application of MLP according to the decision of the distinguishing function. To investigate the effectiveness of the developed method, a sequence of benchmark simulations such as spherical wave propagation, nonlinear wave propagation, shock tube problem and vortex preservation test problem are executed. Also, throughout more realistic shock-vortex interaction and muzzle blast flow problems, the utility of the new method for aeroacoustic applications is verified by comparing with the previous numerical or experimental results.

Using primary care electronic health record data for comparative effectiveness research: experience of data quality assessment and preprocessing in The Netherlands.

PubMed

Huang, Yunyu; Voorham, Jaco; Haaijer-Ruskamp, Flora M

2016-07-01

Details of data quality and how quality issues were solved have not been reported in published comparative effectiveness studies using electronic health record data. We developed a conceptual framework of data quality assessment and preprocessing and apply it to a study comparing angiotensin-converting enzyme inhibitors with angiotensin receptor blockerss on renal function decline in diabetes patients. The framework establishes a line of thought to identify and act on data issues. The core concept is to evaluate whether data are fit-for-use for research tasks. Possible quality problems are listed through specific signal detections, and verified whether they are true problems. Optimal solutions are selected for the identified problems. This framework can be used in observational studies to improve validity of results.

Spatial data analytics on heterogeneous multi- and many-core parallel architectures using python

USGS Publications Warehouse

Laura, Jason R.; Rey, Sergio J.

2017-01-01

Parallel vector spatial analysis concerns the application of parallel computational methods to facilitate vector-based spatial analysis. The history of parallel computation in spatial analysis is reviewed, and this work is placed into the broader context of high-performance computing (HPC) and parallelization research. The rise of cyber infrastructure and its manifestation in spatial analysis as CyberGIScience is seen as a main driver of renewed interest in parallel computation in the spatial sciences. Key problems in spatial analysis that have been the focus of parallel computing are covered. Chief among these are spatial optimization problems, computational geometric problems including polygonization and spatial contiguity detection, the use of Monte Carlo Markov chain simulation in spatial statistics, and parallel implementations of spatial econometric methods. Future directions for research on parallelization in computational spatial analysis are outlined.

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

Trędak, Przemysław, E-mail: przemyslaw.tredak@fuw.edu.pl; Rudnicki, Witold R.; Interdisciplinary Centre for Mathematical and Computational Modelling, University of Warsaw, ul. Pawińskiego 5a, 02-106 Warsaw

The second generation Reactive Bond Order (REBO) empirical potential is commonly used to accurately model a wide range hydrocarbon materials. It is also extensible to other atom types and interactions. REBO potential assumes complex multi-body interaction model, that is difficult to represent efficiently in the SIMD or SIMT programming model. Hence, despite its importance, no efficient GPGPU implementation has been developed for this potential. Here we present a detailed description of a highly efficient GPGPU implementation of molecular dynamics algorithm using REBO potential. The presented algorithm takes advantage of rarely used properties of the SIMT architecture of a modern GPUmore » to solve difficult synchronizations issues that arise in computations of multi-body potential. Techniques developed for this problem may be also used to achieve efficient solutions of different problems. The performance of proposed algorithm is assessed using a range of model systems. It is compared to highly optimized CPU implementation (both single core and OpenMP) available in LAMMPS package. These experiments show up to 6x improvement in forces computation time using single processor of the NVIDIA Tesla K80 compared to high end 16-core Intel Xeon processor.« less

Leveraging human decision making through the optimal management of centralized resources

NASA Astrophysics Data System (ADS)

Hyden, Paul; McGrath, Richard G.

2016-05-01

Combining results from mixed integer optimization, stochastic modeling and queuing theory, we will advance the interdisciplinary problem of efficiently and effectively allocating centrally managed resources. Academia currently fails to address this, as the esoteric demands of each of these large research areas limits work across traditional boundaries. The commercial space does not currently address these challenges due to the absence of a profit metric. By constructing algorithms that explicitly use inputs across boundaries, we are able to incorporate the advantages of using human decision makers. Key improvements in the underlying algorithms are made possible by aligning decision maker goals with the feedback loops introduced between the core optimization step and the modeling of the overall stochastic process of supply and demand. A key observation is that human decision-makers must be explicitly included in the analysis for these approaches to be ultimately successful. Transformative access gives warfighters and mission owners greater understanding of global needs and allows for relationships to guide optimal resource allocation decisions. Mastery of demand processes and optimization bottlenecks reveals long term maximum marginal utility gaps in capabilities.

Rapid indirect trajectory optimization on highly parallel computing architectures

NASA Astrophysics Data System (ADS)

Antony, Thomas

Trajectory optimization is a field which can benefit greatly from the advantages offered by parallel computing. The current state-of-the-art in trajectory optimization focuses on the use of direct optimization methods, such as the pseudo-spectral method. These methods are favored due to their ease of implementation and large convergence regions while indirect methods have largely been ignored in the literature in the past decade except for specific applications in astrodynamics. It has been shown that the shortcomings conventionally associated with indirect methods can be overcome by the use of a continuation method in which complex trajectory solutions are obtained by solving a sequence of progressively difficult optimization problems. High performance computing hardware is trending towards more parallel architectures as opposed to powerful single-core processors. Graphics Processing Units (GPU), which were originally developed for 3D graphics rendering have gained popularity in the past decade as high-performance, programmable parallel processors. The Compute Unified Device Architecture (CUDA) framework, a parallel computing architecture and programming model developed by NVIDIA, is one of the most widely used platforms in GPU computing. GPUs have been applied to a wide range of fields that require the solution of complex, computationally demanding problems. A GPU-accelerated indirect trajectory optimization methodology which uses the multiple shooting method and continuation is developed using the CUDA platform. The various algorithmic optimizations used to exploit the parallelism inherent in the indirect shooting method are described. The resulting rapid optimal control framework enables the construction of high quality optimal trajectories that satisfy problem-specific constraints and fully satisfy the necessary conditions of optimality. The benefits of the framework are highlighted by construction of maximum terminal velocity trajectories for a hypothetical long range weapon system. The techniques used to construct an initial guess from an analytic near-ballistic trajectory and the methods used to formulate the necessary conditions of optimality in a manner that is transparent to the designer are discussed. Various hypothetical mission scenarios that enforce different combinations of initial, terminal, interior point and path constraints demonstrate the rapid construction of complex trajectories without requiring any a-priori insight into the structure of the solutions. Trajectory problems of this kind were previously considered impractical to solve using indirect methods. The performance of the GPU-accelerated solver is found to be 2x--4x faster than MATLAB's bvp4c, even while running on GPU hardware that is five years behind the state-of-the-art.

Accelerating 3D Elastic Wave Equations on Knights Landing based Intel Xeon Phi processors

NASA Astrophysics Data System (ADS)

Sourouri, Mohammed; Birger Raknes, Espen

2017-04-01

In advanced imaging methods like reverse-time migration (RTM) and full waveform inversion (FWI) the elastic wave equation (EWE) is numerically solved many times to create the seismic image or the elastic parameter model update. Thus, it is essential to optimize the solution time for solving the EWE as this will have a major impact on the total computational cost in running RTM or FWI. From a computational point of view applications implementing EWEs are associated with two major challenges. The first challenge is the amount of memory-bound computations involved, while the second challenge is the execution of such computations over very large datasets. So far, multi-core processors have not been able to tackle these two challenges, which eventually led to the adoption of accelerators such as Graphics Processing Units (GPUs). Compared to conventional CPUs, GPUs are densely populated with many floating-point units and fast memory, a type of architecture that has proven to map well to many scientific computations. Despite its architectural advantages, full-scale adoption of accelerators has yet to materialize. First, accelerators require a significant programming effort imposed by programming models such as CUDA or OpenCL. Second, accelerators come with a limited amount of memory, which also require explicit data transfers between the CPU and the accelerator over the slow PCI bus. The second generation of the Xeon Phi processor based on the Knights Landing (KNL) architecture, promises the computational capabilities of an accelerator but require the same programming effort as traditional multi-core processors. The high computational performance is realized through many integrated cores (number of cores and tiles and memory varies with the model) organized in tiles that are connected via a 2D mesh based interconnect. In contrary to accelerators, KNL is a self-hosted system, meaning explicit data transfers over the PCI bus are no longer required. However, like most accelerators, KNL sports a memory subsystem consisting of low-level caches and 16GB of high-bandwidth MCDRAM memory. For capacity computing, up to 400GB of conventional DDR4 memory is provided. Such a strict hierarchical memory layout means that data locality is imperative if the true potential of this product is to be harnessed. In this work, we study a series of optimizations specifically targeting KNL for our EWE based application to reduce the time-to-solution time for the following 3D model sizes in grid points: 1283, 2563 and 5123. We compare the results with an optimized version for multi-core CPUs running on a dual-socket Xeon E5 2680v3 system using OpenMP. Our initial naive implementation on the KNL is roughly 20% faster than the multi-core version, but by using only one thread per core and careful memory placement using the memkind library, we could achieve higher speedups. Additionally, by using the MCDRAM as cache for problem sizes that are smaller than 16 GB further performance improvements were unlocked. Depending on the problem size, our overall results indicate that the KNL based system is approximately 2.2x faster than the 24-core Xeon E5 2680v3 system, with only modest changes to the code.

Optimizing the updated Goddard shortwave radiation Weather Research and Forecasting (WRF) scheme for Intel Many Integrated Core (MIC) architecture

NASA Astrophysics Data System (ADS)

Mielikainen, Jarno; Huang, Bormin; Huang, Allen H.-L.

2015-05-01

Intel Many Integrated Core (MIC) ushers in a new era of supercomputing speed, performance, and compatibility. It allows the developers to run code at trillions of calculations per second using the familiar programming model. In this paper, we present our results of optimizing the updated Goddard shortwave radiation Weather Research and Forecasting (WRF) scheme on Intel Many Integrated Core Architecture (MIC) hardware. The Intel Xeon Phi coprocessor is the first product based on Intel MIC architecture, and it consists of up to 61 cores connected by a high performance on-die bidirectional interconnect. The co-processor supports all important Intel development tools. Thus, the development environment is familiar one to a vast number of CPU developers. Although, getting a maximum performance out of Xeon Phi will require using some novel optimization techniques. Those optimization techniques are discusses in this paper. The results show that the optimizations improved performance of the original code on Xeon Phi 7120P by a factor of 1.3x.

Passively stabilized 215-W monolithic CW LMA-fiber laser with innovative transversal mode filter

NASA Astrophysics Data System (ADS)

Stutzki, Fabian; Jauregui, Cesar; Voigtländer, Christian; Thomas, Jens U.; Limpert, Jens; Nolte, Stefan; Tünnermann, Andreas

2010-02-01

We report on the development of a high power monolithic CW fiber oscillator with an output power of 215 W in a 20μm core diameter few-mode Large Mode Area fiber (LMA). The key parameters for stable operation are reviewed. With these optimizations the root mean square of the output power fluctuations can be reduced to less than 0.5 % on a timescale of 20 s, which represents an improvement of more than a factor 5 over a non-optimized fiber laser. With a real-time measurement of the mode content of the fiber laser it can be shown that the few-mode nature of LMA fibers is the main factor for the residual instability of our optimized fiber laser. The root of the problem is that Fiber Bragg Gratings (FBGs) written in multimode fibers exhibit a multi-peak reflexion spectrum in which each resonance corresponds to a different transversal mode. This reflectivity spectrum stimulates multimode laser operation, which results in power and pointing instabilities due to gain competition between the different transversal modes . To stabilize the temporal and spatial behavior of the laser output, we propose an innovative passive in-fiber transversal mode filter based on modified FBG-Fabry Perot structure. This structure provides different reflectivities to the different transversal modes according to the transversal distribution of their intensity profile. Furthermore, this structure can be completely written into the active fiber using fs-laser pulses. Moreover, this concept scales very well with the fiber core diameter, which implies that there is no performance loss in fibers with even larger cores. In consequence this structure is inherently power scalable and can, therefore, be used in kW-level fiber laser systems.

The development of optimal lightweight truss-core sandwich panels

NASA Astrophysics Data System (ADS)

Langhorst, Benjamin Robert

Sandwich structures effectively provide lightweight stiffness and strength by sandwiching a low-density core between stiff face sheets. The performance of lightweight truss-core sandwich panels is enhanced through the design of novel truss arrangements and the development of methods by which the panels may be optimized. An introduction to sandwich panels is presented along with an overview of previous research of truss-core sandwich panels. Three alternative truss arrangements are developed and their corresponding advantages, disadvantages, and optimization routines are discussed. Finally, performance is investigated by theoretical and numerical methods, and it is shown that the relative structural efficiency of alternative truss cores varies with panel weight and load-carrying capacity. Discrete truss core sandwich panels can be designed to serve bending applications more efficiently than traditional pyramidal truss arrangements at low panel weights and load capacities. Additionally, discrete-truss cores permit the design of heterogeneous cores, which feature unit cells that vary in geometry throughout the panel according to the internal loads present at each unit cell's location. A discrete-truss core panel may be selectively strengthened to more efficiently support bending loads. Future research is proposed and additional areas for lightweight sandwich panel development are explained.

Methodes d'optimisation des parametres 2D du reflecteur dans un reacteur a eau pressurisee

NASA Astrophysics Data System (ADS)

Clerc, Thomas

With a third of the reactors in activity, the Pressurized Water Reactor (PWR) is today the most used reactor design in the world. This technology equips all the 19 EDF power plants. PWRs fit into the category of thermal reactors, because it is mainly the thermal neutrons that contribute to the fission reaction. The pressurized light water is both used as the moderator of the reaction and as the coolant. The active part of the core is composed of uranium, slightly enriched in uranium 235. The reflector is a region surrounding the active core, and containing mostly water and stainless steel. The purpose of the reflector is to protect the vessel from radiations, and also to slow down the neutrons and reflect them into the core. Given that the neutrons participate to the reaction of fission, the study of their behavior within the core is capital to understand the general functioning of how the reactor works. The neutrons behavior is ruled by the transport equation, which is very complex to solve numerically, and requires very long calculation. This is the reason why the core codes that will be used in this study solve simplified equations to approach the neutrons behavior in the core, in an acceptable calculation time. In particular, we will focus our study on the diffusion equation and approximated transport equations, such as SPN or S N equations. The physical properties of the reflector are radically different from those of the fissile core, and this structural change causes important tilt in the neutron flux at the core/reflector interface. This is why it is very important to accurately design the reflector, in order to precisely recover the neutrons behavior over the whole core. Existing reflector calculation techniques are based on the Lefebvre-Lebigot method. This method is only valid if the energy continuum of the neutrons is discretized in two energy groups, and if the diffusion equation is used. The method leads to the calculation of a homogeneous reflector. The aim of this study is to create a computational scheme able to compute the parameters of heterogeneous, multi-group reflectors, with both diffusion and SPN/SN operators. For this purpose, two computational schemes are designed to perform such a reflector calculation. The strategy used in both schemes is to minimize the discrepancies between a power distribution computed with a core code and a reference distribution, which will be obtained with an APOLLO2 calculation based on the method Method Of Characteristics (MOC). In both computational schemes, the optimization parameters, also called control variables, are the diffusion coefficients in each zone of the reflector, for diffusion calculations, and the P-1 corrected macroscopic total cross-sections in each zone of the reflector, for SPN/SN calculations (or correction factors on these parameters). After a first validation of our computational schemes, the results are computed, always by optimizing the fast diffusion coefficient for each zone of the reflector. All the tools of the data assimilation have been used to reflect the different behavior of the solvers in the different parts of the core. Moreover, the reflector is refined in six separated zones, corresponding to the physical structure of the reflector. There will be then six control variables for the optimization algorithms. [special characters omitted]. Our computational schemes are then able to compute heterogeneous, 2-group or multi-group reflectors, using diffusion or SPN/SN operators. The optimization performed reduces the discrepancies distribution between the power computed with the core codes and the reference power. However, there are two main limitations to this study: first the homogeneous modeling of the reflector assemblies doesn't allow to properly describe its physical structure near the core/reflector interface. Moreover, the fissile assemblies are modeled in infinite medium, and this model reaches its limit at the core/reflector interface. These two problems should be tackled in future studies. (Abstract shortened by UMI.).

Shared protection based virtual network mapping in space division multiplexing optical networks

NASA Astrophysics Data System (ADS)

Zhang, Huibin; Wang, Wei; Zhao, Yongli; Zhang, Jie

2018-05-01

Space Division Multiplexing (SDM) has been introduced to improve the capacity of optical networks. In SDM optical networks, there are multiple cores/modes in each fiber link, and spectrum resources are multiplexed in both frequency and core/modes dimensions. Enabled by network virtualization technology, one SDM optical network substrate can be shared by several virtual networks operators. Similar with point-to-point connection services, virtual networks (VN) also need certain survivability to guard against network failures. Based on customers' heterogeneous requirements on the survivability of their virtual networks, this paper studies the shared protection based VN mapping problem and proposes a Minimum Free Frequency Slots (MFFS) mapping algorithm to improve spectrum efficiency. Simulation results show that the proposed algorithm can optimize SDM optical networks significantly in terms of blocking probability and spectrum utilization.

Design of material management system of mining group based on Hadoop

NASA Astrophysics Data System (ADS)

Xia, Zhiyuan; Tan, Zhuoying; Qi, Kuan; Li, Wen

2018-01-01

Under the background of persistent slowdown in mining market at present, improving the management level in mining group has become the key link to improve the economic benefit of the mine. According to the practical material management in mining group, three core components of Hadoop are applied: distributed file system HDFS, distributed computing framework Map/Reduce and distributed database HBase. Material management system of mining group based on Hadoop is constructed with the three core components of Hadoop and SSH framework technology. This system was found to strengthen collaboration between mining group and affiliated companies, and then the problems such as inefficient management, server pressure, hardware equipment performance deficiencies that exist in traditional mining material-management system are solved, and then mining group materials management is optimized, the cost of mining management is saved, the enterprise profit is increased.

Promoting the energy structure optimization around Chinese Beijing-Tianjin area by developing biomass energy

NASA Astrophysics Data System (ADS)

Zhao, Li; Sun, Du; Wang, Shi-Yu; Zhao, Feng-Qing

2017-06-01

In recent years, remarkable achievements in the utilization of biomass energy have been made in China. However, there are still some problems, such as irrational industry layout, immature existing market survival mechanism and lack of core competitiveness. On the basis of investigation and research, some recommendations and strategies are proposed for the development of biomass energy around Chinese Beijing-Tianjin area: scientific planning and precise laying out of biomass industry; rationalizing the relationship between government and enterprises and promoting the establishment of a market-oriented survival mechanism; combining ‘supply side’ with ‘demand side’ to optimize product structure; extending industrial chain to promote industry upgrading and sustainable development; and comprehensive co-ordinating various types of biomass resources and extending product chain to achieve better economic benefits.

Numerical optimization of three-dimensional coils for NSTX-U

DOE PAGES

Lazerson, S. A.; Park, J. -K.; Logan, N.; ...

2015-09-03

A tool for the calculation of optimal three-dimensional (3D) perturbative magnetic fields in tokamaks has been developed. The IPECOPT code builds upon the stellarator optimization code STELLOPT to allow for optimization of linear ideal magnetohydrodynamic perturbed equilibrium (IPEC). This tool has been applied to NSTX-U equilibria, addressing which fields are the most effective at driving NTV torques. The NTV torque calculation is performed by the PENT code. Optimization of the normal field spectrum shows that fields with n = 1 character can drive a large core torque. It is also shown that fields with n = 3 features are capablemore » of driving edge torque and some core torque. Coil current optimization (using the planned in-vessel and existing RWM coils) on NSTX-U suggest the planned coils set is adequate for core and edge torque control. In conclusion, comparison between error field correction experiments on DIII-D and the optimizer show good agreement.« less

Parallelization of the preconditioned IDR solver for modern multicore computer systems

NASA Astrophysics Data System (ADS)

Bessonov, O. A.; Fedoseyev, A. I.

2012-10-01

This paper present the analysis, parallelization and optimization approach for the large sparse matrix solver CNSPACK for modern multicore microprocessors. CNSPACK is an advanced solver successfully used for coupled solution of stiff problems arising in multiphysics applications such as CFD, semiconductor transport, kinetic and quantum problems. It employs iterative IDR algorithm with ILU preconditioning (user chosen ILU preconditioning order). CNSPACK has been successfully used during last decade for solving problems in several application areas, including fluid dynamics and semiconductor device simulation. However, there was a dramatic change in processor architectures and computer system organization in recent years. Due to this, performance criteria and methods have been revisited, together with involving the parallelization of the solver and preconditioner using Open MP environment. Results of the successful implementation for efficient parallelization are presented for the most advances computer system (Intel Core i7-9xx or two-processor Xeon 55xx/56xx).

Virtual optical network mapping and core allocation in elastic optical networks using multi-core fibers

NASA Astrophysics Data System (ADS)

Xuan, Hejun; Wang, Yuping; Xu, Zhanqi; Hao, Shanshan; Wang, Xiaoli

2017-11-01

Virtualization technology can greatly improve the efficiency of the networks by allowing the virtual optical networks to share the resources of the physical networks. However, it will face some challenges, such as finding the efficient strategies for virtual nodes mapping, virtual links mapping and spectrum assignment. It is even more complex and challenging when the physical elastic optical networks using multi-core fibers. To tackle these challenges, we establish a constrained optimization model to determine the optimal schemes of optical network mapping, core allocation and spectrum assignment. To solve the model efficiently, tailor-made encoding scheme, crossover and mutation operators are designed. Based on these, an efficient genetic algorithm is proposed to obtain the optimal schemes of the virtual nodes mapping, virtual links mapping, core allocation. The simulation experiments are conducted on three widely used networks, and the experimental results show the effectiveness of the proposed model and algorithm.

Transmission loss optimization in acoustic sandwich panels

NASA Astrophysics Data System (ADS)

Makris, S. E.; Dym, C. L.; MacGregor Smith, J.

1986-06-01

Considering the sound transmission loss (TL) of a sandwich panel as the single objective, different optimization techniques are examined and a sophisticated computer program is used to find the optimum TL. Also, for one of the possible case studies such as core optimization, closed-form expressions are given between TL and the core-design variables for different sets of skins. The significance of these functional relationships lies in the fact that the panel designer can bypass the necessity of using a sophisticated software package in order to assess explicitly the dependence of the TL on core thickness and density.

S-Genius, a universal software platform with versatile inverse problem resolution for scatterometry

NASA Astrophysics Data System (ADS)

Fuard, David; Troscompt, Nicolas; El Kalyoubi, Ismael; Soulan, Sébastien; Besacier, Maxime

2013-05-01

S-Genius is a new universal scatterometry platform, which gathers all the LTM-CNRS know-how regarding the rigorous electromagnetic computation and several inverse problem solver solutions. This software platform is built to be a userfriendly, light, swift, accurate, user-oriented scatterometry tool, compatible with any ellipsometric measurements to fit and any types of pattern. It aims to combine a set of inverse problem solver capabilities — via adapted Levenberg- Marquard optimization, Kriging, Neural Network solutions — that greatly improve the reliability and the velocity of the solution determination. Furthermore, as the model solution is mainly vulnerable to materials optical properties, S-Genius may be coupled with an innovative material refractive indices determination. This paper will a little bit more focuses on the modified Levenberg-Marquardt optimization, one of the indirect method solver built up in parallel with the total SGenius software coding by yours truly. This modified Levenberg-Marquardt optimization corresponds to a Newton algorithm with an adapted damping parameter regarding the definition domains of the optimized parameters. Currently, S-Genius is technically ready for scientific collaboration, python-powered, multi-platform (windows/linux/macOS), multi-core, ready for 2D- (infinite features along the direction perpendicular to the incident plane), conical, and 3D-features computation, compatible with all kinds of input data from any possible ellipsometers (angle or wavelength resolved) or reflectometers, and widely used in our laboratory for resist trimming studies, etching features characterization (such as complex stack) or nano-imprint lithography measurements for instance. The work about kriging solver, neural network solver and material refractive indices determination is done (or about to) by other LTM members and about to be integrated on S-Genius platform.

Research on an uplink carrier sense multiple access algorithm of large indoor visible light communication networks based on an optical hard core point process.

PubMed

Nan, Zhufen; Chi, Xuefen

2016-12-20

The IEEE 802.15.7 protocol suggests that it could coordinate the channel access process based on the competitive method of carrier sensing. However, the directionality of light and randomness of diffuse reflection would give rise to a serious imperfect carrier sense (ICS) problem [e.g., hidden node (HN) problem and exposed node (EN) problem], which brings great challenges in realizing the optical carrier sense multiple access (CSMA) mechanism. In this paper, the carrier sense process implemented by diffuse reflection light is modeled as the choice of independent sets. We establish an ICS model with the presence of ENs and HNs for the multi-point to multi-point visible light communication (VLC) uplink communications system. Considering the severe optical ICS problem, an optical hard core point process (OHCPP) is developed, which characterizes the optical CSMA for the indoor VLC uplink communications system. Due to the limited coverage of the transmitted optical signal, in our OHCPP, the ENs within the transmitters' carrier sense region could be retained provided that they could not corrupt the ongoing communications. Moreover, because of the directionality of both light emitting diode (LED) transmitters and receivers, theoretical analysis of the HN problem becomes difficult. In this paper, we derive the closed-form expression for approximating the outage probability and transmission capacity of VLC networks with the presence of HNs and ENs. Simulation results validate the analysis and also show the existence of an optimal physical carrier-sensing threshold that maximizes the transmission capacity for a given emission angle of LED.

An evaluation of MPI message rate on hybrid-core processors

DOE PAGES

Barrett, Brian W.; Brightwell, Ron; Grant, Ryan; ...

2014-11-01

Power and energy concerns are motivating chip manufacturers to consider future hybrid-core processor designs that may combine a small number of traditional cores optimized for single-thread performance with a large number of simpler cores optimized for throughput performance. This trend is likely to impact the way in which compute resources for network protocol processing functions are allocated and managed. In particular, the performance of MPI match processing is critical to achieving high message throughput. In this paper, we analyze the ability of simple and more complex cores to perform MPI matching operations for various scenarios in order to gain insightmore » into how MPI implementations for future hybrid-core processors should be designed.« less

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.

Analytic energy gradient of projected Hartree-Fock within projection after variation

NASA Astrophysics Data System (ADS)

Uejima, Motoyuki; Ten-no, Seiichiro

2017-03-01

We develop a geometrical optimization technique for the projection-after-variation (PAV) scheme of the recently refined projected Hartree-Fock (PHF) as a fast alternative to the variation-after-projection (VAP) approach for optimizing the structures of molecules/clusters in symmetry-adapted electronic states at the mean-field computational cost. PHF handles the nondynamic correlation effects by restoring the symmetry of a broken-symmetry single reference wavefunction and moreover enables a black-box treatment of orbital selections. Using HF orbitals instead of PHF orbitals, our approach saves the computational cost for the orbital optimization, avoiding the convergence problem that sometimes emerges in the VAP scheme. We show that PAV-PHF provides geometries comparable to those of the complete active space self-consistent field and VAP-PHF for the tested systems, namely, CH2, O3, and the [Cu2O2 ] 2 + core, where nondynamic correlation is abundant. The proposed approach is useful for large systems mainly dominated by nondynamic correlation to find stable structures in many symmetry-adapted states.

STARS: A general-purpose finite element computer program for analysis of engineering structures

NASA Technical Reports Server (NTRS)

Gupta, K. K.

1984-01-01

STARS (Structural Analysis Routines) is primarily an interactive, graphics-oriented, finite-element computer program for analyzing the static, stability, free vibration, and dynamic responses of damped and undamped structures, including rotating systems. The element library consists of one-dimensional (1-D) line elements, two-dimensional (2-D) triangular and quadrilateral shell elements, and three-dimensional (3-D) tetrahedral and hexahedral solid elements. These elements enable the solution of structural problems that include truss, beam, space frame, plane, plate, shell, and solid structures, or any combination thereof. Zero, finite, and interdependent deflection boundary conditions can be implemented by the program. The associated dynamic response analysis capability provides for initial deformation and velocity inputs, whereas the transient excitation may be either forces or accelerations. An effective in-core or out-of-core solution strategy is automatically employed by the program, depending on the size of the problem. Data input may be at random within a data set, and the program offers certain automatic data-generation features. Input data are formatted as an optimal combination of free and fixed formats. Interactive graphics capabilities enable convenient display of nodal deformations, mode shapes, and element stresses.

Multi channel thermal hydraulic analysis of gas cooled fast reactor using genetic algorithm

NASA Astrophysics Data System (ADS)

Drajat, R. Z.; Su'ud, Z.; Soewono, E.; Gunawan, A. Y.

2012-05-01

There are three analyzes to be done in the design process of nuclear reactor i.e. neutronic analysis, thermal hydraulic analysis and thermodynamic analysis. The focus in this article is the thermal hydraulic analysis, which has a very important role in terms of system efficiency and the selection of the optimal design. This analysis is performed in a type of Gas Cooled Fast Reactor (GFR) using cooling Helium (He). The heat from nuclear fission reactions in nuclear reactors will be distributed through the process of conduction in fuel elements. Furthermore, the heat is delivered through a process of heat convection in the fluid flow in cooling channel. Temperature changes that occur in the coolant channels cause a decrease in pressure at the top of the reactor core. The governing equations in each channel consist of mass balance, momentum balance, energy balance, mass conservation and ideal gas equation. The problem is reduced to finding flow rates in each channel such that the pressure drops at the top of the reactor core are all equal. The problem is solved numerically with the genetic algorithm method. Flow rates and temperature distribution in each channel are obtained here.

Efficient implementation of the many-body Reactive Bond Order (REBO) potential on GPU

NASA Astrophysics Data System (ADS)

Trędak, Przemysław; Rudnicki, Witold R.; Majewski, Jacek A.

2016-09-01

The second generation Reactive Bond Order (REBO) empirical potential is commonly used to accurately model a wide range hydrocarbon materials. It is also extensible to other atom types and interactions. REBO potential assumes complex multi-body interaction model, that is difficult to represent efficiently in the SIMD or SIMT programming model. Hence, despite its importance, no efficient GPGPU implementation has been developed for this potential. Here we present a detailed description of a highly efficient GPGPU implementation of molecular dynamics algorithm using REBO potential. The presented algorithm takes advantage of rarely used properties of the SIMT architecture of a modern GPU to solve difficult synchronizations issues that arise in computations of multi-body potential. Techniques developed for this problem may be also used to achieve efficient solutions of different problems. The performance of proposed algorithm is assessed using a range of model systems. It is compared to highly optimized CPU implementation (both single core and OpenMP) available in LAMMPS package. These experiments show up to 6x improvement in forces computation time using single processor of the NVIDIA Tesla K80 compared to high end 16-core Intel Xeon processor.

First principles of Hamiltonian medicine.

PubMed

Crespi, Bernard; Foster, Kevin; Úbeda, Francisco

2014-05-19

We introduce the field of Hamiltonian medicine, which centres on the roles of genetic relatedness in human health and disease. Hamiltonian medicine represents the application of basic social-evolution theory, for interactions involving kinship, to core issues in medicine such as pathogens, cancer, optimal growth and mental illness. It encompasses three domains, which involve conflict and cooperation between: (i) microbes or cancer cells, within humans, (ii) genes expressed in humans, (iii) human individuals. A set of six core principles, based on these domains and their interfaces, serves to conceptually organize the field, and contextualize illustrative examples. The primary usefulness of Hamiltonian medicine is that, like Darwinian medicine more generally, it provides novel insights into what data will be productive to collect, to address important clinical and public health problems. Our synthesis of this nascent field is intended predominantly for evolutionary and behavioural biologists who aspire to address questions directly relevant to human health and disease.

Bilayer tablets of Paliperidone for Extended release osmotic drug delivery

NASA Astrophysics Data System (ADS)

Chowdary, K. Sunil; Napoleon, A. A.

2017-11-01

The purpose of this study is to develop and optimize the formulation of paliperidone bilayer tablet core and coating which should meet in vitro performance of trilayered Innovator sample Invega. Optimization of core formulations prepared by different ratio of polyox grades and optimization of coating of (i) sub-coating build-up with hydroxy ethyl cellulose (HEC) and (ii).enteric coating build-up with cellulose acetate (CA). Some important influence factors such as different core tablet compositions and different coating solution ingredients involved in the formulation procedure were investigated. The optimization of formulation and process was conducted by comparing different in vitro release behaviours of Paliperidone. In vitro dissolution studies of Innovator sample (Invega) with formulations of different release rate which ever close release pattern during the whole 24 h test is finalized.

Optimization of 200 MWth and 250 MWt Ship Based Small Long Life NPP

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

Fitriyani, Dian; Su'ud, Zaki

2010-06-22

Design optimization of ship-based 200 MWth and 250 MWt nuclear power reactors have been performed. The neutronic and thermo-hydraulic programs of the three-dimensional X-Y-Z geometry have been developed for the analysis of ship-based nuclear power plant. Quasi-static approach is adopted to treat seawater effect. The reactor are loop type lead bismuth cooled fast reactor with nitride fuel and with relatively large coolant pipe above reactor core, the heat from primary coolant system is directly transferred to watersteam loop through steam generators. Square core type are selected and optimized. As the optimization result, the core outlet temperature distribution is changing withmore » the elevation angle of the reactor system and the characteristics are discussed.« less

Parallelized Kalman-Filter-Based Reconstruction of Particle Tracks on Many-Core Processors and GPUs

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

Cerati, Giuseppe; Elmer, Peter; Krutelyov, Slava

2017-01-01

For over a decade now, physical and energy constraints have limited clock speed improvements in commodity microprocessors. Instead, chipmakers have been pushed into producing lower-power, multi-core processors such as Graphical Processing Units (GPU), ARM CPUs, and Intel MICs. Broad-based efforts from manufacturers and developers have been devoted to making these processors user-friendly enough to perform general computations. However, extracting performance from a larger number of cores, as well as specialized vector or SIMD units, requires special care in algorithm design and code optimization. One of the most computationally challenging problems in high-energy particle experiments is finding and fitting the charged-particlemore » tracks during event reconstruction. This is expected to become by far the dominant problem at the High-Luminosity Large Hadron Collider (HL-LHC), for example. Today the most common track finding methods are those based on the Kalman filter. Experience with Kalman techniques on real tracking detector systems has shown that they are robust and provide high physics performance. This is why they are currently in use at the LHC, both in the trigger and offine. Previously we reported on the significant parallel speedups that resulted from our investigations to adapt Kalman filters to track fitting and track building on Intel Xeon and Xeon Phi. Here, we discuss our progresses toward the understanding of these processors and the new developments to port the Kalman filter to NVIDIA GPUs.« less

Parallelized Kalman-Filter-Based Reconstruction of Particle Tracks on Many-Core Processors and GPUs

NASA Astrophysics Data System (ADS)

Cerati, Giuseppe; Elmer, Peter; Krutelyov, Slava; Lantz, Steven; Lefebvre, Matthieu; Masciovecchio, Mario; McDermott, Kevin; Riley, Daniel; Tadel, Matevž; Wittich, Peter; Würthwein, Frank; Yagil, Avi

2017-08-01

For over a decade now, physical and energy constraints have limited clock speed improvements in commodity microprocessors. Instead, chipmakers have been pushed into producing lower-power, multi-core processors such as Graphical Processing Units (GPU), ARM CPUs, and Intel MICs. Broad-based efforts from manufacturers and developers have been devoted to making these processors user-friendly enough to perform general computations. However, extracting performance from a larger number of cores, as well as specialized vector or SIMD units, requires special care in algorithm design and code optimization. One of the most computationally challenging problems in high-energy particle experiments is finding and fitting the charged-particle tracks during event reconstruction. This is expected to become by far the dominant problem at the High-Luminosity Large Hadron Collider (HL-LHC), for example. Today the most common track finding methods are those based on the Kalman filter. Experience with Kalman techniques on real tracking detector systems has shown that they are robust and provide high physics performance. This is why they are currently in use at the LHC, both in the trigger and offine. Previously we reported on the significant parallel speedups that resulted from our investigations to adapt Kalman filters to track fitting and track building on Intel Xeon and Xeon Phi. Here, we discuss our progresses toward the understanding of these processors and the new developments to port the Kalman filter to NVIDIA GPUs.

Partial differential equations constrained combinatorial optimization on an adiabatic quantum computer

NASA Astrophysics Data System (ADS)

Chandra, Rishabh

Partial differential equation-constrained combinatorial optimization (PDECCO) problems are a mixture of continuous and discrete optimization problems. PDECCO problems have discrete controls, but since the partial differential equations (PDE) are continuous, the optimization space is continuous as well. Such problems have several applications, such as gas/water network optimization, traffic optimization, micro-chip cooling optimization, etc. Currently, no efficient classical algorithm which guarantees a global minimum for PDECCO problems exists. A new mapping has been developed that transforms PDECCO problem, which only have linear PDEs as constraints, into quadratic unconstrained binary optimization (QUBO) problems that can be solved using an adiabatic quantum optimizer (AQO). The mapping is efficient, it scales polynomially with the size of the PDECCO problem, requires only one PDE solve to form the QUBO problem, and if the QUBO problem is solved correctly and efficiently on an AQO, guarantees a global optimal solution for the original PDECCO problem.

Research on NGN network control technology

NASA Astrophysics Data System (ADS)

Li, WenYao; Zhou, Fang; Wu, JianXue; Li, ZhiGuang

2004-04-01

Nowadays NGN (Next Generation Network) is the hotspot for discussion and research in IT section. The NGN core technology is the network control technology. The key goal of NGN is to realize the network convergence and evolution. Referring to overlay network model core on Softswitch technology, circuit switch network and IP network convergence realized. Referring to the optical transmission network core on ASTN/ASON, service layer (i.e. IP layer) and optical transmission convergence realized. Together with the distributing feature of NGN network control technology, on NGN platform, overview of combining Softswitch and ASTN/ASON control technology, the solution whether IP should be the NGN core carrier platform attracts general attention, and this is also a QoS problem on NGN end to end. This solution produces the significant practical meaning on equipment development, network deployment, network design and optimization, especially on realizing present network smooth evolving to the NGN. This is why this paper puts forward the research topic on the NGN network control technology. This paper introduces basics on NGN network control technology, then proposes NGN network control reference model, at the same time describes a realizable network structure of NGN. Based on above, from the view of function realization, NGN network control technology is discussed and its work mechanism is analyzed.

Particle swarm optimization of ascent trajectories of multistage launch vehicles

NASA Astrophysics Data System (ADS)

Pontani, Mauro

2014-02-01

Multistage launch vehicles are commonly employed to place spacecraft and satellites in their operational orbits. If the rocket characteristics are specified, the optimization of its ascending trajectory consists of determining the optimal control law that leads to maximizing the final mass at orbit injection. The numerical solution of a similar problem is not trivial and has been pursued with different methods, for decades. This paper is concerned with an original approach based on the joint use of swarming theory and the necessary conditions for optimality. The particle swarm optimization technique represents a heuristic population-based optimization method inspired by the natural motion of bird flocks. Each individual (or particle) that composes the swarm corresponds to a solution of the problem and is associated with a position and a velocity vector. The formula for velocity updating is the core of the method and is composed of three terms with stochastic weights. As a result, the population migrates toward different regions of the search space taking advantage of the mechanism of information sharing that affects the overall swarm dynamics. At the end of the process the best particle is selected and corresponds to the optimal solution to the problem of interest. In this work the three-dimensional trajectory of the multistage rocket is assumed to be composed of four arcs: (i) first stage propulsion, (ii) second stage propulsion, (iii) coast arc (after release of the second stage), and (iv) third stage propulsion. The Euler-Lagrange equations and the Pontryagin minimum principle, in conjunction with the Weierstrass-Erdmann corner conditions, are employed to express the thrust angles as functions of the adjoint variables conjugate to the dynamics equations. The use of these analytical conditions coming from the calculus of variations leads to obtaining the overall rocket dynamics as a function of seven parameters only, namely the unknown values of the initial state and costate components, the coast duration, and the upper stage thrust duration. In addition, a simple approach is introduced and successfully applied with the purpose of satisfying exactly the path constraint related to the maximum dynamical pressure in the atmospheric phase. The basic version of the swarming technique, which is used in this research, is extremely simple and easy to program. Nevertheless, the algorithm proves to be capable of yielding the optimal rocket trajectory with a very satisfactory numerical accuracy.

Preparation, process optimization and characterization of core-shell polyurethane/chitosan nanofibers as a potential platform for bioactive scaffolds.

PubMed

Maleknia, Laleh; Dilamian, Mandana; Pilehrood, Mohammad Kazemi; Sadeghi-Aliabadi, Hojjat; Hekmati, Amir Houshang

2018-06-01

In this paper, polyurethane (PU), chitosan (Cs)/polyethylene oxide (PEO), and core-shell PU/Cs nanofibers were produced at the optimal processing conditions using electrospinning technique. Several methods including SEM, TEM, FTIR, XRD, DSC, TGA and image analysis were utilized to characterize these nanofibrous structures. SEM images exhibited that the core-shell PU/Cs nanofibers were spun without any structural imperfections at the optimized processing conditions. TEM image confirmed the PU/Cs core-shell nanofibers were formed apparently. It that seems the inclusion of Cs/PEO to the shell, did not induce the significant variations in the crystallinity in the core-shell nanofibers. DSC analysis showed that the inclusion of Cs/PEO led to the glass temperature of the composition increased significantly compared to those of neat PU nanofibers. The thermal degradation of core-shell PU/Cs was similar to PU nanofibers degradation due to the higher PU concentration compared to other components. It was hypothesized that the core-shell PU/Cs nanofibers can be used as a potential platform for the bioactive scaffolds in tissue engineering. Further biological tests should be conducted to evaluate this platform as a three dimensional scaffold with the capabilities of releasing the bioactive molecules in a sustained manner.

[Site selection of nature reserve based on the self-learning tabu search algorithm with space-ecology set covering problem: An example from Daiyun Mountain, Southeast China].

PubMed

Huang, Jia Hang; Liu, Jin Fu; Lin, Zhi Wei; Zheng, Shi Qun; He, Zhong Sheng; Zhang, Hui Guang; Li, Wen Zhou

2017-01-01

Designing the nature reserves is an effective approach to protecting biodiversity. The traditional approaches to designing the nature reserves could only identify the core area for protecting the species without specifying an appropriate land area of the nature reserve. The site selection approaches, which are based on mathematical model, can select part of the land from the planning area to compose the nature reserve and to protect specific species or ecosystem. They are useful approaches to alleviating the contradiction between ecological protection and development. The existing site selection methods do not consider the ecological differences between each unit and has the bottleneck of computational efficiency in optimization algorithm. In this study, we first constructed the ecological value assessment system which was appropriated for forest ecosystem and that was used for calculating ecological value of Daiyun Mountain and for drawing its distribution map. Then, the Ecological Set Covering Problem (ESCP) was established by integrating the ecological values and then the Space-ecology Set Covering Problem (SSCP) was generated based on the spatial compactness of ESCP. Finally, the STS algorithm which possessed good optimizing performance was utilized to search the approximate optimal solution under diverse protection targets, and the optimization solution of the built-up area of Daiyun Mountain was proposed. According to the experimental results, the difference of ecological values in the spatial distribution was obvious. The ecological va-lue of selected sites of ESCP was higher than that of SCP. SSCP could aggregate the sites with high ecological value based on ESCP. From the results, the level of the aggregation increased with the weight of the perimeter. We suggested that the range of the existing reserve could be expanded for about 136 km 2 and the site of Tsuga longibracteata should be included, which was located in the northwest of the study area. Our research aimed at providing an optimization scheme for the sustai-nable development of Daiyun Mountain nature reserve and the optimal allocation of land resource, and a novel idea for designing the nature reserve of forest ecosystem in China.

SCORPIO: A Scalable Two-Phase Parallel I/O Library With Application To A Large Scale Subsurface Simulator

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

Sreepathi, Sarat; Sripathi, Vamsi; Mills, Richard T

2013-01-01

Inefficient parallel I/O is known to be a major bottleneck among scientific applications employed on supercomputers as the number of processor cores grows into the thousands. Our prior experience indicated that parallel I/O libraries such as HDF5 that rely on MPI-IO do not scale well beyond 10K processor cores, especially on parallel file systems (like Lustre) with single point of resource contention. Our previous optimization efforts for a massively parallel multi-phase and multi-component subsurface simulator (PFLOTRAN) led to a two-phase I/O approach at the application level where a set of designated processes participate in the I/O process by splitting themore » I/O operation into a communication phase and a disk I/O phase. The designated I/O processes are created by splitting the MPI global communicator into multiple sub-communicators. The root process in each sub-communicator is responsible for performing the I/O operations for the entire group and then distributing the data to rest of the group. This approach resulted in over 25X speedup in HDF I/O read performance and 3X speedup in write performance for PFLOTRAN at over 100K processor cores on the ORNL Jaguar supercomputer. This research describes the design and development of a general purpose parallel I/O library, SCORPIO (SCalable block-ORiented Parallel I/O) that incorporates our optimized two-phase I/O approach. The library provides a simplified higher level abstraction to the user, sitting atop existing parallel I/O libraries (such as HDF5) and implements optimized I/O access patterns that can scale on larger number of processors. Performance results with standard benchmark problems and PFLOTRAN indicate that our library is able to maintain the same speedups as before with the added flexibility of being applicable to a wider range of I/O intensive applications.« less

Optimized Diffusion of Run-and-Tumble Particles in Crowded Environments

NASA Astrophysics Data System (ADS)

Bertrand, Thibault; Zhao, Yongfeng; Bénichou, Olivier; Tailleur, Julien; Voituriez, Raphaël

2018-05-01

We study the transport of self-propelled particles in dynamic complex environments. To obtain exact results, we introduce a model of run-and-tumble particles (RTPs) moving in discrete time on a d -dimensional cubic lattice in the presence of diffusing hard-core obstacles. We derive an explicit expression for the diffusivity of the RTP, which is exact in the limit of low density of fixed obstacles. To do so, we introduce a generalization of Kac's theorem on the mean return times of Markov processes, which we expect to be relevant for a large class of lattice gas problems. Our results show the diffusivity of RTPs to be nonmonotonic in the tumbling probability for low enough obstacle mobility. These results prove the potential for the optimization of the transport of RTPs in crowded and disordered environments with applications to motile artificial and biological systems.

Constant Communities in Complex Networks

NASA Astrophysics Data System (ADS)

Chakraborty, Tanmoy; Srinivasan, Sriram; Ganguly, Niloy; Bhowmick, Sanjukta; Mukherjee, Animesh

2013-05-01

Identifying community structure is a fundamental problem in network analysis. Most community detection algorithms are based on optimizing a combinatorial parameter, for example modularity. This optimization is generally NP-hard, thus merely changing the vertex order can alter their assignments to the community. However, there has been less study on how vertex ordering influences the results of the community detection algorithms. Here we identify and study the properties of invariant groups of vertices (constant communities) whose assignment to communities are, quite remarkably, not affected by vertex ordering. The percentage of constant communities can vary across different applications and based on empirical results we propose metrics to evaluate these communities. Using constant communities as a pre-processing step, one can significantly reduce the variation of the results. Finally, we present a case study on phoneme network and illustrate that constant communities, quite strikingly, form the core functional units of the larger communities.

Firefly Mating Algorithm for Continuous Optimization Problems

PubMed Central

Ritthipakdee, Amarita; Premasathian, Nol; Jitkongchuen, Duangjai

2017-01-01

This paper proposes a swarm intelligence algorithm, called firefly mating algorithm (FMA), for solving continuous optimization problems. FMA uses genetic algorithm as the core of the algorithm. The main feature of the algorithm is a novel mating pair selection method which is inspired by the following 2 mating behaviors of fireflies in nature: (i) the mutual attraction between males and females causes them to mate and (ii) fireflies of both sexes are of the multiple-mating type, mating with multiple opposite sex partners. A female continues mating until her spermatheca becomes full, and, in the same vein, a male can provide sperms for several females until his sperm reservoir is depleted. This new feature enhances the global convergence capability of the algorithm. The performance of FMA was tested with 20 benchmark functions (sixteen 30-dimensional functions and four 2-dimensional ones) against FA, ALC-PSO, COA, MCPSO, LWGSODE, MPSODDS, DFOA, SHPSOS, LSA, MPDPGA, DE, and GABC algorithms. The experimental results showed that the success rates of our proposed algorithm with these functions were higher than those of other algorithms and the proposed algorithm also required fewer numbers of iterations to reach the global optima. PMID:28808442

Firefly Mating Algorithm for Continuous Optimization Problems.

PubMed

Ritthipakdee, Amarita; Thammano, Arit; Premasathian, Nol; Jitkongchuen, Duangjai

2017-01-01

This paper proposes a swarm intelligence algorithm, called firefly mating algorithm (FMA), for solving continuous optimization problems. FMA uses genetic algorithm as the core of the algorithm. The main feature of the algorithm is a novel mating pair selection method which is inspired by the following 2 mating behaviors of fireflies in nature: (i) the mutual attraction between males and females causes them to mate and (ii) fireflies of both sexes are of the multiple-mating type, mating with multiple opposite sex partners. A female continues mating until her spermatheca becomes full, and, in the same vein, a male can provide sperms for several females until his sperm reservoir is depleted. This new feature enhances the global convergence capability of the algorithm. The performance of FMA was tested with 20 benchmark functions (sixteen 30-dimensional functions and four 2-dimensional ones) against FA, ALC-PSO, COA, MCPSO, LWGSODE, MPSODDS, DFOA, SHPSOS, LSA, MPDPGA, DE, and GABC algorithms. The experimental results showed that the success rates of our proposed algorithm with these functions were higher than those of other algorithms and the proposed algorithm also required fewer numbers of iterations to reach the global optima.

The application of dynamic programming in production planning

NASA Astrophysics Data System (ADS)

Wu, Run

2017-05-01

Nowadays, with the popularity of the computers, various industries and fields are widely applying computer information technology, which brings about huge demand for a variety of application software. In order to develop software meeting various needs with most economical cost and best quality, programmers must design efficient algorithms. A superior algorithm can not only soul up one thing, but also maximize the benefits and generate the smallest overhead. As one of the common algorithms, dynamic programming algorithms are used to solving problems with some sort of optimal properties. When solving problems with a large amount of sub-problems that needs repetitive calculations, the ordinary sub-recursive method requires to consume exponential time, and dynamic programming algorithm can reduce the time complexity of the algorithm to the polynomial level, according to which we can conclude that dynamic programming algorithm is a very efficient compared to other algorithms reducing the computational complexity and enriching the computational results. In this paper, we expound the concept, basic elements, properties, core, solving steps and difficulties of the dynamic programming algorithm besides, establish the dynamic programming model of the production planning problem.

Freud, Problem Solving, Ethnicity, and Race: Integrating Psychology into the Interdisciplinary Core Curriculum.

ERIC Educational Resources Information Center

Dunn, Dana S.

The new core curriculum at Moravian College, in Pennsylvania, utilizes an interdisciplinary approach, integrating topics of psychology into three of the seven core courses: "Microcosm/Macrocosm"; "Quantitative Problem Solving"; and the seminar "Gender, Ethnicity, and Race." The course "Microcosm/Macrocosm"…

Multi level optimization of burnable poison utilization for advanced PWR fuel management

NASA Astrophysics Data System (ADS)

Yilmaz, Serkan

The objective of this study was to develop an unique methodology and a practical tool for designing burnable poison (BP) pattern for a given PWR core. Two techniques were studied in developing this tool. First, the deterministic technique called Modified Power Shape Forced Diffusion (MPSFD) method followed by a fine tuning algorithm, based on some heuristic rules, was developed to achieve this goal. Second, an efficient and a practical genetic algorithm (GA) tool was developed and applied successfully to Burnable Poisons (BPs) placement optimization problem for a reference Three Mile Island-1 (TMI-1) core. This thesis presents the step by step progress in developing such a tool. The developed deterministic method appeared to perform as expected. The GA technique produced excellent BP designs. It was discovered that the Beginning of Cycle (BOC) Kinf of a BP fuel assembly (FA) design is a good filter to eliminate invalid BP designs created during the optimization process. By eliminating all BP designs having BOC Kinf above a set limit, the computational time was greatly reduced since the evaluation process with reactor physics calculations for an invalid solution is canceled. Moreover, the GA was applied to develop the BP loading pattern to minimize the total Gadolinium (Gd) amount in the core together with the residual binding at End-of-Cycle (EOC) and to keep the maximum peak pin power during core depletion and Soluble boron concentration at BOC both less than their limit values. The number of UO2/Gd2O3 pins and Gd 2O3 concentrations for each fresh fuel location in the core are the decision variables and the total amount of the Gd in the core and maximum peak pin power during core depletion are in the fitness functions. The use of different fitness function definition and forcing the solution movement towards to desired region in the solution space accelerated the GA runs. Special emphasize is given to minimizing the residual binding to increase core lifetime as well as minimizing the total Gd amount in the core. The GA code developed many good solutions that satisfy all of the design constraints. For these solutions, the EOC soluble boron concentration changes from 68.9 to 97.2 ppm. It is important to note that the difference of 28.3 ppm between the best and the worst solution in the good solutions region represent the potential of 12.5 Effective-Full-Power-Day (EPFD) savings in cycle length. As a comparison, the best BP loading design has 97.2 ppm soluble boron concentration at EOC while the BP loading with available vendors' U/Gd FA designs has 94.4 ppm SOB at EOC. It was estimated that the difference of 2.8 ppm reflected the potential savings of 1.25 EFPD in cycle length. Moreover, the total Gd amount was reduced by 6.89% in mass that provided extra savings in fuel cost compared to the BP loading pattern with available vendor's U/Gd FA designs. (Abstract shortened by UMI.)

Role of CT scanning in formation evaluation

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

Bergosh, J.L.; Dibona, B.G.

1988-01-01

The use of the computerized tomographic (CT) scanner in formation evaluation of difficult to analyze core samples has moved from the research and development phase to daily, routine use in the core-analysis laboratory. The role of the CT scanner has become increasingly important as geologists try to obtain more representative core material for accurate formation evaluation. The most common problem facing the core analyst when preparing to measure petrophysical properties is the selection of representative and unaltered core samples for routine and special core testing. Recent data have shown that heterogeneous reservoir rock can be very difficult, if not impossible,more » to assess correctly when using standard core examination procedures, because many features, such as fractures, are not visible on the core surface. Another problem is the invasion of drilling mud into the core sample. Flushing formation oil and water from the core can greatly alter the saturation and distribution of fluids and lead to serious formation evaluation problems. Because the quality and usefulness of the core date are directly tied to proper sample selection, it has become imperative that the CT scanner be used whenever possible.« less

CQPSO scheduling algorithm for heterogeneous multi-core DAG task model

NASA Astrophysics Data System (ADS)

Zhai, Wenzheng; Hu, Yue-Li; Ran, Feng

2017-07-01

Efficient task scheduling is critical to achieve high performance in a heterogeneous multi-core computing environment. The paper focuses on the heterogeneous multi-core directed acyclic graph (DAG) task model and proposes a novel task scheduling method based on an improved chaotic quantum-behaved particle swarm optimization (CQPSO) algorithm. A task priority scheduling list was built. A processor with minimum cumulative earliest finish time (EFT) was acted as the object of the first task assignment. The task precedence relationships were satisfied and the total execution time of all tasks was minimized. The experimental results show that the proposed algorithm has the advantage of optimization abilities, simple and feasible, fast convergence, and can be applied to the task scheduling optimization for other heterogeneous and distributed environment.

Model-Based Thermal System Design Optimization for the James Webb Space Telescope

NASA Technical Reports Server (NTRS)

Cataldo, Giuseppe; Niedner, Malcolm B.; Fixsen, Dale J.; Moseley, Samuel H.

2017-01-01

Spacecraft thermal model validation is normally performed by comparing model predictions with thermal test data and reducing their discrepancies to meet the mission requirements. Based on thermal engineering expertise, the model input parameters are adjusted to tune the model output response to the test data. The end result is not guaranteed to be the best solution in terms of reduced discrepancy and the process requires months to complete. A model-based methodology was developed to perform the validation process in a fully automated fashion and provide mathematical bases to the search for the optimal parameter set that minimizes the discrepancies between model and data. The methodology was successfully applied to several thermal subsystems of the James Webb Space Telescope (JWST). Global or quasiglobal optimal solutions were found and the total execution time of the model validation process was reduced to about two weeks. The model sensitivities to the parameters, which are required to solve the optimization problem, can be calculated automatically before the test begins and provide a library for sensitivity studies. This methodology represents a crucial commodity when testing complex, large-scale systems under time and budget constraints. Here, results for the JWST Core thermal system will be presented in detail.

Model-based thermal system design optimization for the James Webb Space Telescope

NASA Astrophysics Data System (ADS)

Cataldo, Giuseppe; Niedner, Malcolm B.; Fixsen, Dale J.; Moseley, Samuel H.

2017-10-01

Spacecraft thermal model validation is normally performed by comparing model predictions with thermal test data and reducing their discrepancies to meet the mission requirements. Based on thermal engineering expertise, the model input parameters are adjusted to tune the model output response to the test data. The end result is not guaranteed to be the best solution in terms of reduced discrepancy and the process requires months to complete. A model-based methodology was developed to perform the validation process in a fully automated fashion and provide mathematical bases to the search for the optimal parameter set that minimizes the discrepancies between model and data. The methodology was successfully applied to several thermal subsystems of the James Webb Space Telescope (JWST). Global or quasiglobal optimal solutions were found and the total execution time of the model validation process was reduced to about two weeks. The model sensitivities to the parameters, which are required to solve the optimization problem, can be calculated automatically before the test begins and provide a library for sensitivity studies. This methodology represents a crucial commodity when testing complex, large-scale systems under time and budget constraints. Here, results for the JWST Core thermal system will be presented in detail.

Revisiting Intel Xeon Phi optimization of Thompson cloud microphysics scheme in Weather Research and Forecasting (WRF) model

NASA Astrophysics Data System (ADS)

Mielikainen, Jarno; Huang, Bormin; Huang, Allen

2015-10-01

The Thompson cloud microphysics scheme is a sophisticated cloud microphysics scheme in the Weather Research and Forecasting (WRF) model. The scheme is very suitable for massively parallel computation as there are no interactions among horizontal grid points. Compared to the earlier microphysics schemes, the Thompson scheme incorporates a large number of improvements. Thus, we have optimized the speed of this important part of WRF. Intel Many Integrated Core (MIC) ushers in a new era of supercomputing speed, performance, and compatibility. It allows the developers to run code at trillions of calculations per second using the familiar programming model. In this paper, we present our results of optimizing the Thompson microphysics scheme on Intel Many Integrated Core Architecture (MIC) hardware. The Intel Xeon Phi coprocessor is the first product based on Intel MIC architecture, and it consists of up to 61 cores connected by a high performance on-die bidirectional interconnect. The coprocessor supports all important Intel development tools. Thus, the development environment is familiar one to a vast number of CPU developers. Although, getting a maximum performance out of MICs will require using some novel optimization techniques. New optimizations for an updated Thompson scheme are discusses in this paper. The optimizations improved the performance of the original Thompson code on Xeon Phi 7120P by a factor of 1.8x. Furthermore, the same optimizations improved the performance of the Thompson on a dual socket configuration of eight core Intel Xeon E5-2670 CPUs by a factor of 1.8x compared to the original Thompson code.

Analytical methods in the high conversion reactor core design

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

Zeggel, W.; Oldekop, W.; Axmann, J.K.

High conversion reactor (HCR) design methods have been used at the Technical University of Braunschweig (TUBS) with the technological support of Kraftwerk Union (KWU). The present state and objectives of this cooperation between KWU and TUBS in the field of HCRs have been described using existing design models and current activities aimed at further development and validation of the codes. The hard physical and thermal-hydraulic boundary conditions of pressurized water reactor (PWR) cores with a high degree of fuel utilization result from the tight packing of the HCR fuel rods and the high fissionable plutonium content of the fuel. Inmore » terms of design, the problem will be solved with rod bundles whose fuel rods are adjusted by helical spacers to the proposed small rod pitches. These HCR properties require novel computational models for neutron physics, thermal hydraulics, and fuel rod design. By means of a survey of the codes, the analytical procedure for present-day HCR core design is presented. The design programs are currently under intensive development, as design tools with a solid, scientific foundation and with essential parameters that are widely valid and are required for a promising optimization of the HCR core. Design results and a survey of future HCR development are given. In this connection, the reoptimization of the PWR core in the direction of an HCR is considered a fascinating scientific task, with respect to both economic and safety aspects.« less

Creation of Novel Cores for β-Secretase (BACE-1) Inhibitors: A Multiparameter Lead Generation Strategy

PubMed Central

2014-01-01

In order to find optimal core structures as starting points for lead optimization, a multiparameter lead generation workflow was designed with the goal of finding BACE-1 inhibitors as a treatment for Alzheimer’s disease. De novo design of core fragments was connected with three predictive in silico models addressing target affinity, permeability, and hERG activity, in order to guide synthesis. Taking advantage of an additive SAR, the prioritized cores were decorated with a few, well-characterized substituents from known BACE-1 inhibitors in order to allow for core-to-core comparisons. Prediction methods and analyses of how physicochemical properties of the core structures correlate to in vitro data are described. The syntheses and in vitro data of the test compounds are reported in a separate paper by Ginman et al. [J. Med. Chem.2013, 56, 4181–4205]. The affinity predictions are described in detail by Roos et al. [J. Chem. Inf.2014, DOI: 10.1021/ci400374z]. PMID:24900855

Creation of Novel Cores for β-Secretase (BACE-1) Inhibitors: A Multiparameter Lead Generation Strategy.

PubMed

Viklund, Jenny; Kolmodin, Karin; Nordvall, Gunnar; Swahn, Britt-Marie; Svensson, Mats; Gravenfors, Ylva; Rahm, Fredrik

2014-04-10

In order to find optimal core structures as starting points for lead optimization, a multiparameter lead generation workflow was designed with the goal of finding BACE-1 inhibitors as a treatment for Alzheimer's disease. De novo design of core fragments was connected with three predictive in silico models addressing target affinity, permeability, and hERG activity, in order to guide synthesis. Taking advantage of an additive SAR, the prioritized cores were decorated with a few, well-characterized substituents from known BACE-1 inhibitors in order to allow for core-to-core comparisons. Prediction methods and analyses of how physicochemical properties of the core structures correlate to in vitro data are described. The syntheses and in vitro data of the test compounds are reported in a separate paper by Ginman et al. [J. Med. Chem. 2013, 56, 4181-4205]. The affinity predictions are described in detail by Roos et al. [J. Chem. Inf. 2014, DOI: 10.1021/ci400374z].

Bioprinting Using Mechanically Robust Core-Shell Cell-Laden Hydrogel Strands.

PubMed

Mistry, Pritesh; Aied, Ahmed; Alexander, Morgan; Shakesheff, Kevin; Bennett, Andrew; Yang, Jing

2017-06-01

The strand material in extrusion-based bioprinting determines the microenvironments of the embedded cells and the initial mechanical properties of the constructs. One unmet challenge is the combination of optimal biological and mechanical properties in bioprinted constructs. Here, a novel bioprinting method that utilizes core-shell cell-laden strands with a mechanically robust shell and an extracellular matrix-like core has been developed. Cells encapsulated in the strands demonstrate high cell viability and tissue-like functions during cultivation. This process of bioprinting using core-shell strands with optimal biochemical and biomechanical properties represents a new strategy for fabricating functional human tissues and organs. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Development of Lipid-Shell and Polymer Core Nanoparticles with Water-Soluble Salidroside for Anti-Cancer Therapy

PubMed Central

Fang, Dai-Long; Chen, Yan; Xu, Bei; Ren, Ke; He, Zhi-Yao; He, Li-Li; Lei, Yi; Fan, Chun-Mei; Song, Xiang-Rong

2014-01-01

Salidroside (Sal) is a potent antitumor drug with high water-solubility. The clinic application of Sal in cancer therapy has been significantly restricted by poor oral absorption and low tumor cell uptake. To solve this problem, lipid-shell and polymer-core nanoparticles (Sal-LPNPs) loaded with Sal were developed by a double emulsification method. The processing parameters including the polymer types, organic phase, PVA types and amount were systemically investigated. The obtained optimal Sal-LPNPs, composed of PLGA-PEG-PLGA triblock copolymers and lipids, had high entrapment efficiency (65%), submicron size (150 nm) and negatively charged surface (−23 mV). DSC analysis demonstrated the successful encapsulation of Sal into LPNPs. The core-shell structure of Sal-LPNPs was verified by TEM. Sal released slowly from the LPNPs without apparent burst release. MTT assay revealed that 4T1 and PANC-1 cancer cell lines were sensitive to Sal treatment. Sal-LPNPs had significantly higher antitumor activities than free Sal in 4T1 and PANC-1 cells. The data indicate that LPNPs are a promising Sal vehicle for anti-cancer therapy and worthy of further investigation. PMID:24573250

Development of lipid-shell and polymer core nanoparticles with water-soluble salidroside for anti-cancer therapy.

PubMed

Fang, Dai-Long; Chen, Yan; Xu, Bei; Ren, Ke; He, Zhi-Yao; He, Li-Li; Lei, Yi; Fan, Chun-Mei; Song, Xiang-Rong

2014-02-25

Salidroside (Sal) is a potent antitumor drug with high water-solubility. The clinic application of Sal in cancer therapy has been significantly restricted by poor oral absorption and low tumor cell uptake. To solve this problem, lipid-shell and polymer-core nanoparticles (Sal-LPNPs) loaded with Sal were developed by a double emulsification method. The processing parameters including the polymer types, organic phase, PVA types and amount were systemically investigated. The obtained optimal Sal-LPNPs, composed of PLGA-PEG-PLGA triblock copolymers and lipids, had high entrapment efficiency (65%), submicron size (150 nm) and negatively charged surface (-23 mV). DSC analysis demonstrated the successful encapsulation of Sal into LPNPs. The core-shell structure of Sal-LPNPs was verified by TEM. Sal released slowly from the LPNPs without apparent burst release. MTT assay revealed that 4T1 and PANC-1 cancer cell lines were sensitive to Sal treatment. Sal-LPNPs had significantly higher antitumor activities than free Sal in 4T1 and PANC-1 cells. The data indicate that LPNPs are a promising Sal vehicle for anti-cancer therapy and worthy of further investigation.

Facilitating Teamwork in Adolescent and Young Adult Oncology

PubMed Central

Macpherson, Catherine Fiona; Smith, Ashley W.; Block, Rebecca G.; Keyton, Joann

2016-01-01

A case of a young adult patient in the days immediately after a cancer diagnosis illustrates the critical importance of three interrelated core coordinating mechanisms—closed-loop communication, shared mental models, and mutual trust—of teamwork in an adolescent and young adult multidisciplinary oncology team. The case illustrates both the opportunities to increase team member coordination and the problems that can occur when coordination breaks down. A model for teamwork is presented, which highlights the relationships among these coordinating mechanisms and demonstrates how balance among them works to optimize team function and patient care. Implications for clinical practice and research suggested by the case are presented. PMID:27624944

A discrete mechanics approach to dislocation dynamics in BCC crystals

NASA Astrophysics Data System (ADS)

Ramasubramaniam, A.; Ariza, M. P.; Ortiz, M.

2007-03-01

A discrete mechanics approach to modeling the dynamics of dislocations in BCC single crystals is presented. Ideas are borrowed from discrete differential calculus and algebraic topology and suitably adapted to crystal lattices. In particular, the extension of a crystal lattice to a CW complex allows for convenient manipulation of forms and fields defined over the crystal. Dislocations are treated within the theory as energy-minimizing structures that lead to locally lattice-invariant but globally incompatible eigendeformations. The discrete nature of the theory eliminates the need for regularization of the core singularity and inherently allows for dislocation reactions and complicated topological transitions. The quantization of slip to integer multiples of the Burgers' vector leads to a large integer optimization problem. A novel approach to solving this NP-hard problem based on considerations of metastability is proposed. A numerical example that applies the method to study the emanation of dislocation loops from a point source of dilatation in a large BCC crystal is presented. The structure and energetics of BCC screw dislocation cores, as obtained via the present formulation, are also considered and shown to be in good agreement with available atomistic studies. The method thus provides a realistic avenue for mesoscale simulations of dislocation based crystal plasticity with fully atomistic resolution.

Accelerating large-scale simulation of seismic wave propagation by multi-GPUs and three-dimensional domain decomposition

NASA Astrophysics Data System (ADS)

Okamoto, Taro; Takenaka, Hiroshi; Nakamura, Takeshi; Aoki, Takayuki

2010-12-01

We adopted the GPU (graphics processing unit) to accelerate the large-scale finite-difference simulation of seismic wave propagation. The simulation can benefit from the high-memory bandwidth of GPU because it is a "memory intensive" problem. In a single-GPU case we achieved a performance of about 56 GFlops, which was about 45-fold faster than that achieved by a single core of the host central processing unit (CPU). We confirmed that the optimized use of fast shared memory and registers were essential for performance. In the multi-GPU case with three-dimensional domain decomposition, the non-contiguous memory alignment in the ghost zones was found to impose quite long time in data transfer between GPU and the host node. This problem was solved by using contiguous memory buffers for ghost zones. We achieved a performance of about 2.2 TFlops by using 120 GPUs and 330 GB of total memory: nearly (or more than) 2200 cores of host CPUs would be required to achieve the same performance. The weak scaling was nearly proportional to the number of GPUs. We therefore conclude that GPU computing for large-scale simulation of seismic wave propagation is a promising approach as a faster simulation is possible with reduced computational resources compared to CPUs.

Problem solving as a core strategy in the prevention of schizophrenia and other mental disorders.

PubMed

Falloon, I R

2000-11-01

To outline the rationale for implementing training in structured problem solving as a primary prevention strategy for major mental disorders. The evidence that training people in a structured method of solving their personal problems is an effective strategy in the treatment of established cases of schizophrenic and major mood disorders, is selectively reviewed. Most of the relevant research focused on the prevention of major recurrent episodes of psychosis. There is some evidence to support the hypothesis that this strategy may assist many people to achieve a full and sustained recovery from the clinical and social impairments of these disorders, especially when patients are taught to use structured problem solving with members of their personal resource groups, and they continue to take optimal doses of psychoactive medication. There is support for the hypothesis that the key therapeutic factor associated with these benefits is the improved efficiency of the management of life stress. The simplicity of problem solving, the educational methods used, and the widespread application to a person's lifestyle would appear to make this a possible candidate for a primary prevention program for major mental disorders. Guidebooks and teaching aids have been developed and show excellent consumer acceptance.

Comprehensive Benchmark Suite for Simulation of Particle Laden Flows Using the Discrete Element Method with Performance Profiles from the Multiphase Flow with Interface eXchanges (MFiX) Code

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

Liu, Peiyuan; Brown, Timothy; Fullmer, William D.

Five benchmark problems are developed and simulated with the computational fluid dynamics and discrete element model code MFiX. The benchmark problems span dilute and dense regimes, consider statistically homogeneous and inhomogeneous (both clusters and bubbles) particle concentrations and a range of particle and fluid dynamic computational loads. Several variations of the benchmark problems are also discussed to extend the computational phase space to cover granular (particles only), bidisperse and heat transfer cases. A weak scaling analysis is performed for each benchmark problem and, in most cases, the scalability of the code appears reasonable up to approx. 103 cores. Profiling ofmore » the benchmark problems indicate that the most substantial computational time is being spent on particle-particle force calculations, drag force calculations and interpolating between discrete particle and continuum fields. Hardware performance analysis was also carried out showing significant Level 2 cache miss ratios and a rather low degree of vectorization. These results are intended to serve as a baseline for future developments to the code as well as a preliminary indicator of where to best focus performance optimizations.« less

Graphics Processing Unit–Enhanced Genetic Algorithms for Solving the Temporal Dynamics of Gene Regulatory Networks

PubMed Central

García-Calvo, Raúl; Guisado, JL; Diaz-del-Rio, Fernando; Córdoba, Antonio; Jiménez-Morales, Francisco

2018-01-01

Understanding the regulation of gene expression is one of the key problems in current biology. A promising method for that purpose is the determination of the temporal dynamics between known initial and ending network states, by using simple acting rules. The huge amount of rule combinations and the nonlinear inherent nature of the problem make genetic algorithms an excellent candidate for finding optimal solutions. As this is a computationally intensive problem that needs long runtimes in conventional architectures for realistic network sizes, it is fundamental to accelerate this task. In this article, we study how to develop efficient parallel implementations of this method for the fine-grained parallel architecture of graphics processing units (GPUs) using the compute unified device architecture (CUDA) platform. An exhaustive and methodical study of various parallel genetic algorithm schemes—master-slave, island, cellular, and hybrid models, and various individual selection methods (roulette, elitist)—is carried out for this problem. Several procedures that optimize the use of the GPU’s resources are presented. We conclude that the implementation that produces better results (both from the performance and the genetic algorithm fitness perspectives) is simulating a few thousands of individuals grouped in a few islands using elitist selection. This model comprises 2 mighty factors for discovering the best solutions: finding good individuals in a short number of generations, and introducing genetic diversity via a relatively frequent and numerous migration. As a result, we have even found the optimal solution for the analyzed gene regulatory network (GRN). In addition, a comparative study of the performance obtained by the different parallel implementations on GPU versus a sequential application on CPU is carried out. In our tests, a multifold speedup was obtained for our optimized parallel implementation of the method on medium class GPU over an equivalent sequential single-core implementation running on a recent Intel i7 CPU. This work can provide useful guidance to researchers in biology, medicine, or bioinformatics in how to take advantage of the parallelization on massively parallel devices and GPUs to apply novel metaheuristic algorithms powered by nature for real-world applications (like the method to solve the temporal dynamics of GRNs). PMID:29662297

Graphics Processing Unit-Enhanced Genetic Algorithms for Solving the Temporal Dynamics of Gene Regulatory Networks.

PubMed

García-Calvo, Raúl; Guisado, J L; Diaz-Del-Rio, Fernando; Córdoba, Antonio; Jiménez-Morales, Francisco

2018-01-01

Understanding the regulation of gene expression is one of the key problems in current biology. A promising method for that purpose is the determination of the temporal dynamics between known initial and ending network states, by using simple acting rules. The huge amount of rule combinations and the nonlinear inherent nature of the problem make genetic algorithms an excellent candidate for finding optimal solutions. As this is a computationally intensive problem that needs long runtimes in conventional architectures for realistic network sizes, it is fundamental to accelerate this task. In this article, we study how to develop efficient parallel implementations of this method for the fine-grained parallel architecture of graphics processing units (GPUs) using the compute unified device architecture (CUDA) platform. An exhaustive and methodical study of various parallel genetic algorithm schemes-master-slave, island, cellular, and hybrid models, and various individual selection methods (roulette, elitist)-is carried out for this problem. Several procedures that optimize the use of the GPU's resources are presented. We conclude that the implementation that produces better results (both from the performance and the genetic algorithm fitness perspectives) is simulating a few thousands of individuals grouped in a few islands using elitist selection. This model comprises 2 mighty factors for discovering the best solutions: finding good individuals in a short number of generations, and introducing genetic diversity via a relatively frequent and numerous migration. As a result, we have even found the optimal solution for the analyzed gene regulatory network (GRN). In addition, a comparative study of the performance obtained by the different parallel implementations on GPU versus a sequential application on CPU is carried out. In our tests, a multifold speedup was obtained for our optimized parallel implementation of the method on medium class GPU over an equivalent sequential single-core implementation running on a recent Intel i7 CPU. This work can provide useful guidance to researchers in biology, medicine, or bioinformatics in how to take advantage of the parallelization on massively parallel devices and GPUs to apply novel metaheuristic algorithms powered by nature for real-world applications (like the method to solve the temporal dynamics of GRNs).

Optimizing the Betts-Miller-Janjic cumulus parameterization with Intel Many Integrated Core (MIC) architecture

NASA Astrophysics Data System (ADS)

Huang, Melin; Huang, Bormin; Huang, Allen H.-L.

2015-10-01

The schemes of cumulus parameterization are responsible for the sub-grid-scale effects of convective and/or shallow clouds, and intended to represent vertical fluxes due to unresolved updrafts and downdrafts and compensating motion outside the clouds. Some schemes additionally provide cloud and precipitation field tendencies in the convective column, and momentum tendencies due to convective transport of momentum. The schemes all provide the convective component of surface rainfall. Betts-Miller-Janjic (BMJ) is one scheme to fulfill such purposes in the weather research and forecast (WRF) model. National Centers for Environmental Prediction (NCEP) has tried to optimize the BMJ scheme for operational application. As there are no interactions among horizontal grid points, this scheme is very suitable for parallel computation. With the advantage of Intel Xeon Phi Many Integrated Core (MIC) architecture, efficient parallelization and vectorization essentials, it allows us to optimize the BMJ scheme. If compared to the original code respectively running on one CPU socket (eight cores) and on one CPU core with Intel Xeon E5-2670, the MIC-based optimization of this scheme running on Xeon Phi coprocessor 7120P improves the performance by 2.4x and 17.0x, respectively.

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

Gaitsgory, Vladimir, E-mail: vladimir.gaitsgory@mq.edu.au; Rossomakhine, Sergey, E-mail: serguei.rossomakhine@flinders.edu.au

The paper aims at the development of an apparatus for analysis and construction of near optimal solutions of singularly perturbed (SP) optimal controls problems (that is, problems of optimal control of SP systems) considered on the infinite time horizon. We mostly focus on problems with time discounting criteria but a possibility of the extension of results to periodic optimization problems is discussed as well. Our consideration is based on earlier results on averaging of SP control systems and on linear programming formulations of optimal control problems. The idea that we exploit is to first asymptotically approximate a given problem ofmore » optimal control of the SP system by a certain averaged optimal control problem, then reformulate this averaged problem as an infinite-dimensional linear programming (LP) problem, and then approximate the latter by semi-infinite LP problems. We show that the optimal solution of these semi-infinite LP problems and their duals (that can be found with the help of a modification of an available LP software) allow one to construct near optimal controls of the SP system. We demonstrate the construction with two numerical examples.« less

A New Combinatorial Optimization Approach for Integrated Feature Selection Using Different Datasets: A Prostate Cancer Transcriptomic Study

PubMed Central

Puthiyedth, Nisha; Riveros, Carlos; Berretta, Regina; Moscato, Pablo

2015-01-01

Background The joint study of multiple datasets has become a common technique for increasing statistical power in detecting biomarkers obtained from smaller studies. The approach generally followed is based on the fact that as the total number of samples increases, we expect to have greater power to detect associations of interest. This methodology has been applied to genome-wide association and transcriptomic studies due to the availability of datasets in the public domain. While this approach is well established in biostatistics, the introduction of new combinatorial optimization models to address this issue has not been explored in depth. In this study, we introduce a new model for the integration of multiple datasets and we show its application in transcriptomics. Methods We propose a new combinatorial optimization problem that addresses the core issue of biomarker detection in integrated datasets. Optimal solutions for this model deliver a feature selection from a panel of prospective biomarkers. The model we propose is a generalised version of the (α,β)-k-Feature Set problem. We illustrate the performance of this new methodology via a challenging meta-analysis task involving six prostate cancer microarray datasets. The results are then compared to the popular RankProd meta-analysis tool and to what can be obtained by analysing the individual datasets by statistical and combinatorial methods alone. Results Application of the integrated method resulted in a more informative signature than the rank-based meta-analysis or individual dataset results, and overcomes problems arising from real world datasets. The set of genes identified is highly significant in the context of prostate cancer. The method used does not rely on homogenisation or transformation of values to a common scale, and at the same time is able to capture markers associated with subgroups of the disease. PMID:26106884

Optimization and photomodification of extremely broadband optical response of plasmonic core-shell obscurants.

PubMed

de Silva, Vashista C; Nyga, Piotr; Drachev, Vladimir P

2016-12-15

Plasmonic resonances of the metallic shells depend on their nanostructure and geometry of the core, which can be optimized for the broadband extinction normalized by mass. The fractal nanostructures can provide a broadband extinction. It allows as well for a laser photoburning of holes in the extinction spectra and consequently windows of transparency in a controlled manner. The studied core-shell microparticles synthesized using colloidal chemistry consist of gold fractal nanostructures grown on precipitated calcium carbonate (PCC) microparticles or silica (SiO 2 ) microspheres. The optimization includes different core sizes and shapes, and shell nanostructures. It shows that the rich surface of the PCC flakes is the best core for the fractal shells providing the highest mass normalized extinction over the extremely broad spectral range. The mass normalized extinction cross section up to 3m 2 /g has been demonstrated in the broad spectral range from the visible to mid-infrared. Essentially, the broadband response is a characteristic feature of each core-shell microparticle in contrast to a combination of several structures resonant at different wavelengths, for example nanorods with different aspect ratios. The photomodification at an IR wavelength makes the window of transparency at the longer wavelength side. Copyright © 2016 Elsevier Inc. All rights reserved.

The maximum vector-angular margin classifier and its fast training on large datasets using a core vector machine.

PubMed

Hu, Wenjun; Chung, Fu-Lai; Wang, Shitong

2012-03-01

Although pattern classification has been extensively studied in the past decades, how to effectively solve the corresponding training on large datasets is a problem that still requires particular attention. Many kernelized classification methods, such as SVM and SVDD, can be formulated as the corresponding quadratic programming (QP) problems, but computing the associated kernel matrices requires O(n2)(or even up to O(n3)) computational complexity, where n is the size of the training patterns, which heavily limits the applicability of these methods for large datasets. In this paper, a new classification method called the maximum vector-angular margin classifier (MAMC) is first proposed based on the vector-angular margin to find an optimal vector c in the pattern feature space, and all the testing patterns can be classified in terms of the maximum vector-angular margin ρ, between the vector c and all the training data points. Accordingly, it is proved that the kernelized MAMC can be equivalently formulated as the kernelized Minimum Enclosing Ball (MEB), which leads to a distinctive merit of MAMC, i.e., it has the flexibility of controlling the sum of support vectors like v-SVC and may be extended to a maximum vector-angular margin core vector machine (MAMCVM) by connecting the core vector machine (CVM) method with MAMC such that the corresponding fast training on large datasets can be effectively achieved. Experimental results on artificial and real datasets are provided to validate the power of the proposed methods. Copyright © 2011 Elsevier Ltd. All rights reserved.

Do Imitation Problems Reflect a Core Characteristic in Autism? Evidence from a Literature Review

ERIC Educational Resources Information Center

Vanvuchelen, Marleen; Roeyers, Herbert; De Weerdt, Willy

2011-01-01

Although imitation problems have been associated with autism for many years, the issue if these problems are a core deficit in autism remains subject of debate. In this review article, the question if autism imitation problems fulfil the criteria of uniqueness, specificity, universality, persistency, precedence and broadness is explored and…

Comparison of Acceleration Techniques for Selected Low-Level Bioinformatics Operations

PubMed Central

Langenkämper, Daniel; Jakobi, Tobias; Feld, Dustin; Jelonek, Lukas; Goesmann, Alexander; Nattkemper, Tim W.

2016-01-01

Within the recent years clock rates of modern processors stagnated while the demand for computing power continued to grow. This applied particularly for the fields of life sciences and bioinformatics, where new technologies keep on creating rapidly growing piles of raw data with increasing speed. The number of cores per processor increased in an attempt to compensate for slight increments of clock rates. This technological shift demands changes in software development, especially in the field of high performance computing where parallelization techniques are gaining in importance due to the pressing issue of large sized datasets generated by e.g., modern genomics. This paper presents an overview of state-of-the-art manual and automatic acceleration techniques and lists some applications employing these in different areas of sequence informatics. Furthermore, we provide examples for automatic acceleration of two use cases to show typical problems and gains of transforming a serial application to a parallel one. The paper should aid the reader in deciding for a certain techniques for the problem at hand. We compare four different state-of-the-art automatic acceleration approaches (OpenMP, PluTo-SICA, PPCG, and OpenACC). Their performance as well as their applicability for selected use cases is discussed. While optimizations targeting the CPU worked better in the complex k-mer use case, optimizers for Graphics Processing Units (GPUs) performed better in the matrix multiplication example. But performance is only superior at a certain problem size due to data migration overhead. We show that automatic code parallelization is feasible with current compiler software and yields significant increases in execution speed. Automatic optimizers for CPU are mature and usually no additional manual adjustment is required. In contrast, some automatic parallelizers targeting GPUs still lack maturity and are limited to simple statements and structures. PMID:26904094

Passive motion paradigm: an alternative to optimal control.

PubMed

Mohan, Vishwanathan; Morasso, Pietro

2011-01-01

IN THE LAST YEARS, OPTIMAL CONTROL THEORY (OCT) HAS EMERGED AS THE LEADING APPROACH FOR INVESTIGATING NEURAL CONTROL OF MOVEMENT AND MOTOR COGNITION FOR TWO COMPLEMENTARY RESEARCH LINES: behavioral neuroscience and humanoid robotics. In both cases, there are general problems that need to be addressed, such as the "degrees of freedom (DoFs) problem," the common core of production, observation, reasoning, and learning of "actions." OCT, directly derived from engineering design techniques of control systems quantifies task goals as "cost functions" and uses the sophisticated formal tools of optimal control to obtain desired behavior (and predictions). We propose an alternative "softer" approach passive motion paradigm (PMP) that we believe is closer to the biomechanics and cybernetics of action. The basic idea is that actions (overt as well as covert) are the consequences of an internal simulation process that "animates" the body schema with the attractor dynamics of force fields induced by the goal and task-specific constraints. This internal simulation offers the brain a way to dynamically link motor redundancy with task-oriented constraints "at runtime," hence solving the "DoFs problem" without explicit kinematic inversion and cost function computation. We argue that the function of such computational machinery is not only restricted to shaping motor output during action execution but also to provide the self with information on the feasibility, consequence, understanding and meaning of "potential actions." In this sense, taking into account recent developments in neuroscience (motor imagery, simulation theory of covert actions, mirror neuron system) and in embodied robotics, PMP offers a novel framework for understanding motor cognition that goes beyond the engineering control paradigm provided by OCT. Therefore, the paper is at the same time a review of the PMP rationale, as a computational theory, and a perspective presentation of how to develop it for designing better cognitive architectures.

Comparison of Acceleration Techniques for Selected Low-Level Bioinformatics Operations.

PubMed

Langenkämper, Daniel; Jakobi, Tobias; Feld, Dustin; Jelonek, Lukas; Goesmann, Alexander; Nattkemper, Tim W

2016-01-01

Within the recent years clock rates of modern processors stagnated while the demand for computing power continued to grow. This applied particularly for the fields of life sciences and bioinformatics, where new technologies keep on creating rapidly growing piles of raw data with increasing speed. The number of cores per processor increased in an attempt to compensate for slight increments of clock rates. This technological shift demands changes in software development, especially in the field of high performance computing where parallelization techniques are gaining in importance due to the pressing issue of large sized datasets generated by e.g., modern genomics. This paper presents an overview of state-of-the-art manual and automatic acceleration techniques and lists some applications employing these in different areas of sequence informatics. Furthermore, we provide examples for automatic acceleration of two use cases to show typical problems and gains of transforming a serial application to a parallel one. The paper should aid the reader in deciding for a certain techniques for the problem at hand. We compare four different state-of-the-art automatic acceleration approaches (OpenMP, PluTo-SICA, PPCG, and OpenACC). Their performance as well as their applicability for selected use cases is discussed. While optimizations targeting the CPU worked better in the complex k-mer use case, optimizers for Graphics Processing Units (GPUs) performed better in the matrix multiplication example. But performance is only superior at a certain problem size due to data migration overhead. We show that automatic code parallelization is feasible with current compiler software and yields significant increases in execution speed. Automatic optimizers for CPU are mature and usually no additional manual adjustment is required. In contrast, some automatic parallelizers targeting GPUs still lack maturity and are limited to simple statements and structures.

Negative core affect and employee silence: How differences in activation, cognitive rumination, and problem-solving demands matter.

PubMed

Madrid, Hector P; Patterson, Malcolm G; Leiva, Pedro I

2015-11-01

Employees can help to improve organizational performance by sharing ideas, suggestions, or concerns about practices, but sometimes they keep silent because of the experience of negative affect. Drawing and expanding on this stream of research, this article builds a theoretical rationale based on core affect and cognitive appraisal theories to describe how differences in affect activation and boundary conditions associated with cognitive rumination and cognitive problem-solving demands can explain employee silence. Results of a diary study conducted with professionals from diverse organizations indicated that within-person low-activated negative core affect increased employee silence when, as an invariant factor, cognitive rumination was high. Furthermore, within-person high-activated negative core affect decreased employee silence when, as an invariant factor, cognitive problem-solving demand was high. Thus, organizations should manage conditions to reduce experiences of low-activated negative core affect because these feelings increase silence in individuals high in rumination. In turn, effective management of experiences of high-activated negative core affect can reduce silence for individuals working under high problem-solving demand situations. (c) 2015 APA, all rights reserved).

Performance of Grey Wolf Optimizer on large scale problems

NASA Astrophysics Data System (ADS)

Gupta, Shubham; Deep, Kusum

2017-01-01

For solving nonlinear continuous problems of optimization numerous nature inspired optimization techniques are being proposed in literature which can be implemented to solve real life problems wherein the conventional techniques cannot be applied. Grey Wolf Optimizer is one of such technique which is gaining popularity since the last two years. The objective of this paper is to investigate the performance of Grey Wolf Optimization Algorithm on large scale optimization problems. The Algorithm is implemented on 5 common scalable problems appearing in literature namely Sphere, Rosenbrock, Rastrigin, Ackley and Griewank Functions. The dimensions of these problems are varied from 50 to 1000. The results indicate that Grey Wolf Optimizer is a powerful nature inspired Optimization Algorithm for large scale problems, except Rosenbrock which is a unimodal function.

Behavior-aware cache hierarchy optimization for low-power multi-core embedded systems

NASA Astrophysics Data System (ADS)

Zhao, Huatao; Luo, Xiao; Zhu, Chen; Watanabe, Takahiro; Zhu, Tianbo

2017-07-01

In modern embedded systems, the increasing number of cores requires efficient cache hierarchies to ensure data throughput, but such cache hierarchies are restricted by their tumid size and interference accesses which leads to both performance degradation and wasted energy. In this paper, we firstly propose a behavior-aware cache hierarchy (BACH) which can optimally allocate the multi-level cache resources to many cores and highly improved the efficiency of cache hierarchy, resulting in low energy consumption. The BACH takes full advantage of the explored application behaviors and runtime cache resource demands as the cache allocation bases, so that we can optimally configure the cache hierarchy to meet the runtime demand. The BACH was implemented on the GEM5 simulator. The experimental results show that energy consumption of a three-level cache hierarchy can be saved from 5.29% up to 27.94% compared with other key approaches while the performance of the multi-core system even has a slight improvement counting in hardware overhead.

Preparation of monolithic osmotic pump system by coating the indented core tablet.

PubMed

Liu, Longxiao; Che, Binjie

2006-10-01

A method for the preparation of monolithic osmotic pump tablet was obtained by coating the indented core tablet compressed by the punch with a needle. Atenolol was used as the model drug, sodium chloride as osmotic agent and polyethylene oxide as suspending agent. Ethyl cellulose was employed as semipermeable membrane containing polyethylene glycol 400 as plasticizer for controlling membrane permeability. The formulation of atenolol osmotic pump tablet was optimized by orthogonal design and evaluated by similarity factor (f2). The optimal formulation was evaluated in various release media and agitation rates. Indentation size of core tablet hardly affected drug release in the range of (1.00-1.14) mm. The optimal osmotic tablet was found to be able to deliver atenolol at an approximately constant rate up to 24h, independent of both release media and agitation rate. The method that is simplified by coating the indented core tablet with the elimination of laser drilling may be promising in the field of the preparation of osmotic pump tablet.

Inductance optimization of miniature Broadband transformers with racetrack shaped ferrite cores for Ethernet applications

NASA Astrophysics Data System (ADS)

Bowen, David; Krafft, Charles; Mayergoyz, Isaak D.

2017-05-01

There is strong commercial interest in the ability to fabricate the windings of traditional miniature wire-wound inductive circuit components, such as Ethernet transformers, lithographically. For greater inductance devices, thick cores are required, making the process of embedding the ferrite material within circuit board one of few options for lithographic winding fabrication. In this paper, a non-traditional core shape, suitable for embedding in circuit board, is examined analytically and experimentally; the racetrack shape is two halves of a toroid connected by straight legs. With regard to the high inductance requirements for Ethernet applications (350μH), the racetrack transformer inductance is analytically optimized, determining the optimal physical dimensions. Two sizes of racetrack-core transformers were fabricated and measured. The measured inductance was in reasonable agreement with the analytical prediction, though large variations in material permeability are expected from the mechanical processing of the ferrite. Some of the experimental transformers were observed to satisfy the Ethernet inductance requirement.

Feed Forward Neural Network and Optimal Control Problem with Control and State Constraints

NASA Astrophysics Data System (ADS)

Kmet', Tibor; Kmet'ová, Mária

2009-09-01

A feed forward neural network based optimal control synthesis is presented for solving optimal control problems with control and state constraints. The paper extends adaptive critic neural network architecture proposed by [5] to the optimal control problems with control and state constraints. The optimal control problem is transcribed into a nonlinear programming problem which is implemented with adaptive critic neural network. The proposed simulation method is illustrated by the optimal control problem of nitrogen transformation cycle model. Results show that adaptive critic based systematic approach holds promise for obtaining the optimal control with control and state constraints.

Binary optimization for source localization in the inverse problem of ECG.

PubMed

Potyagaylo, Danila; Cortés, Elisenda Gil; Schulze, Walther H W; Dössel, Olaf

2014-09-01

The goal of ECG-imaging (ECGI) is to reconstruct heart electrical activity from body surface potential maps. The problem is ill-posed, which means that it is extremely sensitive to measurement and modeling errors. The most commonly used method to tackle this obstacle is Tikhonov regularization, which consists in converting the original problem into a well-posed one by adding a penalty term. The method, despite all its practical advantages, has however a serious drawback: The obtained solution is often over-smoothed, which can hinder precise clinical diagnosis and treatment planning. In this paper, we apply a binary optimization approach to the transmembrane voltage (TMV)-based problem. For this, we assume the TMV to take two possible values according to a heart abnormality under consideration. In this work, we investigate the localization of simulated ischemic areas and ectopic foci and one clinical infarction case. This affects only the choice of the binary values, while the core of the algorithms remains the same, making the approximation easily adjustable to the application needs. Two methods, a hybrid metaheuristic approach and the difference of convex functions (DC), algorithm were tested. For this purpose, we performed realistic heart simulations for a complex thorax model and applied the proposed techniques to the obtained ECG signals. Both methods enabled localization of the areas of interest, hence showing their potential for application in ECGI. For the metaheuristic algorithm, it was necessary to subdivide the heart into regions in order to obtain a stable solution unsusceptible to the errors, while the analytical DC scheme can be efficiently applied for higher dimensional problems. With the DC method, we also successfully reconstructed the activation pattern and origin of a simulated extrasystole. In addition, the DC algorithm enables iterative adjustment of binary values ensuring robust performance.

Parallel and Preemptable Dynamically Dimensioned Search Algorithms for Single and Multi-objective Optimization in Water Resources

NASA Astrophysics Data System (ADS)

Tolson, B.; Matott, L. S.; Gaffoor, T. A.; Asadzadeh, M.; Shafii, M.; Pomorski, P.; Xu, X.; Jahanpour, M.; Razavi, S.; Haghnegahdar, A.; Craig, J. R.

2015-12-01

We introduce asynchronous parallel implementations of the Dynamically Dimensioned Search (DDS) family of algorithms including DDS, discrete DDS, PA-DDS and DDS-AU. These parallel algorithms are unique from most existing parallel optimization algorithms in the water resources field in that parallel DDS is asynchronous and does not require an entire population (set of candidate solutions) to be evaluated before generating and then sending a new candidate solution for evaluation. One key advance in this study is developing the first parallel PA-DDS multi-objective optimization algorithm. The other key advance is enhancing the computational efficiency of solving optimization problems (such as model calibration) by combining a parallel optimization algorithm with the deterministic model pre-emption concept. These two efficiency techniques can only be combined because of the asynchronous nature of parallel DDS. Model pre-emption functions to terminate simulation model runs early, prior to completely simulating the model calibration period for example, when intermediate results indicate the candidate solution is so poor that it will definitely have no influence on the generation of further candidate solutions. The computational savings of deterministic model preemption available in serial implementations of population-based algorithms (e.g., PSO) disappear in synchronous parallel implementations as these algorithms. In addition to the key advances above, we implement the algorithms across a range of computation platforms (Windows and Unix-based operating systems from multi-core desktops to a supercomputer system) and package these for future modellers within a model-independent calibration software package called Ostrich as well as MATLAB versions. Results across multiple platforms and multiple case studies (from 4 to 64 processors) demonstrate the vast improvement over serial DDS-based algorithms and highlight the important role model pre-emption plays in the performance of parallel, pre-emptable DDS algorithms. Case studies include single- and multiple-objective optimization problems in water resources model calibration and in many cases linear or near linear speedups are observed.

Hamiltonian Systems and Optimal Control in Computational Anatomy: 100 Years Since D'Arcy Thompson.

PubMed

Miller, Michael I; Trouvé, Alain; Younes, Laurent

2015-01-01

The Computational Anatomy project is the morphome-scale study of shape and form, which we model as an orbit under diffeomorphic group action. Metric comparison calculates the geodesic length of the diffeomorphic flow connecting one form to another. Geodesic connection provides a positioning system for coordinatizing the forms and positioning their associated functional information. This article reviews progress since the Euler-Lagrange characterization of the geodesics a decade ago. Geodesic positioning is posed as a series of problems in Hamiltonian control, which emphasize the key reduction from the Eulerian momentum with dimension of the flow of the group, to the parametric coordinates appropriate to the dimension of the submanifolds being positioned. The Hamiltonian viewpoint provides important extensions of the core setting to new, object-informed positioning systems. Several submanifold mapping problems are discussed as they apply to metamorphosis, multiple shape spaces, and longitudinal time series studies of growth and atrophy via shape splines.

Design of Energy Storage Management System Based on FPGA in Micro-Grid

NASA Astrophysics Data System (ADS)

Liang, Yafeng; Wang, Yanping; Han, Dexiao

2018-01-01

Energy storage system is the core to maintain the stable operation of smart micro-grid. Aiming at the existing problems of the energy storage management system in the micro-grid such as Low fault tolerance, easy to cause fluctuations in micro-grid, a new intelligent battery management system based on field programmable gate array is proposed : taking advantage of FPGA to combine the battery management system with the intelligent micro-grid control strategy. Finally, aiming at the problem that during estimation of battery charge State by neural network, initialization of weights and thresholds are not accurate leading to large errors in prediction results, the genetic algorithm is proposed to optimize the neural network method, and the experimental simulation is carried out. The experimental results show that the algorithm has high precision and provides guarantee for the stable operation of micro-grid.

MUTILS - a set of efficient modeling tools for multi-core CPUs implemented in MEX

NASA Astrophysics Data System (ADS)

Krotkiewski, Marcin; Dabrowski, Marcin

2013-04-01

The need for computational performance is common in scientific applications, and in particular in numerical simulations, where high resolution models require efficient processing of large amounts of data. Especially in the context of geological problems the need to increase the model resolution to resolve physical and geometrical complexities seems to have no limits. Alas, the performance of new generations of CPUs does not improve any longer by simply increasing clock speeds. Current industrial trends are to increase the number of computational cores. As a result, parallel implementations are required in order to fully utilize the potential of new processors, and to study more complex models. We target simulations on small to medium scale shared memory computers: laptops and desktop PCs with ~8 CPU cores and up to tens of GB of memory to high-end servers with ~50 CPU cores and hundereds of GB of memory. In this setting MATLAB is often the environment of choice for scientists that want to implement their own models with little effort. It is a useful general purpose mathematical software package, but due to its versatility some of its functionality is not as efficient as it could be. In particular, the challanges of modern multi-core architectures are not fully addressed. We have developed MILAMIN 2 - an efficient FEM modeling environment written in native MATLAB. Amongst others, MILAMIN provides functions to define model geometry, generate and convert structured and unstructured meshes (also through interfaces to external mesh generators), compute element and system matrices, apply boundary conditions, solve the system of linear equations, address non-linear and transient problems, and perform post-processing. MILAMIN strives to combine the ease of code development and the computational efficiency. Where possible, the code is optimized and/or parallelized within the MATLAB framework. Native MATLAB is augmented with the MUTILS library - a set of MEX functions that implement the computationally intensive, performance critical parts of the code, which we have identified to be bottlenecks. Here, we discuss the functionality and performance of the MUTILS library. Currently, it includes: 1. time and memory efficient assembly of sparse matrices for FEM simulations 2. parallel sparse matrix - vector product with optimizations speficic to symmetric matrices and multiple degrees of freedom per node 3. parallel point in triangle location and point in tetrahedron location for unstructured, adaptive 2D and 3D meshes (useful for 'marker in cell' type of methods) 4. parallel FEM interpolation for 2D and 3D meshes of elements of different types and orders, and for different number of degrees of freedom per node 5. a stand-alone, MEX implementation of the Conjugate Gradients iterative solver 6. interface to METIS graph partitioning and a fast implementation of RCM reordering

COPS: Large-scale nonlinearly constrained optimization problems

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

Bondarenko, A.S.; Bortz, D.M.; More, J.J.

2000-02-10

The authors have started the development of COPS, a collection of large-scale nonlinearly Constrained Optimization Problems. The primary purpose of this collection is to provide difficult test cases for optimization software. Problems in the current version of the collection come from fluid dynamics, population dynamics, optimal design, and optimal control. For each problem they provide a short description of the problem, notes on the formulation of the problem, and results of computational experiments with general optimization solvers. They currently have results for DONLP2, LANCELOT, MINOS, SNOPT, and LOQO.

Design and optimization of a flexible high-peak-power laser-to-fiber coupled illumination system used in digital particle image velocimetry

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

Robinson, Ronald A.; Ilev, Ilko K.

We present a study on the design and parameter optimization of a flexible high-peak-power fiber-optic laser delivery system using commercially available solid-core silica fibers and an experimental glass hollow waveguide (HW). The fiber-optic delivery system provides a flexible, safe, and easily and precisely positioned laser irradiation for many applications including uniform illumination for digital particle image velocimetry (DPIV). The delivery fibers, when coupled through a line-generating lens, produce a uniform thin laser sheet illumination for accurate and repeatable DPIV two-dimensional velocity measurements. We report experimental results on homogenizing the laser beam profile using various mode-mixing techniques. Furthermore, because a fundamentalmore » problem for fiber-optic-based high-peak-power laser delivery systems is the possible damage effects of the fiber material, we determine experimentally the peak power density damage threshold of various delivery fibers designed for the visible spectral range at a typical DPIV laser wavelength of 532 nm. In the case of solid-core silica delivery fibers using conventional lens-based laser-to-fiber coupling, the damage threshold varies from 3.7 GW/cm{sup 2} for a 100-{mu}m-core-diameter high-temperature fiber to 3.9 GW/cm{sup 2} for a 200-{mu}m-core-diameter high-power delivery fiber, with a total output laser energy delivered of at least 3-10 mJ for those respective fibers. Therefore, these fibers are marginally suitable for most macro-DPIV applications. However, to improve the high-power delivery capability for close-up micro-DPIV applications, we propose and validate an experimental fiber link with much higher laser power delivery capability than the solid-core fiber links. We use an uncoated grazing-incidence-based tapered glass funnel coupled to a glass HW with hollow air-core diameter of 700 {mu}m, a low numerical aperture of 0.05, and a thin inside cladding of cyclic olefin polymer coating for optimum transmission at 532 nm. Because of the mode homogenizing effect and lower power density, the taper-waveguide laser delivery technique ensured high damage threshold for the delivery HW, and as a result, no damage occurred at the maximum measured input laser energy of 33 mJ used in this study.« less

P-Hint-Hunt: a deep parallelized whole genome DNA methylation detection tool.

PubMed

Peng, Shaoliang; Yang, Shunyun; Gao, Ming; Liao, Xiangke; Liu, Jie; Yang, Canqun; Wu, Chengkun; Yu, Wenqiang

2017-03-14

The increasing studies have been conducted using whole genome DNA methylation detection as one of the most important part of epigenetics research to find the significant relationships among DNA methylation and several typical diseases, such as cancers and diabetes. In many of those studies, mapping the bisulfite treated sequence to the whole genome has been the main method to study DNA cytosine methylation. However, today's relative tools almost suffer from inaccuracies and time-consuming problems. In our study, we designed a new DNA methylation prediction tool ("Hint-Hunt") to solve the problem. By having an optimal complex alignment computation and Smith-Waterman matrix dynamic programming, Hint-Hunt could analyze and predict the DNA methylation status. But when Hint-Hunt tried to predict DNA methylation status with large-scale dataset, there are still slow speed and low temporal-spatial efficiency problems. In order to solve the problems of Smith-Waterman dynamic programming and low temporal-spatial efficiency, we further design a deep parallelized whole genome DNA methylation detection tool ("P-Hint-Hunt") on Tianhe-2 (TH-2) supercomputer. To the best of our knowledge, P-Hint-Hunt is the first parallel DNA methylation detection tool with a high speed-up to process large-scale dataset, and could run both on CPU and Intel Xeon Phi coprocessors. Moreover, we deploy and evaluate Hint-Hunt and P-Hint-Hunt on TH-2 supercomputer in different scales. The experimental results illuminate our tools eliminate the deviation caused by bisulfite treatment in mapping procedure and the multi-level parallel program yields a 48 times speed-up with 64 threads. P-Hint-Hunt gain a deep acceleration on CPU and Intel Xeon Phi heterogeneous platform, which gives full play of the advantages of multi-cores (CPU) and many-cores (Phi).

Generalized bipartite quantum state discrimination problems with sequential measurements

NASA Astrophysics Data System (ADS)

Nakahira, Kenji; Kato, Kentaro; Usuda, Tsuyoshi Sasaki

2018-02-01

We investigate an optimization problem of finding quantum sequential measurements, which forms a wide class of state discrimination problems with the restriction that only local operations and one-way classical communication are allowed. Sequential measurements from Alice to Bob on a bipartite system are considered. Using the fact that the optimization problem can be formulated as a problem with only Alice's measurement and is convex programming, we derive its dual problem and necessary and sufficient conditions for an optimal solution. Our results are applicable to various practical optimization criteria, including the Bayes criterion, the Neyman-Pearson criterion, and the minimax criterion. In the setting of the problem of finding an optimal global measurement, its dual problem and necessary and sufficient conditions for an optimal solution have been widely used to obtain analytical and numerical expressions for optimal solutions. Similarly, our results are useful to obtain analytical and numerical expressions for optimal sequential measurements. Examples in which our results can be used to obtain an analytical expression for an optimal sequential measurement are provided.

A Cascade Optimization Strategy for Solution of Difficult Multidisciplinary Design Problems

NASA Technical Reports Server (NTRS)

Patnaik, Surya N.; Coroneos, Rula M.; Hopkins, Dale A.; Berke, Laszlo

1996-01-01

A research project to comparatively evaluate 10 nonlinear optimization algorithms was recently completed. A conclusion was that no single optimizer could successfully solve all 40 problems in the test bed, even though most optimizers successfully solved at least one-third of the problems. We realized that improved search directions and step lengths, available in the 10 optimizers compared, were not likely to alleviate the convergence difficulties. For the solution of those difficult problems we have devised an alternative approach called cascade optimization strategy. The cascade strategy uses several optimizers, one followed by another in a specified sequence, to solve a problem. A pseudorandom scheme perturbs design variables between the optimizers. The cascade strategy has been tested successfully in the design of supersonic and subsonic aircraft configurations and air-breathing engines for high-speed civil transport applications. These problems could not be successfully solved by an individual optimizer. The cascade optimization strategy, however, generated feasible optimum solutions for both aircraft and engine problems. This paper presents the cascade strategy and solutions to a number of these problems.

Delivery of prazosin hydrochloride from osmotic pump system prepared by coating the core tablet with an indentation.

PubMed

Liu, Longxiao; Wang, Jinchao; Zhu, Suyan

2007-04-01

The preparation of an osmotic pump tablet was simplified by elimination of laser drilling using prazosin hydrochloride as the model drug. The osmotic pump system was obtained by coating the indented core tablet compressed by the punch with a needle. A multiple regression equation was achieved with the experimental data of core tablet formulations, and then the formulation was optimized. The influences of the indentation size of the core tablet, environmental media, and agitation rate on drug release profile were investigated. The optimal osmotic pump tablet was found to deliver prazosin hydrochloride at an approximately constant rate up to 24 hr, and independent on both release media and agitation rate. Indentation size of core tablet hardly affected drug release in the range of 0.80-1.15 mm. The method that is simplified by elimination of laser drilling may be promising for preparation of an osmotic pump tablet.

Efficient parallel and out of core algorithms for constructing large bi-directed de Bruijn graphs.

PubMed

Kundeti, Vamsi K; Rajasekaran, Sanguthevar; Dinh, Hieu; Vaughn, Matthew; Thapar, Vishal

2010-11-15

Assembling genomic sequences from a set of overlapping reads is one of the most fundamental problems in computational biology. Algorithms addressing the assembly problem fall into two broad categories - based on the data structures which they employ. The first class uses an overlap/string graph and the second type uses a de Bruijn graph. However with the recent advances in short read sequencing technology, de Bruijn graph based algorithms seem to play a vital role in practice. Efficient algorithms for building these massive de Bruijn graphs are very essential in large sequencing projects based on short reads. In an earlier work, an O(n/p) time parallel algorithm has been given for this problem. Here n is the size of the input and p is the number of processors. This algorithm enumerates all possible bi-directed edges which can overlap with a node and ends up generating Θ(nΣ) messages (Σ being the size of the alphabet). In this paper we present a Θ(n/p) time parallel algorithm with a communication complexity that is equal to that of parallel sorting and is not sensitive to Σ. The generality of our algorithm makes it very easy to extend it even to the out-of-core model and in this case it has an optimal I/O complexity of Θ(nlog(n/B)Blog(M/B)) (M being the main memory size and B being the size of the disk block). We demonstrate the scalability of our parallel algorithm on a SGI/Altix computer. A comparison of our algorithm with the previous approaches reveals that our algorithm is faster--both asymptotically and practically. We demonstrate the scalability of our sequential out-of-core algorithm by comparing it with the algorithm used by VELVET to build the bi-directed de Bruijn graph. Our experiments reveal that our algorithm can build the graph with a constant amount of memory, which clearly outperforms VELVET. We also provide efficient algorithms for the bi-directed chain compaction problem. The bi-directed de Bruijn graph is a fundamental data structure for any sequence assembly program based on Eulerian approach. Our algorithms for constructing Bi-directed de Bruijn graphs are efficient in parallel and out of core settings. These algorithms can be used in building large scale bi-directed de Bruijn graphs. Furthermore, our algorithms do not employ any all-to-all communications in a parallel setting and perform better than the prior algorithms. Finally our out-of-core algorithm is extremely memory efficient and can replace the existing graph construction algorithm in VELVET.

A CFD-based aerodynamic design procedure for hypersonic wind-tunnel nozzles

NASA Technical Reports Server (NTRS)

Korte, John J.

1993-01-01

A new procedure which unifies the best of current classical design practices, computational fluid dynamics (CFD), and optimization procedures is demonstrated for designing the aerodynamic lines of hypersonic wind-tunnel nozzles. The new procedure can be used to design hypersonic wind tunnel nozzles with thick boundary layers where the classical design procedure has been shown to break down. An efficient CFD code, which solves the parabolized Navier-Stokes (PNS) equations using an explicit upwind algorithm, is coupled to a least-squares (LS) optimization procedure. A LS problem is formulated to minimize the difference between the computed flow field and the objective function, consisting of the centerline Mach number distribution and the exit Mach number and flow angle profiles. The aerodynamic lines of the nozzle are defined using a cubic spline, the slopes of which are optimized with the design procedure. The advantages of the new procedure are that it allows full use of powerful CFD codes in the design process, solves an optimization problem to determine the new contour, can be used to design new nozzles or improve sections of existing nozzles, and automatically compensates the nozzle contour for viscous effects as part of the unified design procedure. The new procedure is demonstrated by designing two Mach 15, a Mach 12, and a Mach 18 helium nozzles. The flexibility of the procedure is demonstrated by designing the two Mach 15 nozzles using different constraints, the first nozzle for a fixed length and exit diameter and the second nozzle for a fixed length and throat diameter. The computed flow field for the Mach 15 least squares parabolized Navier-Stokes (LS/PNS) designed nozzle is compared with the classically designed nozzle and demonstrates a significant improvement in the flow expansion process and uniform core region.

Numerical optimization of three-dimensional coils for NSTX-U

NASA Astrophysics Data System (ADS)

Lazerson, S. A.; Park, J.-K.; Logan, N.; Boozer, A.

2015-10-01

A tool for the calculation of optimal three-dimensional (3D) perturbative magnetic fields in tokamaks has been developed. The IPECOPT code builds upon the stellarator optimization code STELLOPT to allow for optimization of linear ideal magnetohydrodynamic perturbed equilibrium (IPEC). This tool has been applied to NSTX-U equilibria, addressing which fields are the most effective at driving NTV torques. The NTV torque calculation is performed by the PENT code. Optimization of the normal field spectrum shows that fields with n  =  1 character can drive a large core torque. It is also shown that fields with n  =  3 features are capable of driving edge torque and some core torque. Coil current optimization (using the planned in-vessel and existing RWM coils) on NSTX-U suggest the planned coils set is adequate for core and edge torque control. Comparison between error field correction experiments on DIII-D and the optimizer show good agreement. Notice: This manuscript has been authored by Princeton University under Contract Number DE-AC02-09CH11466 with the U.S. Department of Energy. The publisher, by accepting the article for publication acknowledges, that the United States Government retains a non-exclusive,paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes.

Critical Resolution and Physical Dependenices of Supernovae: Stars in Heat and Under Pressure

NASA Astrophysics Data System (ADS)

Vartanyan, David; Burrows, Adam Seth

2017-01-01

For over five decades, the mechanism of explosion in core-collapse supernova continues to remain one of the last untoppled bastions in astrophysics, presenting both a technical and physical problem.Motivated by advances in computation and nuclear physics and the resilience of the core-collapse problem, collaborators Adam Burrows (Princeton), Joshua Dolence (LANL), and Aaron Skinner (LNL) have developed FORNAX - a highly parallelizable multidimensional supernova simulation code featuring an explicit hydrodynamic and radiation-transfer solver.We present the results (Vartanyan et. al 2016, Burrows et. al 2016, both in preparation) of a sequence of two-dimensional axisymmetric simulations of core-collapse supernovae using FORNAX, probing both progenitor mass dependence and the effect of physical inputs in explosiveness in our study on the revival of the stalled shock via the neutrino heating mechanism. We also performed a resolution study, testing spatial and energy group resolutions as well as compilation flags. We illustrate that, when the protoneutron star bounded by a stalled shock is close to the critical explosion condition (Burrows & Goshy 1993), small changes of order 10% in neutrino energies and luminosities can result in explosion, and that these effects couple nonlinearly.We show that many-body medium effects due to neutrino-nucleon scattering as well as inelastic neutrino-nucleon and neutrino-electron scattering are strongly favorable to earlier and more vigorous explosions by depositing energy in the gain region. Additionally, we probe the effects of a ray-by-ray+ transport solver (which does not include transverse velocity terms) employed by many groups and confirm that it artificially accelerates explosion (see also Skinner et. al 2016).In the coming year, we are gearing up for the first set of 3D simulations yet performed in the context of core-collapse supernovae employing 20 energy groups, and one of the most complete nuclear physics modules in the field with the ambitious goal of simulating supernova remants like Cas A. The current environment for core-collapse supernova provides for invigorating optimism that a robust explosion mechanism is within reach on graduate student lifetimes.

Creation of an Upper Stage Trajectory Capability Boundary to Enable Booster System Trade Space Exploration

NASA Technical Reports Server (NTRS)

Walsh, Ptrick; Coulon, Adam; Edwards, Stephen; Mavris, Dimitri N.

2012-01-01

The problem of trajectory optimization is important in all space missions. The solution of this problem enables one to specify the optimum thrust steering program which should be followed to achieve a specified mission objective, simultaneously satisfying the constraints.1 It is well known that whether or not the ascent trajectory is optimal can have a significant impact on propellant usage for a given payload, or on payload weight for the same gross vehicle weight.2 Consequently, ascent guidance commands are usually optimized in some fashion. Multi-stage vehicles add complexity to this analysis process as changes in vehicle properties in one stage propagate to the other stages through gear ratios and changes in the optimal trajectory. These effects can cause an increase in analysis time as more variables are added and convergence of the optimizer to system closure requires more analysis iterations. In this paper, an approach to simplifying this multi-stage problem through the creation of an upper stage capability boundary is presented. This work was completed as part of a larger study focused on trade space exploration for the advanced booster system that will eventually form a part of NASA s new Space Launch System.3 The approach developed leverages Design of Experiments and Surrogate Modeling4 techniques to create a predictive model of the SLS upper stage performance. The design of the SLS core stages is considered fixed for the purposes of this study, which results in trajectory parameters such as staging conditions being the only variables relevant to the upper stage. Through the creation of a surrogate model, which takes staging conditions as inputs and predicts the payload mass delivered by the SLS upper stage to a reference orbit as the response, it is possible to identify a "surface" of staging conditions which all satisfy the SLS requirement of placing 130 metric tons into low-Earth orbit (LEO).3 This identified surface represents the 130 metric ton capability boundary for the upper stage, such that if the combined first stage and boosters can achieve any one staging point on that surface, then the design is identified as feasible. With the surrogate model created, design and analysis of advanced booster concepts is streamlined, as optimization of the upper stage trajectory is no longer required in every design loop.

Low-Velocity Impact Response of Sandwich Beams with Functionally Graded Core

NASA Technical Reports Server (NTRS)

Apetre, N. A.; Sankar, B. V.; Ambur, D. R.

2006-01-01

The problem of low-speed impact of a one-dimensional sandwich panel by a rigid cylindrical projectile is considered. The core of the sandwich panel is functionally graded such that the density, and hence its stiffness, vary through the thickness. The problem is a combination of static contact problem and dynamic response of the sandwich panel obtained via a simple nonlinear spring-mass model (quasi-static approximation). The variation of core Young's modulus is represented by a polynomial in the thickness coordinate, but the Poisson's ratio is kept constant. The two-dimensional elasticity equations for the plane sandwich structure are solved using a combination of Fourier series and Galerkin method. The contact problem is solved using the assumed contact stress distribution method. For the impact problem we used a simple dynamic model based on quasi-static behavior of the panel - the sandwich beam was modeled as a combination of two springs, a linear spring to account for the global deflection and a nonlinear spring to represent the local indentation effects. Results indicate that the contact stiffness of thc beam with graded core Increases causing the contact stresses and other stress components in the vicinity of contact to increase. However, the values of maximum strains corresponding to the maximum impact load arc reduced considerably due to grading of thc core properties. For a better comparison, the thickness of the functionally graded cores was chosen such that the flexural stiffness was equal to that of a beam with homogeneous core. The results indicate that functionally graded cores can be used effectively to mitigate or completely prevent impact damage in sandwich composites.

Biospecimen Core Resource - TCGA

Cancer.gov

The Cancer Genome Atlas (TCGA) Biospecimen Core Resource centralized laboratory reviews and processes blood and tissue samples and their associated data using optimized standard operating procedures for the entire TCGA Research Network.

Blast protection of infrastructure using advanced composites

NASA Astrophysics Data System (ADS)

Brodsky, Evan

This research was a systematic investigation detailing the energy absorption mechanisms of an E-glass web core composite sandwich panel subjected to an impulse loading applied orthogonal to the facesheet. Key roles of the fiberglass and polyisocyanurate foam material were identified, characterized, and analyzed. A quasi-static test fixture was used to compressively load a unit cell web core specimen machined from the sandwich panel. The web and foam both exhibited non-linear stress-strain responses during axial compressive loading. Through several analyses, the composite web situated in the web core had failed in axial compression. Optimization studies were performed on the sandwich panel unit cell in order to maximize the energy absorption capabilities of the web core. Ultimately, a sandwich panel was designed to optimize the energy dissipation subjected to through-the-thickness compressive loading.

Entropic One-Class Classifiers.

PubMed

Livi, Lorenzo; Sadeghian, Alireza; Pedrycz, Witold

2015-12-01

The one-class classification problem is a well-known research endeavor in pattern recognition. The problem is also known under different names, such as outlier and novelty/anomaly detection. The core of the problem consists in modeling and recognizing patterns belonging only to a so-called target class. All other patterns are termed nontarget, and therefore, they should be recognized as such. In this paper, we propose a novel one-class classification system that is based on an interplay of different techniques. Primarily, we follow a dissimilarity representation-based approach; we embed the input data into the dissimilarity space (DS) by means of an appropriate parametric dissimilarity measure. This step allows us to process virtually any type of data. The dissimilarity vectors are then represented by weighted Euclidean graphs, which we use to determine the entropy of the data distribution in the DS and at the same time to derive effective decision regions that are modeled as clusters of vertices. Since the dissimilarity measure for the input data is parametric, we optimize its parameters by means of a global optimization scheme, which considers both mesoscopic and structural characteristics of the data represented through the graphs. The proposed one-class classifier is designed to provide both hard (Boolean) and soft decisions about the recognition of test patterns, allowing an accurate description of the classification process. We evaluate the performance of the system on different benchmarking data sets, containing either feature-based or structured patterns. Experimental results demonstrate the effectiveness of the proposed technique.

Algorithms for bilevel optimization

NASA Technical Reports Server (NTRS)

Alexandrov, Natalia; Dennis, J. E., Jr.

1994-01-01

General multilevel nonlinear optimization problems arise in design of complex systems and can be used as a means of regularization for multi-criteria optimization problems. Here, for clarity in displaying our ideas, we restrict ourselves to general bi-level optimization problems, and we present two solution approaches. Both approaches use a trust-region globalization strategy, and they can be easily extended to handle the general multilevel problem. We make no convexity assumptions, but we do assume that the problem has a nondegenerate feasible set. We consider necessary optimality conditions for the bi-level problem formulations and discuss results that can be extended to obtain multilevel optimization formulations with constraints at each level.

Near-Optimal Guidance Method for Maximizing the Reachable Domain of Gliding Aircraft

NASA Astrophysics Data System (ADS)

Tsuchiya, Takeshi

This paper proposes a guidance method for gliding aircraft by using onboard computers to calculate a near-optimal trajectory in real-time, and thereby expanding the reachable domain. The results are applicable to advanced aircraft and future space transportation systems that require high safety. The calculation load of the optimal control problem that is used to maximize the reachable domain is too large for current computers to calculate in real-time. Thus the optimal control problem is divided into two problems: a gliding distance maximization problem in which the aircraft motion is limited to a vertical plane, and an optimal turning flight problem in a horizontal direction. First, the former problem is solved using a shooting method. It can be solved easily because its scale is smaller than that of the original problem, and because some of the features of the optimal solution are obtained in the first part of this paper. Next, in the latter problem, the optimal bank angle is computed from the solution of the former; this is an analytical computation, rather than an iterative computation. Finally, the reachable domain obtained from the proposed near-optimal guidance method is compared with that obtained from the original optimal control problem.

Direct Method Transcription for a Human-Class Translunar Injection Trajectory Optimization

NASA Technical Reports Server (NTRS)

Witzberger, Kevin E.; Zeiler, Tom

2012-01-01

This paper presents a new trajectory optimization software package developed in the framework of a low-to-high fidelity 3 degrees-of-freedom (DOF)/6-DOF vehicle simulation program named Mission Analysis Simulation Tool in Fortran (MASTIF) and its application to a translunar trajectory optimization problem. The functionality of the developed optimization package is implemented as a new "mode" in generalized settings to make it applicable for a general trajectory optimization problem. In doing so, a direct optimization method using collocation is employed for solving the problem. Trajectory optimization problems in MASTIF are transcribed to a constrained nonlinear programming (NLP) problem and solved with SNOPT, a commercially available NLP solver. A detailed description of the optimization software developed is provided as well as the transcription specifics for the translunar injection (TLI) problem. The analysis includes a 3-DOF trajectory TLI optimization and a 3-DOF vehicle TLI simulation using closed-loop guidance.

Applying graph partitioning methods in measurement-based dynamic load balancing

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

Bhatele, Abhinav; Fourestier, Sebastien; Menon, Harshitha

Load imbalance leads to an increasing waste of resources as an application is scaled to more and more processors. Achieving the best parallel efficiency for a program requires optimal load balancing which is a NP-hard problem. However, finding near-optimal solutions to this problem for complex computational science and engineering applications is becoming increasingly important. Charm++, a migratable objects based programming model, provides a measurement-based dynamic load balancing framework. This framework instruments and then migrates over-decomposed objects to balance computational load and communication at runtime. This paper explores the use of graph partitioning algorithms, traditionally used for partitioning physical domains/meshes, formore » measurement-based dynamic load balancing of parallel applications. In particular, we present repartitioning methods developed in a graph partitioning toolbox called SCOTCH that consider the previous mapping to minimize migration costs. We also discuss a new imbalance reduction algorithm for graphs with irregular load distributions. We compare several load balancing algorithms using microbenchmarks on Intrepid and Ranger and evaluate the effect of communication, number of cores and number of objects on the benefit achieved from load balancing. New algorithms developed in SCOTCH lead to better performance compared to the METIS partitioners for several cases, both in terms of the application execution time and fewer number of objects migrated.« less

Synthesis of fluorescent core-shell nanomaterials and strategies to generate white light

NASA Astrophysics Data System (ADS)

Singh, Amandeep; Kaur, Ramanjot; Pandey, O. P.; Wei, Xueyong; Sharma, Manoj

2015-07-01

In this work, cadmium free core-shell ZnS:X/ZnS (X = Mn, Cu) nanoparticles have been synthesized and used for white light generation. First, the doping concentration of Manganese (Mn) was varied from 1% to 4% to optimize the dopant related emission and its optimal value was found to be 1%. Then, ZnS shell was grown over ZnS:Mn(1%) core to passivate the surface defects. Similarly, the optimal concentration of Copper (Cu) was found to be 0.8% in the range varied from 0.6% to 1.2%. In order to obtain an emission in the whole visible spectrum, dual doping of Mn and Cu was done in the core and the shell, respectively. A solid-solid mixing in different ratios of separately doped quantum dots (QDs) emitting in the blue green and the orange region was performed. Results show that the optimum mixture of QDs excited at 300 nm gives Commission Internationale del'Éclairage color coordinates of (0.35, 0.36), high color rendering index of 88, and correlated color temperature of 4704 K with minimum self-absorption.

Composition of web services using Markov decision processes and dynamic programming.

PubMed

Uc-Cetina, Víctor; Moo-Mena, Francisco; Hernandez-Ucan, Rafael

2015-01-01

We propose a Markov decision process model for solving the Web service composition (WSC) problem. Iterative policy evaluation, value iteration, and policy iteration algorithms are used to experimentally validate our approach, with artificial and real data. The experimental results show the reliability of the model and the methods employed, with policy iteration being the best one in terms of the minimum number of iterations needed to estimate an optimal policy, with the highest Quality of Service attributes. Our experimental work shows how the solution of a WSC problem involving a set of 100,000 individual Web services and where a valid composition requiring the selection of 1,000 services from the available set can be computed in the worst case in less than 200 seconds, using an Intel Core i5 computer with 6 GB RAM. Moreover, a real WSC problem involving only 7 individual Web services requires less than 0.08 seconds, using the same computational power. Finally, a comparison with two popular reinforcement learning algorithms, sarsa and Q-learning, shows that these algorithms require one or two orders of magnitude and more time than policy iteration, iterative policy evaluation, and value iteration to handle WSC problems of the same complexity.

A new nonlinear conjugate gradient coefficient under strong Wolfe-Powell line search

NASA Astrophysics Data System (ADS)

Mohamed, Nur Syarafina; Mamat, Mustafa; Rivaie, Mohd

2017-08-01

A nonlinear conjugate gradient method (CG) plays an important role in solving a large-scale unconstrained optimization problem. This method is widely used due to its simplicity. The method is known to possess sufficient descend condition and global convergence properties. In this paper, a new nonlinear of CG coefficient βk is presented by employing the Strong Wolfe-Powell inexact line search. The new βk performance is tested based on number of iterations and central processing unit (CPU) time by using MATLAB software with Intel Core i7-3470 CPU processor. Numerical experimental results show that the new βk converge rapidly compared to other classical CG method.

[Care continuity for patients with Prader-Willi syndrome during transition from childhood to adulthood].

PubMed

Saitoh, Shinji

2010-01-01

Prader-Willi syndrome(PWS) is a complex multisystem genetic disorder, of which characteristic phenotypes include neonatal hypotonia, hyperphagia resulting in obesity, mental retardation, hypogonadism, and behavioral and psychiatric problems. The diagnosis can be obtained as early as during neonatal period thanks to development of genetic testing. Clinical features of PWS will change depending on age, although core phenotypes of hyperphagia, obesity and psychiatric issues stay for lifetime. Therefore, integrated multidisciplinary approach starting from neonatal period is mandatory to ensure optimal management to improve lifelong quality of life. For successful transition from childhood to adulthood, multidisciplinary team need to share clinical information, and should keep the same policy about food, environment and psychiatric issues.

Method for depleting BWRs using optimal control rod patterns

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

Taner, M.S.; Levine, S.H.; Hsiao, M.Y.

1991-01-01

Control rod (CR) programming is an essential core management activity for boiling water reactors (BWRs). After establishing a core reload design for a BWR, CR programming is performed to develop a sequence of exposure-dependent CR patterns that assure the safe and effective depletion of the core through a reactor cycle. A time-variant target power distribution approach has been assumed in this study. The authors have developed OCTOPUS to implement a new two-step method for designing semioptimal CR programs for BWRs. The optimization procedure of OCTOPUS is based on the method of approximation programming and uses the SIMULATE-E code for nucleonicsmore » calculations.« less

Optimizing performance by improving core stability and core strength.

PubMed

Hibbs, Angela E; Thompson, Kevin G; French, Duncan; Wrigley, Allan; Spears, Iain

2008-01-01

Core stability and core strength have been subject to research since the early 1980s. Research has highlighted benefits of training these processes for people with back pain and for carrying out everyday activities. However, less research has been performed on the benefits of core training for elite athletes and how this training should be carried out to optimize sporting performance. Many elite athletes undertake core stability and core strength training as part of their training programme, despite contradictory findings and conclusions as to their efficacy. This is mainly due to the lack of a gold standard method for measuring core stability and strength when performing everyday tasks and sporting movements. A further confounding factor is that because of the differing demands on the core musculature during everyday activities (low load, slow movements) and sporting activities (high load, resisted, dynamic movements), research performed in the rehabilitation sector cannot be applied to the sporting environment and, subsequently, data regarding core training programmes and their effectiveness on sporting performance are lacking. There are many articles in the literature that promote core training programmes and exercises for performance enhancement without providing a strong scientific rationale of their effectiveness, especially in the sporting sector. In the rehabilitation sector, improvements in lower back injuries have been reported by improving core stability. Few studies have observed any performance enhancement in sporting activities despite observing improvements in core stability and core strength following a core training programme. A clearer understanding of the roles that specific muscles have during core stability and core strength exercises would enable more functional training programmes to be implemented, which may result in a more effective transfer of these skills to actual sporting activities.

Incorporating the Common Core's Problem Solving Standard for Mathematical Practice into an Early Elementary Inclusive Classroom

ERIC Educational Resources Information Center

Fletcher, Nicole

2014-01-01

Mathematics curriculum designers and policy decision makers are beginning to recognize the importance of problem solving, even at the earliest stages of mathematics learning. The Common Core includes sense making and perseverance in solving problems in its standards for mathematical practice for students at all grade levels. Incorporating problem…

Promoting Access to Common Core Mathematics for Students with Severe Disabilities through Mathematical Problem Solving

ERIC Educational Resources Information Center

Spooner, Fred; Saunders, Alicia; Root, Jenny; Brosh, Chelsi

2017-01-01

There is a need to teach the pivotal skill of mathematical problem solving to students with severe disabilities, moving beyond basic skills like computation to higher level thinking skills. Problem solving is emphasized as a Standard for Mathematical Practice in the Common Core State Standards across grade levels. This article describes a…

Horticulture Materials for Agricultural Education Programs. Core Agricultural Education Curriculum, Central Cluster.

ERIC Educational Resources Information Center

Illinois Univ., Urbana. Office of Agricultural Communications and Education.

This curriculum guide contains five units with relevant problem areas for horticulture. These problem areas have been selected as suggested areas of study to be included in a core curriculum for secondary students enrolled in an agricultural education program. Each problem area includes some or all of the following components: related problem…

Multidisciplinary Optimization of a Transport Aircraft Wing using Particle Swarm Optimization

NASA Technical Reports Server (NTRS)

Sobieszczanski-Sobieski, Jaroslaw; Venter, Gerhard

2002-01-01

The purpose of this paper is to demonstrate the application of particle swarm optimization to a realistic multidisciplinary optimization test problem. The paper's new contributions to multidisciplinary optimization is the application of a new algorithm for dealing with the unique challenges associated with multidisciplinary optimization problems, and recommendations as to the utility of the algorithm in future multidisciplinary optimization applications. The selected example is a bi-level optimization problem that demonstrates severe numerical noise and has a combination of continuous and truly discrete design variables. The use of traditional gradient-based optimization algorithms is thus not practical. The numerical results presented indicate that the particle swarm optimization algorithm is able to reliably find the optimum design for the problem presented here. The algorithm is capable of dealing with the unique challenges posed by multidisciplinary optimization as well as the numerical noise and truly discrete variables present in the current example problem.

Sequential estimation of intrinsic activity and synaptic input in single neurons by particle filtering with optimal importance density

NASA Astrophysics Data System (ADS)

Closas, Pau; Guillamon, Antoni

2017-12-01

This paper deals with the problem of inferring the signals and parameters that cause neural activity to occur. The ultimate challenge being to unveil brain's connectivity, here we focus on a microscopic vision of the problem, where single neurons (potentially connected to a network of peers) are at the core of our study. The sole observation available are noisy, sampled voltage traces obtained from intracellular recordings. We design algorithms and inference methods using the tools provided by stochastic filtering that allow a probabilistic interpretation and treatment of the problem. Using particle filtering, we are able to reconstruct traces of voltages and estimate the time course of auxiliary variables. By extending the algorithm, through PMCMC methodology, we are able to estimate hidden physiological parameters as well, like intrinsic conductances or reversal potentials. Last, but not least, the method is applied to estimate synaptic conductances arriving at a target cell, thus reconstructing the synaptic excitatory/inhibitory input traces. Notably, the performance of these estimations achieve the theoretical lower bounds even in spiking regimes.

Optimal recombination in genetic algorithms for flowshop scheduling problems

NASA Astrophysics Data System (ADS)

Kovalenko, Julia

2016-10-01

The optimal recombination problem consists in finding the best possible offspring as a result of a recombination operator in a genetic algorithm, given two parent solutions. We prove NP-hardness of the optimal recombination for various variants of the flowshop scheduling problem with makespan criterion and criterion of maximum lateness. An algorithm for solving the optimal recombination problem for permutation flowshop problems is built, using enumeration of prefect matchings in a special bipartite graph. The algorithm is adopted for the classical flowshop scheduling problem and for the no-wait flowshop problem. It is shown that the optimal recombination problem for the permutation flowshop scheduling problem is solvable in polynomial time for almost all pairs of parent solutions as the number of jobs tends to infinity.

An efficient and accurate solution methodology for bilevel multi-objective programming problems using a hybrid evolutionary-local-search algorithm.

PubMed

Deb, Kalyanmoy; Sinha, Ankur

2010-01-01

Bilevel optimization problems involve two optimization tasks (upper and lower level), in which every feasible upper level solution must correspond to an optimal solution to a lower level optimization problem. These problems commonly appear in many practical problem solving tasks including optimal control, process optimization, game-playing strategy developments, transportation problems, and others. However, they are commonly converted into a single level optimization problem by using an approximate solution procedure to replace the lower level optimization task. Although there exist a number of theoretical, numerical, and evolutionary optimization studies involving single-objective bilevel programming problems, not many studies look at the context of multiple conflicting objectives in each level of a bilevel programming problem. In this paper, we address certain intricate issues related to solving multi-objective bilevel programming problems, present challenging test problems, and propose a viable and hybrid evolutionary-cum-local-search based algorithm as a solution methodology. The hybrid approach performs better than a number of existing methodologies and scales well up to 40-variable difficult test problems used in this study. The population sizing and termination criteria are made self-adaptive, so that no additional parameters need to be supplied by the user. The study indicates a clear niche of evolutionary algorithms in solving such difficult problems of practical importance compared to their usual solution by a computationally expensive nested procedure. The study opens up many issues related to multi-objective bilevel programming and hopefully this study will motivate EMO and other researchers to pay more attention to this important and difficult problem solving activity.

Acceleration of the Particle Swarm Optimization for Peierls-Nabarro modeling of dislocations in conventional and high-entropy alloys

NASA Astrophysics Data System (ADS)

Pei, Zongrui; Eisenbach, Markus

2017-06-01

Dislocations are among the most important defects in determining the mechanical properties of both conventional alloys and high-entropy alloys. The Peierls-Nabarro model supplies an efficient pathway to their geometries and mobility. The difficulty in solving the integro-differential Peierls-Nabarro equation is how to effectively avoid the local minima in the energy landscape of a dislocation core. Among the other methods to optimize the dislocation core structures, we choose the algorithm of Particle Swarm Optimization, an algorithm that simulates the social behaviors of organisms. By employing more particles (bigger swarm) and more iterative steps (allowing them to explore for longer time), the local minima can be effectively avoided. But this would require more computational cost. The advantage of this algorithm is that it is readily parallelized in modern high computing architecture. We demonstrate the performance of our parallelized algorithm scales linearly with the number of employed cores.

Variational data assimilation for the initial-value dynamo problem.

PubMed

Li, Kuan; Jackson, Andrew; Livermore, Philip W

2011-11-01

The secular variation of the geomagnetic field as observed at the Earth's surface results from the complex magnetohydrodynamics taking place in the fluid core of the Earth. One way to analyze this system is to use the data in concert with an underlying dynamical model of the system through the technique of variational data assimilation, in much the same way as is employed in meteorology and oceanography. The aim is to discover an optimal initial condition that leads to a trajectory of the system in agreement with observations. Taking the Earth's core to be an electrically conducting fluid sphere in which convection takes place, we develop the continuous adjoint forms of the magnetohydrodynamic equations that govern the dynamical system together with the corresponding numerical algorithms appropriate for a fully spectral method. These adjoint equations enable a computationally fast iterative improvement of the initial condition that determines the system evolution. The initial condition depends on the three dimensional form of quantities such as the magnetic field in the entire sphere. For the magnetic field, conservation of the divergence-free condition for the adjoint magnetic field requires the introduction of an adjoint pressure term satisfying a zero boundary condition. We thus find that solving the forward and adjoint dynamo system requires different numerical algorithms. In this paper, an efficient algorithm for numerically solving this problem is developed and tested for two illustrative problems in a whole sphere: one is a kinematic problem with prescribed velocity field, and the second is associated with the Hall-effect dynamo, exhibiting considerable nonlinearity. The algorithm exhibits reliable numerical accuracy and stability. Using both the analytical and the numerical techniques of this paper, the adjoint dynamo system can be solved directly with the same order of computational complexity as that required to solve the forward problem. These numerical techniques form a foundation for ultimate application to observations of the geomagnetic field over the time scale of centuries.

Real science at the petascale.

PubMed

Saksena, Radhika S; Boghosian, Bruce; Fazendeiro, Luis; Kenway, Owain A; Manos, Steven; Mazzeo, Marco D; Sadiq, S Kashif; Suter, James L; Wright, David; Coveney, Peter V

2009-06-28

We describe computational science research that uses petascale resources to achieve scientific results at unprecedented scales and resolution. The applications span a wide range of domains, from investigation of fundamental problems in turbulence through computational materials science research to biomedical applications at the forefront of HIV/AIDS research and cerebrovascular haemodynamics. This work was mainly performed on the US TeraGrid 'petascale' resource, Ranger, at Texas Advanced Computing Center, in the first half of 2008 when it was the largest computing system in the world available for open scientific research. We have sought to use this petascale supercomputer optimally across application domains and scales, exploiting the excellent parallel scaling performance found on up to at least 32 768 cores for certain of our codes in the so-called 'capability computing' category as well as high-throughput intermediate-scale jobs for ensemble simulations in the 32-512 core range. Furthermore, this activity provides evidence that conventional parallel programming with MPI should be successful at the petascale in the short to medium term. We also report on the parallel performance of some of our codes on up to 65 636 cores on the IBM Blue Gene/P system at the Argonne Leadership Computing Facility, which has recently been named the fastest supercomputer in the world for open science.

Optimal design for crosstalk analysis in 12-core 5-LP mode homogeneous multicore fiber for different lattice structure

NASA Astrophysics Data System (ADS)

Kumar, Dablu; Ranjan, Rakesh

2018-03-01

12-Core 5-LP mode homogeneous multicore fibers have been proposed for analysis of inter-core crosstalk and dispersion, with four different lattice structures (circular, 2-ring, square lattice, and triangular lattice) having cladding diameter of 200 μm and a fixed cladding thickness of 35 μm. The core-to-core crosstalk impact has been studied numerically with respect to bending radius, core pitch, transmission distance, wavelength, and core diameter for all 5-LP modes. In anticipation of further reduction in crosstalk levels, the trench-assisted cores have been incorporated for all respective designs. Ultra-low crosstalk (-138 dB/100 km) has been achieved through the triangular lattice arrangement, with trench depth Δ2 = -1.40% for fundamental (LP01) mode. It has been noted that the impact of mode polarization on crosstalk behavior is minor, with difference in crosstalk levels between two polarized spatial modes as ≤0.2 dB. Moreover, the optimized cladding diameter has been obtained for all 5-LP modes for a target value of crosstalk of -50 dB/100 km, with all the core arrangements. The dispersion characteristic has also been analyzed with respect to wavelength, which is nearly 2.5 ps/nm km at operating wavelength 1550 nm. The relative core multiplicity factor (RCMF) for the proposed design is obtained as 64.

Merging spatially variant physical process models under an optimized systems dynamics framework.

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

Cain, William O.; Lowry, Thomas Stephen; Pierce, Suzanne A.

The complexity of water resource issues, its interconnectedness to other systems, and the involvement of competing stakeholders often overwhelm decision-makers and inhibit the creation of clear management strategies. While a range of modeling tools and procedures exist to address these problems, they tend to be case specific and generally emphasize either a quantitative and overly analytic approach or present a qualitative dialogue-based approach lacking the ability to fully explore consequences of different policy decisions. The integration of these two approaches is needed to drive toward final decisions and engender effective outcomes. Given these limitations, the Computer Assisted Dispute Resolution systemmore » (CADRe) was developed to aid in stakeholder inclusive resource planning. This modeling and negotiation system uniquely addresses resource concerns by developing a spatially varying system dynamics model as well as innovative global optimization search techniques to maximize outcomes from participatory dialogues. Ultimately, the core system architecture of CADRe also serves as the cornerstone upon which key scientific innovation and challenges can be addressed.« less

Discussion on teaching reform of environmental planning and management

NASA Astrophysics Data System (ADS)

Zhang, Qiugen; Chen, Suhua; Xie, Yu; Wei, Li'an; Ding, Yuan

2018-05-01

The curriculum of environmental planning and management is an environmental engineering major curriculum established by the teaching steering committee of environmental science and engineering of Education Ministry, which is the core curriculum of Chinese engineering education professional certification. It plays an important role in cultivating environmental planning and environmental management ability of environmental engineering major. The selection and optimization of the course teaching content of environmental planning and management were discussed which including curriculum teaching content updating and optimizing and teaching resource system construction. The comprehensive application of teaching method was discussed which including teaching method synthesis and teaching method. The final combination of the assessment method was also discussed which including the formative assessment normal grades and the final result of the course examination. Through the curriculum comprehensive teaching reform, students' knowledge had been broadened, the subject status and autonomy of learning had been enhanced, students' learning interest had been motivated, the ability of students' finding, analyzing and solving problems had been improved. Students' innovative ability and positive spirit had been well cultivated.

Finite dimensional approximation of a class of constrained nonlinear optimal control problems

NASA Technical Reports Server (NTRS)

Gunzburger, Max D.; Hou, L. S.

1994-01-01

An abstract framework for the analysis and approximation of a class of nonlinear optimal control and optimization problems is constructed. Nonlinearities occur in both the objective functional and in the constraints. The framework includes an abstract nonlinear optimization problem posed on infinite dimensional spaces, and approximate problem posed on finite dimensional spaces, together with a number of hypotheses concerning the two problems. The framework is used to show that optimal solutions exist, to show that Lagrange multipliers may be used to enforce the constraints, to derive an optimality system from which optimal states and controls may be deduced, and to derive existence results and error estimates for solutions of the approximate problem. The abstract framework and the results derived from that framework are then applied to three concrete control or optimization problems and their approximation by finite element methods. The first involves the von Karman plate equations of nonlinear elasticity, the second, the Ginzburg-Landau equations of superconductivity, and the third, the Navier-Stokes equations for incompressible, viscous flows.

Expanding the scope of health information systems. Challenges and developments.

PubMed

Kuhn, K A; Wurst, S H R; Bott, O J; Giuse, D A

2006-01-01

To identify current challenges and developments in health information systems. Reports on HIS, eHealth and process support were analyzed, core problems and challenges were identified. Health information systems are extending their scope towards regional networks and health IT infrastructures. Integration, interoperability and interaction design are still today's core problems. Additional problems arise through the integration of genetic information into the health care process. There are noticeable trends towards solutions for these problems.

LDRD Final Report: Global Optimization for Engineering Science Problems

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

HART,WILLIAM E.

1999-12-01

For a wide variety of scientific and engineering problems the desired solution corresponds to an optimal set of objective function parameters, where the objective function measures a solution's quality. The main goal of the LDRD ''Global Optimization for Engineering Science Problems'' was the development of new robust and efficient optimization algorithms that can be used to find globally optimal solutions to complex optimization problems. This SAND report summarizes the technical accomplishments of this LDRD, discusses lessons learned and describes open research issues.

Feasibility of optimizing trimetazidine dihydrochloride release from controlled porosity osmotic pump tablets of directly compressed cores

PubMed Central

Habib, Basant A.; Rehim, Randa T. Abd El; Nour, Samia A.

2013-01-01

The aim of this study was to develop and optimize Trimetazidine dihydrochloride (TM) controlled porosity osmotic pump (CPOP) tablets of directly compressed cores. A 23 full factorial design was used to study the influence of three factors namely: PEG400 (10% and 25% based on coating polymer weight), coating level (10% and 20% of tablet core weight) and hole diameter (0 “no hole” and 1 mm). Other variables such as tablet cores, coating mixture of ethylcellulose (4%) and dibutylphthalate (2%) in 95% ethanol and pan coating conditions were kept constant. The responses studied (Yi) were cumulative percentage released after 2 h (Q%2h), 6 h (Q%6h), 12 h (Q%12h) and regression coefficient of release data fitted to zero order equation (RSQzero), for Y1, Y2, Y3, and Y4, respectively. Polynomial equations were used to study the influence of different factors on each response individually. Response surface methodology and multiple response optimization were used to search for an optimized formula. Response variables for the optimized formula were restricted to 10% ⩽ Y1 ⩽ 20%, 40% ⩽ Y2 ⩽ 60%, 80% ⩽ Y3 ⩽ 100%, and Y4 > 0.9. The statistical analysis of the results revealed that PEG400 had positive effects on Q%2h, Q%6h and Q%12h, hole diameter had positive effects on all responses and coating level had positive effect on Q%6h, Q%12h and negative effect on RSQzero. Full three factor interaction (3FI) equations were used for representation of all responses except Q%2h which was represented by reduced (3FI) equation. Upon exploring the experimental space, no formula in the tested range could satisfy the required constraints. Thus, direct compression of TM cores was not suitable for formation of CPOP tablets. Preliminary trials of CPOP tablets with wet granulated cores were promising with an intact membrane for 12 h and high RSQzero. Further improvement of these formulations to optimize TM release will be done in further studies. PMID:25685502

Steady induction effects in geomagnetism. Part 1B: Geomagnetic estimation of steady surficial core motions: A non-linear inverse problem

NASA Technical Reports Server (NTRS)

Voorhies, Coerte V.

1993-01-01

The problem of estimating a steady fluid velocity field near the top of Earth's core which induces the secular variation (SV) indicated by models of the observed geomagnetic field is examined in the source-free mantle/frozen-flux core (SFI/VFFC) approximation. This inverse problem is non-linear because solutions of the forward problem are deterministically chaotic. The SFM/FFC approximation is inexact, and neither the models nor the observations they represent are either complete or perfect. A method is developed for solving the non-linear inverse motional induction problem posed by the hypothesis of (piecewise, statistically) steady core surface flow and the supposition of a complete initial geomagnetic condition. The method features iterative solution of the weighted, linearized least-squares problem and admits optional biases favoring surficially geostrophic flow and/or spatially simple flow. Two types of weights are advanced radial field weights for fitting the evolution of the broad-scale portion of the radial field component near Earth's surface implied by the models, and generalized weights for fitting the evolution of the broad-scale portion of the scalar potential specified by the models.

Research on NC laser combined cutting optimization model of sheet metal parts

NASA Astrophysics Data System (ADS)

Wu, Z. Y.; Zhang, Y. L.; Li, L.; Wu, L. H.; Liu, N. B.

2017-09-01

The optimization problem for NC laser combined cutting of sheet metal parts was taken as the research object in this paper. The problem included two contents: combined packing optimization and combined cutting path optimization. In the problem of combined packing optimization, the method of “genetic algorithm + gravity center NFP + geometric transformation” was used to optimize the packing of sheet metal parts. In the problem of combined cutting path optimization, the mathematical model of cutting path optimization was established based on the parts cutting constraint rules of internal contour priority and cross cutting. The model played an important role in the optimization calculation of NC laser combined cutting.

On the improvement of blood sample collection at clinical laboratories

PubMed Central

2014-01-01

Background Blood samples are usually collected daily from different collection points, such hospitals and health centers, and transported to a core laboratory for testing. This paper presents a project to improve the collection routes of two of the largest clinical laboratories in Spain. These routes must be designed in a cost-efficient manner while satisfying two important constraints: (i) two-hour time windows between collection and delivery, and (ii) vehicle capacity. Methods A heuristic method based on a genetic algorithm has been designed to solve the problem of blood sample collection. The user enters the following information for each collection point: postal address, average collecting time, and average demand (in thermal containers). After implementing the algorithm using C programming, this is run and, in few seconds, it obtains optimal (or near-optimal) collection routes that specify the collection sequence for each vehicle. Different scenarios using various types of vehicles have been considered. Unless new collection points are added or problem parameters are changed substantially, routes need to be designed only once. Results The two laboratories in this study previously planned routes manually for 43 and 74 collection points, respectively. These routes were covered by an external carrier company. With the implementation of this algorithm, the number of routes could be reduced from ten to seven in one laboratory and from twelve to nine in the other, which represents significant annual savings in transportation costs. Conclusions The algorithm presented can be easily implemented in other laboratories that face this type of problem, and it is particularly interesting and useful as the number of collection points increases. The method designs blood collection routes with reduced costs that meet the time and capacity constraints of the problem. PMID:24406140

Quadratic constrained mixed discrete optimization with an adiabatic quantum optimizer

NASA Astrophysics Data System (ADS)

Chandra, Rishabh; Jacobson, N. Tobias; Moussa, Jonathan E.; Frankel, Steven H.; Kais, Sabre

2014-07-01

We extend the family of problems that may be implemented on an adiabatic quantum optimizer (AQO). When a quadratic optimization problem has at least one set of discrete controls and the constraints are linear, we call this a quadratic constrained mixed discrete optimization (QCMDO) problem. QCMDO problems are NP-hard, and no efficient classical algorithm for their solution is known. Included in the class of QCMDO problems are combinatorial optimization problems constrained by a linear partial differential equation (PDE) or system of linear PDEs. An essential complication commonly encountered in solving this type of problem is that the linear constraint may introduce many intermediate continuous variables into the optimization while the computational cost grows exponentially with problem size. We resolve this difficulty by developing a constructive mapping from QCMDO to quadratic unconstrained binary optimization (QUBO) such that the size of the QUBO problem depends only on the number of discrete control variables. With a suitable embedding, taking into account the physical constraints of the realizable coupling graph, the resulting QUBO problem can be implemented on an existing AQO. The mapping itself is efficient, scaling cubically with the number of continuous variables in the general case and linearly in the PDE case if an efficient preconditioner is available.

An Enhanced Memetic Algorithm for Single-Objective Bilevel Optimization Problems.

PubMed

Islam, Md Monjurul; Singh, Hemant Kumar; Ray, Tapabrata; Sinha, Ankur

2017-01-01

Bilevel optimization, as the name reflects, deals with optimization at two interconnected hierarchical levels. The aim is to identify the optimum of an upper-level  leader problem, subject to the optimality of a lower-level follower problem. Several problems from the domain of engineering, logistics, economics, and transportation have an inherent nested structure which requires them to be modeled as bilevel optimization problems. Increasing size and complexity of such problems has prompted active theoretical and practical interest in the design of efficient algorithms for bilevel optimization. Given the nested nature of bilevel problems, the computational effort (number of function evaluations) required to solve them is often quite high. In this article, we explore the use of a Memetic Algorithm (MA) to solve bilevel optimization problems. While MAs have been quite successful in solving single-level optimization problems, there have been relatively few studies exploring their potential for solving bilevel optimization problems. MAs essentially attempt to combine advantages of global and local search strategies to identify optimum solutions with low computational cost (function evaluations). The approach introduced in this article is a nested Bilevel Memetic Algorithm (BLMA). At both upper and lower levels, either a global or a local search method is used during different phases of the search. The performance of BLMA is presented on twenty-five standard test problems and two real-life applications. The results are compared with other established algorithms to demonstrate the efficacy of the proposed approach.

Optimal Price Decision Problem for Simultaneous Multi-article Auction and Its Optimal Price Searching Method by Particle Swarm Optimization

NASA Astrophysics Data System (ADS)

Masuda, Kazuaki; Aiyoshi, Eitaro

We propose a method for solving optimal price decision problems for simultaneous multi-article auctions. An auction problem, originally formulated as a combinatorial problem, determines both every seller's whether or not to sell his/her article and every buyer's which article(s) to buy, so that the total utility of buyers and sellers will be maximized. Due to the duality theory, we transform it equivalently into a dual problem in which Lagrange multipliers are interpreted as articles' transaction price. As the dual problem is a continuous optimization problem with respect to the multipliers (i.e., the transaction prices), we propose a numerical method to solve it by applying heuristic global search methods. In this paper, Particle Swarm Optimization (PSO) is used to solve the dual problem, and experimental results are presented to show the validity of the proposed method.

Techniques for shuttle trajectory optimization

NASA Technical Reports Server (NTRS)

Edge, E. R.; Shieh, C. J.; Powers, W. F.

1973-01-01

The application of recently developed function-space Davidon-type techniques to the shuttle ascent trajectory optimization problem is discussed along with an investigation of the recently developed PRAXIS algorithm for parameter optimization. At the outset of this analysis, the major deficiency of the function-space algorithms was their potential storage problems. Since most previous analyses of the methods were with relatively low-dimension problems, no storage problems were encountered. However, in shuttle trajectory optimization, storage is a problem, and this problem was handled efficiently. Topics discussed include: the shuttle ascent model and the development of the particular optimization equations; the function-space algorithms; the operation of the algorithm and typical simulations; variable final-time problem considerations; and a modification of Powell's algorithm.

On l(1): Optimal decentralized performance

NASA Technical Reports Server (NTRS)

Sourlas, Dennis; Manousiouthakis, Vasilios

1993-01-01

In this paper, the Manousiouthakis parametrization of all decentralized stabilizing controllers is employed in mathematically formulating the l(sup 1) optimal decentralized controller synthesis problem. The resulting optimization problem is infinite dimensional and therefore not directly amenable to computations. It is shown that finite dimensional optimization problems that have value arbitrarily close to the infinite dimensional one can be constructed. Based on this result, an algorithm that solves the l(sup 1) decentralized performance problems is presented. A global optimization approach to the solution of the infinite dimensional approximating problems is also discussed.

Execution of Multidisciplinary Design Optimization Approaches on Common Test Problems

NASA Technical Reports Server (NTRS)

Balling, R. J.; Wilkinson, C. A.

1997-01-01

A class of synthetic problems for testing multidisciplinary design optimization (MDO) approaches is presented. These test problems are easy to reproduce because all functions are given as closed-form mathematical expressions. They are constructed in such a way that the optimal value of all variables and the objective is unity. The test problems involve three disciplines and allow the user to specify the number of design variables, state variables, coupling functions, design constraints, controlling design constraints, and the strength of coupling. Several MDO approaches were executed on two sample synthetic test problems. These approaches included single-level optimization approaches, collaborative optimization approaches, and concurrent subspace optimization approaches. Execution results are presented, and the robustness and efficiency of these approaches an evaluated for these sample problems.

Time-domain finite elements in optimal control with application to launch-vehicle guidance. PhD. Thesis

NASA Technical Reports Server (NTRS)

Bless, Robert R.

1991-01-01

A time-domain finite element method is developed for optimal control problems. The theory derived is general enough to handle a large class of problems including optimal control problems that are continuous in the states and controls, problems with discontinuities in the states and/or system equations, problems with control inequality constraints, problems with state inequality constraints, or problems involving any combination of the above. The theory is developed in such a way that no numerical quadrature is necessary regardless of the degree of nonlinearity in the equations. Also, the same shape functions may be employed for every problem because all strong boundary conditions are transformed into natural or weak boundary conditions. In addition, the resulting nonlinear algebraic equations are very sparse. Use of sparse matrix solvers allows for the rapid and accurate solution of very difficult optimization problems. The formulation is applied to launch-vehicle trajectory optimization problems, and results show that real-time optimal guidance is realizable with this method. Finally, a general problem solving environment is created for solving a large class of optimal control problems. The algorithm uses both FORTRAN and a symbolic computation program to solve problems with a minimum of user interaction. The use of symbolic computation eliminates the need for user-written subroutines which greatly reduces the setup time for solving problems.

Influence of item distribution pattern and abundance on efficiency of benthic core sampling

USGS Publications Warehouse

Behney, Adam C.; O'Shaughnessy, Ryan; Eichholz, Michael W.; Stafford, Joshua D.

2014-01-01

ore sampling is a commonly used method to estimate benthic item density, but little information exists about factors influencing the accuracy and time-efficiency of this method. We simulated core sampling in a Geographic Information System framework by generating points (benthic items) and polygons (core samplers) to assess how sample size (number of core samples), core sampler size (cm2), distribution of benthic items, and item density affected the bias and precision of estimates of density, the detection probability of items, and the time-costs. When items were distributed randomly versus clumped, bias decreased and precision increased with increasing sample size and increased slightly with increasing core sampler size. Bias and precision were only affected by benthic item density at very low values (500–1,000 items/m2). Detection probability (the probability of capturing ≥ 1 item in a core sample if it is available for sampling) was substantially greater when items were distributed randomly as opposed to clumped. Taking more small diameter core samples was always more time-efficient than taking fewer large diameter samples. We are unable to present a single, optimal sample size, but provide information for researchers and managers to derive optimal sample sizes dependent on their research goals and environmental conditions.

Gas turbine bucket wall thickness control

DOEpatents

Stathopoulos, Dimitrios; Xu, Liming; Lewis, Doyle C.

2002-01-01

A core for use in casting a turbine bucket including serpentine cooling passages is divided into two pieces including a leading edge core section and a trailing edge core section. Wall thicknesses at the leading edge and the trailing edge of the turbine bucket can be controlled independent of each other by separately positioning the leading edge core section and the trailing edge core section in the casting die. The controlled leading and trailing edge thicknesses can thus be optimized for efficient cooling, resulting in more efficient turbine operation.

Optimization of the injection molding process for development of high performance calcium oxide -based ceramic cores

NASA Astrophysics Data System (ADS)

Zhou, P. P.; Wu, G. Q.; Tao, Y.; Cheng, X.; Zhao, J. Q.; Nan, H.

2018-02-01

The binder composition used for ceramic injection molding plays a crucial role on the final properties of sintered ceramic and to avoid defects on green parts. In this study, the effects of binder compositions on the rheological, microstructures and the mechanical properties of CaO based ceramic cores were investigated. It was found that the optimized formulation for dispersant, solid loading was 1.5 wt% and 84 wt%, respectively. The microstructures, such as porosity, pore size distribution and grain boundary density were closely related to the plasticizer contents. The decrease of plasticizer contents can enhance the strength of the ceramic cores but with decreased shrinkage. Meanwhile, the creep resistance of ceramic cores was enhanced by decreasing of plasticizer contents. The flexural strength of the core was found to decrease with the increase of the porosity, the improvement of creep resistance is closely related to the decrease of porosity and grain boundary density.

Influence of particle size and shell thickness of core-shell packing materials on optimum experimental conditions in preparative chromatography.

PubMed

Horváth, Krisztián; Felinger, Attila

2015-08-14

The applicability of core-shell phases in preparative separations was studied by a modeling approach. The preparative separations were optimized for two compounds having bi-Langmuir isotherms. The differential mass balance equation of chromatography was solved by the Rouchon algorithm. The results show that as the size of the core increases, larger particles can be used in separations, resulting in higher applicable flow rates, shorter cycle times. Due to the decreasing volume of porous layer, the loadability of the column dropped significantly. As a result, the productivity and economy of the separation decreases. It is shown that if it is possible to optimize the size of stationary phase particles for the given separation task, the use of core-shell phases are not beneficial. The use of core-shell phases proved to be advantageous when the goal is to build preparative column for general purposes (e.g. for purification of different products) in small scale separations. Copyright © 2015 Elsevier B.V. All rights reserved.

Steady induction effects in geomagnetism. Part 1A: Steady motional induction of geomagnetic chaos

NASA Technical Reports Server (NTRS)

Voorhies, Coerte V.

1992-01-01

Geomagnetic effects of magnetic induction by hypothetically steady fluid motion and steady magnetic flux diffusion near the top of Earth's core are investigated using electromagnetic theory, simple magnetic earth models, and numerical experiments with geomagnetic field models. The problem of estimating a steady fluid velocity field near the top of Earth's core which induces the secular variation indicated by broad-scale models of the observed geomagnetic field is examined and solved. In Part 1, the steady surficial core flow estimation problem is solved in the context of the source-free mantle/frozen-flux core model. In the first paper (IA), the theory underlying such estimates is reviewed and some consequences of various kinematic and dynamic flow hypotheses are derived. For a frozen-flux core, fluid downwelling is required to change the mean square normal magnetic flux density averaged over the core-mantle boundary. For surficially geostrophic flow, downwelling implies poleward flow. The solution of the forward steady motional induction problem at the surface of a frozen-flux core is derived and found to be a fine, easily visualized example of deterministic chaos. Geomagnetic effects of statistically steady core surface flow may well dominate secular variation over several decades. Indeed, effects of persistent, if not steady, surficially geostrophic core flow are described which may help explain certain features of the present broad-scale geomagnetic field and perhaps paleomagnetic secular variation.

An Empirical Comparison of Seven Iterative and Evolutionary Function Optimization Heuristics

NASA Technical Reports Server (NTRS)

Baluja, Shumeet

1995-01-01

This report is a repository of the results obtained from a large scale empirical comparison of seven iterative and evolution-based optimization heuristics. Twenty-seven static optimization problems, spanning six sets of problem classes which are commonly explored in genetic algorithm literature, are examined. The problem sets include job-shop scheduling, traveling salesman, knapsack, binpacking, neural network weight optimization, and standard numerical optimization. The search spaces in these problems range from 2368 to 22040. The results indicate that using genetic algorithms for the optimization of static functions does not yield a benefit, in terms of the final answer obtained, over simpler optimization heuristics. Descriptions of the algorithms tested and the encodings of the problems are described in detail for reproducibility.

A benchmarking tool to evaluate computer tomography perfusion infarct core predictions against a DWI standard.

PubMed

Cereda, Carlo W; Christensen, Søren; Campbell, Bruce Cv; Mishra, Nishant K; Mlynash, Michael; Levi, Christopher; Straka, Matus; Wintermark, Max; Bammer, Roland; Albers, Gregory W; Parsons, Mark W; Lansberg, Maarten G

2016-10-01

Differences in research methodology have hampered the optimization of Computer Tomography Perfusion (CTP) for identification of the ischemic core. We aim to optimize CTP core identification using a novel benchmarking tool. The benchmarking tool consists of an imaging library and a statistical analysis algorithm to evaluate the performance of CTP. The tool was used to optimize and evaluate an in-house developed CTP-software algorithm. Imaging data of 103 acute stroke patients were included in the benchmarking tool. Median time from stroke onset to CT was 185 min (IQR 180-238), and the median time between completion of CT and start of MRI was 36 min (IQR 25-79). Volumetric accuracy of the CTP-ROIs was optimal at an rCBF threshold of <38%; at this threshold, the mean difference was 0.3 ml (SD 19.8 ml), the mean absolute difference was 14.3 (SD 13.7) ml, and CTP was 67% sensitive and 87% specific for identification of DWI positive tissue voxels. The benchmarking tool can play an important role in optimizing CTP software as it provides investigators with a novel method to directly compare the performance of alternative CTP software packages. © The Author(s) 2015.

Multiobjective optimization of temporal processes.

PubMed

Song, Zhe; Kusiak, Andrew

2010-06-01

This paper presents a dynamic predictive-optimization framework of a nonlinear temporal process. Data-mining (DM) and evolutionary strategy algorithms are integrated in the framework for solving the optimization model. DM algorithms learn dynamic equations from the process data. An evolutionary strategy algorithm is then applied to solve the optimization problem guided by the knowledge extracted by the DM algorithm. The concept presented in this paper is illustrated with the data from a power plant, where the goal is to maximize the boiler efficiency and minimize the limestone consumption. This multiobjective optimization problem can be either transformed into a single-objective optimization problem through preference aggregation approaches or into a Pareto-optimal optimization problem. The computational results have shown the effectiveness of the proposed optimization framework.

Three-dimensional geoelectric modelling with optimal work/accuracy rate using an adaptive wavelet algorithm

NASA Astrophysics Data System (ADS)

Plattner, A.; Maurer, H. R.; Vorloeper, J.; Dahmen, W.

2010-08-01

Despite the ever-increasing power of modern computers, realistic modelling of complex 3-D earth models is still a challenging task and requires substantial computing resources. The overwhelming majority of current geophysical modelling approaches includes either finite difference or non-adaptive finite element algorithms and variants thereof. These numerical methods usually require the subsurface to be discretized with a fine mesh to accurately capture the behaviour of the physical fields. However, this may result in excessive memory consumption and computing times. A common feature of most of these algorithms is that the modelled data discretizations are independent of the model complexity, which may be wasteful when there are only minor to moderate spatial variations in the subsurface parameters. Recent developments in the theory of adaptive numerical solvers have the potential to overcome this problem. Here, we consider an adaptive wavelet-based approach that is applicable to a large range of problems, also including nonlinear problems. In comparison with earlier applications of adaptive solvers to geophysical problems we employ here a new adaptive scheme whose core ingredients arose from a rigorous analysis of the overall asymptotically optimal computational complexity, including in particular, an optimal work/accuracy rate. Our adaptive wavelet algorithm offers several attractive features: (i) for a given subsurface model, it allows the forward modelling domain to be discretized with a quasi minimal number of degrees of freedom, (ii) sparsity of the associated system matrices is guaranteed, which makes the algorithm memory efficient and (iii) the modelling accuracy scales linearly with computing time. We have implemented the adaptive wavelet algorithm for solving 3-D geoelectric problems. To test its performance, numerical experiments were conducted with a series of conductivity models exhibiting varying degrees of structural complexity. Results were compared with a non-adaptive finite element algorithm, which incorporates an unstructured mesh to best-fitting subsurface boundaries. Such algorithms represent the current state-of-the-art in geoelectric modelling. An analysis of the numerical accuracy as a function of the number of degrees of freedom revealed that the adaptive wavelet algorithm outperforms the finite element solver for simple and moderately complex models, whereas the results become comparable for models with high spatial variability of electrical conductivities. The linear dependence of the modelling error and the computing time proved to be model-independent. This feature will allow very efficient computations using large-scale models as soon as our experimental code is optimized in terms of its implementation.

Parallel Mutual Information Based Construction of Genome-Scale Networks on the Intel® Xeon Phi™ Coprocessor.

PubMed

Misra, Sanchit; Pamnany, Kiran; Aluru, Srinivas

2015-01-01

Construction of whole-genome networks from large-scale gene expression data is an important problem in systems biology. While several techniques have been developed, most cannot handle network reconstruction at the whole-genome scale, and the few that can, require large clusters. In this paper, we present a solution on the Intel Xeon Phi coprocessor, taking advantage of its multi-level parallelism including many x86-based cores, multiple threads per core, and vector processing units. We also present a solution on the Intel® Xeon® processor. Our solution is based on TINGe, a fast parallel network reconstruction technique that uses mutual information and permutation testing for assessing statistical significance. We demonstrate the first ever inference of a plant whole genome regulatory network on a single chip by constructing a 15,575 gene network of the plant Arabidopsis thaliana from 3,137 microarray experiments in only 22 minutes. In addition, our optimization for parallelizing mutual information computation on the Intel Xeon Phi coprocessor holds out lessons that are applicable to other domains.

Chemistry in CESM-SE: Evaluation, Performance and Optimization

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

Lamarque, Jean-Francois; Conley, Andrew; Vitt, Francis

2016-01-06

The purpose of the proposed work focused on development of chemistry representation within the Spectral Element (SE) dynamical core as implemented in the Community Earth System Model (CESM). More specifically, a main focus was on the ability of SE to accurately represent tracer transport. The proposed approach was to incrementally increase the complexity of the problem, starting from specified two-dimensional flow and tracers to simulations using specified dynamics and full chemistry. As demonstrated below, we have successfully studied all aspects of the proposed work, although only part of the work has been published in the refereed literature so far. Furthermore,more » because the SE dynamical core has been found to have several deficiencies that are still being investigated for solution, not all proposed tasks were finalized. In addition to the tests for SE performance, in an effort to decrease the computational burden of interactive chemistry, especially in the case of a large number of chemical species and chemical reactions, development on a faster chemical solver and implementation on GPUs has been implemented in CESM under the leadership of John Drake (U. Tennessee).« less

Social Emotional Optimization Algorithm for Nonlinear Constrained Optimization Problems

NASA Astrophysics Data System (ADS)

Xu, Yuechun; Cui, Zhihua; Zeng, Jianchao

Nonlinear programming problem is one important branch in operational research, and has been successfully applied to various real-life problems. In this paper, a new approach called Social emotional optimization algorithm (SEOA) is used to solve this problem which is a new swarm intelligent technique by simulating the human behavior guided by emotion. Simulation results show that the social emotional optimization algorithm proposed in this paper is effective and efficiency for the nonlinear constrained programming problems.

Evolutionary optimization methods for accelerator design

NASA Astrophysics Data System (ADS)

Poklonskiy, Alexey A.

Many problems from the fields of accelerator physics and beam theory can be formulated as optimization problems and, as such, solved using optimization methods. Despite growing efficiency of the optimization methods, the adoption of modern optimization techniques in these fields is rather limited. Evolutionary Algorithms (EAs) form a relatively new and actively developed optimization methods family. They possess many attractive features such as: ease of the implementation, modest requirements on the objective function, a good tolerance to noise, robustness, and the ability to perform a global search efficiently. In this work we study the application of EAs to problems from accelerator physics and beam theory. We review the most commonly used methods of unconstrained optimization and describe the GATool, evolutionary algorithm and the software package, used in this work, in detail. Then we use a set of test problems to assess its performance in terms of computational resources, quality of the obtained result, and the tradeoff between them. We justify the choice of GATool as a heuristic method to generate cutoff values for the COSY-GO rigorous global optimization package for the COSY Infinity scientific computing package. We design the model of their mutual interaction and demonstrate that the quality of the result obtained by GATool increases as the information about the search domain is refined, which supports the usefulness of this model. We Giscuss GATool's performance on the problems suffering from static and dynamic noise and study useful strategies of GATool parameter tuning for these and other difficult problems. We review the challenges of constrained optimization with EAs and methods commonly used to overcome them. We describe REPA, a new constrained optimization method based on repairing, in exquisite detail, including the properties of its two repairing techniques: REFIND and REPROPT. We assess REPROPT's performance on the standard constrained optimization test problems for EA with a variety of different configurations and suggest optimal default parameter values based on the results. Then we study the performance of the REPA method on the same set of test problems and compare the obtained results with those of several commonly used constrained optimization methods with EA. Based on the obtained results, particularly on the outstanding performance of REPA on test problem that presents significant difficulty for other reviewed EAs, we conclude that the proposed method is useful and competitive. We discuss REPA parameter tuning for difficult problems and critically review some of the problems from the de-facto standard test problem set for the constrained optimization with EA. In order to demonstrate the practical usefulness of the developed method, we study several problems of accelerator design and demonstrate how they can be solved with EAs. These problems include a simple accelerator design problem (design a quadrupole triplet to be stigmatically imaging, find all possible solutions), a complex real-life accelerator design problem (an optimization of the front end section for the future neutrino factory), and a problem of the normal form defect function optimization which is used to rigorously estimate the stability of the beam dynamics in circular accelerators. The positive results we obtained suggest that the application of EAs to problems from accelerator theory can be very beneficial and has large potential. The developed optimization scenarios and tools can be used to approach similar problems.

A weak Hamiltonian finite element method for optimal control problems

NASA Technical Reports Server (NTRS)

Hodges, Dewey H.; Bless, Robert R.

1989-01-01

A temporal finite element method based on a mixed form of the Hamiltonian weak principle is developed for dynamics and optimal control problems. The mixed form of Hamilton's weak principle contains both displacements and momenta as primary variables that are expanded in terms of nodal values and simple polynomial shape functions. Unlike other forms of Hamilton's principle, however, time derivatives of the momenta and displacements do not appear therein; instead, only the virtual momenta and virtual displacements are differentiated with respect to time. Based on the duality that is observed to exist between the mixed form of Hamilton's weak principle and variational principles governing classical optimal control problems, a temporal finite element formulation of the latter can be developed in a rather straightforward manner. Several well-known problems in dynamics and optimal control are illustrated. The example dynamics problem involves a time-marching problem. As optimal control examples, elementary trajectory optimization problems are treated.

A weak Hamiltonian finite element method for optimal control problems

NASA Technical Reports Server (NTRS)

Hodges, Dewey H.; Bless, Robert R.

1990-01-01

A temporal finite element method based on a mixed form of the Hamiltonian weak principle is developed for dynamics and optimal control problems. The mixed form of Hamilton's weak principle contains both displacements and momenta as primary variables that are expanded in terms of nodal values and simple polynomial shape functions. Unlike other forms of Hamilton's principle, however, time derivatives of the momenta and displacements do not appear therein; instead, only the virtual momenta and virtual displacements are differentiated with respect to time. Based on the duality that is observed to exist between the mixed form of Hamilton's weak principle and variational principles governing classical optimal control problems, a temporal finite element formulation of the latter can be developed in a rather straightforward manner. Several well-known problems in dynamics and optimal control are illustrated. The example dynamics problem involves a time-marching problem. As optimal control examples, elementary trajectory optimization problems are treated.

Weak Hamiltonian finite element method for optimal control problems

NASA Technical Reports Server (NTRS)

Hodges, Dewey H.; Bless, Robert R.

1991-01-01

A temporal finite element method based on a mixed form of the Hamiltonian weak principle is developed for dynamics and optimal control problems. The mixed form of Hamilton's weak principle contains both displacements and momenta as primary variables that are expanded in terms of nodal values and simple polynomial shape functions. Unlike other forms of Hamilton's principle, however, time derivatives of the momenta and displacements do not appear therein; instead, only the virtual momenta and virtual displacements are differentiated with respect to time. Based on the duality that is observed to exist between the mixed form of Hamilton's weak principle and variational principles governing classical optimal control problems, a temporal finite element formulation of the latter can be developed in a rather straightforward manner. Several well-known problems in dynamics and optimal control are illustrated. The example dynamics problem involves a time-marching problem. As optimal control examples, elementary trajectory optimization problems are treated.

Core Today! Rationale and Implications. Revised Edition.

ERIC Educational Resources Information Center

Vars, Gordon, Ed.; Larson, Craig, Ed.

This pamphlet is designed to help educators apply the core concept to current problems and situations in educational settings. The preface establishes the position of the National Association for Core Curriculum. A definition of the core curriculum concept is stated in the introduction. Ten assumptions and beliefs on which the core concept is…

Ada Integrated Environment III Computer Program Development Specification. Volume III. Ada Optimizing Compiler.

DTIC Science & Technology

1981-12-01

file.library-unit{.subunit).SYMAP Statement Map: library-file. library-unit.subunit).SMAP Type Map: 1 ibrary.fi le. 1 ibrary-unit{.subunit). TMAP The library...generator SYMAP Symbol Map code generator SMAP Updated Statement Map code generator TMAP Type Map code generator A.3.5 The PUNIT Command The P UNIT...Core.Stmtmap) NAME Tmap (Core.Typemap) END Example A-3 Compiler Command Stream for the Code Generator Texas Instruments A-5 Ada Optimizing Compiler

Optimality conditions for the numerical solution of optimization problems with PDE constraints :

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

Aguilo Valentin, Miguel Alejandro; Ridzal, Denis

2014-03-01

A theoretical framework for the numerical solution of partial di erential equation (PDE) constrained optimization problems is presented in this report. This theoretical framework embodies the fundamental infrastructure required to e ciently implement and solve this class of problems. Detail derivations of the optimality conditions required to accurately solve several parameter identi cation and optimal control problems are also provided in this report. This will allow the reader to further understand how the theoretical abstraction presented in this report translates to the application.

FRANOPP: Framework for analysis and optimization problems user's guide

NASA Technical Reports Server (NTRS)

Riley, K. M.

1981-01-01

Framework for analysis and optimization problems (FRANOPP) is a software aid for the study and solution of design (optimization) problems which provides the driving program and plotting capability for a user generated programming system. In addition to FRANOPP, the programming system also contains the optimization code CONMIN, and two user supplied codes, one for analysis and one for output. With FRANOPP the user is provided with five options for studying a design problem. Three of the options utilize the plot capability and present an indepth study of the design problem. The study can be focused on a history of the optimization process or on the interaction of variables within the design problem.

Foam Core Shielding for Spacecraft

NASA Technical Reports Server (NTRS)

Adams, Marc

2007-01-01

A foam core shield (FCS) system is now being developed to supplant multilayer insulation (MLI) systems heretofore installed on spacecraft for thermal management and protection against meteoroid impacts. A typical FCS system consists of a core sandwiched between a face sheet and a back sheet. The core can consist of any of a variety of low-to-medium-density polymeric or inorganic foams chosen to satisfy application-specific requirements regarding heat transfer and temperature. The face sheet serves to shock and thereby shatter incident meteoroids, and is coated on its outer surface to optimize its absorptance and emittance for regulation of temperature. The back sheet can be dimpled to minimize undesired thermal contact with the underlying spacecraft component and can be metallized on the surface facing the component to optimize its absorptance and emittance. The FCS systems can perform better than do MLI systems, at lower mass and lower cost and with greater volumetric efficiency.

Fast data reconstructed method of Fourier transform imaging spectrometer based on multi-core CPU

NASA Astrophysics Data System (ADS)

Yu, Chunchao; Du, Debiao; Xia, Zongze; Song, Li; Zheng, Weijian; Yan, Min; Lei, Zhenggang

2017-10-01

Imaging spectrometer can gain two-dimensional space image and one-dimensional spectrum at the same time, which shows high utility in color and spectral measurements, the true color image synthesis, military reconnaissance and so on. In order to realize the fast reconstructed processing of the Fourier transform imaging spectrometer data, the paper designed the optimization reconstructed algorithm with OpenMP parallel calculating technology, which was further used for the optimization process for the HyperSpectral Imager of `HJ-1' Chinese satellite. The results show that the method based on multi-core parallel computing technology can control the multi-core CPU hardware resources competently and significantly enhance the calculation of the spectrum reconstruction processing efficiency. If the technology is applied to more cores workstation in parallel computing, it will be possible to complete Fourier transform imaging spectrometer real-time data processing with a single computer.

Evaluation of Genetic Algorithm Concepts Using Model Problems. Part 2; Multi-Objective Optimization

NASA Technical Reports Server (NTRS)

Holst, Terry L.; Pulliam, Thomas H.

2003-01-01

A genetic algorithm approach suitable for solving multi-objective optimization problems is described and evaluated using a series of simple model problems. Several new features including a binning selection algorithm and a gene-space transformation procedure are included. The genetic algorithm is suitable for finding pareto optimal solutions in search spaces that are defined by any number of genes and that contain any number of local extrema. Results indicate that the genetic algorithm optimization approach is flexible in application and extremely reliable, providing optimal results for all optimization problems attempted. The binning algorithm generally provides pareto front quality enhancements and moderate convergence efficiency improvements for most of the model problems. The gene-space transformation procedure provides a large convergence efficiency enhancement for problems with non-convoluted pareto fronts and a degradation in efficiency for problems with convoluted pareto fronts. The most difficult problems --multi-mode search spaces with a large number of genes and convoluted pareto fronts-- require a large number of function evaluations for GA convergence, but always converge.

Discovery and Mechanistic Study of Benzamide Derivatives That Modulate Hepatitis B Virus Capsid Assembly.

PubMed

Wu, Shuo; Zhao, Qiong; Zhang, Pinghu; Kulp, John; Hu, Lydia; Hwang, Nicky; Zhang, Jiming; Block, Timothy M; Xu, Xiaodong; Du, Yanming; Chang, Jinhong; Guo, Ju-Tao

2017-08-15

Chronic hepatitis B virus (HBV) infection is a global public health problem. Although the currently approved medications can reliably reduce the viral load and prevent the progression of liver diseases, they fail to cure the viral infection. In an effort toward discovery of novel antiviral agents against HBV, a group of benzamide (BA) derivatives that significantly reduced the amount of cytoplasmic HBV DNA were discovered. The initial lead optimization efforts identified two BA derivatives with improved antiviral activity for further mechanistic studies. Interestingly, similar to our previously reported sulfamoylbenzamides (SBAs), the BAs promote the formation of empty capsids through specific interaction with HBV core protein but not other viral and host cellular components. Genetic evidence suggested that both SBAs and BAs inhibited HBV nucleocapsid assembly by binding to the heteroaryldihydropyrimidine (HAP) pocket between core protein dimer-dimer interfaces. However, unlike SBAs, BA compounds uniquely induced the formation of empty capsids that migrated more slowly in native agarose gel electrophoresis from A36V mutant than from the wild-type core protein. Moreover, we showed that the assembly of chimeric capsids from wild-type and drug-resistant core proteins was susceptible to multiple capsid assembly modulators. Hence, HBV core protein is a dominant antiviral target that may suppress the selection of drug-resistant viruses during core protein-targeting antiviral therapy. Our studies thus indicate that BAs are a chemically and mechanistically unique type of HBV capsid assembly modulators and warranted for further development as antiviral agents against HBV. IMPORTANCE HBV core protein plays essential roles in many steps of the viral replication cycle. In addition to packaging viral pregenomic RNA (pgRNA) and DNA polymerase complex into nucleocapsids for reverse transcriptional DNA replication to take place, the core protein dimers, existing in several different quaternary structures in infected hepatocytes, participate in and regulate HBV virion assembly, capsid uncoating, and covalently closed circular DNA (cccDNA) formation. It is anticipated that small molecular core protein assembly modulators may disrupt one or multiple steps of HBV replication, depending on their interaction with the distinct quaternary structures of core protein. The discovery of novel core protein-targeting antivirals, such as benzamide derivatives reported here, and investigation of their antiviral mechanism may lead to the identification of antiviral therapeutics for the cure of chronic hepatitis B. Copyright © 2017 American Society for Microbiology.

Evolutionary optimization of biopolymers and sequence structure maps

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

Reidys, C.M.; Kopp, S.; Schuster, P.

1996-06-01

Searching for biopolymers having a predefined function is a core problem of biotechnology, biochemistry and pharmacy. On the level of RNA sequences and their corresponding secondary structures we show that this problem can be analyzed mathematically. The strategy will be to study the properties of the RNA sequence to secondary structure mapping that is essential for the understanding of the search process. We show that to each secondary structure s there exists a neutral network consisting of all sequences folding into s. This network can be modeled as a random graph and has the following generic properties: it is densemore » and has a giant component within the graph of compatible sequences. The neutral network percolates sequence space and any two neutral nets come close in terms of Hamming distance. We investigate the distribution of the orders of neutral nets and show that above a certain threshold the topology of neutral nets allows to find practically all frequent secondary structures.« less

Wireless Sensor Network Optimization: Multi-Objective Paradigm.

PubMed

Iqbal, Muhammad; Naeem, Muhammad; Anpalagan, Alagan; Ahmed, Ashfaq; Azam, Muhammad

2015-07-20

Optimization problems relating to wireless sensor network planning, design, deployment and operation often give rise to multi-objective optimization formulations where multiple desirable objectives compete with each other and the decision maker has to select one of the tradeoff solutions. These multiple objectives may or may not conflict with each other. Keeping in view the nature of the application, the sensing scenario and input/output of the problem, the type of optimization problem changes. To address different nature of optimization problems relating to wireless sensor network design, deployment, operation, planing and placement, there exist a plethora of optimization solution types. We review and analyze different desirable objectives to show whether they conflict with each other, support each other or they are design dependent. We also present a generic multi-objective optimization problem relating to wireless sensor network which consists of input variables, required output, objectives and constraints. A list of constraints is also presented to give an overview of different constraints which are considered while formulating the optimization problems in wireless sensor networks. Keeping in view the multi facet coverage of this article relating to multi-objective optimization, this will open up new avenues of research in the area of multi-objective optimization relating to wireless sensor networks.

From play to problem solving to Common Core: The development of fluid reasoning.

PubMed

Prince, Pauline

2017-01-01

How and when does fluid reasoning develop and what does it look like at different ages, from a neurodevelopmental and functional perspective? The goal of this article is to discuss the development of fluid reasoning from a practical perspective of our children's lives: from play to problem solving to Common Core Curriculum. A review of relevant and current literature supports a connection between movement, including movement through free play, and the development of novel problem solving. As our children grow and develop, motor routines can become cognitive routines and can be evidenced not only in games, such as chess, but also in the acquisition and demonstration of academic skills. Finally, this article describes the connection between novel problem solving and the demands of the Common Core Curriculum.

Uncertainty Aware Structural Topology Optimization Via a Stochastic Reduced Order Model Approach

NASA Technical Reports Server (NTRS)

Aguilo, Miguel A.; Warner, James E.

2017-01-01

This work presents a stochastic reduced order modeling strategy for the quantification and propagation of uncertainties in topology optimization. Uncertainty aware optimization problems can be computationally complex due to the substantial number of model evaluations that are necessary to accurately quantify and propagate uncertainties. This computational complexity is greatly magnified if a high-fidelity, physics-based numerical model is used for the topology optimization calculations. Stochastic reduced order model (SROM) methods are applied here to effectively 1) alleviate the prohibitive computational cost associated with an uncertainty aware topology optimization problem; and 2) quantify and propagate the inherent uncertainties due to design imperfections. A generic SROM framework that transforms the uncertainty aware, stochastic topology optimization problem into a deterministic optimization problem that relies only on independent calls to a deterministic numerical model is presented. This approach facilitates the use of existing optimization and modeling tools to accurately solve the uncertainty aware topology optimization problems in a fraction of the computational demand required by Monte Carlo methods. Finally, an example in structural topology optimization is presented to demonstrate the effectiveness of the proposed uncertainty aware structural topology optimization approach.

A Kind of Nonlinear Programming Problem Based on Mixed Fuzzy Relation Equations Constraints

NASA Astrophysics Data System (ADS)

Li, Jinquan; Feng, Shuang; Mi, Honghai

In this work, a kind of nonlinear programming problem with non-differential objective function and under the constraints expressed by a system of mixed fuzzy relation equations is investigated. First, some properties of this kind of optimization problem are obtained. Then, a polynomial-time algorithm for this kind of optimization problem is proposed based on these properties. Furthermore, we show that this algorithm is optimal for the considered optimization problem in this paper. Finally, numerical examples are provided to illustrate our algorithms.

[Agricultural eco-economic system coupling in Zhifanggou watershed in hilly-gully region of Loess Plateau].

PubMed

Wang, Ji-Jun

2009-11-01

Agricultural eco-economic system coupling is an organic unit formed by the inherent interaction between agricultural ecosystem and economic system, and regulated and controlled by mankind moderate interference. Its status can be expressed by the circular chain-net structure of agricultural resources and agricultural industry. The agricultural eco-economic system in Zhifanggou watershed has gone through the process of system coupling, system conflict, system coupling, and partial conflict in high leverage, which is caused by the farmers' requirement and the state's macro-policy, economic means, and administrative means. To cope with the problems of agricultural eco-economics system coupling in Zhifanggou watershed, the optimal coupling model should be established, with tree-grass resources and related industries as the core.

Determining Training Device Requirements in Army Aviation Systems

NASA Technical Reports Server (NTRS)

Poumade, M. L.

1984-01-01

A decision making methodology which applies the systems approach to the training problem is discussed. Training is viewed as a total system instead of a collection of individual devices and unrelated techniques. The core of the methodology is the use of optimization techniques such as the transportation algorithm and multiobjective goal programming with training task and training device specific data. The role of computers, especially automated data bases and computer simulation models, in the development of training programs is also discussed. The approach can provide significant training enhancement and cost savings over the more traditional, intuitive form of training development and device requirements process. While given from an aviation perspective, the methodology is equally applicable to other training development efforts.

Radiation-tolerant microprocessors in Japanese scientific space vehicles: how to maximize the benefits of commercial SOI technologies

NASA Astrophysics Data System (ADS)

Kobayashi, Daisuke; Hirose, Kazuyuki; Saito, Hirobumi

2013-05-01

Development of semiconductor devices not only for harsh radiation environments such as space but also for ground-based applications now faces a major hurdle of radiation problems. Necessary is protecting chips from malfunctions due to sub-nanosecond transient noises induced by radiation. As a protection technique using the silicon-on-insulator structure is often suggested, but the use in fact requires devices and circuits carefully optimized for maximizing its benefits. Mainly describing theoretical and experimental characterization of the transient effects, this paper presents a comprehensive study on radiation responses of commercial silicon-on- insulator technologies, which study results in a space-use low-power system-on-chip with a 100-MIPS RISC-based core.

Construction and Application of a Refined Hospital Management Chain.

PubMed

Lihua, Yi

2016-01-01

Large scale development was quite common in the later period of hospital industrialization in China. Today, Chinese hospital management faces such problems as service inefficiency, high human resources cost, and low rate of capital use. This study analyzes the refined management chain of Wuxi No.2 People's Hospital. This consists of six gears namely, "organizational structure, clinical practice, outpatient service, medical technology, and nursing care and logistics." The gears are based on "flat management system targets, chief of medical staff, centralized outpatient service, intensified medical examinations, vertical nursing management and socialized logistics." The core concepts of refined hospital management are optimizing flow process, reducing waste, improving efficiency, saving costs, and taking good care of patients as most important. Keywords: Hospital, Refined, Management chain

Optimization of rotor shaft shrink fit method for motor using "Robust design"

NASA Astrophysics Data System (ADS)

Toma, Eiji

2018-01-01

This research is collaborative investigation with the general-purpose motor manufacturer. To review construction method in production process, we applied the parameter design method of quality engineering and tried to approach the optimization of construction method. Conventionally, press-fitting method has been adopted in process of fitting rotor core and shaft which is main component of motor, but quality defects such as core shaft deflection occurred at the time of press fitting. In this research, as a result of optimization design of "shrink fitting method by high-frequency induction heating" devised as a new construction method, its construction method was feasible, and it was possible to extract the optimum processing condition.

Optimization design of turbo-expander gas bearing for a 500W helium refrigerator

NASA Astrophysics Data System (ADS)

Li, S. S.; Fu, B.; Y Zhang, Q.

2017-12-01

Turbo-expander is the core machinery of the helium refrigerator. Bearing as the supporting element is the core technology to impact the design of turbo-expander. The perfect design and performance study for the gas bearing are essential to ensure the stability of turbo-expander. In this paper, numerical simulation is used to analyze the performance of gas bearing for a 500W helium refrigerator turbine, and the optimization design of the gas bearing has been completed. And the results of the gas bearing optimization have a guiding role in the processing technology. Finally, the turbine experiments verify that the gas bearing has good performance, and ensure the stable operation of the turbine.

Acceleration of the Particle Swarm Optimization for Peierls–Nabarro modeling of dislocations in conventional and high-entropy alloys

DOE PAGES

Pei, Zongrui; Max-Planck-Inst. fur Eisenforschung, Duseldorf; Eisenbach, Markus

2017-02-06

Dislocations are among the most important defects in determining the mechanical properties of both conventional alloys and high-entropy alloys. The Peierls-Nabarro model supplies an efficient pathway to their geometries and mobility. The difficulty in solving the integro-differential Peierls-Nabarro equation is how to effectively avoid the local minima in the energy landscape of a dislocation core. Among the other methods to optimize the dislocation core structures, we choose the algorithm of Particle Swarm Optimization, an algorithm that simulates the social behaviors of organisms. By employing more particles (bigger swarm) and more iterative steps (allowing them to explore for longer time), themore » local minima can be effectively avoided. But this would require more computational cost. The advantage of this algorithm is that it is readily parallelized in modern high computing architecture. We demonstrate the performance of our parallelized algorithm scales linearly with the number of employed cores.« less

Reconstruction of the unknown optimization cost functions from experimental recordings during static multi-finger prehension

PubMed Central

Niu, Xun; Terekhov, Alexander V.; Latash, Mark L.; Zatsiorsky, Vladimir M.

2013-01-01

The goal of the research is to reconstruct the unknown cost (objective) function(s) presumably used by the neural controller for sharing the total force among individual fingers in multi-finger prehension. The cost function was determined from experimental data by applying the recently developed Analytical Inverse Optimization (ANIO) method (Terekhov et al 2010). The core of the ANIO method is the Theorem of Uniqueness that specifies conditions for unique (with some restrictions) estimation of the objective functions. In the experiment, subjects (n=8) grasped an instrumented handle and maintained it at rest in the air with various external torques, loads, and target grasping forces applied to the object. The experimental data recorded from 80 trials showed a tendency to lie on a 2-dimensional hyperplane in the 4-dimensional finger-force space. Because the constraints in each trial were different, such a propensity is a manifestation of a neural mechanism (not the task mechanics). In agreement with the Lagrange principle for the inverse optimization, the plane of experimental observations was close to the plane resulting from the direct optimization. The latter plane was determined using the ANIO method. The unknown cost function was reconstructed successfully for each performer, as well as for the group data. The cost functions were found to be quadratic with non-zero linear terms. The cost functions obtained with the ANIO method yielded more accurate results than other optimization methods. The ANIO method has an evident potential for addressing the problem of optimization in motor control. PMID:22104742

SOPRA: Scaffolding algorithm for paired reads via statistical optimization.

PubMed

Dayarian, Adel; Michael, Todd P; Sengupta, Anirvan M

2010-06-24

High throughput sequencing (HTS) platforms produce gigabases of short read (<100 bp) data per run. While these short reads are adequate for resequencing applications, de novo assembly of moderate size genomes from such reads remains a significant challenge. These limitations could be partially overcome by utilizing mate pair technology, which provides pairs of short reads separated by a known distance along the genome. We have developed SOPRA, a tool designed to exploit the mate pair/paired-end information for assembly of short reads. The main focus of the algorithm is selecting a sufficiently large subset of simultaneously satisfiable mate pair constraints to achieve a balance between the size and the quality of the output scaffolds. Scaffold assembly is presented as an optimization problem for variables associated with vertices and with edges of the contig connectivity graph. Vertices of this graph are individual contigs with edges drawn between contigs connected by mate pairs. Similar graph problems have been invoked in the context of shotgun sequencing and scaffold building for previous generation of sequencing projects. However, given the error-prone nature of HTS data and the fundamental limitations from the shortness of the reads, the ad hoc greedy algorithms used in the earlier studies are likely to lead to poor quality results in the current context. SOPRA circumvents this problem by treating all the constraints on equal footing for solving the optimization problem, the solution itself indicating the problematic constraints (chimeric/repetitive contigs, etc.) to be removed. The process of solving and removing of constraints is iterated till one reaches a core set of consistent constraints. For SOLiD sequencer data, SOPRA uses a dynamic programming approach to robustly translate the color-space assembly to base-space. For assessing the quality of an assembly, we report the no-match/mismatch error rate as well as the rates of various rearrangement errors. Applying SOPRA to real data from bacterial genomes, we were able to assemble contigs into scaffolds of significant length (N50 up to 200 Kb) with very few errors introduced in the process. In general, the methodology presented here will allow better scaffold assemblies of any type of mate pair sequencing data.

Fast Optimization for Aircraft Descent and Approach Trajectory

NASA Technical Reports Server (NTRS)

Luchinsky, Dmitry G.; Schuet, Stefan; Brenton, J.; Timucin, Dogan; Smith, David; Kaneshige, John

2017-01-01

We address problem of on-line scheduling of the aircraft descent and approach trajectory. We formulate a general multiphase optimal control problem for optimization of the descent trajectory and review available methods of its solution. We develop a fast algorithm for solution of this problem using two key components: (i) fast inference of the dynamical and control variables of the descending trajectory from the low dimensional flight profile data and (ii) efficient local search for the resulting reduced dimensionality non-linear optimization problem. We compare the performance of the proposed algorithm with numerical solution obtained using optimal control toolbox General Pseudospectral Optimal Control Software. We present results of the solution of the scheduling problem for aircraft descent using novel fast algorithm and discuss its future applications.

Research on cutting path optimization of sheet metal parts based on ant colony algorithm

NASA Astrophysics Data System (ADS)

Wu, Z. Y.; Ling, H.; Li, L.; Wu, L. H.; Liu, N. B.

2017-09-01

In view of the disadvantages of the current cutting path optimization methods of sheet metal parts, a new method based on ant colony algorithm was proposed in this paper. The cutting path optimization problem of sheet metal parts was taken as the research object. The essence and optimization goal of the optimization problem were presented. The traditional serial cutting constraint rule was improved. The cutting constraint rule with cross cutting was proposed. The contour lines of parts were discretized and the mathematical model of cutting path optimization was established. Thus the problem was converted into the selection problem of contour lines of parts. Ant colony algorithm was used to solve the problem. The principle and steps of the algorithm were analyzed.

Optimizing zonal advection of the Advanced Research WRF (ARW) dynamics for Intel MIC

NASA Astrophysics Data System (ADS)

Mielikainen, Jarno; Huang, Bormin; Huang, Allen H.

2014-10-01

The Weather Research and Forecast (WRF) model is the most widely used community weather forecast and research model in the world. There are two distinct varieties of WRF. The Advanced Research WRF (ARW) is an experimental, advanced research version featuring very high resolution. The WRF Nonhydrostatic Mesoscale Model (WRF-NMM) has been designed for forecasting operations. WRF consists of dynamics code and several physics modules. The WRF-ARW core is based on an Eulerian solver for the fully compressible nonhydrostatic equations. In the paper, we will use Intel Intel Many Integrated Core (MIC) architecture to substantially increase the performance of a zonal advection subroutine for optimization. It is of the most time consuming routines in the ARW dynamics core. Advection advances the explicit perturbation horizontal momentum equations by adding in the large-timestep tendency along with the small timestep pressure gradient tendency. We will describe the challenges we met during the development of a high-speed dynamics code subroutine for MIC architecture. Furthermore, lessons learned from the code optimization process will be discussed. The results show that the optimizations improved performance of the original code on Xeon Phi 5110P by a factor of 2.4x.

A Bandwidth-Optimized Multi-Core Architecture for Irregular Applications

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

Secchi, Simone; Tumeo, Antonino; Villa, Oreste

This paper presents an architecture template for next-generation high performance computing systems specifically targeted to irregular applications. We start our work by considering that future generation interconnection and memory bandwidth full-system numbers are expected to grow by a factor of 10. In order to keep up with such a communication capacity, while still resorting to fine-grained multithreading as the main way to tolerate unpredictable memory access latencies of irregular applications, we show how overall performance scaling can benefit from the multi-core paradigm. At the same time, we also show how such an architecture template must be coupled with specific techniquesmore » in order to optimize bandwidth utilization and achieve the maximum scalability. We propose a technique based on memory references aggregation, together with the related hardware implementation, as one of such optimization techniques. We explore the proposed architecture template by focusing on the Cray XMT architecture and, using a dedicated simulation infrastructure, validate the performance of our template with two typical irregular applications. Our experimental results prove the benefits provided by both the multi-core approach and the bandwidth optimization reference aggregation technique.« less

Large-scale structural optimization

NASA Technical Reports Server (NTRS)

Sobieszczanski-Sobieski, J.

1983-01-01

Problems encountered by aerospace designers in attempting to optimize whole aircraft are discussed, along with possible solutions. Large scale optimization, as opposed to component-by-component optimization, is hindered by computational costs, software inflexibility, concentration on a single, rather than trade-off, design methodology and the incompatibility of large-scale optimization with single program, single computer methods. The software problem can be approached by placing the full analysis outside of the optimization loop. Full analysis is then performed only periodically. Problem-dependent software can be removed from the generic code using a systems programming technique, and then embody the definitions of design variables, objective function and design constraints. Trade-off algorithms can be used at the design points to obtain quantitative answers. Finally, decomposing the large-scale problem into independent subproblems allows systematic optimization of the problems by an organization of people and machines.

Application of the gravity search algorithm to multi-reservoir operation optimization

NASA Astrophysics Data System (ADS)

Bozorg-Haddad, Omid; Janbaz, Mahdieh; Loáiciga, Hugo A.

2016-12-01

Complexities in river discharge, variable rainfall regime, and drought severity merit the use of advanced optimization tools in multi-reservoir operation. The gravity search algorithm (GSA) is an evolutionary optimization algorithm based on the law of gravity and mass interactions. This paper explores the GSA's efficacy for solving benchmark functions, single reservoir, and four-reservoir operation optimization problems. The GSA's solutions are compared with those of the well-known genetic algorithm (GA) in three optimization problems. The results show that the GSA's results are closer to the optimal solutions than the GA's results in minimizing the benchmark functions. The average values of the objective function equal 1.218 and 1.746 with the GSA and GA, respectively, in solving the single-reservoir hydropower operation problem. The global solution equals 1.213 for this same problem. The GSA converged to 99.97% of the global solution in its average-performing history, while the GA converged to 97% of the global solution of the four-reservoir problem. Requiring fewer parameters for algorithmic implementation and reaching the optimal solution in fewer number of functional evaluations are additional advantages of the GSA over the GA. The results of the three optimization problems demonstrate a superior performance of the GSA for optimizing general mathematical problems and the operation of reservoir systems.

Discrete particle swarm optimization to solve multi-objective limited-wait hybrid flow shop scheduling problem

NASA Astrophysics Data System (ADS)

Santosa, B.; Siswanto, N.; Fiqihesa

2018-04-01

This paper proposes a discrete Particle Swam Optimization (PSO) to solve limited-wait hybrid flowshop scheduing problem with multi objectives. Flow shop schedulimg represents the condition when several machines are arranged in series and each job must be processed at each machine with same sequence. The objective functions are minimizing completion time (makespan), total tardiness time, and total machine idle time. Flow shop scheduling model always grows to cope with the real production system accurately. Since flow shop scheduling is a NP-Hard problem then the most suitable method to solve is metaheuristics. One of metaheuristics algorithm is Particle Swarm Optimization (PSO), an algorithm which is based on the behavior of a swarm. Originally, PSO was intended to solve continuous optimization problems. Since flow shop scheduling is a discrete optimization problem, then, we need to modify PSO to fit the problem. The modification is done by using probability transition matrix mechanism. While to handle multi objectives problem, we use Pareto Optimal (MPSO). The results of MPSO is better than the PSO because the MPSO solution set produced higher probability to find the optimal solution. Besides the MPSO solution set is closer to the optimal solution

Test Scheduling for Core-Based SOCs Using Genetic Algorithm Based Heuristic Approach

NASA Astrophysics Data System (ADS)

Giri, Chandan; Sarkar, Soumojit; Chattopadhyay, Santanu

This paper presents a Genetic algorithm (GA) based solution to co-optimize test scheduling and wrapper design for core based SOCs. Core testing solutions are generated as a set of wrapper configurations, represented as rectangles with width equal to the number of TAM (Test Access Mechanism) channels and height equal to the corresponding testing time. A locally optimal best-fit heuristic based bin packing algorithm has been used to determine placement of rectangles minimizing the overall test times, whereas, GA has been utilized to generate the sequence of rectangles to be considered for placement. Experimental result on ITC'02 benchmark SOCs shows that the proposed method provides better solutions compared to the recent works reported in the literature.

Optimization of Benzoisothiazole dioxide inhibitory activity of the NS5B polymerase of HCV genotype 4 using ligand-steered homological modeling, reaction-driven scaffold-hopping and Enovo workflow.

PubMed

Mahmoud, Amr Hamed; Mohamed Abouzid, Khaled Abouzid; El Ella, Dalal Abd El Rahman Abou; Hamid Ismail, Mohamed Abdel

2011-01-01

Infection caused by hepatitis C virus (HCV) is a significant world health problem for which novel therapies are in urgent demand. The virus is highly prevalent in the Middle East and Africa particularly Egypt with more than 90% of infections due to genotype 4. Nonstructural (NS5B) viral proteins have emerged as an attractive target for HCV antivirals discovery. A potent class of inhibitors having benzisothiazole dioxide scaffold has been identified on this target, however they were mainly active on genotype 1 while exhibiting much lowered activity on other genotypes due to the high degree of mutation of its binding site. Based on this fact, we employed a novel strategy to optimize this class on genotype 4. This strategy depends on using a refined ligand-steered homological model of this genotype to study the mutation binding energies of the binding site amino acid residues, the essential features for interaction and provide a structure-based pharmacophore model that can aid optimization. This model was applied on a focused library which was generated using a reaction-driven scaffold-hopping strategy. The hits retrieved were subjected to Enovo pipeline pilot optimization workflow that employs R-group enumeration, core-constrained protein docking using modified CDOCKER and finally ranking of poses using an accurate molecular mechanics generalized Born with surface area method.

Optimization of deformation monitoring networks using finite element strain analysis

NASA Astrophysics Data System (ADS)

Alizadeh-Khameneh, M. Amin; Eshagh, Mehdi; Jensen, Anna B. O.

2018-04-01

An optimal design of a geodetic network can fulfill the requested precision and reliability of the network, and decrease the expenses of its execution by removing unnecessary observations. The role of an optimal design is highlighted in deformation monitoring network due to the repeatability of these networks. The core design problem is how to define precision and reliability criteria. This paper proposes a solution, where the precision criterion is defined based on the precision of deformation parameters, i. e. precision of strain and differential rotations. A strain analysis can be performed to obtain some information about the possible deformation of a deformable object. In this study, we split an area into a number of three-dimensional finite elements with the help of the Delaunay triangulation and performed the strain analysis on each element. According to the obtained precision of deformation parameters in each element, the precision criterion of displacement detection at each network point is then determined. The developed criterion is implemented to optimize the observations from the Global Positioning System (GPS) in Skåne monitoring network in Sweden. The network was established in 1989 and straddled the Tornquist zone, which is one of the most active faults in southern Sweden. The numerical results show that 17 out of all 21 possible GPS baseline observations are sufficient to detect minimum 3 mm displacement at each network point.

Constraint Optimization Literature Review

DTIC Science & Technology

2015-11-01

COPs. 15. SUBJECT TERMS high-performance computing, mobile ad hoc network, optimization, constraint, satisfaction 16. SECURITY CLASSIFICATION OF: 17...Optimization Problems 1 2.1 Constraint Satisfaction Problems 1 2.2 Constraint Optimization Problems 3 3. Constraint Optimization Algorithms 9 3.1...Constraint Satisfaction Algorithms 9 3.1.1 Brute-Force search 9 3.1.2 Constraint Propagation 10 3.1.3 Depth-First Search 13 3.1.4 Local Search 18

Exploring performance and energy tradeoffs for irregular applications: A case study on the Tilera many-core architecture

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

Panyala, Ajay; Chavarría-Miranda, Daniel; Manzano, Joseph B.

High performance, parallel applications with irregular data accesses are becoming a critical workload class for modern systems. In particular, the execution of such workloads on emerging many-core systems is expected to be a significant component of applications in data mining, machine learning, scientific computing and graph analytics. However, power and energy constraints limit the capabilities of individual cores, memory hierarchy and on-chip interconnect of such systems, thus leading to architectural and software trade-os that must be understood in the context of the intended application’s behavior. Irregular applications are notoriously hard to optimize given their data-dependent access patterns, lack of structuredmore » locality and complex data structures and code patterns. We have ported two irregular applications, graph community detection using the Louvain method (Grappolo) and high-performance conjugate gradient (HPCCG), to the Tilera many-core system and have conducted a detailed study of platform-independent and platform-specific optimizations that improve their performance as well as reduce their overall energy consumption. To conduct this study, we employ an auto-tuning based approach that explores the optimization design space along three dimensions - memory layout schemes, GCC compiler flag choices and OpenMP loop scheduling options. We leverage MIT’s OpenTuner auto-tuning framework to explore and recommend energy optimal choices for different combinations of parameters. We then conduct an in-depth architectural characterization to understand the memory behavior of the selected workloads. Finally, we perform a correlation study to demonstrate the interplay between the hardware behavior and application characteristics. Using auto-tuning, we demonstrate whole-node energy savings and performance improvements of up to 49:6% and 60% relative to a baseline instantiation, and up to 31% and 45:4% relative to manually optimized variants.« less

Trade-offs and efficiencies in optimal budget-constrained multispecies corridor networks

Treesearch

Bistra Dilkina; Rachel Houtman; Carla P. Gomes; Claire A. Montgomery; Kevin S. McKelvey; Katherine Kendall; Tabitha A. Graves; Richard Bernstein; Michael K. Schwartz

2016-01-01

Conservation biologists recognize that a system of isolated protected areas will be necessary but insufficient to meet biodiversity objectives. Current approaches to connecting core conservation areas through corridors consider optimal corridor placement based on a single optimization goal: commonly, maximizing the movement for a target species across a...

Structural Analysis and Optimization of a Composite Fan Blade for Future Aircraft Engine

NASA Astrophysics Data System (ADS)

Coroneos, Rula M.; Gorla, Rama Subba Reddy

2012-09-01

This paper addresses the structural analysis and optimization of a composite sandwich ply lay-up of a NASA baseline solid metallic fan blade comparable to a future Boeing 737 MAX aircraft engine. Sandwich construction with a polymer matrix composite face sheet and honeycomb aluminum core replaces the original baseline solid metallic fan model made of Titanium. The focus of this work is to design the sandwich composite blade with the optimum number of plies for the face sheet that will withstand the combined pressure and centrifugal loads while the constraints are satisfied and the baseline aerodynamic and geometric parameters are maintained. To satisfy the requirements a sandwich construction for the blade is proposed with composite face sheets and a weak core made of honeycomb aluminum material. For aerodynamic considerations, the thickness of the core is optimized where as the overall blade thickness is held fixed in order not to alter the original airfoil geometry. Weight reduction is taken as the objective function by varying the core thickness of the blade within specified upper and lower bounds. Constraints are imposed on radial displacement limitations and ply failure strength. From the optimum design, the minimum number of plies, which will not fail, is back-calculated. The ply lay-up of the blade is adjusted from the calculated number of plies and final structural analysis is performed. Analyses were carried out by utilizing the OpenMDAO Framework, developed at NASA Glenn Research Center combining optimization with structural assessment.

Comparison of Optimal Design Methods in Inverse Problems

PubMed Central

Banks, H. T.; Holm, Kathleen; Kappel, Franz

2011-01-01

Typical optimal design methods for inverse or parameter estimation problems are designed to choose optimal sampling distributions through minimization of a specific cost function related to the resulting error in parameter estimates. It is hoped that the inverse problem will produce parameter estimates with increased accuracy using data collected according to the optimal sampling distribution. Here we formulate the classical optimal design problem in the context of general optimization problems over distributions of sampling times. We present a new Prohorov metric based theoretical framework that permits one to treat succinctly and rigorously any optimal design criteria based on the Fisher Information Matrix (FIM). A fundamental approximation theory is also included in this framework. A new optimal design, SE-optimal design (standard error optimal design), is then introduced in the context of this framework. We compare this new design criteria with the more traditional D-optimal and E-optimal designs. The optimal sampling distributions from each design are used to compute and compare standard errors; the standard errors for parameters are computed using asymptotic theory or bootstrapping and the optimal mesh. We use three examples to illustrate ideas: the Verhulst-Pearl logistic population model [13], the standard harmonic oscillator model [13] and a popular glucose regulation model [16, 19, 29]. PMID:21857762

Design of an integral thermal protection system for future space vehicles

NASA Astrophysics Data System (ADS)

Bapanapalli, Satish Kumar

Thermal protection systems (TPS) are the features incorporated into a spacecraft's design to protect it from severe aerodynamic heating during high-speed travel through planetary atmospheres. The ablative TPS on the space capsule Apollo and ceramic tiles and blankets on the Space Shuttle Orbiter were designed as add-ons to the main load-bearing structure of the vehicles. They are usually incompatible with the structure due to mismatch in coefficient of thermal expansion and as a result the robustness of the external surface of the spacecraft is compromised. This could potentially lead to catastrophic consequences because the TPS forms the external surface of the vehicle and is subjected to numerous other loads like aerodynamic pressure loads, small object high-speed impacts and handling damages during maintenance. In order to make the spacecraft external surface robust, an Integral Thermal Protection System (ITPS) concept has been proposed in this research in which the load-bearing structure and the TPS are combined into one single structure. The design of an ITPS is a formidable task because the requirement of a load-bearing structure and a TPS are often contradictory to one another. The design process has been formulated as an optimization problem with mass per unit area of the ITPS as the objective function and the various functions of the ITPS were formulated as constraints. This is a multidisciplinary design optimization problem involving heat transfer and structural analysis fields. The constraints were expressed as response surface approximations obtained from a large number of finite element analyses, which were carried out with combinations of design variables obtained from an optimized Latin-Hypercube sampling scheme. A MATLABRTM code has been developed to carry out these FE analyses automatically in conjunction with ABAQUSRTM . Corrugated-core structures were designed for ITPS applications with loads and boundary conditions similar to that of a Space Shuttle-like vehicle. Temperature, buckling, deflection and stress constraints were considered for the design process. An optimized mass ranging between 3.5--5 lb/ft2 was achieved by the design. This is considerably heavier when compared to conventional TPS designs. However, the ITPS can withstand substantially large mechanical loads when compared to the conventional TPS. Truss-core geometries used for ITPS design in this research were found to be unsuitable as they could not withstand large thermal gradients frequently encountered in ITPS applications. The corrugated-core design was used for further studying the influence of the various input parameters on the final design weight of the ITPS. It was observed that boundary conditions not only significantly influence the ITPS design but also have a major impact on the effect of various input parameters. It was found that even a small amount of heat loss from bottom face sheet leads to significant reduction in ITPS weight. Aluminum and Beryllium are the most suitable materials for bottom face sheet with Beryllium having considerable advantages in terms of heat capacity, stiffness and density. Although ceramic matrix composites have many superior properties when compared to metal alloys (Titanium alloys and Inconel), their low tensile strength presents difficulties in ITPS applications.

The comparison of numerical models of a sandwich panel in the context of the core deformations at the supports

NASA Astrophysics Data System (ADS)

Pozorska, Jolanta; Pozorski, Zbigniew

2018-01-01

The paper presents the problem of static structural behavior of sandwich panels at the supports. The panels have a soft core and correspond to typical structures applied in civil engineering. To analyze the problem, five different 3-D numerical models were created. The results were compared in the context of core compression and stress redistribution. The numerical solutions verify methods of evaluating the capacity of the sandwich panel that are known from the literature.

Multiobjective optimization approach: thermal food processing.

PubMed

Abakarov, A; Sushkov, Y; Almonacid, S; Simpson, R

2009-01-01

The objective of this study was to utilize a multiobjective optimization technique for the thermal sterilization of packaged foods. The multiobjective optimization approach used in this study is based on the optimization of well-known aggregating functions by an adaptive random search algorithm. The applicability of the proposed approach was illustrated by solving widely used multiobjective test problems taken from the literature. The numerical results obtained for the multiobjective test problems and for the thermal processing problem show that the proposed approach can be effectively used for solving multiobjective optimization problems arising in the food engineering field.

Constructing probabilistic scenarios for wide-area solar power generation

DOE PAGES

Woodruff, David L.; Deride, Julio; Staid, Andrea; ...

2017-12-22

Optimizing thermal generation commitments and dispatch in the presence of high penetrations of renewable resources such as solar energy requires a characterization of their stochastic properties. In this study, we describe novel methods designed to create day-ahead, wide-area probabilistic solar power scenarios based only on historical forecasts and associated observations of solar power production. Each scenario represents a possible trajectory for solar power in next-day operations with an associated probability computed by algorithms that use historical forecast errors. Scenarios are created by segmentation of historic data, fitting non-parametric error distributions using epi-splines, and then computing specific quantiles from these distributions.more » Additionally, we address the challenge of establishing an upper bound on solar power output. Our specific application driver is for use in stochastic variants of core power systems operations optimization problems, e.g., unit commitment and economic dispatch. These problems require as input a range of possible future realizations of renewables production. However, the utility of such probabilistic scenarios extends to other contexts, e.g., operator and trader situational awareness. Finally, we compare the performance of our approach to a recently proposed method based on quantile regression, and demonstrate that our method performs comparably to this approach in terms of two widely used methods for assessing the quality of probabilistic scenarios: the Energy score and the Variogram score.« less

Parallelization of TWOPORFLOW, a Cartesian Grid based Two-phase Porous Media Code for Transient Thermo-hydraulic Simulations

NASA Astrophysics Data System (ADS)

Trost, Nico; Jiménez, Javier; Imke, Uwe; Sanchez, Victor

2014-06-01

TWOPORFLOW is a thermo-hydraulic code based on a porous media approach to simulate single- and two-phase flow including boiling. It is under development at the Institute for Neutron Physics and Reactor Technology (INR) at KIT. The code features a 3D transient solution of the mass, momentum and energy conservation equations for two inter-penetrating fluids with a semi-implicit continuous Eulerian type solver. The application domain of TWOPORFLOW includes the flow in standard porous media and in structured porous media such as micro-channels and cores of nuclear power plants. In the latter case, the fluid domain is coupled to a fuel rod model, describing the heat flow inside the solid structure. In this work, detailed profiling tools have been utilized to determine the optimization potential of TWOPORFLOW. As a result, bottle-necks were identified and reduced in the most feasible way, leading for instance to an optimization of the water-steam property computation. Furthermore, an OpenMP implementation addressing the routines in charge of inter-phase momentum-, energy- and mass-coupling delivered good performance together with a high scalability on shared memory architectures. In contrast to that, the approach for distributed memory systems was to solve sub-problems resulting by the decomposition of the initial Cartesian geometry. Thread communication for the sub-problem boundary updates was accomplished by the Message Passing Interface (MPI) standard.

Constructing probabilistic scenarios for wide-area solar power generation

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

Woodruff, David L.; Deride, Julio; Staid, Andrea

Optimizing thermal generation commitments and dispatch in the presence of high penetrations of renewable resources such as solar energy requires a characterization of their stochastic properties. In this study, we describe novel methods designed to create day-ahead, wide-area probabilistic solar power scenarios based only on historical forecasts and associated observations of solar power production. Each scenario represents a possible trajectory for solar power in next-day operations with an associated probability computed by algorithms that use historical forecast errors. Scenarios are created by segmentation of historic data, fitting non-parametric error distributions using epi-splines, and then computing specific quantiles from these distributions.more » Additionally, we address the challenge of establishing an upper bound on solar power output. Our specific application driver is for use in stochastic variants of core power systems operations optimization problems, e.g., unit commitment and economic dispatch. These problems require as input a range of possible future realizations of renewables production. However, the utility of such probabilistic scenarios extends to other contexts, e.g., operator and trader situational awareness. Finally, we compare the performance of our approach to a recently proposed method based on quantile regression, and demonstrate that our method performs comparably to this approach in terms of two widely used methods for assessing the quality of probabilistic scenarios: the Energy score and the Variogram score.« less

Efficiently Scheduling Multi-core Guest Virtual Machines on Multi-core Hosts in Network Simulation

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

Yoginath, Srikanth B; Perumalla, Kalyan S

2011-01-01

Virtual machine (VM)-based simulation is a method used by network simulators to incorporate realistic application behaviors by executing actual VMs as high-fidelity surrogates for simulated end-hosts. A critical requirement in such a method is the simulation time-ordered scheduling and execution of the VMs. Prior approaches such as time dilation are less efficient due to the high degree of multiplexing possible when multiple multi-core VMs are simulated on multi-core host systems. We present a new simulation time-ordered scheduler to efficiently schedule multi-core VMs on multi-core real hosts, with a virtual clock realized on each virtual core. The distinguishing features of ourmore » approach are: (1) customizable granularity of the VM scheduling time unit on the simulation time axis, (2) ability to take arbitrary leaps in virtual time by VMs to maximize the utilization of host (real) cores when guest virtual cores idle, and (3) empirically determinable optimality in the tradeoff between total execution (real) time and time-ordering accuracy levels. Experiments show that it is possible to get nearly perfect time-ordered execution, with a slight cost in total run time, relative to optimized non-simulation VM schedulers. Interestingly, with our time-ordered scheduler, it is also possible to reduce the time-ordering error from over 50% of non-simulation scheduler to less than 1% realized by our scheduler, with almost the same run time efficiency as that of the highly efficient non-simulation VM schedulers.« less

Composition of Web Services Using Markov Decision Processes and Dynamic Programming

PubMed Central

Uc-Cetina, Víctor; Moo-Mena, Francisco; Hernandez-Ucan, Rafael

2015-01-01

We propose a Markov decision process model for solving the Web service composition (WSC) problem. Iterative policy evaluation, value iteration, and policy iteration algorithms are used to experimentally validate our approach, with artificial and real data. The experimental results show the reliability of the model and the methods employed, with policy iteration being the best one in terms of the minimum number of iterations needed to estimate an optimal policy, with the highest Quality of Service attributes. Our experimental work shows how the solution of a WSC problem involving a set of 100,000 individual Web services and where a valid composition requiring the selection of 1,000 services from the available set can be computed in the worst case in less than 200 seconds, using an Intel Core i5 computer with 6 GB RAM. Moreover, a real WSC problem involving only 7 individual Web services requires less than 0.08 seconds, using the same computational power. Finally, a comparison with two popular reinforcement learning algorithms, sarsa and Q-learning, shows that these algorithms require one or two orders of magnitude and more time than policy iteration, iterative policy evaluation, and value iteration to handle WSC problems of the same complexity. PMID:25874247

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.

The coral reefs optimization algorithm: a novel metaheuristic for efficiently solving optimization problems.

PubMed

Salcedo-Sanz, S; Del Ser, J; Landa-Torres, I; Gil-López, S; Portilla-Figueras, J A

2014-01-01

This paper presents a novel bioinspired algorithm to tackle complex optimization problems: the coral reefs optimization (CRO) algorithm. The CRO algorithm artificially simulates a coral reef, where different corals (namely, solutions to the optimization problem considered) grow and reproduce in coral colonies, fighting by choking out other corals for space in the reef. This fight for space, along with the specific characteristics of the corals' reproduction, produces a robust metaheuristic algorithm shown to be powerful for solving hard optimization problems. In this research the CRO algorithm is tested in several continuous and discrete benchmark problems, as well as in practical application scenarios (i.e., optimum mobile network deployment and off-shore wind farm design). The obtained results confirm the excellent performance of the proposed algorithm and open line of research for further application of the algorithm to real-world problems.

The Coral Reefs Optimization Algorithm: A Novel Metaheuristic for Efficiently Solving Optimization Problems

PubMed Central

Salcedo-Sanz, S.; Del Ser, J.; Landa-Torres, I.; Gil-López, S.; Portilla-Figueras, J. A.

2014-01-01

This paper presents a novel bioinspired algorithm to tackle complex optimization problems: the coral reefs optimization (CRO) algorithm. The CRO algorithm artificially simulates a coral reef, where different corals (namely, solutions to the optimization problem considered) grow and reproduce in coral colonies, fighting by choking out other corals for space in the reef. This fight for space, along with the specific characteristics of the corals' reproduction, produces a robust metaheuristic algorithm shown to be powerful for solving hard optimization problems. In this research the CRO algorithm is tested in several continuous and discrete benchmark problems, as well as in practical application scenarios (i.e., optimum mobile network deployment and off-shore wind farm design). The obtained results confirm the excellent performance of the proposed algorithm and open line of research for further application of the algorithm to real-world problems. PMID:25147860

Quantum Heterogeneous Computing for Satellite Positioning Optimization

NASA Astrophysics Data System (ADS)

Bass, G.; Kumar, V.; Dulny, J., III

2016-12-01

Hard optimization problems occur in many fields of academic study and practical situations. We present results in which quantum heterogeneous computing is used to solve a real-world optimization problem: satellite positioning. Optimization problems like this can scale very rapidly with problem size, and become unsolvable with traditional brute-force methods. Typically, such problems have been approximately solved with heuristic approaches; however, these methods can take a long time to calculate and are not guaranteed to find optimal solutions. Quantum computing offers the possibility of producing significant speed-up and improved solution quality. There are now commercially available quantum annealing (QA) devices that are designed to solve difficult optimization problems. These devices have 1000+ quantum bits, but they have significant hardware size and connectivity limitations. We present a novel heterogeneous computing stack that combines QA and classical machine learning and allows the use of QA on problems larger than the quantum hardware could solve in isolation. We begin by analyzing the satellite positioning problem with a heuristic solver, the genetic algorithm. The classical computer's comparatively large available memory can explore the full problem space and converge to a solution relatively close to the true optimum. The QA device can then evolve directly to the optimal solution within this more limited space. Preliminary experiments, using the Quantum Monte Carlo (QMC) algorithm to simulate QA hardware, have produced promising results. Working with problem instances with known global minima, we find a solution within 8% in a matter of seconds, and within 5% in a few minutes. Future studies include replacing QMC with commercially available quantum hardware and exploring more problem sets and model parameters. Our results have important implications for how heterogeneous quantum computing can be used to solve difficult optimization problems in any field.

Candidate molten salt investigation for an accelerator driven subcritical core

NASA Astrophysics Data System (ADS)

Sooby, E.; Baty, A.; Beneš, O.; McIntyre, P.; Pogue, N.; Salanne, M.; Sattarov, A.

2013-09-01

We report a design for accelerator-driven subcritical fission in a molten salt core (ADSMS) that utilizes a fuel salt composed of NaCl and transuranic (TRU) chlorides. The ADSMS core is designed for fast neutronics (28% of neutrons >1 MeV) to optimize TRU destruction. The choice of a NaCl-based salt offers benefits for corrosion, operating temperature, and actinide solubility as compared with LiF-based fuel salts. A molecular dynamics (MD) code has been used to estimate properties of the molten salt system which are important for ADSMS design but have never been measured experimentally. Results from the MD studies are reported. Experimental measurements of fuel salt properties and studies of corrosion and radiation damage on candidate metals for the core vessel are anticipated. A special thanks is due to Prof. Paul Madden for introducing the ADSMS group to the concept of using the molten salt as the spallation target, rather than a conventional heavy metal spallation target. This feature helps to optimize this core as a Pu/TRU burner.

Elastic stability of cylindrical shells with soft elastic cores: Biomimicking natural tubular structures

NASA Astrophysics Data System (ADS)

Karam, Gebran Nizar

1994-01-01

Thin walled cylindrical shell structures are widespread in nature: examples include plant stems, porcupine quills, and hedgehog spines. All have an outer shell of almost fully dense material supported by a low density, cellular core. In nature, all are loaded in combination of axial compression and bending: failure is typically by buckling. Natural structures are often optimized. Here we have analyzed the elastic buckling of a thin cylindrical shell supported by an elastic core to show that this structural configuration achieves significant weight saving over a hollow cylinder. The results of the analysis are compared with data from an extensive experimental program on uniaxial compression and four point bending tests on silicone rubber shells with and without compliant foam cores. The analysis describes the results of the mechanical tests well. Characterization of the microstructures of several natural tubular structures with foamlike cores (plant stems, quills, and spines) revealed them to be close to the optimal configurations predicted by the analytical model. Biomimicking of natural cylindrical shell structures and evolutionary design processes may offer the potential to increase the mechanical efficiency of engineering cylindrical shells.

Wireless Sensor Network Optimization: Multi-Objective Paradigm

PubMed Central

Iqbal, Muhammad; Naeem, Muhammad; Anpalagan, Alagan; Ahmed, Ashfaq; Azam, Muhammad

2015-01-01

Optimization problems relating to wireless sensor network planning, design, deployment and operation often give rise to multi-objective optimization formulations where multiple desirable objectives compete with each other and the decision maker has to select one of the tradeoff solutions. These multiple objectives may or may not conflict with each other. Keeping in view the nature of the application, the sensing scenario and input/output of the problem, the type of optimization problem changes. To address different nature of optimization problems relating to wireless sensor network design, deployment, operation, planing and placement, there exist a plethora of optimization solution types. We review and analyze different desirable objectives to show whether they conflict with each other, support each other or they are design dependent. We also present a generic multi-objective optimization problem relating to wireless sensor network which consists of input variables, required output, objectives and constraints. A list of constraints is also presented to give an overview of different constraints which are considered while formulating the optimization problems in wireless sensor networks. Keeping in view the multi facet coverage of this article relating to multi-objective optimization, this will open up new avenues of research in the area of multi-objective optimization relating to wireless sensor networks. PMID:26205271

Cost component analysis.

PubMed

Lörincz, András; Póczos, Barnabás

2003-06-01

In optimizations the dimension of the problem may severely, sometimes exponentially increase optimization time. Parametric function approximatiors (FAPPs) have been suggested to overcome this problem. Here, a novel FAPP, cost component analysis (CCA) is described. In CCA, the search space is resampled according to the Boltzmann distribution generated by the energy landscape. That is, CCA converts the optimization problem to density estimation. Structure of the induced density is searched by independent component analysis (ICA). The advantage of CCA is that each independent ICA component can be optimized separately. In turn, (i) CCA intends to partition the original problem into subproblems and (ii) separating (partitioning) the original optimization problem into subproblems may serve interpretation. Most importantly, (iii) CCA may give rise to high gains in optimization time. Numerical simulations illustrate the working of the algorithm.

The design of multirate digital control systems

NASA Technical Reports Server (NTRS)

Berg, M. C.

1986-01-01

The successive loop closures synthesis method is the only method for multirate (MR) synthesis in common use. A new method for MR synthesis is introduced which requires a gradient-search solution to a constrained optimization problem. Some advantages of this method are that the control laws for all control loops are synthesized simultaneously, taking full advantage of all cross-coupling effects, and that simple, low-order compensator structures are easily accomodated. The algorithm and associated computer program for solving the constrained optimization problem are described. The successive loop closures , optimal control, and constrained optimization synthesis methods are applied to two example design problems. A series of compensator pairs are synthesized for each example problem. The succesive loop closure, optimal control, and constrained optimization synthesis methods are compared, in the context of the two design problems.

An introductory pharmacy practice experience based on a medication therapy management service model.

PubMed

Agness, Chanel F; Huynh, Donna; Brandt, Nicole

2011-06-10

To implement and evaluate an introductory pharmacy practice experience (IPPE) based on the medication therapy management (MTM) service model. Patient Care 2 is an IPPE that introduces third-year pharmacy students to the MTM service model. Students interacted with older adults to identify medication-related problems and develop recommendations using core MTM elements. Course outcome evaluations were based on number of documented medication-related problems, recommendations, and student reviews. Fifty-seven older adults participated in the course. Students identified 52 medication-related problems and 66 medical problems, and documented 233 recommendations relating to health maintenance and wellness, pharmacotherapy, referrals, and education. Students reported having adequate experience performing core MTM elements. Patient Care 2 may serve as an experiential learning model for pharmacy schools to teach the core elements of MTM and provide patient care services to the community.

OpenMP GNU and Intel Fortran programs for solving the time-dependent Gross-Pitaevskii equation

NASA Astrophysics Data System (ADS)

Young-S., Luis E.; Muruganandam, Paulsamy; Adhikari, Sadhan K.; Lončar, Vladimir; Vudragović, Dušan; Balaž, Antun

2017-11-01

We present Open Multi-Processing (OpenMP) version of Fortran 90 programs for solving the Gross-Pitaevskii (GP) equation for a Bose-Einstein condensate in one, two, and three spatial dimensions, optimized for use with GNU and Intel compilers. We use the split-step Crank-Nicolson algorithm for imaginary- and real-time propagation, which enables efficient calculation of stationary and non-stationary solutions, respectively. The present OpenMP programs are designed for computers with multi-core processors and optimized for compiling with both commercially-licensed Intel Fortran and popular free open-source GNU Fortran compiler. The programs are easy to use and are elaborated with helpful comments for the users. All input parameters are listed at the beginning of each program. Different output files provide physical quantities such as energy, chemical potential, root-mean-square sizes, densities, etc. We also present speedup test results for new versions of the programs. Program files doi:http://dx.doi.org/10.17632/y8zk3jgn84.2 Licensing provisions: Apache License 2.0 Programming language: OpenMP GNU and Intel Fortran 90. Computer: Any multi-core personal computer or workstation with the appropriate OpenMP-capable Fortran compiler installed. Number of processors used: All available CPU cores on the executing computer. Journal reference of previous version: Comput. Phys. Commun. 180 (2009) 1888; ibid.204 (2016) 209. Does the new version supersede the previous version?: Not completely. It does supersede previous Fortran programs from both references above, but not OpenMP C programs from Comput. Phys. Commun. 204 (2016) 209. Nature of problem: The present Open Multi-Processing (OpenMP) Fortran programs, optimized for use with commercially-licensed Intel Fortran and free open-source GNU Fortran compilers, solve the time-dependent nonlinear partial differential (GP) equation for a trapped Bose-Einstein condensate in one (1d), two (2d), and three (3d) spatial dimensions for six different trap symmetries: axially and radially symmetric traps in 3d, circularly symmetric traps in 2d, fully isotropic (spherically symmetric) and fully anisotropic traps in 2d and 3d, as well as 1d traps, where no spatial symmetry is considered. Solution method: We employ the split-step Crank-Nicolson algorithm to discretize the time-dependent GP equation in space and time. The discretized equation is then solved by imaginary- or real-time propagation, employing adequately small space and time steps, to yield the solution of stationary and non-stationary problems, respectively. Reasons for the new version: Previously published Fortran programs [1,2] have now become popular tools [3] for solving the GP equation. These programs have been translated to the C programming language [4] and later extended to the more complex scenario of dipolar atoms [5]. Now virtually all computers have multi-core processors and some have motherboards with more than one physical computer processing unit (CPU), which may increase the number of available CPU cores on a single computer to several tens. The C programs have been adopted to be very fast on such multi-core modern computers using general-purpose graphic processing units (GPGPU) with Nvidia CUDA and computer clusters using Message Passing Interface (MPI) [6]. Nevertheless, previously developed Fortran programs are also commonly used for scientific computation and most of them use a single CPU core at a time in modern multi-core laptops, desktops, and workstations. Unless the Fortran programs are made aware and capable of making efficient use of the available CPU cores, the solution of even a realistic dynamical 1d problem, not to mention the more complicated 2d and 3d problems, could be time consuming using the Fortran programs. Previously, we published auto-parallel Fortran programs [2] suitable for Intel (but not GNU) compiler for solving the GP equation. Hence, a need for the full OpenMP version of the Fortran programs to reduce the execution time cannot be overemphasized. To address this issue, we provide here such OpenMP Fortran programs, optimized for both Intel and GNU Fortran compilers and capable of using all available CPU cores, which can significantly reduce the execution time. Summary of revisions: Previous Fortran programs [1] for solving the time-dependent GP equation in 1d, 2d, and 3d with different trap symmetries have been parallelized using the OpenMP interface to reduce the execution time on multi-core processors. There are six different trap symmetries considered, resulting in six programs for imaginary-time propagation and six for real-time propagation, totaling to 12 programs included in BEC-GP-OMP-FOR software package. All input data (number of atoms, scattering length, harmonic oscillator trap length, trap anisotropy, etc.) are conveniently placed at the beginning of each program, as before [2]. Present programs introduce a new input parameter, which is designated by Number_of_Threads and defines the number of CPU cores of the processor to be used in the calculation. If one sets the value 0 for this parameter, all available CPU cores will be used. For the most efficient calculation it is advisable to leave one CPU core unused for the background system's jobs. For example, on a machine with 20 CPU cores such that we used for testing, it is advisable to use up to 19 CPU cores. However, the total number of used CPU cores can be divided into more than one job. For instance, one can run three simulations simultaneously using 10, 4, and 5 CPU cores, respectively, thus totaling to 19 used CPU cores on a 20-core computer. The Fortran source programs are located in the directory src, and can be compiled by the make command using the makefile in the root directory BEC-GP-OMP-FOR of the software package. The examples of produced output files can be found in the directory output, although some large density files are omitted, to save space. The programs calculate the values of actually used dimensionless nonlinearities from the physical input parameters, where the input parameters correspond to the identical nonlinearity values as in the previously published programs [1], so that the output files of the old and new programs can be directly compared. The output files are conveniently named such that their contents can be easily identified, following the naming convention introduced in Ref. [2]. For example, a file named -out.txt, where is a name of the individual program, represents the general output file containing input data, time and space steps, nonlinearity, energy and chemical potential, and was named fort.7 in the old Fortran version of programs [1]. A file named -den.txt is the output file with the condensate density, which had the names fort.3 and fort.4 in the old Fortran version [1] for imaginary- and real-time propagation programs, respectively. Other possible density outputs, such as the initial density, are commented out in the programs to have a simpler set of output files, but users can uncomment and re-enable them, if needed. In addition, there are output files for reduced (integrated) 1d and 2d densities for different programs. In the real-time programs there is also an output file reporting the dynamics of evolution of root-mean-square sizes after a perturbation is introduced. The supplied real-time programs solve the stationary GP equation, and then calculate the dynamics. As the imaginary-time programs are more accurate than the real-time programs for the solution of a stationary problem, one can first solve the stationary problem using the imaginary-time programs, adapt the real-time programs to read the pre-calculated wave function and then study the dynamics. In that case the parameter NSTP in the real-time programs should be set to zero and the space mesh and nonlinearity parameters should be identical in both programs. The reader is advised to consult our previous publication where a complete description of the output files is given [2]. A readme.txt file, included in the root directory, explains the procedure to compile and run the programs. We tested our programs on a workstation with two 10-core Intel Xeon E5-2650 v3 CPUs. The parameters used for testing are given in sample input files, provided in the corresponding directory together with the programs. In Table 1 we present wall-clock execution times for runs on 1, 6, and 19 CPU cores for programs compiled using Intel and GNU Fortran compilers. The corresponding columns "Intel speedup" and "GNU speedup" give the ratio of wall-clock execution times of runs on 1 and 19 CPU cores, and denote the actual measured speedup for 19 CPU cores. In all cases and for all numbers of CPU cores, although the GNU Fortran compiler gives excellent results, the Intel Fortran compiler turns out to be slightly faster. Note that during these tests we always ran only a single simulation on a workstation at a time, to avoid any possible interference issues. Therefore, the obtained wall-clock times are more reliable than the ones that could be measured with two or more jobs running simultaneously. We also studied the speedup of the programs as a function of the number of CPU cores used. The performance of the Intel and GNU Fortran compilers is illustrated in Fig. 1, where we plot the speedup and actual wall-clock times as functions of the number of CPU cores for 2d and 3d programs. We see that the speedup increases monotonically with the number of CPU cores in all cases and has large values (between 10 and 14 for 3d programs) for the maximal number of cores. This fully justifies the development of OpenMP programs, which enable much faster and more efficient solving of the GP equation. However, a slow saturation in the speedup with the further increase in the number of CPU cores is observed in all cases, as expected. The speedup tends to increase for programs in higher dimensions, as they become more complex and have to process more data. This is why the speedups of the supplied 2d and 3d programs are larger than those of 1d programs. Also, for a single program the speedup increases with the size of the spatial grid, i.e., with the number of spatial discretization points, since this increases the amount of calculations performed by the program. To demonstrate this, we tested the supplied real2d-th program and varied the number of spatial discretization points NX=NY from 20 to 1000. The measured speedup obtained when running this program on 19 CPU cores as a function of the number of discretization points is shown in Fig. 2. The speedup first increases rapidly with the number of discretization points and eventually saturates. Additional comments: Example inputs provided with the programs take less than 30 minutes to run on a workstation with two Intel Xeon E5-2650 v3 processors (2 QPI links, 10 CPU cores, 25 MB cache, 2.3 GHz).