Heuristics in Problem Solving: The Role of Direction in Controlling Search Space

ERIC Educational Resources Information Center

Chu, Yun; Li, Zheng; Su, Yong; Pizlo, Zygmunt

2010-01-01

Isomorphs of a puzzle called m+m resulted in faster solution times and an easily reproduced solution path in a labeled version of the problem compared to a more difficult binary version. We conjecture that performance is related to a type of heuristic called direction that not only constrains search space in the labeled version, but also…

A Bat Algorithm with Mutation for UCAV Path Planning

PubMed Central

Wang, Gaige; Guo, Lihong; Duan, Hong; Liu, Luo; Wang, Heqi

2012-01-01

Path planning for uninhabited combat air vehicle (UCAV) is a complicated high dimension optimization problem, which mainly centralizes on optimizing the flight route considering the different kinds of constrains under complicated battle field environments. Original bat algorithm (BA) is used to solve the UCAV path planning problem. Furthermore, a new bat algorithm with mutation (BAM) is proposed to solve the UCAV path planning problem, and a modification is applied to mutate between bats during the process of the new solutions updating. Then, the UCAV can find the safe path by connecting the chosen nodes of the coordinates while avoiding the threat areas and costing minimum fuel. This new approach can accelerate the global convergence speed while preserving the strong robustness of the basic BA. The realization procedure for original BA and this improved metaheuristic approach BAM is also presented. To prove the performance of this proposed metaheuristic method, BAM is compared with BA and other population-based optimization methods, such as ACO, BBO, DE, ES, GA, PBIL, PSO, and SGA. The experiment shows that the proposed approach is more effective and feasible in UCAV path planning than the other models. PMID:23365518

Finite-time convergent recurrent neural network with a hard-limiting activation function for constrained optimization with piecewise-linear objective functions.

PubMed

Liu, Qingshan; Wang, Jun

2011-04-01

This paper presents a one-layer recurrent neural network for solving a class of constrained nonsmooth optimization problems with piecewise-linear objective functions. The proposed neural network is guaranteed to be globally convergent in finite time to the optimal solutions under a mild condition on a derived lower bound of a single gain parameter in the model. The number of neurons in the neural network is the same as the number of decision variables of the optimization problem. Compared with existing neural networks for optimization, the proposed neural network has a couple of salient features such as finite-time convergence and a low model complexity. Specific models for two important special cases, namely, linear programming and nonsmooth optimization, are also presented. In addition, applications to the shortest path problem and constrained least absolute deviation problem are discussed with simulation results to demonstrate the effectiveness and characteristics of the proposed neural network.

A Hybrid Metaheuristic DE/CS Algorithm for UCAV Three-Dimension Path Planning

PubMed Central

Wang, Gaige; Guo, Lihong; Duan, Hong; Wang, Heqi; Liu, Luo; Shao, Mingzhen

2012-01-01

Three-dimension path planning for uninhabited combat air vehicle (UCAV) is a complicated high-dimension optimization problem, which primarily centralizes on optimizing the flight route considering the different kinds of constrains under complicated battle field environments. A new hybrid metaheuristic differential evolution (DE) and cuckoo search (CS) algorithm is proposed to solve the UCAV three-dimension path planning problem. DE is applied to optimize the process of selecting cuckoos of the improved CS model during the process of cuckoo updating in nest. The cuckoos can act as an agent in searching the optimal UCAV path. And then, the UCAV can find the safe path by connecting the chosen nodes of the coordinates while avoiding the threat areas and costing minimum fuel. This new approach can accelerate the global convergence speed while preserving the strong robustness of the basic CS. The realization procedure for this hybrid metaheuristic approach DE/CS is also presented. In order to make the optimized UCAV path more feasible, the B-Spline curve is adopted for smoothing the path. To prove the performance of this proposed hybrid metaheuristic method, it is compared with basic CS algorithm. The experiment shows that the proposed approach is more effective and feasible in UCAV three-dimension path planning than the basic CS model. PMID:23193383

A hybrid metaheuristic DE/CS algorithm for UCAV three-dimension path planning.

PubMed

Wang, Gaige; Guo, Lihong; Duan, Hong; Wang, Heqi; Liu, Luo; Shao, Mingzhen

2012-01-01

Three-dimension path planning for uninhabited combat air vehicle (UCAV) is a complicated high-dimension optimization problem, which primarily centralizes on optimizing the flight route considering the different kinds of constrains under complicated battle field environments. A new hybrid metaheuristic differential evolution (DE) and cuckoo search (CS) algorithm is proposed to solve the UCAV three-dimension path planning problem. DE is applied to optimize the process of selecting cuckoos of the improved CS model during the process of cuckoo updating in nest. The cuckoos can act as an agent in searching the optimal UCAV path. And then, the UCAV can find the safe path by connecting the chosen nodes of the coordinates while avoiding the threat areas and costing minimum fuel. This new approach can accelerate the global convergence speed while preserving the strong robustness of the basic CS. The realization procedure for this hybrid metaheuristic approach DE/CS is also presented. In order to make the optimized UCAV path more feasible, the B-Spline curve is adopted for smoothing the path. To prove the performance of this proposed hybrid metaheuristic method, it is compared with basic CS algorithm. The experiment shows that the proposed approach is more effective and feasible in UCAV three-dimension path planning than the basic CS model.

Improving the Air Mobility Command’s Air Refueler Route Building Capabilities

DTIC Science & Technology

2014-03-27

routing tool. Sundar and Rathinam [18] also study a traveling salesman version of the problem in the unmanned aerial vehicle realm. Their focus is on...constrained shortest path with fuel limitations. The objective is to minimize the distance traveled . Some aircraft routing problems involve...radius and network density their only limitations. 4 O’Rourke et al. [15] examine a traveling salesman version of aircraft routing in the unmanned aerial

The dynamics of search, impasse, and representational change provide a coherent explanation of difficulty in the nine-dot problem.

PubMed

Öllinger, Michael; Jones, Gary; Knoblich, Günther

2014-03-01

The nine-dot problem is often used to demonstrate and explain mental impasse, creativity, and out of the box thinking. The present study investigated the interplay of a restricted initial search space, the likelihood of invoking a representational change, and the subsequent constraining of an unrestricted search space. In three experimental conditions, participants worked on different versions of the nine-dot problem that hinted at removing particular sources of difficulty from the standard problem. The hints were incremental such that the first suggested a possible route for a solution attempt; the second additionally indicated the dot at which lines meet on the solution path; and the final condition also provided non-dot locations that appear in the solution path. The results showed that in the experimental conditions, representational change is encountered more quickly and problems are solved more often than for the control group. We propose a cognitive model that focuses on general problem-solving heuristics and representational change to explain problem difficulty.

Minimal entropy probability paths between genome families.

PubMed

Ahlbrandt, Calvin; Benson, Gary; Casey, William

2004-05-01

We develop a metric for probability distributions with applications to biological sequence analysis. Our distance metric is obtained by minimizing a functional defined on the class of paths over probability measures on N categories. The underlying mathematical theory is connected to a constrained problem in the calculus of variations. The solution presented is a numerical solution, which approximates the true solution in a set of cases called rich paths where none of the components of the path is zero. The functional to be minimized is motivated by entropy considerations, reflecting the idea that nature might efficiently carry out mutations of genome sequences in such a way that the increase in entropy involved in transformation is as small as possible. We characterize sequences by frequency profiles or probability vectors, in the case of DNA where N is 4 and the components of the probability vector are the frequency of occurrence of each of the bases A, C, G and T. Given two probability vectors a and b, we define a distance function based as the infimum of path integrals of the entropy function H( p) over all admissible paths p(t), 0 < or = t< or =1, with p(t) a probability vector such that p(0)=a and p(1)=b. If the probability paths p(t) are parameterized as y(s) in terms of arc length s and the optimal path is smooth with arc length L, then smooth and "rich" optimal probability paths may be numerically estimated by a hybrid method of iterating Newton's method on solutions of a two point boundary value problem, with unknown distance L between the abscissas, for the Euler-Lagrange equations resulting from a multiplier rule for the constrained optimization problem together with linear regression to improve the arc length estimate L. Matlab code for these numerical methods is provided which works only for "rich" optimal probability vectors. These methods motivate a definition of an elementary distance function which is easier and faster to calculate, works on non-rich vectors, does not involve variational theory and does not involve differential equations, but is a better approximation of the minimal entropy path distance than the distance //b-a//(2). We compute minimal entropy distance matrices for examples of DNA myostatin genes and amino-acid sequences across several species. Output tree dendograms for our minimal entropy metric are compared with dendograms based on BLAST and BLAST identity scores.

Sensor-Oriented Path Planning for Multiregion Surveillance with a Single Lightweight UAV SAR

PubMed Central

Li, Jincheng; Chen, Jie; Wang, Pengbo; Li, Chunsheng

2018-01-01

In the surveillance of interested regions by unmanned aerial vehicle (UAV), system performance relies greatly on the motion control strategy of the UAV and the operation characteristics of the onboard sensors. This paper investigates the 2D path planning problem for the lightweight UAV synthetic aperture radar (SAR) system in an environment of multiple regions of interest (ROIs), the sizes of which are comparable to the radar swath width. Taking into account the special requirements of the SAR system on the motion of the platform, we model path planning for UAV SAR as a constrained multiobjective optimization problem (MOP). Based on the fact that the UAV route can be designed in the map image, an image-based path planner is proposed in this paper. First, the neighboring ROIs are merged by the morphological operation. Then, the parts of routes for data collection of the ROIs can be located according to the geometric features of the ROIs and the observation geometry of UAV SAR. Lastly, the route segments for ROIs surveillance are connected by a path planning algorithm named the sampling-based sparse A* search (SSAS) algorithm. Simulation experiments in real scenarios demonstrate that the proposed sensor-oriented path planner can improve the reconnaissance performance of lightweight UAV SAR greatly compared with the conventional zigzag path planner. PMID:29439447

Sensor-Oriented Path Planning for Multiregion Surveillance with a Single Lightweight UAV SAR.

PubMed

Li, Jincheng; Chen, Jie; Wang, Pengbo; Li, Chunsheng

2018-02-11

In the surveillance of interested regions by unmanned aerial vehicle (UAV), system performance relies greatly on the motion control strategy of the UAV and the operation characteristics of the onboard sensors. This paper investigates the 2D path planning problem for the lightweight UAV synthetic aperture radar (SAR) system in an environment of multiple regions of interest (ROIs), the sizes of which are comparable to the radar swath width. Taking into account the special requirements of the SAR system on the motion of the platform, we model path planning for UAV SAR as a constrained multiobjective optimization problem (MOP). Based on the fact that the UAV route can be designed in the map image, an image-based path planner is proposed in this paper. First, the neighboring ROIs are merged by the morphological operation. Then, the parts of routes for data collection of the ROIs can be located according to the geometric features of the ROIs and the observation geometry of UAV SAR. Lastly, the route segments for ROIs surveillance are connected by a path planning algorithm named the sampling-based sparse A* search (SSAS) algorithm. Simulation experiments in real scenarios demonstrate that the proposed sensor-oriented path planner can improve the reconnaissance performance of lightweight UAV SAR greatly compared with the conventional zigzag path planner.

Dynamic discrete tomography

NASA Astrophysics Data System (ADS)

Alpers, Andreas; Gritzmann, Peter

2018-03-01

We consider the problem of reconstructing the paths of a set of points over time, where, at each of a finite set of moments in time the current positions of points in space are only accessible through some small number of their x-rays. This particular particle tracking problem, with applications, e.g. in plasma physics, is the basic problem in dynamic discrete tomography. We introduce and analyze various different algorithmic models. In particular, we determine the computational complexity of the problem (and various of its relatives) and derive algorithms that can be used in practice. As a byproduct we provide new results on constrained variants of min-cost flow and matching problems.

Quantum annealing of the traveling-salesman problem.

PubMed

Martonák, Roman; Santoro, Giuseppe E; Tosatti, Erio

2004-11-01

We propose a path-integral Monte Carlo quantum annealing scheme for the symmetric traveling-salesman problem, based on a highly constrained Ising-like representation, and we compare its performance against standard thermal simulated annealing. The Monte Carlo moves implemented are standard, and consist in restructuring a tour by exchanging two links (two-opt moves). The quantum annealing scheme, even with a drastically simple form of kinetic energy, appears definitely superior to the classical one, when tested on a 1002-city instance of the standard TSPLIB.

A New Understanding for the Rain Rate retrieval of Attenuating Radars Measurement

NASA Astrophysics Data System (ADS)

Koner, P.; Battaglia, A.; Simmer, C.

2009-04-01

The retrieval of rain rate from the attenuated radar (e.g. Cloud Profiling Radar on board of CloudSAT in orbit since June 2006) is a challenging problem. ĹEcuyer and Stephens [1] underlined this difficulty (for rain rates larger than 1.5 mm/h) and suggested the need of additional information (like path-integrated attenuations (PIA) derived from surface reference techniques or precipitation water path estimated from co-located passive microwave radiometer) to constrain the retrieval. It is generally discussed based on the optimal estimation theory that there are no solutions without constraining the problem in a case of visible attenuation because there is no enough information content to solve the problem. However, when the problem is constrained by the additional measurement of PIA, there is a reasonable solution. This raises the spontaneous question: Is all information enclosed in this additional measurement? This also contradicts with the information theory because one measurement can introduce only one degree of freedom in the retrieval. Why is one degree of freedom so important in the above problem? This question cannot be explained using the estimation and information theories of OEM. On the other hand, Koner and Drummond [2] argued that the OEM is basically a regularization method, where a-priori covariance is used as a stabilizer and the regularization strength is determined by the choices of the a-priori and error covariance matrices. The regularization is required for the reduction of the condition number of Jacobian, which drives the noise injection from the measurement and inversion spaces to the state space in an ill-posed inversion. In this work, the above mentioned question will be discussed based on the regularization theory, error mitigation and eigenvalue mathematics. References 1. L'Ecuyer TS and Stephens G. An estimation based precipitation retrieval algorithm for attenuating radar. J. Appl. Met., 2002, 41, 272-85. 2. Koner PK, Drummond JR. A comparison of regularization techniques for atmospheric trace gases retrievals. JQSRT 2008; 109:514-26.

Reflected stochastic differential equation models for constrained animal movement

USGS Publications Warehouse

Hanks, Ephraim M.; Johnson, Devin S.; Hooten, Mevin B.

2017-01-01

Movement for many animal species is constrained in space by barriers such as rivers, shorelines, or impassable cliffs. We develop an approach for modeling animal movement constrained in space by considering a class of constrained stochastic processes, reflected stochastic differential equations. Our approach generalizes existing methods for modeling unconstrained animal movement. We present methods for simulation and inference based on augmenting the constrained movement path with a latent unconstrained path and illustrate this augmentation with a simulation example and an analysis of telemetry data from a Steller sea lion (Eumatopias jubatus) in southeast Alaska.

Bridging the Gap: The 'Soft Path' for Improving Resilience and Adaptability of Water Systems (Invited)

NASA Astrophysics Data System (ADS)

Gleick, P. H.

2010-12-01

The failure of traditional water management systems in the 20th century -- what I call the "hard path for water" -- is evident in several ways, including the persistent inability to meet basic human needs for safe water and adequate sanitation for vast populations, ongoing and accelerating aquatic ecosystem collapses , and growing political disputes over water allocation, management, and use, even in regions where substantial investment in water has been made. Progress in resolving these problems, especially in the face of unavoidable climate changes, growing populations, and constrained financial systems, will require bridging hydrologic and social sciences in new ways. Integrating social and cultural knowledge with new economic and technological tools and classical hydrologic and climatological sciences can produce a new “soft path for water” that offers the opportunity to move toward sustainable water systems. This talk will define the soft path for water and offer examples of innovative steps already being taken along that path in the western United States, South Africa, India, and elsewhere.

Computational path planner for product assembly in complex environments

NASA Astrophysics Data System (ADS)

Shang, Wei; Liu, Jianhua; Ning, Ruxin; Liu, Mi

2013-03-01

Assembly path planning is a crucial problem in assembly related design and manufacturing processes. Sampling based motion planning algorithms are used for computational assembly path planning. However, the performance of such algorithms may degrade much in environments with complex product structure, narrow passages or other challenging scenarios. A computational path planner for automatic assembly path planning in complex 3D environments is presented. The global planning process is divided into three phases based on the environment and specific algorithms are proposed and utilized in each phase to solve the challenging issues. A novel ray test based stochastic collision detection method is proposed to evaluate the intersection between two polyhedral objects. This method avoids fake collisions in conventional methods and degrades the geometric constraint when a part has to be removed with surface contact with other parts. A refined history based rapidly-exploring random tree (RRT) algorithm which bias the growth of the tree based on its planning history is proposed and employed in the planning phase where the path is simple but the space is highly constrained. A novel adaptive RRT algorithm is developed for the path planning problem with challenging scenarios and uncertain environment. With extending values assigned on each tree node and extending schemes applied, the tree can adapts its growth to explore complex environments more efficiently. Experiments on the key algorithms are carried out and comparisons are made between the conventional path planning algorithms and the presented ones. The comparing results show that based on the proposed algorithms, the path planner can compute assembly path in challenging complex environments more efficiently and with higher success. This research provides the references to the study of computational assembly path planning under complex environments.

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

PubMed

Dasgupta, Rumpa; Yoon, Seokhoon

2017-04-01

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

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

PubMed Central

Dasgupta, Rumpa; Yoon, Seokhoon

2017-01-01

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

Optimization for Service Routes of Pallet Service Center Based on the Pallet Pool Mode

PubMed Central

He, Shiwei; Song, Rui

2016-01-01

Service routes optimization (SRO) of pallet service center should meet customers' demand firstly and then, through the reasonable method of lines organization, realize the shortest path of vehicle driving. The routes optimization of pallet service center is similar to the distribution problems of vehicle routing problem (VRP) and Chinese postman problem (CPP), but it has its own characteristics. Based on the relevant research results, the conditions of determining the number of vehicles, the one way of the route, the constraints of loading, and time windows are fully considered, and a chance constrained programming model with stochastic constraints is constructed taking the shortest path of all vehicles for a delivering (recycling) operation as an objective. For the characteristics of the model, a hybrid intelligent algorithm including stochastic simulation, neural network, and immune clonal algorithm is designed to solve the model. Finally, the validity and rationality of the optimization model and algorithm are verified by the case. PMID:27528865

Analysis of explicit model predictive control for path-following control

PubMed Central

2018-01-01

In this paper, explicit Model Predictive Control(MPC) is employed for automated lane-keeping systems. MPC has been regarded as the key to handle such constrained systems. However, the massive computational complexity of MPC, which employs online optimization, has been a major drawback that limits the range of its target application to relatively small and/or slow problems. Explicit MPC can reduce this computational burden using a multi-parametric quadratic programming technique(mp-QP). The control objective is to derive an optimal front steering wheel angle at each sampling time so that autonomous vehicles travel along desired paths, including straight, circular, and clothoid parts, at high entry speeds. In terms of the design of the proposed controller, a method of choosing weighting matrices in an optimization problem and the range of horizons for path-following control are described through simulations. For the verification of the proposed controller, simulation results obtained using other control methods such as MPC, Linear-Quadratic Regulator(LQR), and driver model are employed, and CarSim, which reflects the features of a vehicle more realistically than MATLAB/Simulink, is used for reliable demonstration. PMID:29534080

Analysis of explicit model predictive control for path-following control.

PubMed

Lee, Junho; Chang, Hyuk-Jun

2018-01-01

In this paper, explicit Model Predictive Control(MPC) is employed for automated lane-keeping systems. MPC has been regarded as the key to handle such constrained systems. However, the massive computational complexity of MPC, which employs online optimization, has been a major drawback that limits the range of its target application to relatively small and/or slow problems. Explicit MPC can reduce this computational burden using a multi-parametric quadratic programming technique(mp-QP). The control objective is to derive an optimal front steering wheel angle at each sampling time so that autonomous vehicles travel along desired paths, including straight, circular, and clothoid parts, at high entry speeds. In terms of the design of the proposed controller, a method of choosing weighting matrices in an optimization problem and the range of horizons for path-following control are described through simulations. For the verification of the proposed controller, simulation results obtained using other control methods such as MPC, Linear-Quadratic Regulator(LQR), and driver model are employed, and CarSim, which reflects the features of a vehicle more realistically than MATLAB/Simulink, is used for reliable demonstration.

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

NASA Astrophysics Data System (ADS)

Kovtunenko, V. A.

2006-10-01

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

Joint Chance-Constrained Dynamic Programming

NASA Technical Reports Server (NTRS)

Ono, Masahiro; Kuwata, Yoshiaki; Balaram, J. Bob

2012-01-01

This paper presents a novel dynamic programming algorithm with a joint chance constraint, which explicitly bounds the risk of failure in order to maintain the state within a specified feasible region. A joint chance constraint cannot be handled by existing constrained dynamic programming approaches since their application is limited to constraints in the same form as the cost function, that is, an expectation over a sum of one-stage costs. We overcome this challenge by reformulating the joint chance constraint into a constraint on an expectation over a sum of indicator functions, which can be incorporated into the cost function by dualizing the optimization problem. As a result, the primal variables can be optimized by a standard dynamic programming, while the dual variable is optimized by a root-finding algorithm that converges exponentially. Error bounds on the primal and dual objective values are rigorously derived. We demonstrate the algorithm on a path planning problem, as well as an optimal control problem for Mars entry, descent and landing. The simulations are conducted using a real terrain data of Mars, with four million discrete states at each time step.

Transversely-illuminated high current photoconductive switches with geometry-constrained conductivity path

DOEpatents

Nelson, Scott D.

2016-05-10

A photoconductive switch having a wide bandgap semiconductor material substrate between opposing electrodes, with one of the electrodes having an aperture or apertures at an electrode-substrate interface for transversely directing radiation therethrough from a radiation source into a triple junction region of the substrate, so as to geometrically constrain the conductivity path to within the triple junction region.

Rough-Cut Capacity Planning in Multimodal Freight Transportation Networks

DTIC Science & Technology

2012-09-30

transportation system to losses in es - tablished routes or assets? That is, what is the nature and length of system capability degradation due to these...Multimodal Rough-Cut Capacity Planning is mod- eled using the Resource Constrained Shortest Path Problem. We demonstrate how this approach supports...of non-zero ele - ments and the 0 entries depict appropriately dimensioned blocks of 0 entries.∣∣∣∣∑ k Ck ∣∣∣∣ Σ 0 0 0 0 Σ 0 0

A Sequential Linear Quadratic Approach for Constrained Nonlinear Optimal Control with Adaptive Time Discretization and Application to Higher Elevation Mars Landing Problem

NASA Astrophysics Data System (ADS)

Sandhu, Amit

A sequential quadratic programming method is proposed for solving nonlinear optimal control problems subject to general path constraints including mixed state-control and state only constraints. The proposed algorithm further develops on the approach proposed in [1] with objective to eliminate the use of a high number of time intervals for arriving at an optimal solution. This is done by introducing an adaptive time discretization to allow formation of a desirable control profile without utilizing a lot of intervals. The use of fewer time intervals reduces the computation time considerably. This algorithm is further used in this thesis to solve a trajectory planning problem for higher elevation Mars landing.

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

Bromberger, Seth A.; Klymko, Christine F.; Henderson, Keith A.

Betweenness centrality is a graph statistic used to nd vertices that are participants in a large number of shortest paths in a graph. This centrality measure is commonly used in path and network interdiction problems and its complete form requires the calculation of all-pairs shortest paths for each vertex. This leads to a time complexity of O(jV jjEj), which is impractical for large graphs. Estimation of betweenness centrality has focused on performing shortest-path calculations on a subset of randomly- selected vertices. This reduces the complexity of the centrality estimation to O(jSjjEj); jSj < jV j, which can be scaled appropriatelymore » based on the computing resources available. An estimation strategy that uses random selection of vertices for seed selection is fast and simple to implement, but may not provide optimal estimation of betweenness centrality when the number of samples is constrained. Our experimentation has identi ed a number of alternate seed-selection strategies that provide lower error than random selection in common scale-free graphs. These strategies are discussed and experimental results are presented.« less

Explaining evolution via constrained persistent perfect phylogeny

PubMed Central

2014-01-01

Background The perfect phylogeny is an often used model in phylogenetics since it provides an efficient basic procedure for representing the evolution of genomic binary characters in several frameworks, such as for example in haplotype inference. The model, which is conceptually the simplest, is based on the infinite sites assumption, that is no character can mutate more than once in the whole tree. A main open problem regarding the model is finding generalizations that retain the computational tractability of the original model but are more flexible in modeling biological data when the infinite site assumption is violated because of e.g. back mutations. A special case of back mutations that has been considered in the study of the evolution of protein domains (where a domain is acquired and then lost) is persistency, that is the fact that a character is allowed to return back to the ancestral state. In this model characters can be gained and lost at most once. In this paper we consider the computational problem of explaining binary data by the Persistent Perfect Phylogeny model (referred as PPP) and for this purpose we investigate the problem of reconstructing an evolution where some constraints are imposed on the paths of the tree. Results We define a natural generalization of the PPP problem obtained by requiring that for some pairs (character, species), neither the species nor any of its ancestors can have the character. In other words, some characters cannot be persistent for some species. This new problem is called Constrained PPP (CPPP). Based on a graph formulation of the CPPP problem, we are able to provide a polynomial time solution for the CPPP problem for matrices whose conflict graph has no edges. Using this result, we develop a parameterized algorithm for solving the CPPP problem where the parameter is the number of characters. Conclusions A preliminary experimental analysis shows that the constrained persistent perfect phylogeny model allows to explain efficiently data that do not conform with the classical perfect phylogeny model. PMID:25572381

The Use of Non-Standard Devices in Finite Element Analysis

NASA Technical Reports Server (NTRS)

Schur, Willi W.; Broduer, Steve (Technical Monitor)

2001-01-01

A general mathematical description of the response behavior of thin-skin pneumatic envelopes and many other membrane and cable structures produces under-constrained systems that pose severe difficulties to analysis. These systems are mobile, and the general mathematical description exposes the mobility. Yet the response behavior of special under-constrained structures under special loadings can be accurately predicted using a constrained mathematical description. The static response behavior of systems that are infinitesimally mobile, such as a non-slack membrane subtended from a rigid or elastic boundary frame, can be easily analyzed using such general mathematical description as afforded by the non-linear, finite element method using an implicit solution scheme if the incremental uploading is guided through a suitable path. Similarly, if such structures are assembled with structural lack of fit that provides suitable self-stress, then dynamic response behavior can be predicted by the non-linear, finite element method and an implicit solution scheme. An explicit solution scheme is available for evolution problems. Such scheme can be used via the method of dynamic relaxation to obtain the solution to a static problem. In some sense, pneumatic envelopes and many other compliant structures can be said to have destiny under a specified loading system. What that means to the analyst is that what happens on the evolution path of the solution is irrelevant as long as equilibrium is achieved at destiny under full load and that the equilibrium is stable in the vicinity of that load. The purpose of this paper is to alert practitioners to the fact that non-standard procedures in finite element analysis are useful and can be legitimate although they burden their users with the requirement to use special caution. Some interesting findings that are useful to the US Scientific Balloon Program and that could not be obtained without non-standard techniques are presented.

Open-path FTIR data reduction algorithm with atmospheric absorption corrections: the NONLIN code

NASA Astrophysics Data System (ADS)

Phillips, William; Russwurm, George M.

1999-02-01

This paper describes the progress made to date in developing, testing, and refining a data reduction computer code, NONLIN, that alleviates many of the difficulties experienced in the analysis of open path FTIR data. Among the problems that currently effect FTIR open path data quality are: the inability to obtain a true I degree or background, spectral interferences of atmospheric gases such as water vapor and carbon dioxide, and matching the spectral resolution and shift of the reference spectra to a particular field instrument. This algorithm is based on a non-linear fitting scheme and is therefore not constrained by many of the assumptions required for the application of linear methods such as classical least squares (CLS). As a result, a more realistic mathematical model of the spectral absorption measurement process can be employed in the curve fitting process. Applications of the algorithm have proven successful in circumventing open path data reduction problems. However, recent studies, by one of the authors, of the temperature and pressure effects on atmospheric absorption indicate there exist temperature and water partial pressure effects that should be incorporated into the NONLIN algorithm for accurate quantification of gas concentrations. This paper investigates the sources of these phenomena. As a result of this study a partial pressure correction has been employed in NONLIN computer code. Two typical field spectra are examined to determine what effect the partial pressure correction has on gas quantification.

Development and demonstration of an on-board mission planner for helicopters

NASA Technical Reports Server (NTRS)

Deutsch, Owen L.; Desai, Mukund

1988-01-01

Mission management tasks can be distributed within a planning hierarchy, where each level of the hierarchy addresses a scope of action, and associated time scale or planning horizon, and requirements for plan generation response time. The current work is focused on the far-field planning subproblem, with a scope and planning horizon encompassing the entire mission and with a response time required to be about two minutes. The far-feld planning problem is posed as a constrained optimization problem and algorithms and structural organizations are proposed for the solution. Algorithms are implemented in a developmental environment, and performance is assessed with respect to optimality and feasibility for the intended application and in comparison with alternative algorithms. This is done for the three major components of far-field planning: goal planning, waypoint path planning, and timeline management. It appears feasible to meet performance requirements on a 10 Mips flyable processor (dedicated to far-field planning) using a heuristically-guided simulated annealing technique for the goal planner, a modified A* search for the waypoint path planner, and a speed scheduling technique developed for this project.

The fastclime Package for Linear Programming and Large-Scale Precision Matrix Estimation in R.

PubMed

Pang, Haotian; Liu, Han; Vanderbei, Robert

2014-02-01

We develop an R package fastclime for solving a family of regularized linear programming (LP) problems. Our package efficiently implements the parametric simplex algorithm, which provides a scalable and sophisticated tool for solving large-scale linear programs. As an illustrative example, one use of our LP solver is to implement an important sparse precision matrix estimation method called CLIME (Constrained L 1 Minimization Estimator). Compared with existing packages for this problem such as clime and flare, our package has three advantages: (1) it efficiently calculates the full piecewise-linear regularization path; (2) it provides an accurate dual certificate as stopping criterion; (3) it is completely coded in C and is highly portable. This package is designed to be useful to statisticians and machine learning researchers for solving a wide range of problems.

Constrained motion estimation-based error resilient coding for HEVC

NASA Astrophysics Data System (ADS)

Guo, Weihan; Zhang, Yongfei; Li, Bo

2018-04-01

Unreliable communication channels might lead to packet losses and bit errors in the videos transmitted through it, which will cause severe video quality degradation. This is even worse for HEVC since more advanced and powerful motion estimation methods are introduced to further remove the inter-frame dependency and thus improve the coding efficiency. Once a Motion Vector (MV) is lost or corrupted, it will cause distortion in the decoded frame. More importantly, due to motion compensation, the error will propagate along the motion prediction path, accumulate over time, and significantly degrade the overall video presentation quality. To address this problem, we study the problem of encoder-sider error resilient coding for HEVC and propose a constrained motion estimation scheme to mitigate the problem of error propagation to subsequent frames. The approach is achieved by cutting off MV dependencies and limiting the block regions which are predicted by temporal motion vector. The experimental results show that the proposed method can effectively suppress the error propagation caused by bit errors of motion vector and can improve the robustness of the stream in the bit error channels. When the bit error probability is 10-5, an increase of the decoded video quality (PSNR) by up to1.310dB and on average 0.762 dB can be achieved, compared to the reference HEVC.

Real-time fuzzy inference based robot path planning

NASA Technical Reports Server (NTRS)

Pacini, Peter J.; Teichrow, Jon S.

1990-01-01

This project addresses the problem of adaptive trajectory generation for a robot arm. Conventional trajectory generation involves computing a path in real time to minimize a performance measure such as expended energy. This method can be computationally intensive, and it may yield poor results if the trajectory is weakly constrained. Typically some implicit constraints are known, but cannot be encoded analytically. The alternative approach used here is to formulate domain-specific knowledge, including implicit and ill-defined constraints, in terms of fuzzy rules. These rules utilize linguistic terms to relate input variables to output variables. Since the fuzzy rulebase is determined off-line, only high-level, computationally light processing is required in real time. Potential applications for adaptive trajectory generation include missile guidance and various sophisticated robot control tasks, such as automotive assembly, high speed electrical parts insertion, stepper alignment, and motion control for high speed parcel transfer systems.

In Praise of Ignorance

ERIC Educational Resources Information Center

Formica, Piero

2014-01-01

In this article Piero Formica examines the difference between incremental and revolutionary innovation, distinguishing between the constrained "path finders" and the unconstrained "path creators". He argues that an acceptance of "ignorance" and a willingness to venture into the unknown are critical elements in…

Planning maximally smooth hand movements constrained to nonplanar workspaces.

PubMed

Liebermann, Dario G; Krasovsky, Tal; Berman, Sigal

2008-11-01

The article characterizes hand paths and speed profiles for movements performed in a nonplanar, 2-dimensional workspace (a hemisphere of constant curvature). The authors assessed endpoint kinematics (i.e., paths and speeds) under the minimum-jerk model assumptions and calculated minimal amplitude paths (geodesics) and the corresponding speed profiles. The authors also calculated hand speeds using the 2/3 power law. They then compared modeled results with the empirical observations. In all, 10 participants moved their hands forward and backward from a common starting position toward 3 targets located within a hemispheric workspace of small or large curvature. Comparisons of modeled observed differences using 2-way RM-ANOVAs showed that movement direction had no clear influence on hand kinetics (p < .05). Workspace curvature affected the hand paths, which seldom followed geodesic lines. Constraining the paths to different curvatures did not affect the hand speed profiles. Minimum-jerk speed profiles closely matched the observations and were superior to those predicted by 2/3 power law (p < .001). The authors conclude that speed and path cannot be unambiguously linked under the minimum-jerk assumption when individuals move the hand in a nonplanar 2-dimensional workspace. In such a case, the hands do not follow geodesic paths, but they preserve the speed profile, regardless of the geometric features of the workspace.

Air Vehicle Path Planning

DTIC Science & Technology

2001-12-13

6-18 6.13. Apollonius Circle for the Case of Two Unequal Power Radars . . . 6-20 6.14. Solution Triangle...Voronoi edge is an Apollonius circle [32, 19]. In this section, we are concerned with the optimality of the Voronoi path for the two radar exposure...Comparison of Cost vs. Path Length for Constrained Trajectories Around and Between Two Radars 6-18 from the two radars is an Apollonius circle

Constrained motion model of mobile robots and its applications.

PubMed

Zhang, Fei; Xi, Yugeng; Lin, Zongli; Chen, Weidong

2009-06-01

Target detecting and dynamic coverage are fundamental tasks in mobile robotics and represent two important features of mobile robots: mobility and perceptivity. This paper establishes the constrained motion model and sensor model of a mobile robot to represent these two features and defines the k -step reachable region to describe the states that the robot may reach. We show that the calculation of the k-step reachable region can be reduced from that of 2(k) reachable regions with the fixed motion styles to k + 1 such regions and provide an algorithm for its calculation. Based on the constrained motion model and the k -step reachable region, the problems associated with target detecting and dynamic coverage are formulated and solved. For target detecting, the k-step detectable region is used to describe the area that the robot may detect, and an algorithm for detecting a target and planning the optimal path is proposed. For dynamic coverage, the k-step detected region is used to represent the area that the robot has detected during its motion, and the dynamic-coverage strategy and algorithm are proposed. Simulation results demonstrate the efficiency of the coverage algorithm in both convex and concave environments.

Improved multi-objective ant colony optimization algorithm and its application in complex reasoning

NASA Astrophysics Data System (ADS)

Wang, Xinqing; Zhao, Yang; Wang, Dong; Zhu, Huijie; Zhang, Qing

2013-09-01

The problem of fault reasoning has aroused great concern in scientific and engineering fields. However, fault investigation and reasoning of complex system is not a simple reasoning decision-making problem. It has become a typical multi-constraint and multi-objective reticulate optimization decision-making problem under many influencing factors and constraints. So far, little research has been carried out in this field. This paper transforms the fault reasoning problem of complex system into a paths-searching problem starting from known symptoms to fault causes. Three optimization objectives are considered simultaneously: maximum probability of average fault, maximum average importance, and minimum average complexity of test. Under the constraints of both known symptoms and the causal relationship among different components, a multi-objective optimization mathematical model is set up, taking minimizing cost of fault reasoning as the target function. Since the problem is non-deterministic polynomial-hard(NP-hard), a modified multi-objective ant colony algorithm is proposed, in which a reachability matrix is set up to constrain the feasible search nodes of the ants and a new pseudo-random-proportional rule and a pheromone adjustment mechinism are constructed to balance conflicts between the optimization objectives. At last, a Pareto optimal set is acquired. Evaluation functions based on validity and tendency of reasoning paths are defined to optimize noninferior set, through which the final fault causes can be identified according to decision-making demands, thus realize fault reasoning of the multi-constraint and multi-objective complex system. Reasoning results demonstrate that the improved multi-objective ant colony optimization(IMACO) can realize reasoning and locating fault positions precisely by solving the multi-objective fault diagnosis model, which provides a new method to solve the problem of multi-constraint and multi-objective fault diagnosis and reasoning of complex system.

Socioeconomic Influences on the Educational and Career Paths of Kentucky High School Seniors.

ERIC Educational Resources Information Center

Rever, Philip R.; And Others

The report is a response to Kentucky Higher Education Assistance Authority's concern about limiting factors which constrain students' choices of paths to follow after leaving high school, specifically unacceptable factors that prohibit students (1) from pursuing postsecondary education and (2) from entering postsecondary institutions of their…

NEUTRON SOURCE

DOEpatents

Foster, J.S. Jr.

1960-04-19

A compact electronic device capable of providing short time high density outputs of neutrons is described. The device of the invention includes an evacuated vacuum housing adapted to be supplied with a deuterium, tritium, or other atmosphere and means for establishing an electrical discharge along a path through the gas. An energized solenoid is arranged to constrain the ionized gas (plasma) along the path. An anode bearing adsorbed or adherent target material is arranged to enclose the constrained plasma. To produce neutrons a high voltage is applied from appropriate supply means between the plasma and anode to accelerate ions from the plasma to impinge upcn the target material, e.g., comprising deuterium.

The Thinnest Path Problem

DTIC Science & Technology

2016-07-22

their corresponding transmission powers . At first glance, one may wonder whether the thinnest path problem is simply a shortest path problem with the...nature of the shortest path problem. Another aspect that complicates the problem is the choice of the transmission power at each node (within a maximum...fixed transmission power at each node (in this case, the resulting hypergraph degenerates to a standard graph), the thinnest path problem is NP

Motion on Cycloid Paths: A Project

ERIC Educational Resources Information Center

Gluck, P.

2010-01-01

This article reports a high school laboratory project whose theme is the motion of a small ball on cycloidal tracks. Models were built both of a brachistochrone and of a Huygens pendulum clock whose bob is constrained to move on a cycloidal path. Photogates and a data acquisition system were employed in order to investigate experimentally the…

A Reactive-Transport Model Describing Methanogen Growth and Methane Production in Diffuse Flow Vents at Axial Seamount

NASA Astrophysics Data System (ADS)

Algar, C. K.

2015-12-01

Hydrogenotrophic methanogenesis is an important mode of metabolism in deep-sea hydrothermal vents. Diffuse vent fluids often show a depletion in hydrogen with a corresponding increase in methane relative to pure-mixing of end member fluid and seawater, and genomic surveys show an enrichment in genetic sequences associated with known methanogens. However, because we cannot directly sample the subseafloor habitat where these organisms are living, constraining the size and activity of these populations remains a challenge and limits our ability to quantify the role they play in vent biogeochemistry. Reactive-transport modeling may provide a useful tool for approaching this problem. Here we present a reactive-transport model describing methane production along the flow-path of hydrothermal fluid from its high temperature end-member to diffuse venting at the seafloor. The model is set up to reflect conditions at several diffuse vents in the Axial Seamount. The model describes the growth of the two dominant thermophilic methanogens, Methanothermococcus and Methanocaldococcus, observed at Axial seamount. Monod and Arrhenius constants for Methanothermococcus thermolithotrophicus and Methanocaldococcus jannaschii were obtained for the model using chemostat and bottle experiments at varying temperatures. The model is used to investigate the influence of different mixing regimes on the subseafloor populations of these methanogens. By varying the model flow path length and subseafloor cell concentrations, and fitting to observed hydrogen and methane concentrations in the venting fluid, the subseafloor biomass, fluid residence time, and methane production rate can be constrained.

Optimal Power Allocation Strategy in a Joint Bistatic Radar and Communication System Based on Low Probability of Intercept

PubMed Central

Wang, Fei; Salous, Sana; Zhou, Jianjiang

2017-01-01

In this paper, we investigate a low probability of intercept (LPI)-based optimal power allocation strategy for a joint bistatic radar and communication system, which is composed of a dedicated transmitter, a radar receiver, and a communication receiver. The joint system is capable of fulfilling the requirements of both radar and communications simultaneously. First, assuming that the signal-to-noise ratio (SNR) corresponding to the target surveillance path is much weaker than that corresponding to the line of sight path at radar receiver, the analytically closed-form expression for the probability of false alarm is calculated, whereas the closed-form expression for the probability of detection is not analytically tractable and is approximated due to the fact that the received signals are not zero-mean Gaussian under target presence hypothesis. Then, an LPI-based optimal power allocation strategy is presented to minimize the total transmission power for information signal and radar waveform, which is constrained by a specified information rate for the communication receiver and the desired probabilities of detection and false alarm for the radar receiver. The well-known bisection search method is employed to solve the resulting constrained optimization problem. Finally, numerical simulations are provided to reveal the effects of several system parameters on the power allocation results. It is also demonstrated that the LPI performance of the joint bistatic radar and communication system can be markedly improved by utilizing the proposed scheme. PMID:29186850

Optimal Power Allocation Strategy in a Joint Bistatic Radar and Communication System Based on Low Probability of Intercept.

PubMed

Shi, Chenguang; Wang, Fei; Salous, Sana; Zhou, Jianjiang

2017-11-25

In this paper, we investigate a low probability of intercept (LPI)-based optimal power allocation strategy for a joint bistatic radar and communication system, which is composed of a dedicated transmitter, a radar receiver, and a communication receiver. The joint system is capable of fulfilling the requirements of both radar and communications simultaneously. First, assuming that the signal-to-noise ratio (SNR) corresponding to the target surveillance path is much weaker than that corresponding to the line of sight path at radar receiver, the analytically closed-form expression for the probability of false alarm is calculated, whereas the closed-form expression for the probability of detection is not analytically tractable and is approximated due to the fact that the received signals are not zero-mean Gaussian under target presence hypothesis. Then, an LPI-based optimal power allocation strategy is presented to minimize the total transmission power for information signal and radar waveform, which is constrained by a specified information rate for the communication receiver and the desired probabilities of detection and false alarm for the radar receiver. The well-known bisection search method is employed to solve the resulting constrained optimization problem. Finally, numerical simulations are provided to reveal the effects of several system parameters on the power allocation results. It is also demonstrated that the LPI performance of the joint bistatic radar and communication system can be markedly improved by utilizing the proposed scheme.

On the formation of granulites

USGS Publications Warehouse

Bohlen, S.R.

1991-01-01

The tectonic settings for the formation and evolution of regional granulite terranes and the lowermost continental crust can be deduced from pressure-temperature-time (P-T-time) paths and constrained by petrological and geophysical considerations. P-T conditions deduced for regional granulites require transient, average geothermal gradients of greater than 35??C km-1, implying minimum heat flow in excess of 100 mW m-2. Such high heat flow is probably caused by magmatic heating. Tectonic settings wherein such conditions are found include convergent plate margins, continental rifts, hot spots and at the margins of large, deep-seated batholiths. Cooling paths can be constrained by solid-solid and devolatilization equilibria and geophysical modelling. -from Author

Multi Robot Path Planning for Budgeted Active Perception with Self-Organising Maps

DTIC Science & Technology

2016-10-04

Multi- Robot Path Planning for Budgeted Active Perception with Self-Organising Maps Graeme Best1, Jan Faigl2 and Robert Fitch1 Abstract— We propose a...optimise paths for a multi- robot team that aims to maximally observe a set of nodes in the environment. The selected nodes are observed by visiting...regions, each node has an observation reward, and the robots are constrained by travel budgets. The SOM algorithm jointly selects and allocates nodes

Momentum-weighted conjugate gradient descent algorithm for gradient coil optimization.

PubMed

Lu, Hanbing; Jesmanowicz, Andrzej; Li, Shi-Jiang; Hyde, James S

2004-01-01

MRI gradient coil design is a type of nonlinear constrained optimization. A practical problem in transverse gradient coil design using the conjugate gradient descent (CGD) method is that wire elements move at different rates along orthogonal directions (r, phi, z), and tend to cross, breaking the constraints. A momentum-weighted conjugate gradient descent (MW-CGD) method is presented to overcome this problem. This method takes advantage of the efficiency of the CGD method combined with momentum weighting, which is also an intrinsic property of the Levenberg-Marquardt algorithm, to adjust step sizes along the three orthogonal directions. A water-cooled, 12.8 cm inner diameter, three axis torque-balanced gradient coil for rat imaging was developed based on this method, with an efficiency of 2.13, 2.08, and 4.12 mT.m(-1).A(-1) along X, Y, and Z, respectively. Experimental data demonstrate that this method can improve efficiency by 40% and field uniformity by 27%. This method has also been applied to the design of a gradient coil for the human brain, employing remote current return paths. The benefits of this design include improved gradient field uniformity and efficiency, with a shorter length than gradient coil designs using coaxial return paths. Copyright 2003 Wiley-Liss, Inc.

CPMC-Lab: A MATLAB package for Constrained Path Monte Carlo calculations

NASA Astrophysics Data System (ADS)

Nguyen, Huy; Shi, Hao; Xu, Jie; Zhang, Shiwei

2014-12-01

We describe CPMC-Lab, a MATLAB program for the constrained-path and phaseless auxiliary-field Monte Carlo methods. These methods have allowed applications ranging from the study of strongly correlated models, such as the Hubbard model, to ab initio calculations in molecules and solids. The present package implements the full ground-state constrained-path Monte Carlo (CPMC) method in MATLAB with a graphical interface, using the Hubbard model as an example. The package can perform calculations in finite supercells in any dimensions, under periodic or twist boundary conditions. Importance sampling and all other algorithmic details of a total energy calculation are included and illustrated. This open-source tool allows users to experiment with various model and run parameters and visualize the results. It provides a direct and interactive environment to learn the method and study the code with minimal overhead for setup. Furthermore, the package can be easily generalized for auxiliary-field quantum Monte Carlo (AFQMC) calculations in many other models for correlated electron systems, and can serve as a template for developing a production code for AFQMC total energy calculations in real materials. Several illustrative studies are carried out in one- and two-dimensional lattices on total energy, kinetic energy, potential energy, and charge- and spin-gaps.

Constrained VPH+: a local path planning algorithm for a bio-inspired crawling robot with customized ultrasonic scanning sensor.

PubMed

Rao, Akshay; Elara, Mohan Rajesh; Elangovan, Karthikeyan

This paper aims to develop a local path planning algorithm for a bio-inspired, reconfigurable crawling robot. A detailed description of the robotic platform is first provided, and the suitability for deployment of each of the current state-of-the-art local path planners is analyzed after an extensive literature review. The Enhanced Vector Polar Histogram algorithm is described and reformulated to better fit the requirements of the platform. The algorithm is deployed on the robotic platform in crawling configuration and favorably compared with other state-of-the-art local path planning algorithms.

Polynomial algorithms for the Maximal Pairing Problem: efficient phylogenetic targeting on arbitrary trees

PubMed Central

2010-01-01

Background The Maximal Pairing Problem (MPP) is the prototype of a class of combinatorial optimization problems that are of considerable interest in bioinformatics: Given an arbitrary phylogenetic tree T and weights ωxy for the paths between any two pairs of leaves (x, y), what is the collection of edge-disjoint paths between pairs of leaves that maximizes the total weight? Special cases of the MPP for binary trees and equal weights have been described previously; algorithms to solve the general MPP are still missing, however. Results We describe a relatively simple dynamic programming algorithm for the special case of binary trees. We then show that the general case of multifurcating trees can be treated by interleaving solutions to certain auxiliary Maximum Weighted Matching problems with an extension of this dynamic programming approach, resulting in an overall polynomial-time solution of complexity (n4 log n) w.r.t. the number n of leaves. The source code of a C implementation can be obtained under the GNU Public License from http://www.bioinf.uni-leipzig.de/Software/Targeting. For binary trees, we furthermore discuss several constrained variants of the MPP as well as a partition function approach to the probabilistic version of the MPP. Conclusions The algorithms introduced here make it possible to solve the MPP also for large trees with high-degree vertices. This has practical relevance in the field of comparative phylogenetics and, for example, in the context of phylogenetic targeting, i.e., data collection with resource limitations. PMID:20525185

A study of snake-like locomotion through the analysis of a flexible robot model

PubMed Central

Cicconofri, Giancarlo; DeSimone, Antonio

2015-01-01

We examine the problem of snake-like locomotion by studying a system consisting of a planar inextensible elastic rod with adjustable spontaneous curvature, which provides an internal actuation mechanism that mimics muscular action in a snake. Using a Cosserat model, we derive the equations of motion in two special cases: one in which the rod can only move along a prescribed curve, and one in which the rod is constrained to slide longitudinally without slipping laterally, but the path is not fixed a priori (free-path case). The second setting is inspired by undulatory locomotion of snakes on flat surfaces. The presence of constraints leads in both cases to non-standard boundary conditions that allow us to close and solve the equations of motion. The kinematics and dynamics of the system can be recovered from a one-dimensional equation, without any restrictive assumption on the followed trajectory or the actuation. We derive explicit formulae highlighting the role of spontaneous curvature in providing the driving force (and the steering, in the free-path case) needed for locomotion. We also provide analytical solutions for a special class of serpentine motions, which enable us to discuss the connection between observed trajectories, internal actuation and forces exchanged with the environment. PMID:26807040

Singular perturbation analysis of AOTV-related trajectory optimization problems

NASA Technical Reports Server (NTRS)

Calise, Anthony J.; Bae, Gyoung H.

1990-01-01

The problem of real time guidance and optimal control of Aeroassisted Orbit Transfer Vehicles (AOTV's) was addressed using singular perturbation theory as an underlying method of analysis. Trajectories were optimized with the objective of minimum energy expenditure in the atmospheric phase of the maneuver. Two major problem areas were addressed: optimal reentry, and synergetic plane change with aeroglide. For the reentry problem, several reduced order models were analyzed with the objective of optimal changes in heading with minimum energy loss. It was demonstrated that a further model order reduction to a single state model is possible through the application of singular perturbation theory. The optimal solution for the reduced problem defines an optimal altitude profile dependent on the current energy level of the vehicle. A separate boundary layer analysis is used to account for altitude and flight path angle dynamics, and to obtain lift and bank angle control solutions. By considering alternative approximations to solve the boundary layer problem, three guidance laws were derived, each having an analytic feedback form. The guidance laws were evaluated using a Maneuvering Reentry Research Vehicle model and all three laws were found to be near optimal. For the problem of synergetic plane change with aeroglide, a difficult terminal boundary layer control problem arises which to date is found to be analytically intractable. Thus a predictive/corrective solution was developed to satisfy the terminal constraints on altitude and flight path angle. A composite guidance solution was obtained by combining the optimal reentry solution with the predictive/corrective guidance method. Numerical comparisons with the corresponding optimal trajectory solutions show that the resulting performance is very close to optimal. An attempt was made to obtain numerically optimized trajectories for the case where heating rate is constrained. A first order state variable inequality constraint was imposed on the full order AOTV point mass equations of motion, using a simple aerodynamic heating rate model.

An Anatomically Constrained Model for Path Integration in the Bee Brain.

PubMed

Stone, Thomas; Webb, Barbara; Adden, Andrea; Weddig, Nicolai Ben; Honkanen, Anna; Templin, Rachel; Wcislo, William; Scimeca, Luca; Warrant, Eric; Heinze, Stanley

2017-10-23

Path integration is a widespread navigational strategy in which directional changes and distance covered are continuously integrated on an outward journey, enabling a straight-line return to home. Bees use vision for this task-a celestial-cue-based visual compass and an optic-flow-based visual odometer-but the underlying neural integration mechanisms are unknown. Using intracellular electrophysiology, we show that polarized-light-based compass neurons and optic-flow-based speed-encoding neurons converge in the central complex of the bee brain, and through block-face electron microscopy, we identify potential integrator cells. Based on plausible output targets for these cells, we propose a complete circuit for path integration and steering in the central complex, with anatomically identified neurons suggested for each processing step. The resulting model circuit is thus fully constrained biologically and provides a functional interpretation for many previously unexplained architectural features of the central complex. Moreover, we show that the receptive fields of the newly discovered speed neurons can support path integration for the holonomic motion (i.e., a ground velocity that is not precisely aligned with body orientation) typical of bee flight, a feature not captured in any previously proposed model of path integration. In a broader context, the model circuit presented provides a general mechanism for producing steering signals by comparing current and desired headings-suggesting a more basic function for central complex connectivity, from which path integration may have evolved. Copyright © 2017 Elsevier Ltd. All rights reserved.

Resilient Wireless Sensor Networks Using Topology Control: A Review

PubMed Central

Huang, Yuanjiang; Martínez, José-Fernán; Sendra, Juana; López, Lourdes

2015-01-01

Wireless sensor networks (WSNs) may be deployed in failure-prone environments, and WSNs nodes easily fail due to unreliable wireless connections, malicious attacks and resource-constrained features. Nevertheless, if WSNs can tolerate at most losing k − 1 nodes while the rest of nodes remain connected, the network is called k − connected. k is one of the most important indicators for WSNs’ self-healing capability. Following a WSN design flow, this paper surveys resilience issues from the topology control and multi-path routing point of view. This paper provides a discussion on transmission and failure models, which have an important impact on research results. Afterwards, this paper reviews theoretical results and representative topology control approaches to guarantee WSNs to be k − connected at three different network deployment stages: pre-deployment, post-deployment and re-deployment. Multi-path routing protocols are discussed, and many NP-complete or NP-hard problems regarding topology control are identified. The challenging open issues are discussed at the end. This paper can serve as a guideline to design resilient WSNs. PMID:26404272

A Time-constrained Network Voronoi Construction and Accessibility Analysis in Location-based Service Technology

NASA Astrophysics Data System (ADS)

Yu, W.; Ai, T.

2014-11-01

Accessibility analysis usually requires special models of spatial location analysis based on some geometric constructions, such as Voronoi diagram (abbreviated to VD). There are many achievements in classic Voronoi model research, however suffering from the following limitations for location-based services (LBS) applications. (1) It is difficult to objectively reflect the actual service areas of facilities by using traditional planar VDs, because human activities in LBS are usually constrained only to the network portion of the planar space. (2) Although some researchers have adopted network distance to construct VDs, their approaches are used in a static environment, where unrealistic measures of shortest path distance based on assumptions about constant travel speeds through the network were often used. (3) Due to the computational complexity of the shortest-path distance calculating, previous researches tend to be very time consuming, especially for large datasets and if multiple runs are required. To solve the above problems, a novel algorithm is developed in this paper. We apply network-based quadrat system and 1-D sequential expansion to find the corresponding subnetwork for each focus. The idea is inspired by the natural phenomenon that water flow extends along certain linear channels until meets others or arrives at the end of route. In order to accommodate the changes in traffic conditions, the length of network-quadrat is set upon the traffic condition of the corresponding street. The method has the advantage over Dijkstra's algorithm in that the time cost is avoided, and replaced with a linear time operation.

Constraining Path-Dependent Processes During Basalt-CO2 Interactions with Observations From Flow-Through and Batch Experiments

NASA Astrophysics Data System (ADS)

Thomas, D.; Garing, C.; Zahasky, C.; Harrison, A. L.; Bird, D. K.; Benson, S. M.; Oelkers, E. H.; Maher, K.

2017-12-01

Predicting the timing and magnitude of CO2 storage in basaltic rocks relies partly on quantifying the dependence of reactivity on flow path and mineral distribution. Flow-through experiments that use intact cores are advantageous because the spatial heterogeneity of pore space and reactive phases is preserved. Combining aqueous geochemical analyses and petrologic characterization with non-destructive imaging techniques (e.g. micro-computed tomography) constrains the relationship between irreversible reactions, pore connectivity and accessible surface area. Our work enhances these capabilities by dynamically imaging flow through vesicular basalts with Positron Emission Tomography (PET) scanning. PET highlights the path a fluid takes by detecting photons produced during radioactive decay of an injected radiotracer (FDG). We have performed single-phase, CO2-saturated flow-through experiments with basaltic core from Iceland at CO2 sequestration conditions (50 °C; 76-90 bar Ptot). Constant flow rate and continuous pressure measurements at the inlet and outlet of the core constrain permeability. We monitor geochemical evolution through cation and anion analysis of outlet fluid sampled periodically. Before and after reaction, we perform PET scans and characterize the core using micro-CT. The PET scans indicate a discrete, localized flow path that appears to be a micro-crack connecting vesicles, suggesting that vesicle-lining minerals are immediately accessible and important reactants. Rapid increases in aqueous cation concentration, pH and HCO3- indicate that the rock reacts nearly immediately after CO2 injection. After 24 hours the solute release decreases, which may reflect a transition to reaction with phases with slower kinetic dissolution rates (e.g. zeolites and glasses to feldspar), a decrease in available reactive surface area or precipitation. We have performed batch experiments using crushed material of the same rock to elucidate the effect of flow path geometry and mineral accessibility on geochemical evolution. Interestingly, surface area-normalized dissolution rates as evinced by SiO2 release in all experiments approach similar values ( 10-15 mol/cm2/s). Our experiments show how imaging techniques are helpful in interpreting path-dependent processes in open systems.

Self-organization and solution of shortest-path optimization problems with memristive networks

NASA Astrophysics Data System (ADS)

Pershin, Yuriy V.; Di Ventra, Massimiliano

2013-07-01

We show that memristive networks, namely networks of resistors with memory, can efficiently solve shortest-path optimization problems. Indeed, the presence of memory (time nonlocality) promotes self organization of the network into the shortest possible path(s). We introduce a network entropy function to characterize the self-organized evolution, show the solution of the shortest-path problem and demonstrate the healing property of the solution path. Finally, we provide an algorithm to solve the traveling salesman problem. Similar considerations apply to networks of memcapacitors and meminductors, and networks with memory in various dimensions.

Graphs and matroids weighted in a bounded incline algebra.

PubMed

Lu, Ling-Xia; Zhang, Bei

2014-01-01

Firstly, for a graph weighted in a bounded incline algebra (or called a dioid), a longest path problem (LPP, for short) is presented, which can be considered the uniform approach to the famous shortest path problem, the widest path problem, and the most reliable path problem. The solutions for LPP and related algorithms are given. Secondly, for a matroid weighted in a linear matroid, the maximum independent set problem is studied.

Smell Detection Agent Based Optimization Algorithm

NASA Astrophysics Data System (ADS)

Vinod Chandra, S. S.

2016-09-01

In this paper, a novel nature-inspired optimization algorithm has been employed and the trained behaviour of dogs in detecting smell trails is adapted into computational agents for problem solving. The algorithm involves creation of a surface with smell trails and subsequent iteration of the agents in resolving a path. This algorithm can be applied in different computational constraints that incorporate path-based problems. Implementation of the algorithm can be treated as a shortest path problem for a variety of datasets. The simulated agents have been used to evolve the shortest path between two nodes in a graph. This algorithm is useful to solve NP-hard problems that are related to path discovery. This algorithm is also useful to solve many practical optimization problems. The extensive derivation of the algorithm can be enabled to solve shortest path problems.

Constrained optimization via simulation models for new product innovation

NASA Astrophysics Data System (ADS)

Pujowidianto, Nugroho A.

2017-11-01

We consider the problem of constrained optimization where the decision makers aim to optimize the primary performance measure while constraining the secondary performance measures. This paper provides a brief overview of stochastically constrained optimization via discrete event simulation. Most review papers tend to be methodology-based. This review attempts to be problem-based as decision makers may have already decided on the problem formulation. We consider constrained optimization models as there are usually constraints on secondary performance measures as trade-off in new product development. It starts by laying out different possible methods and the reasons using constrained optimization via simulation models. It is then followed by the review of different simulation optimization approach to address constrained optimization depending on the number of decision variables, the type of constraints, and the risk preferences of the decision makers in handling uncertainties.

A 2-dimensional optical architecture for solving Hamiltonian path problem based on micro ring resonators

NASA Astrophysics Data System (ADS)

Shakeri, Nadim; Jalili, Saeed; Ahmadi, Vahid; Rasoulzadeh Zali, Aref; Goliaei, Sama

2015-01-01

The problem of finding the Hamiltonian path in a graph, or deciding whether a graph has a Hamiltonian path or not, is an NP-complete problem. No exact solution has been found yet, to solve this problem using polynomial amount of time and space. In this paper, we propose a two dimensional (2-D) optical architecture based on optical electronic devices such as micro ring resonators, optical circulators and MEMS based mirror (MEMS-M) to solve the Hamiltonian Path Problem, for undirected graphs in linear time. It uses a heuristic algorithm and employs n+1 different wavelengths of a light ray, to check whether a Hamiltonian path exists or not on a graph with n vertices. Then if a Hamiltonian path exists, it reports the path. The device complexity of the proposed architecture is O(n2).

Matrix methods applied to engineering rigid body mechanics

NASA Astrophysics Data System (ADS)

Crouch, T.

The purpose of this book is to present the solution of a range of rigorous body mechanics problems using a matrix formulation of vector algebra. Essential theory concerning kinematics and dynamics is formulated in terms of matrix algebra. The solution of kinematics and dynamics problems is discussed, taking into account the velocity and acceleration of a point moving in a circular path, the velocity and acceleration determination for a linkage, the angular velocity and angular acceleration of a roller in a taper-roller thrust race, Euler's theroem on the motion of rigid bodies, an automotive differential, a rotating epicyclic, the motion of a high speed rotor mounted in gimbals, and the vibration of a spinning projectile. Attention is given to the activity of a force, the work done by a conservative force, the work and potential in a conservative system, the equilibrium of a mechanism, bearing forces due to rotor misalignment, and the frequency of vibrations of a constrained rod.

Self-consistent collective coordinate for reaction path and inertial mass

NASA Astrophysics Data System (ADS)

Wen, Kai; Nakatsukasa, Takashi

2016-11-01

We propose a numerical method to determine the optimal collective reaction path for a nucleus-nucleus collision, based on the adiabatic self-consistent collective coordinate (ASCC) method. We use an iterative method, combining the imaginary-time evolution and the finite amplitude method, for the solution of the ASCC coupled equations. It is applied to the simplest case, α -α scattering. We determine the collective path, the potential, and the inertial mass. The results are compared with other methods, such as the constrained Hartree-Fock method, Inglis's cranking formula, and the adiabatic time-dependent Hartree-Fock (ATDHF) method.

Surface Wave Tomography with Spatially Varying Smoothing Based on Continuous Model Regionalization

NASA Astrophysics Data System (ADS)

Liu, Chuanming; Yao, Huajian

2017-03-01

Surface wave tomography based on continuous regionalization of model parameters is widely used to invert for 2-D phase or group velocity maps. An inevitable problem is that the distribution of ray paths is far from homogeneous due to the spatially uneven distribution of stations and seismic events, which often affects the spatial resolution of the tomographic model. We present an improved tomographic method with a spatially varying smoothing scheme that is based on the continuous regionalization approach. The smoothness of the inverted model is constrained by the Gaussian a priori model covariance function with spatially varying correlation lengths based on ray path density. In addition, a two-step inversion procedure is used to suppress the effects of data outliers on tomographic models. Both synthetic and real data are used to evaluate this newly developed tomographic algorithm. In the synthetic tests, when the contrived model has different scales of anomalies but with uneven ray path distribution, we compare the performance of our spatially varying smoothing method with the traditional inversion method, and show that the new method is capable of improving the recovery in regions of dense ray sampling. For real data applications, the resulting phase velocity maps of Rayleigh waves in SE Tibet produced using the spatially varying smoothing method show similar features to the results with the traditional method. However, the new results contain more detailed structures and appears to better resolve the amplitude of anomalies. From both synthetic and real data tests we demonstrate that our new approach is useful to achieve spatially varying resolution when used in regions with heterogeneous ray path distribution.

An Application of Multi-Criteria Shortest Path to a Customizable Hex-Map Environment

DTIC Science & Technology

2015-03-26

forces which could act as intermediate destinations or obstacles to movement through the network. This is similar to a traveling salesman problem ...118 Abstract The shortest path problem of finding the optimal path through a complex network is well-studied in the field of operations research. This...research presents an applica- tion of the shortest path problem to a customizable map with terrain features and enemy engagement risk. The PathFinder

Path querying system on mobile devices

NASA Astrophysics Data System (ADS)

Lin, Xing; Wang, Yifei; Tian, Yuan; Wu, Lun

2006-01-01

Traditional approaches to path querying problems are not efficient and convenient under most circumstances. A more convenient and reliable approach to this problem has to be found. This paper is devoted to a path querying solution on mobile devices. By using an improved Dijkstra's shortest path algorithm and a natural language translating module, this system can help people find the shortest path between two places through their cell phones or other mobile devices. The chosen path is prompted in text of natural language, as well as a map picture. This system would be useful in solving best path querying problems and have potential to be a profitable business system.

A sequential solution for anisotropic total variation image denoising with interval constraints

NASA Astrophysics Data System (ADS)

Xu, Jingyan; Noo, Frédéric

2017-09-01

We show that two problems involving the anisotropic total variation (TV) and interval constraints on the unknown variables admit, under some conditions, a simple sequential solution. Problem 1 is a constrained TV penalized image denoising problem; problem 2 is a constrained fused lasso signal approximator. The sequential solution entails finding first the solution to the unconstrained problem, and then applying a thresholding to satisfy the constraints. If the interval constraints are uniform, this sequential solution solves problem 1. If the interval constraints furthermore contain zero, the sequential solution solves problem 2. Here uniform interval constraints refer to all unknowns being constrained to the same interval. A typical example of application is image denoising in x-ray CT, where the image intensities are non-negative as they physically represent linear attenuation coefficient in the patient body. Our results are simple yet seem unknown; we establish them using the Karush-Kuhn-Tucker conditions for constrained convex optimization.

Hybrid Motion Planning with Multiple Destinations

NASA Technical Reports Server (NTRS)

Clouse, Jeffery

1998-01-01

In our initial proposal, we laid plans for developing a hybrid motion planning system that combines the concepts of visibility-based motion planning, artificial potential field based motion planning, evolutionary constrained optimization, and reinforcement learning. Our goal was, and still is, to produce a hybrid motion planning system that outperforms the best traditional motion planning systems on problems with dynamic environments. The proposed hybrid system will be in two parts the first is a global motion planning system and the second is a local motion planning system. The global system will take global information about the environment, such as the placement of the obstacles and goals, and produce feasible paths through those obstacles. We envision a system that combines the evolutionary-based optimization and visibility-based motion planning to achieve this end.

Northeast Parallel Architectures Center (NPAC) at Syracuse University

DTIC Science & Technology

1990-12-01

lattice models. On the CM-2 we will fun a lattice gauge theory simulation of quantum chromodynamics ( QCD ), and on the CM-1 we will investigate the...into a three-dimensional grid with the stipulation that adjacent processors in the lattice correspond to proximate regions of space. Light paths will...be constrained to follow lattice links and the sum over all paths from light sources to each lattice site will be computed inductively by all

Temporal Constraint Reasoning With Preferences

NASA Technical Reports Server (NTRS)

Khatib, Lina; Morris, Paul; Morris, Robert; Rossi, Francesca

2001-01-01

A number of reasoning problems involving the manipulation of temporal information can naturally be viewed as implicitly inducing an ordering of potential local decisions involving time (specifically, associated with durations or orderings of events) on the basis of preferences. For example. a pair of events might be constrained to occur in a certain order, and, in addition. it might be preferable that the delay between them be as large, or as small, as possible. This paper explores problems in which a set of temporal constraints is specified, where each constraint is associated with preference criteria for making local decisions about the events involved in the constraint, and a reasoner must infer a complete solution to the problem such that, to the extent possible, these local preferences are met in the best way. A constraint framework for reasoning about time is generalized to allow for preferences over event distances and durations, and we study the complexity of solving problems in the resulting formalism. It is shown that while in general such problems are NP-hard, some restrictions on the shape of the preference functions, and on the structure of the preference set, can be enforced to achieve tractability. In these cases, a simple generalization of a single-source shortest path algorithm can be used to compute a globally preferred solution in polynomial time.

Provisional-Ideal-Point-Based Multi-objective Optimization Method for Drone Delivery Problem

NASA Astrophysics Data System (ADS)

Omagari, Hiroki; Higashino, Shin-Ichiro

2018-04-01

In this paper, we proposed a new evolutionary multi-objective optimization method for solving drone delivery problems (DDP). It can be formulated as a constrained multi-objective optimization problem. In our previous research, we proposed the "aspiration-point-based method" to solve multi-objective optimization problems. However, this method needs to calculate the optimal values of each objective function value in advance. Moreover, it does not consider the constraint conditions except for the objective functions. Therefore, it cannot apply to DDP which has many constraint conditions. To solve these issues, we proposed "provisional-ideal-point-based method." The proposed method defines a "penalty value" to search for feasible solutions. It also defines a new reference solution named "provisional-ideal point" to search for the preferred solution for a decision maker. In this way, we can eliminate the preliminary calculations and its limited application scope. The results of the benchmark test problems show that the proposed method can generate the preferred solution efficiently. The usefulness of the proposed method is also demonstrated by applying it to DDP. As a result, the delivery path when combining one drone and one truck drastically reduces the traveling distance and the delivery time compared with the case of using only one truck.

When Dijkstra Meets Vanishing Point: A Stereo Vision Approach for Road Detection.

PubMed

Zhang, Yigong; Su, Yingna; Yang, Jian; Ponce, Jean; Kong, Hui

2018-05-01

In this paper, we propose a vanishing-point constrained Dijkstra road model for road detection in a stereo-vision paradigm. First, the stereo-camera is used to generate the u- and v-disparity maps of road image, from which the horizon can be extracted. With the horizon and ground region constraints, we can robustly locate the vanishing point of road region. Second, a weighted graph is constructed using all pixels of the image, and the detected vanishing point is treated as the source node of the graph. By computing a vanishing-point constrained Dijkstra minimum-cost map, where both disparity and gradient of gray image are used to calculate cost between two neighbor pixels, the problem of detecting road borders in image is transformed into that of finding two shortest paths that originate from the vanishing point to two pixels in the last row of image. The proposed approach has been implemented and tested over 2600 grayscale images of different road scenes in the KITTI data set. The experimental results demonstrate that this training-free approach can detect horizon, vanishing point, and road regions very accurately and robustly. It can achieve promising performance.

Option pricing, stochastic volatility, singular dynamics and constrained path integrals

NASA Astrophysics Data System (ADS)

Contreras, Mauricio; Hojman, Sergio A.

2014-01-01

Stochastic volatility models have been widely studied and used in the financial world. The Heston model (Heston, 1993) [7] is one of the best known models to deal with this issue. These stochastic volatility models are characterized by the fact that they explicitly depend on a correlation parameter ρ which relates the two Brownian motions that drive the stochastic dynamics associated to the volatility and the underlying asset. Solutions to the Heston model in the context of option pricing, using a path integral approach, are found in Lemmens et al. (2008) [21] while in Baaquie (2007,1997) [12,13] propagators for different stochastic volatility models are constructed. In all previous cases, the propagator is not defined for extreme cases ρ=±1. It is therefore necessary to obtain a solution for these extreme cases and also to understand the origin of the divergence of the propagator. In this paper we study in detail a general class of stochastic volatility models for extreme values ρ=±1 and show that in these two cases, the associated classical dynamics corresponds to a system with second class constraints, which must be dealt with using Dirac’s method for constrained systems (Dirac, 1958,1967) [22,23] in order to properly obtain the propagator in the form of a Euclidean Hamiltonian path integral (Henneaux and Teitelboim, 1992) [25]. After integrating over momenta, one gets an Euclidean Lagrangian path integral without constraints, which in the case of the Heston model corresponds to a path integral of a repulsive radial harmonic oscillator. In all the cases studied, the price of the underlying asset is completely determined by one of the second class constraints in terms of volatility and plays no active role in the path integral.

Neighboring extremals of dynamic optimization problems with path equality constraints

NASA Technical Reports Server (NTRS)

Lee, A. Y.

1988-01-01

Neighboring extremals of dynamic optimization problems with path equality constraints and with an unknown parameter vector are considered in this paper. With some simplifications, the problem is reduced to solving a linear, time-varying two-point boundary-value problem with integral path equality constraints. A modified backward sweep method is used to solve this problem. Two example problems are solved to illustrate the validity and usefulness of the solution technique.

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.

The terminal area automated path generation problem

NASA Technical Reports Server (NTRS)

Hsin, C.-C.

1977-01-01

The automated terminal area path generation problem in the advanced Air Traffic Control System (ATC), has been studied. Definitions, input, output and the interrelationships with other ATC functions have been discussed. Alternatives in modeling the problem have been identified. Problem formulations and solution techniques are presented. In particular, the solution of a minimum effort path stretching problem (path generation on a given schedule) has been carried out using the Newton-Raphson trajectory optimization method. Discussions are presented on the effect of different delivery time, aircraft entry position, initial guess on the boundary conditions, etc. Recommendations are made on real-world implementations.

Congestion patterns of electric vehicles with limited battery capacity.

PubMed

Jing, Wentao; Ramezani, Mohsen; An, Kun; Kim, Inhi

2018-01-01

The path choice behavior of battery electric vehicle (BEV) drivers is influenced by the lack of public charging stations, limited battery capacity, range anxiety and long battery charging time. This paper investigates the congestion/flow pattern captured by stochastic user equilibrium (SUE) traffic assignment problem in transportation networks with BEVs, where the BEV paths are restricted by their battery capacities. The BEV energy consumption is assumed to be a linear function of path length and path travel time, which addresses both path distance limit problem and road congestion effect. A mathematical programming model is proposed for the path-based SUE traffic assignment where the path cost is the sum of the corresponding link costs and a path specific out-of-energy penalty. We then apply the convergent Lagrangian dual method to transform the original problem into a concave maximization problem and develop a customized gradient projection algorithm to solve it. A column generation procedure is incorporated to generate the path set. Finally, two numerical examples are presented to demonstrate the applicability of the proposed model and the solution algorithm.

Congestion patterns of electric vehicles with limited battery capacity

PubMed Central

2018-01-01

The path choice behavior of battery electric vehicle (BEV) drivers is influenced by the lack of public charging stations, limited battery capacity, range anxiety and long battery charging time. This paper investigates the congestion/flow pattern captured by stochastic user equilibrium (SUE) traffic assignment problem in transportation networks with BEVs, where the BEV paths are restricted by their battery capacities. The BEV energy consumption is assumed to be a linear function of path length and path travel time, which addresses both path distance limit problem and road congestion effect. A mathematical programming model is proposed for the path-based SUE traffic assignment where the path cost is the sum of the corresponding link costs and a path specific out-of-energy penalty. We then apply the convergent Lagrangian dual method to transform the original problem into a concave maximization problem and develop a customized gradient projection algorithm to solve it. A column generation procedure is incorporated to generate the path set. Finally, two numerical examples are presented to demonstrate the applicability of the proposed model and the solution algorithm. PMID:29543875

A distributed algorithm to maintain and repair the trail networks of arboreal ants.

PubMed

Chandrasekhar, Arjun; Gordon, Deborah M; Navlakha, Saket

2018-06-18

We study how the arboreal turtle ant (Cephalotes goniodontus) solves a fundamental computing problem: maintaining a trail network and finding alternative paths to route around broken links in the network. Turtle ants form a routing backbone of foraging trails linking several nests and temporary food sources. This species travels only in the trees, so their foraging trails are constrained to lie on a natural graph formed by overlapping branches and vines in the tangled canopy. Links between branches, however, can be ephemeral, easily destroyed by wind, rain, or animal movements. Here we report a biologically feasible distributed algorithm, parameterized using field data, that can plausibly describe how turtle ants maintain the routing backbone and find alternative paths to circumvent broken links in the backbone. We validate the ability of this probabilistic algorithm to circumvent simulated breaks in synthetic and real-world networks, and we derive an analytic explanation for why certain features are crucial to improve the algorithm's success. Our proposed algorithm uses fewer computational resources than common distributed graph search algorithms, and thus may be useful in other domains, such as for swarm computing or for coordinating molecular robots.

Optimal Control Problems with Switching Points. Ph.D. Thesis, 1990 Final Report

NASA Technical Reports Server (NTRS)

Seywald, Hans

1991-01-01

The main idea of this report is to give an overview of the problems and difficulties that arise in solving optimal control problems with switching points. A brief discussion of existing optimality conditions is given and a numerical approach for solving the multipoint boundary value problems associated with the first-order necessary conditions of optimal control is presented. Two real-life aerospace optimization problems are treated explicitly. These are altitude maximization for a sounding rocket (Goddard Problem) in the presence of a dynamic pressure limit, and range maximization for a supersonic aircraft flying in the vertical, also in the presence of a dynamic pressure limit. In the second problem singular control appears along arcs with active dynamic pressure limit, which in the context of optimal control, represents a first-order state inequality constraint. An extension of the Generalized Legendre-Clebsch Condition to the case of singular control along state/control constrained arcs is presented and is applied to the aircraft range maximization problem stated above. A contribution to the field of Jacobi Necessary Conditions is made by giving a new proof for the non-optimality of conjugate paths in the Accessory Minimum Problem. Because of its simple and explicit character, the new proof may provide the basis for an extension of Jacobi's Necessary Condition to the case of the trajectories with interior point constraints. Finally, the result that touch points cannot occur for first-order state inequality constraints is extended to the case of vector valued control functions.

Cooperative path following control of multiple nonholonomic mobile robots.

PubMed

Cao, Ke-Cai; Jiang, Bin; Yue, Dong

2017-11-01

Cooperative path following control problem of multiple nonholonomic mobile robots has been considered in this paper. Based on the framework of decomposition, the cooperative path following problem has been transformed into path following problem and cooperative control problem; Then cascaded theory of non-autonomous system has been employed in the design of controllers without resorting to feedback linearization. One time-varying coordinate transformation based on dilation has been introduced to solve the uncontrollable problem of nonholonomic robots when the whole group's reference converges to stationary point. Cooperative path following controllers for nonholonomic robots have been proposed under persistent reference or reference target that converges to stationary point respectively. Simulation results using Matlab have illustrated the effectiveness of the obtained theoretical results. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Neck Muscle Moment Arms Obtained In-Vivo from MRI: Effect of Curved and Straight Modeled Paths.

PubMed

Suderman, Bethany L; Vasavada, Anita N

2017-08-01

Musculoskeletal models of the cervical spine commonly represent neck muscles with straight paths. However, straight lines do not best represent the natural curvature of muscle paths in the neck, because the paths are constrained by bone and soft tissue. The purpose of this study was to estimate moment arms of curved and straight neck muscle paths using different moment arm calculation methods: tendon excursion, geometric, and effective torque. Curved and straight muscle paths were defined for two subject-specific cervical spine models derived from in vivo magnetic resonance images (MRI). Modeling neck muscle paths with curvature provides significantly different moment arm estimates than straight paths for 10 of 15 neck muscles (p < 0.05, repeated measures two-way ANOVA). Moment arm estimates were also found to be significantly different among moment arm calculation methods for 11 of 15 neck muscles (p < 0.05, repeated measures two-way ANOVA). In particular, using straight lines to model muscle paths can lead to overestimating neck extension moment. However, moment arm methods for curved paths should be investigated further, as different methods of calculating moment arm can provide different estimates.

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

NASA Astrophysics Data System (ADS)

Yu, Xiang; Zhang, Shiqing

2018-05-01

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

The covariance matrix for the solution vector of an equality-constrained least-squares problem

NASA Technical Reports Server (NTRS)

Lawson, C. L.

1976-01-01

Methods are given for computing the covariance matrix for the solution vector of an equality-constrained least squares problem. The methods are matched to the solution algorithms given in the book, 'Solving Least Squares Problems.'

Pathways from maternal distress and child problem behavior to adolescent depressive symptoms: a prospective examination from early childhood to adolescence.

PubMed

Nilsen, Wendy; Gustavson, Kristin; Røysamb, Espen; Kjeldsen, Anne; Karevold, Evalill

2013-06-01

The main aim of this study was to identify the pathways from maternal distress and child problem behaviors (i.e., internalizing and externalizing problems) across childhood and their impact on depressive symptoms during adolescence among girls and boys. Data from families of 921 Norwegian children in a 15-year longitudinal community sample were used. Using structural equation modeling, the authors explored the interplay between maternal-reported distress and child problem behaviors measured at 5 time points from early (ages 1.5, 2.5, and 4.5 years) and middle (age 8.5 years) childhood to early adolescence (age 12.5 years), and their prediction of self-reported depressive symptoms during adolescence (ages 14.5 and 16.5 years). The findings revealed paths from internalizing and externalizing problems throughout the development for corresponding problems (homotypic paths) and paths from early externalizing to subsequent internalizing problems (heterotypic paths). The findings suggest 2 pathways linking maternal-rated risk factors to self-reported adolescent depressive symptoms. There was a direct path from early externalizing problems to depressive symptoms. There was an indirect path from early maternal distress going through child problem behavior to depressive symptoms. In general, girls and boys were similar, but some gender-specific effects appeared. Problem behaviors in middle childhood had heterotypic paths to subsequent problems only for girls. The findings highlight the developmental importance of child externalizing problems, as well as the impact of maternal distress as early as age 1.5 years for the development of adolescent depressive symptoms. Findings also indicate a certain vulnerable period in middle childhood for girls. NOTE: See Supplemental Digital Content 1, at http://links.lww.com/JDBP/A45, for a video introduction to this article.

Assessment of the Performance of a Dual-Frequency Surface Reference Technique

NASA Technical Reports Server (NTRS)

Meneghini, Robert; Liao, Liang; Tanelli, Simone; Durden, Stephen

2013-01-01

The high correlation of the rain-free surface cross sections at two frequencies implies that the estimate of differential path integrated attenuation (PIA) caused by precipitation along the radar beam can be obtained to a higher degree of accuracy than the path-attenuation at either frequency. We explore this finding first analytically and then by examining data from the JPL dual-frequency airborne radar using measurements from the TC4 experiment obtained during July-August 2007. Despite this improvement in the accuracy of the differential path attenuation, solving the constrained dual-wavelength radar equations for parameters of the particle size distribution requires not only this quantity but the single-wavelength path attenuation as well. We investigate a simple method of estimating the single-frequency path attenuation from the differential attenuation and compare this with the estimate derived directly from the surface return.

Optimal Patrol to Detect Attacks at Dispersed Heterogeneous Locations

DTIC Science & Technology

2013-12-01

path with one revisit SPR2 Shortest path with two revisits SPR3 Shortest path with three revisits TSP Traveling salesman problem UAV Unmanned aerial...path patrol pattern. Finding the shortest-path patrol pattern is an example of solving a traveling salesman problem , as described in Section 16.5 of...use of patrol paths based on the traveling salesman prob- lem (TSP), where patrollers follow the shortest Hamiltonian cycle in a graph in order to

Autonomous Path Planning for On-Orbit Servicing Vehicles

NASA Astrophysics Data System (ADS)

McInnes, C. R.

On-orbit servicing has long been considered as a means of reducing mission costs. While automated on-orbit servicing of satellites in LEO and GEO has yet to be realised, the International Space Station (ISS) will require servicing in a number of forms for re-supply, external visual inspection and maintenance. This paper will discuss a unified approach to path planning for such servicing vehicles using artificial potential field methods. In particular, path constrained rendezvous and docking of the ESA Automated Transfer Vehicle (ATV) at the ISS will be investigated as will mission and path planning tools for the Daimler-Chrysler Aerospace ISS Inspector free-flying camera. Future applications for free-flying microcameras and co-operative control between multiple free-flyers for on-orbit assembly will also be considered.

Dual stage potential field method for robotic path planning

NASA Astrophysics Data System (ADS)

Singh, Pradyumna Kumar; Parida, Pramod Kumar

2018-04-01

Path planning for autonomous mobile robots are the root for all autonomous mobile systems. Various methods are used for optimization of path to be followed by the autonomous mobile robots. Artificial potential field based path planning method is one of the most used methods for the researchers. Various algorithms have been proposed using the potential field approach. But in most of the common problems are encounters while heading towards the goal or target. i.e. local minima problem, zero potential regions problem, complex shaped obstacles problem, target near obstacle problem. In this paper we provide a new algorithm in which two types of potential functions are used one after another. The former one is to use to get the probable points and later one for getting the optimum path. In this algorithm we consider only the static obstacle and goal.

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

PubMed Central

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

2014-01-01

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

On size-constrained minimum s–t cut problems and size-constrained dense subgraph problems

DOE PAGES

Chen, Wenbin; Samatova, Nagiza F.; Stallmann, Matthias F.; ...

2015-10-30

In some application cases, the solutions of combinatorial optimization problems on graphs should satisfy an additional vertex size constraint. In this paper, we consider size-constrained minimum s–t cut problems and size-constrained dense subgraph problems. We introduce the minimum s–t cut with at-least-k vertices problem, the minimum s–t cut with at-most-k vertices problem, and the minimum s–t cut with exactly k vertices problem. We prove that they are NP-complete. Thus, they are not polynomially solvable unless P = NP. On the other hand, we also study the densest at-least-k-subgraph problem (DalkS) and the densest at-most-k-subgraph problem (DamkS) introduced by Andersen andmore » Chellapilla [1]. We present a polynomial time algorithm for DalkS when k is bounded by some constant c. We also present two approximation algorithms for DamkS. In conclusion, the first approximation algorithm for DamkS has an approximation ratio of n-1/k-1, where n is the number of vertices in the input graph. The second approximation algorithm for DamkS has an approximation ratio of O (n δ), for some δ < 1/3.« less

A two-stage path planning approach for multiple car-like robots based on PH curves and a modified harmony search algorithm

NASA Astrophysics Data System (ADS)

Zeng, Wenhui; Yi, Jin; Rao, Xiao; Zheng, Yun

2017-11-01

In this article, collision-avoidance path planning for multiple car-like robots with variable motion is formulated as a two-stage objective optimization problem minimizing both the total length of all paths and the task's completion time. Accordingly, a new approach based on Pythagorean Hodograph (PH) curves and Modified Harmony Search algorithm is proposed to solve the two-stage path-planning problem subject to kinematic constraints such as velocity, acceleration, and minimum turning radius. First, a method of path planning based on PH curves for a single robot is proposed. Second, a mathematical model of the two-stage path-planning problem for multiple car-like robots with variable motion subject to kinematic constraints is constructed that the first-stage minimizes the total length of all paths and the second-stage minimizes the task's completion time. Finally, a modified harmony search algorithm is applied to solve the two-stage optimization problem. A set of experiments demonstrate the effectiveness of the proposed approach.

The automation of remote vehicle control. [in Mars roving vehicles

NASA Technical Reports Server (NTRS)

Paine, G.

1977-01-01

The automation of remote vehicles is becoming necessary to overcome the requirement of having man present as a controller. By removing man, remote vehicles can be operated in areas where the environment is too hostile for man, his reaction times are too slow, time delays are too long, and where his presence is too costly, or where system performance can be improved. This paper addresses the development of automated remote vehicle control for nonspace and space tasks from warehouse vehicles to proposed Mars rovers. The state-of-the-art and the availability of new technology for implementing automated control are reviewed and the major problem areas are outlined. The control strategies are divided into those where the path is planned in advance or constrained, or where the system is a teleoperator, or where automation or robotics have been introduced.

The bidirectional pathways between internalizing and externalizing problems and academic performance from 6 to 18 years.

PubMed

Van der Ende, Jan; Verhulst, Frank C; Tiemeier, Henning

2016-08-01

Internalizing and externalizing problems are associated with poor academic performance, both concurrently and longitudinally. Important questions are whether problems precede academic performance or vice versa, whether both internalizing and externalizing are associated with academic problems when simultaneously tested, and whether associations and their direction depend on the informant providing information. These questions were addressed in a sample of 816 children who were assessed four times. The children were 6-10 years at baseline and 14-18 years at the last assessment. Parent-reported internalizing and externalizing problems and teacher-reported academic performance were tested in cross-lagged models to examine bidirectional paths between these constructs. These models were compared with cross-lagged models testing paths between teacher-reported internalizing and externalizing problems and parent-reported academic performance. Both final models revealed similar pathways from mostly externalizing problems to academic performance. No paths emerged from internalizing problems to academic performance. Moreover, paths from academic performance to internalizing and externalizing problems were only found when teachers reported on children's problems and not for parent-reported problems. Additional model tests revealed that paths were observed in both childhood and adolescence. Externalizing problems place children at increased risk of poor academic performance and should therefore be the target for interventions.

Minimal complexity control law synthesis

NASA Technical Reports Server (NTRS)

Bernstein, Dennis S.; Haddad, Wassim M.; Nett, Carl N.

1989-01-01

A paradigm for control law design for modern engineering systems is proposed: Minimize control law complexity subject to the achievement of a specified accuracy in the face of a specified level of uncertainty. Correspondingly, the overall goal is to make progress towards the development of a control law design methodology which supports this paradigm. Researchers achieve this goal by developing a general theory of optimal constrained-structure dynamic output feedback compensation, where here constrained-structure means that the dynamic-structure (e.g., dynamic order, pole locations, zero locations, etc.) of the output feedback compensation is constrained in some way. By applying this theory in an innovative fashion, where here the indicated iteration occurs over the choice of the compensator dynamic-structure, the paradigm stated above can, in principle, be realized. The optimal constrained-structure dynamic output feedback problem is formulated in general terms. An elegant method for reducing optimal constrained-structure dynamic output feedback problems to optimal static output feedback problems is then developed. This reduction procedure makes use of star products, linear fractional transformations, and linear fractional decompositions, and yields as a byproduct a complete characterization of the class of optimal constrained-structure dynamic output feedback problems which can be reduced to optimal static output feedback problems. Issues such as operational/physical constraints, operating-point variations, and processor throughput/memory limitations are considered, and it is shown how anti-windup/bumpless transfer, gain-scheduling, and digital processor implementation can be facilitated by constraining the controller dynamic-structure in an appropriate fashion.

Alternative Constraint Handling Technique for Four-Bar Linkage Path Generation

NASA Astrophysics Data System (ADS)

Sleesongsom, S.; Bureerat, S.

2018-03-01

This paper proposes an extension of a new concept for path generation from our previous work by adding a new constraint handling technique. The propose technique was initially designed for problems without prescribed timing by avoiding the timing constraint, while remain constraints are solving with a new constraint handling technique. The technique is one kind of penalty technique. The comparative study is optimisation of path generation problems are solved using self-adaptive population size teaching-learning based optimization (SAP-TLBO) and original TLBO. In this study, two traditional path generation test problem are used to test the proposed technique. The results show that the new technique can be applied with the path generation problem without prescribed timing and gives better results than the previous technique. Furthermore, SAP-TLBO outperforms the original one.

Path optimization method for the sign problem

NASA Astrophysics Data System (ADS)

Ohnishi, Akira; Mori, Yuto; Kashiwa, Kouji

2018-03-01

We propose a path optimization method (POM) to evade the sign problem in the Monte-Carlo calculations for complex actions. Among many approaches to the sign problem, the Lefschetz-thimble path-integral method and the complex Langevin method are promising and extensively discussed. In these methods, real field variables are complexified and the integration manifold is determined by the flow equations or stochastically sampled. When we have singular points of the action or multiple critical points near the original integral surface, however, we have a risk to encounter the residual and global sign problems or the singular drift term problem. One of the ways to avoid the singular points is to optimize the integration path which is designed not to hit the singular points of the Boltzmann weight. By specifying the one-dimensional integration-path as z = t +if(t)(f ɛ R) and by optimizing f(t) to enhance the average phase factor, we demonstrate that we can avoid the sign problem in a one-variable toy model for which the complex Langevin method is found to fail. In this proceedings, we propose POM and discuss how we can avoid the sign problem in a toy model. We also discuss the possibility to utilize the neural network to optimize the path.

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.

The Edge-Disjoint Path Problem on Random Graphs by Message-Passing.

PubMed

Altarelli, Fabrizio; Braunstein, Alfredo; Dall'Asta, Luca; De Bacco, Caterina; Franz, Silvio

2015-01-01

We present a message-passing algorithm to solve a series of edge-disjoint path problems on graphs based on the zero-temperature cavity equations. Edge-disjoint paths problems are important in the general context of routing, that can be defined by incorporating under a unique framework both traffic optimization and total path length minimization. The computation of the cavity equations can be performed efficiently by exploiting a mapping of a generalized edge-disjoint path problem on a star graph onto a weighted maximum matching problem. We perform extensive numerical simulations on random graphs of various types to test the performance both in terms of path length minimization and maximization of the number of accommodated paths. In addition, we test the performance on benchmark instances on various graphs by comparison with state-of-the-art algorithms and results found in the literature. Our message-passing algorithm always outperforms the others in terms of the number of accommodated paths when considering non trivial instances (otherwise it gives the same trivial results). Remarkably, the largest improvement in performance with respect to the other methods employed is found in the case of benchmarks with meshes, where the validity hypothesis behind message-passing is expected to worsen. In these cases, even though the exact message-passing equations do not converge, by introducing a reinforcement parameter to force convergence towards a sub optimal solution, we were able to always outperform the other algorithms with a peak of 27% performance improvement in terms of accommodated paths. On random graphs, we numerically observe two separated regimes: one in which all paths can be accommodated and one in which this is not possible. We also investigate the behavior of both the number of paths to be accommodated and their minimum total length.

The Edge-Disjoint Path Problem on Random Graphs by Message-Passing

PubMed Central

2015-01-01

We present a message-passing algorithm to solve a series of edge-disjoint path problems on graphs based on the zero-temperature cavity equations. Edge-disjoint paths problems are important in the general context of routing, that can be defined by incorporating under a unique framework both traffic optimization and total path length minimization. The computation of the cavity equations can be performed efficiently by exploiting a mapping of a generalized edge-disjoint path problem on a star graph onto a weighted maximum matching problem. We perform extensive numerical simulations on random graphs of various types to test the performance both in terms of path length minimization and maximization of the number of accommodated paths. In addition, we test the performance on benchmark instances on various graphs by comparison with state-of-the-art algorithms and results found in the literature. Our message-passing algorithm always outperforms the others in terms of the number of accommodated paths when considering non trivial instances (otherwise it gives the same trivial results). Remarkably, the largest improvement in performance with respect to the other methods employed is found in the case of benchmarks with meshes, where the validity hypothesis behind message-passing is expected to worsen. In these cases, even though the exact message-passing equations do not converge, by introducing a reinforcement parameter to force convergence towards a sub optimal solution, we were able to always outperform the other algorithms with a peak of 27% performance improvement in terms of accommodated paths. On random graphs, we numerically observe two separated regimes: one in which all paths can be accommodated and one in which this is not possible. We also investigate the behavior of both the number of paths to be accommodated and their minimum total length. PMID:26710102

A real-time approximate optimal guidance law for flight in a plane

NASA Technical Reports Server (NTRS)

Feeley, Timothy S.; Speyer, Jason L.

1990-01-01

A real-time guidance scheme is presented for the problem of maximizing the payload into orbit subject to the equations of motion of a rocket over a nonrotating spherical earth. The flight is constrained to a path in the equatorial plane while reaching an orbital altitude at orbital injection speeds. The dynamics of the problem can be separated into primary and perturbation effects by a small parameter, epsilon, which is the ratio of the atmospheric scale height to the radius of the earth. The Hamilton-Jacobi-Bellman or dynamic programming equation is expanded in an asymptotic series where the zeroth-order term (epsilon = 0) can be obtained in closed form. The neglected perturbation terms are included in the higher-order terms of the expansion, which are determined from the solution of first-order linear partial differential equations requiring only integrations which are quadratures. The quadratures can be performed rapidly with emerging computer capability, so that real-time approximate optimization can be used to construct the launch guidance law. The application of this technique to flight in three-dimensions is made apparent from the solution presented.

A Framework for a Supervisory Expert System for Robotic Manipulators with Joint-Position Limits and Joint-Rate Limits

NASA Technical Reports Server (NTRS)

Mutambara, Arthur G. O.; Litt, Jonathan

1998-01-01

This report addresses the problem of path planning and control of robotic manipulators which have joint-position limits and joint-rate limits. The manipulators move autonomously and carry out variable tasks in a dynamic, unstructured and cluttered environment. The issue considered is whether the robotic manipulator can achieve all its tasks, and if it cannot, the objective is to identify the closest achievable goal. This problem is formalized and systematically solved for generic manipulators by using inverse kinematics and forward kinematics. Inverse kinematics are employed to define the subspace, workspace and constrained workspace, which are then used to identify when a task is not achievable. The closest achievable goal is obtained by determining weights for an optimal control redistribution scheme. These weights are quantified by using forward kinematics. Conditions leading to joint rate limits are identified, in particular it is established that all generic manipulators have singularities at the boundary of their workspace, while some have loci of singularities inside their workspace. Once the manipulator singularity is identified the command redistribution scheme is used to compute the closest achievable Cartesian velocities. Two examples are used to illustrate the use of the algorithm: A three link planar manipulator and the Unimation Puma 560. Implementation of the derived algorithm is effected by using a supervisory expert system to check whether the desired goal lies in the constrained workspace and if not, to evoke the redistribution scheme which determines the constraint relaxation between end effector position and orientation, and then computes optimal gains.

Analysis of elastically tailored viscoelastic damping member

NASA Technical Reports Server (NTRS)

Chen, G.-S.; Dolgin, B. P.

1990-01-01

For more than two decades, viscoelastic materials have been commonly used as a passive damping source in a variety of structures because of their high material loss factors. In most of the applications, viscoelastic materials are used either in series with or parallel to the structural load path. The latter is also known as the constrained-layer damping treatment. The advantage of the constrained-layer damping treatment is that it can be incorporated without loss in structural integrity, namely, stiffness and strength. However, the disadvantages are that: (1) it is not the most effective use of the viscoelastic material when compared with the series-type application, and (2) weight penalty from the stiff constraining layer requirement can be excessive. To overcome the disadvantages of the constrained-layer damping treatment, a new approach for using viscoelastic material in axial-type structural components, e.g., truss members, was studied in this investigation.

On the nullspace of TLS multi-station adjustment

NASA Astrophysics Data System (ADS)

Sterle, Oskar; Kogoj, Dušan; Stopar, Bojan; Kregar, Klemen

2018-07-01

In the article we present an analytic aspect of TLS multi-station least-squares adjustment with the main focus on the datum problem. The datum problem is, compared to previously published researches, theoretically analyzed and solved, where the solution is based on nullspace derivation of the mathematical model. The importance of datum problem solution is seen in a complete description of TLS multi-station adjustment solutions from a set of all minimally constrained least-squares solutions. On a basis of known nullspace, estimable parameters are described and the geometric interpretation of all minimally constrained least squares solutions is presented. At the end a simulated example is used to analyze the results of TLS multi-station minimally constrained and inner constrained least-squares adjustment solutions.

Folded path LWIR system for SWAP constrained platforms

NASA Astrophysics Data System (ADS)

Fleet, Erin F.; Wilson, Michael L.; Linne von Berg, Dale; Giallorenzi, Thomas; Mathieu, Barry

2014-06-01

Folded path reflection and catadioptric optics are of growing interest, especially in the long wave infrared (LWIR), due to continuing demands for reductions in imaging system size, weight and power (SWAP). We present the optical design and laboratory data for a 50 mm focal length low f/# folded-path compact LWIR imaging system. The optical design uses 4 concentric aspheric mirrors, each of which is described by annular aspheric functions well suited to the folded path design space. The 4 mirrors are diamond turned onto two thin air-spaced aluminum plates which can be manually focused onto the uncooled LWIR microbolometer array detector. Stray light analysis will be presented to show how specialized internal baffling can be used to reduce stray light propagation through the folded path optical train. The system achieves near diffraction limited performance across the FOV with a 15 mm long optical train and a 5 mm back focal distance. The completed system is small enough to reside within a 3 inch diameter ball gimbal.

Constrained maximum consistency multi-path mitigation

NASA Astrophysics Data System (ADS)

Smith, George B.

2003-10-01

Blind deconvolution algorithms can be useful as pre-processors for signal classification algorithms in shallow water. These algorithms remove the distortion of the signal caused by multipath propagation when no knowledge of the environment is available. A framework in which filters that produce signal estimates from each data channel that are as consistent with each other as possible in a least-squares sense has been presented [Smith, J. Acoust. Soc. Am. 107 (2000)]. This framework provides a solution to the blind deconvolution problem. One implementation of this framework yields the cross-relation on which EVAM [Gurelli and Nikias, IEEE Trans. Signal Process. 43 (1995)] and Rietsch [Rietsch, Geophysics 62(6) (1997)] processing are based. In this presentation, partially blind implementations that have good noise stability properties are compared using Classification Operating Characteristics (CLOC) analysis. [Work supported by ONR under Program Element 62747N and NRL, Stennis Space Center, MS.

Gender differences in visuospatial planning: an eye movements study.

PubMed

Cazzato, Valentina; Basso, Demis; Cutini, Simone; Bisiacchi, Patrizia

2010-01-20

Gender studies report a male advantage in several visuospatial abilities. Only few studies however, have evaluated differences in visuospatial planning behaviour with regard to gender. This study was aimed at exploring whether gender may affect the choice of cognitive strategies in a visuospatial planning task and, if oculomotor measures could assist in disentangling the cognitive processes involved. A computerised task based on the travelling salesperson problem paradigm, the Maps test, was used to investigate these issues. Participants were required to optimise time and space of a path travelling among a set of sub-goals in a spatially constrained environment. Behavioural results suggest that there are no gender differences in the initial visual processing of the stimuli, but rather during the execution of the plan, with males showing a shorter execution time and a higher path length optimisation than females. Males often showed changes of heuristics during the execution while females seemed to prefer a constant strategy. Moreover, a better performance in behavioural and oculomotor measures seemed to suggest that males are more able than females in either the optimisation of spatial features or the realisation of the planned scheme. Despite inconclusive findings, the results support previous research and provide insight into the level of cognitive processing involved in navigation and planning tasks, with regard to the influence of gender.

Adaptive Constrained Optimal Control Design for Data-Based Nonlinear Discrete-Time Systems With Critic-Only Structure.

PubMed

Luo, Biao; Liu, Derong; Wu, Huai-Ning

2018-06-01

Reinforcement learning has proved to be a powerful tool to solve optimal control problems over the past few years. However, the data-based constrained optimal control problem of nonaffine nonlinear discrete-time systems has rarely been studied yet. To solve this problem, an adaptive optimal control approach is developed by using the value iteration-based Q-learning (VIQL) with the critic-only structure. Most of the existing constrained control methods require the use of a certain performance index and only suit for linear or affine nonlinear systems, which is unreasonable in practice. To overcome this problem, the system transformation is first introduced with the general performance index. Then, the constrained optimal control problem is converted to an unconstrained optimal control problem. By introducing the action-state value function, i.e., Q-function, the VIQL algorithm is proposed to learn the optimal Q-function of the data-based unconstrained optimal control problem. The convergence results of the VIQL algorithm are established with an easy-to-realize initial condition . To implement the VIQL algorithm, the critic-only structure is developed, where only one neural network is required to approximate the Q-function. The converged Q-function obtained from the critic-only VIQL method is employed to design the adaptive constrained optimal controller based on the gradient descent scheme. Finally, the effectiveness of the developed adaptive control method is tested on three examples with computer simulation.

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.

Solving LP Relaxations of Large-Scale Precedence Constrained Problems

NASA Astrophysics Data System (ADS)

Bienstock, Daniel; Zuckerberg, Mark

We describe new algorithms for solving linear programming relaxations of very large precedence constrained production scheduling problems. We present theory that motivates a new set of algorithmic ideas that can be employed on a wide range of problems; on data sets arising in the mining industry our algorithms prove effective on problems with many millions of variables and constraints, obtaining provably optimal solutions in a few minutes of computation.

A feasible DY conjugate gradient method for linear equality constraints

NASA Astrophysics Data System (ADS)

LI, Can

2017-09-01

In this paper, we propose a feasible conjugate gradient method for solving linear equality constrained optimization problem. The method is an extension of the Dai-Yuan conjugate gradient method proposed by Dai and Yuan to linear equality constrained optimization problem. It can be applied to solve large linear equality constrained problem due to lower storage requirement. An attractive property of the method is that the generated direction is always feasible and descent direction. Under mild conditions, the global convergence of the proposed method with exact line search is established. Numerical experiments are also given which show the efficiency of the method.

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

PubMed Central

Zhang, Gongxuan; Wang, Yongli; Wang, Tianshu

2018-01-01

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

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

PubMed

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

2018-05-04

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

In Defense of Core Competencies, Quantitative Change, and Continuity

ERIC Educational Resources Information Center

Quinn, Paul C.

2008-01-01

J. Kagan (2008) urges contemporary developmentalists to (a) be cautious when attributing conceptual knowledge to infants based on looking-time performance, (b) constrain their interpretation of infant performance with multiple methodologies, and (c) reconsider the possibility that qualitative development may be the path by which perceptual infants…

Siblings versus parents and friends: longitudinal linkages to adolescent externalizing problems.

PubMed

Defoe, Ivy N; Keijsers, Loes; Hawk, Skyler T; Branje, Susan; Dubas, Judith Semon; Buist, Kirsten; Frijns, Tom; van Aken, Marcel A G; Koot, Hans M; van Lier, Pol A C; Meeus, Wim

2013-08-01

It is well documented that friends' externalizing problems and negative parent-child interactions predict externalizing problems in adolescence, but relatively little is known about the role of siblings. This four-wave, multi-informant study investigated linkages of siblings' externalizing problems and sibling-adolescent negative interactions on adolescents' externalizing problems, while examining and controlling for similar linkages with friends and parents. Questionnaire data on externalizing problems and negative interactions were annually collected from 497 Dutch adolescents (M = 13.03 years, SD = 0.52, at baseline), as well as their siblings, mothers, fathers, and friends. Cross-lagged panel analyses revealed modest unique longitudinal paths from sibling externalizing problems to adolescent externalizing problems, for male and female adolescents, and for same-sex and mixed-sex sibling dyads, but only from older to younger siblings. Moreover, these paths were above and beyond significant paths from mother-adolescent negative interaction and friend externalizing problems to adolescent externalizing problems, 1 year later. No cross-lagged paths existed between sibling-adolescent negative interaction and adolescent externalizing problems. Taken together, it appears that especially older sibling externalizing problems may be a unique social risk factor for adolescent externalizing problems, equal in strength to significant parents' and friends' risk factors. © 2013 The Authors. Journal of Child Psychology and Psychiatry © 2013 Association for Child and Adolescent Mental Health.

Siblings versus parents and friends: longitudinal linkages to adolescent externalizing problems

PubMed Central

Defoe, Ivy N; Keijsers, Loes; Hawk, Skyler T; Branje, Susan; Dubas, Judith Semon; Buist, Kirsten; Frijns, Tom; van Aken, Marcel AG; Koot, Hans M; van Lier, Pol AC; Meeus, Wim

2013-01-01

Background: It is well documented that friends’ externalizing problems and negative parent–child interactions predict externalizing problems in adolescence, but relatively little is known about the role of siblings. This four-wave, multi-informant study investigated linkages of siblings’ externalizing problems and sibling–adolescent negative interactions on adolescents’ externalizing problems, while examining and controlling for similar linkages with friends and parents. Methods: Questionnaire data on externalizing problems and negative interactions were annually collected from 497 Dutch adolescents (M = 13.03 years, SD = 0.52, at baseline), as well as their siblings, mothers, fathers, and friends. Results: Cross-lagged panel analyses revealed modest unique longitudinal paths from sibling externalizing problems to adolescent externalizing problems, for male and female adolescents, and for same-sex and mixed-sex sibling dyads, but only from older to younger siblings. Moreover, these paths were above and beyond significant paths from mother–adolescent negative interaction and friend externalizing problems to adolescent externalizing problems, 1 year later. No cross-lagged paths existed between sibling–adolescent negative interaction and adolescent externalizing problems. Conclusions: Taken together, it appears that especially older sibling externalizing problems may be a unique social risk factor for adolescent externalizing problems, equal in strength to significant parents’ and friends’ risk factors. PMID:23398022

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.

Solving the Curriculum Sequencing Problem with DNA Computing Approach

ERIC Educational Resources Information Center

Debbah, Amina; Ben Ali, Yamina Mohamed

2014-01-01

In the e-learning systems, a learning path is known as a sequence of learning materials linked to each others to help learners achieving their learning goals. As it is impossible to have the same learning path that suits different learners, the Curriculum Sequencing problem (CS) consists of the generation of a personalized learning path for each…

Spacecraft inertia estimation via constrained least squares

NASA Technical Reports Server (NTRS)

Keim, Jason A.; Acikmese, Behcet A.; Shields, Joel F.

2006-01-01

This paper presents a new formulation for spacecraft inertia estimation from test data. Specifically, the inertia estimation problem is formulated as a constrained least squares minimization problem with explicit bounds on the inertia matrix incorporated as LMIs [linear matrix inequalities). The resulting minimization problem is a semidefinite optimization that can be solved efficiently with guaranteed convergence to the global optimum by readily available algorithms. This method is applied to data collected from a robotic testbed consisting of a freely rotating body. The results show that the constrained least squares approach produces more accurate estimates of the inertia matrix than standard unconstrained least squares estimation methods.

Two arm robot path planning in a static environment using polytopes and string stretching. Thesis

NASA Technical Reports Server (NTRS)

Schima, Francis J., III

1990-01-01

The two arm robot path planning problem has been analyzed and reduced into components to be simplified. This thesis examines one component in which two Puma-560 robot arms are simultaneously holding a single object. The problem is to find a path between two points around obstacles which is relatively fast and minimizes the distance. The thesis involves creating a structure on which to form an advanced path planning algorithm which could ideally find the optimum path. An actual path planning method is implemented which is simple though effective in most common situations. Given the limits of computer technology, a 'good' path is currently found. Objects in the workspace are modeled with polytopes. These are used because they can be used for rapid collision detection and still provide a representation which is adequate for path planning.

Modeling of tool path for the CNC sheet cutting machines

NASA Astrophysics Data System (ADS)

Petunin, Aleksandr A.

2015-11-01

In the paper the problem of tool path optimization for CNC (Computer Numerical Control) cutting machines is considered. The classification of the cutting techniques is offered. We also propose a new classification of toll path problems. The tasks of cost minimization and time minimization for standard cutting technique (Continuous Cutting Problem, CCP) and for one of non-standard cutting techniques (Segment Continuous Cutting Problem, SCCP) are formalized. We show that the optimization tasks can be interpreted as discrete optimization problem (generalized travel salesman problem with additional constraints, GTSP). Formalization of some constraints for these tasks is described. For the solution GTSP we offer to use mathematical model of Prof. Chentsov based on concept of a megalopolis and dynamic programming.

Microprobe monazite geochronology: new techniques for dating deformation and metamorphism

NASA Astrophysics Data System (ADS)

Williams, M.; Jercinovic, M.; Goncalves, P.; Mahan, K.

2003-04-01

High-resolution compositional mapping, age mapping, and precise dating of monazite on the electron microprobe are powerful additions to microstructural and petrologic analysis and important tools for tectonic studies. The in-situ nature and high spatial resolution of the technique offer an entirely new level of structurally and texturally specific geochronologic data that can be used to put absolute time constraints on P-T-D paths, constrain the rates of sedimentary, metamorphic, and deformational processes, and provide new links between metamorphism and deformation. New analytical techniques (including background modeling, sample preparation, and interference analysis) have significantly improved the precision and accuracy of the technique and new mapping and image analysis techniques have increased the efficiency and strengthened the correlation with fabrics and textures. Microprobe geochronology is particularly applicable to three persistent microstructural-microtextural problem areas: (1) constraining the chronology of metamorphic assemblages; (2) constraining the timing of deformational fabrics; and (3) interpreting other geochronological results. In addition, authigenic monazite can be used to date sedimentary basins, and detrital monazite can fingerprint sedimentary source areas, both critical for tectonic analysis. Although some monazite generations can be directly tied to metamorphism or deformation, at present, the most common constraints rely on monazite inclusion relations in porphyroblasts that, in turn, can be tied to the deformation and/or metamorphic history. Examples will be presented from deep-crustal rocks of northern Saskatchewan and from mid-crustal rocks from the southwestern USA. Microprobe monazite geochronology has been used in both regions to deconvolute overprinting deformation and metamorphic events and to clarify the interpretation of other geochronologic data. Microprobe mapping and dating are powerful companions to mass spectroscopic dating techniques. They allow geochronology to be incorporated into the microstructural analytical process, resulting in a new level of integration of time (t) into P-T-D histories.

Scientific and Engineering Studies: Spectral Estimation

DTIC Science & Technology

1989-08-11

PROBLEM SOLUTION Four different constrained problems will be addressed in this section: con- strained window duration L ; constrained equivalent...sm(frtp + C, ^ smk ) » 0. (B_18) (B-19) The simultaneous solution of (B-ll) and (B-18), with smallest *< , is then given by q =.?0n l^fi

Effective Teaching of Economics: A Constrained Optimization Problem?

ERIC Educational Resources Information Center

Hultberg, Patrik T.; Calonge, David Santandreu

2017-01-01

One of the fundamental tenets of economics is that decisions are often the result of optimization problems subject to resource constraints. Consumers optimize utility, subject to constraints imposed by prices and income. As economics faculty, instructors attempt to maximize student learning while being constrained by their own and students'…

Train repathing in emergencies based on fuzzy linear programming.

PubMed

Meng, Xuelei; Cui, Bingmou

2014-01-01

Train pathing is a typical problem which is to assign the train trips on the sets of rail segments, such as rail tracks and links. This paper focuses on the train pathing problem, determining the paths of the train trips in emergencies. We analyze the influencing factors of train pathing, such as transferring cost, running cost, and social adverse effect cost. With the overall consideration of the segment and station capability constraints, we build the fuzzy linear programming model to solve the train pathing problem. We design the fuzzy membership function to describe the fuzzy coefficients. Furthermore, the contraction-expansion factors are introduced to contract or expand the value ranges of the fuzzy coefficients, coping with the uncertainty of the value range of the fuzzy coefficients. We propose a method based on triangular fuzzy coefficient and transfer the train pathing (fuzzy linear programming model) to a determinate linear model to solve the fuzzy linear programming problem. An emergency is supposed based on the real data of the Beijing-Shanghai Railway. The model in this paper was solved and the computation results prove the availability of the model and efficiency of the algorithm.

Enhanced Fuel-Optimal Trajectory-Generation Algorithm for Planetary Pinpoint Landing

NASA Technical Reports Server (NTRS)

Acikmese, Behcet; Blackmore, James C.; Scharf, Daniel P.

2011-01-01

An enhanced algorithm is developed that builds on a previous innovation of fuel-optimal powered-descent guidance (PDG) for planetary pinpoint landing. The PDG problem is to compute constrained, fuel-optimal trajectories to land a craft at a prescribed target on a planetary surface, starting from a parachute cut-off point and using a throttleable descent engine. The previous innovation showed the minimal-fuel PDG problem can be posed as a convex optimization problem, in particular, as a Second-Order Cone Program, which can be solved to global optimality with deterministic convergence properties, and hence is a candidate for onboard implementation. To increase the speed and robustness of this convex PDG algorithm for possible onboard implementation, the following enhancements are incorporated: 1) Fast detection of infeasibility (i.e., control authority is not sufficient for soft-landing) for subsequent fault response. 2) The use of a piecewise-linear control parameterization, providing smooth solution trajectories and increasing computational efficiency. 3) An enhanced line-search algorithm for optimal time-of-flight, providing quicker convergence and bounding the number of path-planning iterations needed. 4) An additional constraint that analytically guarantees inter-sample satisfaction of glide-slope and non-sub-surface flight constraints, allowing larger discretizations and, hence, faster optimization. 5) Explicit incorporation of Mars rotation rate into the trajectory computation for improved targeting accuracy. These enhancements allow faster convergence to the fuel-optimal solution and, more importantly, remove the need for a "human-in-the-loop," as constraints will be satisfied over the entire path-planning interval independent of step-size (as opposed to just at the discrete time points) and infeasible initial conditions are immediately detected. Finally, while the PDG stage is typically only a few minutes, ignoring the rotation rate of Mars can introduce 10s of meters of error. By incorporating it, the enhanced PDG algorithm becomes capable of pinpoint targeting.

Adaptive Fuzzy Output-Constrained Fault-Tolerant Control of Nonlinear Stochastic Large-Scale Systems With Actuator Faults.

PubMed

Li, Yongming; Ma, Zhiyao; Tong, Shaocheng

2017-09-01

The problem of adaptive fuzzy output-constrained tracking fault-tolerant control (FTC) is investigated for the large-scale stochastic nonlinear systems of pure-feedback form. The nonlinear systems considered in this paper possess the unstructured uncertainties, unknown interconnected terms and unknown nonaffine nonlinear faults. The fuzzy logic systems are employed to identify the unknown lumped nonlinear functions so that the problems of structured uncertainties can be solved. An adaptive fuzzy state observer is designed to solve the nonmeasurable state problem. By combining the barrier Lyapunov function theory, adaptive decentralized and stochastic control principles, a novel fuzzy adaptive output-constrained FTC approach is constructed. All the signals in the closed-loop system are proved to be bounded in probability and the system outputs are constrained in a given compact set. Finally, the applicability of the proposed controller is well carried out by a simulation example.

Study on high-resolution representation of terraces in Shanxi Loess Plateau area

NASA Astrophysics Data System (ADS)

Zhao, Weidong; Tang, Guo'an; Ma, Lei

2008-10-01

A new elevation points sampling method, namely TIN-based Sampling Method (TSM) and a new visual method called Elevation Addition Method (EAM), are put forth for representing the typical terraces in Shanxi loess plateau area. The DEM Feature Points and Lines Classification (DEPLC) put forth by the authors in 2007 is perfected for depicting the main path in the study area. The EAM is used to visualize the terraces and the path in the study area. 406 key elevation points and 15 feature constrained lines sampled by this method are used to construct CD-TINs which can depict the terraces and path correctly and effectively. Our case study shows that the new sampling method called TSM is reasonable and feasible. The complicated micro-terrains like terraces and path can be represented with high resolution and high efficiency successfully by use of the perfected DEPLC, TSM and CD-TINs. And both the terraces and the main path are visualized very well by use of EAM even when the terrace height is not more than 1m.

Column Number Density Expressions Through M = 0 and M = 1 Point Source Plumes Along Any Straight Path

NASA Technical Reports Server (NTRS)

Woronowicz, Michael S.

2016-01-01

Analytical expressions for column number density (CND) are developed for optical line of sight paths through a variety of steady free molecule point source models including directionally-constrained effusion (Mach number M = 0) and flow from a sonic orifice (M 1). Sonic orifice solutions are approximate, developed using a fair simulacrum fitted to the free molecule solution. Expressions are also developed for a spherically-symmetric thermal expansion (M = 0). CND solutions are found for the most general paths relative to these sources and briefly explored. It is determined that the maximum CND from a distant location through directed effusion and sonic orifice cases occurs along the path parallel to the source plane that intersects the plume axis. For the effusive case this value is exactly twice the CND found along the ray originating from that point of intersection and extending to infinity along the plumes axis. For sonic plumes this ratio is reduced to about 43. For high Mach number cases the maximum CND will be found along the axial centerline path.

Fast Combinatorial Algorithm for the Solution of Linearly Constrained Least Squares Problems

DOEpatents

Van Benthem, Mark H.; Keenan, Michael R.

2008-11-11

A fast combinatorial algorithm can significantly reduce the computational burden when solving general equality and inequality constrained least squares problems with large numbers of observation vectors. The combinatorial algorithm provides a mathematically rigorous solution and operates at great speed by reorganizing the calculations to take advantage of the combinatorial nature of the problems to be solved. The combinatorial algorithm exploits the structure that exists in large-scale problems in order to minimize the number of arithmetic operations required to obtain a solution.

Domain decomposition in time for PDE-constrained optimization

DOE PAGES

Barker, Andrew T.; Stoll, Martin

2015-08-28

Here, PDE-constrained optimization problems have a wide range of applications, but they lead to very large and ill-conditioned linear systems, especially if the problems are time dependent. In this paper we outline an approach for dealing with such problems by decomposing them in time and applying an additive Schwarz preconditioner in time, so that we can take advantage of parallel computers to deal with the very large linear systems. We then illustrate the performance of our method on a variety of problems.

Kleinberg Complex Networks

DTIC Science & Technology

2014-10-21

linear combinations of paths. This project featured research on two classes of routing problems , namely traveling salesman problems and multicommodity...flows. One highlight of this research was our discovery of a polynomial-time algorithm for the metric traveling salesman s-t path problem whose...metric TSP would resolve one of the most venerable open problems in the theory of approximation algorithms. Our research on traveling salesman

Airborne Remote sensing of the OH tropospheric column with an Integrated Path Differential LIDAR.

NASA Astrophysics Data System (ADS)

Hanisco, T. F.; Liang, Q.; Nicely, J. M.; Brune, W. H.; Miller, D. O.; Thames, A. B.

2017-12-01

The Hydroxyl radical, OH, is central to the photochemistry that controls tropospheric oxidation including the removal of atmospheric methane. Measurements of this important species are thus critical to testing our understanding and for constraining model results. Until now, tropospheric measurements have been limited to airborne or ground-based in situ instruments best suited to test photochemical box models. However, because of the growing recognition of the importance of the global methane abundance, we have a growing need to better quantify OH at the regional to global scales that are best sampled with airborne or space-based remote sensing instruments. To address this need, we have developed an instrument concept and have begun work on a laser transmitter for an airborne integrated path differential absorption LIDAR for the detection of OH. We will describe the instrument and present the expected performance characteristics. As a demonstration, we will use measurements from the recent ATOM-1 NASA airborne campaign to show measured OH columns can be used to constrain regional and global models.

A Gauge Invariant Description for the General Conic Constrained Particle from the FJBW Iteration Algorithm

NASA Astrophysics Data System (ADS)

Barbosa, Gabriel D.; Thibes, Ronaldo

2018-06-01

We consider a second-degree algebraic curve describing a general conic constraint imposed on the motion of a massive spinless particle. The problem is trivial at classical level but becomes involved and interesting concerning its quantum counterpart with subtleties in its symplectic structure and symmetries. We start with a second-class version of the general conic constrained particle, which encompasses previous versions of circular and elliptical paths discussed in the literature. By applying the symplectic FJBW iteration program, we proceed on to show how a gauge invariant version for the model can be achieved from the originally second-class system. We pursue the complete constraint analysis in phase space and perform the Faddeev-Jackiw symplectic quantization following the Barcelos-Wotzasek iteration program to unravel the essential aspects of the constraint structure. While in the standard Dirac-Bergmann approach there are four second-class constraints, in the FJBW they reduce to two. By using the symplectic potential obtained in the last step of the FJBW iteration process, we construct a gauge invariant model exhibiting explicitly its BRST symmetry. We obtain the quantum BRST charge and write the Green functions generator for the gauge invariant version. Our results reproduce and neatly generalize the known BRST symmetry of the rigid rotor, clearly showing that this last one constitutes a particular case of a broader class of theories.

Task Assignment and Path Planning for Multiple Autonomous Underwater Vehicles Using 3D Dubins Curves †

PubMed Central

Zhang, Meiyan; Zheng, Yahong Rosa

2017-01-01

This paper investigates the task assignment and path planning problem for multiple AUVs in three dimensional (3D) underwater wireless sensor networks where nonholonomic motion constraints of underwater AUVs in 3D space are considered. The multi-target task assignment and path planning problem is modeled by the Multiple Traveling Sales Person (MTSP) problem and the Genetic Algorithm (GA) is used to solve the MTSP problem with Euclidean distance as the cost function and the Tour Hop Balance (THB) or Tour Length Balance (TLB) constraints as the stop criterion. The resulting tour sequences are mapped to 2D Dubins curves in the X−Y plane, and then interpolated linearly to obtain the Z coordinates. We demonstrate that the linear interpolation fails to achieve G1 continuity in the 3D Dubins path for multiple targets. Therefore, the interpolated 3D Dubins curves are checked against the AUV dynamics constraint and the ones satisfying the constraint are accepted to finalize the 3D Dubins curve selection. Simulation results demonstrate that the integration of the 3D Dubins curve with the MTSP model is successful and effective for solving the 3D target assignment and path planning problem. PMID:28696377

Task Assignment and Path Planning for Multiple Autonomous Underwater Vehicles Using 3D Dubins Curves †.

PubMed

Cai, Wenyu; Zhang, Meiyan; Zheng, Yahong Rosa

2017-07-11

This paper investigates the task assignment and path planning problem for multiple AUVs in three dimensional (3D) underwater wireless sensor networks where nonholonomic motion constraints of underwater AUVs in 3D space are considered. The multi-target task assignment and path planning problem is modeled by the Multiple Traveling Sales Person (MTSP) problem and the Genetic Algorithm (GA) is used to solve the MTSP problem with Euclidean distance as the cost function and the Tour Hop Balance (THB) or Tour Length Balance (TLB) constraints as the stop criterion. The resulting tour sequences are mapped to 2D Dubins curves in the X - Y plane, and then interpolated linearly to obtain the Z coordinates. We demonstrate that the linear interpolation fails to achieve G 1 continuity in the 3D Dubins path for multiple targets. Therefore, the interpolated 3D Dubins curves are checked against the AUV dynamics constraint and the ones satisfying the constraint are accepted to finalize the 3D Dubins curve selection. Simulation results demonstrate that the integration of the 3D Dubins curve with the MTSP model is successful and effective for solving the 3D target assignment and path planning problem.

A Dynamic Bioinspired Neural Network Based Real-Time Path Planning Method for Autonomous Underwater Vehicles

PubMed Central

2017-01-01

Real-time path planning for autonomous underwater vehicle (AUV) is a very difficult and challenging task. Bioinspired neural network (BINN) has been used to deal with this problem for its many distinct advantages: that is, no learning process is needed and realization is also easy. However, there are some shortcomings when BINN is applied to AUV path planning in a three-dimensional (3D) unknown environment, including complex computing problem when the environment is very large and repeated path problem when the size of obstacles is bigger than the detection range of sensors. To deal with these problems, an improved dynamic BINN is proposed in this paper. In this proposed method, the AUV is regarded as the core of the BINN and the size of the BINN is based on the detection range of sensors. Then the BINN will move with the AUV and the computing could be reduced. A virtual target is proposed in the path planning method to ensure that the AUV can move to the real target effectively and avoid big-size obstacles automatically. Furthermore, a target attractor concept is introduced to improve the computing efficiency of neural activities. Finally, some experiments are conducted under various 3D underwater environments. The experimental results show that the proposed BINN based method can deal with the real-time path planning problem for AUV efficiently. PMID:28255297

A Dynamic Bioinspired Neural Network Based Real-Time Path Planning Method for Autonomous Underwater Vehicles.

PubMed

Ni, Jianjun; Wu, Liuying; Shi, Pengfei; Yang, Simon X

2017-01-01

Real-time path planning for autonomous underwater vehicle (AUV) is a very difficult and challenging task. Bioinspired neural network (BINN) has been used to deal with this problem for its many distinct advantages: that is, no learning process is needed and realization is also easy. However, there are some shortcomings when BINN is applied to AUV path planning in a three-dimensional (3D) unknown environment, including complex computing problem when the environment is very large and repeated path problem when the size of obstacles is bigger than the detection range of sensors. To deal with these problems, an improved dynamic BINN is proposed in this paper. In this proposed method, the AUV is regarded as the core of the BINN and the size of the BINN is based on the detection range of sensors. Then the BINN will move with the AUV and the computing could be reduced. A virtual target is proposed in the path planning method to ensure that the AUV can move to the real target effectively and avoid big-size obstacles automatically. Furthermore, a target attractor concept is introduced to improve the computing efficiency of neural activities. Finally, some experiments are conducted under various 3D underwater environments. The experimental results show that the proposed BINN based method can deal with the real-time path planning problem for AUV efficiently.

A chance-constrained stochastic approach to intermodal container routing problems.

PubMed

Zhao, Yi; Liu, Ronghui; Zhang, Xi; Whiteing, Anthony

2018-01-01

We consider a container routing problem with stochastic time variables in a sea-rail intermodal transportation system. The problem is formulated as a binary integer chance-constrained programming model including stochastic travel times and stochastic transfer time, with the objective of minimising the expected total cost. Two chance constraints are proposed to ensure that the container service satisfies ship fulfilment and cargo on-time delivery with pre-specified probabilities. A hybrid heuristic algorithm is employed to solve the binary integer chance-constrained programming model. Two case studies are conducted to demonstrate the feasibility of the proposed model and to analyse the impact of stochastic variables and chance-constraints on the optimal solution and total cost.

A chance-constrained stochastic approach to intermodal container routing problems

PubMed Central

Zhao, Yi; Zhang, Xi; Whiteing, Anthony

2018-01-01

We consider a container routing problem with stochastic time variables in a sea-rail intermodal transportation system. The problem is formulated as a binary integer chance-constrained programming model including stochastic travel times and stochastic transfer time, with the objective of minimising the expected total cost. Two chance constraints are proposed to ensure that the container service satisfies ship fulfilment and cargo on-time delivery with pre-specified probabilities. A hybrid heuristic algorithm is employed to solve the binary integer chance-constrained programming model. Two case studies are conducted to demonstrate the feasibility of the proposed model and to analyse the impact of stochastic variables and chance-constraints on the optimal solution and total cost. PMID:29438389

A subgradient approach for constrained binary optimization via quantum adiabatic evolution

NASA Astrophysics Data System (ADS)

Karimi, Sahar; Ronagh, Pooya

2017-08-01

Outer approximation method has been proposed for solving the Lagrangian dual of a constrained binary quadratic programming problem via quantum adiabatic evolution in the literature. This should be an efficient prescription for solving the Lagrangian dual problem in the presence of an ideally noise-free quantum adiabatic system. However, current implementations of quantum annealing systems demand methods that are efficient at handling possible sources of noise. In this paper, we consider a subgradient method for finding an optimal primal-dual pair for the Lagrangian dual of a constrained binary polynomial programming problem. We then study the quadratic stable set (QSS) problem as a case study. We see that this method applied to the QSS problem can be viewed as an instance-dependent penalty-term approach that avoids large penalty coefficients. Finally, we report our experimental results of using the D-Wave 2X quantum annealer and conclude that our approach helps this quantum processor to succeed more often in solving these problems compared to the usual penalty-term approaches.

The path integral on the Poincaré upper half-plane with a magnetic field and for the Morse potential

NASA Astrophysics Data System (ADS)

Grosche, Christian

1988-10-01

Rigorous path integral treatments on the Poincaré upper half-plane with a magnetic field and for the Morse potential are presented. The calculation starts with the path integral on the Poincaré upper half-plane with a magnetic field. By a Fourier expansion and a non-linear transformation this problem is reformulated in terms of the path integral for the Morse potential. This latter problem can be reduced by an appropriate space-time transformation to the path integral for the harmonic oscillator with generalised angular momentum, a technique which has been developed in recent years. The well-known solution for the last problem enables one to give explicit expressions for the Feynman kernels for the Morse potential and for the Poincaré upper half-plane with magnetic field, respectively. The wavefunctions and the energy spectrum for the bound and scattering states are given, respectively.

Multiply-Constrained Semantic Search in the Remote Associates Test

ERIC Educational Resources Information Center

Smith, Kevin A.; Huber, David E.; Vul, Edward

2013-01-01

Many important problems require consideration of multiple constraints, such as choosing a job based on salary, location, and responsibilities. We used the Remote Associates Test to study how people solve such multiply-constrained problems by asking participants to make guesses as they came to mind. We evaluated how people generated these guesses…

Solving a Hamiltonian Path Problem with a bacterial computer

PubMed Central

Baumgardner, Jordan; Acker, Karen; Adefuye, Oyinade; Crowley, Samuel Thomas; DeLoache, Will; Dickson, James O; Heard, Lane; Martens, Andrew T; Morton, Nickolaus; Ritter, Michelle; Shoecraft, Amber; Treece, Jessica; Unzicker, Matthew; Valencia, Amanda; Waters, Mike; Campbell, A Malcolm; Heyer, Laurie J; Poet, Jeffrey L; Eckdahl, Todd T

2009-01-01

Background The Hamiltonian Path Problem asks whether there is a route in a directed graph from a beginning node to an ending node, visiting each node exactly once. The Hamiltonian Path Problem is NP complete, achieving surprising computational complexity with modest increases in size. This challenge has inspired researchers to broaden the definition of a computer. DNA computers have been developed that solve NP complete problems. Bacterial computers can be programmed by constructing genetic circuits to execute an algorithm that is responsive to the environment and whose result can be observed. Each bacterium can examine a solution to a mathematical problem and billions of them can explore billions of possible solutions. Bacterial computers can be automated, made responsive to selection, and reproduce themselves so that more processing capacity is applied to problems over time. Results We programmed bacteria with a genetic circuit that enables them to evaluate all possible paths in a directed graph in order to find a Hamiltonian path. We encoded a three node directed graph as DNA segments that were autonomously shuffled randomly inside bacteria by a Hin/hixC recombination system we previously adapted from Salmonella typhimurium for use in Escherichia coli. We represented nodes in the graph as linked halves of two different genes encoding red or green fluorescent proteins. Bacterial populations displayed phenotypes that reflected random ordering of edges in the graph. Individual bacterial clones that found a Hamiltonian path reported their success by fluorescing both red and green, resulting in yellow colonies. We used DNA sequencing to verify that the yellow phenotype resulted from genotypes that represented Hamiltonian path solutions, demonstrating that our bacterial computer functioned as expected. Conclusion We successfully designed, constructed, and tested a bacterial computer capable of finding a Hamiltonian path in a three node directed graph. This proof-of-concept experiment demonstrates that bacterial computing is a new way to address NP-complete problems using the inherent advantages of genetic systems. The results of our experiments also validate synthetic biology as a valuable approach to biological engineering. We designed and constructed basic parts, devices, and systems using synthetic biology principles of standardization and abstraction. PMID:19630940

A quasi-Newton approach to optimization problems with probability density constraints. [problem solving in mathematical programming

NASA Technical Reports Server (NTRS)

Tapia, R. A.; Vanrooy, D. L.

1976-01-01

A quasi-Newton method is presented for minimizing a nonlinear function while constraining the variables to be nonnegative and sum to one. The nonnegativity constraints were eliminated by working with the squares of the variables and the resulting problem was solved using Tapia's general theory of quasi-Newton methods for constrained optimization. A user's guide for a computer program implementing this algorithm is provided.

Hopfield networks for solving Tower of Hanoi problems

NASA Astrophysics Data System (ADS)

Kaplan, G. B.; Güzeliş, Cüneyt

2001-08-01

In this paper, Hopfield neural networks have been considered in solving the Tower of Hanoi test which is used in the determining of deficit of planning capability of the human prefrontal cortex. The main difference between this paper and the ones in the literature which use neural networks is that the Tower of Hanoi problem has been formulated here as a special shortest-path problem. In the literature, some Hopfield networks are developed for solving the shortest path problem which is a combinatorial optimization problem having a diverse field of application. The approach given in this paper gives the possibility of solving the Tower of Hanoi problem using these Hopfield networks. Also, the paper proposes new Hopfield network models for the shortest path and hence the Tower of Hanoi problems and compares them to the available ones in terms of the memory and time (number of steps) needed in the simulations.

Semismooth Newton method for gradient constrained minimization problem

NASA Astrophysics Data System (ADS)

Anyyeva, Serbiniyaz; Kunisch, Karl

2012-08-01

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

Firefly algorithm for cardinality constrained mean-variance portfolio optimization problem with entropy diversity constraint.

PubMed

Bacanin, Nebojsa; Tuba, Milan

2014-01-01

Portfolio optimization (selection) problem is an important and hard optimization problem that, with the addition of necessary realistic constraints, becomes computationally intractable. Nature-inspired metaheuristics are appropriate for solving such problems; however, literature review shows that there are very few applications of nature-inspired metaheuristics to portfolio optimization problem. This is especially true for swarm intelligence algorithms which represent the newer branch of nature-inspired algorithms. No application of any swarm intelligence metaheuristics to cardinality constrained mean-variance (CCMV) portfolio problem with entropy constraint was found in the literature. This paper introduces modified firefly algorithm (FA) for the CCMV portfolio model with entropy constraint. Firefly algorithm is one of the latest, very successful swarm intelligence algorithm; however, it exhibits some deficiencies when applied to constrained problems. To overcome lack of exploration power during early iterations, we modified the algorithm and tested it on standard portfolio benchmark data sets used in the literature. Our proposed modified firefly algorithm proved to be better than other state-of-the-art algorithms, while introduction of entropy diversity constraint further improved results.

Firefly Algorithm for Cardinality Constrained Mean-Variance Portfolio Optimization Problem with Entropy Diversity Constraint

PubMed Central

2014-01-01

Portfolio optimization (selection) problem is an important and hard optimization problem that, with the addition of necessary realistic constraints, becomes computationally intractable. Nature-inspired metaheuristics are appropriate for solving such problems; however, literature review shows that there are very few applications of nature-inspired metaheuristics to portfolio optimization problem. This is especially true for swarm intelligence algorithms which represent the newer branch of nature-inspired algorithms. No application of any swarm intelligence metaheuristics to cardinality constrained mean-variance (CCMV) portfolio problem with entropy constraint was found in the literature. This paper introduces modified firefly algorithm (FA) for the CCMV portfolio model with entropy constraint. Firefly algorithm is one of the latest, very successful swarm intelligence algorithm; however, it exhibits some deficiencies when applied to constrained problems. To overcome lack of exploration power during early iterations, we modified the algorithm and tested it on standard portfolio benchmark data sets used in the literature. Our proposed modified firefly algorithm proved to be better than other state-of-the-art algorithms, while introduction of entropy diversity constraint further improved results. PMID:24991645

Numerical methods for the inverse problem of density functional theory

DOE PAGES

Jensen, Daniel S.; Wasserman, Adam

2017-07-17

Here, the inverse problem of Kohn–Sham density functional theory (DFT) is often solved in an effort to benchmark and design approximate exchange-correlation potentials. The forward and inverse problems of DFT rely on the same equations but the numerical methods for solving each problem are substantially different. We examine both problems in this tutorial with a special emphasis on the algorithms and error analysis needed for solving the inverse problem. Two inversion methods based on partial differential equation constrained optimization and constrained variational ideas are introduced. We compare and contrast several different inversion methods applied to one-dimensional finite and periodic modelmore » systems.« less

Numerical methods for the inverse problem of density functional theory

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

Jensen, Daniel S.; Wasserman, Adam

Here, the inverse problem of Kohn–Sham density functional theory (DFT) is often solved in an effort to benchmark and design approximate exchange-correlation potentials. The forward and inverse problems of DFT rely on the same equations but the numerical methods for solving each problem are substantially different. We examine both problems in this tutorial with a special emphasis on the algorithms and error analysis needed for solving the inverse problem. Two inversion methods based on partial differential equation constrained optimization and constrained variational ideas are introduced. We compare and contrast several different inversion methods applied to one-dimensional finite and periodic modelmore » systems.« less

On-Line Path Generation and Tracking for High-Speed Wheeled Autonomous Vehicles

DTIC Science & Technology

2006-02-17

On-Line Path Generation and Tracking for High-Speed Wheeled Autonomous Vehicles Report Title ABSTRACT In this work we proposed two semi-analytic...298-102 Enclosure 1 On-Line Path Generation and Tracking for High-Speed Wheeled Autonomous Vehicles by...Specifically, the following problems will be addressed during this project: 2.1 Challenges The problem of trajectory planning for high-speed autonomous vehicles is

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.

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

Graf, Peter; Dykes, Katherine; Scott, George

The layout of turbines in a wind farm is already a challenging nonlinear, nonconvex, nonlinearly constrained continuous global optimization problem. Here we begin to address the next generation of wind farm optimization problems by adding the complexity that there is more than one turbine type to choose from. The optimization becomes a nonlinear constrained mixed integer problem, which is a very difficult class of problems to solve. Furthermore, this document briefly summarizes the algorithm and code we have developed, the code validation steps we have performed, and the initial results for multi-turbine type and placement optimization (TTP_OPT) we have run.

Graph-Theoretic Representations for Proximity Matrices through Strongly-Anti-Robinson or Circular Strongly-Anti-Robinson Matrices.

ERIC Educational Resources Information Center

Hubert, Lawrence; Arabie, Phipps; Meulman, Jacqueline

1998-01-01

Introduces a method for fitting order-constrained matrices that satisfy the strongly anti-Robinson restrictions (SAR). The method permits a representation of the fitted values in a (least-squares) SAR approximating matrix as lengths of paths in a graph. The approach is illustrated with a published proximity matrix. (SLD)

Network Design for Reliability and Resilience to Attack

DTIC Science & Technology

2014-03-01

attacker can destroy n arcs in the network SPNI Shortest-Path Network-Interdiction problem TSP Traveling Salesman Problem UB upper bound UKR Ukraine...elimination from the traveling salesman problem (TSP). Literature calls a walk that does not contain a cycle a path [19]. The objective function in...arc lengths as random variables with known probability distributions. The m-median problem seeks to design a network with minimum average travel cost

Solution of a Complex Least Squares Problem with Constrained Phase.

PubMed

Bydder, Mark

2010-12-30

The least squares solution of a complex linear equation is in general a complex vector with independent real and imaginary parts. In certain applications in magnetic resonance imaging, a solution is desired such that each element has the same phase. A direct method for obtaining the least squares solution to the phase constrained problem is described.

Shortest path problem on a grid network with unordered intermediate points

NASA Astrophysics Data System (ADS)

Saw, Veekeong; Rahman, Amirah; Eng Ong, Wen

2017-10-01

We consider a shortest path problem with single cost factor on a grid network with unordered intermediate points. A two stage heuristic algorithm is proposed to find a feasible solution path within a reasonable amount of time. To evaluate the performance of the proposed algorithm, computational experiments are performed on grid maps of varying size and number of intermediate points. Preliminary results for the problem are reported. Numerical comparisons against brute forcing show that the proposed algorithm consistently yields solutions that are within 10% of the optimal solution and uses significantly less computation time.

Structural factoring approach for analyzing stochastic networks

NASA Technical Reports Server (NTRS)

Hayhurst, Kelly J.; Shier, Douglas R.

1991-01-01

The problem of finding the distribution of the shortest path length through a stochastic network is investigated. A general algorithm for determining the exact distribution of the shortest path length is developed based on the concept of conditional factoring, in which a directed, stochastic network is decomposed into an equivalent set of smaller, generally less complex subnetworks. Several network constructs are identified and exploited to reduce significantly the computational effort required to solve a network problem relative to complete enumeration. This algorithm can be applied to two important classes of stochastic path problems: determining the critical path distribution for acyclic networks and the exact two-terminal reliability for probabilistic networks. Computational experience with the algorithm was encouraging and allowed the exact solution of networks that have been previously analyzed only by approximation techniques.

Vervet monkeys use paths consistent with context-specific spatial movement heuristics.

PubMed

Teichroeb, Julie A

2015-10-01

Animal foraging routes are analogous to the computationally demanding "traveling salesman problem" (TSP), where individuals must find the shortest path among several locations before returning to the start. Humans approximate solutions to TSPs using simple heuristics or "rules of thumb," but our knowledge of how other animals solve multidestination routing problems is incomplete. Most nonhuman primate species have shown limited ability to route plan. However, captive vervets were shown to solve a TSP for six sites. These results were consistent with either planning three steps ahead or a risk-avoidance strategy. I investigated how wild vervet monkeys (Chlorocebus pygerythrus) solved a path problem with six, equally rewarding food sites; where site arrangement allowed assessment of whether vervets found the shortest route and/or used paths consistent with one of three simple heuristics to navigate. Single vervets took the shortest possible path in fewer than half of the trials, usually in ways consistent with the most efficient heuristic (the convex hull). When in competition, vervets' paths were consistent with different, more efficient heuristics dependent on their dominance rank (a cluster strategy for dominants and the nearest neighbor rule for subordinates). These results suggest that, like humans, vervets may solve multidestination routing problems by applying simple, adaptive, context-specific "rules of thumb." The heuristics that were consistent with vervet paths in this study are the same as some of those asserted to be used by humans. These spatial movement strategies may have common evolutionary roots and be part of a universal mental navigational toolkit. Alternatively, they may have emerged through convergent evolution as the optimal way to solve multidestination routing problems.

UCAV path planning in the presence of radar-guided surface-to-air missile threats

NASA Astrophysics Data System (ADS)

Zeitz, Frederick H., III

This dissertation addresses the problem of path planning for unmanned combat aerial vehicles (UCAVs) in the presence of radar-guided surface-to-air missiles (SAMs). The radars, collocated with SAM launch sites, operate within the structure of an Integrated Air Defense System (IADS) that permits communication and cooperation between individual radars. The problem is formulated in the framework of the interaction between three sub-systems: the aircraft, the IADS, and the missile. The main features of this integrated model are: The aircraft radar cross section (RCS) depends explicitly on both the aspect and bank angles; hence, the RCS and aircraft dynamics are coupled. The probabilistic nature of IADS tracking is accounted for; namely, the probability that the aircraft has been continuously tracked by the IADS depends on the aircraft RCS and range from the perspective of each radar within the IADS. Finally, the requirement to maintain tracking prior to missile launch and during missile flyout are also modeled. Based on this model, the problem of UCAV path planning is formulated as a minimax optimal control problem, with the aircraft bank angle serving as control. Necessary conditions of optimality for this minimax problem are derived. Based on these necessary conditions, properties of the optimal paths are derived. These properties are used to discretize the dynamic optimization problem into a finite-dimensional, nonlinear programming problem that can be solved numerically. Properties of the optimal paths are also used to initialize the numerical procedure. A homotopy method is proposed to solve the finite-dimensional, nonlinear programming problem, and a heuristic method is proposed to improve the discretization during the homotopy process. Based upon the properties of numerical solutions, a method is proposed for parameterizing and storing information for later recall in flight to permit rapid replanning in response to changing threats. Illustrative examples are presented that confirm the standard flying tactics of "denying range, aspect, and aim," by yielding flight paths that "weave" to avoid long exposures of aspects with large RCS.

Finite Element Analysis in Concurrent Processing: Computational Issues

NASA Technical Reports Server (NTRS)

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

2004-01-01

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

Simultaneous travel time tomography for updating both velocity and reflector geometry in triangular/tetrahedral cell model

NASA Astrophysics Data System (ADS)

Bai, Chao-ying; He, Lei-yu; Li, Xing-wang; Sun, Jia-yu

2018-05-01

To conduct forward and simultaneous inversion in a complex geological model, including an irregular topography (or irregular reflector or velocity anomaly), we in this paper combined our previous multiphase arrival tracking method (referred as triangular shortest-path method, TSPM) in triangular (2D) or tetrahedral (3D) cell model and a linearized inversion solver (referred to as damped minimum norms and constrained least squares problem solved using the conjugate gradient method, DMNCLS-CG) to formulate a simultaneous travel time inversion method for updating both velocity and reflector geometry by using multiphase arrival times. In the triangular/tetrahedral cells, we deduced the partial derivative of velocity variation with respective to the depth change of reflector. The numerical simulation results show that the computational accuracy can be tuned to a high precision in forward modeling and the irregular velocity anomaly and reflector geometry can be accurately captured in the simultaneous inversion, because the triangular/tetrahedral cell can be easily used to stitch the irregular topography or subsurface interface.

Modelling Equilibrium and Fractional Crystallization in the System MgO-FeO-CaO-Al2O3-SiO2

NASA Technical Reports Server (NTRS)

Herbert, F.

1985-01-01

A mathematical modelling technique for use in petrogenesis calculations in the system MgO-FeO-CaO-Al2O3-SiO2 is reported. Semiempirical phase boundary and elemental distribution information was combined with mass balance to compute approximate equilibrium crystallization paths for arbitrary system compositions. The calculation is applicable to a range of system compositions and fractionation calculations are possible. The goal of the calculation is the computation of the composition and quantity of each phase present as a function of the degree of solidification. The degree of solidification is parameterized by the heat released by the solidifying phases. The mathematical requirement for the solution of this problem is: (1) An equation constraining the composition of the magma for each solid phase in equilibrium with the liquidus phase, and (2) an equation for each solid phase and each component giving the distribution of that element between that phase and the magma.

Simultaneous travel time tomography for updating both velocity and reflector geometry in triangular/tetrahedral cell model

NASA Astrophysics Data System (ADS)

Bai, Chao-ying; He, Lei-yu; Li, Xing-wang; Sun, Jia-yu

2017-12-01

To conduct forward and simultaneous inversion in a complex geological model, including an irregular topography (or irregular reflector or velocity anomaly), we in this paper combined our previous multiphase arrival tracking method (referred as triangular shortest-path method, TSPM) in triangular (2D) or tetrahedral (3D) cell model and a linearized inversion solver (referred to as damped minimum norms and constrained least squares problem solved using the conjugate gradient method, DMNCLS-CG) to formulate a simultaneous travel time inversion method for updating both velocity and reflector geometry by using multiphase arrival times. In the triangular/tetrahedral cells, we deduced the partial derivative of velocity variation with respective to the depth change of reflector. The numerical simulation results show that the computational accuracy can be tuned to a high precision in forward modeling and the irregular velocity anomaly and reflector geometry can be accurately captured in the simultaneous inversion, because the triangular/tetrahedral cell can be easily used to stitch the irregular topography or subsurface interface.

A New Control Paradigm for Stochastic Differential Equations

NASA Astrophysics Data System (ADS)

Schmid, Matthias J. A.

This study presents a novel comprehensive approach to the control of dynamic systems under uncertainty governed by stochastic differential equations (SDEs). Large Deviations (LD) techniques are employed to arrive at a control law for a large class of nonlinear systems minimizing sample path deviations. Thereby, a paradigm shift is suggested from point-in-time to sample path statistics on function spaces. A suitable formal control framework which leverages embedded Freidlin-Wentzell theory is proposed and described in detail. This includes the precise definition of the control objective and comprises an accurate discussion of the adaptation of the Freidlin-Wentzell theorem to the particular situation. The new control design is enabled by the transformation of an ill-posed control objective into a well-conditioned sequential optimization problem. A direct numerical solution process is presented using quadratic programming, but the emphasis is on the development of a closed-form expression reflecting the asymptotic deviation probability of a particular nominal path. This is identified as the key factor in the success of the new paradigm. An approach employing the second variation and the differential curvature of the effective action is suggested for small deviation channels leading to the Jacobi field of the rate function and the subsequently introduced Jacobi field performance measure. This closed-form solution is utilized in combination with the supplied parametrization of the objective space. For the first time, this allows for an LD based control design applicable to a large class of nonlinear systems. Thus, Minimum Large Deviations (MLD) control is effectively established in a comprehensive structured framework. The construction of the new paradigm is completed by an optimality proof for the Jacobi field performance measure, an interpretive discussion, and a suggestion for efficient implementation. The potential of the new approach is exhibited by its extension to scalar systems subject to state-dependent noise and to systems of higher order. The suggested control paradigm is further advanced when a sequential application of MLD control is considered. This technique yields a nominal path corresponding to the minimum total deviation probability on the entire time domain. It is demonstrated that this sequential optimization concept can be unified in a single objective function which is revealed to be the Jacobi field performance index on the entire domain subject to an endpoint deviation. The emerging closed-form term replaces the previously required nested optimization and, thus, results in a highly efficient application-ready control design. This effectively substantiates Minimum Path Deviation (MPD) control. The proposed control paradigm allows the specific problem of stochastic cost control to be addressed as a special case. This new technique is employed within this study for the stochastic cost problem giving rise to Cost Constrained MPD (CCMPD) as well as to Minimum Quadratic Cost Deviation (MQCD) control. An exemplary treatment of a generic scalar nonlinear system subject to quadratic costs is performed for MQCD control to demonstrate the elementary expandability of the new control paradigm. This work concludes with a numerical evaluation of both MPD and CCMPD control for three exemplary benchmark problems. Numerical issues associated with the simulation of SDEs are briefly discussed and illustrated. The numerical examples furnish proof of the successful design. This study is complemented by a thorough review of statistical control methods, stochastic processes, Large Deviations techniques and the Freidlin-Wentzell theory, providing a comprehensive, self-contained account. The presentation of the mathematical tools and concepts is of a unique character, specifically addressing an engineering audience.

Prediction-Correction Algorithms for Time-Varying Constrained Optimization

DOE PAGES

Simonetto, Andrea; Dall'Anese, Emiliano

2017-07-26

This article develops online algorithms to track solutions of time-varying constrained optimization problems. Particularly, resembling workhorse Kalman filtering-based approaches for dynamical systems, the proposed methods involve prediction-correction steps to provably track the trajectory of the optimal solutions of time-varying convex problems. The merits of existing prediction-correction methods have been shown for unconstrained problems and for setups where computing the inverse of the Hessian of the cost function is computationally affordable. This paper addresses the limitations of existing methods by tackling constrained problems and by designing first-order prediction steps that rely on the Hessian of the cost function (and do notmore » require the computation of its inverse). In addition, the proposed methods are shown to improve the convergence speed of existing prediction-correction methods when applied to unconstrained problems. Numerical simulations corroborate the analytical results and showcase performance and benefits of the proposed algorithms. A realistic application of the proposed method to real-time control of energy resources is presented.« less

Time-Domain Pure-state Polarization Analysis of Surface Waves Traversing California

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

Zhang, J; Walter, W R; Lay, T

A time-domain pure-state polarization analysis method is used to characterize surface waves traversing California parallel to the plate boundary. The method is applied to data recorded at four broadband stations in California from twenty-six large, shallow earthquakes which occurred since 1988, yielding polarization parameters such as the ellipticity, Euler angles, instantaneous periods, and wave incident azimuths. The earthquakes are located along the circum-Pacific margin and the ray paths cluster into two groups, with great-circle paths connecting stations MHC and PAS or CMB and GSC. The first path (MHC-PAS) is in the vicinity of the San Andreas Fault System (SAFS), andmore » the second (CMB-GSC) traverses the Sierra Nevada Batholith parallel to and east of the SAFS. Both Rayleigh and Love wave data show refractions due to lateral velocity heterogeneities under the path, indicating that accurate phase velocity and attenuation analysis requires array measurements. The Rayleigh waves are strongly affected by low velocity anomalies beneath Central California, with ray paths bending eastward as waves travel toward the south, while Love waves are less affected, providing observables to constrain the depth extent of the anomalies. Strong lateral gradients in the lithospheric structure between the continent and the ocean are the likely cause of the path deflections.« less

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.

Comparison of some evolutionary algorithms for optimization of the path synthesis problem

NASA Astrophysics Data System (ADS)

Grabski, Jakub Krzysztof; Walczak, Tomasz; Buśkiewicz, Jacek; Michałowska, Martyna

2018-01-01

The paper presents comparison of the results obtained in a mechanism synthesis by means of some selected evolutionary algorithms. The optimization problem considered in the paper as an example is the dimensional synthesis of the path generating four-bar mechanism. In order to solve this problem, three different artificial intelligence algorithms are employed in this study.

Planning minimum-energy paths in an off-road environment with anisotropic traversal costs and motion constraints. Doctoral thesis

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

Ross, R.S.

1989-06-01

For a vehicle operating across arbitrarily-contoured terrain, finding the most fuel-efficient route between two points can be viewed as a high-level global path-planning problem with traversal costs and stability dependent on the direction of travel (anisotropic). The problem assumes a two-dimensional polygonal map of homogeneous cost regions for terrain representation constructed from elevation information. The anisotropic energy cost of vehicle motion has a non-braking component dependent on horizontal distance, a braking component dependent on vertical distance, and a constant path-independent component. The behavior of minimum-energy paths is then proved to be restricted to a small, but optimal set of traversalmore » types. An optimal-path-planning algorithm, using a heuristic search technique, reduces the infinite number of paths between the start and goal points to a finite number by generating sequences of goal-feasible window lists from analyzing the polygonal map and applying pruning criteria. The pruning criteria consist of visibility analysis, heading analysis, and region-boundary constraints. Each goal-feasible window lists specifies an associated convex optimization problem, and the best of all locally-optimal paths through the goal-feasible window lists is the globally-optimal path. These ideas have been implemented in a computer program, with results showing considerably better performance than the exponential average-case behavior predicted.« less

Negative emotionality moderates associations among attachment, toddler sleep, and later problem behaviors.

PubMed

Troxel, Wendy M; Trentacosta, Christopher J; Forbes, Erika E; Campbell, Susan B

2013-02-01

Secure parent-child relationships are implicated in children's self-regulation, including the ability to self-soothe at bedtime. Sleep, in turn, may serve as a pathway linking attachment security with subsequent emotional and behavioral problems in children. We used path analysis to examine the direct relationship between attachment security and maternal reports of sleep problems during toddlerhood and the degree to which sleep serves as a pathway linking attachment with subsequent teacher-reported emotional and behavioral problems. We also examined infant negative emotionality as a vulnerability factor that may potentiate attachment-sleep-adjustment outcomes. Data were drawn from 776 mother-infant dyads participating in the National Institute of Child and Human Development Study of Early Child Care. After statistically adjusting for mother and child characteristics, including child sleep and emotional and behavioral problems at 24 months, we found no evidence for a statistically significant direct path between attachment security and sleep problems at 36 months; however, there was a direct relationship between sleep problems at 36 months and internalizing problems at 54 months. Path models that examined the moderating influence of infant negative emotionality demonstrated significant direct relationships between attachment security and toddler sleep problems and between sleep problems and subsequent emotional and behavioral problems, but only among children characterized by high negative emotionality at 6 months. In addition, among this subset, there was a significant indirect path between attachment and internalizing problems through sleep problems. These longitudinal findings implicate sleep as one critical pathway linking attachment security with adjustment difficulties, particularly among temperamentally vulnerable children. PsycINFO Database Record (c) 2013 APA, all rights reserved.

Negative Emotionality Moderates Associations among Attachment, Toddler Sleep, and Later Problem Behaviors

PubMed Central

Troxel, Wendy M.; Trentacosta, Christopher J.; Forbes, Erika E.; Campbell, Susan B.

2013-01-01

Secure parent-child relationships are implicated in children’s self-regulation, including the ability to self-soothe at bedtime. Sleep, in turn, may serve as a pathway linking attachment security with subsequent emotional and behavioral problems in children. We used path analysis to examine the direct relationship between attachment security and maternal-reports of sleep problems during toddlerhood, and the degree to which sleep serves as a pathway linking attachment with subsequent teacher-reported emotional and behavioral problems. We also examined infant negative emotionality as a vulnerability factor that may potentiate attachment-sleep-adjustment outcomes. Data were drawn from 776 mother-infant dyads participating in the NICHD Study of Early Child Care (SECC). In the full sample, after statistically adjusting for mother and child characteristics, including child sleep and emotional and behavioral problems at 24 months, we did not find evidence for a statistically significant direct path between attachment security and sleep problems at 36 months; however, there was a direct relationship between sleep problems at 36 months and internalizing problems at 54 months. Path models that examined the moderating influence of infant negative emotionality demonstrated significant direct relationships between attachment security and toddler sleep problems, and sleep problems and subsequent emotional and behavioral problems, but only among children characterized by high negative emotionality at 6 months of age. In addition, among this subset, there was a significant indirect path between attachment and internalizing problems through sleep problems. These longitudinal findings implicate sleep as one critical pathway linking attachment security with adjustment difficulties, particularly among temperamentally vulnerable children. PMID:23421840

An historical survey of computational methods in optimal control.

NASA Technical Reports Server (NTRS)

Polak, E.

1973-01-01

Review of some of the salient theoretical developments in the specific area of optimal control algorithms. The first algorithms for optimal control were aimed at unconstrained problems and were derived by using first- and second-variation methods of the calculus of variations. These methods have subsequently been recognized as gradient, Newton-Raphson, or Gauss-Newton methods in function space. A much more recent addition to the arsenal of unconstrained optimal control algorithms are several variations of conjugate-gradient methods. At first, constrained optimal control problems could only be solved by exterior penalty function methods. Later algorithms specifically designed for constrained problems have appeared. Among these are methods for solving the unconstrained linear quadratic regulator problem, as well as certain constrained minimum-time and minimum-energy problems. Differential-dynamic programming was developed from dynamic programming considerations. The conditional-gradient method, the gradient-projection method, and a couple of feasible directions methods were obtained as extensions or adaptations of related algorithms for finite-dimensional problems. Finally, the so-called epsilon-methods combine the Ritz method with penalty function techniques.

A Mixed Integer Linear Programming Approach to Electrical Stimulation Optimization Problems.

PubMed

Abouelseoud, Gehan; Abouelseoud, Yasmine; Shoukry, Amin; Ismail, Nour; Mekky, Jaidaa

2018-02-01

Electrical stimulation optimization is a challenging problem. Even when a single region is targeted for excitation, the problem remains a constrained multi-objective optimization problem. The constrained nature of the problem results from safety concerns while its multi-objectives originate from the requirement that non-targeted regions should remain unaffected. In this paper, we propose a mixed integer linear programming formulation that can successfully address the challenges facing this problem. Moreover, the proposed framework can conclusively check the feasibility of the stimulation goals. This helps researchers to avoid wasting time trying to achieve goals that are impossible under a chosen stimulation setup. The superiority of the proposed framework over alternative methods is demonstrated through simulation examples.

Column Number Density Expressions Through M = 0 and M = 1 Point Source Plumes Along Any Straight Path

NASA Technical Reports Server (NTRS)

Woronowicz, Michael

2016-01-01

Analytical expressions for column number density (CND) are developed for optical line of sight paths through a variety of steady free molecule point source models including directionally-constrained effusion (Mach number M = 0) and flow from a sonic orifice (M = 1). Sonic orifice solutions are approximate, developed using a fair simulacrum fitted to the free molecule solution. Expressions are also developed for a spherically-symmetric thermal expansion (M = 0). CND solutions are found for the most general paths relative to these sources and briefly explored. It is determined that the maximum CND from a distant location through directed effusion and sonic orifice cases occurs along the path parallel to the source plane that intersects the plume axis. For the effusive case this value is exactly twice the CND found along the ray originating from that point of intersection and extending to infinity along the plume's axis. For sonic plumes this ratio is reduced to about 4/3. For high Mach number cases the maximum CND will be found along the axial centerline path. Keywords: column number density, plume flows, outgassing, free molecule flow.

Distributed Method to Optimal Profile Descent

NASA Astrophysics Data System (ADS)

Kim, Geun I.

Current ground automation tools for Optimal Profile Descent (OPD) procedures utilize path stretching and speed profile change to maintain proper merging and spacing requirements at high traffic terminal area. However, low predictability of aircraft's vertical profile and path deviation during decent add uncertainty to computing estimated time of arrival, a key information that enables the ground control center to manage airspace traffic effectively. This paper uses an OPD procedure that is based on a constant flight path angle to increase the predictability of the vertical profile and defines an OPD optimization problem that uses both path stretching and speed profile change while largely maintaining the original OPD procedure. This problem minimizes the cumulative cost of performing OPD procedures for a group of aircraft by assigning a time cost function to each aircraft and a separation cost function to a pair of aircraft. The OPD optimization problem is then solved in a decentralized manner using dual decomposition techniques under inter-aircraft ADS-B mechanism. This method divides the optimization problem into more manageable sub-problems which are then distributed to the group of aircraft. Each aircraft solves its assigned sub-problem and communicate the solutions to other aircraft in an iterative process until an optimal solution is achieved thus decentralizing the computation of the optimization problem.

Fuzzy multi-objective chance-constrained programming model for hazardous materials transportation

NASA Astrophysics Data System (ADS)

Du, Jiaoman; Yu, Lean; Li, Xiang

2016-04-01

Hazardous materials transportation is an important and hot issue of public safety. Based on the shortest path model, this paper presents a fuzzy multi-objective programming model that minimizes the transportation risk to life, travel time and fuel consumption. First, we present the risk model, travel time model and fuel consumption model. Furthermore, we formulate a chance-constrained programming model within the framework of credibility theory, in which the lengths of arcs in the transportation network are assumed to be fuzzy variables. A hybrid intelligent algorithm integrating fuzzy simulation and genetic algorithm is designed for finding a satisfactory solution. Finally, some numerical examples are given to demonstrate the efficiency of the proposed model and algorithm.

Adaptive Fuzzy Output Constrained Control Design for Multi-Input Multioutput Stochastic Nonstrict-Feedback Nonlinear Systems.

PubMed

Li, Yongming; Tong, Shaocheng

2017-12-01

In this paper, an adaptive fuzzy output constrained control design approach is addressed for multi-input multioutput uncertain stochastic nonlinear systems in nonstrict-feedback form. The nonlinear systems addressed in this paper possess unstructured uncertainties, unknown gain functions and unknown stochastic disturbances. Fuzzy logic systems are utilized to tackle the problem of unknown nonlinear uncertainties. The barrier Lyapunov function technique is employed to solve the output constrained problem. In the framework of backstepping design, an adaptive fuzzy control design scheme is constructed. All the signals in the closed-loop system are proved to be bounded in probability and the system outputs are constrained in a given compact set. Finally, the applicability of the proposed controller is well carried out by a simulation example.

Improved Geothermometry Through Multivariate Reaction-path Modeling and Evaluation of Geomicrobiological Influences on Geochemical Temperature Indicators: Final Report

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

Mattson, Earl; Smith, Robert; Fujita, Yoshiko

2015-03-01

The project was aimed at demonstrating that the geothermometric predictions can be improved through the application of multi-element reaction path modeling that accounts for lithologic and tectonic settings, while also accounting for biological influences on geochemical temperature indicators. The limited utilization of chemical signatures by individual traditional geothermometer in the development of reservoir temperature estimates may have been constraining their reliability for evaluation of potential geothermal resources. This project, however, was intended to build a geothermometry tool which can integrate multi-component reaction path modeling with process-optimization capability that can be applied to dilute, low-temperature water samples to consistently predict reservoirmore » temperature within ±30 °C. The project was also intended to evaluate the extent to which microbiological processes can modulate the geochemical signals in some thermal waters and influence the geothermometric predictions.« less

Compact atom interferometer using single laser

NASA Astrophysics Data System (ADS)

Chiow, Sheng-wey; Yu, Nan

2018-06-01

A typical atom interferometer requires vastly different laser frequencies at different stages of operation, e.g., near resonant light for laser cooling and far detuned light for atom optics, such that multiple lasers are typically employed. The number of laser units constrains the achievable minimum size and power in practical devices for resource critical environments such as space. We demonstrate a compact atom interferometer accelerometer operated by a single diode laser. This is achieved by dynamically changing the laser output frequency in GHz range while maintaining spectroscopic reference to an atomic transition via a sideband generated by phase modulation. At the same time, a beam path sharing configuration is also demonstrated for a compact sensor head design, in which atom interferometer beams share the same path as that of the cooling beam. This beam path sharing also significantly simplifies three-axis atomic accelerometry in microgravity using single sensor head.

The Time Window Vehicle Routing Problem Considering Closed Route

NASA Astrophysics Data System (ADS)

Irsa Syahputri, Nenna; Mawengkang, Herman

2017-12-01

The Vehicle Routing Problem (VRP) determines the optimal set of routes used by a fleet of vehicles to serve a given set of customers on a predefined graph; the objective is to minimize the total travel cost (related to the travel times or distances) and operational cost (related to the number of vehicles used). In this paper we study a variant of the predefined graph: given a weighted graph G and vertices a and b, and given a set X of closed paths in G, find the minimum total travel cost of a-b path P such that no path in X is a subpath of P. Path P is allowed to repeat vertices and edges. We use integer programming model to describe the problem. A feasible neighbourhood approach is proposed to solve the model

Vervet monkey (Chlorocebus pygerythrus) behavior in a multi-destination route: Evidence for planning ahead when heuristics fail.

PubMed

Teichroeb, Julie Annette; Smeltzer, Eve Ann

2018-01-01

Animal paths are analogous to intractable mathematical problems like the Traveling Salesman Problem (TSP) and the shortest path problem (SPP). Both the TSP and SPP require an individual to find the shortest path through multiple targets but the TSP demands a return to the start, while the SPP does not. Vervet monkeys are very efficient in solving TSPs but this species is a multiple central place forager that does not always return to the same sleeping site and thus theoretically should be selected to find solutions to SPPs rather than TSPs. We examined path choice by wild vervets in an SPP experimental array where the shortest paths usually differed from those consistent with common heuristic strategies, the nearest-neighbor rule (NNR-go to the closest resource that has not been visited), and the convex hull (put a mental loop around sites, adding inner targets in order of distance from the edge)-an efficient strategy for TSPs but not SPPs. In addition, humans solving SPPs use an initial segment strategy (ISS-choose the straightest path at the beginning, only turning when necessary) and we looked at vervet paths consistent with this strategy. In 615 trials by single foragers, paths usually conformed to the NNR and rarely the slightly more efficient convex hull, supporting that vervets may be selected to solve SPPs. Further, like humans solving SPPs, vervets showed a tendency to use the ISS. Paths consistent with heuristics dropped off sharply, and use of the shortest path increased, when heuristics led to longer paths showing trade-offs in efficiency versus cognitive load. Two individuals out of 17, found the shortest path most often, showing inter-individual variation in path planning. Given support for the NNR and the ISS, we propose a new rule-of-thumb termed the "region heuristic" that vervets may apply in multi-destination routes.

Vervet monkey (Chlorocebus pygerythrus) behavior in a multi-destination route: Evidence for planning ahead when heuristics fail

PubMed Central

Smeltzer, Eve Ann

2018-01-01

Animal paths are analogous to intractable mathematical problems like the Traveling Salesman Problem (TSP) and the shortest path problem (SPP). Both the TSP and SPP require an individual to find the shortest path through multiple targets but the TSP demands a return to the start, while the SPP does not. Vervet monkeys are very efficient in solving TSPs but this species is a multiple central place forager that does not always return to the same sleeping site and thus theoretically should be selected to find solutions to SPPs rather than TSPs. We examined path choice by wild vervets in an SPP experimental array where the shortest paths usually differed from those consistent with common heuristic strategies, the nearest-neighbor rule (NNR–go to the closest resource that has not been visited), and the convex hull (put a mental loop around sites, adding inner targets in order of distance from the edge)–an efficient strategy for TSPs but not SPPs. In addition, humans solving SPPs use an initial segment strategy (ISS–choose the straightest path at the beginning, only turning when necessary) and we looked at vervet paths consistent with this strategy. In 615 trials by single foragers, paths usually conformed to the NNR and rarely the slightly more efficient convex hull, supporting that vervets may be selected to solve SPPs. Further, like humans solving SPPs, vervets showed a tendency to use the ISS. Paths consistent with heuristics dropped off sharply, and use of the shortest path increased, when heuristics led to longer paths showing trade-offs in efficiency versus cognitive load. Two individuals out of 17, found the shortest path most often, showing inter-individual variation in path planning. Given support for the NNR and the ISS, we propose a new rule-of-thumb termed the “region heuristic” that vervets may apply in multi-destination routes. PMID:29813105

Wind Farm Turbine Type and Placement Optimization

NASA Astrophysics Data System (ADS)

Graf, Peter; Dykes, Katherine; Scott, George; Fields, Jason; Lunacek, Monte; Quick, Julian; Rethore, Pierre-Elouan

2016-09-01

The layout of turbines in a wind farm is already a challenging nonlinear, nonconvex, nonlinearly constrained continuous global optimization problem. Here we begin to address the next generation of wind farm optimization problems by adding the complexity that there is more than one turbine type to choose from. The optimization becomes a nonlinear constrained mixed integer problem, which is a very difficult class of problems to solve. This document briefly summarizes the algorithm and code we have developed, the code validation steps we have performed, and the initial results for multi-turbine type and placement optimization (TTP_OPT) we have run.

Wind farm turbine type and placement optimization

DOE PAGES

Graf, Peter; Dykes, Katherine; Scott, George; ...

2016-10-03

The layout of turbines in a wind farm is already a challenging nonlinear, nonconvex, nonlinearly constrained continuous global optimization problem. Here we begin to address the next generation of wind farm optimization problems by adding the complexity that there is more than one turbine type to choose from. The optimization becomes a nonlinear constrained mixed integer problem, which is a very difficult class of problems to solve. Furthermore, this document briefly summarizes the algorithm and code we have developed, the code validation steps we have performed, and the initial results for multi-turbine type and placement optimization (TTP_OPT) we have run.

Mobile transporter path planning

NASA Technical Reports Server (NTRS)

Baffes, Paul; Wang, Lui

1990-01-01

The use of a genetic algorithm (GA) for solving the mobile transporter path planning problem is investigated. The mobile transporter is a traveling robotic vehicle proposed for the space station which must be able to reach any point of the structure autonomously. Elements of the genetic algorithm are explored in both a theoretical and experimental sense. Specifically, double crossover, greedy crossover, and tournament selection techniques are examined. Additionally, the use of local optimization techniques working in concert with the GA are also explored. Recent developments in genetic algorithm theory are shown to be particularly effective in a path planning problem domain, though problem areas can be cited which require more research.

Employment Policy for the Hard-to-Employ: The Path of Progress. A Statement by the Program Committee.

ERIC Educational Resources Information Center

Committee for Economic Development, New York, NY.

At a time of high unemployment and increasingly constrained public resources, more and more attention is being focused on how to further the private sector's involvement in expanding opportunities for individuals, especially the hard-to-employ, to obtain long-term productive employment. Although at least 12 pieces of federal legislation that…

Just in Time Assurance

DTIC Science & Technology

2010-04-01

constrained to the objects it manages A local reference monitor can be maintained, updated, and replaced with minimal effect on the rest of the system...Compositional assurance is the path towards the goal of JIT Assurance Construct individual assurance case for each trusted tcomponen Provide argument that...local policies combine to enforce the overall system policy Composability enables JIT Assurance A component can be patched, upgraded, refreshed

Aiding the search: Examining individual differences in multiply-constrained problem solving.

PubMed

Ellis, Derek M; Brewer, Gene A

2018-07-01

Understanding and resolving complex problems is of vital importance in daily life. Problems can be defined by the limitations they place on the problem solver. Multiply-constrained problems are traditionally examined with the compound remote associates task (CRAT). Performance on the CRAT is partially dependent on an individual's working memory capacity (WMC). These findings suggest that executive processes are critical for problem solving and that there are reliable individual differences in multiply-constrained problem solving abilities. The goals of the current study are to replicate and further elucidate the relation between WMC and CRAT performance. To achieve these goals, we manipulated preexposure to CRAT solutions and measured WMC with complex-span tasks. In Experiment 1, we report evidence that preexposure to CRAT solutions improved problem solving accuracy, WMC was correlated with problem solving accuracy, and that WMC did not moderate the effect of preexposure on problem solving accuracy. In Experiment 2, we preexposed participants to correct and incorrect solutions. We replicated Experiment 1 and found that WMC moderates the effect of exposure to CRAT solutions such that high WMC participants benefit more from preexposure to correct solutions than low WMC (although low WMC participants have preexposure benefits as well). Broadly, these results are consistent with theories of working memory and problem solving that suggest a mediating role of attention control processes. Published by Elsevier Inc.

Optimal solution for travelling salesman problem using heuristic shortest path algorithm with imprecise arc length

NASA Astrophysics Data System (ADS)

Bakar, Sumarni Abu; Ibrahim, Milbah

2017-08-01

The shortest path problem is a popular problem in graph theory. It is about finding a path with minimum length between a specified pair of vertices. In any network the weight of each edge is usually represented in a form of crisp real number and subsequently the weight is used in the calculation of shortest path problem using deterministic algorithms. However, due to failure, uncertainty is always encountered in practice whereby the weight of edge of the network is uncertain and imprecise. In this paper, a modified algorithm which utilized heuristic shortest path method and fuzzy approach is proposed for solving a network with imprecise arc length. Here, interval number and triangular fuzzy number in representing arc length of the network are considered. The modified algorithm is then applied to a specific example of the Travelling Salesman Problem (TSP). Total shortest distance obtained from this algorithm is then compared with the total distance obtained from traditional nearest neighbour heuristic algorithm. The result shows that the modified algorithm can provide not only on the sequence of visited cities which shown to be similar with traditional approach but it also provides a good measurement of total shortest distance which is lesser as compared to the total shortest distance calculated using traditional approach. Hence, this research could contribute to the enrichment of methods used in solving TSP.

Explicit Low-Thrust Guidance for Reference Orbit Targeting

NASA Technical Reports Server (NTRS)

Lam, Try; Udwadia, Firdaus E.

2013-01-01

The problem of a low-thrust spacecraft controlled to a reference orbit is addressed in this paper. A simple and explicit low-thrust guidance scheme with constrained thrust magnitude is developed by combining the fundamental equations of motion for constrained systems from analytical dynamics with a Lyapunov-based method. Examples are given for a spacecraft controlled to a reference trajectory in the circular restricted three body problem.

Critical transition in the constrained traveling salesman problem.

PubMed

Andrecut, M; Ali, M K

2001-04-01

We investigate the finite size scaling of the mean optimal tour length as a function of density of obstacles in a constrained variant of the traveling salesman problem (TSP). The computational experience pointed out a critical transition (at rho(c) approximately 85%) in the dependence between the excess of the mean optimal tour length over the Held-Karp lower bound and the density of obstacles.

Behavior problems and placement change in a national child welfare sample: a prospective study.

PubMed

Aarons, Gregory A; James, Sigrid; Monn, Amy R; Raghavan, Ramesh; Wells, Rebecca S; Leslie, Laurel K

2010-01-01

There is ongoing debate regarding the impact of youth behavior problems on placement change in child welfare compared to the impact of placement change on behavior problems. Existing studies provide support for both perspectives. The purpose of this study was to prospectively examine the relations of behavior problems and placement change in a nationally representative sample of youths in the National Survey of Child and Adolescent Well-Being. The sample consisted of 500 youths in the child welfare system with out-of-home placements over the course of the National Survey of Child and Adolescent Well-Being study. We used a prospective cross-lag design and path analysis to examine reciprocal effects of behavior problems and placement change, testing an overall model and models examining effects of age and gender. In the overall model, out of a total of eight path coefficients, behavior problems significantly predicted placement changes for three paths and placement change predicted behavior problems for one path. Internalizing and externalizing behavior problems at baseline predicted placement change between baseline and 18 months. Behavior problems at an older age and externalizing behavior at 18 months appear to confer an increased risk of placement change. Of note, among female subjects, placement changes later in the study predicted subsequent internalizing and externalizing behavior problems. In keeping with recommendations from a number of professional bodies, we suggest that initial and ongoing screening for internalizing and externalizing behavior problems be instituted as part of standard practice for youths entering or transitioning in the child welfare system.

Research and application of genetic algorithm in path planning of logistics distribution vehicle

NASA Astrophysics Data System (ADS)

Wang, Yong; Zhou, Heng; Wang, Ying

2017-08-01

The core of the logistics distribution system is the vehicle routing planning, research path planning problem, provide a better solution has become an important issue. In order to provide the decision support for logistics and distribution operations, this paper studies the problem of vehicle routing with capacity constraints (CVRP). By establishing a mathematical model, the genetic algorithm is used to plan the path of the logistics vehicle to meet the minimum logistics and transportation costs.

Search Path Mapping: A Versatile Approach for Visualizing Problem-Solving Behavior.

ERIC Educational Resources Information Center

Stevens, Ronald H.

1991-01-01

Computer-based problem-solving examinations in immunology generate graphic representations of students' search paths, allowing evaluation of how organized and focused their knowledge is, how well their organization relates to critical concepts in immunology, where major misconceptions exist, and whether proper knowledge links exist between content…

Path Planning For A Class Of Cutting Operations

NASA Astrophysics Data System (ADS)

Tavora, Jose

1989-03-01

Optimizing processing time in some contour-cutting operations requires solving the so-called no-load path problem. This problem is formulated and an approximate resolution method (based on heuristic search techniques) is described. Results for real-life instances (clothing layouts in the apparel industry) are presented and evaluated.

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.

Parallel Quantum Circuit in a Tunnel Junction

NASA Astrophysics Data System (ADS)

Faizy Namarvar, Omid; Dridi, Ghassen; Joachim, Christian; GNS theory Group Team

In between 2 metallic nanopads, adding identical and independent electron transfer paths in parallel increases the electronic effective coupling between the 2 nanopads through the quantum circuit defined by those paths. Measuring this increase of effective coupling using the tunnelling current intensity can lead for example for 2 paths in parallel to the now standard G =G1 +G2 + 2√{G1 .G2 } conductance superposition law (1). This is only valid for the tunnelling regime (2). For large electronic coupling to the nanopads (or at resonance), G can saturate and even decay as a function of the number of parallel paths added in the quantum circuit (3). We provide here the explanation of this phenomenon: the measurement of the effective Rabi oscillation frequency using the current intensity is constrained by the normalization principle of quantum mechanics. This limits the quantum conductance G for example to go when there is only one channel per metallic nanopads. This ef fect has important consequences for the design of Boolean logic gates at the atomic scale using atomic scale or intramolecular circuits. References: This has the financial support by European PAMS project.

Evolved atmospheric entry corridor with safety factor

NASA Astrophysics Data System (ADS)

Liang, Zixuan; Ren, Zhang; Li, Qingdong

2018-02-01

Atmospheric entry corridors are established in previous research based on the equilibrium glide condition which assumes the flight-path angle to be zero. To get a better understanding of the highly constrained entry flight, an evolved entry corridor that considers the exact flight-path angle is developed in this study. Firstly, the conventional corridor in the altitude vs. velocity plane is extended into a three-dimensional one in the space of altitude, velocity, and flight-path angle. The three-dimensional corridor is generated by a series of constraint boxes. Then, based on a simple mapping method, an evolved two-dimensional entry corridor with safety factor is obtained. The safety factor is defined to describe the flexibility of the flight-path angle for a state within the corridor. Finally, the evolved entry corridor is simulated for the Space Shuttle and the Common Aero Vehicle (CAV) to demonstrate the effectiveness of the corridor generation approach. Compared with the conventional corridor, the evolved corridor is much wider and provides additional information. Therefore, the evolved corridor would benefit more to the entry trajectory design and analysis.

Path analysis of the genetic integration of traits in the sand cricket: a novel use of BLUPs.

PubMed

Roff, D A; Fairbairn, D J

2011-09-01

This study combines path analysis with quantitative genetics to analyse a key life history trade-off in the cricket, Gryllus firmus. We develop a path model connecting five traits associated with the trade-off between flight capability and reproduction and test this model using phenotypic data and estimates of breeding values (best linear unbiased predictors) from a half-sibling experiment. Strong support by both types of data validates our causal model and indicates concordance between the phenotypic and genetic expression of the trade-off. Comparisons of the trade-off between sexes and wing morphs reveal that these discrete phenotypes are not genetically independent and that the evolutionary trajectories of the two wing morphs are more tightly constrained to covary than those of the two sexes. Our results illustrate the benefits of combining a quantitative genetic analysis, which examines statistical correlations between traits, with a path model that focuses upon the causal components of variation. © 2011 The Authors. Journal of Evolutionary Biology © 2011 European Society For Evolutionary Biology.

Thread Graphs, Linear Rank-Width and Their Algorithmic Applications

NASA Astrophysics Data System (ADS)

Ganian, Robert

The introduction of tree-width by Robertson and Seymour [7] was a breakthrough in the design of graph algorithms. A lot of research since then has focused on obtaining a width measure which would be more general and still allowed efficient algorithms for a wide range of NP-hard problems on graphs of bounded width. To this end, Oum and Seymour have proposed rank-width, which allows the solution of many such hard problems on a less restricted graph classes (see e.g. [3,4]). But what about problems which are NP-hard even on graphs of bounded tree-width or even on trees? The parameter used most often for these exceptionally hard problems is path-width, however it is extremely restrictive - for example the graphs of path-width 1 are exactly paths.

Hierarchical Motion Planning for Autonomous Aerial and Terrestrial Vehicles

NASA Astrophysics Data System (ADS)

Cowlagi, Raghvendra V.

Autonomous mobile robots---both aerial and terrestrial vehicles---have gained immense importance due to the broad spectrum of their potential military and civilian applications. One of the indispensable requirements for the autonomy of a mobile vehicle is the vehicle's capability of planning and executing its motion, that is, finding appropriate control inputs for the vehicle such that the resulting vehicle motion satisfies the requirements of the vehicular task. The motion planning and control problem is inherently complex because it involves two disparate sub-problems: (1) satisfaction of the vehicular task requirements, which requires tools from combinatorics and/or formal methods, and (2) design of the vehicle control laws, which requires tools from dynamical systems and control theory. Accordingly, this problem is usually decomposed and solved over two levels of hierarchy. The higher level, called the geometric path planning level, finds a geometric path that satisfies the vehicular task requirements, e.g., obstacle avoidance. The lower level, called the trajectory planning level, involves sufficient smoothening of this geometric path followed by a suitable time parametrization to obtain a reference trajectory for the vehicle. Although simple and efficient, such hierarchical decomposition suffers a serious drawback: the geometric path planner has no information of the kinematical and dynamical constraints of the vehicle. Consequently, the geometric planner may produce paths that the trajectory planner cannot transform into a feasible reference trajectory. Two main ideas appear in the literature to remedy this problem: (a) randomized sampling-based planning, which eliminates the geometric planner altogether by planning in the vehicle state space, and (b) geometric planning supported by feedback control laws. The former class of methods suffer from a lack of optimality of the resultant trajectory, while the latter class of methods makes a restrictive assumption concerning the vehicle kinematical model. We propose a hierarchical motion planning framework based on a novel mode of interaction between these two levels of planning. This interaction rests on the solution of a special shortest-path problem on graphs, namely, one using costs defined on multiple edge transitions in the path instead of the usual single edge transition costs. These costs are provided by a local trajectory generation algorithm, which we implement using model predictive control and the concept of effective target sets for simplifying the non-convex constraints involved in the problem. The proposed motion planner ensures "consistency" between the two levels of planning, i.e., a guarantee that the higher level geometric path is always associated with a kinematically and dynamically feasible trajectory. The main contributions of this thesis are: 1. A motion planning framework based on history-dependent costs (H-costs) in cell decomposition graphs for incorporating vehicle dynamical constraints: this framework offers distinct advantages in comparison with the competing approaches of discretization of the state space, of randomized sampling-based motion planning, and of local feedback-based, decoupled hierarchical motion planning, 2. An efficient and flexible algorithm for finding optimal H-cost paths, 3. A precise and general formulation of a local trajectory problem (the tile motion planning problem) that allows independent development of the discrete planner and the trajectory planner, while maintaining "compatibility" between the two planners, 4. A local trajectory generation algorithm using mpc, and the application of the concept of effective target sets for a significant simplification of the local trajectory generation problem, 5. The geometric analysis of curvature-bounded traversal of rectangular channels, leading to less conservative results in comparison with a result reported in the literature, and also to the efficient construction of effective target sets for the solution of the tile motion planning problem, 6. A wavelet-based multi-resolution path planning scheme, and a proof of completeness of the proposed scheme: such proofs are altogether absent from other works on multi-resolution path planning, 7. A technique for extracting all information about cells---namely, the locations, the sizes, and the associated image intensities---directly from the set of significant detail coefficients considered for path planning at a given iteration, and 8. The extension of the multi-resolution path planning scheme to include vehicle dynamical constraints using the aforementioned history-dependent costs approach. The future work includes an implementation of the proposed framework involving a discrete planner that solves classical planning problems more general than the single-query path planning problem considered thus far, and involving trajectory generation schemes for realistic vehicle dynamical models such as the bicycle model.

Altered perceptual sensitivity to kinematic invariants in Parkinson's disease.

PubMed

Dayan, Eran; Inzelberg, Rivka; Flash, Tamar

2012-01-01

Ample evidence exists for coupling between action and perception in neurologically healthy individuals, yet the precise nature of the internal representations shared between these domains remains unclear. One experimentally derived view is that the invariant properties and constraints characterizing movement generation are also manifested during motion perception. One prominent motor invariant is the "two-third power law," describing the strong relation between the kinematics of motion and the geometrical features of the path followed by the hand during planar drawing movements. The two-thirds power law not only characterizes various movement generation tasks but also seems to constrain visual perception of motion. The present study aimed to assess whether motor invariants, such as the two thirds power law also constrain motion perception in patients with Parkinson's disease (PD). Patients with PD and age-matched controls were asked to observe the movement of a light spot rotating on an elliptical path and to modify its velocity until it appeared to move most uniformly. As in previous reports controls tended to choose those movements close to obeying the two-thirds power law as most uniform. Patients with PD displayed a more variable behavior, choosing on average, movements closer but not equal to a constant velocity. Our results thus demonstrate impairments in how the two-thirds power law constrains motion perception in patients with PD, where this relationship between velocity and curvature appears to be preserved but scaled down. Recent hypotheses on the role of the basal ganglia in motor timing may explain these irregularities. Alternatively, these impairments in perception of movement may reflect similar deficits in motor production.

Analysis of crossing path crashes

DOT National Transportation Integrated Search

2001-07-01

This report defines the problem of crossing path crashes in the United States. This crash type involves one moving vehicle that cuts across the path of another when their initial approach comes from either lateral or opposite directions and they typi...

Parallel-vector computation for structural analysis and nonlinear unconstrained optimization problems

NASA Technical Reports Server (NTRS)

Nguyen, Duc T.

1990-01-01

Practical engineering application can often be formulated in the form of a constrained optimization problem. There are several solution algorithms for solving a constrained optimization problem. One approach is to convert a constrained problem into a series of unconstrained problems. Furthermore, unconstrained solution algorithms can be used as part of the constrained solution algorithms. Structural optimization is an iterative process where one starts with an initial design, a finite element structure analysis is then performed to calculate the response of the system (such as displacements, stresses, eigenvalues, etc.). Based upon the sensitivity information on the objective and constraint functions, an optimizer such as ADS or IDESIGN, can be used to find the new, improved design. For the structural analysis phase, the equation solver for the system of simultaneous, linear equations plays a key role since it is needed for either static, or eigenvalue, or dynamic analysis. For practical, large-scale structural analysis-synthesis applications, computational time can be excessively large. Thus, it is necessary to have a new structural analysis-synthesis code which employs new solution algorithms to exploit both parallel and vector capabilities offered by modern, high performance computers such as the Convex, Cray-2 and Cray-YMP computers. The objective of this research project is, therefore, to incorporate the latest development in the parallel-vector equation solver, PVSOLVE into the widely popular finite-element production code, such as the SAP-4. Furthermore, several nonlinear unconstrained optimization subroutines have also been developed and tested under a parallel computer environment. The unconstrained optimization subroutines are not only useful in their own right, but they can also be incorporated into a more popular constrained optimization code, such as ADS.

Genetic Algorithm for Traveling Salesman Problem with Modified Cycle Crossover Operator

PubMed Central

Mohamd Shoukry, Alaa; Gani, Showkat

2017-01-01

Genetic algorithms are evolutionary techniques used for optimization purposes according to survival of the fittest idea. These methods do not ensure optimal solutions; however, they give good approximation usually in time. The genetic algorithms are useful for NP-hard problems, especially the traveling salesman problem. The genetic algorithm depends on selection criteria, crossover, and mutation operators. To tackle the traveling salesman problem using genetic algorithms, there are various representations such as binary, path, adjacency, ordinal, and matrix representations. In this article, we propose a new crossover operator for traveling salesman problem to minimize the total distance. This approach has been linked with path representation, which is the most natural way to represent a legal tour. Computational results are also reported with some traditional path representation methods like partially mapped and order crossovers along with new cycle crossover operator for some benchmark TSPLIB instances and found improvements. PMID:29209364

Genetic Algorithm for Traveling Salesman Problem with Modified Cycle Crossover Operator.

PubMed

Hussain, Abid; Muhammad, Yousaf Shad; Nauman Sajid, M; Hussain, Ijaz; Mohamd Shoukry, Alaa; Gani, Showkat

2017-01-01

Genetic algorithms are evolutionary techniques used for optimization purposes according to survival of the fittest idea. These methods do not ensure optimal solutions; however, they give good approximation usually in time. The genetic algorithms are useful for NP-hard problems, especially the traveling salesman problem. The genetic algorithm depends on selection criteria, crossover, and mutation operators. To tackle the traveling salesman problem using genetic algorithms, there are various representations such as binary, path, adjacency, ordinal, and matrix representations. In this article, we propose a new crossover operator for traveling salesman problem to minimize the total distance. This approach has been linked with path representation, which is the most natural way to represent a legal tour. Computational results are also reported with some traditional path representation methods like partially mapped and order crossovers along with new cycle crossover operator for some benchmark TSPLIB instances and found improvements.

Optimal Paths in Gliding Flight

NASA Astrophysics Data System (ADS)

Wolek, Artur

Underwater gliders are robust and long endurance ocean sampling platforms that are increasingly being deployed in coastal regions. This new environment is characterized by shallow waters and significant currents that can challenge the mobility of these efficient (but traditionally slow moving) vehicles. This dissertation aims to improve the performance of shallow water underwater gliders through path planning. The path planning problem is formulated for a dynamic particle (or "kinematic car") model. The objective is to identify the path which satisfies specified boundary conditions and minimizes a particular cost. Several cost functions are considered. The problem is addressed using optimal control theory. The length scales of interest for path planning are within a few turn radii. First, an approach is developed for planning minimum-time paths, for a fixed speed glider, that are sub-optimal but are guaranteed to be feasible in the presence of unknown time-varying currents. Next the minimum-time problem for a glider with speed controls, that may vary between the stall speed and the maximum speed, is solved. Last, optimal paths that minimize change in depth (equivalently, maximize range) are investigated. Recognizing that path planning alone cannot overcome all of the challenges associated with significant currents and shallow waters, the design of a novel underwater glider with improved capabilities is explored. A glider with a pneumatic buoyancy engine (allowing large, rapid buoyancy changes) and a cylindrical moving mass mechanism (generating large pitch and roll moments) is designed, manufactured, and tested to demonstrate potential improvements in speed and maneuverability.

Statistical mechanics of budget-constrained auctions

NASA Astrophysics Data System (ADS)

Altarelli, F.; Braunstein, A.; Realpe-Gomez, J.; Zecchina, R.

2009-07-01

Finding the optimal assignment in budget-constrained auctions is a combinatorial optimization problem with many important applications, a notable example being in the sale of advertisement space by search engines (in this context the problem is often referred to as the off-line AdWords problem). On the basis of the cavity method of statistical mechanics, we introduce a message-passing algorithm that is capable of solving efficiently random instances of the problem extracted from a natural distribution, and we derive from its properties the phase diagram of the problem. As the control parameter (average value of the budgets) is varied, we find two phase transitions delimiting a region in which long-range correlations arise.

Approximate error conjugation gradient minimization methods

DOEpatents

Kallman, Jeffrey S

2013-05-21

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

Prevention of adolescent problem behavior: longitudinal impact of the Project P.A.T.H.S. in Hong Kong.

PubMed

Shek, Daniel T L; Yu, Lu

2011-03-07

The present study attempts to examine the longitudinal impact of a curriculum-based positive youth development program, entitled the Project P.A.T.H.S. (Positive Adolescent Training through Holistic Social Programmes), on adolescent problem behavior in Hong Kong. Using a longitudinal randomized group design, six waves of data were collected from 19 experimental schools (n = 3,797 at Wave 1) in which students participated in the Project P.A.T.H.S. and 24 control schools (n = 4,049 at Wave 1). At each wave, students responded to questions asking about their current problem behaviors, including delinquency and use of different types of drugs, and their intentions of engaging in such behaviors in the future. Results based on individual growth curve modeling generally showed that the participants displayed lower levels of substance abuse and delinquent behavior than did the control students. Participants who regarded the program to be helpful also showed lower levels of problem behavior than did the control students. The present findings suggest that the Project P.A.T.H.S. is effective in preventing adolescent problem behavior in the junior secondary school years.

Energy efficient LED layout optimization for near-uniform illumination

NASA Astrophysics Data System (ADS)

Ali, Ramy E.; Elgala, Hany

2016-09-01

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

Shortest multiple disconnected path for the analysis of entanglements in two- and three-dimensional polymeric systems

NASA Astrophysics Data System (ADS)

Kröger, Martin

2005-06-01

We present an algorithm which returns a shortest path and related number of entanglements for a given configuration of a polymeric system in 2 or 3 dimensions. Rubinstein and Helfand, and later Everaers et al. introduced a concept to extract primitive paths for dense polymeric melts made of linear chains (a multiple disconnected multibead 'path'), where each primitive path is defined as a path connecting the (space-fixed) ends of a polymer under the constraint of non-interpenetration (excluded volume) between primitive paths of different chains, such that the multiple disconnected path fulfills a minimization criterion. The present algorithm uses geometrical operations and provides a—model independent—efficient approximate solution to this challenging problem. Primitive paths are treated as 'infinitely' thin (we further allow for finite thickness to model excluded volume), and tensionless lines rather than multibead chains, excluded volume is taken into account without a force law. The present implementation allows to construct a shortest multiple disconnected path (SP) for 2D systems (polymeric chain within spherical obstacles) and an optimal SP for 3D systems (collection of polymeric chains). The number of entanglements is then simply obtained from the SP as either the number of interior kinks, or from the average length of a line segment. Further, information about structure and potentially also the dynamics of entanglements is immediately available from the SP. We apply the method to study the 'concentration' dependence of the degree of entanglement in phantom chain systems. Program summaryTitle of program:Z Catalogue number:ADVG Program summary URL:http://cpc.cs.qub.ac.uk/summaries/ADVG Program obtainable from: CPC Program Library, Queen's University of Belfast, N. Ireland Computer for which the program is designed and others on which it has been tested: Silicon Graphics (Irix), Sun (Solaris), PC (Linux) Operating systems or monitors under which the program has been tested: UNIX, Linux Program language used: USANSI Fortran 77 and Fortran 90 Memory required to execute with typical data: 1 MByte No. of lines in distributed program, including test data, etc.: 10 660 No. of bytes in distributed program, including test data, etc.: 119 551 Distribution formet:tar.gz Nature of physical problem: The problem is to obtain primitive paths substantiating a shortest multiple disconnected path (SP) for a given polymer configuration (chains of particles, with or without additional single particles as obstacles for the 2D case). Primitive paths are here defined as in [M. Rubinstein, E. Helfand, J. Chem. Phys. 82 (1985) 2477; R. Everaers, S.K. Sukumaran, G.S. Grest, C. Svaneborg, A. Sivasubramanian, K. Kremer, Science 303 (2004) 823] as the shortest line (path) respecting 'topological' constraints (from neighboring polymers or point obstacles) between ends of polymers. There is a unique solution for the 2D case. For the 3D case it is unique if we construct a primitive path of a single chain embedded within fixed line obstacles [J.S.B. Mitchell, Geometric shortest paths and network optimization, in: J.-R. Sack, J. Urrutia (Eds.), Handbook of Computational Geometry, Elsevier, Amsterdam, 2000, pp. 633-701]. For a large 3D configuration made of several chains, short is meant to be the Euclidean shortest multiple disconnected path (SP) where primitive paths are constructed for all chains simultaneously. While the latter problem, in general, does not possess a unique solution, the algorithm must return a locally optimal solution, robust against minor displacements of the disconnected path and chain re-labeling. The problem is solved if the number of kinks (or entanglements Z), explicitly deduced from the SP, is quite insensitive to the exact conformation of the SP which allows to estimate Z with a small error. Efficient method of solution: Primitive paths are constructed from the given polymer configuration (a non-shortest multiple disconnected path, including obstacles, if present) by first replacing each polymer contour by a line with a number of 'kinks' (beads, nodes) and 'segments' (edges). To obtain primitive paths, defined to be uncrossable by any other objects (neighboring primitive paths, line or point obstacles), the algorithm minimizes the length of all primitive paths consecutively, until a final minimum Euclidean length of the SP is reached. Fast geometric operations rather than dynamical methods are used to minimize the contour lengths of the primitive paths. Neighbor lists are used to keep track of potentially intersecting segments of other chains. Periodic boundary conditions are employed. A finite small line thickness is used in order to make sure that entanglements are not 'lost' due to finite precision of representation of numbers. Restrictions on the complexity of the problem: For a single chain embedded within fixed line or point obstacles, the algorithm returns the exact SP. For more complex problems, the algorithm returns a locally optimal SP. Except for exotic, probably rare, configurations it turns out that different locally optimal SPs possess quite an identical number of nodes. In general, the problem constructing the SP is known to be NP-hard [J.S.B. Mitchell, Geometric shortest paths and network optimization, in: J.-R. Sack, J. Urrutia (Eds.), Handbook of Computational Geometry, Elsevier, Amsterdam, 2000, pp. 633-701], and we offer a solution which should suffice to analyze physical problems, and gives an estimate about the precision and uniqueness of the result (from a standard deviation by varying the parameter: cyclicswitch). The program is NOT restricted to handle systems for which segment lengths of the SP exceed half the box size. Typical running time: Typical running times are approximately two orders of magnitude shorter compared with the ones needed for a corresponding molecular dynamics approach, and scale mostly linearly with system size. We provide a benchmark table.

Stochastic Evolutionary Algorithms for Planning Robot Paths

NASA Technical Reports Server (NTRS)

Fink, Wolfgang; Aghazarian, Hrand; Huntsberger, Terrance; Terrile, Richard

2006-01-01

A computer program implements stochastic evolutionary algorithms for planning and optimizing collision-free paths for robots and their jointed limbs. Stochastic evolutionary algorithms can be made to produce acceptably close approximations to exact, optimal solutions for path-planning problems while often demanding much less computation than do exhaustive-search and deterministic inverse-kinematics algorithms that have been used previously for this purpose. Hence, the present software is better suited for application aboard robots having limited computing capabilities (see figure). The stochastic aspect lies in the use of simulated annealing to (1) prevent trapping of an optimization algorithm in local minima of an energy-like error measure by which the fitness of a trial solution is evaluated while (2) ensuring that the entire multidimensional configuration and parameter space of the path-planning problem is sampled efficiently with respect to both robot joint angles and computation time. Simulated annealing is an established technique for avoiding local minima in multidimensional optimization problems, but has not, until now, been applied to planning collision-free robot paths by use of low-power computers.

Efficient Compressed Sensing Based MRI Reconstruction using Nonconvex Total Variation Penalties

NASA Astrophysics Data System (ADS)

Lazzaro, D.; Loli Piccolomini, E.; Zama, F.

2016-10-01

This work addresses the problem of Magnetic Resonance Image Reconstruction from highly sub-sampled measurements in the Fourier domain. It is modeled as a constrained minimization problem, where the objective function is a non-convex function of the gradient of the unknown image and the constraints are given by the data fidelity term. We propose an algorithm, Fast Non Convex Reweighted (FNCR), where the constrained problem is solved by a reweighting scheme, as a strategy to overcome the non-convexity of the objective function, with an adaptive adjustment of the penalization parameter. We propose a fast iterative algorithm and we can prove that it converges to a local minimum because the constrained problem satisfies the Kurdyka-Lojasiewicz property. Moreover the adaptation of non convex l0 approximation and penalization parameters, by means of a continuation technique, allows us to obtain good quality solutions, avoiding to get stuck in unwanted local minima. Some numerical experiments performed on MRI sub-sampled data show the efficiency of the algorithm and the accuracy of the solution.

Amalgamation of East Eurasia Since Late Paleozoic: Constraints from the Apparent Polar Wander Paths of the Major China Blocks

NASA Astrophysics Data System (ADS)

Wu, L.; Kravchinsky, V. A.; Potter, D. K.

2014-12-01

It has been a longstanding challenge in the last few decades to quantitatively reconstruct the paleogeographic evolution of East Eurasia because of its great tectonic complexities. As the core region, the major China cratons including North China Block, South China Block and Tarim Block hold the key clues for the understanding of the amalgamation history, tectonic activities and biological affinity among the component blocks and terranes in East Eurasia. Compared with the major Gondwana and Laurentia plates, however, the apparent polar wander paths of China are not well constrained due to the outdated paleomagnetic database and relatively loose pole selection process. With the recruitment of the new high-fidelity poles published in the last decade, the rejection of the low quality data and the strict implementation of Voo's grading scheme, we build an updated paleomagnetic database for the three blocks from which three types of apparent polar wander paths (APWP) are computed. Version 1 running mean paths are constructed during the pole selection and compared with those from the previous publications. Version 2 running mean and spline paths with different sliding time windows are computed from the thoroughly examined poles to find the optimal paths with the steady trend, reasonable speed for the polar drift and plate rotation. The spline paths are recommended for the plate reconstructions, however, considering the poor data coverage during certain periods. Our new China APWPs, together with the latest European reference path, the geological, geochronological and biological evidence from the studied Asian plates allow us to reevaluate the paleogeographic and tectonic history of East Eurasia.

Visual, motor and attentional influences on proprioceptive contributions to perception of hand path rectilinearity during reaching

PubMed Central

Scheidt, Robert A.; Lillis, Kyle P.; Emerson, Scott J.

2010-01-01

We examined how proprioceptive contributions to perception of hand path straightness are influenced by visual, motor and attentional sources of performance variability during horizontal planar reaching. Subjects held the handle of a robot that constrained goal-directed movements of the hand to paths of controlled curvature. Subjects attempted to detect the presence of hand path curvature during both active (subject-driven) and passive (robot-driven) movements that either required active muscle force production or not. Subjects were less able to discriminate curved from straight paths when actively reaching for a target vs. when the robot moved their hand through the same curved paths. This effect was especially evident during robot-driven movements requiring concurrent activation of lengthening but not shortening muscles. Subjects were less likely to report curvature and were more variable in reporting when movements appeared straight in a novel “visual channel” condition previously shown to block adaptive updating of motor commands in response to deviations from a straight-line hand path. Similarly compromised performance was obtained when subjects simultaneously performed a distracting secondary task (key pressing with the contralateral hand). The effects compounded when these last two treatments were combined. It is concluded that environmental, intrinsic and attentional factors all impact the ability to detect deviations from a rectilinear hand path during goal-directed movement by decreasing proprioceptive contributions to limb state estimation. In contrast, response variability increased only in experimental conditions thought to impose additional attentional demands on the observer. Implications of these results for perception and other sensorimotor behaviors are discussed. PMID:20532489

Bit-systolic arithmetic arrays using dynamic differential gallium arsenide circuits

NASA Technical Reports Server (NTRS)

Beagles, Grant; Winters, Kel; Eldin, A. G.

1992-01-01

A new family of gallium arsenide circuits for fine grained bit-systolic arithmetic arrays is introduced. This scheme combines features of two recent techniques of dynamic gallium arsenide FET logic and differential dynamic single-clock CMOS logic. The resulting circuits are fast and compact, with tightly constrained series FET propagation paths, low fanout, no dc power dissipation, and depletion FET implementation without level shifting diodes.

Constrained trajectory optimization for kinematically redundant arms

NASA Technical Reports Server (NTRS)

Carignan, Craig R.; Tarrant, Janice M.

1990-01-01

Two velocity optimization schemes for resolving redundant joint configurations are compared. The Extended Moore-Penrose Technique minimizes the joint velocities and avoids obstacles indirectly by adjoining a cost gradient to the solution. A new method can incorporate inequality constraints directly to avoid obstacles and singularities in the workspace. A four-link arm example is used to illustrate singularity avoidance while tracking desired end-effector paths.

An Application of Calculus: Optimum Parabolic Path Problem

ERIC Educational Resources Information Center

Atasever, Merve; Pakdemirli, Mehmet; Yurtsever, Hasan Ali

2009-01-01

A practical and technological application of calculus problem is posed to motivate freshman students or junior high school students. A variable coefficient of friction is used in modelling air friction. The case in which the coefficient of friction is a decreasing function of altitude is considered. The optimum parabolic path for a flying object…

Robust Flight Path Determination for Mars Precision Landing Using Genetic Algorithms

NASA Technical Reports Server (NTRS)

Bayard, David S.; Kohen, Hamid

1997-01-01

This paper documents the application of genetic algorithms (GAs) to the problem of robust flight path determination for Mars precision landing. The robust flight path problem is defined here as the determination of the flight path which delivers a low-lift open-loop controlled vehicle to its desired final landing location while minimizing the effect of perturbations due to uncertainty in the atmospheric model and entry conditions. The genetic algorithm was capable of finding solutions which reduced the landing error from 111 km RMS radial (open-loop optimal) to 43 km RMS radial (optimized with respect to perturbations) using 200 hours of computation on an Ultra-SPARC workstation. Further reduction in the landing error is possible by going to closed-loop control which can utilize the GA optimized paths as nominal trajectories for linearization.

Modeling the exhumation path of partially melted ultrahigh-pressure metapelites, North-East Greenland Caledonides

NASA Astrophysics Data System (ADS)

Lang, Helen M.; Gilotti, Jane A.

2015-06-01

Pseudosection modeling constrains the pressure-temperature (P-T) exhumation path of partially melted ultrahigh-pressure (UHP) metapelites exposed in the North-East Greenland UHP terrane. A robust peak P and T estimate of 3.6 GPa and 970 °C based on mineral assemblages in nearby kyanite eclogites is the starting point for the P-T path. Although the peak assemblage for the metapelite is not preserved, the calculated modeled peak assemblage contained substantial clinopyroxene, garnet, phengite, K-feldspar and coesite with minor kyanite and rutile. Combining the pseudosection and observed textures, the decompression path crosses the coesite-quartz transition before reaching the dry phengite dehydration melting reaction where phengite is abruptly consumed. In the range of 2.5 to 2.2 GPa, clinopyroxene is completely consumed and garnet grows to its maximum volume and grossular content, matching the high grossular rims of relict megacrysts. Plagioclase joins the assemblage and the pseudosection predicts up to 12-13 vol.% melt in the supersolidus assemblage, which contained garnet, liquid, K-feldspar, plagioclase, kyanite, quartz and rutile. At this stage, the steep decompression path flattened out and became nearly isobaric. The melt crystallization assemblage that formed when the path crossed the solidus with decreasing temperature contains phengite, garnet, biotite, 2 feldspars, kyanite, quartz and rutile. Therefore, the path must have intersected the solidus at approximately 1.2 GPa, 825 °C. The pseudosection predicts that garnet is consumed on the cooling path, but little evidence of late garnet consumption or other retrograde effects is observed. This may be due to partial melt loss from the rock. Isochemical PT-n and PT-X sections calculated along the P-T path display changes in mineral assemblage and composition that are consistent with preserved assemblages.

Traveling salesman problem with a center.

PubMed

Lipowski, Adam; Lipowska, Dorota

2005-06-01

We study a traveling salesman problem where the path is optimized with a cost function that includes its length L as well as a certain measure C of its distance from the geometrical center of the graph. Using simulated annealing (SA) we show that such a problem has a transition point that separates two phases differing in the scaling behavior of L and C, in efficiency of SA, and in the shape of minimal paths.

Neural Network Assisted Inverse Dynamic Guidance for Terminally Constrained Entry Flight

PubMed Central

Chen, Wanchun

2014-01-01

This paper presents a neural network assisted entry guidance law that is designed by applying Bézier approximation. It is shown that a fully constrained approximation of a reference trajectory can be made by using the Bézier curve. Applying this approximation, an inverse dynamic system for an entry flight is solved to generate guidance command. The guidance solution thus gotten ensures terminal constraints for position, flight path, and azimuth angle. In order to ensure terminal velocity constraint, a prediction of the terminal velocity is required, based on which, the approximated Bézier curve is adjusted. An artificial neural network is used for this prediction of the terminal velocity. The method enables faster implementation in achieving fully constrained entry flight. Results from simulations indicate improved performance of the neural network assisted method. The scheme is expected to have prospect for further research on automated onboard control of terminal velocity for both reentry and terminal guidance laws. PMID:24723821

Development and Application of a Paleomagnetic/Geochemical Method for Constraining the Timing of Burial Diagenetic and Fluid

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

Elmore, Richard D.; Engel, Michael H.

2005-03-10

Studies of diagenesis caused by fluid migration or other events are commonly hindered by a lack of temporal control. Our results to date demonstrate that a paleomagnetic/geochemical approach can be used to date fluid migration as well as burial diagenetic events. Our principal working hypothesis is that burial diagenetic processes (e.g., maturation of organic-rich sediments and clay diagenesis) and the migration of fluids can trigger the authigenesis of magnetic mineral phases. The ages of these events can be constrained by comparing chemical remanent magnetizations (CRMs) to independently established Apparent Polar Wander Paths. While geochemical (e.g. stable isotope and organic analyses)more » and petrographic studies provide important clues for establishing these relationships, the ultimate test of this hypothesis requires the application of independent dating methods to verify the paleomagnetic ages. Towards this end, we have used K-Ar dating of illitization as an alternative method for constraining the ages of magnetic mineral phases in our field areas.« less

The Pendulum: From Constrained Fall to the Concept of Potential

NASA Astrophysics Data System (ADS)

Bevilacqua, Fabio; Falomo, Lidia; Fregonese, Lucio; Giannetto, Enrico; Giudice, Franco; Mascheretti, Paolo

2006-08-01

Kuhn underlined the relevance of Galileo’s gestalt switch in the interpretation of a swinging body from constrained fall to time metre. But the new interpretation did not eliminate the older one. The constrained fall, both in the motion of pendulums and along inclined planes, led Galileo to the law of free fall. Experimenting with physical pendulums and assuming the impossibility of perpetual motion Huygens obtained a law of conservation of vis viva at specific positions, beautifully commented by Mach. Daniel Bernoulli generalised Huygens results introducing the concept of potential and the related independence of the ‘work’ done from the trajectories (paths) followed: vis viva conservation at specific positions is now linked with the potential. Feynman’s modern way of teaching the subject shows striking similarities with Bernoulli’s approach. A number of animations and simulations can help to visualise and teach some of the pendulum’s interpretations related to what we now see as instances of energy conservation.

A Scheduling Algorithm for Cloud Computing System Based on the Driver of Dynamic Essential Path.

PubMed

Xie, Zhiqiang; Shao, Xia; Xin, Yu

2016-01-01

To solve the problem of task scheduling in the cloud computing system, this paper proposes a scheduling algorithm for cloud computing based on the driver of dynamic essential path (DDEP). This algorithm applies a predecessor-task layer priority strategy to solve the problem of constraint relations among task nodes. The strategy assigns different priority values to every task node based on the scheduling order of task node as affected by the constraint relations among task nodes, and the task node list is generated by the different priority value. To address the scheduling order problem in which task nodes have the same priority value, the dynamic essential long path strategy is proposed. This strategy computes the dynamic essential path of the pre-scheduling task nodes based on the actual computation cost and communication cost of task node in the scheduling process. The task node that has the longest dynamic essential path is scheduled first as the completion time of task graph is indirectly influenced by the finishing time of task nodes in the longest dynamic essential path. Finally, we demonstrate the proposed algorithm via simulation experiments using Matlab tools. The experimental results indicate that the proposed algorithm can effectively reduce the task Makespan in most cases and meet a high quality performance objective.

A Scheduling Algorithm for Cloud Computing System Based on the Driver of Dynamic Essential Path

PubMed Central

Xie, Zhiqiang; Shao, Xia; Xin, Yu

2016-01-01

To solve the problem of task scheduling in the cloud computing system, this paper proposes a scheduling algorithm for cloud computing based on the driver of dynamic essential path (DDEP). This algorithm applies a predecessor-task layer priority strategy to solve the problem of constraint relations among task nodes. The strategy assigns different priority values to every task node based on the scheduling order of task node as affected by the constraint relations among task nodes, and the task node list is generated by the different priority value. To address the scheduling order problem in which task nodes have the same priority value, the dynamic essential long path strategy is proposed. This strategy computes the dynamic essential path of the pre-scheduling task nodes based on the actual computation cost and communication cost of task node in the scheduling process. The task node that has the longest dynamic essential path is scheduled first as the completion time of task graph is indirectly influenced by the finishing time of task nodes in the longest dynamic essential path. Finally, we demonstrate the proposed algorithm via simulation experiments using Matlab tools. The experimental results indicate that the proposed algorithm can effectively reduce the task Makespan in most cases and meet a high quality performance objective. PMID:27490901

Constrained Multi-Level Algorithm for Trajectory Optimization

NASA Astrophysics Data System (ADS)

Adimurthy, V.; Tandon, S. R.; Jessy, Antony; Kumar, C. Ravi

The emphasis on low cost access to space inspired many recent developments in the methodology of trajectory optimization. Ref.1 uses a spectral patching method for optimization, where global orthogonal polynomials are used to describe the dynamical constraints. A two-tier approach of optimization is used in Ref.2 for a missile mid-course trajectory optimization. A hybrid analytical/numerical approach is described in Ref.3, where an initial analytical vacuum solution is taken and gradually atmospheric effects are introduced. Ref.4 emphasizes the fact that the nonlinear constraints which occur in the initial and middle portions of the trajectory behave very nonlinearly with respect the variables making the optimization very difficult to solve in the direct and indirect shooting methods. The problem is further made complex when different phases of the trajectory have different objectives of optimization and also have different path constraints. Such problems can be effectively addressed by multi-level optimization. In the multi-level methods reported so far, optimization is first done in identified sub-level problems, where some coordination variables are kept fixed for global iteration. After all the sub optimizations are completed, higher-level optimization iteration with all the coordination and main variables is done. This is followed by further sub system optimizations with new coordination variables. This process is continued until convergence. In this paper we use a multi-level constrained optimization algorithm which avoids the repeated local sub system optimizations and which also removes the problem of non-linear sensitivity inherent in the single step approaches. Fall-zone constraints, structural load constraints and thermal constraints are considered. In this algorithm, there is only a single multi-level sequence of state and multiplier updates in a framework of an augmented Lagrangian. Han Tapia multiplier updates are used in view of their special role in diagonalised methods, being the only single update with quadratic convergence. For a single level, the diagonalised multiplier method (DMM) is described in Ref.5. The main advantage of the two-level analogue of the DMM approach is that it avoids the inner loop optimizations required in the other methods. The scheme also introduces a gradient change measure to reduce the computational time needed to calculate the gradients. It is demonstrated that the new multi-level scheme leads to a robust procedure to handle the sensitivity of the constraints, and the multiple objectives of different trajectory phases. Ref. 1. Fahroo, F and Ross, M., " A Spectral Patching Method for Direct Trajectory Optimization" The Journal of the Astronautical Sciences, Vol.48, 2000, pp.269-286 Ref. 2. Phililps, C.A. and Drake, J.C., "Trajectory Optimization for a Missile using a Multitier Approach" Journal of Spacecraft and Rockets, Vol.37, 2000, pp.663-669 Ref. 3. Gath, P.F., and Calise, A.J., " Optimization of Launch Vehicle Ascent Trajectories with Path Constraints and Coast Arcs", Journal of Guidance, Control, and Dynamics, Vol. 24, 2001, pp.296-304 Ref. 4. Betts, J.T., " Survey of Numerical Methods for Trajectory Optimization", Journal of Guidance, Control, and Dynamics, Vol.21, 1998, pp. 193-207 Ref. 5. Adimurthy, V., " Launch Vehicle Trajectory Optimization", Acta Astronautica, Vol.15, 1987, pp.845-850.

MO-FG-CAMPUS-TeP2-01: A Graph Form ADMM Algorithm for Constrained Quadratic Radiation Treatment Planning

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

Liu, X; Belcher, AH; Wiersma, R

Purpose: In radiation therapy optimization the constraints can be either hard constraints which must be satisfied or soft constraints which are included but do not need to be satisfied exactly. Currently the voxel dose constraints are viewed as soft constraints and included as a part of the objective function and approximated as an unconstrained problem. However in some treatment planning cases the constraints should be specified as hard constraints and solved by constrained optimization. The goal of this work is to present a computation efficiency graph form alternating direction method of multipliers (ADMM) algorithm for constrained quadratic treatment planning optimizationmore » and compare it with several commonly used algorithms/toolbox. Method: ADMM can be viewed as an attempt to blend the benefits of dual decomposition and augmented Lagrangian methods for constrained optimization. Various proximal operators were first constructed as applicable to quadratic IMRT constrained optimization and the problem was formulated in a graph form of ADMM. A pre-iteration operation for the projection of a point to a graph was also proposed to further accelerate the computation. Result: The graph form ADMM algorithm was tested by the Common Optimization for Radiation Therapy (CORT) dataset including TG119, prostate, liver, and head & neck cases. Both unconstrained and constrained optimization problems were formulated for comparison purposes. All optimizations were solved by LBFGS, IPOPT, Matlab built-in toolbox, CVX (implementing SeDuMi) and Mosek solvers. For unconstrained optimization, it was found that LBFGS performs the best, and it was 3–5 times faster than graph form ADMM. However, for constrained optimization, graph form ADMM was 8 – 100 times faster than the other solvers. Conclusion: A graph form ADMM can be applied to constrained quadratic IMRT optimization. It is more computationally efficient than several other commercial and noncommercial optimizers and it also used significantly less computer memory.« less

Degree-constrained multicast routing for multimedia communications

NASA Astrophysics Data System (ADS)

Wang, Yanlin; Sun, Yugeng; Li, Guidan

2005-02-01

Multicast services have been increasingly used by many multimedia applications. As one of the key techniques to support multimedia applications, the rational and effective multicast routing algorithms are very important to networks performance. When switch nodes in networks have different multicast capability, multicast routing problem is modeled as the degree-constrained Steiner problem. We presented two heuristic algorithms, named BMSTA and BSPTA, for the degree-constrained case in multimedia communications. Both algorithms are used to generate degree-constrained multicast trees with bandwidth and end to end delay bound. Simulations over random networks were carried out to compare the performance of the two proposed algorithms. Experimental results show that the proposed algorithms have advantages in traffic load balancing, which can avoid link blocking and enhance networks performance efficiently. BMSTA has better ability in finding unsaturated links and (or) unsaturated nodes to generate multicast trees than BSPTA. The performance of BMSTA is affected by the variation of degree constraints.

Four-dimensional guidance algorithms for aircraft in an air traffic control environment

NASA Technical Reports Server (NTRS)

Pecsvaradi, T.

1975-01-01

Theoretical development and computer implementation of three guidance algorithms are presented. From a small set of input parameters the algorithms generate the ground track, altitude profile, and speed profile required to implement an experimental 4-D guidance system. Given a sequence of waypoints that define a nominal flight path, the first algorithm generates a realistic, flyable ground track consisting of a sequence of straight line segments and circular arcs. Each circular turn is constrained by the minimum turning radius of the aircraft. The ground track and the specified waypoint altitudes are used as inputs to the second algorithm which generates the altitude profile. The altitude profile consists of piecewise constant flight path angle segments, each segment lying within specified upper and lower bounds. The third algorithm generates a feasible speed profile subject to constraints on the rate of change in speed, permissible speed ranges, and effects of wind. Flight path parameters are then combined into a chronological sequence to form the 4-D guidance vectors. These vectors can be used to drive the autopilot/autothrottle of the aircraft so that a 4-D flight path could be tracked completely automatically; or these vectors may be used to drive the flight director and other cockpit displays, thereby enabling the pilot to track a 4-D flight path manually.

Statistical estimation of ultrasonic propagation path parameters for aberration correction.

PubMed

Waag, Robert C; Astheimer, Jeffrey P

2005-05-01

Parameters in a linear filter model for ultrasonic propagation are found using statistical estimation. The model uses an inhomogeneous-medium Green's function that is decomposed into a homogeneous-transmission term and a path-dependent aberration term. Power and cross-power spectra of random-medium scattering are estimated over the frequency band of the transmit-receive system by using closely situated scattering volumes. The frequency-domain magnitude of the aberration is obtained from a normalization of the power spectrum. The corresponding phase is reconstructed from cross-power spectra of subaperture signals at adjacent receive positions by a recursion. The subapertures constrain the receive sensitivity pattern to eliminate measurement system phase contributions. The recursion uses a Laplacian-based algorithm to obtain phase from phase differences. Pulse-echo waveforms were acquired from a point reflector and a tissue-like scattering phantom through a tissue-mimicking aberration path from neighboring volumes having essentially the same aberration path. Propagation path aberration parameters calculated from the measurements of random scattering through the aberration phantom agree with corresponding parameters calculated for the same aberrator and array position by using echoes from the point reflector. The results indicate the approach describes, in addition to time shifts, waveform amplitude and shape changes produced by propagation through distributed aberration under realistic conditions.

Parameterized LMI Based Diagonal Dominance Compensator Study for Polynomial Linear Parameter Varying System

NASA Astrophysics Data System (ADS)

Han, Xiaobao; Li, Huacong; Jia, Qiusheng

2017-12-01

For dynamic decoupling of polynomial linear parameter varying(PLPV) system, a robust dominance pre-compensator design method is given. The parameterized precompensator design problem is converted into an optimal problem constrained with parameterized linear matrix inequalities(PLMI) by using the conception of parameterized Lyapunov function(PLF). To solve the PLMI constrained optimal problem, the precompensator design problem is reduced into a normal convex optimization problem with normal linear matrix inequalities (LMI) constraints on a new constructed convex polyhedron. Moreover, a parameter scheduling pre-compensator is achieved, which satisfies robust performance and decoupling performances. Finally, the feasibility and validity of the robust diagonal dominance pre-compensator design method are verified by the numerical simulation on a turbofan engine PLPV model.

Knowledge-based design of generate-and-patch problem solvers that solve global resource assignment problems

NASA Technical Reports Server (NTRS)

Voigt, Kerstin

1992-01-01

We present MENDER, a knowledge based system that implements software design techniques that are specialized to automatically compile generate-and-patch problem solvers that satisfy global resource assignments problems. We provide empirical evidence of the superior performance of generate-and-patch over generate-and-test: even with constrained generation, for a global constraint in the domain of '2D-floorplanning'. For a second constraint in '2D-floorplanning' we show that even when it is possible to incorporate the constraint into a constrained generator, a generate-and-patch problem solver may satisfy the constraint more rapidly. We also briefly summarize how an extended version of our system applies to a constraint in the domain of 'multiprocessor scheduling'.

Near-field observations of microearthquake source physics using dense array

NASA Astrophysics Data System (ADS)

Chen, X.; Nakata, N.; Abercrombie, R. E.

2017-12-01

The recorded waveform includes contributions from earthquake source properties and propagation effects, leading to long-standing trade-off problems between site/path effects and source effects. This problem is especially significant for small earthquakes where the corner frequencies are within similar ranges of near-site attenuation effects. Fortunately, this problem can be remedied by dense near-field recordings at high frequency, and large databases with wide magnitude range. The 2016 IRIS wavefield experiment provides high-quality recordings of earthquake sequences in north-central Oklahoma with about 400 sensors in 15 km area. Preliminary processing of the IRIS wavefield array resulted with about 20,000 microearthquakes ranging from M-1 to M2, while only 2 earthquakes are listed in the catalog during the same time period. A preliminary examination of the catalog reveals three similar magnitude earthquakes (M 2) occurred at similar locations within 9 seconds of each other. Utilizing this catalog, we will combine individual empirical Green's function (EGF) analysis and stacking over multiple EGFs to examine if there are any systematic variations of source time functions and spectral ratios across the array, which will provide constrains of rupture complexity, directivity and earthquake interactions. For example, this would help us to understand if these three earthquakes rupture overlapping fault patches from cascading failure, or from repeated rupture at the same slip patch due to external stress loading. Deciphering the interaction at smaller scales with near-field observations is important for a controlled earthquake experiment.

An Efficient Augmented Lagrangian Method with Applications to Total Variation Minimization

DTIC Science & Technology

2012-08-17

the classic augmented Lagrangian multiplier method, we propose, analyze and test an algorithm for solving a class of equality-constrained non-smooth...method, we propose, analyze and test an algorithm for solving a class of equality-constrained non-smooth optimization problems (chie y but not...significantly outperforming several state-of-the-art solvers on most tested problems. The resulting MATLAB solver, called TVAL3, has been posted online [23]. 2

Stress-Constrained Structural Topology Optimization with Design-Dependent Loads

NASA Astrophysics Data System (ADS)

Lee, Edmund

Topology optimization is commonly used to distribute a given amount of material to obtain the stiffest structure, with predefined fixed loads. The present work investigates the result of applying stress constraints to topology optimization, for problems with design-depending loading, such as self-weight and pressure. In order to apply pressure loading, a material boundary identification scheme is proposed, iteratively connecting points of equal density. In previous research, design-dependent loading problems have been limited to compliance minimization. The present study employs a more practical approach by minimizing mass subject to failure constraints, and uses a stress relaxation technique to avoid stress constraint singularities. The results show that these design dependent loading problems may converge to a local minimum when stress constraints are enforced. Comparisons between compliance minimization solutions and stress-constrained solutions are also given. The resulting topologies of these two solutions are usually vastly different, demonstrating the need for stress-constrained topology optimization.

Homotopy approach to optimal, linear quadratic, fixed architecture compensation

NASA Technical Reports Server (NTRS)

Mercadal, Mathieu

1991-01-01

Optimal linear quadratic Gaussian compensators with constrained architecture are a sensible way to generate good multivariable feedback systems meeting strict implementation requirements. The optimality conditions obtained from the constrained linear quadratic Gaussian are a set of highly coupled matrix equations that cannot be solved algebraically except when the compensator is centralized and full order. An alternative to the use of general parameter optimization methods for solving the problem is to use homotopy. The benefit of the method is that it uses the solution to a simplified problem as a starting point and the final solution is then obtained by solving a simple differential equation. This paper investigates the convergence properties and the limitation of such an approach and sheds some light on the nature and the number of solutions of the constrained linear quadratic Gaussian problem. It also demonstrates the usefulness of homotopy on an example of an optimal decentralized compensator.

CONORBIT: constrained optimization by radial basis function interpolation in trust regions

DOE PAGES

Regis, Rommel G.; Wild, Stefan M.

2016-09-26

Here, this paper presents CONORBIT (CONstrained Optimization by Radial Basis function Interpolation in Trust regions), a derivative-free algorithm for constrained black-box optimization where the objective and constraint functions are computationally expensive. CONORBIT employs a trust-region framework that uses interpolating radial basis function (RBF) models for the objective and constraint functions, and is an extension of the ORBIT algorithm. It uses a small margin for the RBF constraint models to facilitate the generation of feasible iterates, and extensive numerical tests confirm that such a margin is helpful in improving performance. CONORBIT is compared with other algorithms on 27 test problems, amore » chemical process optimization problem, and an automotive application. Numerical results show that CONORBIT performs better than COBYLA, a sequential penalty derivative-free method, an augmented Lagrangian method, a direct search method, and another RBF-based algorithm on the test problems and on the automotive application.« less

Visually based path-planning by Japanese monkeys.

PubMed

Mushiake, H; Saito, N; Sakamoto, K; Sato, Y; Tanji, J

2001-03-01

To construct an animal model of strategy formation, we designed a maze path-finding task. First, we asked monkeys to capture a goal in the maze by moving a cursor on the screen. Cursor movement was linked to movements of each wrist. When the animals learned the association between cursor movement and wrist movement, we established a start and a goal in the maze, and asked them to find a path between them. We found that the animals took the shortest pathway, rather than approaching the goal randomly. We further found that the animals adopted a strategy of selecting a fixed intermediate point in the visually presented maze to select one of the shortest pathways, suggesting a visually based path planning. To examine their capacity to use that strategy flexibly, we transformed the task by blocking pathways in the maze, providing a problem to solve. The animals then developed a strategy of solving the problem by planning a novel shortest path from the start to the goal and rerouting the path to bypass the obstacle.

Towards Detection of Learner Misconceptions in a Medical Learning Environment: A Subgroup Discovery Approach

ERIC Educational Resources Information Center

Poitras, Eric G.; Doleck, Tenzin; Lajoie, Susanne P.

2018-01-01

Ill-structured problems, by definition, have multiple paths to a solution and are multifaceted making automated assessment and feedback a difficult challenge. Diagnostic reasoning about medical cases meet the criteria of ill-structured problem solving since there are multiple solution paths. The goal of this study was to develop an adaptive…

Object permanence in cats: Analysis in locomotor space.

PubMed

Thinus-Blanc, C; Poucet, B; Chapuis, N

1982-04-01

Stages IV and V object permanence were studied with 38-40-week-old cats. A constraining apparatus preventing animals from pursuing the bowl containing meat before it was concealed was used. Either the bowl was seen moving and disappeared from view behind a screen (stage IV trials), or after this sequence, it reappeared from behind the first screen and disappeared behind a second screen (stage V trials). In both situations cats performed significantly above chance but the paths taken to reach the food were different according to the stage. In stage V trials, cats expressed a preference for the path leading to the end of the second screen where the food was last seen disappearing. Copyright © 1982. Published by Elsevier B.V.

A Constrained-Clustering Approach to the Analysis of Remote Sensing Data.

DTIC Science & Technology

1983-01-01

One old and two new clustering methods were applied to the constrained-clustering problem of separating different agricultural fields based on multispectral remote sensing satellite data. (Constrained-clustering involves double clustering in multispectral measurement similarity and geographical location.) The results of applying the three methods are provided along with a discussion of their relative strengths and weaknesses and a detailed description of their algorithms.

Implementation of fast handover for proxy mobile IPv6: Resolving out-of-order packets

PubMed Central

Anh, Khuong Quoc; Choo, Hyunseung

2017-01-01

Mobile IP allows for location-independent routing of IP datagrams on the Internet. Mobile IP specifies how a mobile node (MN) registers with its home agent and how the home agent routes datagrams to the MN through the tunnel. Current Mobile IP protocols have difficulties meeting the stringent handover delay requirements of future wireless networks. Fast handover for Proxy Mobile IPv6 (FPMIPv6) is used to resolve handover latency and packet loss problems that occur in the Proxy Mobile IPv6 (PMIPv6) protocol. However, while implementing the FPMIPv6 scheme in a testbed, we encounter the out-of-order packet (OoOP) problem. The cause of this problem is the existence of two paths for data transmitted from a correspondent node (CN) to an MN. Since the problem affects the quality of service (QoS) of the network and the performance of the MN, we propose a new scheme using the last packet marker and packet buffering to solve this problem in FPMIPv6. The new Mobile Access Gateway (MAG) can control and deliver the data transmitted via the old path or the new path to an MN in order, using the last packet marker to notify the end of the data delivery in the old path and the packet buffering for holding the data delivered in the new path. We implement both the proposed scheme and FPMIPv6 in a testbed as a real network environment to demonstrate the correctness, cost effectiveness, and performance of the proposed scheme. A performance evaluation reveals that the proposed scheme can handle the OoOP problem efficiently. PMID:28968450

Implementation of fast handover for proxy mobile IPv6: Resolving out-of-order packets.

PubMed

Kang, Byungseok; Anh, Khuong Quoc; Choo, Hyunseung

2017-01-01

Mobile IP allows for location-independent routing of IP datagrams on the Internet. Mobile IP specifies how a mobile node (MN) registers with its home agent and how the home agent routes datagrams to the MN through the tunnel. Current Mobile IP protocols have difficulties meeting the stringent handover delay requirements of future wireless networks. Fast handover for Proxy Mobile IPv6 (FPMIPv6) is used to resolve handover latency and packet loss problems that occur in the Proxy Mobile IPv6 (PMIPv6) protocol. However, while implementing the FPMIPv6 scheme in a testbed, we encounter the out-of-order packet (OoOP) problem. The cause of this problem is the existence of two paths for data transmitted from a correspondent node (CN) to an MN. Since the problem affects the quality of service (QoS) of the network and the performance of the MN, we propose a new scheme using the last packet marker and packet buffering to solve this problem in FPMIPv6. The new Mobile Access Gateway (MAG) can control and deliver the data transmitted via the old path or the new path to an MN in order, using the last packet marker to notify the end of the data delivery in the old path and the packet buffering for holding the data delivered in the new path. We implement both the proposed scheme and FPMIPv6 in a testbed as a real network environment to demonstrate the correctness, cost effectiveness, and performance of the proposed scheme. A performance evaluation reveals that the proposed scheme can handle the OoOP problem efficiently.

Short paths in expander graphs

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

Kleinberg, J.; Rubinfeld, R.

Graph expansion has proved to be a powerful general tool for analyzing the behavior of routing algorithms and the interconnection networks on which they run. We develop new routing algorithms and structural results for bounded-degree expander graphs. Our results are unified by the fact that they are all based upon, and extend, a body of work asserting that expanders are rich in short, disjoint paths. In particular, our work has consequences for the disjoint paths problem, multicommodify flow, and graph minor containment. We show: (i) A greedy algorithm for approximating the maximum disjoint paths problem achieves a polylogarithmic approximation ratiomore » in bounded-degree expanders. Although our algorithm is both deterministic and on-line, its performance guarantee is an improvement over previous bounds in expanders. (ii) For a multicommodily flow problem with arbitrary demands on a bounded-degree expander, there is a (1 + {epsilon})-optimal solution using only flow paths of polylogarithmic length. It follows that the multicommodity flow algorithm of Awerbuch and Leighton runs in nearly linear time per commodity in expanders. Our analysis is based on establishing the following: given edge weights on an expander G, one can increase some of the weights very slightly so the resulting shortest-path metric is smooth - the min-weight path between any pair of nodes uses a polylogarithmic number of edges. (iii) Every bounded-degree expander on n nodes contains every graph with O(n/log{sup O(1)} n) nodes and edges as a minor.« less

Calibration of neural networks using genetic algorithms, with application to optimal path planning

NASA Technical Reports Server (NTRS)

Smith, Terence R.; Pitney, Gilbert A.; Greenwood, Daniel

1987-01-01

Genetic algorithms (GA) are used to search the synaptic weight space of artificial neural systems (ANS) for weight vectors that optimize some network performance function. GAs do not suffer from some of the architectural constraints involved with other techniques and it is straightforward to incorporate terms into the performance function concerning the metastructure of the ANS. Hence GAs offer a remarkably general approach to calibrating ANS. GAs are applied to the problem of calibrating an ANS that finds optimal paths over a given surface. This problem involves training an ANS on a relatively small set of paths and then examining whether the calibrated ANS is able to find good paths between arbitrary start and end points on the surface.

E-Center: A Collaborative Platform for Wide Area Network Users

NASA Astrophysics Data System (ADS)

Grigoriev, M.; DeMar, P.; Tierney, B.; Lake, A.; Metzger, J.; Frey, M.; Calyam, P.

2012-12-01

The E-Center is a social collaborative web-based platform for assisting network users in understanding network conditions across network paths of interest to them. It is designed to give a user the necessary tools to isolate, identify, and resolve network performance-related problems. E-Center provides network path information on a link-by-link level, as well as from an end-to-end perspective. In addition to providing current and recent network path data, E-Center is intended to provide a social media environment for them to share issues, ideas, concerns, and problems. The product has a modular design that accommodates integration of other network services that make use of the same network path and performance data.

Solving fuzzy shortest path problem by genetic algorithm

NASA Astrophysics Data System (ADS)

Syarif, A.; Muludi, K.; Adrian, R.; Gen, M.

2018-03-01

Shortest Path Problem (SPP) is known as one of well-studied fields in the area Operations Research and Mathematical Optimization. It has been applied for many engineering and management designs. The objective is usually to determine path(s) in the network with minimum total cost or traveling time. In the past, the cost value for each arc was usually assigned or estimated as a deteministic value. For some specific real world applications, however, it is often difficult to determine the cost value properly. One way of handling such uncertainty in decision making is by introducing fuzzy approach. With this situation, it will become difficult to solve the problem optimally. This paper presents the investigations on the application of Genetic Algorithm (GA) to a new SPP model in which the cost values are represented as Triangular Fuzzy Number (TFN). We adopts the concept of ranking fuzzy numbers to determine how good the solutions. Here, by giving his/her degree value, the decision maker can determine the range of objective value. This would be very valuable for decision support system in the real world applications.Simulation experiments were carried out by modifying several test problems with 10-25 nodes. It is noted that the proposed approach is capable attaining a good solution with different degree of optimism for the tested problems.

Benchmarking Gas Path Diagnostic Methods: A Public Approach

NASA Technical Reports Server (NTRS)

Simon, Donald L.; Bird, Jeff; Davison, Craig; Volponi, Al; Iverson, R. Eugene

2008-01-01

Recent technology reviews have identified the need for objective assessments of engine health management (EHM) technology. The need is two-fold: technology developers require relevant data and problems to design and validate new algorithms and techniques while engine system integrators and operators need practical tools to direct development and then evaluate the effectiveness of proposed solutions. This paper presents a publicly available gas path diagnostic benchmark problem that has been developed by the Propulsion and Power Systems Panel of The Technical Cooperation Program (TTCP) to help address these needs. The problem is coded in MATLAB (The MathWorks, Inc.) and coupled with a non-linear turbofan engine simulation to produce "snap-shot" measurements, with relevant noise levels, as if collected from a fleet of engines over their lifetime of use. Each engine within the fleet will experience unique operating and deterioration profiles, and may encounter randomly occurring relevant gas path faults including sensor, actuator and component faults. The challenge to the EHM community is to develop gas path diagnostic algorithms to reliably perform fault detection and isolation. An example solution to the benchmark problem is provided along with associated evaluation metrics. A plan is presented to disseminate this benchmark problem to the engine health management technical community and invite technology solutions.

Insight and search in Katona's five-square problem.

PubMed

Ollinger, Michael; Jones, Gary; Knoblich, Günther

2014-01-01

Insights are often productive outcomes of human thinking. We provide a cognitive model that explains insight problem solving by the interplay of problem space search and representational change, whereby the problem space is constrained or relaxed based on the problem representation. By introducing different experimental conditions that either constrained the initial search space or helped solvers to initiate a representational change, we investigated the interplay of problem space search and representational change in Katona's five-square problem. Testing 168 participants, we demonstrated that independent hints relating to the initial search space and to representational change had little effect on solution rates. However, providing both hints caused a significant increase in solution rates. Our results show the interplay between problem space search and representational change in insight problem solving: The initial problem space can be so large that people fail to encounter impasse, but even when representational change is achieved the resulting problem space can still provide a major obstacle to finding the solution.

Fast marching methods for the continuous traveling salesman problem.

PubMed

Andrews, June; Sethian, J A

2007-01-23

We consider a problem in which we are given a domain, a cost function which depends on position at each point in the domain, and a subset of points ("cities") in the domain. The goal is to determine the cheapest closed path that visits each city in the domain once. This can be thought of as a version of the traveling salesman problem, in which an underlying known metric determines the cost of moving through each point of the domain, but in which the actual shortest path between cities is unknown at the outset. We describe algorithms for both a heuristic and an optimal solution to this problem. The complexity of the heuristic algorithm is at worst case M.N log N, where M is the number of cities, and N the size of the computational mesh used to approximate the solutions to the shortest paths problems. The average runtime of the heuristic algorithm is linear in the number of cities and O(N log N) in the size N of the mesh.

An Effective Evolutionary Approach for Bicriteria Shortest Path Routing Problems

NASA Astrophysics Data System (ADS)

Lin, Lin; Gen, Mitsuo

Routing problem is one of the important research issues in communication network fields. In this paper, we consider a bicriteria shortest path routing (bSPR) model dedicated to calculating nondominated paths for (1) the minimum total cost and (2) the minimum transmission delay. To solve this bSPR problem, we propose a new multiobjective genetic algorithm (moGA): (1) an efficient chromosome representation using the priority-based encoding method; (2) a new operator of GA parameters auto-tuning, which is adaptively regulation of exploration and exploitation based on the change of the average fitness of parents and offspring which is occurred at each generation; and (3) an interactive adaptive-weight fitness assignment mechanism is implemented that assigns weights to each objective and combines the weighted objectives into a single objective function. Numerical experiments with various scales of network design problems show the effectiveness and the efficiency of our approach by comparing with the recent researches.

Constraint shapes convergence in tetrodotoxin-resistant sodium channels of snakes.

PubMed

Feldman, Chris R; Brodie, Edmund D; Brodie, Edmund D; Pfrender, Michael E

2012-03-20

Natural selection often produces convergent changes in unrelated lineages, but the degree to which such adaptations occur via predictable genetic paths is unknown. If only a limited subset of possible mutations is fixed in independent lineages, then it is clear that constraint in the production or function of molecular variants is an important determinant of adaptation. We demonstrate remarkably constrained convergence during the evolution of resistance to the lethal poison, tetrodotoxin, in six snake species representing three distinct lineages from around the globe. Resistance-conferring amino acid substitutions in a voltage-gated sodium channel, Na(v)1.4, are clustered in only two regions of the protein, and a majority of the replacements are confined to the same three positions. The observed changes represent only a small fraction of the experimentally validated mutations known to increase Na(v)1.4 resistance to tetrodotoxin. These results suggest that constraints resulting from functional tradeoffs between ion channel function and toxin resistance led to predictable patterns of evolutionary convergence at the molecular level. Our data are consistent with theoretical predictions and recent microcosm work that suggest a predictable path is followed during an adaptive walk along a mutational landscape, and that natural selection may be frequently constrained to produce similar genetic outcomes even when operating on independent lineages.

A path-integral approach to the problem of time

NASA Astrophysics Data System (ADS)

Amaral, M. M.; Bojowald, Martin

2018-01-01

Quantum transition amplitudes are formulated for model systems with local internal time, using intuition from path integrals. The amplitudes are shown to be more regular near a turning point of internal time than could be expected based on existing canonical treatments. In particular, a successful transition through a turning point is provided in the model systems, together with a new definition of such a transition in general terms. Some of the results rely on a fruitful relation between the problem of time and general Gribov problems.

Planning Minimum-Energy Paths in an Off-Road Environment with Anisotropic Traversal Costs and Motion Constraints

DTIC Science & Technology

1989-06-01

problems, and (3) weighted-region problems. Since the minimum-energy path-planning problem addressed in this dissertation is a hybrid between the two...contains components that are strictly vehicle dependent, components that are strictly terrain dependent, and components representing a hybrid of...Single Segment Braking/Multiple Segment Hybrid Using Eq. (3.46), the traversal cost U 1,.-1 can be rewritten as Uop- 1 = mgD Itan01 , (4.12a) and the

An analysis of running skyline load path.

Treesearch

Ward W. Carson; Charles N. Mann

1971-01-01

This paper is intended for those who wish to prepare an algorithm to determine the load path of a running skyline. The mathematics of a simplified approach to this running skyline design problem are presented. The approach employs assumptions which reduce the complexity of the problem to the point where it can be solved on desk-top computers of limited capacities. The...

Behavioral and Emotional Regulation and Adolescent Substance Use Problems: A Test of Moderation Effects in a Dual-Process Model

PubMed Central

Wills, Thomas A.; Pokhrel, Pallav; Morehouse, Ellen; Fenster, Bonnie

2011-01-01

In a structural model, we tested how relations of predictors to level of adolescent substance use (tobacco, alcohol, marijuana), and to substance-related impaired-control and behavior problems, are moderated by good self-control and poor regulation in behavioral and emotional domains. The participants were a sample of 1,116 public high-school students. In a multiple-group analysis for good self-control, the paths from negative life events to substance use level and from level to behavior problems were lower among persons scoring higher on good behavioral self-control. In a multiple-group analysis for poor regulation, the paths from negative life events to level and from peer substance use to level were greater among persons scoring higher on poor behavioral (but not emotional) regulation; an inverse path from academic competence to level was greater among persons scoring higher on both aspects of poor regulation. Paths from level to impaired-control and behavior problems were greater among persons scoring higher on both poor behavioral and poor emotional regulation. Theoretical implications for the basis of moderation effects are discussed. PMID:21443302

Path spectra derived from inversion of source and site spectra for earthquakes in Southern California

NASA Astrophysics Data System (ADS)

Klimasewski, A.; Sahakian, V. J.; Baltay, A.; Boatwright, J.; Fletcher, J. B.; Baker, L. M.

2017-12-01

A large source of epistemic uncertainty in Ground Motion Prediction Equations (GMPEs) is derived from the path term, currently represented as a simple geometric spreading and intrinsic attenuation term. Including additional physical relationships between the path properties and predicted ground motions would produce more accurate and precise, region-specific GMPEs by reclassifying some of the random, aleatory uncertainty as epistemic. This study focuses on regions of Southern California, using data from the Anza network and Southern California Seismic network to create a catalog of events magnitude 2.5 and larger from 1998 to 2016. The catalog encompasses regions of varying geology and therefore varying path and site attenuation. Within this catalog of events, we investigate several collections of event region-to-station pairs, each of which share similar origin locations and stations so that all events have similar paths. Compared with a simple regional GMPE, these paths consistently have high or low residuals. By working with events that have the same path, we can isolate source and site effects, and focus on the remaining residual as path effects. We decompose the recordings into source and site spectra for each unique event and site in our greater Southern California regional database using the inversion method of Andrews (1986). This model represents each natural log record spectra as the sum of its natural log event and site spectra, while constraining each record to a reference site or Brune source spectrum. We estimate a regional, path-specific anelastic attenuation (Q) and site attenuation (t*) from the inversion site spectra and corner frequency from the inversion event spectra. We then compute the residuals between the observed record data, and the inversion model prediction (event*site spectra). This residual is representative of path effects, likely anelastic attenuation along the path that varies from the regional median attenuation. We examine the residuals for our different sets independently to see how path terms differ between event-to-station collections. The path-specific information gained from this can inform development of terms for regional GMPEs, through understanding of these seismological phenomena.

Comments on "The multisynapse neural network and its application to fuzzy clustering".

PubMed

Yu, Jian; Hao, Pengwei

2005-05-01

In the above-mentioned paper, Wei and Fahn proposed a neural architecture, the multisynapse neural network, to solve constrained optimization problems including high-order, logarithmic, and sinusoidal forms, etc. As one of its main applications, a fuzzy bidirectional associative clustering network (FBACN) was proposed for fuzzy-partition clustering according to the objective-functional method. The connection between the objective-functional-based fuzzy c-partition algorithms and FBACN is the Lagrange multiplier approach. Unfortunately, the Lagrange multiplier approach was incorrectly applied so that FBACN does not equivalently minimize its corresponding constrained objective-function. Additionally, Wei and Fahn adopted traditional definition of fuzzy c-partition, which is not satisfied by FBACN. Therefore, FBACN can not solve constrained optimization problems, either.

Relationship between Air Pollution and Weather Conditions under Complicated Geographical conditions

NASA Astrophysics Data System (ADS)

Cheng, Q.; Jiang, P.; Li, M.

2017-12-01

Air pollution is one of the most serious issues all over the world, especially in megacities with constrained geographical conditions for air pollution diffusion. However, the dynamic mechanism of air pollution diffusion under complicated geographical conditions is still be confused. Researches to explore relationship between air pollution and weather conditions from the perspective of local atmospheric circulations can contribute more to solve such problem. We selected three megacities (Beijing, Shanghai and Guangzhou) under different geographical condition (mountain-plain transition region, coastal alluvial plain and coastal hilly terrain) to explore the relationship between air pollution and weather conditions. RDA (Redundancy analysis) model was used to analyze how the local atmospheric circulation acts on the air pollutant diffusion. The results show that there was a positive correlation between the concentration of air pollutants and air pressure, while temperature, precipitation and wind speed have negative correlations with the concentration of air pollutants. Furthermore, geographical conditions, such as topographic relief, have significant effects on the direction, path and intensity of local atmospheric circulation. As a consequence, air pollutants diffusion modes in different cities under various geographical conditions are diverse from each other.

Empirical Green's function analysis: Taking the next step

USGS Publications Warehouse

Hough, S.E.

1997-01-01

An extension of the empirical Green's function (EGF) method is presented that involves determination of source parameters using standard EGF deconvolution, followed by inversion for a common attenuation parameter for a set of colocated events. Recordings of three or more colocated events can thus be used to constrain a single path attenuation estimate. I apply this method to recordings from the 1995-1996 Ridgecrest, California, earthquake sequence; I analyze four clusters consisting of 13 total events with magnitudes between 2.6 and 4.9. I first obtain corner frequencies, which are used to infer Brune stress drop estimates. I obtain stress drop values of 0.3-53 MPa (with all but one between 0.3 and 11 MPa), with no resolved increase of stress drop with moment. With the corner frequencies constrained, the inferred attenuation parameters are very consistent; they imply an average shear wave quality factor of approximately 20-25 for alluvial sediments within the Indian Wells Valley. Although the resultant spectral fitting (using corner frequency and ??) is good, the residuals are consistent among the clusters analyzed. Their spectral shape is similar to the the theoretical one-dimensional response of a layered low-velocity structure in the valley (an absolute site response cannot be determined by this method, because of an ambiguity between absolute response and source spectral amplitudes). I show that even this subtle site response can significantly bias estimates of corner frequency and ??, if it is ignored in an inversion for only source and path effects. The multiple-EGF method presented in this paper is analogous to a joint inversion for source, path, and site effects; the use of colocated sets of earthquakes appears to offer significant advantages in improving resolution of all three estimates, especially if data are from a single site or sites with similar site response.

Mechanical and hydraulic properties of Nankai accretionary prism sediments: Effect of stress path

NASA Astrophysics Data System (ADS)

Kitajima, Hiroko; Chester, Frederick M.; Biscontin, Giovanna

2012-10-01

We have conducted triaxial deformation experiments along different loading paths on prism sediments from the Nankai Trough. Different load paths of isotropic loading, uniaxial strain loading, triaxial compression (at constant confining pressure, Pc), undrained Pc reduction, drained Pc reduction, and triaxial unloading at constant Pc, were used to understand the evolution of mechanical and hydraulic properties under complicated stress states and loading histories in accretionary subduction zones. Five deformation experiments were conducted on three sediment core samples for the Nankai prism, specifically from older accreted sediments at the forearc basin, underthrust slope sediments beneath the megasplay fault, and overthrust Upper Shikoku Basin sediments along the frontal thrust. Yield envelopes for each sample were constructed based on the stress paths of Pc-reduction using the modified Cam-clay model, and in situ stress states of the prism were constrained using the results from the other load paths and accounting for horizontal stress. Results suggest that the sediments in the vicinity of the megasplay fault and frontal thrust are highly overconsolidated, and thus likely to deform brittle rather than ductile. The porosity of sediments decreases as the yield envelope expands, while the reduction in permeability mainly depends on the effective mean stress before yield, and the differential stress after yield. An improved understanding of sediment yield strength and hydromechanical properties along different load paths is necessary to treat accurately the coupling of deformation and fluid flow in accretionary subduction zones.

Path optimization with limited sensing ability

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

Kang, Sung Ha, E-mail: kang@math.gatech.edu; Kim, Seong Jun, E-mail: skim396@math.gatech.edu; Zhou, Haomin, E-mail: hmzhou@math.gatech.edu

2015-10-15

We propose a computational strategy to find the optimal path for a mobile sensor with limited coverage to traverse a cluttered region. The goal is to find one of the shortest feasible paths to achieve the complete scan of the environment. We pose the problem in the level set framework, and first consider a related question of placing multiple stationary sensors to obtain the full surveillance of the environment. By connecting the stationary locations using the nearest neighbor strategy, we form the initial guess for the path planning problem of the mobile sensor. Then the path is optimized by reducingmore » its length, via solving a system of ordinary differential equations (ODEs), while maintaining the complete scan of the environment. Furthermore, we use intermittent diffusion, which converts the ODEs into stochastic differential equations (SDEs), to find an optimal path whose length is globally minimal. To improve the computation efficiency, we introduce two techniques, one to remove redundant connecting points to reduce the dimension of the system, and the other to deal with the entangled path so the solution can escape the local traps. Numerical examples are shown to illustrate the effectiveness of the proposed method.« less

Kinematic analyses of the golf swing hub path and its role in golfer/club kinetic transfers.

PubMed

Nesbit, Steven M; McGinnis, Ryan

2009-01-01

This study analyzed the fundamental geometric and kinematic characteristics of the swing hub path of the golf shot for four diverse subjects. In addition, the role of the hub path geometry in transferring the kinetic quantities from the golfer to the club were investigated. The hub path was found to have a complex geometry with significantly changing radii, and a constantly moving center-of-curvature during the downswing for all subjects. While the size and shape of the hub path differed considerably among the subjects, a three phase radius-based pattern was revealed that aligned with distinct stages of the downswing. Artificially controlling and optimizing the hub path of the better golfer in the group indicated that a non-circular hub path was superior to a constant radius path in minimizing the kinetic loading while generating the highest possible club head velocity. The shape and purpose of the hub path geometry appears to result from a complex combination of achieving equilibrium between the golfer and the club, and a purposeful configuring of the path to control the outward movement of the club while minimizing the kinetic loading on the golfer yet transferring the maximum kinetic quantities to the club. Describing the downswing relative to the hub path phasing is presented and was found to be informative since the phases align with significant swing, kinetic and kinematic markers. These findings challenge golf swing modeling methodologies which fix the center-of-curvature of the hub path thus constraining it to constant radius motion. Key pointsThe golf swing hub path was found to have a complex geometry with significantly changing radii, and a constantly moving center-of-curvature during the downswing.The hub path differed considerably among subjects, however a three phase radius-based pattern was revealed that aligned with distinct stages of the downswing.The shape and purpose of the hub path geometry appears to result from a complex combination of achieving equilibrium between the golfer and the club, and a purposeful configuring of the path to control the outward movement of the club while minimizing the kinetic loading on the golfer yet transferring the maximum kinetic quantities to the club.

Processor Would Find Best Paths On Map

NASA Technical Reports Server (NTRS)

Eberhardt, Silvio P.

1990-01-01

Proposed very-large-scale integrated (VLSI) circuit image-data processor finds path of least cost from specified origin to any destination on map. Cost of traversal assigned to each picture element of map. Path of least cost from originating picture element to every other picture element computed as path that preserves as much as possible of signal transmitted by originating picture element. Dedicated microprocessor at each picture element stores cost of traversal and performs its share of computations of paths of least cost. Least-cost-path problem occurs in research, military maneuvers, and in planning routes of vehicles.

Traffic-engineering-aware shortest-path routing and its application in IP-over-WDM networks [Invited

NASA Astrophysics Data System (ADS)

Lee, Youngseok; Mukherjee, Biswanath

2004-03-01

Single shortest-path routing is known to perform poorly for Internet traffic engineering (TE) where the typical optimization objective is to minimize the maximum link load. Splitting traffic uniformly over equal-cost multiple shortest paths in open shortest path first and intermediate system-intermediate system protocols does not always minimize the maximum link load when multiple paths are not carefully selected for the global traffic demand matrix. However, a TE-aware shortest path among all the equal-cost multiple shortest paths between each ingress-egress pair can be selected such that the maximum link load is significantly reduced. IP routers can use the globally optimal TE-aware shortest path without any change to existing routing protocols and without any serious configuration overhead. While calculating TE-aware shortest paths, the destination-based forwarding constraint at a node should be satisfied, because an IP router will forward a packet to the next hop toward the destination by looking up the destination prefix. We present a mathematical problem formulation for finding a set of TE-aware shortest paths for the given network as an integer linear program, and we propose a simple heuristic for solving large instances of the problem. Then we explore the usage of our proposed algorithm for the integrated TE method in IP-over-WDM networks. The proposed algorithm is evaluated through simulations in IP networks as well as in IP-over-WDM networks.

Method of interplanetary trajectory optimization for the spacecraft with low thrust and swing-bys

NASA Astrophysics Data System (ADS)

Konstantinov, M. S.; Thein, M.

2017-07-01

The method developed to avoid the complexity of solving the multipoint boundary value problem while optimizing interplanetary trajectories of the spacecraft with electric propulsion and a sequence of swing-bys is presented in the paper. This method is based on the use of the preliminary problem solutions for the impulsive trajectories. The preliminary problem analyzed at the first stage of the study is formulated so that the analysis and optimization of a particular flight path is considered as the unconstrained minimum in the space of the selectable parameters. The existing methods can effectively solve this problem and make it possible to identify rational flight paths (the sequence of swing-bys) to receive the initial approximation for the main characteristics of the flight path (dates, values of the hyperbolic excess velocity, etc.). These characteristics can be used to optimize the trajectory of the spacecraft with electric propulsion. The special feature of the work is the introduction of the second (intermediate) stage of the research. At this stage some characteristics of the analyzed flight path (e.g. dates of swing-bys) are fixed and the problem is formulated so that the trajectory of the spacecraft with electric propulsion is optimized on selected sites of the flight path. The end-to-end optimization is carried out at the third (final) stage of the research. The distinctive feature of this stage is the analysis of the full set of optimal conditions for the considered flight path. The analysis of the characteristics of the optimal flight trajectories to Jupiter with Earth, Venus and Mars swing-bys for the spacecraft with electric propulsion are presented. The paper shows that the spacecraft weighing more than 7150 kg can be delivered into the vicinity of Jupiter along the trajectory with two Earth swing-bys by use of the space transportation system based on the "Angara A5" rocket launcher, the chemical upper stage "KVTK" and the electric propulsion system with input electrical power of 100 kW.

SU-E-T-58: A Novel Monte Carlo Photon Transport Simulation Scheme and Its Application in Cone Beam CT Projection Simulation

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

Xu, Y; Southern Medical University, Guangzhou; Tian, Z

Purpose: Monte Carlo (MC) simulation is an important tool to solve radiotherapy and medical imaging problems. Low computational efficiency hinders its wide applications. Conventionally, MC is performed in a particle-by -particle fashion. The lack of control on particle trajectory is a main cause of low efficiency in some applications. Take cone beam CT (CBCT) projection simulation as an example, significant amount of computations were wasted on transporting photons that do not reach the detector. To solve this problem, we propose an innovative MC simulation scheme with a path-by-path sampling method. Methods: Consider a photon path starting at the x-ray source.more » After going through a set of interactions, it ends at the detector. In the proposed scheme, we sampled an entire photon path each time. Metropolis-Hasting algorithm was employed to accept/reject a sampled path based on a calculated acceptance probability, in order to maintain correct relative probabilities among different paths, which are governed by photon transport physics. We developed a package gMMC on GPU with this new scheme implemented. The performance of gMMC was tested in a sample problem of CBCT projection simulation for a homogeneous object. The results were compared to those obtained using gMCDRR, a GPU-based MC tool with the conventional particle-by-particle simulation scheme. Results: Calculated scattered photon signals in gMMC agreed with those from gMCDRR with a relative difference of 3%. It took 3.1 hr. for gMCDRR to simulate 7.8e11 photons and 246.5 sec for gMMC to simulate 1.4e10 paths. Under this setting, both results attained the same ∼2% statistical uncertainty. Hence, a speed-up factor of ∼45.3 was achieved by this new path-by-path simulation scheme, where all the computations were spent on those photons contributing to the detector signal. Conclusion: We innovatively proposed a novel path-by-path simulation scheme that enabled a significant efficiency enhancement for MC particle transport simulations.« less

Thermal Stability of Al2O3/Silicone Composites as High-Temperature Encapsulants

NASA Astrophysics Data System (ADS)

Yao, Yiying

Underwater gliders are robust and long endurance ocean sampling platforms that are increasingly being deployed in coastal regions. This new environment is characterized by shallow waters and significant currents that can challenge the mobility of these efficient (but traditionally slow moving) vehicles. This dissertation aims to improve the performance of shallow water underwater gliders through path planning. The path planning problem is formulated for a dynamic particle (or "kinematic car") model. The objective is to identify the path which satisfies specified boundary conditions and minimizes a particular cost. Several cost functions are considered. The problem is addressed using optimal control theory. The length scales of interest for path planning are within a few turn radii. First, an approach is developed for planning minimum-time paths, for a fixed speed glider, that are sub-optimal but are guaranteed to be feasible in the presence of unknown time-varying currents. Next the minimum-time problem for a glider with speed controls, that may vary between the stall speed and the maximum speed, is solved. Last, optimal paths that minimize change in depth (equivalently, maximize range) are investigated. Recognizing that path planning alone cannot overcome all of the challenges associated with significant currents and shallow waters, the design of a novel underwater glider with improved capabilities is explored. A glider with a pneumatic buoyancy engine (allowing large, rapid buoyancy changes) and a cylindrical moving mass mechanism (generating large pitch and roll moments) is designed, manufactured, and tested to demonstrate potential improvements in speed and maneuverability.

The inverse problem of the calculus of variations for discrete systems

NASA Astrophysics Data System (ADS)

Barbero-Liñán, María; Farré Puiggalí, Marta; Ferraro, Sebastián; Martín de Diego, David

2018-05-01

We develop a geometric version of the inverse problem of the calculus of variations for discrete mechanics and constrained discrete mechanics. The geometric approach consists of using suitable Lagrangian and isotropic submanifolds. We also provide a transition between the discrete and the continuous problems and propose variationality as an interesting geometric property to take into account in the design and computer simulation of numerical integrators for constrained systems. For instance, nonholonomic mechanics is generally non variational but some special cases admit an alternative variational description. We apply some standard nonholonomic integrators to such an example to study which ones conserve this property.

On Profit-Maximizing Pricing for the Highway and Tollbooth Problems

NASA Astrophysics Data System (ADS)

Elbassioni, Khaled; Raman, Rajiv; Ray, Saurabh; Sitters, René

In the tollbooth problem on trees, we are given a tree T= (V,E) with n edges, and a set of m customers, each of whom is interested in purchasing a path on the graph. Each customer has a fixed budget, and the objective is to price the edges of T such that the total revenue made by selling the paths to the customers that can afford them is maximized. An important special case of this problem, known as the highway problem, is when T is restricted to be a line. For the tollbooth problem, we present an O(logn)-approximation, improving on the current best O(logm)-approximation. We also study a special case of the tollbooth problem, when all the paths that customers are interested in purchasing go towards a fixed root of T. In this case, we present an algorithm that returns a (1 - ɛ)-approximation, for any ɛ> 0, and runs in quasi-polynomial time. On the other hand, we rule out the existence of an FPTAS by showing that even for the line case, the problem is strongly NP-hard. Finally, we show that in the discount model, when we allow some items to be priced below zero to improve the overall profit, the problem becomes even APX-hard.

A shrinking hypersphere PSO for engineering optimisation problems

NASA Astrophysics Data System (ADS)

Yadav, Anupam; Deep, Kusum

2016-03-01

Many real-world and engineering design problems can be formulated as constrained optimisation problems (COPs). Swarm intelligence techniques are a good approach to solve COPs. In this paper an efficient shrinking hypersphere-based particle swarm optimisation (SHPSO) algorithm is proposed for constrained optimisation. The proposed SHPSO is designed in such a way that the movement of the particle is set to move under the influence of shrinking hyperspheres. A parameter-free approach is used to handle the constraints. The performance of the SHPSO is compared against the state-of-the-art algorithms for a set of 24 benchmark problems. An exhaustive comparison of the results is provided statistically as well as graphically. Moreover three engineering design problems namely welded beam design, compressed string design and pressure vessel design problems are solved using SHPSO and the results are compared with the state-of-the-art algorithms.

Time optimal paths for high speed maneuvering

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

Reister, D.B.; Lenhart, S.M.

1993-01-01

Recent theoretical results have completely solved the problem of determining the minimum length path for a vehicle with a minimum turning radius moving from an initial configuration to a final configuration. Time optimal paths for a constant speed vehicle are a subset of the minimum length paths. This paper uses the Pontryagin maximum principle to find time optimal paths for a constant speed vehicle. The time optimal paths consist of sequences of axes of circles and straight lines. The maximum principle introduces concepts (dual variables, bang-bang solutions, singular solutions, and transversality conditions) that provide important insight into the nature ofmore » the time optimal paths. We explore the properties of the optimal paths and present some experimental results for a mobile robot following an optimal path.« less

A Bayesian framework for infrasound location

NASA Astrophysics Data System (ADS)

Modrak, Ryan T.; Arrowsmith, Stephen J.; Anderson, Dale N.

2010-04-01

We develop a framework for location of infrasound events using backazimuth and infrasonic arrival times from multiple arrays. Bayesian infrasonic source location (BISL) developed here estimates event location and associated credibility regions. BISL accounts for unknown source-to-array path or phase by formulating infrasonic group velocity as random. Differences between observed and predicted source-to-array traveltimes are partitioned into two additive Gaussian sources, measurement error and model error, the second of which accounts for the unknown influence of wind and temperature on path. By applying the technique to both synthetic tests and ground-truth events, we highlight the complementary nature of back azimuths and arrival times for estimating well-constrained event locations. BISL is an extension to methods developed earlier by Arrowsmith et al. that provided simple bounds on location using a grid-search technique.

Cost efficient environmental survey paths for detecting continuous tracer discharges

NASA Astrophysics Data System (ADS)

Alendal, G.

2017-07-01

Designing monitoring programs for detecting potential tracer discharges from unknown locations is challenging. The high variability of the environment may camouflage the anticipated anisotropic signal from a discharge, and there are a number of discharge scenarios. Monitoring operations may also be costly, constraining the number of measurements taken. By assuming that a discharge is active, and a prior belief on the most likely seep location, a method that uses Bayes' theorem combined with discharge footprint predictions is used to update the probability map. Measurement locations with highest reduction in the overall probability of a discharge to be active can be identified. The relative cost between reallocating and measurements can be taken into account. Three different strategies are suggested to enable cost efficient paths for autonomous vessels.

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

Reister, D.B.; Lenhart, S.M.

Recent theoretical results have completely solved the problem of determining the minimum length path for a vehicle with a minimum turning radius moving from an initial configuration to a final configuration. Time optimal paths for a constant speed vehicle are a subset of the minimum length paths. This paper uses the Pontryagin maximum principle to find time optimal paths for a constant speed vehicle. The time optimal paths consist of sequences of axes of circles and straight lines. The maximum principle introduces concepts (dual variables, bang-bang solutions, singular solutions, and transversality conditions) that provide important insight into the nature ofmore » the time optimal paths. We explore the properties of the optimal paths and present some experimental results for a mobile robot following an optimal path.« less

Experimental characterization of seasonal variations in infrasonic traveltimes on the Korean Peninsula with implications for infrasound event location

NASA Astrophysics Data System (ADS)

Che, Il-Young; Stump, Brian W.; Lee, Hee-Il

2011-04-01

The dependence of infrasound propagation on the season and path environment was quantified by the analysis of more than 1000 repetitive infrasonic ground-truth events at an active, open-pit mine over two years. Blast-associated infrasonic signals were analysed from two infrasound arrays (CHNAR and ULDAR) located at similar distances of 181 and 169 km, respectively, from the source but in different azimuthal directions and with different path environments. The CHNAR array is located to the NW of the source area with primarily a continental path, whereas the ULDAR is located East of the source with a path dominated by open ocean. As a result, CHNAR observations were dominated by stratospheric phases with characteristic celerities of 260-289 m s-1 and large seasonal variations in the traveltime, whereas data from ULDAR consisted primarily of tropospheric phases with larger celerities from 322 to 361 m s-1 and larger daily than seasonal variation in the traveltime. The interpretation of these observations is verified by ray tracing using atmospheric models incorporating daily weather balloon data that characterizes the shallow atmosphere for the two years of the study. Finally, experimental celerity models that included seasonal path effects were constructed from the long-term data set. These experimental celerity models were used to constrain traveltime variations in infrasonic location algorithms providing improved location estimates as illustrated with the empirical data set.

Modified Backtracking Search Optimization Algorithm Inspired by Simulated Annealing for Constrained Engineering Optimization Problems

PubMed Central

Wang, Hailong; Sun, Yuqiu; Su, Qinghua; Xia, Xuewen

2018-01-01

The backtracking search optimization algorithm (BSA) is a population-based evolutionary algorithm for numerical optimization problems. BSA has a powerful global exploration capacity while its local exploitation capability is relatively poor. This affects the convergence speed of the algorithm. In this paper, we propose a modified BSA inspired by simulated annealing (BSAISA) to overcome the deficiency of BSA. In the BSAISA, the amplitude control factor (F) is modified based on the Metropolis criterion in simulated annealing. The redesigned F could be adaptively decreased as the number of iterations increases and it does not introduce extra parameters. A self-adaptive ε-constrained method is used to handle the strict constraints. We compared the performance of the proposed BSAISA with BSA and other well-known algorithms when solving thirteen constrained benchmarks and five engineering design problems. The simulation results demonstrated that BSAISA is more effective than BSA and more competitive with other well-known algorithms in terms of convergence speed. PMID:29666635

Vehicle routing problem with time windows using natural inspired algorithms

NASA Astrophysics Data System (ADS)

Pratiwi, A. B.; Pratama, A.; Sa’diyah, I.; Suprajitno, H.

2018-03-01

Process of distribution of goods needs a strategy to make the total cost spent for operational activities minimized. But there are several constrains have to be satisfied which are the capacity of the vehicles and the service time of the customers. This Vehicle Routing Problem with Time Windows (VRPTW) gives complex constrains problem. This paper proposes natural inspired algorithms for dealing with constrains of VRPTW which involves Bat Algorithm and Cat Swarm Optimization. Bat Algorithm is being hybrid with Simulated Annealing, the worst solution of Bat Algorithm is replaced by the solution from Simulated Annealing. Algorithm which is based on behavior of cats, Cat Swarm Optimization, is improved using Crow Search Algorithm to make simplier and faster convergence. From the computational result, these algorithms give good performances in finding the minimized total distance. Higher number of population causes better computational performance. The improved Cat Swarm Optimization with Crow Search gives better performance than the hybridization of Bat Algorithm and Simulated Annealing in dealing with big data.

Constrained State Estimation for Individual Localization in Wireless Body Sensor Networks

PubMed Central

Feng, Xiaoxue; Snoussi, Hichem; Liang, Yan; Jiao, Lianmeng

2014-01-01

Wireless body sensor networks based on ultra-wideband radio have recently received much research attention due to its wide applications in health-care, security, sports and entertainment. Accurate localization is a fundamental problem to realize the development of effective location-aware applications above. In this paper the problem of constrained state estimation for individual localization in wireless body sensor networks is addressed. Priori knowledge about geometry among the on-body nodes as additional constraint is incorporated into the traditional filtering system. The analytical expression of state estimation with linear constraint to exploit the additional information is derived. Furthermore, for nonlinear constraint, first-order and second-order linearizations via Taylor series expansion are proposed to transform the nonlinear constraint to the linear case. Examples between the first-order and second-order nonlinear constrained filters based on interacting multiple model extended kalman filter (IMM-EKF) show that the second-order solution for higher order nonlinearity as present in this paper outperforms the first-order solution, and constrained IMM-EKF obtains superior estimation than IMM-EKF without constraint. Another brownian motion individual localization example also illustrates the effectiveness of constrained nonlinear iterative least square (NILS), which gets better filtering performance than NILS without constraint. PMID:25390408

Agency as a mediator in the pathway from transactional sex to HIV among pregnant women in Swaziland: a multigroup path analysis.

PubMed

Fielding-Miller, Rebecca; Dunkle, Kristin L; Hadley, Craig; Cooper, Hannah Lf; Windle, Michael

2017-07-18

Transactional sex is a structural driver of HIV for women and girls in sub-Saharan Africa. In transactional relationships, sexual and economic obligations intertwine and may have positive and negative effects on women's financial standing and social status. We conducted a clinic-based survey with pregnant women in Swaziland using a locally validated transactional sex scale to measure the association between subjective social status, transactional sex, and HIV status, and to assess whether this association differed according to a woman's agency within her relationship. We recruited a convenience sample of 406 pregnant women at one rural and one urban public antenatal clinic in Swaziland and administered a behavioural survey that was linked to participant HIV status using clinic records. We then conducted a multigroup path analysis to test three hypotheses: (1) that more engagement in transactional sex is associated with decreased condom use and increased subjective social status; (2) that subjective social status mediates the relationship between transactional sex and HIV status; and (3) that these relationships are different across groups according to whether or not a woman reported any indicator of constrained agency within her relationship. The amount and value of material goods received from a sexual partner was significantly and positively associated with higher subjective social status among all participants. As the amount of material goods received from a partner increased, women who reported no indicators of constrained agency were less likely to use condoms. Conversely, there was no relationship between transactional sex and condom use among women who reported any indicator of constrained relationship agency. Among women who reported any indicator of constrained agency, HIV was significantly associated with lower subjective social status. Relationship agency likely plays a key role in determining which mechanisms create HIV risk for women in transactional relationships. Interventions to mitigate these risks must address social forces that penalize women who engage in sexual relationships as well as structural drivers of gendered economic disparity that reduce women's agency within their sexual and romantic relationships.

Simulating reservoir lithologies by an actively conditioned Markov chain model

NASA Astrophysics Data System (ADS)

Feng, Runhai; Luthi, Stefan M.; Gisolf, Dries

2018-06-01

The coupled Markov chain model can be used to simulate reservoir lithologies between wells, by conditioning them on the observed data in the cored wells. However, with this method, only the state at the same depth as the current cell is going to be used for conditioning, which may be a problem if the geological layers are dipping. This will cause the simulated lithological layers to be broken or to become discontinuous across the reservoir. In order to address this problem, an actively conditioned process is proposed here, in which a tolerance angle is predefined. The states contained in the region constrained by the tolerance angle will be employed for conditioning in the horizontal chain first, after which a coupling concept with the vertical chain is implemented. In order to use the same horizontal transition matrix for different future states, the tolerance angle has to be small. This allows the method to work in reservoirs without complex structures caused by depositional processes or tectonic deformations. Directional artefacts in the modeling process are avoided through a careful choice of the simulation path. The tolerance angle and dipping direction of the strata can be obtained from a correlation between wells, or from seismic data, which are available in most hydrocarbon reservoirs, either by interpretation or by inversion that can also assist the construction of a horizontal probability matrix.

A parallel algorithm for finding the shortest exit paths in mines

NASA Astrophysics Data System (ADS)

Jastrzab, Tomasz; Buchcik, Agata

2017-11-01

In the paper we study the problem of finding the shortest exit path in an underground mine in case of emergency. Since emergency situations, such as underground fires, can put the miners' lives at risk, the ability to quickly determine the safest exit path is crucial. We propose a parallel algorithm capable of finding the shortest path between the safe exit point and any other point in the mine. The algorithm is also able to take into account the characteristics of individual miners, to make the path determination more reliable.

Exposure Time Distributions reveal Denitrification Rates along Groundwater Flow Path of an Agricultural Unconfined Aquifer

NASA Astrophysics Data System (ADS)

Kolbe, T.; Abbott, B. W.; Thomas, Z.; Labasque, T.; Aquilina, L.; Laverman, A.; Babey, T.; Marçais, J.; Fleckenstein, J. H.; Peiffer, S.; De Dreuzy, J. R.; Pinay, G.

2016-12-01

Groundwater contamination by nitrate is nearly ubiquitous in agricultural regions. Nitrate is highly mobile in groundwater and though it can be denitrified in the aquifer (reduced to inert N2 gas), this process requires the simultaneous occurrence of anoxia, an electron donor (e.g. organic carbon, pyrite), nitrate, and microorganisms capable of denitrification. In addition to this the ratio of the time groundwater spent in a denitrifying environment (exposure time) to the characteristic denitrification reaction time plays an important role, because denitrification can only occur if the exposure time is longer than the characteristic reaction time. Despite a long history of field studies and numerical models, it remains exceedingly difficult to measure or model exposure times in the subsurface at the catchment scale. To approach this problem, we developed a unified modelling approach combining measured environmental proxies with an exposure time based reactive transport model. We measured groundwater age, nitrogen and sulfur isotopes, and water chemistry from agricultural wells in an unconfined aquifer in Brittany, France, to quantify changes in nitrate concentration due to dilution and denitrification. Field data showed large differences in nitrate concentrations among wells, associated with differences in the exposure time distributions. By constraining a catchment-scale characteristic reaction time for denitrification with water chemistry proxies and exposure times, we were able to assess rates of denitrification along groundwater flow paths. This unified modeling approach is transferable to other catchments and could be further used to investigate how catchment structure and flow dynamics interact with biogeochemical processes such as denitrification.

An expert system for choosing the best combination of options in a general purpose program for automated design synthesis

NASA Technical Reports Server (NTRS)

Rogers, J. L.; Barthelemy, J.-F. M.

1986-01-01

An expert system called EXADS has been developed to aid users of the Automated Design Synthesis (ADS) general purpose optimization program. ADS has approximately 100 combinations of strategy, optimizer, and one-dimensional search options from which to choose. It is difficult for a nonexpert to make this choice. This expert system aids the user in choosing the best combination of options based on the users knowledge of the problem and the expert knowledge stored in the knowledge base. The knowledge base is divided into three categories; constrained problems, unconstrained problems, and constrained problems being treated as unconstrained problems. The inference engine and rules are written in LISP, contains about 200 rules, and executes on DEC-VAX (with Franz-LISP) and IBM PC (with IQ-LISP) computers.

Mobile robot dynamic path planning based on improved genetic algorithm

NASA Astrophysics Data System (ADS)

Wang, Yong; Zhou, Heng; Wang, Ying

2017-08-01

In dynamic unknown environment, the dynamic path planning of mobile robots is a difficult problem. In this paper, a dynamic path planning method based on genetic algorithm is proposed, and a reward value model is designed to estimate the probability of dynamic obstacles on the path, and the reward value function is applied to the genetic algorithm. Unique coding techniques reduce the computational complexity of the algorithm. The fitness function of the genetic algorithm fully considers three factors: the security of the path, the shortest distance of the path and the reward value of the path. The simulation results show that the proposed genetic algorithm is efficient in all kinds of complex dynamic environments.

The Relationship Between Resilience and Internet Addiction: A Multiple Mediation Model Through Peer Relationship and Depression.

PubMed

Zhou, Pingyan; Zhang, Cai; Liu, Jian; Wang, Zhe

2017-10-01

Heavy use of the Internet may lead to profound academic problems in elementary students, such as poor grades, academic probation, and even expulsion from school. It is of great concern that Internet addiction problems in elementary school students have increased sharply in recent years. In this study, 58,756 elementary school students from the Henan province of China completed four questionnaires to explore the mechanisms of Internet addiction. The results showed that resilience was negatively correlated with Internet addiction. There were three mediational paths in the model: (a) the mediational path through peer relationship with an effect size of 50.0 percent, (b) the mediational path through depression with an effect size of 15.6 percent, (c) the mediational path through peer relationship and depression with an effect size of 13.7 percent. The total mediational effect size was 79.27 percent. The effect size through peer relationship was the strongest among the three mediation paths. The current findings suggest that resilience is a predictor of Internet addiction. Improving children's resilience (such as toughness, emotional control, and problem solving) can be an effective way to reduce Internet addiction behavior. The current findings provide useful information for early detection and intervention for Internet addiction.

Privacy-Preserving Relationship Path Discovery in Social Networks

NASA Astrophysics Data System (ADS)

Mezzour, Ghita; Perrig, Adrian; Gligor, Virgil; Papadimitratos, Panos

As social networks sites continue to proliferate and are being used for an increasing variety of purposes, the privacy risks raised by the full access of social networking sites over user data become uncomfortable. A decentralized social network would help alleviate this problem, but offering the functionalities of social networking sites is a distributed manner is a challenging problem. In this paper, we provide techniques to instantiate one of the core functionalities of social networks: discovery of paths between individuals. Our algorithm preserves the privacy of relationship information, and can operate offline during the path discovery phase. We simulate our algorithm on real social network topologies.

A path-oriented knowledge representation system: Defusing the combinatorial system

NASA Technical Reports Server (NTRS)

Karamouzis, Stamos T.; Barry, John S.; Smith, Steven L.; Feyock, Stefan

1995-01-01

LIMAP is a programming system oriented toward efficient information manipulation over fixed finite domains, and quantification over paths and predicates. A generalization of Warshall's Algorithm to precompute paths in a sparse matrix representation of semantic nets is employed to allow questions involving paths between components to be posed and answered easily. LIMAP's ability to cache all paths between two components in a matrix cell proved to be a computational obstacle, however, when the semantic net grew to realistic size. The present paper describes a means of mitigating this combinatorial explosion to an extent that makes the use of the LIMAP representation feasible for problems of significant size. The technique we describe radically reduces the size of the search space in which LIMAP must operate; semantic nets of more than 500 nodes have been attacked successfully. Furthermore, it appears that the procedure described is applicable not only to LIMAP, but to a number of other combinatorially explosive search space problems found in AI as well.

A Linear Kernel for Co-Path/Cycle Packing

NASA Astrophysics Data System (ADS)

Chen, Zhi-Zhong; Fellows, Michael; Fu, Bin; Jiang, Haitao; Liu, Yang; Wang, Lusheng; Zhu, Binhai

Bounded-Degree Vertex Deletion is a fundamental problem in graph theory that has new applications in computational biology. In this paper, we address a special case of Bounded-Degree Vertex Deletion, the Co-Path/Cycle Packing problem, which asks to delete as few vertices as possible such that the graph of the remaining (residual) vertices is composed of disjoint paths and simple cycles. The problem falls into the well-known class of 'node-deletion problems with hereditary properties', is hence NP-complete and unlikely to admit a polynomial time approximation algorithm with approximation factor smaller than 2. In the framework of parameterized complexity, we present a kernelization algorithm that produces a kernel with at most 37k vertices, improving on the super-linear kernel of Fellows et al.'s general theorem for Bounded-Degree Vertex Deletion. Using this kernel,and the method of bounded search trees, we devise an FPT algorithm that runs in time O *(3.24 k ). On the negative side, we show that the problem is APX-hard and unlikely to have a kernel smaller than 2k by a reduction from Vertex Cover.

Fast marching methods for the continuous traveling salesman problem

PubMed Central

Andrews, June; Sethian, J. A.

2007-01-01

We consider a problem in which we are given a domain, a cost function which depends on position at each point in the domain, and a subset of points (“cities”) in the domain. The goal is to determine the cheapest closed path that visits each city in the domain once. This can be thought of as a version of the traveling salesman problem, in which an underlying known metric determines the cost of moving through each point of the domain, but in which the actual shortest path between cities is unknown at the outset. We describe algorithms for both a heuristic and an optimal solution to this problem. The complexity of the heuristic algorithm is at worst case M·N log N, where M is the number of cities, and N the size of the computational mesh used to approximate the solutions to the shortest paths problems. The average runtime of the heuristic algorithm is linear in the number of cities and O(N log N) in the size N of the mesh. PMID:17220271

Fast marching methods for the continuous traveling salesman problem

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

Andrews, J.; Sethian, J.A.

We consider a problem in which we are given a domain, a cost function which depends on position at each point in the domain, and a subset of points ('cities') in the domain. The goal is to determine the cheapest closed path that visits each city in the domain once. This can be thought of as a version of the Traveling Salesman Problem, in which an underlying known metric determines the cost of moving through each point of the domain, but in which the actual shortest path between cities is unknown at the outset. We describe algorithms for both amore » heuristic and an optimal solution to this problem. The order of the heuristic algorithm is at worst case M * N logN, where M is the number of cities, and N the size of the computational mesh used to approximate the solutions to the shortest paths problems. The average runtime of the heuristic algorithm is linear in the number of cities and O(N log N) in the size N of the mesh.« less

Optimal exploration systems

NASA Astrophysics Data System (ADS)

Klesh, Andrew T.

This dissertation studies optimal exploration, defined as the collection of information about given objects of interest by a mobile agent (the explorer) using imperfect sensors. The key aspects of exploration are kinematics (which determine how the explorer moves in response to steering commands), energetics (which determine how much energy is consumed by motion and maneuvers), informatics (which determine the rate at which information is collected) and estimation (which determines the states of the objects). These aspects are coupled by the steering decisions of the explorer. We seek to improve exploration by finding trade-offs amongst these couplings and the components of exploration: the Mission, the Path and the Agent. A comprehensive model of exploration is presented that, on one hand, accounts for these couplings and on the other hand is simple enough to allow analysis. This model is utilized to pose and solve several exploration problems where an objective function is to be minimized. Specific functions to be considered are the mission duration and the total energy. These exploration problems are formulated as optimal control problems and necessary conditions for optimality are obtained in the form of two-point boundary value problems. An analysis of these problems reveals characteristics of optimal exploration paths. Several regimes are identified for the optimal paths including the Watchtower, Solar and Drag regime, and several non-dimensional parameters are derived that determine the appropriate regime of travel. The so-called Power Ratio is shown to predict the qualitative features of the optimal paths, provide a metric to evaluate an aircrafts design and determine an aircrafts capability for flying perpetually. Optimal exploration system drivers are identified that provide perspective as to the importance of these various regimes of flight. A bank-to-turn solar-powered aircraft flying at constant altitude on Mars is used as a specific platform for analysis using the coupled model. Flight-paths found with this platform are presented that display the optimal exploration problem characteristics. These characteristics are used to form heuristics, such as a Generalized Traveling Salesman Problem solver, to simplify the exploration problem. These heuristics are used to empirically show the successful completion of an exploration mission by a physical explorer.

Applications of a constrained mechanics methodology in economics

NASA Astrophysics Data System (ADS)

Janová, Jitka

2011-11-01

This paper presents instructive interdisciplinary applications of constrained mechanics calculus in economics on a level appropriate for undergraduate physics education. The aim of the paper is (i) to meet the demand for illustrative examples suitable for presenting the background of the highly expanding research field of econophysics even at the undergraduate level and (ii) to enable the students to gain a deeper understanding of the principles and methods routinely used in mechanics by looking at the well-known methodology from the different perspective of economics. Two constrained dynamic economic problems are presented using the economic terminology in an intuitive way. First, the Phillips model of the business cycle is presented as a system of forced oscillations and the general problem of two interacting economies is solved by the nonholonomic dynamics approach. Second, the Cass-Koopmans-Ramsey model of economical growth is solved as a variational problem with a velocity-dependent constraint using the vakonomic approach. The specifics of the solution interpretation in economics compared to mechanics is discussed in detail, a discussion of the nonholonomic and vakonomic approaches to constrained problems in mechanics and economics is provided and an economic interpretation of the Lagrange multipliers (possibly surprising for the students of physics) is carefully explained. This paper can be used by the undergraduate students of physics interested in interdisciplinary physics applications to gain an understanding of the current scientific approach to economics based on a physical background, or by university teachers as an attractive supplement to classical mechanics lessons.

Optimization of OSPF Routing in IP Networks

NASA Astrophysics Data System (ADS)

Bley, Andreas; Fortz, Bernard; Gourdin, Eric; Holmberg, Kaj; Klopfenstein, Olivier; Pióro, Michał; Tomaszewski, Artur; Ümit, Hakan

The Internet is a huge world-wide packet switching network comprised of more than 13,000 distinct subnetworks, referred to as Autonomous Systems (ASs) autonomous system AS . They all rely on the Internet Protocol (IP) internet protocol IP for transport of packets across the network. And most of them use shortest path routing protocols shortest path routing!protocols , such as OSPF or IS-IS, to control the routing of IP packets routing!of IP packets within an AS. The idea of the routing is extremely simple — every packet is forwarded on IP links along the shortest route between its source and destination nodes of the AS. The AS network administrator can manage the routing of packets in the AS by supplying the so-called administrative weights of IP links, which specify the link lengths that are used by the routing protocols for their shortest path computations. The main advantage of the shortest path routing policy is its simplicity, allowing for little administrative overhead. From the network engineering perspective, however, shortest path routing can pose problems in achieving satisfactory traffic handling efficiency. As all routing paths depend on the same routing metric routing!metric , it is not possible to configure the routing paths for the communication demands between different pairs of nodes explicitly or individually; the routing can be controlled only indirectly and only as a whole by modifying the routing metric. Thus, one of the main tasks when planning such networks is to find administrative link weights that induce a globally efficient traffic routing traffic!routing configuration of an AS. It turns out that this task leads to very difficult mathematical optimization problems. In this chapter, we discuss and describe exact integer programming models and solution approaches as well as practically efficient smart heuristics for such shortest path routing problems shortest path routing!problems .

A shifted hyperbolic augmented Lagrangian-based artificial fish two-swarm algorithm with guaranteed convergence for constrained global optimization

NASA Astrophysics Data System (ADS)

Rocha, Ana Maria A. C.; Costa, M. Fernanda P.; Fernandes, Edite M. G. P.

2016-12-01

This article presents a shifted hyperbolic penalty function and proposes an augmented Lagrangian-based algorithm for non-convex constrained global optimization problems. Convergence to an ?-global minimizer is proved. At each iteration k, the algorithm requires the ?-global minimization of a bound constrained optimization subproblem, where ?. The subproblems are solved by a stochastic population-based metaheuristic that relies on the artificial fish swarm paradigm and a two-swarm strategy. To enhance the speed of convergence, the algorithm invokes the Nelder-Mead local search with a dynamically defined probability. Numerical experiments with benchmark functions and engineering design problems are presented. The results show that the proposed shifted hyperbolic augmented Lagrangian compares favorably with other deterministic and stochastic penalty-based methods.

CAMPways: constrained alignment framework for the comparative analysis of a pair of metabolic pathways.

PubMed

Abaka, Gamze; Bıyıkoğlu, Türker; Erten, Cesim

2013-07-01

Given a pair of metabolic pathways, an alignment of the pathways corresponds to a mapping between similar substructures of the pair. Successful alignments may provide useful applications in phylogenetic tree reconstruction, drug design and overall may enhance our understanding of cellular metabolism. We consider the problem of providing one-to-many alignments of reactions in a pair of metabolic pathways. We first provide a constrained alignment framework applicable to the problem. We show that the constrained alignment problem even in a primitive setting is computationally intractable, which justifies efforts for designing efficient heuristics. We present our Constrained Alignment of Metabolic Pathways (CAMPways) algorithm designed for this purpose. Through extensive experiments involving a large pathway database, we demonstrate that when compared with a state-of-the-art alternative, the CAMPways algorithm provides better alignment results on metabolic networks as far as measures based on same-pathway inclusion and biochemical significance are concerned. The execution speed of our algorithm constitutes yet another important improvement over alternative algorithms. Open source codes, executable binary, useful scripts, all the experimental data and the results are freely available as part of the Supplementary Material at http://code.google.com/p/campways/. Supplementary data are available at Bioinformatics online.

Lefschetz thimbles in fermionic effective models with repulsive vector-field

NASA Astrophysics Data System (ADS)

Mori, Yuto; Kashiwa, Kouji; Ohnishi, Akira

2018-06-01

We discuss two problems in complexified auxiliary fields in fermionic effective models, the auxiliary sign problem associated with the repulsive vector-field and the choice of the cut for the scalar field appearing from the logarithmic function. In the fermionic effective models with attractive scalar and repulsive vector-type interaction, the auxiliary scalar and vector fields appear in the path integral after the bosonization of fermion bilinears. When we make the path integral well-defined by the Wick rotation of the vector field, the oscillating Boltzmann weight appears in the partition function. This "auxiliary" sign problem can be solved by using the Lefschetz-thimble path-integral method, where the integration path is constructed in the complex plane. Another serious obstacle in the numerical construction of Lefschetz thimbles is caused by singular points and cuts induced by multivalued functions of the complexified scalar field in the momentum integration. We propose a new prescription which fixes gradient flow trajectories on the same Riemann sheet in the flow evolution by performing the momentum integration in the complex domain.

Fat water decomposition using globally optimal surface estimation (GOOSE) algorithm.

PubMed

Cui, Chen; Wu, Xiaodong; Newell, John D; Jacob, Mathews

2015-03-01

This article focuses on developing a novel noniterative fat water decomposition algorithm more robust to fat water swaps and related ambiguities. Field map estimation is reformulated as a constrained surface estimation problem to exploit the spatial smoothness of the field, thus minimizing the ambiguities in the recovery. Specifically, the differences in the field map-induced frequency shift between adjacent voxels are constrained to be in a finite range. The discretization of the above problem yields a graph optimization scheme, where each node of the graph is only connected with few other nodes. Thanks to the low graph connectivity, the problem is solved efficiently using a noniterative graph cut algorithm. The global minimum of the constrained optimization problem is guaranteed. The performance of the algorithm is compared with that of state-of-the-art schemes. Quantitative comparisons are also made against reference data. The proposed algorithm is observed to yield more robust fat water estimates with fewer fat water swaps and better quantitative results than other state-of-the-art algorithms in a range of challenging applications. The proposed algorithm is capable of considerably reducing the swaps in challenging fat water decomposition problems. The experiments demonstrate the benefit of using explicit smoothness constraints in field map estimation and solving the problem using a globally convergent graph-cut optimization algorithm. © 2014 Wiley Periodicals, Inc.

Path Planning Algorithms for Autonomous Border Patrol Vehicles

NASA Astrophysics Data System (ADS)

Lau, George Tin Lam

This thesis presents an online path planning algorithm developed for unmanned vehicles in charge of autonomous border patrol. In this Pursuit-Evasion game, the unmanned vehicle is required to capture multiple trespassers on its own before any of them reach a target safe house where they are safe from capture. The problem formulation is based on Isaacs' Target Guarding problem, but extended to the case of multiple evaders. The proposed path planning method is based on Rapidly-exploring random trees (RRT) and is capable of producing trajectories within several seconds to capture 2 or 3 evaders. Simulations are carried out to demonstrate that the resulting trajectories approach the optimal solution produced by a nonlinear programming-based numerical optimal control solver. Experiments are also conducted on unmanned ground vehicles to show the feasibility of implementing the proposed online path planning algorithm on physical applications.

Benchmarking optimization software with COPS 3.0.

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

Dolan, E. D.; More, J. J.; Munson, T. S.

2004-05-24

The authors describe version 3.0 of the COPS set of nonlinearly constrained optimization problems. They have added new problems, as well as streamlined and improved most of the problems. They also provide a comparison of the FILTER, KNITRO, LOQO, MINOS, and SNOPT solvers on these problems.

Quantum Adiabatic Algorithms and Large Spin Tunnelling

NASA Technical Reports Server (NTRS)

Boulatov, A.; Smelyanskiy, V. N.

2003-01-01

We provide a theoretical study of the quantum adiabatic evolution algorithm with different evolution paths proposed in this paper. The algorithm is applied to a random binary optimization problem (a version of the 3-Satisfiability problem) where the n-bit cost function is symmetric with respect to the permutation of individual bits. The evolution paths are produced, using the generic control Hamiltonians H (r) that preserve the bit symmetry of the underlying optimization problem. In the case where the ground state of H(0) coincides with the totally-symmetric state of an n-qubit system the algorithm dynamics is completely described in terms of the motion of a spin-n/2. We show that different control Hamiltonians can be parameterized by a set of independent parameters that are expansion coefficients of H (r) in a certain universal set of operators. Only one of these operators can be responsible for avoiding the tunnelling in the spin-n/2 system during the quantum adiabatic algorithm. We show that it is possible to select a coefficient for this operator that guarantees a polynomial complexity of the algorithm for all problem instances. We show that a successful evolution path of the algorithm always corresponds to the trajectory of a classical spin-n/2 and provide a complete characterization of such paths.

H2, fixed architecture, control design for large scale systems. Ph.D. Thesis

NASA Technical Reports Server (NTRS)

Mercadal, Mathieu

1990-01-01

The H2, fixed architecture, control problem is a classic linear quadratic Gaussian (LQG) problem whose solution is constrained to be a linear time invariant compensator with a decentralized processing structure. The compensator can be made of p independent subcontrollers, each of which has a fixed order and connects selected sensors to selected actuators. The H2, fixed architecture, control problem allows the design of simplified feedback systems needed to control large scale systems. Its solution becomes more complicated, however, as more constraints are introduced. This work derives the necessary conditions for optimality for the problem and studies their properties. It is found that the filter and control problems couple when the architecture constraints are introduced, and that the different subcontrollers must be coordinated in order to achieve global system performance. The problem requires the simultaneous solution of highly coupled matrix equations. The use of homotopy is investigated as a numerical tool, and its convergence properties studied. It is found that the general constrained problem may have multiple stabilizing solutions, and that these solutions may be local minima or saddle points for the quadratic cost. The nature of the solution is not invariant when the parameters of the system are changed. Bifurcations occur, and a solution may continuously transform into a nonstabilizing compensator. Using a modified homotopy procedure, fixed architecture compensators are derived for models of large flexible structures to help understand the properties of the constrained solutions and compare them to the corresponding unconstrained ones.

Kinetic constrained optimization of the golf swing hub path.

PubMed

Nesbit, Steven M; McGinnis, Ryan S

2014-12-01

This study details an optimization of the golf swing, where the hand path and club angular trajectories are manipulated. The optimization goal was to maximize club head velocity at impact within the interaction kinetic limitations (force, torque, work, and power) of the golfer as determined through the analysis of a typical swing using a two-dimensional dynamic model. The study was applied to four subjects with diverse swing capabilities and styles. It was determined that it is possible for all subjects to increase their club head velocity at impact within their respective kinetic limitations through combined modifications to their respective hand path and club angular trajectories. The manner of the modifications, the degree of velocity improvement, the amount of kinetic reduction, and the associated kinetic limitation quantities were subject dependent. By artificially minimizing selected kinetic inputs within the optimization algorithm, it was possible to identify swing trajectory characteristics that indicated relative kinetic weaknesses of a subject. Practical implications are offered based upon the findings of the study. Key PointsThe hand path trajectory is an important characteristic of the golf swing and greatly affects club head velocity and golfer/club energy transfer.It is possible to increase the energy transfer from the golfer to the club by modifying the hand path and swing trajectories without increasing the kinetic output demands on the golfer.It is possible to identify relative kinetic output strengths and weakness of a golfer through assessment of the hand path and swing trajectories.Increasing any one of the kinetic outputs of the golfer can potentially increase the club head velocity at impact.The hand path trajectory has important influences over the club swing trajectory.

Kinetic Constrained Optimization of the Golf Swing Hub Path

PubMed Central

Nesbit, Steven M.; McGinnis, Ryan S.

2014-01-01

This study details an optimization of the golf swing, where the hand path and club angular trajectories are manipulated. The optimization goal was to maximize club head velocity at impact within the interaction kinetic limitations (force, torque, work, and power) of the golfer as determined through the analysis of a typical swing using a two-dimensional dynamic model. The study was applied to four subjects with diverse swing capabilities and styles. It was determined that it is possible for all subjects to increase their club head velocity at impact within their respective kinetic limitations through combined modifications to their respective hand path and club angular trajectories. The manner of the modifications, the degree of velocity improvement, the amount of kinetic reduction, and the associated kinetic limitation quantities were subject dependent. By artificially minimizing selected kinetic inputs within the optimization algorithm, it was possible to identify swing trajectory characteristics that indicated relative kinetic weaknesses of a subject. Practical implications are offered based upon the findings of the study. Key Points The hand path trajectory is an important characteristic of the golf swing and greatly affects club head velocity and golfer/club energy transfer. It is possible to increase the energy transfer from the golfer to the club by modifying the hand path and swing trajectories without increasing the kinetic output demands on the golfer. It is possible to identify relative kinetic output strengths and weakness of a golfer through assessment of the hand path and swing trajectories. Increasing any one of the kinetic outputs of the golfer can potentially increase the club head velocity at impact. The hand path trajectory has important influences over the club swing trajectory. PMID:25435779

Generic Equations for Constructing Smooth Paths Along Circles and Tangent Lines With Application to Airport Ground Paths

NASA Technical Reports Server (NTRS)

Barker, L. Keith

1998-01-01

The primary purpose of this publication is to develop a mathematical model to describe smooth paths along any combination of circles and tangent lines. Two consecutive circles in a path are either tangent (externally or internally) or they appear on the same (lateral) or opposite (transverse) sides of a connecting tangent line. A path may start or end on either a segment or circle. The approach is to use mathematics common to robotics to design the path as a multilink manipulator. This approach allows a hierarchical view of the problem and keeps the notation manageable. A user simply specifies a few parameters to configure a path. Necessary and sufficient conditions automatically ensure the consistency of the inputs for a smooth path. Two example runway exit paths are given, and an angle to go assists in knowing when to switch from one path element to the next.

Path planning of decentralized multi-quadrotor based on fuzzy-cell decomposition algorithm

NASA Astrophysics Data System (ADS)

Iswanto, Wahyunggoro, Oyas; Cahyadi, Adha Imam

2017-04-01

The paper aims to present a design algorithm for multi quadrotor lanes in order to move towards the goal quickly and avoid obstacles in an area with obstacles. There are several problems in path planning including how to get to the goal position quickly and avoid static and dynamic obstacles. To overcome the problem, therefore, the paper presents fuzzy logic algorithm and fuzzy cell decomposition algorithm. Fuzzy logic algorithm is one of the artificial intelligence algorithms which can be applied to robot path planning that is able to detect static and dynamic obstacles. Cell decomposition algorithm is an algorithm of graph theory used to make a robot path map. By using the two algorithms the robot is able to get to the goal position and avoid obstacles but it takes a considerable time because they are able to find the shortest path. Therefore, this paper describes a modification of the algorithms by adding a potential field algorithm used to provide weight values on the map applied for each quadrotor by using decentralized controlled, so that the quadrotor is able to move to the goal position quickly by finding the shortest path. The simulations conducted have shown that multi-quadrotor can avoid various obstacles and find the shortest path by using the proposed algorithms.

An Application of Self-Organizing Map for Multirobot Multigoal Path Planning with Minmax Objective.

PubMed

Faigl, Jan

2016-01-01

In this paper, Self-Organizing Map (SOM) for the Multiple Traveling Salesman Problem (MTSP) with minmax objective is applied to the robotic problem of multigoal path planning in the polygonal domain. The main difficulty of such SOM deployment is determination of collision-free paths among obstacles that is required to evaluate the neuron-city distances in the winner selection phase of unsupervised learning. Moreover, a collision-free path is also needed in the adaptation phase, where neurons are adapted towards the presented input signal (city) to the network. Simple approximations of the shortest path are utilized to address this issue and solve the robotic MTSP by SOM. Suitability of the proposed approximations is verified in the context of cooperative inspection, where cities represent sensing locations that guarantee to "see" the whole robots' workspace. The inspection task formulated as the MTSP-Minmax is solved by the proposed SOM approach and compared with the combinatorial heuristic GENIUS. The results indicate that the proposed approach provides competitive results to GENIUS and support applicability of SOM for robotic multigoal path planning with a group of cooperating mobile robots. The proposed combination of approximate shortest paths with unsupervised learning opens further applications of SOM in the field of robotic planning.

An Application of Self-Organizing Map for Multirobot Multigoal Path Planning with Minmax Objective

PubMed Central

Faigl, Jan

2016-01-01

In this paper, Self-Organizing Map (SOM) for the Multiple Traveling Salesman Problem (MTSP) with minmax objective is applied to the robotic problem of multigoal path planning in the polygonal domain. The main difficulty of such SOM deployment is determination of collision-free paths among obstacles that is required to evaluate the neuron-city distances in the winner selection phase of unsupervised learning. Moreover, a collision-free path is also needed in the adaptation phase, where neurons are adapted towards the presented input signal (city) to the network. Simple approximations of the shortest path are utilized to address this issue and solve the robotic MTSP by SOM. Suitability of the proposed approximations is verified in the context of cooperative inspection, where cities represent sensing locations that guarantee to “see” the whole robots' workspace. The inspection task formulated as the MTSP-Minmax is solved by the proposed SOM approach and compared with the combinatorial heuristic GENIUS. The results indicate that the proposed approach provides competitive results to GENIUS and support applicability of SOM for robotic multigoal path planning with a group of cooperating mobile robots. The proposed combination of approximate shortest paths with unsupervised learning opens further applications of SOM in the field of robotic planning. PMID:27340395

Stock management in hospital pharmacy using chance-constrained model predictive control.

PubMed

Jurado, I; Maestre, J M; Velarde, P; Ocampo-Martinez, C; Fernández, I; Tejera, B Isla; Prado, J R Del

2016-05-01

One of the most important problems in the pharmacy department of a hospital is stock management. The clinical need for drugs must be satisfied with limited work labor while minimizing the use of economic resources. The complexity of the problem resides in the random nature of the drug demand and the multiple constraints that must be taken into account in every decision. In this article, chance-constrained model predictive control is proposed to deal with this problem. The flexibility of model predictive control allows taking into account explicitly the different objectives and constraints involved in the problem while the use of chance constraints provides a trade-off between conservativeness and efficiency. The solution proposed is assessed to study its implementation in two Spanish hospitals. Copyright © 2015 Elsevier Ltd. All rights reserved.

Constrained Null Space Component Analysis for Semiblind Source Separation Problem.

PubMed

Hwang, Wen-Liang; Lu, Keng-Shih; Ho, Jinn

2018-02-01

The blind source separation (BSS) problem extracts unknown sources from observations of their unknown mixtures. A current trend in BSS is the semiblind approach, which incorporates prior information on sources or how the sources are mixed. The constrained independent component analysis (ICA) approach has been studied to impose constraints on the famous ICA framework. We introduced an alternative approach based on the null space component (NCA) framework and referred to the approach as the c-NCA approach. We also presented the c-NCA algorithm that uses signal-dependent semidefinite operators, which is a bilinear mapping, as signatures for operator design in the c-NCA approach. Theoretically, we showed that the source estimation of the c-NCA algorithm converges with a convergence rate dependent on the decay of the sequence, obtained by applying the estimated operators on corresponding sources. The c-NCA can be formulated as a deterministic constrained optimization method, and thus, it can take advantage of solvers developed in optimization society for solving the BSS problem. As examples, we demonstrated electroencephalogram interference rejection problems can be solved by the c-NCA with proximal splitting algorithms by incorporating a sparsity-enforcing separation model and considering the case when reference signals are available.

New Polyazine-Bridged RuII,RhIII and RuII,RhI Supramolecular Photocatalysts for Water Reduction to Hydrogen Applicable for Solar Energy Conversion and Mechanistic Investigation of the Photocatalytic Cycle

NASA Astrophysics Data System (ADS)

Zhou, Rongwei

Underwater gliders are robust and long endurance ocean sampling platforms that are increasingly being deployed in coastal regions. This new environment is characterized by shallow waters and significant currents that can challenge the mobility of these efficient (but traditionally slow moving) vehicles. This dissertation aims to improve the performance of shallow water underwater gliders through path planning. The path planning problem is formulated for a dynamic particle (or "kinematic car") model. The objective is to identify the path which satisfies specified boundary conditions and minimizes a particular cost. Several cost functions are considered. The problem is addressed using optimal control theory. The length scales of interest for path planning are within a few turn radii. First, an approach is developed for planning minimum-time paths, for a fixed speed glider, that are sub-optimal but are guaranteed to be feasible in the presence of unknown time-varying currents. Next the minimum-time problem for a glider with speed controls, that may vary between the stall speed and the maximum speed, is solved. Last, optimal paths that minimize change in depth (equivalently, maximize range) are investigated. Recognizing that path planning alone cannot overcome all of the challenges associated with significant currents and shallow waters, the design of a novel underwater glider with improved capabilities is explored. A glider with a pneumatic buoyancy engine (allowing large, rapid buoyancy changes) and a cylindrical moving mass mechanism (generating large pitch and roll moments) is designed, manufactured, and tested to demonstrate potential improvements in speed and maneuverability.

Constrained Surface-Level Gateway Placement for Underwater Acoustic Wireless Sensor Networks

NASA Astrophysics Data System (ADS)

Li, Deying; Li, Zheng; Ma, Wenkai; Chen, Hong

One approach to guarantee the performance of underwater acoustic sensor networks is to deploy multiple Surface-level Gateways (SGs) at the surface. This paper addresses the connected (or survivable) Constrained Surface-level Gateway Placement (C-SGP) problem for 3-D underwater acoustic sensor networks. Given a set of candidate locations where SGs can be placed, our objective is to place minimum number of SGs at a subset of candidate locations such that it is connected (or 2-connected) from any USN to the base station. We propose a polynomial time approximation algorithm for the connected C-SGP problem and survivable C-SGP problem, respectively. Simulations are conducted to verify our algorithms' efficiency.

An adaptive finite element method for the inequality-constrained Reynolds equation

NASA Astrophysics Data System (ADS)

Gustafsson, Tom; Rajagopal, Kumbakonam R.; Stenberg, Rolf; Videman, Juha

2018-07-01

We present a stabilized finite element method for the numerical solution of cavitation in lubrication, modeled as an inequality-constrained Reynolds equation. The cavitation model is written as a variable coefficient saddle-point problem and approximated by a residual-based stabilized method. Based on our recent results on the classical obstacle problem, we present optimal a priori estimates and derive novel a posteriori error estimators. The method is implemented as a Nitsche-type finite element technique and shown in numerical computations to be superior to the usually applied penalty methods.

Multiple utility constrained multi-objective programs using Bayesian theory

NASA Astrophysics Data System (ADS)

Abbasian, Pooneh; Mahdavi-Amiri, Nezam; Fazlollahtabar, Hamed

2018-03-01

A utility function is an important tool for representing a DM's preference. We adjoin utility functions to multi-objective optimization problems. In current studies, usually one utility function is used for each objective function. Situations may arise for a goal to have multiple utility functions. Here, we consider a constrained multi-objective problem with each objective having multiple utility functions. We induce the probability of the utilities for each objective function using Bayesian theory. Illustrative examples considering dependence and independence of variables are worked through to demonstrate the usefulness of the proposed model.

Necessary optimality conditions for infinite dimensional state constrained control problems

NASA Astrophysics Data System (ADS)

Frankowska, H.; Marchini, E. M.; Mazzola, M.

2018-06-01

This paper is concerned with first order necessary optimality conditions for state constrained control problems in separable Banach spaces. Assuming inward pointing conditions on the constraint, we give a simple proof of Pontryagin maximum principle, relying on infinite dimensional neighboring feasible trajectories theorems proved in [20]. Further, we provide sufficient conditions guaranteeing normality of the maximum principle. We work in the abstract semigroup setting, but nevertheless we apply our results to several concrete models involving controlled PDEs. Pointwise state constraints (as positivity of the solutions) are allowed.

Local structure of equality constrained NLP problems

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

Mari, J.

We show that locally around a feasible point, the behavior of an equality constrained nonlinear program is described by the gradient and the Hessian of the Lagrangian on the tangent subspace. In particular this holds true for reduced gradient approaches. Applying the same ideas to the control of nonlinear ODE:s, one can device first and second order methods that can be applied also to stiff problems. We finally describe an application of these ideas to the optimization of the production of human growth factor by fed-batch fermentation.

Maximum principle for a stochastic delayed system involving terminal state constraints.

PubMed

Wen, Jiaqiang; Shi, Yufeng

2017-01-01

We investigate a stochastic optimal control problem where the controlled system is depicted as a stochastic differential delayed equation; however, at the terminal time, the state is constrained in a convex set. We firstly introduce an equivalent backward delayed system depicted as a time-delayed backward stochastic differential equation. Then a stochastic maximum principle is obtained by virtue of Ekeland's variational principle. Finally, applications to a state constrained stochastic delayed linear-quadratic control model and a production-consumption choice problem are studied to illustrate the main obtained result.

Grand Coulee - Bell 500-kV Transmission Line Project, Draft Environmental Impact Statement

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

N /A

2002-08-09

BPA is proposing to construct a 500-kilovolt (kV) transmission line that would extend approximately 84 miles between the Grand Coulee 500-kV Switchyard, near Grand Coulee Dam, and the Bell Substation, in Mead just north of Spokane. The new line would cross portions of Douglas, Grant, Lincoln, and Spokane counties. In addition to the transmission line, new equipment would be installed at the substations at each end of the new line and at other facilities. The proposed action would remove an existing 115-kV transmission line and replace it with the new 500-kV line on existing right-of-way for most of its length.more » Additional right-of-way would be needed in the first 3.5 miles out of the Grand Coulee Switchyard to connect to the existing 115-kV right-of-way. Since the mid-1990s, the transmission path west of Spokane, called the West of Hatwai transmission pathway, has grown increasingly constrained. To date, BPA has been able to manage operation of the path through available operating practices, and customer needed have been met while maintaining the reliability of the path. however, in early 2001, operations showed that the amount of electricity that needs to flow from east to west along this path creates severe transmission congestion. Under these conditions, the system is at risk of overloads and violation of industry safety and reliability standards. The problem is particularly acute in the spring and summer months because of the large amount of power generated by dams east of the path. Large amounts of water cannot be spilled during that time in order for BPA to fulfill its obligation to protect threatened and endangered fish. The amount of power that needs to move through this area during these months at times could exceed the carrying capacity of the existing transmission lines. In additional capacity is not added, BPA will run a significant risk that it will not be able to continue to meet its contractual obligations to deliver power and maintain reliability standards that minimize risks to public safety and to equipment. BPA is considering two construction alternatives, the Agency Proposed Action and the Alternative Action. The Alternative Action would include all the components of the Preferred Action except a double-circuit line would be constructed in the Spokane area between a point about 2 miles west of the Spokane River and Bell Substation, a distance of about 9 miles. BPA is also considering the No Action Alternative.« less

Path coloring on the Mesh

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

Rabani, Y.

In the minimum path coloring problem, we are given a list of pairs of vertices of a graph. We are asked to connect each pair by a colored path. Paths of the same color must be edge disjoint. Our objective is to minimize the number of colors used. This problem was raised by Aggarwal et al and Raghavan and Upfal as a model for routing in all-optical networks. It is also related to questions in circuit routing. In this paper, we improve the O (ln N) approximation result of Kleinberg and Tardos for path coloring on the N x Nmore » mesh. We give an O(1) approximation algorithm to the number of colors needed, and a poly(ln ln N) approximation algorithm to the choice of paths and colors. To the best of our knowledge, these are the first sub-logarithmic bounds for any network other than trees, rings, or trees of rings. Our results are based on developing new techniques for randomized rounding. These techniques iteratively improve a fractional solution until it approaches integrality. They are motivated by the method used by Leighton, Maggs, and Rao for packet routing.« less

The problem of the driverless vehicle specified path stability control

NASA Astrophysics Data System (ADS)

Buznikov, S. E.; Endachev, D. V.; Elkin, D. S.; Strukov, V. O.

2018-02-01

Currently the effort of many leading foreign companies is focused on creation of driverless transport for transportation of cargo and passengers. Among many practical problems arising while creating driverless vehicles, the problem of the specified path stability control occupies a central place. The purpose of this paper is formalization of the problem in question in terms of the quadratic functional of the control quality, the comparative analysis of the possible solutions and justification of the choice of the optimum technical solution. As square value of the integral of the deviation from the specified path is proposed as the quadratic functional of the control quality. For generation of the set of software and hardware solution variants the Zwicky “morphological box” method is used within the hardware and software environments. The heading control algorithms use the wheel steering angle data and the deviation from the lane centerline (specified path) calculated based on the navigation data and the data from the video system. Where the video system does not detect the road marking, the control is carried out based on the wheel navigation system data and where recognizable road marking exits - based on to the video system data. The analysis of the test results allows making the conclusion that the application of the combined navigation system algorithms that provide quasi-optimum solution of the problem while meeting the strict functional limits for the technical and economic indicators of the driverless vehicle control system under development is effective.

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

Dufour, F., E-mail: dufour@math.u-bordeaux1.fr; Prieto-Rumeau, T., E-mail: tprieto@ccia.uned.es

We consider a discrete-time constrained discounted Markov decision process (MDP) with Borel state and action spaces, compact action sets, and lower semi-continuous cost functions. We introduce a set of hypotheses related to a positive weight function which allow us to consider cost functions that might not be bounded below by a constant, and which imply the solvability of the linear programming formulation of the constrained MDP. In particular, we establish the existence of a constrained optimal stationary policy. Our results are illustrated with an application to a fishery management problem.

Numerical study of a matrix-free trust-region SQP method for equality constrained optimization.

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

Heinkenschloss, Matthias; Ridzal, Denis; Aguilo, Miguel Antonio

2011-12-01

This is a companion publication to the paper 'A Matrix-Free Trust-Region SQP Algorithm for Equality Constrained Optimization' [11]. In [11], we develop and analyze a trust-region sequential quadratic programming (SQP) method that supports the matrix-free (iterative, in-exact) solution of linear systems. In this report, we document the numerical behavior of the algorithm applied to a variety of equality constrained optimization problems, with constraints given by partial differential equations (PDEs).

Geometric constrained variational calculus. II: The second variation (Part I)

NASA Astrophysics Data System (ADS)

Massa, Enrico; Bruno, Danilo; Luria, Gianvittorio; Pagani, Enrico

2016-10-01

Within the geometrical framework developed in [Geometric constrained variational calculus. I: Piecewise smooth extremals, Int. J. Geom. Methods Mod. Phys. 12 (2015) 1550061], the problem of minimality for constrained calculus of variations is analyzed among the class of differentiable curves. A fully covariant representation of the second variation of the action functional, based on a suitable gauge transformation of the Lagrangian, is explicitly worked out. Both necessary and sufficient conditions for minimality are proved, and reinterpreted in terms of Jacobi fields.

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.

Aircraft Engine Gas Path Diagnostic Methods: Public Benchmarking Results

NASA Technical Reports Server (NTRS)

Simon, Donald L.; Borguet, Sebastien; Leonard, Olivier; Zhang, Xiaodong (Frank)

2013-01-01

Recent technology reviews have identified the need for objective assessments of aircraft engine health management (EHM) technologies. To help address this issue, a gas path diagnostic benchmark problem has been created and made publicly available. This software tool, referred to as the Propulsion Diagnostic Method Evaluation Strategy (ProDiMES), has been constructed based on feedback provided by the aircraft EHM community. It provides a standard benchmark problem enabling users to develop, evaluate and compare diagnostic methods. This paper will present an overview of ProDiMES along with a description of four gas path diagnostic methods developed and applied to the problem. These methods, which include analytical and empirical diagnostic techniques, will be described and associated blind-test-case metric results will be presented and compared. Lessons learned along with recommendations for improving the public benchmarking processes will also be presented and discussed.

The data-driven null models for information dissemination tree in social networks

NASA Astrophysics Data System (ADS)

Zhang, Zhiwei; Wang, Zhenyu

2017-10-01

For the purpose of detecting relatedness and co-occurrence between users, as well as the distribution features of nodes in spreading path of a social network, this paper explores topological characteristics of information dissemination trees (IDT) that can be employed indirectly to probe the information dissemination laws within social networks. Hence, three different null models of IDT are presented in this article, including the statistical-constrained 0-order IDT null model, the random-rewire-broken-edge 0-order IDT null model and the random-rewire-broken-edge 2-order IDT null model. These null models firstly generate the corresponding randomized copy of an actual IDT; then the extended significance profile, which is developed by adding the cascade ratio of information dissemination path, is exploited not only to evaluate degree correlation of two nodes associated with an edge, but also to assess the cascade ratio of different length of information dissemination paths. The experimental correspondences of the empirical analysis for several SinaWeibo IDTs and Twitter IDTs indicate that the IDT null models presented in this paper perform well in terms of degree correlation of nodes and dissemination path cascade ratio, which can be better to reveal the features of information dissemination and to fit the situation of real social networks.

Energy aware path planning in complex four dimensional environments

NASA Astrophysics Data System (ADS)

Chakrabarty, Anjan

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

A variational dynamic programming approach to robot-path planning with a distance-safety criterion

NASA Technical Reports Server (NTRS)

Suh, Suk-Hwan; Shin, Kang G.

1988-01-01

An approach to robot-path planning is developed by considering both the traveling distance and the safety of the robot. A computationally-efficient algorithm is developed to find a near-optimal path with a weighted distance-safety criterion by using a variational calculus and dynamic programming (VCDP) method. The algorithm is readily applicable to any factory environment by representing the free workspace as channels. A method for deriving these channels is also proposed. Although it is developed mainly for two-dimensional problems, this method can be easily extended to a class of three-dimensional problems. Numerical examples are presented to demonstrate the utility and power of this method.

Path integral measure, constraints and ghosts for massive gravitons with a cosmological constant

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

Metaxas, Dimitrios

2009-12-15

For massive gravity in a de Sitter background one encounters problems of stability when the curvature is larger than the graviton mass. I analyze this situation from the path integral point of view and show that it is related to the conformal factor problem of Euclidean quantum (massless) gravity. When a constraint for massive gravity is incorporated and the proper treatment of the path integral measure is taken into account one finds that, for particular choices of the DeWitt metric on the space of metrics (in fact, the same choices as in the massless case), one obtains the opposite boundmore » on the graviton mass.« less

The Study of Intelligent Vehicle Navigation Path Based on Behavior Coordination of Particle Swarm.

PubMed

Han, Gaining; Fu, Weiping; Wang, Wen

2016-01-01

In the behavior dynamics model, behavior competition leads to the shock problem of the intelligent vehicle navigation path, because of the simultaneous occurrence of the time-variant target behavior and obstacle avoidance behavior. Considering the safety and real-time of intelligent vehicle, the particle swarm optimization (PSO) algorithm is proposed to solve these problems for the optimization of weight coefficients of the heading angle and the path velocity. Firstly, according to the behavior dynamics model, the fitness function is defined concerning the intelligent vehicle driving characteristics, the distance between intelligent vehicle and obstacle, and distance of intelligent vehicle and target. Secondly, behavior coordination parameters that minimize the fitness function are obtained by particle swarm optimization algorithms. Finally, the simulation results show that the optimization method and its fitness function can improve the perturbations of the vehicle planning path and real-time and reliability.

The Study of Intelligent Vehicle Navigation Path Based on Behavior Coordination of Particle Swarm

PubMed Central

Han, Gaining; Fu, Weiping; Wang, Wen

2016-01-01

In the behavior dynamics model, behavior competition leads to the shock problem of the intelligent vehicle navigation path, because of the simultaneous occurrence of the time-variant target behavior and obstacle avoidance behavior. Considering the safety and real-time of intelligent vehicle, the particle swarm optimization (PSO) algorithm is proposed to solve these problems for the optimization of weight coefficients of the heading angle and the path velocity. Firstly, according to the behavior dynamics model, the fitness function is defined concerning the intelligent vehicle driving characteristics, the distance between intelligent vehicle and obstacle, and distance of intelligent vehicle and target. Secondly, behavior coordination parameters that minimize the fitness function are obtained by particle swarm optimization algorithms. Finally, the simulation results show that the optimization method and its fitness function can improve the perturbations of the vehicle planning path and real-time and reliability. PMID:26880881

Constrained model predictive control, state estimation and coordination

NASA Astrophysics Data System (ADS)

Yan, Jun

In this dissertation, we study the interaction between the control performance and the quality of the state estimation in a constrained Model Predictive Control (MPC) framework for systems with stochastic disturbances. This consists of three parts: (i) the development of a constrained MPC formulation that adapts to the quality of the state estimation via constraints; (ii) the application of such a control law in a multi-vehicle formation coordinated control problem in which each vehicle operates subject to a no-collision constraint posed by others' imperfect prediction computed from finite bit-rate, communicated data; (iii) the design of the predictors and the communication resource assignment problem that satisfy the performance requirement from Part (ii). Model Predictive Control (MPC) is of interest because it is one of the few control design methods which preserves standard design variables and yet handles constraints. MPC is normally posed as a full-state feedback control and is implemented in a certainty-equivalence fashion with best estimates of the states being used in place of the exact state. However, if the state constraints were handled in the same certainty-equivalence fashion, the resulting control law could drive the real state to violate the constraints frequently. Part (i) focuses on exploring the inclusion of state estimates into the constraints. It does this by applying constrained MPC to a system with stochastic disturbances. The stochastic nature of the problem requires re-posing the constraints in a probabilistic form. In Part (ii), we consider applying constrained MPC as a local control law in a coordinated control problem of a group of distributed autonomous systems. Interactions between the systems are captured via constraints. First, we inspect the application of constrained MPC to a completely deterministic case. Formation stability theorems are derived for the subsystems and conditions on the local constraint set are derived in order to guarantee local stability or convergence to a target state. If these conditions are met for all subsystems, then this stability is inherited by the overall system. For the case when each subsystem suffers from disturbances in the dynamics, own self-measurement noises, and quantization errors on neighbors' information due to the finite-bit-rate channels, the constrained MPC strategy developed in Part (i) is appropriate to apply. In Part (iii), we discuss the local predictor design and bandwidth assignment problem in a coordinated vehicle formation context. The MPC controller used in Part (ii) relates the formation control performance and the information quality in the way that large standoff implies conservative performance. We first develop an LMI (Linear Matrix Inequality) formulation for cross-estimator design in a simple two-vehicle scenario with non-standard information: one vehicle does not have access to the other's exact control value applied at each sampling time, but to its known, pre-computed, coupling linear feedback control law. Then a similar LMI problem is formulated for the bandwidth assignment problem that minimizes the total number of bits by adjusting the prediction gain matrices and the number of bits assigned to each variable. (Abstract shortened by UMI.)

Dynamical insurance models with investment: Constrained singular problems for integrodifferential equations

NASA Astrophysics Data System (ADS)

Belkina, T. A.; Konyukhova, N. B.; Kurochkin, S. V.

2016-01-01

Previous and new results are used to compare two mathematical insurance models with identical insurance company strategies in a financial market, namely, when the entire current surplus or its constant fraction is invested in risky assets (stocks), while the rest of the surplus is invested in a risk-free asset (bank account). Model I is the classical Cramér-Lundberg risk model with an exponential claim size distribution. Model II is a modification of the classical risk model (risk process with stochastic premiums) with exponential distributions of claim and premium sizes. For the survival probability of an insurance company over infinite time (as a function of its initial surplus), there arise singular problems for second-order linear integrodifferential equations (IDEs) defined on a semiinfinite interval and having nonintegrable singularities at zero: model I leads to a singular constrained initial value problem for an IDE with a Volterra integral operator, while II model leads to a more complicated nonlocal constrained problem for an IDE with a non-Volterra integral operator. A brief overview of previous results for these two problems depending on several positive parameters is given, and new results are presented. Additional results are concerned with the formulation, analysis, and numerical study of "degenerate" problems for both models, i.e., problems in which some of the IDE parameters vanish; moreover, passages to the limit with respect to the parameters through which we proceed from the original problems to the degenerate ones are singular for small and/or large argument values. Such problems are of mathematical and practical interest in themselves. Along with insurance models without investment, they describe the case of surplus completely invested in risk-free assets, as well as some noninsurance models of surplus dynamics, for example, charity-type models.

NEWSUMT: A FORTRAN program for inequality constrained function minimization, users guide

NASA Technical Reports Server (NTRS)

Miura, H.; Schmit, L. A., Jr.

1979-01-01

A computer program written in FORTRAN subroutine form for the solution of linear and nonlinear constrained and unconstrained function minimization problems is presented. The algorithm is the sequence of unconstrained minimizations using the Newton's method for unconstrained function minimizations. The use of NEWSUMT and the definition of all parameters are described.

The Indirect Path From Mindful Parenting to Emotional Problems in Adolescents: The Role of Maternal Warmth and Adolescents’ Mindfulness

PubMed Central

Wang, Yuyin; Liang, Yiying; Fan, Linlin; Lin, Kexiu; Xie, Xiaolin; Pan, Junhao; Zhou, Hui

2018-01-01

Mindfulness has been demonstrated to have positive effects on children’s emotional functioning, and adaptive parenting practices are associated with fewer emotional problems. However, the association between mindful parenting and adolescent emotional problems has not been studied much. In the current study, the indirect path from mindful parenting to adolescent emotional problems was examined, with maternal warmth and adolescent dispositional mindfulness as potential mediators. A sample of 168 mother–child dyads participated in this study. A serial indirect effects model showed mother’s mindful parenting could decrease adolescent emotional problems through adolescent’s perceived maternal warmth and their dispositional mindfulness. Findings of this study imply that intervention in mindful parenting may have benefits for adolescents’ emotional problems through enhancing maternal warmth and children’s trait mindfulness. PMID:29706925

A Measure Approximation for Distributionally Robust PDE-Constrained Optimization Problems

DOE PAGES

Kouri, Drew Philip

2017-12-19

In numerous applications, scientists and engineers acquire varied forms of data that partially characterize the inputs to an underlying physical system. This data is then used to inform decisions such as controls and designs. Consequently, it is critical that the resulting control or design is robust to the inherent uncertainties associated with the unknown probabilistic characterization of the model inputs. Here in this work, we consider optimal control and design problems constrained by partial differential equations with uncertain inputs. We do not assume a known probabilistic model for the inputs, but rather we formulate the problem as a distributionally robustmore » optimization problem where the outer minimization problem determines the control or design, while the inner maximization problem determines the worst-case probability measure that matches desired characteristics of the data. We analyze the inner maximization problem in the space of measures and introduce a novel measure approximation technique, based on the approximation of continuous functions, to discretize the unknown probability measure. Finally, we prove consistency of our approximated min-max problem and conclude with numerical results.« less

A GA based penalty function technique for solving constrained redundancy allocation problem of series system with interval valued reliability of components

NASA Astrophysics Data System (ADS)

Gupta, R. K.; Bhunia, A. K.; Roy, D.

2009-10-01

In this paper, we have considered the problem of constrained redundancy allocation of series system with interval valued reliability of components. For maximizing the overall system reliability under limited resource constraints, the problem is formulated as an unconstrained integer programming problem with interval coefficients by penalty function technique and solved by an advanced GA for integer variables with interval fitness function, tournament selection, uniform crossover, uniform mutation and elitism. As a special case, considering the lower and upper bounds of the interval valued reliabilities of the components to be the same, the corresponding problem has been solved. The model has been illustrated with some numerical examples and the results of the series redundancy allocation problem with fixed value of reliability of the components have been compared with the existing results available in the literature. Finally, sensitivity analyses have been shown graphically to study the stability of our developed GA with respect to the different GA parameters.

Constrained optimization by radial basis function interpolation for high-dimensional expensive black-box problems with infeasible initial points

NASA Astrophysics Data System (ADS)

Regis, Rommel G.

2014-02-01

This article develops two new algorithms for constrained expensive black-box optimization that use radial basis function surrogates for the objective and constraint functions. These algorithms are called COBRA and Extended ConstrLMSRBF and, unlike previous surrogate-based approaches, they can be used for high-dimensional problems where all initial points are infeasible. They both follow a two-phase approach where the first phase finds a feasible point while the second phase improves this feasible point. COBRA and Extended ConstrLMSRBF are compared with alternative methods on 20 test problems and on the MOPTA08 benchmark automotive problem (D.R. Jones, Presented at MOPTA 2008), which has 124 decision variables and 68 black-box inequality constraints. The alternatives include a sequential penalty derivative-free algorithm, a direct search method with kriging surrogates, and two multistart methods. Numerical results show that COBRA algorithms are competitive with Extended ConstrLMSRBF and they generally outperform the alternatives on the MOPTA08 problem and most of the test problems.

Preconditioned Alternating Projection Algorithms for Maximum a Posteriori ECT Reconstruction

PubMed Central

Krol, Andrzej; Li, Si; Shen, Lixin; Xu, Yuesheng

2012-01-01

We propose a preconditioned alternating projection algorithm (PAPA) for solving the maximum a posteriori (MAP) emission computed tomography (ECT) reconstruction problem. Specifically, we formulate the reconstruction problem as a constrained convex optimization problem with the total variation (TV) regularization. We then characterize the solution of the constrained convex optimization problem and show that it satisfies a system of fixed-point equations defined in terms of two proximity operators raised from the convex functions that define the TV-norm and the constrain involved in the problem. The characterization (of the solution) via the proximity operators that define two projection operators naturally leads to an alternating projection algorithm for finding the solution. For efficient numerical computation, we introduce to the alternating projection algorithm a preconditioning matrix (the EM-preconditioner) for the dense system matrix involved in the optimization problem. We prove theoretically convergence of the preconditioned alternating projection algorithm. In numerical experiments, performance of our algorithms, with an appropriately selected preconditioning matrix, is compared with performance of the conventional MAP expectation-maximization (MAP-EM) algorithm with TV regularizer (EM-TV) and that of the recently developed nested EM-TV algorithm for ECT reconstruction. Based on the numerical experiments performed in this work, we observe that the alternating projection algorithm with the EM-preconditioner outperforms significantly the EM-TV in all aspects including the convergence speed, the noise in the reconstructed images and the image quality. It also outperforms the nested EM-TV in the convergence speed while providing comparable image quality. PMID:23271835

Traffic engineering and regenerator placement in GMPLS networks with restoration

NASA Astrophysics Data System (ADS)

Yetginer, Emre; Karasan, Ezhan

2002-07-01

In this paper we study regenerator placement and traffic engineering of restorable paths in Generalized Multipro-tocol Label Switching (GMPLS) networks. Regenerators are necessary in optical networks due to transmission impairments. We study a network architecture where there are regenerators at selected nodes and we propose two heuristic algorithms for the regenerator placement problem. Performances of these algorithms in terms of required number of regenerators and computational complexity are evaluated. In this network architecture with sparse regeneration, offline computation of working and restoration paths is studied with bandwidth reservation and path rerouting as the restoration scheme. We study two approaches for selecting working and restoration paths from a set of candidate paths and formulate each method as an Integer Linear Programming (ILP) prob-lem. Traffic uncertainty model is developed in order to compare these methods based on their robustness with respect to changing traffic patterns. Traffic engineering methods are compared based on number of additional demands due to traffic uncertainty that can be carried. Regenerator placement algorithms are also evaluated from a traffic engineering point of view.

Proportional Topology Optimization: A New Non-Sensitivity Method for Solving Stress Constrained and Minimum Compliance Problems and Its Implementation in MATLAB

PubMed Central

Biyikli, Emre; To, Albert C.

2015-01-01

A new topology optimization method called the Proportional Topology Optimization (PTO) is presented. As a non-sensitivity method, PTO is simple to understand, easy to implement, and is also efficient and accurate at the same time. It is implemented into two MATLAB programs to solve the stress constrained and minimum compliance problems. Descriptions of the algorithm and computer programs are provided in detail. The method is applied to solve three numerical examples for both types of problems. The method shows comparable efficiency and accuracy with an existing optimality criteria method which computes sensitivities. Also, the PTO stress constrained algorithm and minimum compliance algorithm are compared by feeding output from one algorithm to the other in an alternative manner, where the former yields lower maximum stress and volume fraction but higher compliance compared to the latter. Advantages and disadvantages of the proposed method and future works are discussed. The computer programs are self-contained and publicly shared in the website www.ptomethod.org. PMID:26678849

Multi-point objective-oriented sequential sampling strategy for constrained robust design

NASA Astrophysics Data System (ADS)

Zhu, Ping; Zhang, Siliang; Chen, Wei

2015-03-01

Metamodelling techniques are widely used to approximate system responses of expensive simulation models. In association with the use of metamodels, objective-oriented sequential sampling methods have been demonstrated to be effective in balancing the need for searching an optimal solution versus reducing the metamodelling uncertainty. However, existing infilling criteria are developed for deterministic problems and restricted to one sampling point in one iteration. To exploit the use of multiple samples and identify the true robust solution in fewer iterations, a multi-point objective-oriented sequential sampling strategy is proposed for constrained robust design problems. In this article, earlier development of objective-oriented sequential sampling strategy for unconstrained robust design is first extended to constrained problems. Next, a double-loop multi-point sequential sampling strategy is developed. The proposed methods are validated using two mathematical examples followed by a highly nonlinear automotive crashworthiness design example. The results show that the proposed method can mitigate the effect of both metamodelling uncertainty and design uncertainty, and identify the robust design solution more efficiently than the single-point sequential sampling approach.

Constrained Versions of DEDICOM for Use in Unsupervised Part-Of-Speech Tagging

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

Dunlavy, Daniel; Chew, Peter A.

This reports describes extensions of DEDICOM (DEcomposition into DIrectional COMponents) data models [3] that incorporate bound and linear constraints. The main purpose of these extensions is to investigate the use of improved data models for unsupervised part-of-speech tagging, as described by Chew et al. [2]. In that work, a single domain, two-way DEDICOM model was computed on a matrix of bigram fre- quencies of tokens in a corpus and used to identify parts-of-speech as an unsupervised approach to that problem. An open problem identi ed in that work was the com- putation of a DEDICOM model that more closely resembledmore » the matrices used in a Hidden Markov Model (HMM), speci cally through post-processing of the DEDICOM factor matrices. The work reported here consists of the description of several models that aim to provide a direct solution to that problem and a way to t those models. The approach taken here is to incorporate the model requirements as bound and lin- ear constrains into the DEDICOM model directly and solve the data tting problem as a constrained optimization problem. This is in contrast to the typical approaches in the literature, where the DEDICOM model is t using unconstrained optimization approaches, and model requirements are satis ed as a post-processing step.« less

A Stochastic Approach to Path Planning in the Weighted-Region Problem

DTIC Science & Technology

1991-03-01

polynomial time. However, the polyhedrons in this three-dimensional obstacle-avoidance problem are all obstacles (i.e. travel is not permitted within...them). Therefore, optimal paths tend to avoid their vertices, and settle into closest approach tangents across polyhedron edges. So, in a sense...intersection update map database with new vertex for this edge 3. IF (C1 > D) and (C2 > D) THEN edge intersects ellipse at two points OR edge is

The use of 3-D sensing techniques for on-line collision-free path planning

NASA Technical Reports Server (NTRS)

Hayward, V.; Aubry, S.; Jasiukajc, Z.

1987-01-01

The state of the art in collision prevention for manipulators with revolute joints, showing that it is a particularly computationally hard problem, is discussed. Based on the analogy with other hard or undecidable problems such as theorem proving, an extensible multi-resolution architecture for path planning, based on a collection of weak methods is proposed. Finally, the role that sensors can play for an on-line use of sensor data is examined.

Solving the detour problem in navigation: a model of prefrontal and hippocampal interactions.

PubMed

Spiers, Hugo J; Gilbert, Sam J

2015-01-01

Adapting behavior to accommodate changes in the environment is an important function of the nervous system. A universal problem for motile animals is the discovery that a learned route is blocked and a detour is required. Given the substantial neuroscience research on spatial navigation and decision-making it is surprising that so little is known about how the brain solves the detour problem. Here we review the limited number of relevant functional neuroimaging, single unit recording and lesion studies. We find that while the prefrontal cortex (PFC) consistently responds to detours, the hippocampus does not. Recent evidence suggests the hippocampus tracks information about the future path distance to the goal. Based on this evidence we postulate a conceptual model in which: Lateral PFC provides a prediction error signal about the change in the path, frontopolar and superior PFC support the re-formulation of the route plan as a novel subgoal and the hippocampus simulates the new path. More data will be required to validate this model and understand (1) how the system processes the different options; and (2) deals with situations where a new path becomes available (i.e., shortcuts).

Search Problems in Mission Planning and Navigation of Autonomous Aircraft. M.S. Thesis

NASA Technical Reports Server (NTRS)

Krozel, James A.

1988-01-01

An architecture for the control of an autonomous aircraft is presented. The architecture is a hierarchical system representing an anthropomorphic breakdown of the control problem into planner, navigator, and pilot systems. The planner system determines high level global plans from overall mission objectives. This abstract mission planning is investigated by focusing on the Traveling Salesman Problem with variations on local and global constraints. Tree search techniques are applied including the breadth first, depth first, and best first algorithms. The minimum-column and row entries for the Traveling Salesman Problem cost matrix provides a powerful heuristic to guide these search techniques. Mission planning subgoals are directed from the planner to the navigator for planning routes in mountainous terrain with threats. Terrain/threat information is abstracted into a graph of possible paths for which graph searches are performed. It is shown that paths can be well represented by a search graph based on the Voronoi diagram of points representing the vertices of mountain boundaries. A comparison of Dijkstra's dynamic programming algorithm and the A* graph search algorithm from artificial intelligence/operations research is performed for several navigation path planning examples. These examples illustrate paths that minimize a combination of distance and exposure to threats. Finally, the pilot system synthesizes the flight trajectory by creating the control commands to fly the aircraft.

Nonlinear model predictive control of a wave energy converter based on differential flatness parameterisation

NASA Astrophysics Data System (ADS)

Li, Guang

2017-01-01

This paper presents a fast constrained optimization approach, which is tailored for nonlinear model predictive control of wave energy converters (WEC). The advantage of this approach relies on its exploitation of the differential flatness of the WEC model. This can reduce the dimension of the resulting nonlinear programming problem (NLP) derived from the continuous constrained optimal control of WEC using pseudospectral method. The alleviation of computational burden using this approach helps to promote an economic implementation of nonlinear model predictive control strategy for WEC control problems. The method is applicable to nonlinear WEC models, nonconvex objective functions and nonlinear constraints, which are commonly encountered in WEC control problems. Numerical simulations demonstrate the efficacy of this approach.

Missile Guidance Law Based on Robust Model Predictive Control Using Neural-Network Optimization.

PubMed

Li, Zhijun; Xia, Yuanqing; Su, Chun-Yi; Deng, Jun; Fu, Jun; He, Wei

2015-08-01

In this brief, the utilization of robust model-based predictive control is investigated for the problem of missile interception. Treating the target acceleration as a bounded disturbance, novel guidance law using model predictive control is developed by incorporating missile inside constraints. The combined model predictive approach could be transformed as a constrained quadratic programming (QP) problem, which may be solved using a linear variational inequality-based primal-dual neural network over a finite receding horizon. Online solutions to multiple parametric QP problems are used so that constrained optimal control decisions can be made in real time. Simulation studies are conducted to illustrate the effectiveness and performance of the proposed guidance control law for missile interception.

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

Dall-Anese, Emiliano; Simonetto, Andrea

This paper focuses on the design of online algorithms based on prediction-correction steps to track the optimal solution of a time-varying constrained problem. Existing prediction-correction methods have been shown to work well for unconstrained convex problems and for settings where obtaining the inverse of the Hessian of the cost function can be computationally affordable. The prediction-correction algorithm proposed in this paper addresses the limitations of existing methods by tackling constrained problems and by designing a first-order prediction step that relies on the Hessian of the cost function (and do not require the computation of its inverse). Analytical results are establishedmore » to quantify the tracking error. Numerical simulations corroborate the analytical results and showcase performance and benefits of the algorithms.« less

A Near-Optimal Distributed QoS Constrained Routing Algorithm for Multichannel Wireless Sensor Networks

PubMed Central

Lin, Frank Yeong-Sung; Hsiao, Chiu-Han; Yen, Hong-Hsu; Hsieh, Yu-Jen

2013-01-01

One of the important applications in Wireless Sensor Networks (WSNs) is video surveillance that includes the tasks of video data processing and transmission. Processing and transmission of image and video data in WSNs has attracted a lot of attention in recent years. This is known as Wireless Visual Sensor Networks (WVSNs). WVSNs are distributed intelligent systems for collecting image or video data with unique performance, complexity, and quality of service challenges. WVSNs consist of a large number of battery-powered and resource constrained camera nodes. End-to-end delay is a very important Quality of Service (QoS) metric for video surveillance application in WVSNs. How to meet the stringent delay QoS in resource constrained WVSNs is a challenging issue that requires novel distributed and collaborative routing strategies. This paper proposes a Near-Optimal Distributed QoS Constrained (NODQC) routing algorithm to achieve an end-to-end route with lower delay and higher throughput. A Lagrangian Relaxation (LR)-based routing metric that considers the “system perspective” and “user perspective” is proposed to determine the near-optimal routing paths that satisfy end-to-end delay constraints with high system throughput. The empirical results show that the NODQC routing algorithm outperforms others in terms of higher system throughput with lower average end-to-end delay and delay jitter. In this paper, for the first time, the algorithm shows how to meet the delay QoS and at the same time how to achieve higher system throughput in stringently resource constrained WVSNs.

Direct detection of the Yarkovsky effect by radar ranging to ateroid 6489 Golevka

NASA Technical Reports Server (NTRS)

Chesley, S. R.; Ostro, S. J.; Vokrouhlicky, D.; Capek, D.; Giorgini, J. D.; Nolan, M. C.; Margot, J. L.; Hine, A. A.; Benner, L. A. M.; Chamberlin, A. B.

2003-01-01

Radar ranging from Arecibo, Puerto Rico, to the 0.5-kilometer near-Earth asteroid 6489 Golevka unambiguously reveals a small nongravitational acceleration caused by the anisotropic thermal emission of absorbed sunlight. The magnitude of this perturbation, known as the Yarkovsky effect, is a function of the asteroid's mass and surface thermal characteristics. Direct detection of the Yarkovsky effect on asteroids will help constrain their physical properties, such as bulk density, and refine their orbital paths.

Host Immunity via Mutable Virtualized Large-Scale Network Containers

DTIC Science & Technology

2016-07-25

and constrain the distributed persistent inside crawlers that have va.lid credentials to access the web services. The main idea is to add a marker...to each web page URL and use the URL path and user inforn1ation contained in the marker to help accurately detect crawlers at its earliest stage...more than half of all website traffic, and malicious bots contributes almost one third of the traffic. As one type of bots, web crawlers have been

Centrifugal separator devices, systems and related methods

DOEpatents

Meikrantz, David H [Idaho Falls, ID; Law, Jack D [Pocatello, ID; Garn, Troy G [Idaho Falls, ID; Todd, Terry A [Aberdeen, ID; Macaluso, Lawrence L [Carson City, NV

2012-03-20

Centrifugal separator devices, systems and related methods are described. More particularly, fluid transfer connections for a centrifugal separator system having support assemblies with a movable member coupled to a connection tube and coupled to a fixed member, such that the movable member is constrained to movement along a fixed path relative to the fixed member are described. Also, centrifugal separator systems including such fluid transfer connections are described. Additionally, methods of installing, removing and/or replacing centrifugal separators from centrifugal separator systems are described.

Understanding and Mitigating Vortex-Dominated, Tip-Leakage and End-Wall Losses in a Transonic Splittered Rotor Stage

DTIC Science & Technology

2015-04-23

blade geometry parameters the TPL design 9   tool was initiated by running the MATLAB script (*.m) Main_SpeedLine_Auto. Main_SpeedLine_Auto...SolidWorks for solid model generation of the blade shapes. Computational Analysis With solid models generated of the gas -path air wedge, automated...287 mm (11.3 in) Constrained by existing TCR geometry Number of Passages 12 None A blade tip-down design approach was used. The outputs of the

Alpha channeling in a rotating plasma.

PubMed

Fetterman, Abraham J; Fisch, Nathaniel J

2008-11-14

The wave-particle alpha-channeling effect is generalized to include rotating plasma. Specifically, radio frequency waves can resonate with alpha particles in a mirror machine with ExB rotation to diffuse the alpha particles along constrained paths in phase space. Of major interest is that the alpha-particle energy, in addition to amplifying the rf waves, can directly enhance the rotation energy which in turn provides additional plasma confinement in centrifugal fusion reactors. An ancillary benefit is the rapid removal of alpha particles, which increases the fusion reactivity.

Visual Information Processing Based on Spatial Filters Constrained by Biological Data.

DTIC Science & Technology

1978-12-01

was provided by Pantie and Sekuler ( 19681. They found that the detection (if gratings was affected most by adapting isee Section 6.1. 11 to square...evidence for certain eye scans being directed by spatial information in filtered images is given. Eye scan paths of a portrait of a young girl I Figure 08...multistable objects to more complex objects such as the man- girl figure of Fisher 119681, decision boundaries that are a natural concomitant to any pattern

Robust approximate optimal guidance strategies for aeroassisted orbital transfer missions

NASA Astrophysics Data System (ADS)

Ilgen, Marc R.

This thesis presents the application of game theoretic and regular perturbation methods to the problem of determining robust approximate optimal guidance laws for aeroassisted orbital transfer missions with atmospheric density and navigated state uncertainties. The optimal guidance problem is reformulated as a differential game problem with the guidance law designer and Nature as opposing players. The resulting equations comprise the necessary conditions for the optimal closed loop guidance strategy in the presence of worst case parameter variations. While these equations are nonlinear and cannot be solved analytically, the presence of a small parameter in the equations of motion allows the method of regular perturbations to be used to solve the equations approximately. This thesis is divided into five parts. The first part introduces the class of problems to be considered and presents results of previous research. The second part then presents explicit semianalytical guidance law techniques for the aerodynamically dominated region of flight. These guidance techniques are applied to unconstrained and control constrained aeroassisted plane change missions and Mars aerocapture missions, all subject to significant atmospheric density variations. The third part presents a guidance technique for aeroassisted orbital transfer problems in the gravitationally dominated region of flight. Regular perturbations are used to design an implicit guidance technique similar to the second variation technique but that removes the need for numerically computing an optimal trajectory prior to flight. This methodology is then applied to a set of aeroassisted inclination change missions. In the fourth part, the explicit regular perturbation solution technique is extended to include the class of guidance laws with partial state information. This methodology is then applied to an aeroassisted plane change mission using inertial measurements and subject to uncertainties in the initial value of the flight path angle. A summary of performance results for all these guidance laws is presented in the fifth part of this thesis along with recommendations for further research.

Reference View Selection in DIBR-Based Multiview Coding.

PubMed

Maugey, Thomas; Petrazzuoli, Giovanni; Frossard, Pascal; Cagnazzo, Marco; Pesquet-Popescu, Beatrice

2016-04-01

Augmented reality, interactive navigation in 3D scenes, multiview video, and other emerging multimedia applications require large sets of images, hence larger data volumes and increased resources compared with traditional video services. The significant increase in the number of images in multiview systems leads to new challenging problems in data representation and data transmission to provide high quality of experience on resource-constrained environments. In order to reduce the size of the data, different multiview video compression strategies have been proposed recently. Most of them use the concept of reference or key views that are used to estimate other images when there is high correlation in the data set. In such coding schemes, the two following questions become fundamental: 1) how many reference views have to be chosen for keeping a good reconstruction quality under coding cost constraints? And 2) where to place these key views in the multiview data set? As these questions are largely overlooked in the literature, we study the reference view selection problem and propose an algorithm for the optimal selection of reference views in multiview coding systems. Based on a novel metric that measures the similarity between the views, we formulate an optimization problem for the positioning of the reference views, such that both the distortion of the view reconstruction and the coding rate cost are minimized. We solve this new problem with a shortest path algorithm that determines both the optimal number of reference views and their positions in the image set. We experimentally validate our solution in a practical multiview distributed coding system and in the standardized 3D-HEVC multiview coding scheme. We show that considering the 3D scene geometry in the reference view, positioning problem brings significant rate-distortion improvements and outperforms the traditional coding strategy that simply selects key frames based on the distance between cameras.

Path changing methods applied to the 4-D guidance of STOL aircraft.

DOT National Transportation Integrated Search

1971-11-01

Prior to the advent of large-scale commercial STOL service, some challenging navigation and guidance problems must be solved. Proposed terminal area operations may require that these aircraft be capable of accurately flying complex flight paths, and ...

Robot path planning algorithm based on symbolic tags in dynamic environment

NASA Astrophysics Data System (ADS)

Vokhmintsev, A.; Timchenko, M.; Melnikov, A.; Kozko, A.; Makovetskii, A.

2017-09-01

The present work will propose a new heuristic algorithms for path planning of a mobile robot in an unknown dynamic space that have theoretically approved estimates of computational complexity and are approbated for solving specific applied problems.

Developing Tools to Test the Thermo-Mechanical Models, Examples at Crustal and Upper Mantle Scale

NASA Astrophysics Data System (ADS)

Le Pourhiet, L.; Yamato, P.; Burov, E.; Gurnis, M.

2005-12-01

Testing geodynamical model is never an easy task. Depending on the spatio-temporal scale of the model, different testable predictions are needed and no magic reciepe exist. This contribution first presents different methods that have been used to test themo-mechanical modeling results at upper crustal, lithospheric and upper mantle scale using three geodynamical examples : the Gulf of Corinth (Greece), the Western Alps, and the Sierra Nevada. At short spatio-temporal scale (e.g. Gulf of Corinth). The resolution of the numerical models is usually sufficient to catch the timing and kinematics of the faults precisely enough to be tested by tectono-stratigraphic arguments. In active deforming area, microseismicity can be compared to the effective rheology and P and T axes of the focal mechanism can be compared with local orientation of the major component of the stress tensor. At lithospheric scale the resolution of the models doesn't permit anymore to constrain the models by direct observations (i.e. structural data from field or seismic reflection). Instead, synthetic P-T-t path may be computed and compared to natural ones in term of rate of exhumation for ancient orogens. Topography may also help but on continent it mainly depends on erosion laws that are complicated to constrain. Deeper in the mantle, the only available constrain are long wave length topographic data and tomographic "data". The major problem to overcome now at lithospheric and upper mantle scale, is that the so called "data" results actually from inverse models of the real data and that those inverse model are based on synthetic models. Post processing P and S wave velocities is not sufficient to be able to make testable prediction at upper mantle scale. Instead of that, direct wave propagations model must be computed. This allows checking if the differences between two models constitute a testable prediction or not. On longer term, we may be able to use those synthetic models to reduce the residue in the inversion of elastic wave arrival time

Investigations of Desert Dust and Smoke in the North Atlantic in Support of the TOMS Instrument

NASA Technical Reports Server (NTRS)

Toon, Owen B.

2005-01-01

During the initial period of the work we concentrated on Saharan dust storms and published a sequence of papers (Colarco et a1 2002,2003a,b, Toon, 2004). The U.S. Air Force liked the dust model so well that they appropriated it for operational dust storm forecasting (Barnum et al., 2004). The Air Force has used it for about 5 yrs in the Middle East where dust storms cause significant operational problems. The student working on this project, Peter Colarco, has graduated and is now a civil servant at Goddard where he continues to interact with the TOMS team. This work helped constrain the optical properties of dust at TOMS wavelengths, which is useful for climate simulations and for TOMS retrievals of dust properties such as optical depth. We also used TOMS data to constrain the sources of dust in Africa and the Middle East, to determine the actual paths taken by Saharan dust storms, to learn more about the mechanics of variations in the optical depths, and to learn more about the mechanisms controlling the altitudes of the dust. During the last two years we have been working on smoke from fires. Black carbon aerosols are one of the leading factors in radiative forcing. The US Climate Change Science Program calls this area out for specific study. It has been suggested by Jim Hansen, and Mark Jacobsen among others, that by controlling emissions of black carbon we might reduce greenhouse radiative forcing in a relatively painless manner. However, we need a greatly improved understanding of the amount of black carbon in the atmosphere, where it is located, where it comes from, how it is mixed with other particles, what its actual optical properties are, and how it evolves. In order to learn about these issues we are using a numerical model of smoke. We have applied this model to the SAFARI field program data, and used the TOMS satellite observations in that period (Sept. 2000). Our goal is to constrain source function estimates for black carbon, and smoke optical properties.

Energetic Materials Optimization via Constrained Search

DTIC Science & Technology

2015-06-01

steps. 3. Optimization Methodology Our optimization problem is formulated as a constrained maximization: max x∈CCS P (x) s.t. : TED ( x )− 9.75 ≥ 0 SV (x)− 9...0 5− SA(x) ≥ 0, (1) where TED ( x ) is the total energy of detonation (TED) of compound x from the chosen chemical subspace (CCS) of chemical compound...max problem, max x∈CCS min λ∈R3+ P (x)− λTC(x), (2) where C(x) is the vector of constraint violations, i.e., η(9.75 − TED ( x )), η(9 − SV (x)), η(SA(x

Time-resolved, dual heterodyne phase collection transient grating spectroscopy

DOE PAGES

Dennett, Cody A.; Short, Michael P.

2017-05-23

The application of optical heterodyne detection for transient grating spectroscopy (TGS) using a fixed, binary phase mask often relies on taking the difference between signals captured at multiple heterodyne phases. To date, this has been accomplished by manually controlling the heterodyne phase between measurements with an optical flat. In this letter, an optical configuration is presented which allows for collection of TGS measurements at two heterodyne phases concurrently through the use of two independently phase controlled interrogation paths. This arrangement allows for complete, heterodyne amplified TGS measurements to be made in a manner not constrained by a mechanical actuation time.more » Measurements are instead constrained only by the desired signal-to-noise ratio. A temporal resolution of between 1 and 10 s, demonstrated here on single crystal metallic samples, will allow TGS experiments to be used as an in-situ, time-resolved monitoring technique for many material processing applications.« less

Dynamics of non-holonomic systems with stochastic transport

NASA Astrophysics Data System (ADS)

Holm, D. D.; Putkaradze, V.

2018-01-01

This paper formulates a variational approach for treating observational uncertainty and/or computational model errors as stochastic transport in dynamical systems governed by action principles under non-holonomic constraints. For this purpose, we derive, analyse and numerically study the example of an unbalanced spherical ball rolling under gravity along a stochastic path. Our approach uses the Hamilton-Pontryagin variational principle, constrained by a stochastic rolling condition, which we show is equivalent to the corresponding stochastic Lagrange-d'Alembert principle. In the example of the rolling ball, the stochasticity represents uncertainty in the observation and/or error in the computational simulation of the angular velocity of rolling. The influence of the stochasticity on the deterministically conserved quantities is investigated both analytically and numerically. Our approach applies to a wide variety of stochastic, non-holonomically constrained systems, because it preserves the mathematical properties inherited from the variational principle.

A practical method to avoid zero-point leak in molecular dynamics calculations: application to the water dimer.

PubMed

Czakó, Gábor; Kaledin, Alexey L; Bowman, Joel M

2010-04-28

We report the implementation of a previously suggested method to constrain a molecular system to have mode-specific vibrational energy greater than or equal to the zero-point energy in quasiclassical trajectory calculations [J. M. Bowman et al., J. Chem. Phys. 91, 2859 (1989); W. H. Miller et al., J. Chem. Phys. 91, 2863 (1989)]. The implementation is made practical by using a technique described recently [G. Czako and J. M. Bowman, J. Chem. Phys. 131, 244302 (2009)], where a normal-mode analysis is performed during the course of a trajectory and which gives only real-valued frequencies. The method is applied to the water dimer, where its effectiveness is shown by computing mode energies as a function of integration time. Radial distribution functions are also calculated using constrained quasiclassical and standard classical molecular dynamics at low temperature and at 300 K and compared to rigorous quantum path integral calculations.

Time-resolved, dual heterodyne phase collection transient grating spectroscopy

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

Dennett, Cody A.; Short, Michael P.

The application of optical heterodyne detection for transient grating spectroscopy (TGS) using a fixed, binary phase mask often relies on taking the difference between signals captured at multiple heterodyne phases. To date, this has been accomplished by manually controlling the heterodyne phase between measurements with an optical flat. In this letter, an optical configuration is presented which allows for collection of TGS measurements at two heterodyne phases concurrently through the use of two independently phase controlled interrogation paths. This arrangement allows for complete, heterodyne amplified TGS measurements to be made in a manner not constrained by a mechanical actuation time.more » Measurements are instead constrained only by the desired signal-to-noise ratio. A temporal resolution of between 1 and 10 s, demonstrated here on single crystal metallic samples, will allow TGS experiments to be used as an in-situ, time-resolved monitoring technique for many material processing applications.« less

Hybrid real-code ant colony optimisation for constrained mechanical design

NASA Astrophysics Data System (ADS)

Pholdee, Nantiwat; Bureerat, Sujin

2016-01-01

This paper proposes a hybrid meta-heuristic based on integrating a local search simplex downhill (SDH) method into the search procedure of real-code ant colony optimisation (ACOR). This hybridisation leads to five hybrid algorithms where a Monte Carlo technique, a Latin hypercube sampling technique (LHS) and a translational propagation Latin hypercube design (TPLHD) algorithm are used to generate an initial population. Also, two numerical schemes for selecting an initial simplex are investigated. The original ACOR and its hybrid versions along with a variety of established meta-heuristics are implemented to solve 17 constrained test problems where a fuzzy set theory penalty function technique is used to handle design constraints. The comparative results show that the hybrid algorithms are the top performers. Using the TPLHD technique gives better results than the other sampling techniques. The hybrid optimisers are a powerful design tool for constrained mechanical design problems.

Multiple object tracking using the shortest path faster association algorithm.

PubMed

Xi, Zhenghao; Liu, Heping; Liu, Huaping; Yang, Bin

2014-01-01

To solve the persistently multiple object tracking in cluttered environments, this paper presents a novel tracking association approach based on the shortest path faster algorithm. First, the multiple object tracking is formulated as an integer programming problem of the flow network. Then we relax the integer programming to a standard linear programming problem. Therefore, the global optimum can be quickly obtained using the shortest path faster algorithm. The proposed method avoids the difficulties of integer programming, and it has a lower worst-case complexity than competing methods but better robustness and tracking accuracy in complex environments. Simulation results show that the proposed algorithm takes less time than other state-of-the-art methods and can operate in real time.

Multiple Object Tracking Using the Shortest Path Faster Association Algorithm

PubMed Central

Liu, Heping; Liu, Huaping; Yang, Bin

2014-01-01

To solve the persistently multiple object tracking in cluttered environments, this paper presents a novel tracking association approach based on the shortest path faster algorithm. First, the multiple object tracking is formulated as an integer programming problem of the flow network. Then we relax the integer programming to a standard linear programming problem. Therefore, the global optimum can be quickly obtained using the shortest path faster algorithm. The proposed method avoids the difficulties of integer programming, and it has a lower worst-case complexity than competing methods but better robustness and tracking accuracy in complex environments. Simulation results show that the proposed algorithm takes less time than other state-of-the-art methods and can operate in real time. PMID:25215322

Dynamical mechanism in aero-engine gas path system using minimum spanning tree and detrended cross-correlation analysis

NASA Astrophysics Data System (ADS)

Dong, Keqiang; Zhang, Hong; Gao, You

2017-01-01

Identifying the mutual interaction in aero-engine gas path system is a crucial problem that facilitates the understanding of emerging structures in complex system. By employing the multiscale multifractal detrended cross-correlation analysis method to aero-engine gas path system, the cross-correlation characteristics between gas path system parameters are established. Further, we apply multiscale multifractal detrended cross-correlation distance matrix and minimum spanning tree to investigate the mutual interactions of gas path variables. The results can infer that the low-spool rotor speed (N1) and engine pressure ratio (EPR) are main gas path parameters. The application of proposed method contributes to promote our understanding of the internal mechanisms and structures of aero-engine dynamics.

Sampling-Based Coverage Path Planning for Complex 3D Structures

DTIC Science & Technology

2012-09-01

one such task, in which a single robot must sweep its end effector over the entirety of a known workspace. For two-dimensional environments, optimal...structures. First, we introduce a new algorithm for planning feasible coverage paths. It is more computationally efficient in problems of complex geometry...iteratively shortens and smooths a feasible coverage path; robot configurations are adjusted without violating any coverage con- straints. Third, we propose

Energy-Aware Multipath Routing Scheme Based on Particle Swarm Optimization in Mobile Ad Hoc Networks

PubMed Central

Robinson, Y. Harold; Rajaram, M.

2015-01-01

Mobile ad hoc network (MANET) is a collection of autonomous mobile nodes forming an ad hoc network without fixed infrastructure. Dynamic topology property of MANET may degrade the performance of the network. However, multipath selection is a great challenging task to improve the network lifetime. We proposed an energy-aware multipath routing scheme based on particle swarm optimization (EMPSO) that uses continuous time recurrent neural network (CTRNN) to solve optimization problems. CTRNN finds the optimal loop-free paths to solve link disjoint paths in a MANET. The CTRNN is used as an optimum path selection technique that produces a set of optimal paths between source and destination. In CTRNN, particle swarm optimization (PSO) method is primly used for training the RNN. The proposed scheme uses the reliability measures such as transmission cost, energy factor, and the optimal traffic ratio between source and destination to increase routing performance. In this scheme, optimal loop-free paths can be found using PSO to seek better link quality nodes in route discovery phase. PSO optimizes a problem by iteratively trying to get a better solution with regard to a measure of quality. The proposed scheme discovers multiple loop-free paths by using PSO technique. PMID:26819966

Development and Demonstration of an Ada Test Generation System

NASA Technical Reports Server (NTRS)

1996-01-01

In this project we have built a prototype system that performs Feasible Path Analysis on Ada programs: given a description of a set of control flow paths through a procedure, and a predicate at a program point feasible path analysis determines if there is input data which causes execution to flow down some path in the collection reaching the point so that tile predicate is true. Feasible path analysis can be applied to program testing, program slicing, array bounds checking, and other forms of anomaly checking. FPA is central to most applications of program analysis. But, because this problem is formally unsolvable, syntactic-based approximations are used in its place. For example, in dead-code analysis the problem is to determine if there are any input values which cause execution to reach a specified program point. Instead an approximation to this problem is computed: determine whether there is a control flow path from the start of the program to the point. This syntactic approximation is efficiently computable and conservative: if there is no such path the program point is clearly unreachable, but if there is such a path, the analysis is inconclusive, and the code is assumed to be live. Such conservative analysis too often yields unsatisfactory results because the approximation is too weak. As another example, consider data flow analysis. A du-pair is a pair of program points such that the first point is a definition of a variable and the second point a use and for which there exists a definition-free path from the definition to the use. The sharper, semantic definition of a du-pair requires that there be a feasible definition-free path from the definition to the use. A compiler using du-pairs for detecting dead variables may miss optimizations by not considering feasibility. Similarly, a program analyzer computing program slices to merge parallel versions may report conflicts where none exist. In the context of software testing, feasibility analysis plays an important role in identifying testing requirements which are infeasible. This is especially true for data flow testing and modified condition/decision coverage. Our system uses in an essential way symbolic analysis and theorem proving technology, and we believe this work represents one of the few successful uses of a theorem prover working in a completely automatic fashion to solve a problem of practical interest. We believe this work anticipates an important trend away from purely syntactic-based methods for program analysis to semantic methods based on symbolic processing and inference technology. Other results demonstrating the practical use of automatic inference is being reported in hardware verification, although there are significant differences between the hardware work and ours. However, what is common and important is that general purpose theorem provers are being integrated with more special-purpose decision procedures to solve problems in analysis and verification. We are pursuina commercial opportunities for this work, and will use and extend the work in other projects we are engaged in. Ultimately we would like to rework the system to analyze C, C++, or Java as a key step toward commercialization.

Interprocedural Analysis and the Verification of Concurrent Programs

DTIC Science & Technology

2009-01-01

SSPE ) problem is to compute a regular expression that represents paths(s, v) for all vertices v in the graph. The syntax of regular expressions is as...follows: r ::= ∅ | ε | e | r1 ∪ r2 | r1.r2 | r∗, where e stands for an edge in G. We can use any algorithm for SSPE to compute regular expressions for...a closed representation of loops provides an exponential speedup.2 Tarjan’s path-expression algorithm solves the SSPE problem efficiently. It uses

Navigation of the autonomous vehicle reverse movement

NASA Astrophysics Data System (ADS)

Rachkov, M.; Petukhov, S.

2018-02-01

The paper presents a mathematical formulation of the vehicle reverse motion along a multi-link polygonal trajectory consisting of rectilinear segments interconnected by nodal points. Relevance of the problem is caused by the need to solve a number of tasks: to save the vehicle in the event of а communication break by returning along the trajectory already passed, to avoid a turn on the ground in constrained obstacles or dangerous conditions, or a partial return stroke for the subsequent bypass of the obstacle and continuation of the forward movement. The method of navigation with direct movement assumes that the reverse path is elaborated by using landmarks. To measure landmarks on board, a block of cameras is placed on a vehicle controlled by the operator through the radio channel. Errors in estimating deviation from the nominal trajectory of motion are determined using the multidimensional correlation analysis apparatus based on the dynamics of a lateral deviation error and a vehicle speed error. The result of the experiment showed a relatively high accuracy in determining the state vector that provides the vehicle reverse motion relative to the reference trajectory with a practically acceptable error while returning to the start point.

Coupled Long-Term Simulation of Reach-Scale Water and Heat Fluxes Across the River-Groundwater Interface for Retrieving Hyporheic Residence Times and Temperature Dynamics

NASA Astrophysics Data System (ADS)

Munz, Matthias; Oswald, Sascha E.; Schmidt, Christian

2017-11-01

Flow patterns in conjunction with seasonal and diurnal temperature variations control ecological and biogeochemical conditions in hyporheic sediments. In particular, hyporheic temperatures have a great impact on many temperature-sensitive microbial processes. In this study, we used 3-D coupled water flow and heat transport simulations applying the HydroGeoSphere code in combination with high-resolution observations of hydraulic heads and temperatures to quantify reach-scale water and heat flux across the river-groundwater interface and hyporheic temperature dynamics of a lowland gravel bed river. The model was calibrated in order to constrain estimates of the most sensitive model parameters. The magnitude and variations of the simulated temperatures matched the observed ones, with an average mean absolute error of 0.7°C and an average Nash Sutcliffe efficiency of 0.87. Our results indicate that nonsubmerged streambed structures such as gravel bars cause substantial thermal heterogeneity within the saturated sediment at the reach scale. Individual hyporheic flow path temperatures strongly depend on the flow path residence time, flow path depth, river, and groundwater temperature. Variations in individual hyporheic flow path temperatures were up to 7.9°C, significantly higher than the daily average (2.8°C), but still lower than the average seasonal hyporheic temperature difference (19.2°C). The distribution between flow path temperatures and residence times follows a power law relationship with exponent of about 0.37. Based on this empirical relation, we further estimated the influence of hyporheic flow path residence time and temperature on oxygen consumption which was found to partly increase by up to 29% in simulations.

Energy-Efficient Cognitive Radio Sensor Networks: Parametric and Convex Transformations

PubMed Central

Naeem, Muhammad; Illanko, Kandasamy; Karmokar, Ashok; Anpalagan, Alagan; Jaseemuddin, Muhammad

2013-01-01

Designing energy-efficient cognitive radio sensor networks is important to intelligently use battery energy and to maximize the sensor network life. In this paper, the problem of determining the power allocation that maximizes the energy-efficiency of cognitive radio-based wireless sensor networks is formed as a constrained optimization problem, where the objective function is the ratio of network throughput and the network power. The proposed constrained optimization problem belongs to a class of nonlinear fractional programming problems. Charnes-Cooper Transformation is used to transform the nonlinear fractional problem into an equivalent concave optimization problem. The structure of the power allocation policy for the transformed concave problem is found to be of a water-filling type. The problem is also transformed into a parametric form for which a ε-optimal iterative solution exists. The convergence of the iterative algorithms is proven, and numerical solutions are presented. The iterative solutions are compared with the optimal solution obtained from the transformed concave problem, and the effects of different system parameters (interference threshold level, the number of primary users and secondary sensor nodes) on the performance of the proposed algorithms are investigated. PMID:23966194

A path following algorithm for the graph matching problem.

PubMed

Zaslavskiy, Mikhail; Bach, Francis; Vert, Jean-Philippe

2009-12-01

We propose a convex-concave programming approach for the labeled weighted graph matching problem. The convex-concave programming formulation is obtained by rewriting the weighted graph matching problem as a least-square problem on the set of permutation matrices and relaxing it to two different optimization problems: a quadratic convex and a quadratic concave optimization problem on the set of doubly stochastic matrices. The concave relaxation has the same global minimum as the initial graph matching problem, but the search for its global minimum is also a hard combinatorial problem. We, therefore, construct an approximation of the concave problem solution by following a solution path of a convex-concave problem obtained by linear interpolation of the convex and concave formulations, starting from the convex relaxation. This method allows to easily integrate the information on graph label similarities into the optimization problem, and therefore, perform labeled weighted graph matching. The algorithm is compared with some of the best performing graph matching methods on four data sets: simulated graphs, QAPLib, retina vessel images, and handwritten Chinese characters. In all cases, the results are competitive with the state of the art.

Mixed-Strategy Chance Constrained Optimal Control

NASA Technical Reports Server (NTRS)

Ono, Masahiro; Kuwata, Yoshiaki; Balaram, J.

2013-01-01

This paper presents a novel chance constrained optimal control (CCOC) algorithm that chooses a control action probabilistically. A CCOC problem is to find a control input that minimizes the expected cost while guaranteeing that the probability of violating a set of constraints is below a user-specified threshold. We show that a probabilistic control approach, which we refer to as a mixed control strategy, enables us to obtain a cost that is better than what deterministic control strategies can achieve when the CCOC problem is nonconvex. The resulting mixed-strategy CCOC problem turns out to be a convexification of the original nonconvex CCOC problem. Furthermore, we also show that a mixed control strategy only needs to "mix" up to two deterministic control actions in order to achieve optimality. Building upon an iterative dual optimization, the proposed algorithm quickly converges to the optimal mixed control strategy with a user-specified tolerance.

Convex optimisation approach to constrained fuel optimal control of spacecraft in close relative motion

NASA Astrophysics Data System (ADS)

Massioni, Paolo; Massari, Mauro

2018-05-01

This paper describes an interesting and powerful approach to the constrained fuel-optimal control of spacecraft in close relative motion. The proposed approach is well suited for problems under linear dynamic equations, therefore perfectly fitting to the case of spacecraft flying in close relative motion. If the solution of the optimisation is approximated as a polynomial with respect to the time variable, then the problem can be approached with a technique developed in the control engineering community, known as "Sum Of Squares" (SOS), and the constraints can be reduced to bounds on the polynomials. Such a technique allows rewriting polynomial bounding problems in the form of convex optimisation problems, at the cost of a certain amount of conservatism. The principles of the techniques are explained and some application related to spacecraft flying in close relative motion are shown.

Constraining Thermal Histories by Monte Carlo Simulation of Mg-Fe Isotopic Profiles in Olivine

NASA Astrophysics Data System (ADS)

Sio, C. K. I.; Dauphas, N.

2016-12-01

In thermochronology, random time-temperature (t-T) paths are generated and used as inputs to model fission track data. This random search method is used to identify a range of acceptable thermal histories that can describe the data. We have extended this modeling approach to magmatic systems. This approach utilizes both the chemical and stable isotope profiles measured in crystals as model constraints. Specifically, the isotopic profiles are used to determine the relative contribution of crystal growth vs. diffusion in generating chemical profiles, and to detect changes in melt composition. With this information, tighter constraints can be placed on the thermal evolution of magmatic bodies. We use an olivine phenocryst from the Kilauea Iki lava lake, HI, to demonstrate proof of concept. We treat this sample as one with little geologic context, then compare our modeling results to the known thermal history experienced by that sample. To complete forward modeling, we use MELTS to estimate the boundary condition, initial and quench temperatures. We also assume a simple relationship between crystal growth and cooling rate. Another important parameter is the isotopic effect for diffusion (i.e., the relative diffusivity of the light vs. heavy isotope of an element). The isotopic effects for Mg and Fe diffusion in olivine have been estimated based on natural samples; experiments to better constrain these parameters are underway. We find that 40% of the random t-T paths can be used to fit the Mg-Fe chemical profiles. However, only a few can be used to simultaneously fit the Mg-Fe isotopic profiles. These few t-T paths are close to the independently determined t-T history of the sample. This modeling approach can be further extended other igneous and metamorphic systems where data exist for diffusion rates, crystal growth rates, and isotopic effects for diffusion.

Two-dimensional and 3-D images of thick tissue using time-constrained times-of-flight and absorbance spectrophotometry

NASA Astrophysics Data System (ADS)

Benaron, David A.; Lennox, M.; Stevenson, David K.

1992-05-01

Reconstructing deep-tissue images in real time using spectrophotometric data from optically diffusing thick tissues has been problematic. Continuous wave applications (e.g., pulse oximetry, regional cerebral saturation) ignore both the multiple paths traveled by the photons through the tissue and the effects of scattering, allowing scalar measurements but only under limited conditions; interferometry works poorly in thick, highly-scattering media; frequency- modulated approaches may not allow full deconvolution of scattering and absorbance; and pulsed-light techniques allow for preservation of information regarding the multiple paths taken by light through the tissue, but reconstruction is both computation intensive and limited by the relative surface area available for detection of photons. We have developed a picosecond times-of-flight and absorbance (TOFA) optical system, time-constrained to measure only photons with a narrow range of path lengths and arriving within a narrow angel of the emitter-detector axis. The delay until arrival of the earliest arriving photons is a function of both the scattering and absorbance of the tissues in a direct line between the emitter and detector, reducing the influence of surrounding tissues. Measurement using a variety of emitter and detector locations produces spatial information which can be analyzed in a standard 2-D grid, or subject to computer reconstruction to produce tomographic images representing 3-D structure. Using such a technique, we have been able to demonstrate the principles of tc-TOFA, detect and localize diffusive and/or absorptive objects suspended in highly scattering media (such as blood admixed with yeast), and perform simple 3-D reconstructions using phantom objects. We are now attempting to obtain images in vivo. Potential future applications include use as a research tool, and as a continuous, noninvasive, nondestructive monitor in diagnostic imaging, fetal monitoring, neurologic and cardiac assessment. The technique may lead to real-time optical imaging and quantitation of tissues oxygen delivery.

How important is exact knowledge of preferential flowpath locations and orientations for understanding spatiotemporally integrated spring hydrologic and transport response?

NASA Astrophysics Data System (ADS)

Henson, W.; De Rooij, R.; Graham, W. D.

2016-12-01

The Upper Floridian Aquifer is hydrogeologically complex; limestone dissolution has led to vertical and horizontal preferential flow paths. Locations of karst conduits are unknown and conduit properties are poorly constrained. Uncertainty in effects of conduit location, size, and density, network geometry and connectivity on hydrologic and transport responses is not well quantified, leading to limited use of discrete-continuum models that incorporate conduit networks for regional-scale hydrologic regulatory models. However, conduit networks typically dominate flow and contaminant transport in karst aquifers. We evaluated sensitivity of simulated water and nitrate fluxes and flow paths to karst conduit geometry in a springshed representative of Silver Springs, Florida, using a novel calcite dissolution conduit-generation algorithm coupled with a discrete-continuum flow and transport model (DisCo). Monte Carlo simulations of conduit generation, groundwater flow, and conservative solute transport indicate that, if a first magnitude spring system conduit network developed (i.e., spring flow >2.8 m3/s), the uncertainty in hydraulic and solute pulse response metrics at the spring vent was minimally related to locational uncertainty of network elements. Across the ensemble of realizations for various distributions of conduits, first magnitude spring hydraulic pulse metrics (e.g., steady-flow, peak flow, and recession coefficients) had < 0.01 coefficient of variation (CV). Similarly, spring solute breakthrough curve moments had low CV (<0.08); peak arrival had CV=0.06, mean travel time had CV=0.05, and travel time standard deviation had CV=0.08. Nevertheless, hydraulic and solute pulse response metrics were significantly different than those predicted by an equivalent porous-media model. These findings indicate that regional-scale decision models that incorporate karst preferential flow paths within an uncertainty framework can be used to better constrain aquifer-vulnerability estimates, despite lacking information about actual conduit locations.

A study of regional waveform calibration in the eastern Mediterranean

NASA Astrophysics Data System (ADS)

Di Luccio, F.; Pino, N. A.; Thio, H. K.

2003-06-01

We modeled P nl phases from several moderate magnitude earthquakes in the eastern Mediterranean to test methods and develop path calibrations for determining source parameters. The study region, which extends from the eastern part of the Hellenic arc to the eastern Anatolian fault, is dominated by moderate earthquakes that can produce significant damage. Our results are useful for analyzing regional seismicity as well as seismic hazard, because very few broadband seismic stations are available in the selected area. For the whole region we have obtained a single velocity model characterized by a 30 km thick crust, low upper mantle velocities and a very thin lid overlaying a distinct low velocity layer. Our preferred model proved quite reliable for determining focal mechanism and seismic moment across the entire range of selected paths. The source depth is also well constrained, especially for moderate earthquakes.

Principles of Metamorphic Petrology

NASA Astrophysics Data System (ADS)

Williams, Michael L.

2009-05-01

The field of metamorphic petrology has seen spectacular advances in the past decade, including new X-ray mapping techniques for characterizing metamorphic rocks and minerals, new internally consistent thermobarometers, new software for constructing and viewing phase diagrams, new methods to date metamorphic processes, and perhaps most significant, revised petrologic databases and the ability to calculate accurate phase diagrams and pseudosections. These tools and techniques provide new power and resolution for constraining pressure-temperature (P-T) histories and tectonic events. Two books have been fundamental for empowering petrologists and structural geologists during the past decade. Frank Spear's Metamorphic Phase Equilibria and Pressure-Temperature-Time Paths, published in 1993, builds on his seminal papers to provide a quantitative framework for P-T path analysis. Spear's book lays the foundation for modern quantitative metamorphic analysis. Cees Passchier and Rudolph Trouw's Microtectonics, published in 2005, with its superb photos and figures, provides the tools and the theory for interpreting deformation textures and inferring deformation processes.

Control of mechanical systems with rolling constraints: Application to dynamic control of mobile robots

NASA Technical Reports Server (NTRS)

Sarkar, Nilanjan; Yun, Xiaoping; Kumar, Vijay

1994-01-01

There are many examples of mechanical systems that require rolling contacts between two or more rigid bodies. Rolling contacts engender nonholonomic constraints in an otherwise holonomic system. In this article, we develop a unified approach to the control of mechanical systems subject to both holonomic and nonholonomic constraints. We first present a state space realization of a constrained system. We then discuss the input-output linearization and zero dynamics of the system. This approach is applied to the dynamic control of mobile robots. Two types of control algorithms for mobile robots are investigated: trajectory tracking and path following. In each case, a smooth nonlinear feedback is obtained to achieve asymptotic input-output stability and Lagrange stability of the overall system. Simulation results are presented to demonstrate the effectiveness of the control algorithms and to compare the performane of trajectory-tracking and path-following algorithms.

Spreading paths in partially observed social networks

NASA Astrophysics Data System (ADS)

Onnela, Jukka-Pekka; Christakis, Nicholas A.

2012-03-01

Understanding how and how far information, behaviors, or pathogens spread in social networks is an important problem, having implications for both predicting the size of epidemics, as well as for planning effective interventions. There are, however, two main challenges for inferring spreading paths in real-world networks. One is the practical difficulty of observing a dynamic process on a network, and the other is the typical constraint of only partially observing a network. Using static, structurally realistic social networks as platforms for simulations, we juxtapose three distinct paths: (1) the stochastic path taken by a simulated spreading process from source to target; (2) the topologically shortest path in the fully observed network, and hence the single most likely stochastic path, between the two nodes; and (3) the topologically shortest path in a partially observed network. In a sampled network, how closely does the partially observed shortest path (3) emulate the unobserved spreading path (1)? Although partial observation inflates the length of the shortest path, the stochastic nature of the spreading process also frequently derails the dynamic path from the shortest path. We find that the partially observed shortest path does not necessarily give an inflated estimate of the length of the process path; in fact, partial observation may, counterintuitively, make the path seem shorter than it actually is.

Spreading paths in partially observed social networks.

PubMed

Onnela, Jukka-Pekka; Christakis, Nicholas A

2012-03-01

Understanding how and how far information, behaviors, or pathogens spread in social networks is an important problem, having implications for both predicting the size of epidemics, as well as for planning effective interventions. There are, however, two main challenges for inferring spreading paths in real-world networks. One is the practical difficulty of observing a dynamic process on a network, and the other is the typical constraint of only partially observing a network. Using static, structurally realistic social networks as platforms for simulations, we juxtapose three distinct paths: (1) the stochastic path taken by a simulated spreading process from source to target; (2) the topologically shortest path in the fully observed network, and hence the single most likely stochastic path, between the two nodes; and (3) the topologically shortest path in a partially observed network. In a sampled network, how closely does the partially observed shortest path (3) emulate the unobserved spreading path (1)? Although partial observation inflates the length of the shortest path, the stochastic nature of the spreading process also frequently derails the dynamic path from the shortest path. We find that the partially observed shortest path does not necessarily give an inflated estimate of the length of the process path; in fact, partial observation may, counterintuitively, make the path seem shorter than it actually is.

Polynomial Size Formulations for the Distance and Capacity Constrained Vehicle Routing Problem

NASA Astrophysics Data System (ADS)

Kara, Imdat; Derya, Tusan

2011-09-01

The Distance and Capacity Constrained Vehicle Routing Problem (DCVRP) is an extension of the well known Traveling Salesman Problem (TSP). DCVRP arises in distribution and logistics problems. It would be beneficial to construct new formulations, which is the main motivation and contribution of this paper. We focused on two indexed integer programming formulations for DCVRP. One node based and one arc (flow) based formulation for DCVRP are presented. Both formulations have O(n2) binary variables and O(n2) constraints, i.e., the number of the decision variables and constraints grows with a polynomial function of the nodes of the underlying graph. It is shown that proposed arc based formulation produces better lower bound than the existing one (this refers to the Water's formulation in the paper). Finally, various problems from literature are solved with the node based and arc based formulations by using CPLEX 8.0. Preliminary computational analysis shows that, arc based formulation outperforms the node based formulation in terms of linear programming relaxation.

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

Simonetto, Andrea; Dall'Anese, Emiliano

This article develops online algorithms to track solutions of time-varying constrained optimization problems. Particularly, resembling workhorse Kalman filtering-based approaches for dynamical systems, the proposed methods involve prediction-correction steps to provably track the trajectory of the optimal solutions of time-varying convex problems. The merits of existing prediction-correction methods have been shown for unconstrained problems and for setups where computing the inverse of the Hessian of the cost function is computationally affordable. This paper addresses the limitations of existing methods by tackling constrained problems and by designing first-order prediction steps that rely on the Hessian of the cost function (and do notmore » require the computation of its inverse). In addition, the proposed methods are shown to improve the convergence speed of existing prediction-correction methods when applied to unconstrained problems. Numerical simulations corroborate the analytical results and showcase performance and benefits of the proposed algorithms. A realistic application of the proposed method to real-time control of energy resources is presented.« less

Cooperative Surveillance and Pursuit Using Unmanned Aerial Vehicles and Unattended Ground Sensors

PubMed Central

Las Fargeas, Jonathan; Kabamba, Pierre; Girard, Anouck

2015-01-01

This paper considers the problem of path planning for a team of unmanned aerial vehicles performing surveillance near a friendly base. The unmanned aerial vehicles do not possess sensors with automated target recognition capability and, thus, rely on communicating with unattended ground sensors placed on roads to detect and image potential intruders. The problem is motivated by persistent intelligence, surveillance, reconnaissance and base defense missions. The problem is formulated and shown to be intractable. A heuristic algorithm to coordinate the unmanned aerial vehicles during surveillance and pursuit is presented. Revisit deadlines are used to schedule the vehicles' paths nominally. The algorithm uses detections from the sensors to predict intruders' locations and selects the vehicles' paths by minimizing a linear combination of missed deadlines and the probability of not intercepting intruders. An analysis of the algorithm's completeness and complexity is then provided. The effectiveness of the heuristic is illustrated through simulations in a variety of scenarios. PMID:25591168

Grey-Theory-Based Optimization Model of Emergency Logistics Considering Time Uncertainty.

PubMed

Qiu, Bao-Jian; Zhang, Jiang-Hua; Qi, Yuan-Tao; Liu, Yang

2015-01-01

Natural disasters occur frequently in recent years, causing huge casualties and property losses. Nowadays, people pay more and more attention to the emergency logistics problems. This paper studies the emergency logistics problem with multi-center, multi-commodity, and single-affected-point. Considering that the path near the disaster point may be damaged, the information of the state of the paths is not complete, and the travel time is uncertainty, we establish the nonlinear programming model that objective function is the maximization of time-satisfaction degree. To overcome these drawbacks: the incomplete information and uncertain time, this paper firstly evaluates the multiple roads of transportation network based on grey theory and selects the reliable and optimal path. Then simplify the original model under the scenario that the vehicle only follows the optimal path from the emergency logistics center to the affected point, and use Lingo software to solve it. The numerical experiments are presented to show the feasibility and effectiveness of the proposed method.

Grey-Theory-Based Optimization Model of Emergency Logistics Considering Time Uncertainty

PubMed Central

Qiu, Bao-Jian; Zhang, Jiang-Hua; Qi, Yuan-Tao; Liu, Yang

2015-01-01

Natural disasters occur frequently in recent years, causing huge casualties and property losses. Nowadays, people pay more and more attention to the emergency logistics problems. This paper studies the emergency logistics problem with multi-center, multi-commodity, and single-affected-point. Considering that the path near the disaster point may be damaged, the information of the state of the paths is not complete, and the travel time is uncertainty, we establish the nonlinear programming model that objective function is the maximization of time-satisfaction degree. To overcome these drawbacks: the incomplete information and uncertain time, this paper firstly evaluates the multiple roads of transportation network based on grey theory and selects the reliable and optimal path. Then simplify the original model under the scenario that the vehicle only follows the optimal path from the emergency logistics center to the affected point, and use Lingo software to solve it. The numerical experiments are presented to show the feasibility and effectiveness of the proposed method. PMID:26417946

CloudSat-Constrained Cloud Ice Water Path and Cloud Top Height Retrievals from MHS 157 and 183.3 GHz Radiances

NASA Technical Reports Server (NTRS)

Gong, J.; Wu, D. L.

2014-01-01

Ice water path (IWP) and cloud top height (ht) are two of the key variables in determining cloud radiative and thermodynamical properties in climate models. Large uncertainty remains among IWP measurements from satellite sensors, in large part due to the assumptions made for cloud microphysics in these retrievals. In this study, we develop a fast algorithm to retrieve IWP from the 157, 183.3+/-3 and 190.3 GHz radiances of the Microwave Humidity Sounder (MHS) such that the MHS cloud ice retrieval is consistent with CloudSat IWP measurements. This retrieval is obtained by constraining the empirical forward models between collocated and coincident measurements of CloudSat IWP and MHS cloud-induced radiance depression (Tcir) at these channels. The empirical forward model is represented by a lookup table (LUT) of Tcir-IWP relationships as a function of ht and the frequency channel.With ht simultaneously retrieved, the IWP is found to be more accurate. The useful range of the MHS IWP retrieval is between 0.5 and 10 kg/sq m, and agrees well with CloudSat in terms of the normalized probability density function (PDF). Compared to the empirical model, current operational radiative transfer models (RTMs) still have significant uncertainties in characterizing the observed Tcir-IWP relationships. Therefore, the empirical LUT method developed here remains an effective approach to retrieving ice cloud properties from the MHS-like microwave channels.

Optimal mistuning for enhanced aeroelastic stability of transonic fans

NASA Technical Reports Server (NTRS)

Hall, K. C.; Crawley, E. F.

1983-01-01

An inverse design procedure was developed for the design of a mistuned rotor. The design requirements are that the stability margin of the eigenvalues of the aeroelastic system be greater than or equal to some minimum stability margin, and that the mass added to each blade be positive. The objective was to achieve these requirements with a minimal amount of mistuning. Hence, the problem was posed as a constrained optimization problem. The constrained minimization problem was solved by the technique of mathematical programming via augmented Lagrangians. The unconstrained minimization phase of this technique was solved by the variable metric method. The bladed disk was modelled as being composed of a rigid disk mounted on a rigid shaft. Each of the blades were modelled with a single tosional degree of freedom.

Probabilistic Reasoning for Plan Robustness

NASA Technical Reports Server (NTRS)

Schaffer, Steve R.; Clement, Bradley J.; Chien, Steve A.

2005-01-01

A planning system must reason about the uncertainty of continuous variables in order to accurately project the possible system state over time. A method is devised for directly reasoning about the uncertainty in continuous activity duration and resource usage for planning problems. By representing random variables as parametric distributions, computing projected system state can be simplified in some cases. Common approximation and novel methods are compared for over-constrained and lightly constrained domains. The system compares a few common approximation methods for an iterative repair planner. Results show improvements in robustness over the conventional non-probabilistic representation by reducing the number of constraint violations witnessed by execution. The improvement is more significant for larger problems and problems with higher resource subscription levels but diminishes as the system is allowed to accept higher risk levels.

Visual environment recognition for robot path planning using template matched filters

NASA Astrophysics Data System (ADS)

Orozco-Rosas, Ulises; Picos, Kenia; Díaz-Ramírez, Víctor H.; Montiel, Oscar; Sepúlveda, Roberto

2017-08-01

A visual approach in environment recognition for robot navigation is proposed. This work includes a template matching filtering technique to detect obstacles and feasible paths using a single camera to sense a cluttered environment. In this problem statement, a robot can move from the start to the goal by choosing a single path between multiple possible ways. In order to generate an efficient and safe path for mobile robot navigation, the proposal employs a pseudo-bacterial potential field algorithm to derive optimal potential field functions using evolutionary computation. Simulation results are evaluated in synthetic and real scenes in terms of accuracy of environment recognition and efficiency of path planning computation.

Splines and polynomial tools for flatness-based constrained motion planning

NASA Astrophysics Data System (ADS)

Suryawan, Fajar; De Doná, José; Seron, María

2012-08-01

This article addresses the problem of trajectory planning for flat systems with constraints. Flat systems have the useful property that the input and the state can be completely characterised by the so-called flat output. We propose a spline parametrisation for the flat output, the performance output, the states and the inputs. Using this parametrisation the problem of constrained trajectory planning can be cast into a simple quadratic programming problem. An important result is that the B-spline parametrisation used gives exact results for constrained linear continuous-time system. The result is exact in the sense that the constrained signal can be made arbitrarily close to the boundary without having intersampling issues (as one would have in sampled-data systems). Simulation examples are presented, involving the generation of rest-to-rest trajectories. In addition, an experimental result of the method is also presented, where two methods to generate trajectories for a magnetic-levitation (maglev) system in the presence of constraints are compared and each method's performance is discussed. The first method uses the nonlinear model of the plant, which turns out to belong to the class of flat systems. The second method uses a linearised version of the plant model around an operating point. In every case, a continuous-time description is used. The experimental results on a real maglev system reported here show that, in most scenarios, the nonlinear and linearised models produce almost similar, indistinguishable trajectories.

Path-following control of wheeled planetary exploration robots moving on deformable rough terrain.

PubMed

Ding, Liang; Gao, Hai-bo; Deng, Zong-quan; Li, Zhijun; Xia, Ke-rui; Duan, Guang-ren

2014-01-01

The control of planetary rovers, which are high performance mobile robots that move on deformable rough terrain, is a challenging problem. Taking lateral skid into account, this paper presents a rough terrain model and nonholonomic kinematics model for planetary rovers. An approach is proposed in which the reference path is generated according to the planned path by combining look-ahead distance and path updating distance on the basis of the carrot following method. A path-following strategy for wheeled planetary exploration robots incorporating slip compensation is designed. Simulation results of a four-wheeled robot on deformable rough terrain verify that it can be controlled to follow a planned path with good precision, despite the fact that the wheels will obviously skid and slip.

Path-Following Control of Wheeled Planetary Exploration Robots Moving on Deformable Rough Terrain

PubMed Central

Ding, Liang; Gao, Hai-bo; Deng, Zong-quan; Li, Zhijun; Xia, Ke-rui; Duan, Guang-ren

2014-01-01

The control of planetary rovers, which are high performance mobile robots that move on deformable rough terrain, is a challenging problem. Taking lateral skid into account, this paper presents a rough terrain model and nonholonomic kinematics model for planetary rovers. An approach is proposed in which the reference path is generated according to the planned path by combining look-ahead distance and path updating distance on the basis of the carrot following method. A path-following strategy for wheeled planetary exploration robots incorporating slip compensation is designed. Simulation results of a four-wheeled robot on deformable rough terrain verify that it can be controlled to follow a planned path with good precision, despite the fact that the wheels will obviously skid and slip. PMID:24790582

Constrained Low-Rank Learning Using Least Squares-Based Regularization.

PubMed

Li, Ping; Yu, Jun; Wang, Meng; Zhang, Luming; Cai, Deng; Li, Xuelong

2017-12-01

Low-rank learning has attracted much attention recently due to its efficacy in a rich variety of real-world tasks, e.g., subspace segmentation and image categorization. Most low-rank methods are incapable of capturing low-dimensional subspace for supervised learning tasks, e.g., classification and regression. This paper aims to learn both the discriminant low-rank representation (LRR) and the robust projecting subspace in a supervised manner. To achieve this goal, we cast the problem into a constrained rank minimization framework by adopting the least squares regularization. Naturally, the data label structure tends to resemble that of the corresponding low-dimensional representation, which is derived from the robust subspace projection of clean data by low-rank learning. Moreover, the low-dimensional representation of original data can be paired with some informative structure by imposing an appropriate constraint, e.g., Laplacian regularizer. Therefore, we propose a novel constrained LRR method. The objective function is formulated as a constrained nuclear norm minimization problem, which can be solved by the inexact augmented Lagrange multiplier algorithm. Extensive experiments on image classification, human pose estimation, and robust face recovery have confirmed the superiority of our method.

A Risk-Constrained Multi-Stage Decision Making Approach to the Architectural Analysis of Mars Missions

NASA Technical Reports Server (NTRS)

Kuwata, Yoshiaki; Pavone, Marco; Balaram, J. (Bob)

2012-01-01

This paper presents a novel risk-constrained multi-stage decision making approach to the architectural analysis of planetary rover missions. In particular, focusing on a 2018 Mars rover concept, which was considered as part of a potential Mars Sample Return campaign, we model the entry, descent, and landing (EDL) phase and the rover traverse phase as four sequential decision-making stages. The problem is to find a sequence of divert and driving maneuvers so that the rover drive is minimized and the probability of a mission failure (e.g., due to a failed landing) is below a user specified bound. By solving this problem for several different values of the model parameters (e.g., divert authority), this approach enables rigorous, accurate and systematic trade-offs for the EDL system vs. the mobility system, and, more in general, cross-domain trade-offs for the different phases of a space mission. The overall optimization problem can be seen as a chance-constrained dynamic programming problem, with the additional complexity that 1) in some stages the disturbances do not have any probabilistic characterization, and 2) the state space is extremely large (i.e, hundreds of millions of states for trade-offs with high-resolution Martian maps). To this purpose, we solve the problem by performing an unconventional combination of average and minimax cost analysis and by leveraging high efficient computation tools from the image processing community. Preliminary trade-off results are presented.

Integrated optimization of unmanned aerial vehicle task allocation and path planning under steady wind.

PubMed

Luo, He; Liang, Zhengzheng; Zhu, Moning; Hu, Xiaoxuan; Wang, Guoqiang

2018-01-01

Wind has a significant effect on the control of fixed-wing unmanned aerial vehicles (UAVs), resulting in changes in their ground speed and direction, which has an important influence on the results of integrated optimization of UAV task allocation and path planning. The objective of this integrated optimization problem changes from minimizing flight distance to minimizing flight time. In this study, the Euclidean distance between any two targets is expanded to the Dubins path length, considering the minimum turning radius of fixed-wing UAVs. According to the vector relationship between wind speed, UAV airspeed, and UAV ground speed, a method is proposed to calculate the flight time of UAV between targets. On this basis, a variable-speed Dubins path vehicle routing problem (VS-DP-VRP) model is established with the purpose of minimizing the time required for UAVs to visit all the targets and return to the starting point. By designing a crossover operator and mutation operator, the genetic algorithm is used to solve the model, the results of which show that an effective UAV task allocation and path planning solution under steady wind can be provided.

Integrated optimization of unmanned aerial vehicle task allocation and path planning under steady wind

PubMed Central

Liang, Zhengzheng; Zhu, Moning; Hu, Xiaoxuan; Wang, Guoqiang

2018-01-01

Wind has a significant effect on the control of fixed-wing unmanned aerial vehicles (UAVs), resulting in changes in their ground speed and direction, which has an important influence on the results of integrated optimization of UAV task allocation and path planning. The objective of this integrated optimization problem changes from minimizing flight distance to minimizing flight time. In this study, the Euclidean distance between any two targets is expanded to the Dubins path length, considering the minimum turning radius of fixed-wing UAVs. According to the vector relationship between wind speed, UAV airspeed, and UAV ground speed, a method is proposed to calculate the flight time of UAV between targets. On this basis, a variable-speed Dubins path vehicle routing problem (VS-DP-VRP) model is established with the purpose of minimizing the time required for UAVs to visit all the targets and return to the starting point. By designing a crossover operator and mutation operator, the genetic algorithm is used to solve the model, the results of which show that an effective UAV task allocation and path planning solution under steady wind can be provided. PMID:29561888

Tracks of a non-main path traveler: 2011 Thomas Willis Lecture.

PubMed

Hallenbeck, John M

2012-02-01

After an unconventional beginning in stroke research, I veered off the main path repeatedly to view problems from a different perspective. In this lecture summary, I would like to return to several points along the byways that led to research with some continuity.

The Relations among Cumulative Risk, Parenting, and Behavior Problems during Early Childhood

ERIC Educational Resources Information Center

Trentacosta, Christopher J.; Hyde, Luke W.; Shaw, Daniel S.; Dishion, Thomas J.; Gardner, Frances; Wilson, Melvin

2008-01-01

Background: This study examined relations among cumulative risk, nurturant and involved parenting, and behavior problems across early childhood. Methods: Cumulative risk, parenting, and behavior problems were measured in a sample of low-income toddlers participating in a family-centered program to prevent conduct problems. Results: Path analysis…

Development of the PEBLebl Traveling Salesman Problem Computerized Testbed

ERIC Educational Resources Information Center

Mueller, Shane T.; Perelman, Brandon S.; Tan, Yin Yin; Thanasuan, Kejkaew

2015-01-01

The traveling salesman problem (TSP) is a combinatorial optimization problem that requires finding the shortest path through a set of points ("cities") that returns to the starting point. Because humans provide heuristic near-optimal solutions to Euclidean versions of the problem, it has sometimes been used to investigate human visual…

Open problems in artificial life.

PubMed

Bedau, M A; McCaskill, J S; Packard, N H; Rasmussen, S; Adami, C; Green, D G; Ikegami, T; Kaneko, K; Ray, T S

2000-01-01

This article lists fourteen open problems in artificial life, each of which is a grand challenge requiring a major advance on a fundamental issue for its solution. Each problem is briefly explained, and, where deemed helpful, some promising paths to its solution are indicated.

A transformation method for constrained-function minimization

NASA Technical Reports Server (NTRS)

Park, S. K.

1975-01-01

A direct method for constrained-function minimization is discussed. The method involves the construction of an appropriate function mapping all of one finite dimensional space onto the region defined by the constraints. Functions which produce such a transformation are constructed for a variety of constraint regions including, for example, those arising from linear and quadratic inequalities and equalities. In addition, the computational performance of this method is studied in the situation where the Davidon-Fletcher-Powell algorithm is used to solve the resulting unconstrained problem. Good performance is demonstrated for 19 test problems by achieving rapid convergence to a solution from several widely separated starting points.

Novel methods for Solving Economic Dispatch of Security-Constrained Unit Commitment Based on Linear Programming

NASA Astrophysics Data System (ADS)

Guo, Sangang

2017-09-01

There are two stages in solving security-constrained unit commitment problems (SCUC) within Lagrangian framework: one is to obtain feasible units’ states (UC), the other is power economic dispatch (ED) for each unit. The accurate solution of ED is more important for enhancing the efficiency of the solution to SCUC for the fixed feasible units’ statues. Two novel methods named after Convex Combinatorial Coefficient Method and Power Increment Method respectively based on linear programming problem for solving ED are proposed by the piecewise linear approximation to the nonlinear convex fuel cost functions. Numerical testing results show that the methods are effective and efficient.

What is the correct cost functional for variational data assimilation?

NASA Astrophysics Data System (ADS)

Bröcker, Jochen

2018-03-01

Variational approaches to data assimilation, and weakly constrained four dimensional variation (WC-4DVar) in particular, are important in the geosciences but also in other communities (often under different names). The cost functions and the resulting optimal trajectories may have a probabilistic interpretation, for instance by linking data assimilation with maximum aposteriori (MAP) estimation. This is possible in particular if the unknown trajectory is modelled as the solution of a stochastic differential equation (SDE), as is increasingly the case in weather forecasting and climate modelling. In this situation, the MAP estimator (or "most probable path" of the SDE) is obtained by minimising the Onsager-Machlup functional. Although this fact is well known, there seems to be some confusion in the literature, with the energy (or "least squares") functional sometimes been claimed to yield the most probable path. The first aim of this paper is to address this confusion and show that the energy functional does not, in general, provide the most probable path. The second aim is to discuss the implications in practice. Although the mentioned results pertain to stochastic models in continuous time, they do have consequences in practice where SDE's are approximated by discrete time schemes. It turns out that using an approximation to the SDE and calculating its most probable path does not necessarily yield a good approximation to the most probable path of the SDE proper. This suggest that even in discrete time, a version of the Onsager-Machlup functional should be used, rather than the energy functional, at least if the solution is to be interpreted as a MAP estimator.

Algorithms for Heterogeneous, Multiple Depot, Multiple Unmanned Vehicle Path Planning Problems

DOE PAGES

Sundar, Kaarthik; Rathinam, Sivakumar

2016-12-26

Unmanned vehicles, both aerial and ground, are being used in several monitoring applications to collect data from a set of targets. This article addresses a problem where a group of heterogeneous aerial or ground vehicles with different motion constraints located at distinct depots visit a set of targets. The vehicles also may be equipped with different sensors, and therefore, a target may not be visited by any vehicle. The objective is to find an optimal path for each vehicle starting and ending at its respective depot such that each target is visited at least once by some vehicle, the vehicle–targetmore » constraints are satisfied, and the sum of the length of the paths for all the vehicles is minimized. Two variants of this problem are formulated (one for ground vehicles and another for aerial vehicles) as mixed-integer linear programs and a branchand- cut algorithm is developed to compute an optimal solution to each of the variants. Computational results show that optimal solutions for problems involving 100 targets and 5 vehicles can be obtained within 300 seconds on average, further corroborating the effectiveness of the proposed approach.« less

Interactive multi-objective path planning through a palette-based user interface

NASA Astrophysics Data System (ADS)

Shaikh, Meher T.; Goodrich, Michael A.; Yi, Daqing; Hoehne, Joseph

2016-05-01

n a problem where a human uses supervisory control to manage robot path-planning, there are times when human does the path planning, and if satisfied commits those paths to be executed by the robot, and the robot executes that plan. In planning a path, the robot often uses an optimization algorithm that maximizes or minimizes an objective. When a human is assigned the task of path planning for robot, the human may care about multiple objectives. This work proposes a graphical user interface (GUI) designed for interactive robot path-planning when an operator may prefer one objective over others or care about how multiple objectives are traded off. The GUI represents multiple objectives using the metaphor of an artist's palette. A distinct color is used to represent each objective, and tradeoffs among objectives are balanced in a manner that an artist mixes colors to get the desired shade of color. Thus, human intent is analogous to the artist's shade of color. We call the GUI an "Adverb Palette" where the word "Adverb" represents a specific type of objective for the path, such as the adverbs "quickly" and "safely" in the commands: "travel the path quickly", "make the journey safely". The novel interactive interface provides the user an opportunity to evaluate various alternatives (that tradeoff between different objectives) by allowing her to visualize the instantaneous outcomes that result from her actions on the interface. In addition to assisting analysis of various solutions given by an optimization algorithm, the palette has additional feature of allowing the user to define and visualize her own paths, by means of waypoints (guiding locations) thereby spanning variety for planning. The goal of the Adverb Palette is thus to provide a way for the user and robot to find an acceptable solution even though they use very different representations of the problem. Subjective evaluations suggest that even non-experts in robotics can carry out the planning tasks with a great deal of flexibility using the adverb palette.

Comparison of Geothermobarometers with Different Closure Behavior to Constrain P-T Paths

NASA Astrophysics Data System (ADS)

Hora, J.; Simon, K.; Kronz, A.; Xiao, Y.; Worner, G.

2014-12-01

Temperatures obtained from geothermobarometers depend not only on minerals reaching equilibrium, but also on preservation of those compositions through subsequent thermal history. In the case of step-function cooling histories (volcanic systems), each of several geothermometry equations (with P-dependence) can be treated as a line in P-T space for a given composition. In the absence of independent P-constraint, intersection of those lines corresponds to the simultaneous solution of the equations involved and is indicative of crystallization and storage conditions. Multiple calibrations of a given thermometer can be evaluated by their degree of match. For protracted plutonic or metamorphic cooling histories, a single intersection is not expected - instead, calculated temperatures will reflect a sequence of mineral closure based on diffusivity of the element(s) of interest in the various phases. We apply this multi-thermometer approach to quartz, rutile, and titanite formed along the retrograde path in gneiss and eclogite at Bixiling, Dabie UHP terrane, China. Using the Huang and Audetat (2012) Ti-in-quartz thermometer calibration, all available Zr-in-rutile equations intersect at approximately 7-10 kbar and 560-580°C. Zr-in-rutile calibrations diverge at higher P, and intersect the Thomas et al. (2010) Ti-in-quartz calibration over a broader range of 13-16 kbar and 550-600°C. Regardless of which intersection is used, it appears that both of these minerals have reequilibrated far below the previously reported peak conditions of >30 kbar and >750°C. Titanite, where diffusion is slower, is present in the gneiss unit as late-stage overgrowths on rutile, but records T that are approximately 150°C higher than the rutile inclusions at all P. This appears to be consistent with all minerals forming above 750°C and possibly much higher P, with quartz and rutile being reset along the retrograde path due to more rapid diffusion. When crystal sizes are taken into account, rutile and quartz are predicted to close to Zr and Ti exchange at similar T ≈ 600-650°C. Intersections of P-T lines among two or more minerals with similar closure behavior may reflect P,T at points along a retrograde path or can be used to constrain cooling rate, and may have advantages relative to using a single thermobarometer.

Optimal symmetric flight with an intermediate vehicle model

NASA Technical Reports Server (NTRS)

Menon, P. K. A.; Kelley, H. J.; Cliff, E. M.

1983-01-01

Optimal flight in the vertical plane with a vehicle model intermediate in complexity between the point-mass and energy models is studied. Flight-path angle takes on the role of a control variable. Range-open problems feature subarcs of vertical flight and singular subarcs. The class of altitude-speed-range-time optimization problems with fuel expenditure unspecified is investigated and some interesting phenomena uncovered. The maximum-lift-to-drag glide appears as part of the family, final-time-open, with appropriate initial and terminal transient exceeding level-flight drag, some members exhibiting oscillations. Oscillatory paths generally fail the Jacobi test for durations exceeding a period and furnish a minimum only for short-duration problems.

Pseudo-updated constrained solution algorithm for nonlinear heat conduction

NASA Technical Reports Server (NTRS)

Tovichakchaikul, S.; Padovan, J.

1983-01-01

This paper develops efficiency and stability improvements in the incremental successive substitution (ISS) procedure commonly used to generate the solution to nonlinear heat conduction problems. This is achieved by employing the pseudo-update scheme of Broyden, Fletcher, Goldfarb and Shanno in conjunction with the constrained version of the ISS. The resulting algorithm retains the formulational simplicity associated with ISS schemes while incorporating the enhanced convergence properties of slope driven procedures as well as the stability of constrained approaches. To illustrate the enhanced operating characteristics of the new scheme, the results of several benchmark comparisons are presented.

Using the World Health Organization's 4S-Framework to Strengthen National Strategies, Policies and Services to Address Mental Health Problems in Adolescents in Resource-Constrained Settings

PubMed Central

2011-01-01

Background Most adolescents live in resource-constrained countries and their mental health has been less well recognised than other aspects of their health. The World Health Organization's 4-S Framework provides a structure for national initiatives to improve adolescent health through: gathering and using strategic information; developing evidence-informed policies; scaling up provision and use of health services; and strengthening linkages with other government sectors. The aim of this paper is to discuss how the findings of a recent systematic review of mental health problems in adolescents in resource-constrained settings might be applied using the 4-S Framework. Method Analysis of the implications of the findings of a systematic search of the English-language literature for national strategies, policies, services and cross-sectoral linkages to improve the mental health of adolescents in resource-constrained settings. Results Data are available for only 33/112 [29%] resource-constrained countries, but in all where data are available, non-psychotic mental health problems in adolescents are identifiable, prevalent and associated with reduced quality of life, impaired participation and compromised development. In the absence of evidence about effective interventions in these settings expert opinion is that a broad public policy response which addresses direct strategies for prevention, early intervention and treatment; health service and health workforce requirements; social inclusion of marginalised groups of adolescents; and specific education is required. Specific endorsed strategies include public education, parent education, training for teachers and primary healthcare workers, psycho-educational curricula, identification through periodic screening of the most vulnerable and referral for care, and the availability of counsellors or other identified trained staff members in schools from whom adolescents can seek assistance for personal, peer and family relationship problems. Conclusion The predominant endorsed action is not that dedicated mental health services for adolescents are required, but that mental health care should be integrated using cross-sectoral strategies into the communities in which adolescents live, the institutions they attend and the organisations in which they participate. PMID:21923901

Topological charge quantization via path integration: An application of the Kustaanheimo-Stiefel transformation

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

Inomata, A.; Junker, G.; Wilson, R.

1993-08-01

The unified treatment of the Dirac monopole, the Schwinger monopole, and the Aharonov-Bahn problem by Barut and Wilson is revisited via a path integral approach. The Kustaanheimo-Stiefel transformation of space and time is utilized to calculate the path integral for a charged particle in the singular vector potential. In the process of dimensional reduction, a topological charge quantization rule is derived, which contains Dirac's quantization condition as a special case. 32 refs.

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

NASA Astrophysics Data System (ADS)

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

2012-09-01

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

Real-time Feynman path integral with Picard–Lefschetz theory and its applications to quantum tunneling

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

Tanizaki, Yuya, E-mail: yuya.tanizaki@riken.jp; Theoretical Research Division, Nishina Center, RIKEN, Wako 351-0198; Koike, Takayuki, E-mail: tkoike@ms.u-tokyo.ac.jp

Picard–Lefschetz theory is applied to path integrals of quantum mechanics, in order to compute real-time dynamics directly. After discussing basic properties of real-time path integrals on Lefschetz thimbles, we demonstrate its computational method in a concrete way by solving three simple examples of quantum mechanics. It is applied to quantum mechanics of a double-well potential, and quantum tunneling is discussed. We identify all of the complex saddle points of the classical action, and their properties are discussed in detail. However a big theoretical difficulty turns out to appear in rewriting the original path integral into a sum of path integralsmore » on Lefschetz thimbles. We discuss generality of that problem and mention its importance. Real-time tunneling processes are shown to be described by those complex saddle points, and thus semi-classical description of real-time quantum tunneling becomes possible on solid ground if we could solve that problem. - Highlights: • Real-time path integral is studied based on Picard–Lefschetz theory. • Lucid demonstration is given through simple examples of quantum mechanics. • This technique is applied to quantum mechanics of the double-well potential. • Difficulty for practical applications is revealed, and we discuss its generality. • Quantum tunneling is shown to be closely related to complex classical solutions.« less

Link prediction based on local weighted paths for complex networks

NASA Astrophysics Data System (ADS)

Yao, Yabing; Zhang, Ruisheng; Yang, Fan; Yuan, Yongna; Hu, Rongjing; Zhao, Zhili

As a significant problem in complex networks, link prediction aims to find the missing and future links between two unconnected nodes by estimating the existence likelihood of potential links. It plays an important role in understanding the evolution mechanism of networks and has broad applications in practice. In order to improve prediction performance, a variety of structural similarity-based methods that rely on different topological features have been put forward. As one topological feature, the path information between node pairs is utilized to calculate the node similarity. However, many path-dependent methods neglect the different contributions of paths for a pair of nodes. In this paper, a local weighted path (LWP) index is proposed to differentiate the contributions between paths. The LWP index considers the effect of the link degrees of intermediate links and the connectivity influence of intermediate nodes on paths to quantify the path weight in the prediction procedure. The experimental results on 12 real-world networks show that the LWP index outperforms other seven prediction baselines.

Promoting Social and Emotional Competencies among Young Children in Croatia with Preschool PATHS

ERIC Educational Resources Information Center

Mihic, Josipa; Novak, Miranda; Basic, Josipa; Nix, Robert L.

2016-01-01

Preschool PATHS (Promoting Alternative Thinking Strategies) is an evidence-based universal prevention program focused on promoting children's social and emotional competencies and reducing the likelihood of behaviour problems and negative relationships with peers and teachers. This paper examines changes in the social and emotional competencies of…

Earth Education: A New Beginning.

ERIC Educational Resources Information Center

Van Matre, Steve

The Institute for Earth Education is a nonprofit volunteer group made up of an international network of individuals and member organizations devoted to helping people live more lightly on the earth. This book proposes an alternative path to solve environmental problems. The program, called the earth education path, seeks to accomplish one of…

Automated fuel pin loading system

DOEpatents

Christiansen, David W.; Brown, William F.; Steffen, Jim M.

1985-01-01

An automated loading system for nuclear reactor fuel elements utilizes a gravity feed conveyor which permits individual fuel pins to roll along a constrained path perpendicular to their respective lengths. The individual lengths of fuel cladding are directed onto movable transports, where they are aligned coaxially with the axes of associated handling equipment at appropriate production stations. Each fuel pin can be reciprocated axially and/or rotated about its axis as required during handling steps. The fuel pins are inserted as a batch prior to welding of end caps by one of two disclosed welding systems.

Automated fuel pin loading system

DOEpatents

Christiansen, D.W.; Brown, W.F.; Steffen, J.M.

An automated loading system for nuclear reactor fuel elements utilizes a gravity feed conveyor which permits individual fuel pins to roll along a constrained path perpendicular to their respective lengths. The individual lengths of fuel cladding are directed onto movable transports, where they are aligned coaxially with the axes of associated handling equipment at appropriate production stations. Each fuel pin can be be reciprocated axially and/or rotated about its axis as required during handling steps. The fuel pins are inerted as a batch prior to welding of end caps by one of two disclosed welding systems.

Multiobjective Resource-Constrained Project Scheduling with a Time-Varying Number of Tasks

PubMed Central

Abello, Manuel Blanco

2014-01-01

In resource-constrained project scheduling (RCPS) problems, ongoing tasks are restricted to utilizing a fixed number of resources. This paper investigates a dynamic version of the RCPS problem where the number of tasks varies in time. Our previous work investigated a technique called mapping of task IDs for centroid-based approach with random immigrants (McBAR) that was used to solve the dynamic problem. However, the solution-searching ability of McBAR was investigated over only a few instances of the dynamic problem. As a consequence, only a small number of characteristics of McBAR, under the dynamics of the RCPS problem, were found. Further, only a few techniques were compared to McBAR with respect to its solution-searching ability for solving the dynamic problem. In this paper, (a) the significance of the subalgorithms of McBAR is investigated by comparing McBAR to several other techniques; and (b) the scope of investigation in the previous work is extended. In particular, McBAR is compared to a technique called, Estimation Distribution Algorithm (EDA). As with McBAR, EDA is applied to solve the dynamic problem, an application that is unique in the literature. PMID:24883398

Application of Extended Kalman Filter in Persistant Scatterer Interferometry to Enhace the Accuracy of Unwrapping Process

NASA Astrophysics Data System (ADS)

Tavakkoli Estahbanat, A.; Dehghani, M.

2017-09-01

In interferometry technique, phases have been modulated between 0-2π. Finding the number of integer phases missed when they were wrapped is the main goal of unwrapping algorithms. Although the density of points in conventional interferometry is high, this is not effective in some cases such as large temporal baselines or noisy interferograms. Due to existing noisy pixels, not only it does not improve results, but also it leads to some unwrapping errors during interferogram unwrapping. In PS technique, because of the sparse PS pixels, scientists are confronted with a problem to unwrap phases. Due to the irregular data separation, conventional methods are sterile. Unwrapping techniques are divided in to path-independent and path-dependent in the case of unwrapping paths. A region-growing method which is a path-dependent technique has been used to unwrap PS data. In this paper an idea of EKF has been generalized on PS data. This algorithm is applied to consider the nonlinearity of PS unwrapping problem as well as conventional unwrapping problem. A pulse-pair method enhanced with singular value decomposition (SVD) has been used to estimate spectral shift from interferometric power spectral density in 7*7 local windows. Furthermore, a hybrid cost-map is used to manage the unwrapping path. This algorithm has been implemented on simulated PS data. To form a sparse dataset, A few points from regular grid are randomly selected and the RMSE of results and true unambiguous phases in presented to validate presented approach. The results of this algorithm and true unwrapped phases were completely identical.

Optimal impulsive time-fixed orbital rendezvous and interception with path constraints

NASA Technical Reports Server (NTRS)

Taur, D.-R.; Prussing, J. E.; Coverstone-Carroll, V.

1990-01-01

Minimum-fuel, impulsive, time-fixed solutions are obtained for the problem of orbital rendezvous and interception with interior path constraints. Transfers between coplanar circular orbits in an inverse-square gravitational field are considered, subject to a circular path constraint representing a minimum or maximum permissible orbital radius. Primer vector theory is extended to incorporate path constraints. The optimal number of impulses, their times and positions, and the presence of initial or final coasting arcs are determined. The existence of constraint boundary arcs and boundary points is investigated as well as the optimality of a class of singular arc solutions. To illustrate the complexities introduced by path constraints, an analysis is made of optimal rendezvous in field-free space subject to a minimum radius constraint.

A tutorial description of an interior point method and its applications to security-constrained economic dispatch

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

Vargas, L.S.; Quintana, V.H.; Vannelli, A.

This paper deals with the use of Successive Linear Programming (SLP) for the solution of the Security-Constrained Economic Dispatch (SCED) problem. The authors tutorially describe an Interior Point Method (IPM) for the solution of Linear Programming (LP) problems, discussing important implementation issues that really make this method far superior to the simplex method. A study of the convergence of the SLP technique and a practical criterion to avoid oscillatory behavior in the iteration process are also proposed. A comparison of the proposed method with an efficient simplex code (MINOS) is carried out by solving SCED problems on two standard IEEEmore » systems. The results show that the interior point technique is reliable, accurate and more than two times faster than the simplex algorithm.« less

Points on the Path to Probability.

ERIC Educational Resources Information Center

Kiernan, James F.

2001-01-01

Presents the problem of points and the development of the binomial triangle, or Pascal's triangle. Examines various attempts to solve this problem to give students insight into the nature of mathematical discovery. (KHR)

An embodied perspective on expertise in solving the problem of making a geologic map

NASA Astrophysics Data System (ADS)

Callahan, Caitlin Norah

The task of constructing a geologic map is a cognitively and physically demanding field-based problem. The map produced is understood to be an individual's two-dimensional interpretation or mental model of the three-dimensional underlying geology. A popular view within the geoscience community is that teaching students how to make a geologic map is valuable for preparing them to deal with disparate and incomplete data sets, for helping them develop problem-solving skills, and for acquiring expertise in geology. Few previous studies have focused specifically on expertise in geologic mapping. Drawing from literature related to expertise, to problem solving, and to mental models, two overarching research questions were identified: How do geologists of different levels of expertise constrain and solve an ill-structured problem such as making a geologic map? How do geologists address the uncertainties inherent to the processes and interpretations involved in solving a geologic mapping problem? These questions were answered using a methodology that captured the physical actions, expressed thoughts, and navigation paths of geologists as they made a geologic map. Eight geologists, from novice to expert, wore a head-mounted video camera with an attached microphone to record those actions and thoughts, creating "video logs" while in the field. The video logs were also time-stamped, which allowed the visual and audio data to be synchronized with the GPS data that tracked participants' movements in the field. Analysis of the video logs yielded evidence that all eight participants expressed thoughts that reflected the process of becoming mentally situated in the mapping task (e.g. relating between distance on a map and distance in three-dimensional space); the prominence of several of these early thoughts waned in the expressed thoughts later in the day. All participants collected several types of data while in the field; novices, however, did so more continuously throughout the day whereas the experts collected more of their data earlier in the day. Experts and novices also differed in that experts focused more on evaluating certainty in their interpretations; the novices focused more on evaluating the certainty of their observations and sense of location.

Optimal UAS Assignments and Trajectories for Persistent Surveillance and Data Collection from a Wireless Sensor Network

DTIC Science & Technology

2015-12-24

minimizing a weighted sum ofthe time and control effort needed to collect sensor data. This problem formulation is a modified traveling salesman ...29 2.5 The Shortest Path Problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 2.5.1 Traveling Salesman Problem ...48 3.3.1 Initial Guess by Traveling Salesman Problem Solution

When Procedures Discourage Insight: Epistemological Consequences of Prompting Novice Physics Students to Construct Force Diagrams

ERIC Educational Resources Information Center

Kuo, Eric; Hallinen, Nicole R.; Conlin, Luke D.

2017-01-01

One aim of school science instruction is to help students become adaptive problem solvers. Though successful at structuring novice problem solving, step-by-step problem-solving frameworks may also constrain students' thinking. This study utilises a paradigm established by Heckler [(2010). Some consequences of prompting novice physics students to…

We Are All Engineers Now: Delivering Useful Projections Of Sea Level Rise

NASA Astrophysics Data System (ADS)

Pfeffer, W. T.

2011-12-01

Sea level rise is among the most tangible and potentially costly global changes facing society in the near future. Much of the uncertainty in future sea level rise lies in the determination of glacier and ice sheet contributions through melting of ice and through the discharge of icebergs directly into the ocean. As a consequence, many aspects of modern glaciological research have come to be motivated wholly or in part by the need to solve societally relevant problems involving future changes in sea level. To this extent, glaciology has become - temporarily - an applied science, in which the motivating questions are not purely scientific but practical in nature, and entail goals, deadlines and constraints that may or may not mesh comfortably with the skills, resources, and interests of the glaciological research community. This shift in motivation has subtle but important effects on how the glaciological community conducts research: we are no longer fully at liberty to explore only those problems that we judge to be the most intellectually stimulating and novel, or even the most likely to produce immediate results. We are obliged, at least if we are going to claim to be serving a critical societal need, to take on the entire spectrum of problems pertinent to sea level rise: the exciting with the mundane, the low-hanging fruit with the high-hanging, the tractable with the intractable. And in those intractable cases, and in other situations where the path to a solution is unclear, we must explore alternatives to our conventional approaches, and seek the means, if not to actually obtain solutions, to at least constrain the outcome and reduce the uncertainty of future knowledge. This broadening of methods is very much an engineer's approach to problem solving, but it also fits the philosopher/physicist P.W. Bridgman's definition of the scientific method as "Doing your damnedest, no holds barred."

High-order Path Integral Monte Carlo methods for solving strongly correlated fermion problems

NASA Astrophysics Data System (ADS)

Chin, Siu A.

2015-03-01

In solving for the ground state of a strongly correlated many-fermion system, the conventional second-order Path Integral Monte Carlo method is plagued with the sign problem. This is due to the large number of anti-symmetric free fermion propagators that are needed to extract the square of the ground state wave function at large imaginary time. In this work, I show that optimized fourth-order Path Integral Monte Carlo methods, which uses no more than 5 free-fermion propagators, in conjunction with the use of the Hamiltonian energy estimator, can yield accurate ground state energies for quantum dots with up to 20 polarized electrons. The correlations are directly built-in and no explicit wave functions are needed. This work is supported by the Qatar National Research Fund NPRP GRANT #5-674-1-114.

Application of modern control theory to scheduling and path-stretching maneuvers of aircraft in the near terminal area

NASA Technical Reports Server (NTRS)

Athans, M.

1974-01-01

A design concept of the dynamic control of aircraft in the near terminal area is discussed. An arbitrary set of nominal air routes, with possible multiple merging points, all leading to a single runway, is considered. The system allows for the automated determination of acceleration/deceleration of aircraft along the nominal air routes, as well as for the automated determination of path-stretching delay maneuvers. In addition to normal operating conditions, the system accommodates: (1) variable commanded separations over the outer marker to allow for takeoffs and between successive landings and (2) emergency conditions under which aircraft in distress have priority. The system design is based on a combination of three distinct optimal control problems involving a standard linear-quadratic problem, a parameter optimization problem, and a minimum-time rendezvous problem.

Data-Driven Online and Real-Time Combinatorial Optimization

DTIC Science & Technology

2013-10-30

Problem , the online Traveling Salesman Problem , and variations of the online Quota Hamil- tonian Path Problem and the online Traveling ...has the lowest competitive ratio among all algorithms of this kind. Second, we consider the Online Traveling Salesman Problem , and consider randomized...matroid secretary problem on a partition matroid. 6. Jaillet, P. and X. Lu. “Online Traveling Salesman Problems with Rejection Options”, submitted

Ensuring critical event sequences in high consequence computer based systems as inspired by path expressions

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

Kidd, M.E.C.

1997-02-01

The goal of our work is to provide a high level of confidence that critical software driven event sequences are maintained in the face of hardware failures, malevolent attacks and harsh or unstable operating environments. This will be accomplished by providing dynamic fault management measures directly to the software developer and to their varied development environments. The methodology employed here is inspired by previous work in path expressions. This paper discusses the perceived problems, a brief overview of path expressions, the proposed methods, and a discussion of the differences between the proposed methods and traditional path expression usage and implementation.

The Problem-Solving Process in Physics as Observed When Engineering Students at University Level Work in Groups

ERIC Educational Resources Information Center

Gustafsson, Peter; Jonsson, Gunnar; Enghag, Margareta

2015-01-01

The problem-solving process is investigated for five groups of students when solving context-rich problems in an introductory physics course included in an engineering programme. Through transcripts of their conversation, the paths in the problem-solving process have been traced and related to a general problem-solving model. All groups exhibit…

Path perception during rotation: influence of instructions, depth range, and dot density

NASA Technical Reports Server (NTRS)

Li, Li; Warren, William H Jr

2004-01-01

How do observers perceive their direction of self-motion when traveling on a straight path while their eyes are rotating? Our previous findings suggest that information from retinal flow and extra-retinal information about eye movements are each sufficient to solve this problem for both perception and active control of self-motion [Vision Res. 40 (2000) 3873; Psych. Sci. 13 (2002) 485]. In this paper, using displays depicting translation with simulated eye rotation, we investigated how task variables such as instructions, depth range, and dot density influenced the visual system's reliance on retinal vs. extra-retinal information for path perception during rotation. We found that path errors were small when observers expected to travel on a straight path or with neutral instructions, but errors increased markedly when observers expected to travel on a curved path. Increasing depth range or dot density did not improve path judgments. We conclude that the expectation of the shape of an upcoming path can influence the interpretation of the ambiguous retinal flow. A large depth range and dense motion parallax are not essential for accurate path perception during rotation, but reference objects and a large field of view appear to improve path judgments.

Path Analysis on Educational Fiscal Decision-Making Mechanism in China

ERIC Educational Resources Information Center

Zhao, Hongbin; Sun, Baicai

2007-01-01

In China's current educational fiscal decision making, problems are as follows: no law to trust or not abiding by available laws, absence of equity and efficiency, as well as the standardization of decision-making procedures. It is necessary to set up effective fiscal decision-making mechanism in education and rationally devise reliable paths.

A method to stabilize linear systems using eigenvalue gradient information

NASA Technical Reports Server (NTRS)

Wieseman, C. D.

1985-01-01

Formal optimization methods and eigenvalue gradient information are used to develop a stabilizing control law for a closed loop linear system that is initially unstable. The method was originally formulated by using direct, constrained optimization methods with the constraints being the real parts of the eigenvalues. However, because of problems in trying to achieve stabilizing control laws, the problem was reformulated to be solved differently. The method described uses the Davidon-Fletcher-Powell minimization technique to solve an indirect, constrained minimization problem in which the performance index is the Kreisselmeier-Steinhauser function of the real parts of all the eigenvalues. The method is applied successfully to solve two different problems: the determination of a fourth-order control law stabilizes a single-input single-output active flutter suppression system and the determination of a second-order control law for a multi-input multi-output lateral-directional flight control system. Various sets of design variables and initial starting points were chosen to show the robustness of the method.

On the Miller-Tucker-Zemlin Based Formulations for the Distance Constrained Vehicle Routing Problems

NASA Astrophysics Data System (ADS)

Kara, Imdat

2010-11-01

Vehicle Routing Problem (VRP), is an extension of the well known Traveling Salesman Problem (TSP) and has many practical applications in the fields of distribution and logistics. When the VRP consists of distance based constraints it is called Distance Constrained Vehicle Routing Problem (DVRP). However, the literature addressing on the DVRP is scarce. In this paper, existing two-indexed integer programming formulations, having Miller-Tucker-Zemlin based subtour elimination constraints, are reviewed. Existing formulations are simplified and obtained formulation is presented as formulation F1. It is shown that, the distance bounding constraints of the formulation F1, may not generate the distance traveled up to the related node. To do this, we redefine the auxiliary variables of the formulation and propose second formulation F2 with new and easy to use distance bounding constraints. Adaptation of the second formulation to the cases where new restrictions such as minimal distance traveled by each vehicle or other objectives such as minimizing the longest distance traveled is discussed.

A generalized network flow model for the multi-mode resource-constrained project scheduling problem with discounted cash flows

NASA Astrophysics Data System (ADS)

Chen, Miawjane; Yan, Shangyao; Wang, Sin-Siang; Liu, Chiu-Lan

2015-02-01

An effective project schedule is essential for enterprises to increase their efficiency of project execution, to maximize profit, and to minimize wastage of resources. Heuristic algorithms have been developed to efficiently solve the complicated multi-mode resource-constrained project scheduling problem with discounted cash flows (MRCPSPDCF) that characterize real problems. However, the solutions obtained in past studies have been approximate and are difficult to evaluate in terms of optimality. In this study, a generalized network flow model, embedded in a time-precedence network, is proposed to formulate the MRCPSPDCF with the payment at activity completion times. Mathematically, the model is formulated as an integer network flow problem with side constraints, which can be efficiently solved for optimality, using existing mathematical programming software. To evaluate the model performance, numerical tests are performed. The test results indicate that the model could be a useful planning tool for project scheduling in the real world.

Impact of Project P.A.T.H.S. on adolescent developmental outcomes in Hong Kong: findings based on seven waves of data.

PubMed

Shek, Daniel T L; Ma, Cecilia M S

2012-01-18

The present study examined the longitudinal impact of Project P.A.T.H.S. (Positive Adolescent Training through Holistic Social Programmes) on adolescent developmental outcomes in Hong Kong. Using a longitudinal randomized group design, seven waves of data were collected from 24 experimental schools (n=4049 at wave 1) in which students participated in the Tier 1 Program of Project P.A.T.H.S. and 24 control schools (n=3797 at wave 1). Results based on individual growth curve modeling generally showed that, relative to the control participants, participants in the experimental group had: (a) a higher level of positive development; (b) a lower level of substance abuse; and (c) a lower level of delinquent behavior. Participants who regarded the program to be beneficial also showed higher levels of positive development and lower levels of problem behavior than did the control school students. The present findings suggest that Project P.A.T.H.S. is effective in promoting positive development and preventing adolescent problem behavior in Chinese adolescents in Hong Kong.

Information-Constrained Optima with Retrading: An Externality and Its Market-Based Solution☆

PubMed Central

Kilenthong, Weerachart T.; Townsend, Robert M.

2010-01-01

This paper studies the efficiency of competitive equilibria in environments with a moral hazard problem and unobserved states, both with retrading in ex post spot markets. The interaction between private information problems and the possibility of retrade creates an externality, unless preferences have special, restrictive properties. The externality is internalized by allowing agents to contract ex ante on market fundamentals determining the spot price or interest rate, over and above contracting on actions and outputs. Then competitive equilibria are equivalent with the appropriate notion of constrained Pareto optimality. Examples show that it is possible to have multiple market fundamentals or price-islands, created endogenously in equilibrium. PMID:21765540

An iterative algorithm for L1-TV constrained regularization in image restoration

NASA Astrophysics Data System (ADS)

Chen, K.; Loli Piccolomini, E.; Zama, F.

2015-11-01

We consider the problem of restoring blurred images affected by impulsive noise. The adopted method restores the images by solving a sequence of constrained minimization problems where the data fidelity function is the ℓ1 norm of the residual and the constraint, chosen as the image Total Variation, is automatically adapted to improve the quality of the restored images. Although this approach is general, we report here the case of vectorial images where the blurring model involves contributions from the different image channels (cross channel blur). A computationally convenient extension of the Total Variation function to vectorial images is used and the results reported show that this approach is efficient for recovering nearly optimal images.

Metabolic PathFinding: inferring relevant pathways in biochemical networks.

PubMed

Croes, Didier; Couche, Fabian; Wodak, Shoshana J; van Helden, Jacques

2005-07-01

Our knowledge of metabolism can be represented as a network comprising several thousands of nodes (compounds and reactions). Several groups applied graph theory to analyse the topological properties of this network and to infer metabolic pathways by path finding. This is, however, not straightforward, with a major problem caused by traversing irrelevant shortcuts through highly connected nodes, which correspond to pool metabolites and co-factors (e.g. H2O, NADP and H+). In this study, we present a web server implementing two simple approaches, which circumvent this problem, thereby improving the relevance of the inferred pathways. In the simplest approach, the shortest path is computed, while filtering out the selection of highly connected compounds. In the second approach, the shortest path is computed on the weighted metabolic graph where each compound is assigned a weight equal to its connectivity in the network. This approach significantly increases the accuracy of the inferred pathways, enabling the correct inference of relatively long pathways (e.g. with as many as eight intermediate reactions). Available options include the calculation of the k-shortest paths between two specified seed nodes (either compounds or reactions). Multiple requests can be submitted in a queue. Results are returned by email, in textual as well as graphical formats (available in http://www.scmbb.ulb.ac.be/pathfinding/).

SDN-based path hopping communication against eavesdropping attack

NASA Astrophysics Data System (ADS)

Zhang, Chuanhao; Bu, Youjun; Zhao, Zheng

2016-10-01

Network eavesdropping is one of the most popular means used by cyber attackers, which has been a severe threat to network communication security. Adversaries could capture and analyze network communication data from network nodes or links, monitor network status and steal sensitive data such as username and password etc. Traditional network usually uses static network configuration, and existing defense methods, including firewall, IDS, IPS etc., cannot prevent eavesdropping, which has no distinguishing characteristic. Network eavesdropping become silent during most of the time of the attacking process, which is why it is difficult to discover and to defend. But A successful eavesdropping attack also has its' precondition, which is the target path should be relatively stable and has enough time of duration. So, In order to resolve this problem, it has to work on the network architecture. In this paper, a path hopping communication(PHC) mechanism based on Software Define Network (SDN) was proposed to solve this problem. In PHC, Ends in communication packets as well as the routing paths were changed dynamically. Therefore, the traffic would be distributed to multiple flows and transmitted along different paths. so that Network eavesdropping attack could be prevented effectively. It was concluded that PHC was able to increase the overhead of Network eavesdropping, as well as the difficulty of communication data recovery.

On the optimization of electromagnetic geophysical data: Application of the PSO algorithm

NASA Astrophysics Data System (ADS)

Godio, A.; Santilano, A.

2018-01-01

Particle Swarm optimization (PSO) algorithm resolves constrained multi-parameter problems and is suitable for simultaneous optimization of linear and nonlinear problems, with the assumption that forward modeling is based on good understanding of ill-posed problem for geophysical inversion. We apply PSO for solving the geophysical inverse problem to infer an Earth model, i.e. the electrical resistivity at depth, consistent with the observed geophysical data. The method doesn't require an initial model and can be easily constrained, according to external information for each single sounding. The optimization process to estimate the model parameters from the electromagnetic soundings focuses on the discussion of the objective function to be minimized. We discuss the possibility to introduce in the objective function vertical and lateral constraints, with an Occam-like regularization. A sensitivity analysis allowed us to check the performance of the algorithm. The reliability of the approach is tested on synthetic, real Audio-Magnetotelluric (AMT) and Long Period MT data. The method appears able to solve complex problems and allows us to estimate the a posteriori distribution of the model parameters.

A Bankruptcy Problem Approach to Load-shedding in Multiagent-based Microgrid Operation

PubMed Central

Kim, Hak-Man; Kinoshita, Tetsuo; Lim, Yujin; Kim, Tai-Hoon

2010-01-01

A microgrid is composed of distributed power generation systems (DGs), distributed energy storage devices (DSs), and loads. To maintain a specific frequency in the islanded mode as an important requirement, the control of DGs’ output and charge action of DSs are used in supply surplus conditions and load-shedding and discharge action of DSs are used in supply shortage conditions. Recently, multiagent systems for autonomous microgrid operation have been studied. Especially, load-shedding, which is intentional reduction of electricity use, is a critical problem in islanded microgrid operation based on the multiagent system. Therefore, effective schemes for load-shedding are required. Meanwhile, the bankruptcy problem deals with dividing short resources among multiple agents. In order to solve the bankruptcy problem, division rules, such as the constrained equal awards rule (CEA), the constrained equal losses rule (CEL), and the random arrival rule (RA), have been used. In this paper, we approach load-shedding as a bankruptcy problem. We compare load-shedding results by above-mentioned rules in islanded microgrid operation based on wireless sensor network (WSN) as the communication link for an agent’s interactions. PMID:22163386

A bankruptcy problem approach to load-shedding in multiagent-based microgrid operation.

PubMed

Kim, Hak-Man; Kinoshita, Tetsuo; Lim, Yujin; Kim, Tai-Hoon

2010-01-01

A microgrid is composed of distributed power generation systems (DGs), distributed energy storage devices (DSs), and loads. To maintain a specific frequency in the islanded mode as an important requirement, the control of DGs' output and charge action of DSs are used in supply surplus conditions and load-shedding and discharge action of DSs are used in supply shortage conditions. Recently, multiagent systems for autonomous microgrid operation have been studied. Especially, load-shedding, which is intentional reduction of electricity use, is a critical problem in islanded microgrid operation based on the multiagent system. Therefore, effective schemes for load-shedding are required. Meanwhile, the bankruptcy problem deals with dividing short resources among multiple agents. In order to solve the bankruptcy problem, division rules, such as the constrained equal awards rule (CEA), the constrained equal losses rule (CEL), and the random arrival rule (RA), have been used. In this paper, we approach load-shedding as a bankruptcy problem. We compare load-shedding results by above-mentioned rules in islanded microgrid operation based on wireless sensor network (WSN) as the communication link for an agent's interactions.

Classification-Assisted Memetic Algorithms for Equality-Constrained Optimization Problems

NASA Astrophysics Data System (ADS)

Handoko, Stephanus Daniel; Kwoh, Chee Keong; Ong, Yew Soon

Regressions has successfully been incorporated into memetic algorithm (MA) to build surrogate models for the objective or constraint landscape of optimization problems. This helps to alleviate the needs for expensive fitness function evaluations by performing local refinements on the approximated landscape. Classifications can alternatively be used to assist MA on the choice of individuals that would experience refinements. Support-vector-assisted MA were recently proposed to alleviate needs for function evaluations in the inequality-constrained optimization problems by distinguishing regions of feasible solutions from those of the infeasible ones based on some past solutions such that search efforts can be focussed on some potential regions only. For problems having equality constraints, however, the feasible space would obviously be extremely small. It is thus extremely difficult for the global search component of the MA to produce feasible solutions. Hence, the classification of feasible and infeasible space would become ineffective. In this paper, a novel strategy to overcome such limitation is proposed, particularly for problems having one and only one equality constraint. The raw constraint value of an individual, instead of its feasibility class, is utilized in this work.

Spectroscopic factors near the r-process path using (d , p) measurements at two energies

NASA Astrophysics Data System (ADS)

Walter, D.; Cizewski, J. A.; Baugher, T.; Ratkiewicz, A.; Manning, B.; Pain, S. D.; Nunes, F. M.; Ahn, S.; Cerizza, G.; Thornsberry, C.; Jones, K. L.

2016-09-01

To determine spectroscopic factors, it is necessary to use a nuclear reaction model that is dependent on the bound-state potential. A poorly constrained potential can drastically increase uncertainties in extracted spectroscopic factors. Mukhamedzhanov and Nunes have proposed a technique to mitigate this uncertainty by combining transfer reaction measurements at two energies. At peripheral reaction energies ( 5 MeV/u), the external contribution of the wave function can be reliably extracted, and then combined with the higher energy reaction ( 40 MeV/u) with a larger contribution from the interior. The two measurements will constrain the single-particle asymptotic normalization coefficient, ANC, and enable spectroscopic factors to be determined with uncertainties dominated by the cross section measurements rather than in the bound-state potential. Published measurements of 86Kr(d , p) at 5.5 MeV/u have been combined with recent results at 35 MeV/u at the NSCL using the ORRUBA and SIDAR arrays of silicon-strip detectors. Preliminary analysis shows that the single-particle ANC can be constrained. The details of the analysis and prospects for measurements with rare isotope beams will be presented. This research by the ORRUBA Collaboration is supported in part by the NSF and the U.S. DOE.

Hedonic price models with omitted variables and measurement errors: a constrained autoregression-structural equation modeling approach with application to urban Indonesia

NASA Astrophysics Data System (ADS)

Suparman, Yusep; Folmer, Henk; Oud, Johan H. L.

2014-01-01

Omitted variables and measurement errors in explanatory variables frequently occur in hedonic price models. Ignoring these problems leads to biased estimators. In this paper, we develop a constrained autoregression-structural equation model (ASEM) to handle both types of problems. Standard panel data models to handle omitted variables bias are based on the assumption that the omitted variables are time-invariant. ASEM allows handling of both time-varying and time-invariant omitted variables by constrained autoregression. In the case of measurement error, standard approaches require additional external information which is usually difficult to obtain. ASEM exploits the fact that panel data are repeatedly measured which allows decomposing the variance of a variable into the true variance and the variance due to measurement error. We apply ASEM to estimate a hedonic housing model for urban Indonesia. To get insight into the consequences of measurement error and omitted variables, we compare the ASEM estimates with the outcomes of (1) a standard SEM, which does not account for omitted variables, (2) a constrained autoregression model, which does not account for measurement error, and (3) a fixed effects hedonic model, which ignores measurement error and time-varying omitted variables. The differences between the ASEM estimates and the outcomes of the three alternative approaches are substantial.

From the physics of interacting polymers to optimizing routes on the London Underground

PubMed Central

Yeung, Chi Ho; Saad, David; Wong, K. Y. Michael

2013-01-01

Optimizing paths on networks is crucial for many applications, ranging from subway traffic to Internet communication. Because global path optimization that takes account of all path choices simultaneously is computationally hard, most existing routing algorithms optimize paths individually, thus providing suboptimal solutions. We use the physics of interacting polymers and disordered systems to analyze macroscopic properties of generic path optimization problems and derive a simple, principled, generic, and distributed routing algorithm capable of considering all individual path choices simultaneously. We demonstrate the efficacy of the algorithm by applying it to: (i) random graphs resembling Internet overlay networks, (ii) travel on the London Underground network based on Oyster card data, and (iii) the global airport network. Analytically derived macroscopic properties give rise to insightful new routing phenomena, including phase transitions and scaling laws, that facilitate better understanding of the appropriate operational regimes and their limitations, which are difficult to obtain otherwise. PMID:23898198

From the physics of interacting polymers to optimizing routes on the London Underground.

PubMed

Yeung, Chi Ho; Saad, David; Wong, K Y Michael

2013-08-20

Optimizing paths on networks is crucial for many applications, ranging from subway traffic to Internet communication. Because global path optimization that takes account of all path choices simultaneously is computationally hard, most existing routing algorithms optimize paths individually, thus providing suboptimal solutions. We use the physics of interacting polymers and disordered systems to analyze macroscopic properties of generic path optimization problems and derive a simple, principled, generic, and distributed routing algorithm capable of considering all individual path choices simultaneously. We demonstrate the efficacy of the algorithm by applying it to: (i) random graphs resembling Internet overlay networks, (ii) travel on the London Underground network based on Oyster card data, and (iii) the global airport network. Analytically derived macroscopic properties give rise to insightful new routing phenomena, including phase transitions and scaling laws, that facilitate better understanding of the appropriate operational regimes and their limitations, which are difficult to obtain otherwise.

Path Flow Estimation Using Time Varying Coefficient State Space Model

NASA Astrophysics Data System (ADS)

Jou, Yow-Jen; Lan, Chien-Lun

2009-08-01

The dynamic path flow information is very crucial in the field of transportation operation and management, i.e., dynamic traffic assignment, scheduling plan, and signal timing. Time-dependent path information, which is important in many aspects, is nearly impossible to be obtained. Consequently, researchers have been seeking estimation methods for deriving valuable path flow information from less expensive traffic data, primarily link traffic counts of surveillance systems. This investigation considers a path flow estimation problem involving the time varying coefficient state space model, Gibbs sampler, and Kalman filter. Numerical examples with part of a real network of the Taipei Mass Rapid Transit with real O-D matrices is demonstrated to address the accuracy of proposed model. Results of this study show that this time-varying coefficient state space model is very effective in the estimation of path flow compared to time-invariant model.

Constrained Self-adaptive Solutions Procedures for Structure Subject to High Temperature Elastic-plastic Creep Effects

NASA Technical Reports Server (NTRS)

Padovan, J.; Tovichakchaikul, S.

1983-01-01

This paper will develop a new solution strategy which can handle elastic-plastic-creep problems in an inherently stable manner. This is achieved by introducing a new constrained time stepping algorithm which will enable the solution of creep initiated pre/postbuckling behavior where indefinite tangent stiffnesses are encountered. Due to the generality of the scheme, both monotone and cyclic loading histories can be handled. The presentation will give a thorough overview of current solution schemes and their short comings, the development of constrained time stepping algorithms as well as illustrate the results of several numerical experiments which benchmark the new procedure.

From childhood adversity to problem behaviors: Role of psychological and structural social integration.

PubMed

Chao, Lo-Hsin; Tsai, Meng-Che; Liang, Ya-Lun; Strong, Carol; Lin, Chung-Ying

2018-01-01

Childhood adversity (CA) is associated with problem behaviors in adolescence, but the mediators, that is, those factors that help build resilience and prevent some children who experience CA from engaging in problem behaviors, await more exploration, including social integration. The aim of this study was to identify the association between CA and adolescent problem behaviors, and to further examine the mediating role of social integration distinctly as psychological and structural integration. Data used were from the Taiwan Education Panel Survey, a core panel of 4,261 students (age 13) surveyed in 2001 and followed for three more waves until age 18. For psychological integration, an average score was calculated to represent adolescents' feelings about their school. Structural integration was constructed using several items about adolescents' school and extracurricular activities. We used structural equation modeling with the diagonally weighted least squares method to examine the effect of CA on the primary outcome: adolescent problem behaviors via social integration. The hypothesized structural equation model specifying the path from CA to adolescent problem behavior had good fit. Respondents with one CA were indirectly linked to problem behaviors via psychological but not structural integration (e.g. the level of participation in school and non-school activities). On mediation analysis, psychological integration significantly mediated the paths from one CA to all six problem behaviors (all P < 0.05). The presence of only one CA was indirectly associated with problem behavior via psychological integration; two or more CA were not associated with significant paths to problem behaviors. The contribution of social integration is crucial to an adolescent's development from CA to problem behaviors. To form supportive social relationships to achieve better health, we suggest that those adolescents who have been exposed to CA should be helped to join more teams and take part in more activities, thereby increasing their opportunities for social interaction, and improving their communication skills. © 2017 Japan Pediatric Society.

E-Learning Technologies: Employing Matlab Web Server to Facilitate the Education of Mathematical Programming

ERIC Educational Resources Information Center

Karagiannis, P.; Markelis, I.; Paparrizos, K.; Samaras, N.; Sifaleras, A.

2006-01-01

This paper presents new web-based educational software (webNetPro) for "Linear Network Programming." It includes many algorithms for "Network Optimization" problems, such as shortest path problems, minimum spanning tree problems, maximum flow problems and other search algorithms. Therefore, webNetPro can assist the teaching process of courses such…

IIR filtering based adaptive active vibration control methodology with online secondary path modeling using PZT actuators

NASA Astrophysics Data System (ADS)

Boz, Utku; Basdogan, Ipek

2015-12-01

Structural vibrations is a major cause for noise problems, discomfort and mechanical failures in aerospace, automotive and marine systems, which are mainly composed of plate-like structures. In order to reduce structural vibrations on these structures, active vibration control (AVC) is an effective approach. Adaptive filtering methodologies are preferred in AVC due to their ability to adjust themselves for varying dynamics of the structure during the operation. The filtered-X LMS (FXLMS) algorithm is a simple adaptive filtering algorithm widely implemented in active control applications. Proper implementation of FXLMS requires availability of a reference signal to mimic the disturbance and model of the dynamics between the control actuator and the error sensor, namely the secondary path. However, the controller output could interfere with the reference signal and the secondary path dynamics may change during the operation. This interference problem can be resolved by using an infinite impulse response (IIR) filter which considers feedback of the one or more previous control signals to the controller output and the changing secondary path dynamics can be updated using an online modeling technique. In this paper, IIR filtering based filtered-U LMS (FULMS) controller is combined with online secondary path modeling algorithm to suppress the vibrations of a plate-like structure. The results are validated through numerical and experimental studies. The results show that the FULMS with online secondary path modeling approach has more vibration rejection capabilities with higher convergence rate than the FXLMS counterpart.

Understanding Associations Between Neighborhood Socioeconomic Status and Negative Consequences of Drinking: a Moderated Mediation Analysis.

PubMed

Karriker-Jaffe, Katherine J; Liu, HuiGuo; Kaplan, Lauren M

2016-05-01

We explored how neighborhood socioeconomic status (SES) is related to negative consequences of drinking to explain why racial/ethnic minority group members are more at risk than Whites for adverse alcohol outcomes. We tested direct and indirect effects of neighborhood SES on alcohol problems and examined differences by gender and race. We used data from the 2000 and 2005 National Alcohol Surveys (N = 7912 drinkers aged 18 and older; 49 % female) linked with data from the 2000 Decennial Census in multivariate path models adjusting for individual demographics. In the full sample, neighborhood disadvantage had a significant direct path to increased negative consequences, with no indirect paths through depression, positive affect or pro-drinking attitudes. Neighborhood affluence had significant indirect paths to increased negative consequences through greater pro-drinking attitudes and increased heavy drinking. Subgroup analyses showed the indirect path from affluence to consequences held for White men, with no effects of neighborhood disadvantage. For racial/ethnic minority men, significant indirect paths emerged from both neighborhood disadvantage and affluence to increased consequences through greater pro-drinking attitudes and more heavy drinking. For minority women, there was an indirect effect of neighborhood affluence through reduced depression to fewer drinking consequences. There were limited neighborhood effects on alcohol outcomes for White women. Interventions targeting pro-drinking attitudes in both affluent and disadvantaged areas may help reduce alcohol-related problems among men. Initiatives to improve neighborhood conditions could enhance mental health of minority women and reduce alcohol-related health disparities.

Programmable motion of DNA origami mechanisms.

PubMed

Marras, Alexander E; Zhou, Lifeng; Su, Hai-Jun; Castro, Carlos E

2015-01-20

DNA origami enables the precise fabrication of nanoscale geometries. We demonstrate an approach to engineer complex and reversible motion of nanoscale DNA origami machine elements. We first design, fabricate, and characterize the mechanical behavior of flexible DNA origami rotational and linear joints that integrate stiff double-stranded DNA components and flexible single-stranded DNA components to constrain motion along a single degree of freedom and demonstrate the ability to tune the flexibility and range of motion. Multiple joints with simple 1D motion were then integrated into higher order mechanisms. One mechanism is a crank-slider that couples rotational and linear motion, and the other is a Bennett linkage that moves between a compacted bundle and an expanded frame configuration with a constrained 3D motion path. Finally, we demonstrate distributed actuation of the linkage using DNA input strands to achieve reversible conformational changes of the entire structure on ∼ minute timescales. Our results demonstrate programmable motion of 2D and 3D DNA origami mechanisms constructed following a macroscopic machine design approach.

Programmable motion of DNA origami mechanisms

PubMed Central

Marras, Alexander E.; Zhou, Lifeng; Su, Hai-Jun; Castro, Carlos E.

2015-01-01

DNA origami enables the precise fabrication of nanoscale geometries. We demonstrate an approach to engineer complex and reversible motion of nanoscale DNA origami machine elements. We first design, fabricate, and characterize the mechanical behavior of flexible DNA origami rotational and linear joints that integrate stiff double-stranded DNA components and flexible single-stranded DNA components to constrain motion along a single degree of freedom and demonstrate the ability to tune the flexibility and range of motion. Multiple joints with simple 1D motion were then integrated into higher order mechanisms. One mechanism is a crank–slider that couples rotational and linear motion, and the other is a Bennett linkage that moves between a compacted bundle and an expanded frame configuration with a constrained 3D motion path. Finally, we demonstrate distributed actuation of the linkage using DNA input strands to achieve reversible conformational changes of the entire structure on ∼minute timescales. Our results demonstrate programmable motion of 2D and 3D DNA origami mechanisms constructed following a macroscopic machine design approach. PMID:25561550

Combined garnet and zircon geochronology of the ultra-high temperature metamorphism: Constraints on the rise of the Orlica-Śnieżnik Dome, NE Bohemian Massif, SW Poland

NASA Astrophysics Data System (ADS)

Walczak, Katarzyna; Anczkiewicz, Robert; Szczepański, Jacek; Rubatto, Daniela; Košler, Jan

2017-11-01

Garnet and zircon geochronology combined with trace element partitioning and petrological studies provide tight constraints on evolution of the UHT-(U)HP terrain of the Orlica-Śnieżnik Dome (OSD) in the NE Bohemian massif. Lu-Hf dating of peritectic garnet from two mesocratic granulites constrained the time of its initial growth at 346.9 ± 1.2 and 348.3 ± 2.0 Ma recording peak 2.5 GPa pressure and 950 °C temperature. In situ, U-Pb SHRIMP dating of zircon from the same granulite gave a younger age of 341.9 ± 3.4 Ma. Ti-in-zircon thermometry indicates crystallization at 810-860 °C pointing to zircon formation on the retrograde path. Lu partitioning between garnet rim and zircon suggest equilibrium growth and thus U-Pb zircon age constrain the terminal phase of garnet crystallization which lasted about 6 Ma. All Sm-Nd garnet ages obtained for mesocratic and mafic granulites are identical and consistently younger than the corresponding Lu-Hf dates. They are interpreted as reflecting cooling of granulites through the Sm-Nd closure temperature at about 337 Ma. The estimated PTt path documents the ca. 10 Ma evolution cycle of the OSD characterized by two distinct periods: (1) 347 - > 342 Ma period corresponds to nearly isothermal decompression resulting from crustal scale folding and vertical extrusion of granulites, and (2) at > 342-337 Ma which corresponds to a fast, nearly isobaric cooling.

Psychosocial Pathways to Sexually Transmitted Infection (STI) Risk Among Youth Transitioning Out of Foster Care: Evidence from a Longitudinal Cohort Study

PubMed Central

McCarty, Cari; Simoni, Jane; Dworsky, Amy; Courtney, Mark E.

2013-01-01

Purpose To test the fit of a theoretically driven conceptual model of pathways to STI risk among foster youth transitioning to adulthood. The model included: 1) historical abuse and foster care experiences, 2) mental health and attachment style in late adolescence, and 3) STI risk in young adulthood. Methods We used path analysis to analyze data from a longitudinal study of 732 youth transitioning out of foster care. Covariates included gender, race and an inverse probability weight. We also performed moderation analyses comparing models constrained and unconstrained by gender. Results Thirty percent reported they or a partner had been diagnosed with an STI. Probability of other measured STI risk behaviors ranged from 9% (having sex for money) to 79% (inconsistent condom use). Overall model fit was good (Standardized Root Mean Squared Residual of 0.026). Increased risk of oppositional/delinquent behaviors mediated an association between abuse history and STI risk, via increased inconsistent condom use. There was also a borderline association with having greater than 5 partners. Having a very close relationship with a caregiver and remaining in foster care beyond age 18 decreased STI risk. Moderation analysis revealed better model fit when coefficients were allowed to vary by gender versus a constrained model, but few significant differences in individual path coefficients were found between male and female-only models. Conclusions Interventions/policies that: 1) address externalizing trauma sequelae, 2) promote close, stable substitute caregiver relationships, and 3) extend care to age 21 years have the potential to decrease STI risk in this population. PMID:23859955

On the equivalence of the dual-wavelength and polarimetric equations for estimation of the raindrop size distribution

NASA Technical Reports Server (NTRS)

Meneghini, Robert; Liao, Liang

2006-01-01

In writing the integral equations for the median mass diameter and particle concentration, or comparable parameters of the raindrop size distribution, it is apparent that when attenuation effects are included, the forms of the equations for polarimetric and dual wavelength radars are identical. In both sets of equations, differences in the backscattering and extinction cross sections appear: in the polarimetric equations, the differences are taken with respect polarization at a fixed frequency while for the dual wavelength equations, the differences are taken with respect to wavelength at a fixed polarization. Because the forms of the equations are the same, the ways in which they can be solved are similar as well. To avoid instabilities in the forward recursion procedure, the equations can be expressed in the form of a final-value. Solving the equations in this way traditionally has required estimates of the path attenuations to the final gate: either the attenuations at horizontal and vertical polarizations at the same frequency or attenuations at two frequencies with the same polarization. This has been done for dual-frequency (air/spaceborne case) and polarimetric radars by the respective use of the surface reference technique and the differential phase shift. An alternative to solving the constrained version of the equations is an iterative procedure recently proposed in which independent estimates of path attenuation are not required. Although the procedure has limitations, it appears to be quite useful. Simulations of the retrievals help clarify the relationship between the constrained and unconstrained approaches and their application to the polarimetric and dual-wavelength equations.

Calculation of Pareto-optimal solutions to multiple-objective problems using threshold-of-acceptability constraints

NASA Technical Reports Server (NTRS)

Giesy, D. P.

1978-01-01

A technique is presented for the calculation of Pareto-optimal solutions to a multiple-objective constrained optimization problem by solving a series of single-objective problems. Threshold-of-acceptability constraints are placed on the objective functions at each stage to both limit the area of search and to mathematically guarantee convergence to a Pareto optimum.

Decompressional metamorphic P-T paths from kyanite-sillimanite-andalusite bearing rocks in north-central New Mexico

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

Daniel, C.G.; Thompson, A.G.; Grambling, J.A.

1992-01-01

Proterozoic rocks in six uplifts in northern and central New mexico display decompressional metamorphic P-T paths, apparently, related to crustal extension at a time near 1,440 Ma. Metamorphic P-T paths from the Picuris, Rincon, Truchas, Rio Mora, Sandia and southern Manzano Mountains are constrained by three independent techniques: (1) Al[sub 2]SiO[sub 5] and other mineral parageneses; (2) the Gibbs method applied to compositional zoning in garnet and plagioclase; and (3) a new technique based upon Fe[sup 3+] and Mn[sup 3+] zoning in andalusite. Aluminum silicate textures suggest that kyanite and subsequent sillimanite (commonly aligned parallel to the regional foliation andmore » lineation) are earlier than andalusite, which overprints foliation. The decompressional P-T paths are interpreted to reflect crustal extension. Contact metamorphic aureoles associated with the Priest pluton in the southern Manzano and the Sandia Granite in the Sandia Mountains place constraints upon the timing of the Ky-Sil-And metamorphism. In the southern Manzano Mountains metamorphic isograds marking the first appearance of staurolite and (closer to the pluton) sillimanite are parallel to the margin of the 1,440 Ma Priest quartz monzonite pluton. Similarly, isograds also surround the NW margin of the 1,420 Ma Sandia pluton. The regional metamorphism appears to have coincided with and to have been enhanced by the emplacement of the plutons. Because plutonism and regional metamorphism appear to have been synchronous, pluton emplacement occurred during decompression.« less

Indoor 3D Route Modeling Based On Estate Spatial Data

NASA Astrophysics Data System (ADS)

Zhang, H.; Wen, Y.; Jiang, J.; Huang, W.

2014-04-01

Indoor three-dimensional route model is essential for space intelligence navigation and emergency evacuation. This paper is motivated by the need of constructing indoor route model automatically and as far as possible. By comparing existing building data sources, this paper firstly explained the reason why the estate spatial management data is chosen as the data source. Then, an applicable method of construction three-dimensional route model in a building is introduced by establishing the mapping relationship between geographic entities and their topological expression. This data model is a weighted graph consist of "node" and "path" to express the spatial relationship and topological structure of a building components. The whole process of modelling internal space of a building is addressed by two key steps: (1) each single floor route model is constructed, including path extraction of corridor using Delaunay triangulation algorithm with constrained edge, fusion of room nodes into the path; (2) the single floor route model is connected with stairs and elevators and the multi-floor route model is eventually generated. In order to validate the method in this paper, a shopping mall called "Longjiang New City Plaza" in Nanjing is chosen as a case of study. And the whole building space is constructed according to the modelling method above. By integrating of existing path finding algorithm, the usability of this modelling method is verified, which shows the indoor three-dimensional route modelling method based on estate spatial data in this paper can support indoor route planning and evacuation route design very well.

A generalized fuzzy credibility-constrained linear fractional programming approach for optimal irrigation water allocation under uncertainty

NASA Astrophysics Data System (ADS)

Zhang, Chenglong; Guo, Ping

2017-10-01

The vague and fuzzy parametric information is a challenging issue in irrigation water management problems. In response to this problem, a generalized fuzzy credibility-constrained linear fractional programming (GFCCFP) model is developed for optimal irrigation water allocation under uncertainty. The model can be derived from integrating generalized fuzzy credibility-constrained programming (GFCCP) into a linear fractional programming (LFP) optimization framework. Therefore, it can solve ratio optimization problems associated with fuzzy parameters, and examine the variation of results under different credibility levels and weight coefficients of possibility and necessary. It has advantages in: (1) balancing the economic and resources objectives directly; (2) analyzing system efficiency; (3) generating more flexible decision solutions by giving different credibility levels and weight coefficients of possibility and (4) supporting in-depth analysis of the interrelationships among system efficiency, credibility level and weight coefficient. The model is applied to a case study of irrigation water allocation in the middle reaches of Heihe River Basin, northwest China. Therefore, optimal irrigation water allocation solutions from the GFCCFP model can be obtained. Moreover, factorial analysis on the two parameters (i.e. λ and γ) indicates that the weight coefficient is a main factor compared with credibility level for system efficiency. These results can be effective for support reasonable irrigation water resources management and agricultural production.