Optimal Path Selection for Mobile Robot Navigation Using Genetic Algorithm
Directory of Open Access Journals (Sweden)
D Tamilselvi
2011-07-01
Full Text Available The proposed Navigation Strategy using GA(Genetic Algorithm finds an optimal path in the simulated grid environment. GA forces to find a path that is connected to the robot start and target positions via predefined points. Each point in the environmental model is called genome and the path connecting Start and Target is called as Chromosome. According to the problem formulation, the length of the algorithm chromosomes (number of genomes is dynamic. Moreover every genome is not a simple digit. In this case, every genome represents the nodes in the 2D grid environment. After implementing the cross over and mutation concepts the resultant chromosome (path is subjected to optimization process which gives the optimal path as a result. The problem faced with is there may be chances for the loss of the fittest chromosome while performing the reproduction operations. The solution is achieved by inducing the concept of elitism thereby maintaining the population richness. The efficiency of the algorithm is analyzed with respect to execution time and path cost to reach the destination. Path planning, collision avoidance and obstacle avoidance are achieved in both static and dynamic environment.
Energy optimization based path selection algorithm for IEEE 802.11s wireless mesh networks
CSIR Research Space (South Africa)
Mhlanga, MM
2011-09-01
Full Text Available when the network is deployed in rural areas where electricity is a scarce resource. This research therefore presents an energy optimization based path selection algorithm for IEEE 802.11s WMNs which is aimed at addressing the above mentioned constrains...
DEFF Research Database (Denmark)
Mohanty, Sankhya; Hattel, Jesper Henri
2015-01-01
to generate optimized cellular scanning strategies and processing parameters, with an objective of reducing thermal asymmetries and mechanical deformations. The optimized scanning strategies are used for selective laser melting of the standard samples, and experimental and numerical results are compared....... gradients that occur during the process. While process monitoring and control of selective laser melting is an active area of research, establishing the reliability and robustness of the process still remains a challenge.In this paper, a methodology for generating reliable, optimized scanning paths...
DEFF Research Database (Denmark)
Mohanty, Sankhya; Hattel, Jesper Henri
2015-01-01
Selective laser melting is yet to become a standardized industrial manufacturing technique. The process continues to suffer from defects such as distortions, residual stresses, localized deformations and warpage caused primarily due to the localized heating, rapid cooling and high temperature...... gradients that occur during the process. While process monitoring and control of selective laser melting is an active area of research, establishing the reliability and robustness of the process still remains a challenge.In this paper, a methodology for generating reliable, optimized scanning paths...... and process parameters for selective laser melting of a standard sample is introduced. The processing of the sample is simulated by sequentially coupling a calibrated 3D pseudo-analytical thermal model with a 3D finite element mechanical model.The optimized processing parameters are subjected to a Monte Carlo...
Directory of Open Access Journals (Sweden)
Deepak Goyal
2013-07-01
Full Text Available This paper addresses the malicious node detection and path optimization problem for wireless sensor networks. Malicious node detection in neighborhood is a needed because that node may cause incorrect decisions or energy depletion. In this paper APSO (combination of Artificial bee colony and particular swarm optimization is used to choose an optimized path. Through this improved version we will overcome the disadvantage of local optimal which comes when we use PSO approach.
A Multiobjective Optimization Algorithm for QoS-Aware Path Selection in DiffServ and MPLS Networks
Institute of Scientific and Technical Information of China (English)
无
2006-01-01
A multiobjective quality of service (QoS) routing algorithm was proposed and used as the QoS-aware path selection approach in differentiated services and multi-protocol label switching (DiffServ-MPLS) networks. It simultaneously optimizes multiple QoS objectives by a genetic algorithm in conjunction with concept of Pareto dominance. The simulation demonstrates that the proposed algorithm is capable of discovering a set of QoS-based near optimal paths within in a few iterations. In addition, the simulation results also show the scalability of the algorithm with increasing number of network nodes.
Goury, Olivier; Amsallem, David; Bordas, Stéphane Pierre Alain; Liu, Wing Kam; Kerfriden, Pierre
2016-08-01
In this paper, we present new reliable model order reduction strategies for computational micromechanics. The difficulties rely mainly upon the high dimensionality of the parameter space represented by any load path applied onto the representative volume element. We take special care of the challenge of selecting an exhaustive snapshot set. This is treated by first using a random sampling of energy dissipating load paths and then in a more advanced way using Bayesian optimization associated with an interlocked division of the parameter space. Results show that we can insure the selection of an exhaustive snapshot set from which a reliable reduced-order model can be built.
Lane, John S
1977-01-01
The overall purpose of this monograph is to integrate and critically evaluate the existing literature in the area of optimal joint savings population programs. The existing diverse presentations are all seen to be discussions within a unified framework. The central problem is to compare the desirability of alternative inter-temporal sequences of total savings and population sizes. Of critical importance is whether one regards persons as the fundamental moral entities or whether one takes Sidgwick's viewpoint that something good being the result of one's action is the baSic reason for dOing anything. The latter viewpoint is consistent with defining a complete social preference ordering over these alternative sequences. Since part of one's interest is to evaluate the consequences of various ethical beliefs a com parative study of several such orderings is presented; in particular the Mill-Wolfe average utilitarian, and Sidgwick-Meade classical utilitarian) formulations. A possible problem with the social pref...
Vessel tree extraction using locally optimal paths
DEFF Research Database (Denmark)
Lo, Pechin Chien Pau; van Ginneken, Bram; de Bruijne, Marleen
2010-01-01
This paper proposes a method to extract vessel trees by continually extending detected branches with locally optimal paths. Our approach uses a cost function from a multi scale vessel enhancement filter. Optimal paths are selected based on rules that take into account the geometric characteristics...... of the vessel tree. Experiments were performed on 10 low dose chest CT scans for which the pulmonary vessel trees were extracted. The proposed method is shown to extract a better connected vessel tree and extract more of the small peripheral vessels in comparison to applying a threshold on the output...
Directory of Open Access Journals (Sweden)
Meenakshi R Patel
2012-03-01
Full Text Available ACO algorithms for datagram networks was given by Di Caro Dorigo, in year 1996. Basic mechanisms in typical ACO routing algorithms is Ant-like agents are proactively generated at the nodes to find/check paths toward assigned destinations Ants move hop-by-hop according to a exploratory routing policy based on the local routing .After reaching their destination, ants retrace their path and update nodes routing information according to the quality of the path. Routing information is statistical estimates of the time-to-go to the destination maintained in pheromone arrays. Data are probabilistically spread over the paths according to their estimated quality as stored in the pheromone variables. AntNet algorithms may cause the network congestion and stagnation as the routing table converges. In this paper we perform a survey on modified AntNet routing algorithm using Multiple Ant-Colony Optimization. Multiple ant colonies with different pheromone updating mechanism have different searching traits. By leveraging this feature, much of work is done by designing a set of adaptive rules to facilitate the collaboration between these colonies. This approach can balance the diversity and convergence of solutions generated by different ant colonies and also overcome the problem of Stagnation.
Optimal paths as correlated random walks
Perlsman, E.; Havlin, S.
2006-01-01
A numerical study of optimal paths in the directed polymer model shows that the paths are similar to correlated random walks. It is shown that when a directed optimal path of length t is divided into 3 segments whose length is t/3, the correlation between the transversal movements along the first and last path segments is independent of the path length t. It is also shown that the transversal correlations along optimal paths decrease as the paths approach their endpoints. The numerical results obtained for optimal paths in 1+4 dimensions are qualitatively similar to those obtained for optimal paths in lower dimensions, and the data supplies a strong numerical indication that 1+4 is not the upper critical dimension of this model, and of the associated KPZ equation.
Time optimal paths for high speed maneuvering
Energy Technology Data Exchange (ETDEWEB)
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 of 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.
Directory of Open Access Journals (Sweden)
K. Duraiswamy
2012-01-01
Full Text Available The moving object or vehicle location prediction based on their spatial and temporal information is an important task in many applications. Different methods were utilized for performing the vehicle movement detection and prediction process. In such works, there is a lack of analysis in predicting the vehicles location in current as well as in future. Moreover, such methods compute the vehicles movement by finding the topological relationships among trajectories and locations, whereas the representative GPS points are determined by the 30 m circular window. Due to this process, the performance of the method is degraded because such 30 m circular window is selected by calculating the error range in the given input image and such error range may vary from image to image. To reduce the drawback presented in the existing method, in this study a heuristic moving vehicle location prediction algorithm is proposed. The proposed heuristic algorithm mainly comprises two techniques namely, optimization GA algorithm and FFBNN. In this proposed technique, initially the vehicles frequent paths are collected by monitoring all the vehicles movement in a specific period. Among the frequent paths, the vehicles optimal paths are computed by the GA algorithm. The selected optimal paths for each vehicle are utilized to train the FFBNN. The well trained FFBNN is then utilized to find the vehicle movement from the current location. By combining the proposed heuristic algorithm with GA and FFBNN, the vehicles location is predicted efficiently. The implementation result shows the effectiveness of the proposed heuristic algorithm in predicting the vehicles future location from the current location. The performance of the heuristic algorithm is evaluated by comparing the result with the RBF classifier. The comparison result shows our proposed technique acquires an accurate vehicle location prediction ratio than the RBF prediction ratio, in terms of accuracy.
2015-07-06
consider a probabilistically-constrained portfolio optimization problem [16] to determine a minimum cost distribution of a unit investment among n assets...present a branching technique (Section 5). Through computational experiments on the probabilistic portfolio optimization problem (3) and an optimal ...at one. 15 DISTRIBUTION A: Distribution approved for public release. 6.2 Probabilistic Portfolio Optimization The first class of instances we test
Computing the optimal path in stochastic dynamical systems.
Bauver, Martha; Forgoston, Eric; Billings, Lora
2016-08-01
In stochastic systems, one is often interested in finding the optimal path that maximizes the probability of escape from a metastable state or of switching between metastable states. Even for simple systems, it may be impossible to find an analytic form of the optimal path, and in high-dimensional systems, this is almost always the case. In this article, we formulate a constructive methodology that is used to compute the optimal path numerically. The method utilizes finite-time Lyapunov exponents, statistical selection criteria, and a Newton-based iterative minimizing scheme. The method is applied to four examples. The first example is a two-dimensional system that describes a single population with internal noise. This model has an analytical solution for the optimal path. The numerical solution found using our computational method agrees well with the analytical result. The second example is a more complicated four-dimensional system where our numerical method must be used to find the optimal path. The third example, although a seemingly simple two-dimensional system, demonstrates the success of our method in finding the optimal path where other numerical methods are known to fail. In the fourth example, the optimal path lies in six-dimensional space and demonstrates the power of our method in computing paths in higher-dimensional spaces.
Computing the optimal path in stochastic dynamical systems
Energy Technology Data Exchange (ETDEWEB)
Bauver, Martha; Forgoston, Eric, E-mail: eric.forgoston@montclair.edu; Billings, Lora [Department of Mathematical Sciences, Montclair State University, 1 Normal Avenue, Montclair, New Jersey 07043 (United States)
2016-08-15
In stochastic systems, one is often interested in finding the optimal path that maximizes the probability of escape from a metastable state or of switching between metastable states. Even for simple systems, it may be impossible to find an analytic form of the optimal path, and in high-dimensional systems, this is almost always the case. In this article, we formulate a constructive methodology that is used to compute the optimal path numerically. The method utilizes finite-time Lyapunov exponents, statistical selection criteria, and a Newton-based iterative minimizing scheme. The method is applied to four examples. The first example is a two-dimensional system that describes a single population with internal noise. This model has an analytical solution for the optimal path. The numerical solution found using our computational method agrees well with the analytical result. The second example is a more complicated four-dimensional system where our numerical method must be used to find the optimal path. The third example, although a seemingly simple two-dimensional system, demonstrates the success of our method in finding the optimal path where other numerical methods are known to fail. In the fourth example, the optimal path lies in six-dimensional space and demonstrates the power of our method in computing paths in higher-dimensional spaces.
Generating and prioritizing optimal paths using ant colony optimization
Directory of Open Access Journals (Sweden)
Mukesh Mann
2015-03-01
Full Text Available The assurance of software reliability partially depends on testing. Numbers of approaches for software testing are available with their proclaimed advantages and limitations, but accessibility of any one of them is a subject dependent. Time is a critical factor in deciding cost of any project. A deep insight has shown that executing test cases are time consuming and tedious activity. Thus stress has been given to develop algorithms which can suggest better pathways for testing. One such algorithm called Path Prioritization -Ant Colony Optimization (PP-ACO has been suggested in this paper which is inspired by real Ant's foraging behavior to generate optimal paths sequence of a decision to decision (DD path of a graph. The algorithm does full path coverage and suggests the best optimal sequences of path in path testing and prioritizes them according to path strength.
DEFF Research Database (Denmark)
Mohanty, Sankhya; Tutum, Cem Celal; Hattel, Jesper Henri
2013-01-01
Selective laser melting, as a rapid manufacturing technology, is uniquely poised to enforce a paradigm shift in the manufacturing industry by eliminating the gap between job- and batch-production techniques. Products from this process, however, tend to show an increased amount of defects...
Thermodynamic Metrics and Optimal Paths
Energy Technology Data Exchange (ETDEWEB)
Sivak, David; Crooks, Gavin
2012-05-08
A fundamental problem in modern thermodynamics is how a molecular-scale machine performs useful work, while operating away from thermal equilibrium without excessive dissipation. To this end, we derive a friction tensor that induces a Riemannian manifold on the space of thermodynamic states. Within the linear-response regime, this metric structure controls the dissipation of finite-time transformations, and bestows optimal protocols with many useful properties. We discuss the connection to the existing thermodynamic length formalism, and demonstrate the utility of this metric by solving for optimal control parameter protocols in a simple nonequilibrium model.
Airway Tree Extraction with Locally Optimal Paths
DEFF Research Database (Denmark)
Lo, Pechin Chien Pau; Sporring, Jon; Pedersen, Jesper Johannes Holst
2009-01-01
This paper proposes a method to extract the airway tree from CT images by continually extending the tree with locally optimal paths. This is in contrast to commonly used region growing based approaches that only search the space of the immediate neighbors. The result is a much more robust method...... for tree extraction that can overcome local occlusions. The cost function for obtaining the optimal paths takes into account of an airway probability map as well as measures of airway shape and orientation derived from multi-scale Hessian eigen analysis on the airway probability. Significant improvements...
Quad-rotor flight path energy optimization
Kemper, Edward
Quad-Rotor unmanned areal vehicles (UAVs) have been a popular area of research and development in the last decade, especially with the advent of affordable microcontrollers like the MSP 430 and the Raspberry Pi. Path-Energy Optimization is an area that is well developed for linear systems. In this thesis, this idea of path-energy optimization is extended to the nonlinear model of the Quad-rotor UAV. The classical optimization technique is adapted to the nonlinear model that is derived for the problem at hand, coming up with a set of partial differential equations and boundary value conditions to solve these equations. Then, different techniques to implement energy optimization algorithms are tested using simulations in Python. First, a purely nonlinear approach is used. This method is shown to be computationally intensive, with no practical solution available in a reasonable amount of time. Second, heuristic techniques to minimize the energy of the flight path are tested, using Ziegler-Nichols' proportional integral derivative (PID) controller tuning technique. Finally, a brute force look-up table based PID controller is used. Simulation results of the heuristic method show that both reliable control of the system and path-energy optimization are achieved in a reasonable amount of time.
Drilling Path Optimization Based on Particle Swarm Optimization Algorithm
Institute of Scientific and Technical Information of China (English)
ZHU Guangyu; ZHANG Weibo; DU Yuexiang
2006-01-01
This paper presents a new approach based on the particle swarm optimization (PSO) algorithm for solving the drilling path optimization problem belonging to discrete space. Because the standard PSO algorithm is not guaranteed to be global convergence or local convergence, based on the mathematical algorithm model, the algorithm is improved by adopting the method of generate the stop evolution particle over again to get the ability of convergence to the global optimization solution. And the operators are improved by establishing the duality transposition method and the handle manner for the elements of the operator, the improved operator can satisfy the need of integer coding in drilling path optimization. The experiment with small node numbers indicates that the improved algorithm has the characteristics of easy realize, fast convergence speed, and better global convergence characteristics, hence the new PSO can play a role in solving the problem of drilling path optimization in drilling holes.
Institute of Scientific and Technical Information of China (English)
邢建平
2015-01-01
The previous optimal path selection of vehicles is mostly considered the shortest path or the least time. However, in practice, there is often a different behavior preference. The different requirements of the driver in the path selection are considered in this article, and the G1 method is used to determine the weight of the influence factors on the path selection. Further the optimal path method based on TOPSIS combining with G1 method is developed, which considers the driver's behavior preference. An example is used to show that the proposed method is effective and practical.%以前的车辆最优路径选择大多是考虑路程最短或时间最少。然而在实际的情形往往是伴随着驾驶员不同的行为偏好。充分考虑了驾驶员在路径选择中的不同要求，将G1法引入到驾驶员路径选择影响因素的权重确定，同时发展出基于G1-TOPSIS法车辆最优路径方法，并通过应用例子说明了方法的有效性和实用性。
Accurate free energy calculation along optimized paths.
Chen, Changjun; Xiao, Yi
2010-05-01
The path-based methods of free energy calculation, such as thermodynamic integration and free energy perturbation, are simple in theory, but difficult in practice because in most cases smooth paths do not exist, especially for large molecules. In this article, we present a novel method to build the transition path of a peptide. We use harmonic potentials to restrain its nonhydrogen atom dihedrals in the initial state and set the equilibrium angles of the potentials as those in the final state. Through a series of steps of geometrical optimization, we can construct a smooth and short path from the initial state to the final state. This path can be used to calculate free energy difference. To validate this method, we apply it to a small 10-ALA peptide and find that the calculated free energy changes in helix-helix and helix-hairpin transitions are both self-convergent and cross-convergent. We also calculate the free energy differences between different stable states of beta-hairpin trpzip2, and the results show that this method is more efficient than the conventional molecular dynamics method in accurate free energy calculation.
Time optimal paths for a constant speed unicycle
Energy Technology Data Exchange (ETDEWEB)
Reister, D.B.
1991-01-01
This paper uses the Pontryagin maximum principle to find time optimal paths for a constant speed unicycle. The time optimal paths consist of sequences of arcs of circles and straight lines. The maximum principle introduced concepts (dual variables, bang-bang solutions, singular solutions, and transversality conditions) that provide important insight into the nature of the time optimal paths. 10 refs., 6 figs.
Janssen, José
2008-01-01
Jansen, J. (2008). Facilitating Description and Selection of Learning Paths: the learning path specification put to the test. Presentation at the Otec Colloquium. April, 2008, Heerlen, The Netherlands.
Janssen, José
2008-01-01
Jansen, J. (2008). Facilitating Description and Selection of Learning Paths: the learning path specification put to the test. Presentation at the Otec Colloquium. April, 2008, Heerlen, The Netherlands.
Institute of Scientific and Technical Information of China (English)
王建新; 王新辉; 彭革刚
2002-01-01
An important issue for providing better guarantees of Quality of Service (QoS) to applications is QoS rout-ing. The task of QoS routing is to determine a feasible path that satisfies a set of constraints while maintaining high u-tilization of network resources. For the purpose of achieving the latter objective additional optimality requirementsneed to be imposed. In general, multi-constrained path selection problem is NP-hard so it cannot be exactly solved inpolynomial time. Accordingly heuristics and approximation algorithms with polynomial or pseudo-polynomial timecomplexity are often used to deal with this problem. However, many of these algorithms suffer from either excessivecomputational complexity that cannot be used for online network operation or low performance. Moreover, they gen-erally deal with special cases of the problem (e. g. , two constraints without optimization, one constraint with opti-mization, etc. ). In this paper, the authors propose a new efficient algorithm (EAMCOP) for the problem. Makinguse of efficient pruning policy, the algorithm reduces greatly the size of search space and improves the computationalperformance. Although the proposed algorithm has exponential time complexity in the worst case, it can get verygood performance in real networks. The reason is that when the scale of network increases, EAMCOP controls effi-ciently the size of search space by constraint conditions and prior queue that improves computational efficiency. Theresults of simulation show that the algorithm has good performance and can solve effectively multi-constrained opti-mal path (MCOP) problem.
Kinetic constrained optimization of the golf swing hub path.
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.
Sub-optimality analysis of mobile robot rolling path planning
Institute of Scientific and Technical Information of China (English)
张纯刚; 席裕庚
2003-01-01
Rolling planning is an efficient method for path planning in uncertain environment. In this paper, the general principle and algorithm of mobile robot path planning based on rolling windows are studied. The sub-optimality of rolling path planning is analyzed in details and explained with a concrete example.
Global path planning approach based on ant colony optimization algorithm
Institute of Scientific and Technical Information of China (English)
WEN Zhi-qiang; CAI Zi-xing
2006-01-01
Ant colony optimization (ACO) algorithm was modified to optimize the global path. In order to simulate the real ant colonies, according to the foraging behavior of ant colonies and the characteristic of food, conceptions of neighboring area and smell area were presented. The former can ensure the diversity of paths and the latter ensures that each ant can reach the goal. Then the whole path was divided into three parts and ACO was used to search the second part path. When the three parts pathes were adjusted,the final path was found. The valid path and invalid path were defined to ensure the path valid. Finally, the strategies of the pheromone search were applied to search the optimum path. However, when only the pheromone was used to search the optimum path, ACO converges easily. In order to avoid this premature convergence, combining pheromone search and random search, a hybrid ant colony algorithm(HACO) was used to find the optimum path. The comparison between ACO and HACO shows that HACO can be used to find the shortest path.
Kinetic Constrained Optimization of the Golf Swing Hub Path
Directory of Open Access Journals (Sweden)
Steven M. Nesbit
2014-12-01
Full Text Available 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.
Optimal Path Planning for Mobile Robot Using Tailored Genetic Algorithm
Directory of Open Access Journals (Sweden)
Dong Xiao Xian
2013-07-01
Full Text Available During routine inspecting, mobile robot may be requested to visit multiple locations to execute special tasks occasionally. This study aims at optimal path planning for multiple goals visiting task based on tailored genetic algorithm. The proposed algorithm will generate an optimal path that has the least idle time, which is proven to be more effective on evaluating a path in our previous work. In proposed algorithm, customized chromosome representing a path and genetic operators including repair and cut are developed and implemented. Afterwards, simulations are carried out to verify the effectiveness and applicability. Finally, analysis of simulation results is conducted and future work is addressed.
NONMONOTONE PRECONDITIONAL CURVILINEAR PATH ALGORITHMS FOR UNCONSTRAINED OPTIMIZATION
Institute of Scientific and Technical Information of China (English)
朱德通
2003-01-01
This paper presents nonmonotonic quasi-Newton algorithms via two pre-conditional curvilinear paths, the preconditional modified gradient path and the precon-ditional optimal path, for unconstrained optimization problem. We employ the stableBunch-Parlett factorization method to form two curvilinear paths very easily. Thenonmonotone criterion is used to speed up the convergence progress in the contoursof objective function with large curvature. Theoretical analyses are given which provethat the proposed algorithms are globally convergent and have a local superlinear con-vergence rate under some reasonable conditions. The results of numerical experimentsare reported to show the effectiveness of the proposed algorithms.
Optimal Internet Media Selection
Peter J. Danaher; Janghyuk Lee; Laoucine Kerbache
2010-01-01
In this study we develop a method that optimally selects online media vehicles and determines the number of advertising impressions that should be purchased and then served from each chosen website. As a starting point, we apply Danaher's [Danaher, P. J. 2007. Modeling page views across multiple websites with an application to Internet reach and frequency prediction. (3) 422–437] multivariate negative binomial distribution (MNBD) for predicting online media exposure distributions. The MNBD is...
Transition for Optimal Paths in Bimodal Directed Polymers
Institute of Scientific and Technical Information of China (English)
WANG Xiao-Hong
2005-01-01
@@ The problem for optimal paths in bimodal directed polymers is studied. It is shown that the distribution of the thermal average position of the endpoints of the optimal paths is discontinuous below the threshold p ＜ pc. The origin is that there is a finite possibility that only one endpoint takes the global minimum energy for p ＜ pc. Our results suggest that the percolation threshold for directed percolation is also the critical point of the transition for the possibility that the optimal paths converge to one endpoint.
Heuristic optimization of the scanning path of particle therapy beams.
Pardo, J; Donetti, M; Bourhaleb, F; Ansarinejad, A; Attili, A; Cirio, R; Garella, M A; Giordanengo, S; Givehchi, N; La Rosa, A; Marchetto, F; Monaco, V; Pecka, A; Peroni, C; Russo, G; Sacchi, R
2009-06-01
Quasidiscrete scanning is a delivery strategy for proton and ion beam therapy in which the beam is turned off when a slice is finished and a new energy must be set but not during the scanning between consecutive spots. Different scanning paths lead to different dose distributions due to the contribution of the unintended transit dose between spots. In this work an algorithm to optimize the scanning path for quasidiscrete scanned beams is presented. The classical simulated annealing algorithm is used. It is a heuristic algorithm frequently used in combinatorial optimization problems, which allows us to obtain nearly optimal solutions in acceptable running times. A study focused on the best choice of operational parameters on which the algorithm performance depends is presented. The convergence properties of the algorithm have been further improved by using the next-neighbor algorithm to generate the starting paths. Scanning paths for two clinical treatments have been optimized. The optimized paths are found to be shorter than the back-and-forth, top-to-bottom (zigzag) paths generally provided by the treatment planning systems. The gamma method has been applied to quantify the improvement achieved on the dose distribution. Results show a reduction of the transit dose when the optimized paths are used. The benefit is clear especially when the fluence per spot is low, as in the case of repainting. The minimization of the transit dose can potentially allow the use of higher beam intensities, thus decreasing the treatment time. The algorithm implemented for this work can optimize efficiently the scanning path of quasidiscrete scanned particle beams. Optimized scanning paths decrease the transit dose and lead to better dose distributions.
Heuristic optimization of the scanning path of particle therapy beams
Energy Technology Data Exchange (ETDEWEB)
Pardo, J.; Donetti, M.; Bourhaleb, F.; Ansarinejad, A.; Attili, A.; Cirio, R.; Garella, M. A.; Giordanengo, S.; Givehchi, N.; La Rosa, A.; Marchetto, F.; Monaco, V.; Pecka, A.; Peroni, C.; Russo, G.; Sacchi, R. [Istituto Nazionale di Fisica Nucleare, Sezione di Torino, Via P. Giuria 1, I-10125 Torino (Italy); Istituto Nazionale di Fisica Nucleare, Sezione di Torino, Via P. Giuria 1, I-10125 Torino (Italy) and Fondazione CNAO, Via Caminadella 16, I-20123, Milano (Italy); Dipartimento di Fisica Sperimentale, Universita di Torino, Via P. Giuria 1, I-10125 Torino (Italy); Istituto Nazionale di Fisica Nucleare, Sezione di Torino, Via P. Giuria 1, I-10125 Torino (Italy) and Dipartimento di Fisica Sperimentale, Universita di Torino, Via P. Giuria 1, I-10125 Torino (Italy); Istituto Nazionale di Fisica Nucleare, Sezione di Torino, Via P. Giuria 1, I-10125 Torino (Italy); Istituto Nazionale di Fisica Nucleare, Sezione di Torino, Via P. Giuria 1, I-10125 Torino (Italy) and Dipartimento di Fisica Sperimentale, Universita di Torino, Via P. Giuria 1, I-10125 Torino (Italy); Istituto Nazionale di Fisica Nucleare, Sezione di Torino, Via P. Giuria 1, I-10125 Torino (Italy); Istituto Nazionale di Fisica Nucleare, Sezione di Torino, Via P. Giuria 1, I-10125 Torino (Italy) and Dipartimento di Fisica Sperimentale, Universita di Torino, Via P. Giuria 1, I-10125 Torino (Italy); Istituto Nazionale di Fisica Nucleare, Sezione di Torino, Via P. Giuria 1, I-10125 Torino (Italy); Istituto Nazionale di Fisica Nucleare, Sezione di Torino, Via P. Giuria 1, I-10125 Torino (Italy) and Dipartimento di Fisica Sperimentale, Universita di Torino, Via P. Giuria 1, I-10125 Torino (Italy)
2009-06-15
Quasidiscrete scanning is a delivery strategy for proton and ion beam therapy in which the beam is turned off when a slice is finished and a new energy must be set but not during the scanning between consecutive spots. Different scanning paths lead to different dose distributions due to the contribution of the unintended transit dose between spots. In this work an algorithm to optimize the scanning path for quasidiscrete scanned beams is presented. The classical simulated annealing algorithm is used. It is a heuristic algorithm frequently used in combinatorial optimization problems, which allows us to obtain nearly optimal solutions in acceptable running times. A study focused on the best choice of operational parameters on which the algorithm performance depends is presented. The convergence properties of the algorithm have been further improved by using the next-neighbor algorithm to generate the starting paths. Scanning paths for two clinical treatments have been optimized. The optimized paths are found to be shorter than the back-and-forth, top-to-bottom (zigzag) paths generally provided by the treatment planning systems. The gamma method has been applied to quantify the improvement achieved on the dose distribution. Results show a reduction of the transit dose when the optimized paths are used. The benefit is clear especially when the fluence per spot is low, as in the case of repainting. The minimization of the transit dose can potentially allow the use of higher beam intensities, thus decreasing the treatment time. The algorithm implemented for this work can optimize efficiently the scanning path of quasidiscrete scanned particle beams. Optimized scanning paths decrease the transit dose and lead to better dose distributions.
Reaction Path Optimization with Holonomic Constraints and Kinetic Energy Potentials.
Brokaw, Jason B; Haas, Kevin R; Chu, Jhih-Wei
2009-08-11
Two methods are developed to enhance the stability, efficiency, and robustness of reaction path optimization using a chain of replicas. First, distances between replicas are kept equal during path optimization via holonomic constraints. Finding a reaction path is, thus, transformed into a constrained optimization problem. This approach avoids force projections for finding minimum energy paths (MEPs), and fast-converging schemes such as quasi-Newton methods can be readily applied. Second, we define a new objective function - the total Hamiltonian - for reaction path optimization, by combining the kinetic energy potential of each replica with its potential energy function. Minimizing the total Hamiltonian of a chain determines a minimum Hamiltonian path (MHP). If the distances between replicas are kept equal and a consistent force constant is used, then the kinetic energy potentials of all replicas have the same value. The MHP in this case is the most probable isokinetic path. Our results indicate that low-temperature kinetic energy potentials (optimization and can significantly reduce the required steps of minimization by 2-3 times without causing noticeable differences between a MHP and MEP. These methods are applied to three test cases, the C7eq-to-Cax isomerization of an alanine dipeptide, the (4)C1-to-(1)C4 transition of an α-d-glucopyranose, and the helix-to-sheet transition of a GNNQQNY heptapeptide. By applying the methods developed in this work, convergence of reaction path optimization can be achieved for these complex transitions, involving full atomic details and a large number of replicas (>100). For the case of helix-to-sheet transition, we identify pathways whose energy barriers are consistent with experimental measurements. Further, we develop a method based on the work energy theorem to quantify the accuracy of reaction paths and to determine whether the atoms used to define a path are enough to provide quantitative estimation of energy barriers.
PCB Drill Path Optimization by Combinatorial Cuckoo Search Algorithm
Directory of Open Access Journals (Sweden)
Wei Chen Esmonde Lim
2014-01-01
Full Text Available Optimization of drill path can lead to significant reduction in machining time which directly improves productivity of manufacturing systems. In a batch production of a large number of items to be drilled such as printed circuit boards (PCB, the travel time of the drilling device is a significant portion of the overall manufacturing process. To increase PCB manufacturing productivity and to reduce production costs, a good option is to minimize the drill path route using an optimization algorithm. This paper reports a combinatorial cuckoo search algorithm for solving drill path optimization problem. The performance of the proposed algorithm is tested and verified with three case studies from the literature. The computational experience conducted in this research indicates that the proposed algorithm is capable of efficiently finding the optimal path for PCB holes drilling process.
PCB drill path optimization by combinatorial cuckoo search algorithm.
Lim, Wei Chen Esmonde; Kanagaraj, G; Ponnambalam, S G
2014-01-01
Optimization of drill path can lead to significant reduction in machining time which directly improves productivity of manufacturing systems. In a batch production of a large number of items to be drilled such as printed circuit boards (PCB), the travel time of the drilling device is a significant portion of the overall manufacturing process. To increase PCB manufacturing productivity and to reduce production costs, a good option is to minimize the drill path route using an optimization algorithm. This paper reports a combinatorial cuckoo search algorithm for solving drill path optimization problem. The performance of the proposed algorithm is tested and verified with three case studies from the literature. The computational experience conducted in this research indicates that the proposed algorithm is capable of efficiently finding the optimal path for PCB holes drilling process.
Optimal Path Planner for Mobile Robot in 2D Environment
Directory of Open Access Journals (Sweden)
Valeri Kroumov
2004-06-01
Full Text Available The problem of path planning for the case of a mobile robot moving in an environment filled with obstacles with known shapes and positions is studied. A path planner based on the genetic algorithm approach, which generates optimal in length path is proposed. The population member paths are generated by another algorithm, which uses for description of the obstacles an artificial annealing neural network and is based on potential field approach. The resulting path is piecewise linear with changing directions at the corners of the obstacles. Because of this feature, the inverse kinematics problems in controlling differential drive robots are simply solved: to drive the robot to some goal pose (x, y, theta, the robot can be spun in place until it is aimed at (x, y, then driven forward until it is at (x, y, and then spun in place until the required goal orientation
Optimal learning paths in information networks.
Rodi, G C; Loreto, V; Servedio, V D P; Tria, F
2015-06-01
Each sphere of knowledge and information could be depicted as a complex mesh of correlated items. By properly exploiting these connections, innovative and more efficient navigation strategies could be defined, possibly leading to a faster learning process and an enduring retention of information. In this work we investigate how the topological structure embedding the items to be learned can affect the efficiency of the learning dynamics. To this end we introduce a general class of algorithms that simulate the exploration of knowledge/information networks standing on well-established findings on educational scheduling, namely the spacing and lag effects. While constructing their learning schedules, individuals move along connections, periodically revisiting some concepts, and sometimes jumping on very distant ones. In order to investigate the effect of networked information structures on the proposed learning dynamics we focused both on synthetic and real-world graphs such as subsections of Wikipedia and word-association graphs. We highlight the existence of optimal topological structures for the simulated learning dynamics whose efficiency is affected by the balance between hubs and the least connected items. Interestingly, the real-world graphs we considered lead naturally to almost optimal learning performances.
Aircraft path planning for optimal imaging using dynamic cost functions
Christie, Gordon; Chaudhry, Haseeb; Kochersberger, Kevin
2015-05-01
Unmanned aircraft development has accelerated with recent technological improvements in sensing and communications, which has resulted in an "applications lag" for how these aircraft can best be utilized. The aircraft are becoming smaller, more maneuverable and have longer endurance to perform sensing and sampling missions, but operating them aggressively to exploit these capabilities has not been a primary focus in unmanned systems development. This paper addresses a means of aerial vehicle path planning to provide a realistic optimal path in acquiring imagery for structure from motion (SfM) reconstructions and performing radiation surveys. This method will allow SfM reconstructions to occur accurately and with minimal flight time so that the reconstructions can be executed efficiently. An assumption is made that we have 3D point cloud data available prior to the flight. A discrete set of scan lines are proposed for the given area that are scored based on visibility of the scene. Our approach finds a time-efficient path and calculates trajectories between scan lines and over obstacles encountered along those scan lines. Aircraft dynamics are incorporated into the path planning algorithm as dynamic cost functions to create optimal imaging paths in minimum time. Simulations of the path planning algorithm are shown for an urban environment. We also present our approach for image-based terrain mapping, which is able to efficiently perform a 3D reconstruction of a large area without the use of GPS data.
Numerical Tool Path Optimization for Conventional Sheet Metal Spinning Processes
Rentsch, Benedikt; Manopulo, Niko; Hora, Pavel
2016-08-01
To this day, conventional sheet metal spinning processes are designed with a very low degree of automation. They are usually executed by experienced personnel, who actively adjust the tool paths during production. The practically unlimited freedom in designing the tool paths enables the efficient manufacturing of complex geometries on one hand, but is challenging to translate into a standardized procedure on the other. The present study aims to propose a systematic methodology, based on a 3D FEM model combined with a numerical optimization strategy, in order to design tool paths. The accurate numerical modelling of the spinning process is firstly discussed, followed by an analysis of appropriate objective functions and constraints required to obtain a failure free tool path design.
Institute of Scientific and Technical Information of China (English)
徐勇; 贾欣; 王哲; 王翠柳
2015-01-01
公交地铁网络出行线路优选问题是公交网络系统研究的核心问题之一。为此研究了公交地铁一体化条件下的公交网络出行优化模型与算法。构造公交地铁网络的标号模型及映射网络模型，以适当倍数缩小地铁线路上站点之间的权值，进而可将公交与地铁进行一体化处理，缩小后可使地铁线路具有明显的优势以达到优选地铁的目的。运用映射网络图、二分图、半张量积等理论给出了公交地铁一体化网络的最优路选择算法。最后实证了该方法在公交地铁网络线路优选的有效性。%In this paper, the travel optimal model and algorithm of public transit network for the integrated bus and subway system are studied. First, a label model and mapped network model are constructed for the bus and subway network. The weight between two subway stations is appropriately reduced to deal with the bus and subway integra⁃tion problem. The subway has obvious advantages after reduction and subway becomes the preferred option. Next, the optimal path selection algorithm of the integration network of bus and subway is given using the mapping net⁃work graph, bipartite graph, and semi⁃tensor product theory. Finally, the effectiveness of the proposed method in optimized selection of the public transit network is illustrated by a numerical example.
Optimizing path selection of mobile Sink nodes in mobility-assistant WSN%移动协助传感器网络中Sink的路径优化策略
Institute of Scientific and Technical Information of China (English)
张希伟; 沈琳; 蒋益峰
2013-01-01
There inevitably exist some serious problems such as energy hole, overlapping and hot spots in static wireless sensor networks which are composed by all static sensors. The mobile Sink (MS) was used to reduce the energy con-sumption of static sensor nodes through a collection-based approach in which a subset of nodes served as the data collec-tion points (CP) that buffer data originated from sensors and transferred these data to MS when it arrived. An optimiza-tion model named min-energy min-distance (MEMD) of MS’ moving path was introduced and proved this model was NP-hard. A heuristic algorithm was developed combining MS and CPs selection to enable a flexible trade-off between energy consumption and data delivery latency. Furthermore, a probabilistic path selection (PPS) algorithm to make the MS visit as much as possible sensors was proposed. The experimental and simulating results show monotonic decrease of data delivery latency for greater limits on the energy consumption and vice versa.% 在无线传感器网络中引入移动Sink来解决。静态无线传感器网络(所有节点均为静止)存在的能量空洞、冗余覆盖和热点等问题。传感器节点将数据发送给汇聚节点(CP, collection point),移动Sink访问CP节点收集数据。提出了一种最短移动距离最小能耗的路径优化模型(MEMD)。证明了该模型是一个 NP-hard 问题,给出了一种基于效用的贪心启发式方法用于确定最佳的CP 节点队列。为了在规定的最大传输延时的范围内访问尽可能多的 CP 节点,提出了一种基于 CP 节点访问概率的路径选择算法。通过模拟实验以及实验床的真实数据,提出的算法能很好地在满足延时要求的同时节约网络的能量。
Constructs of highly effective heat transport paths by bionic optimization
Institute of Scientific and Technical Information of China (English)
CHENG; Xinguang; (程新广); LI; Zhixin; (李志信); GUO; Zengyuan; (过增元)
2003-01-01
The optimization approach based on the biological evolution principle is used to construct the heat transport paths for volume-to-point problem. The transport paths are constructed by inserting high conductivity materials in the heat conduction domain where uniform or nonuniform heat sources exist. In the bionic optimization process, the optimal constructs of the high conductivity material are obtained by numerically simulating the evolution and degeneration process according to the uniformity principle of the temperature gradient. Finally, preserving the features of the optimal constructs, the constructs are regularized for the convenience of engineering manufacture. The results show that the construct obtained by bionic optimization is approximate to that obtained by the tree-network constructal theory when the heat conduction is enhanced for the domain with a uniform heat source and high conductivity ratio of the inserting material to the substrate, the high conductivity materials are mainly concentrated on the heat outlet for the case with a uniform heat source and low thermal conductivity ratio, and for the case with nonuniform heat sources, the high conductivity material is concentrated in the heat source regions and construacts several highly effective heat transport paths to connect the regions to the outlet.
Going against the flow: finding the optimal path
Talbot, Julian
2010-01-01
We consider the problem of finding the optimum path of a boat traversing a straight in a current. The path of the shortest time is found using the calculus of variations with the constraint that the boat must land directly opposite to its starting point. We compare the optimal trajectory with that where the boat's local orientation is always directed to the arrival point. When analytical solutions cannot be found we use numerical methods. The level of the exposition is suitable for advanced undergraduate students, graduate students and general physicists.
Optimal Path Planning Program for Autonomous Speed Sprayer in Orchard Using Order-Picking Algorithm
Park, T. S.; Park, S. J.; Hwang, K. Y.; Cho, S. I.
This study was conducted to develop a software program which computes optimal path for autonomous navigation in orchard, especially for speed sprayer. Possibilities of autonomous navigation in orchard were shown by other researches which have minimized distance error between planned path and performed path. But, research of planning an optimal path for speed sprayer in orchard is hardly founded. In this study, a digital map and a database for orchard which contains GPS coordinate information (coordinates of trees and boundary of orchard) and entity information (heights and widths of trees, radius of main stem of trees, disease of trees) was designed. An orderpicking algorithm which has been used for management of warehouse was used to calculate optimum path based on the digital map. Database for digital map was created by using Microsoft Access and graphic interface for database was made by using Microsoft Visual C++ 6.0. It was possible to search and display information about boundary of an orchard, locations of trees, daily plan for scattering chemicals and plan optimal path on different orchard based on digital map, on each circumstance (starting speed sprayer in different location, scattering chemicals for only selected trees).
Exploring chemical reaction mechanisms through harmonic Fourier beads path optimization.
Khavrutskii, Ilja V; Smith, Jason B; Wallqvist, Anders
2013-10-28
Here, we apply the harmonic Fourier beads (HFB) path optimization method to study chemical reactions involving covalent bond breaking and forming on quantum mechanical (QM) and hybrid QM∕molecular mechanical (QM∕MM) potential energy surfaces. To improve efficiency of the path optimization on such computationally demanding potentials, we combined HFB with conjugate gradient (CG) optimization. The combined CG-HFB method was used to study two biologically relevant reactions, namely, L- to D-alanine amino acid inversion and alcohol acylation by amides. The optimized paths revealed several unexpected reaction steps in the gas phase. For example, on the B3LYP∕6-31G(d,p) potential, we found that alanine inversion proceeded via previously unknown intermediates, 2-iminopropane-1,1-diol and 3-amino-3-methyloxiran-2-ol. The CG-HFB method accurately located transition states, aiding in the interpretation of complex reaction mechanisms. Thus, on the B3LYP∕6-31G(d,p) potential, the gas phase activation barriers for the inversion and acylation reactions were 50.5 and 39.9 kcal∕mol, respectively. These barriers determine the spontaneous loss of amino acid chirality and cleavage of peptide bonds in proteins. We conclude that the combined CG-HFB method further advances QM and QM∕MM studies of reaction mechanisms.
Optimal Path Planning for Minimizing Base Disturbance of Space Robot
Directory of Open Access Journals (Sweden)
Xiao-Peng Wei
2016-03-01
Full Text Available The path planning of free-floating space robot in space on-orbit service has been paid more and more attention. The problem is more complicated because of the interaction between the space robot and base. Therefore, it is necessary to minimize the base position and attitude disturbance to improve the path planning of free-floating space robot, reducing the fuel consumption for the position and attitude maintenance. In this paper, a reasonable path planning method to solve the problem is presented, which is feasible and relatively simple. First, the kinematic model of 6 degrees of freedom free-floating space robot is established. And then the joint angles are parameterized using the 7th order polynomial sine functions. The fitness function is defined according to the position and attitude of minimizing base disturbance and constraints of space robot. Furthermore, an improved chaotic particle swarm optimization (ICPSO is presented. The proposed algorithm is compared with the standard PSO and CPSO algorithm in the literature by the experimental simulation. The simulation results demonstrate that the proposed algorithm is more effective than the two other approaches, such as easy to find the optimal solution, and this method could provide a satisfactory path for the free-floating space robot.
Study of Multi-objective Fuzzy Optimization for Path Planning
Institute of Scientific and Technical Information of China (English)
WANG Yanyang; WEI Tietao; QU Xiangju
2012-01-01
During path planning,it is necessary to satisfy the requirements of multiple objectives.Multi-objective synthesis is based on the need of flight mission and subjectivity inclination of decision-maker.The decision-maker,however,has illegibility for understanding the requirements of multiple objectives and the subjectivity inclination.It is important to develop a reasonable cost performance index for describing the illegibility of the decision-maker in multi-objective path planning.Based on Voronoi diagram method for the path planning,this paper studies the synthesis method of the multi-objective cost performance index.According to the application of the cost performance index to the path planning based on Voronoi diagram method,this paper analyzes the cost performance index which has been referred to at present.The analysis shows the insufficiency of the cost performance index at present,i.e.,it is difficult to synthesize sub-objective functions because of the great disparity of the sub-objective functions.Thus,a new approach is developed to optimize the cost performance index with the multi-objective fuzzy optimization strategy,and an improved performance index is established,which could coordinate the weight conflict of the sub-objective functions.Finally,the experimental result shows the effectiveness of the proposed approach.
Optimal Path Planning for Minimizing Base Disturbance of Space Robot
Directory of Open Access Journals (Sweden)
Xiao-Peng Wei
2016-03-01
Full Text Available The path planning of free-floating space robot in space on-orbit service has been paid more and more attention. The problem is more complicated because of the interaction between the space robot and base. Therefore, it is necessary to minimize the base position and attitude disturbance to improve the path planning of free-floating space robot, reducing the fuel consumption for the position and attitude maintenance. In this paper, a reasonable path planning method to solve the problem is presented, which is feasible and relatively simple. First, the kinematic model of 6 degrees of freedom free-floating space robot is established. And then the joint angles are parameterized using the 7th order polynomial sine functions. The fitness function is defined according to the position and attitude of minimizing base disturbance and constraints of space robot. Furthermore, an improved chaotic particle swarm optimization (ICPSO is presented. The proposed algorithm is compared with the standard PSO and CPSO algorithm in the literature by the experimental simulation. The simulation results demonstrate that the proposed algorithm is more effective than the two other approaches, such as easy to find the optimal solution, and this method could provide a satisfactory path for the free-floating space robot.
Mobile Path Selection Algorithm of Sink Node for Optimizing Network Lifetime%优化网络生存时间的Sink节点移动路径选择算法
Institute of Scientific and Technical Information of China (English)
王章权; 陈友荣; 尉理哲; 任条娟
2014-01-01
为克服无线传感网的能量空穴问题,采用最优化方法,研究一种优化网络生存时间的Sink节点移动路径选择算法( MPSA)。在MPSA算法中,将单跳传输的无线传感网监测区域分成多个大小一致的网格,Sink节点可移动到任一网格中心,停留收集单跳最大通信范围内的传感节点数据。分析停留位置的全节点覆盖条件和所有传感节点的能耗,建立权衡网络生存时间和Sink节点移动路程的优化模型。提出一种改进的遗传算法,用于求解优化模型,即迭代执行染色体评估、选择、交叉、变异、最小覆盖处理、孤立节点处理等步骤,最终获得优化网络生存时间的Sink节点移动方案。仿真结果表明：MPSA算法能提高网络生存时间,将移动路程保持在较小范围。在提高网络生存时间方面,比RCC算法更优。%To overcome the energy hole problem in wireless sensor networks,optimization method is used and mobile path selection algorithm of Sink node for optimizing network lifetime( MPSA) is researched. In MPSA algorithm,the monitoring area of single-hop transmission wireless sensor network is divided into multiple grids of same size. Sink node can move to any grid's center and stay to gather data in the single-hop maximum communication range. Full node coverage condition of stay location and node energy consumption are analyzed. Then the optimization model which weighs network lifetime and mobile journey is established. The modified genetic algorithm is proposed to solve the model. The steps such as chromosome evaluation, selection, crossover, mutation, minimum coverage processing and isolated nodes processing are iteratively executed. Finally the mobile scheme of Sink node for optimizing network lifetime is obtained. Simulation results show that MPSA algorithm can improve the network lifetime and keep mobile journey at small range. In the aspect of improving network lifetime, it is better than RCC ( range
Parameter optimization for tandemregenerative system based on critical path
Institute of Scientific and Technical Information of China (English)
无
2001-01-01
For a tandem queue system, the regenerative path is constructed. In an inter-regeneration cycle, the sensitivity value of performance measure with respect to the adjustable parameter θ can be acquired based on a fixed length of observation. Furthermore, a new algorithm of parameter optimization for the tandem queue system is given,which requires less simulation and no analysis for the perturbation transmission and makes a better estimation for the sen sitivity.
Nearly time-optimal paths for a ground vehicle
Institute of Scientific and Technical Information of China (English)
David A. ANISI; Johan HAMBERG; Xiaoming HU
2003-01-01
It is well known that the sufficient family of time-optimal paths for both Dubins' as well as Reeds-Shepp' s car models consist of the concatenation of circular arcs with maxmum curvature and straight line segments, all tangentially connected.These time-optimal solutions suffer from some drawbacks. Their discontinuous curvature profde, together with the wear and impairment on the control equipment that the bang-bang solutions induce, calls for "smoother" and more supple reference paths to follow. Avoiding the bang-bang solutions also raises the robustness with respect to any possible uncertainties. In this paper, our main tool for generating these "nearly time-optimal", but nevertheless continuous-curvature paths, is to use the Pontryagin Maximum Principle (PMP) and make an appropriate and cunning choice of the Lagrangian function. Despite some rewarding simuhtion results, this concept tums out to be numerically divergent at some instances. Upon a more careful investigation, it can be concluded that the problem at hand is nearly singular. This is seen by applying the PMP to Dubins' car and studying the corresponding two point boundary value problem, which turn out to be singuhr. Realizing this, one is able to contradict the widespread belief that all the information about the motion of a mobile platform lies in the initial values of the auxiliary variables associated with the PMP.
Vibration Transfer Path Analysis and Path Ranking for NVH Optimization of a Vehicle Interior
Directory of Open Access Journals (Sweden)
B. Sakhaei
2014-01-01
Full Text Available By new advancements in vehicle manufacturing, evaluation of vehicle quality assurance has got a more critical issue. Today noise and vibration generated inside and outside the vehicles are more important factors for customers than before. So far several researchers have focused on interior noise transfer path analysis and the results have been published in related papers but each method has its own limitations. In present work, the vibration transfer path analysis and vibration path ranking of a car interior have been performed. As interior vibration is a source of structural borne noise problem, thus, the results of this research can be used to present the structural borne noise state in a vehicle. The proposed method in this paper does not need to disassemble the powertrain from the chassis. The procedure shows a good ability of vibration path ranking in a vehicle and is an effective tool to diagnose the vibration problem inside the vehicle. The simulated vibration spectrums in different speeds of the engine have a good compliance with the tested results; however, some incompatibilities exist and have been discussed in detail. The simulated results show the strength of the method in engine mount optimization.
Selection of the Optimal Path for Established Flight Mission of Fixed-wing UAV%固定翼无人机定点飞行最优路径选择
Institute of Scientific and Technical Information of China (English)
梁爽
2016-01-01
针对固定翼无人机路径规划复杂、航迹冗余、偏离度高等特点，通过建立三维空间空气动力学模型，标定预置坐标，根据常规气动布局下的空气动力学原理、PID算法、环境等因素对航迹网格点进行管理。采用改进的动态规划算法，对如何准确、快速地计算出连贯预定坐标的最佳路径进行了研究。飞控计算机通过动态对比、状态预测算法，对路径进行实时对比、矫正、重新规划，使无人机能沿着贯穿预定坐标的最佳路径完成既定飞行任务。%For the features of fixed-wing UAV, e. g. , complex path planning, redundant flight track, and high degree of deviation, through setting up the aerodynamics model of three -dimensional space, calibrating the preset coordinates, and according to the factors of aerodynamic principle, PID algorithm, and environment under conventional aerodynamic layout, the flight track grid points are managed. With the improved dynamic planning algorithm, the method for accurately and quickly calculating the optimal path of coherence predetermined coordinates is researched. The paths are compared, corrected and re-planned in real time by flight control computer through dynamic contrast and state prediction algorithms, thus the UAV can accomplish the established flight mission along the optimal path with predetermined coordinates.
Institute of Scientific and Technical Information of China (English)
张鹏; 胡传雨
2011-01-01
To unify regulatory organization structure has been a trend to change organization structure of financial regulatory. But due to the different financial development levels and operating environment in various countries, the path selection to optimize the organization structure has also differences. The factors which affect the changes of organization structure of financial regulatory are institutional factors, financial factors and management factors. In this paper, we do the empirical analysis on the impact factors of organization structure changes of financial regulatory by selecting representative samples of 33 countries and constructing the ordered Logit model, and then analyze the organization structure of financial regulatory in China. The results show that China＇s current organizational structure of separate supervision is appropriate. With the development of China＇s financial sector and mixed - depth, we must first improve governance, and then integrate the financial regulatory agencies gradually, ultimately establish a unified regulatory organization.%统一监管组织架构已成为金融监管组织架构变迁的一种趋势，但由于各国金融业的发展程度和经营环境不同，金融监管组织架构优化的路径选择也就有差异。影响金融监管组织架构变迁的因素有体制因素、金融因素和治理因素。选取33个代表性国家样本，构建有序Logit模型对影响金融监管组织架构变迁的因素进行实证分析，并运用有序蚴模型对中国金融监管组织架构进行分析，结果表明，目前中国分业监管组织架构是合适的，随著中国金融业的发展和混业经营的深入，中国应首先提高治理水平，然后逐渐整合金融监管机构，最终建立统一监管组织架构。
Visibility-based optimal path and motion planning
Wang, Paul Keng-Chieh
2015-01-01
This monograph deals with various visibility-based path and motion planning problems motivated by real-world applications such as exploration and mapping planetary surfaces, environmental surveillance using stationary or mobile robots, and imaging of global air/pollutant circulation. The formulation and solution of these problems call for concepts and methods from many areas of applied mathematics including computational geometry, set-covering, non-smooth optimization, combinatorial optimization and optimal control. Emphasis is placed on the formulation of new problems and methods of approach to these problems. Since geometry and visualization play important roles in the understanding of these problems, intuitive interpretations of the basic concepts are presented before detailed mathematical development. The development of a particular topic begins with simple cases illustrated by specific examples, and then progresses forward to more complex cases. The intended readers of this monograph are primarily studen...
ESTIMATION OF OPTIMAL PATH ON URBAN ROAD NETWORKS USING AHP ALGORITHM
Directory of Open Access Journals (Sweden)
Surendra Kukadapwar
2016-03-01
Full Text Available This paper describes to develop a multi criteria decision based methodology to find optimal path in real urban road network. Over the year several studies were conducted but most of which rely on single variable like travel distance or travel time as cost function. In this study, seven different attributes influencing the traffic network i.e. distance, time, traffic volume, road width, no. of intersection, parking and encroachment on road are used to define cost function using multi criterion decision making approach. These variables are combined using a Multi-Dimensional Cost Model (MDCM using the Analytical Hierarchical Process (AHP. The models developed were implemented and closely evaluated in Nagpur city of India. Model is considered for determining optimal path between various Origins and Destinations in real urban traffic network. Composite weighted AHP scored were used to generate AHP decision surface. Finally, the best decision was proposed by generating the least cost path which is considered as optimal path. The resulting routes showed to be more accurate than those obtained utilizing one-dimensional cost functions and AHP is found to be effective tool to deal with optimal route selection problem.
Path Planning Optimization for Teaching and Playback Welding Robot
Directory of Open Access Journals (Sweden)
Yuehai Wang
2013-02-01
Full Text Available Path planning for the industrial robot plays an important role in the intelligent control of robot. Tradition strategies, including model-based methods and human taught based methods, find it is difficult to control manipulator intelligently and optically. Thus, it is hard to ensure the better performance and lower energy consumption even if the same welding task was executed repeatedly. A path planning optimization method was proposed to add learning ability to teaching and playback welding robot. The optimization was divided into the welding points sequence improvement and trajectory improvement, which was done both on-line and off-line. Points sequence optimization was modeled as TSP and was continuously improved by genetic algorithm based strategy, while the trajectory between two welding points was on-line improved by an try-and-error strategy where the robot try different trajectory from time to time so as to search a better plan. Simulation results verified that this control strategy reduced the time and energy cost as compared with the man-made fix-order sequence. Our method prevents the robot from the computation-intensive model-based control, and offers a convenient way for self-improvement on the basis of human teaching.
A genetic algorithm for the pareto optimal solution set of multi-objective shortest path problem
Institute of Scientific and Technical Information of China (English)
HU Shi-cheng; XU Xiao-fei; ZHAN De-chen
2005-01-01
Unlike the shortest path problem that has only one optimal solution and can be solved in polynomial time, the multi-objective shortest path problem (MSPP) has a set of pareto optimal solutions and cannot be solved in polynomial time. The present algorithms focused mainly on how to obtain a precisely pareto optimal solution for MSPP resulting in a long time to obtain multiple pareto optimal solutions with them. In order to obtain a set of satisfied solutions for MSPP in reasonable time to meet the demand of a decision maker, a genetic algorithm MSPP-GA is presented to solve the MSPP with typically competing objectives, cost and time, in this paper. The encoding of the solution and the operators such as crossover, mutation and selection are developed.The algorithm introduced pareto domination tournament and sharing based selection operator, which can not only directly search the pareto optimal frontier but also maintain the diversity of populations in the process of evolutionary computation. Experimental results show that MSPP-GA can obtain most efficient solutions distributed all along the pareto frontier in less time than an exact algorithm. The algorithm proposed in this paper provides a new and effective method of how to obtain the set of pareto optimal solutions for other multiple objective optimization problems in a short time.
Research on Optimal Path of Data Migration among Multisupercomputer Centers
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Gang Li
2016-01-01
Full Text Available Data collaboration between supercomputer centers requires a lot of data migration. In order to increase the efficiency of data migration, it is necessary to design optimal path of data transmission among multisupercomputer centers. Based on the situation that the target center which finished receiving data can be regarded as the new source center to migrate data to others, we present a parallel scheme for the data migration among multisupercomputer centers with different interconnection topologies using graph theory analysis and calculations. Finally, we verify that this method is effective via numeric simulation.
Autonomous guided vehicles methods and models for optimal path planning
Fazlollahtabar, Hamed
2015-01-01
This book provides readers with extensive information on path planning optimization for both single and multiple Autonomous Guided Vehicles (AGVs), and discusses practical issues involved in advanced industrial applications of AGVs. After discussing previously published research in the field and highlighting the current gaps, it introduces new models developed by the authors with the goal of reducing costs and increasing productivity and effectiveness in the manufacturing industry. The new models address the increasing complexity of manufacturing networks, due for example to the adoption of flexible manufacturing systems that involve automated material handling systems, robots, numerically controlled machine tools, and automated inspection stations, while also considering the uncertainty and stochastic nature of automated equipment such as AGVs. The book discusses and provides solutions to important issues concerning the use of AGVs in the manufacturing industry, including material flow optimization with A...
Feature selection for portfolio optimization
DEFF Research Database (Denmark)
Bjerring, Thomas Trier; Ross, Omri; Weissensteiner, Alex
2016-01-01
Most portfolio selection rules based on the sample mean and covariance matrix perform poorly out-of-sample. Moreover, there is a growing body of evidence that such optimization rules are not able to beat simple rules of thumb, such as 1/N. Parameter uncertainty has been identified as one major...... reason for these findings. A strand of literature addresses this problem by improving the parameter estimation and/or by relying on more robust portfolio selection methods. Independent of the chosen portfolio selection rule, we propose using feature selection first in order to reduce the asset menu....... While most of the diversification benefits are preserved, the parameter estimation problem is alleviated. We conduct out-of-sample back-tests to show that in most cases different well-established portfolio selection rules applied on the reduced asset universe are able to improve alpha relative...
Feature selection for portfolio optimization
DEFF Research Database (Denmark)
Bjerring, Thomas Trier; Ross, Omri; Weissensteiner, Alex
2016-01-01
Most portfolio selection rules based on the sample mean and covariance matrix perform poorly out-of-sample. Moreover, there is a growing body of evidence that such optimization rules are not able to beat simple rules of thumb, such as 1/N. Parameter uncertainty has been identified as one major...... reason for these findings. A strand of literature addresses this problem by improving the parameter estimation and/or by relying on more robust portfolio selection methods. Independent of the chosen portfolio selection rule, we propose using feature selection first in order to reduce the asset menu....... While most of the diversification benefits are preserved, the parameter estimation problem is alleviated. We conduct out-of-sample back-tests to show that in most cases different well-established portfolio selection rules applied on the reduced asset universe are able to improve alpha relative...
A numerical scheme for optimal transition paths of stochastic chemical kinetic systems
Liu, Di
2008-10-01
We present a new framework for finding the optimal transition paths of metastable stochastic chemical kinetic systems with large system size. The optimal transition paths are identified to be the most probable paths according to the Large Deviation Theory of stochastic processes. Dynamical equations for the optimal transition paths are derived using the variational principle. A modified Minimum Action Method (MAM) is proposed as a numerical scheme to solve the optimal transition paths. Applications to Gene Regulatory Networks such as the toggle switch model and the Lactose Operon Model in Escherichia coli are presented as numerical examples.
Optimum Strategies for Selecting Descent Flight-Path Angles
Wu, Minghong G. (Inventor); Green, Steven M. (Inventor)
2016-01-01
An information processing system and method for adaptively selecting an aircraft descent flight path for an aircraft, are provided. The system receives flight adaptation parameters, including aircraft flight descent time period, aircraft flight descent airspace region, and aircraft flight descent flyability constraints. The system queries a plurality of flight data sources and retrieves flight information including any of winds and temperatures aloft data, airspace/navigation constraints, airspace traffic demand, and airspace arrival delay model. The system calculates a set of candidate descent profiles, each defined by at least one of a flight path angle and a descent rate, and each including an aggregated total fuel consumption value for the aircraft following a calculated trajectory, and a flyability constraints metric for the calculated trajectory. The system selects a best candidate descent profile having the least fuel consumption value while the fly ability constraints metric remains within aircraft flight descent flyability constraints.
A new efficient optimal path planner for mobile robot based on Invasive Weed Optimization algorithm
Mohanty, Prases K.; Parhi, Dayal R.
2014-12-01
Planning of the shortest/optimal route is essential for efficient operation of autonomous mobile robot or vehicle. In this paper Invasive Weed Optimization (IWO), a new meta-heuristic algorithm, has been implemented for solving the path planning problem of mobile robot in partially or totally unknown environments. This meta-heuristic optimization is based on the colonizing property of weeds. First we have framed an objective function that satisfied the conditions of obstacle avoidance and target seeking behavior of robot in partially or completely unknown environments. Depending upon the value of objective function of each weed in colony, the robot avoids obstacles and proceeds towards destination. The optimal trajectory is generated with this navigational algorithm when robot reaches its destination. The effectiveness, feasibility, and robustness of the proposed algorithm has been demonstrated through series of simulation and experimental results. Finally, it has been found that the developed path planning algorithm can be effectively applied to any kinds of complex situation.
Chattopadhyay, Ishanu; Ray, Asok
2010-01-01
We report a globally-optimal approach to robotic path planning under uncertainty, based on the theory of quantitative measures of formal languages. A significant generalization to the language-measure-theoretic path planning algorithm $\
Wang, Xuewu; Shi, Yingpan; Ding, Dongyan; Gu, Xingsheng
2016-02-01
Spot-welding robots have a wide range of applications in manufacturing industries. There are usually many weld joints in a welding task, and a reasonable welding path to traverse these weld joints has a significant impact on welding efficiency. Traditional manual path planning techniques can handle a few weld joints effectively, but when the number of weld joints is large, it is difficult to obtain the optimal path. The traditional manual path planning method is also time consuming and inefficient, and cannot guarantee optimality. Double global optimum genetic algorithm-particle swarm optimization (GA-PSO) based on the GA and PSO algorithms is proposed to solve the welding robot path planning problem, where the shortest collision-free paths are used as the criteria to optimize the welding path. Besides algorithm effectiveness analysis and verification, the simulation results indicate that the algorithm has strong searching ability and practicality, and is suitable for welding robot path planning.
An Optimal Path Computation Architecture for the Cloud-Network on Software-Defined Networking
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Hyunhun Cho
2015-05-01
Full Text Available Legacy networks do not open the precise information of the network domain because of scalability, management and commercial reasons, and it is very hard to compute an optimal path to the destination. According to today’s ICT environment change, in order to meet the new network requirements, the concept of software-defined networking (SDN has been developed as a technological alternative to overcome the limitations of the legacy network structure and to introduce innovative concepts. The purpose of this paper is to propose the application that calculates the optimal paths for general data transmission and real-time audio/video transmission, which consist of the major services of the National Research & Education Network (NREN in the SDN environment. The proposed SDN routing computation (SRC application is designed and applied in a multi-domain network for the efficient use of resources, selection of the optimal path between the multi-domains and optimal establishment of end-to-end connections.
Optimal Contracting under Adverse Selection
DEFF Research Database (Denmark)
Lenells, Jonatan; Stea, Diego; Foss, Nicolai Juul
2015-01-01
We study a model of adverse selection, hard and soft information, and mentalizing ability--the human capacity to represent others' intentions, knowledge, and beliefs. By allowing for a continuous range of different information types, as well as for different means of acquiring information, we dev...... of that information. This strategy affects the properties of the optimal contract, which grows closer to the first best. This research provides insights into the implications of mentalizing for agency theory....
Institute of Scientific and Technical Information of China (English)
冯小燕
2011-01-01
The optimal allocation of experimental resources is vital to the improvement of teaching and research level, as well as to the quality of serving local economic and social development for local colleges. Based on the understanding and definition of Experimental Resources of Local College, this paper explores the goals and the path design of resource optimization at local colleges, and discusses some relative problems of controversy and uncertainty, and consequently provides some thoughts and methods.%实验资源优化配置是地方高校提高教学、科研水平和服务地方经济社会发展质量的重要途径.基于对地方高校实验资源概念的理解和界定,对地方高校实验资源优化配置的目标和路径作了设计,并就相关争议性和不确定性问题进行了讨论.
Order-Optimal Consensus through Randomized Path Averaging
Benezit, F; Thiran, P; Vetterli, M
2008-01-01
Gossip algorithms have recently received significant attention, mainly because they constitute simple and robust message-passing schemes for distributed information processing over networks. However for many topologies that are realistic for wireless ad-hoc and sensor networks (like grids and random geometric graphs), the standard nearest-neighbor gossip converges as slowly as flooding ($O(n^2)$ messages). A recently proposed algorithm called geographic gossip improves gossip efficiency by a $\\sqrt{n}$ factor, by exploiting geographic information to enable multi-hop long distance communications. In this paper we prove that a variation of geographic gossip that averages along routed paths, improves efficiency by an additional $\\sqrt{n}$ factor and is order optimal ($O(n)$ messages) for grids and random geometric graphs. We develop a general technique (travel agency method) based on Markov chain mixing time inequalities, which can give bounds on the performance of randomized message-passing algorithms operating...
The path planning of UAV based on orthogonal particle swarm optimization
Liu, Xin; Wei, Haiguang; Zhou, Chengping; Li, Shujing
2013-10-01
To ensure the attack mission success rate, a trajectory with high survivability and accepted path length and multiple paths with different attack angles must be planned. This paper proposes a novel path planning algorithm based on orthogonal particle swarm optimization, which divides population individual and speed vector into independent orthogonal parts, velocity and individual part update independently, this improvement advances optimization effect of traditional particle swarm optimization in the field of path planning, multiple paths are produced by setting different attacking angles, this method is simulated on electronic chart, the simulation result shows the effect of this method.
Sample-Path Optimization of Buffer Allocations in a Tandem Queue - Part I : Theoretical Issues
Gürkan, G.; Ozge, A.Y.
1996-01-01
This is the first of two papers dealing with the optimal bu er allocation problem in tandem manufacturing lines with unreliable machines.We address the theoretical issues that arise when using sample-path optimization, a simulation-based optimization method, to solve this problem.Sample-path optimiz
Directory of Open Access Journals (Sweden)
Syed Bilal Hussain Shah
2017-01-01
Full Text Available In Wireless Sensors Networks (WSNs, researcher’s main focus is on energy preservation and prolonging network lifetime. More energy resources are required in case of remote applications of WSNs, where some of the nodes die early that shorten the lifetime and decrease the stability of the network. It is mainly caused due to the non-optimal Cluster Heads (CHs selection based on single criterion and uneven distribution of energy. We propose a new clustering protocol for both homogeneous and heterogeneous environments, named as Optimized Path planning algorithm with Energy efficiency and Extending Network lifetime in WSN (OPEN. In the proposed protocol, timer value concept is used for CH selection based on multiple criteria. Simulation results prove that OPEN performs better than the existing protocols in terms of the network lifetime, throughput and stability. The results explicitly explain the cluster head selection of OPEN protocol and efficient solution of uneven energy distribution problem.
Sequential Optimization of Paths in Directed Graphs Relative to Different Cost Functions
Abubeker, Jewahir Ali
2011-05-14
This paper is devoted to the consideration of an algorithm for sequential optimization of paths in directed graphs relative to di_erent cost functions. The considered algorithm is based on an extension of dynamic programming which allows to represent the initial set of paths and the set of optimal paths after each application of optimization procedure in the form of a directed acyclic graph.
Application of particle swarm optimization in path planning of mobile robot
Wang, Yong; Cai, Feng; Wang, Ying
2017-08-01
In order to realize the optimal path planning of mobile robot in unknown environment, a particle swarm optimization algorithm based on path length as fitness function is proposed. The location of the global optimal particle is determined by the minimum fitness value, and the robot moves along the points of the optimal particles to the target position. The process of moving to the target point is done with MATLAB R2014a. Compared with the standard particle swarm optimization algorithm, the simulation results show that this method can effectively avoid all obstacles and get the optimal path.
Institute of Scientific and Technical Information of China (English)
无
2006-01-01
A novel method of global optimal path planning for mobile robot was proposed based on the improved Dijkstra algorithm and ant system algorithm. This method includes three steps: the first step is adopting the MAKLINK graph theory to establish the free space model of the mobile robot, the second step is adopting the improved Dijkstra algorithm to find out a sub-optimal collision-free path, and the third step is using the ant system algorithm to adjust and optimize the location of the sub-optimal path so as to generate the global optimal path for the mobile robot. The computer simulation experiment was carried out and the results show that this method is correct and effective. The comparison of the results confirms that the proposed method is better than the hybrid genetic algorithm in the global optimal path planning.
von Thienen, Wolfhard; Metzler, Dirk; Witte, Volker
2015-05-07
The emergence of self-organizing behavior in ants has been modeled in various theoretical approaches in the past decades. One model explains experimental observations in which Argentine ants (Linepithema humile) selected the shorter of two alternative paths from their nest to a food source (shortest path experiments). This model serves as an important example for the emergence of collective behavior and self-organization in biological systems. In addition, it inspired the development of computer algorithms for optimization problems called ant colony optimization (ACO). In the model, a choice function describing how ants react to different pheromone concentrations is fundamental. However, the parameters of the choice function were not deduced experimentally but freely adapted so that the model fitted the observations of the shortest path experiments. Thus, important knowledge was lacking about crucial model assumptions. A recent study on the Argentine ant provided this information by measuring the response of the ants to varying pheromone concentrations. In said study, the above mentioned choice function was fitted to the experimental data and its parameters were deduced. In addition, a psychometric function was fitted to the data and its parameters deduced. Based on these findings, it is possible to test the shortest path model by applying realistic parameter values. Here we present the results of such tests using Monte Carlo simulations of shortest path experiments with Argentine ants. We compare the choice function and the psychometric function, both with parameter values deduced from the above-mentioned experiments. Our results show that by applying the psychometric function, the shortest path experiments can be explained satisfactorily by the model. The study represents the first example of how psychophysical theory can be used to understand and model collective foraging behavior of ants based on trail pheromones. These findings may be important for other
Path analysis for selection of feijoa with greater pulp weight
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Joel Donazzolo
Full Text Available ABSTRACT: The objective of this paper was to identify the direct and indirect effects of feijoa fruits (Acca sellowiana traitson pulp weight, in order to use these traits in indirect genotypes selection. Fruits of five feijoa plants were collected in Rio Grande do Sul, in the years of 2009, 2010 and 2011. Six traits were evaluated: diameter, length, total weight, pulp weight, peel thickness and number of seeds per fruit. In the path analysis, with or without ridge regression, pulp weight was considered as the basic variable, and the other traits were considered as explanatory variables. Total weight and fruit diameter had high direct effect, and are the main traits associated with pulp weight. These traits may serve as criteria for indirect selection to increase feijoa pulp weight, since they are easy to be measured.
Optimal Contracting under Adverse Selection
DEFF Research Database (Denmark)
Lenells, Jonatan; Stea, Diego; Foss, Nicolai Juul
2015-01-01
We study a model of adverse selection, hard and soft information, and mentalizing ability--the human capacity to represent others' intentions, knowledge, and beliefs. By allowing for a continuous range of different information types, as well as for different means of acquiring information, we dev...... of that information. This strategy affects the properties of the optimal contract, which grows closer to the first best. This research provides insights into the implications of mentalizing for agency theory....... develop a model that captures how principals differentially obtain information on agents. We show that principals that combine conventional data collection techniques with mentalizing benefit from a synergistic effect that impacts both the amount of information that is accessed and the overall cost...
Cooperative 3D Path Optimization (C3PO) Simulation
2015-11-10
knowledge, the group would elect a leader, plan a path using Rapidly-Exploring Random Trees (RRTs), and move to the goal using Artificial Potential... Field . The simulation was created in the MASON multi-agent simulation framework, and we were able to show that RRTs are a viable solution for path...to do so without a home base for communication. Starting out with full map knowledge, the group would elect a leader, plan a path using Rapidly
Institute of Scientific and Technical Information of China (English)
无
1999-01-01
An enveloping theory based method for the determination of path interval in three-axis NC machining of free form surface is presented, and a practical algorithm and the measures for improving the calculating efficiency of the algorithm are given. Not only the given algorithm can be used for ball end cutter, flat end cutter, torus cutter and drum cutter, but also the proposed method can be extended to arbitrary milling cutters. Thus, the problem how to strictly calculate path interval in the occasion of three-axis NC machining of free form surfaces with non-ball end cutters has been resolved effectively. On this basis, the factors that affect path interval are analyzed, and the methods for optimizing tool path are explored.
Directory of Open Access Journals (Sweden)
Aristeidis Antonakis
2017-04-01
Full Text Available In this article, a new multi-objective approach to the aircraft climb path optimization problem, based on the Particle Swarm Optimization algorithm, is introduced to be used for aircraft–engine integration studies. This considers a combination of a simulation with a traditional Energy approach, which incorporates, among others, the use of a proposed path-tracking scheme for guidance in the Altitude–Mach plane. The adoption of population-based solver serves to simplify case setup, allowing for direct interfaces between the optimizer and aircraft/engine performance codes. A two-level optimization scheme is employed and is shown to improve search performance compared to the basic PSO algorithm. The effectiveness of the proposed methodology is demonstrated in a hypothetic engine upgrade scenario for the F-4 aircraft considering the replacement of the aircraft’s J79 engine with the EJ200; a clear advantage of the EJ200-equipped configuration is unveiled, resulting, on average, in 15% faster climbs with 20% less fuel.
Birkholz, Adam B; Schlegel, H Bernhard
2015-12-28
The development of algorithms to optimize reaction pathways between reactants and products is an active area of study. Existing algorithms typically describe the path as a discrete series of images (chain of states) which are moved downhill toward the path, using various reparameterization schemes, constraints, or fictitious forces to maintain a uniform description of the reaction path. The Variational Reaction Coordinate (VRC) method is a novel approach that finds the reaction path by minimizing the variational reaction energy (VRE) of Quapp and Bofill. The VRE is the line integral of the gradient norm along a path between reactants and products and minimization of VRE has been shown to yield the steepest descent reaction path. In the VRC method, we represent the reaction path by a linear expansion in a set of continuous basis functions and find the optimized path by minimizing the VRE with respect to the linear expansion coefficients. Improved convergence is obtained by applying constraints to the spacing of the basis functions and coupling the minimization of the VRE to the minimization of one or more points along the path that correspond to intermediates and transition states. The VRC method is demonstrated by optimizing the reaction path for the Müller-Brown surface and by finding a reaction path passing through 5 transition states and 4 intermediates for a 10 atom Lennard-Jones cluster.
Simultaneous Camera Path Optimization and Distraction Removal for Improving Amateur Video.
Zhang, Fang-Lue; Wang, Jue; Zhao, Han; Martin, Ralph R; Hu, Shi-Min
2015-12-01
A major difference between amateur and professional video lies in the quality of camera paths. Previous work on video stabilization has considered how to improve amateur video by smoothing the camera path. In this paper, we show that additional changes to the camera path can further improve video aesthetics. Our new optimization method achieves multiple simultaneous goals: 1) stabilizing video content over short time scales; 2) ensuring simple and consistent camera paths over longer time scales; and 3) improving scene composition by automatically removing distractions, a common occurrence in amateur video. Our approach uses an L(1) camera path optimization framework, extended to handle multiple constraints. Two passes of optimization are used to address both low-level and high-level constraints on the camera path. The experimental and user study results show that our approach outputs video that is perceptually better than the input, or the results of using stabilization only.
TRAFFIC SENSITIVE AND TRAFFIC LOAD AWARE PATH SELECTION ALGORITHM FOR MMR WIMAX NETWORKS
Directory of Open Access Journals (Sweden)
Sandhya Kulkarni
2011-08-01
Full Text Available The recent developments in the broadband wireless access (BWA communication systems haveintroduced several major changes to the existing systems. Legacy IEEE 802.16j is one such amendment tothe existing IEEE 802.16 WiMAX family. The key modification introduced by 802.16j system is theconcept of relay station (RS, which may be used to enhance the system coverage or to make systemthroughput optimal. The end terminals, subscriber stations (SS are unchanged in the standard. Theoverall change pertinent to the system has raised many unresolved issues related to RS and multi-hoprelay base station (MR-BS. The selection of path from a SS to MR-BS via a RS is also one of the issues,need to be addressed. The path selection of a SS in both uplink and downlink directions is left open in thestandard. It is very significant to satisfy the traffics of stringent quality of service (QoS requirements andto appropriately manage the resources of a cell under different circumstances. This paper proposes apath selection algorithm to achieve the aforementioned qualities in the network. The path selectionmetrics include traffic load of the transparent relay station and traffic sensitivity factor of the SS. Anextensive simulation work discusses the performance evaluation of the proposed work using QualNetsimulator.
Evolutionary path control strategy for solving many-objective optimization problem.
Roy, Proteek Chandan; Islam, Md Monirul; Murase, Kazuyuki; Yao, Xin
2015-04-01
The number of objectives in many-objective optimization problems (MaOPs) is typically high and evolutionary algorithms face severe difficulties in solving such problems. In this paper, we propose a new scalable evolutionary algorithm, called evolutionary path control strategy (EPCS), for solving MaOPs. The central component of our algorithm is the use of a reference vector that helps simultaneously minimizing all the objectives of an MaOP. In doing so, EPCS employs a new fitness assignment strategy for survival selection. This strategy consists of two procedures and our algorithm applies them sequentially. It encourages a population of solutions to follow a certain path reaching toward the Pareto optimal front. The essence of our strategy is that it reduces the number of nondominated solutions to increase selection pressure in evolution. Furthermore, unlike previous work, EPCS is able to apply the classical Pareto-dominance relation with the new fitness assignment strategy. Our algorithm has been tested extensively on several scalable test problems, namely five DTLZ problems with 5 to 40 objectives and six WFG problems with 2 to 13 objectives. Furthermore, the algorithm has been tested on six CEC09 problems having 2 or 3 objectives. The experimental results show that EPCS is capable of finding better solutions compared to other existing algorithms for problems with an increasing number of objectives.
Self-organization and solution of shortest-path optimization problems with memristive networks
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.
Optimal Path Identification Using ANT Colony Optimisation in Wireless Sensor Network
Directory of Open Access Journals (Sweden)
Aniket. A. Gurav
2013-05-01
Full Text Available Wireless Sensor Network WSN is tightly constrained for energy, computational power and memory. All applications of WSN require to forward data from remote sensor node SN to base station BS. The path length and numbers of nodes in path by which data is forwarded affect the basic performance of WSN. In this paper we present bio-Inspired Ant Colony Optimisation ACO algorithm for Optimal path Identification OPI for p acket transmission to communicate between SN to BS. Our modified algorithm OPI using ACO cons iders the path length and the number of hops in path for data packet transmission, with an aim to reduce communication overheads .
Study on Tool Path Optimization in Multi-axis NC Machining
Directory of Open Access Journals (Sweden)
Niu Xinghua
2015-01-01
Full Text Available This paper presents a new generation algorithm for tool path based on the optimization of traditional algorithms. Then, the tool path on an impeller is generated with UG software, and it is used to make contrasts and verifications for the effect of optimization. Finally, VERICUT software with the function of the simulating on the whole manufacturing process is utilized to verify the feasibility of the optimized algorithm.
An adaptive dual-optimal path-planning technique for unmanned air vehicles
Directory of Open Access Journals (Sweden)
Whitfield Clifford A.
2016-01-01
Full Text Available A multi-objective technique for unmanned air vehicle path-planning generation through task allocation has been developed. The dual-optimal path-planning technique generates real-time adaptive flight paths based on available flight windows and environmental influenced objectives. The environmentally-influenced flight condition determines the aircraft optimal orientation within a downstream virtual window of possible vehicle destinations that is based on the vehicle’s kinematics. The intermittent results are then pursued by a dynamic optimization technique to determine the flight path. This path-planning technique is a multi-objective optimization procedure consisting of two goals that do not require additional information to combine the conflicting objectives into a single-objective. The technique was applied to solar-regenerative high altitude long endurance flight which can benefit significantly from an adaptive real-time path-planning technique. The objectives were to determine the minimum power required flight paths while maintaining maximum solar power for continual surveillance over an area of interest (AOI. The simulated path generation technique prolonged the flight duration over a sustained turn loiter flight path by approximately 2 months for a year of flight. The potential for prolonged solar powered flight was consistent for all latitude locations, including 2 months of available flight at 60° latitude, where sustained turn flight was no longer capable.
Sensor Selection for Aircraft Engine Performance Estimation and Gas Path Fault Diagnostics
Simon, Donald L.; Rinehart, Aidan W.
2016-01-01
This paper presents analytical techniques for aiding system designers in making aircraft engine health management sensor selection decisions. The presented techniques, which are based on linear estimation and probability theory, are tailored for gas turbine engine performance estimation and gas path fault diagnostics applications. They enable quantification of the performance estimation and diagnostic accuracy offered by different candidate sensor suites. For performance estimation, sensor selection metrics are presented for two types of estimators including a Kalman filter and a maximum a posteriori estimator. For each type of performance estimator, sensor selection is based on minimizing the theoretical sum of squared estimation errors in health parameters representing performance deterioration in the major rotating modules of the engine. For gas path fault diagnostics, the sensor selection metric is set up to maximize correct classification rate for a diagnostic strategy that performs fault classification by identifying the fault type that most closely matches the observed measurement signature in a weighted least squares sense. Results from the application of the sensor selection metrics to a linear engine model are presented and discussed. Given a baseline sensor suite and a candidate list of optional sensors, an exhaustive search is performed to determine the optimal sensor suites for performance estimation and fault diagnostics. For any given sensor suite, Monte Carlo simulation results are found to exhibit good agreement with theoretical predictions of estimation and diagnostic accuracies.
Optimal paths planning in dynamic transportation networks with random link travel times
Institute of Scientific and Technical Information of China (English)
孙世超; 段征宇; 杨东援
2014-01-01
A theoretical study was conducted on finding optimal paths in transportation networks where link travel times were stochastic and time-dependent (STD). The methodology of relative robust optimization was applied as measures for comparing time-varying, random path travel times for a priori optimization. In accordance with the situation in real world, a stochastic consistent condition was provided for the STD networks and under this condition, a mathematical proof was given that the STD robust optimal path problem can be simplified into a minimum problem in specific time-dependent networks. A label setting algorithm was designed and tested to find travelers’ robust optimal path in a sampled STD network with computation complexity of O(n2+n·m). The validity of the robust approach and the designed algorithm were confirmed in the computational tests. Compared with conventional probability approach, the proposed approach is simple and efficient, and also has a good application prospect in navigation system.
Differential-Evolution Control Parameter Optimization for Unmanned Aerial Vehicle Path Planning.
Kok, Kai Yit; Rajendran, Parvathy
2016-01-01
The differential evolution algorithm has been widely applied on unmanned aerial vehicle (UAV) path planning. At present, four random tuning parameters exist for differential evolution algorithm, namely, population size, differential weight, crossover, and generation number. These tuning parameters are required, together with user setting on path and computational cost weightage. However, the optimum settings of these tuning parameters vary according to application. Instead of trial and error, this paper presents an optimization method of differential evolution algorithm for tuning the parameters of UAV path planning. The parameters that this research focuses on are population size, differential weight, crossover, and generation number. The developed algorithm enables the user to simply define the weightage desired between the path and computational cost to converge with the minimum generation required based on user requirement. In conclusion, the proposed optimization of tuning parameters in differential evolution algorithm for UAV path planning expedites and improves the final output path and computational cost.
Differential-Evolution Control Parameter Optimization for Unmanned Aerial Vehicle Path Planning.
Directory of Open Access Journals (Sweden)
Kai Yit Kok
Full Text Available The differential evolution algorithm has been widely applied on unmanned aerial vehicle (UAV path planning. At present, four random tuning parameters exist for differential evolution algorithm, namely, population size, differential weight, crossover, and generation number. These tuning parameters are required, together with user setting on path and computational cost weightage. However, the optimum settings of these tuning parameters vary according to application. Instead of trial and error, this paper presents an optimization method of differential evolution algorithm for tuning the parameters of UAV path planning. The parameters that this research focuses on are population size, differential weight, crossover, and generation number. The developed algorithm enables the user to simply define the weightage desired between the path and computational cost to converge with the minimum generation required based on user requirement. In conclusion, the proposed optimization of tuning parameters in differential evolution algorithm for UAV path planning expedites and improves the final output path and computational cost.
Multiple Optimal Path Identification using Ant Colony Optimisation in Wireless Sensor Network
Directory of Open Access Journals (Sweden)
Aniket. A. Gurav
2013-10-01
Full Text Available Wireless Sensor Network WSN is tightly constrained for resources like energy, computational power andmemory. Many applications of WSN require to communicate sensitive information at sensor nodes SN toBase station BS. The basic performance of WSN depends upon the path length and numbers of nodes in thepath by which data is forwarded to BS. In this paper we present bio-inspired Ant Colony Optimisation ACOalgorithm for Optimal Path Identification OPI for packet transmission to communicate between SN to BS.Our modified algorithm OPI using ACO is base-station driven which considers the path length and thenumber of hops in path for data packet transmission. This modified algorithm finds optimal path OP aswell as several suboptimal paths between SN & BS which are useful for effective communication.
New SRLG-diverse path selection algorithm in survivable GMPLS networks
Institute of Scientific and Technical Information of China (English)
Wang Yan; Zheng Junhui; Zeng Jiazhi
2009-01-01
In conventional shared risk link group (SRLG)-diverse path selection (CSPS) algorithm in survivable GMPLS networks, SRLG is taken into account when selecting the backup paths, while the primary path selection method is the same as the algorithms without SRLG constraint. A problem of CSPS algorithm is that, after a primary path is selected, the success probability to select an SRLG-diverse backup path for it is low. If SRLG is taken into account when computing the primary path, then the probability to successfully select an SRLG-diverse backup path will be much increased. Based on this idea, an active SRLG-diverse path selection (ASPS) algorithm is proposed. To actively avoid selecting those SRLG links, when computing the primary path, a link that share risk with more links is assigned a larger link cost. To improve the resource utilization ratio, it is permitted that the bandwidth resources are shared among backup paths. What is more, differentiated reliability (DiR) requirements of different customers are considered in ASPS algorithm. The simulation results show that, compared with CSPS algorithm, ASPS algorithm not only increases successful protection probability but also improves resource utilization ratio.
An optimization approach for mapping and measuring the divergence and correspondence between paths.
Mueller, Shane T; Perelman, Brandon S; Veinott, Elizabeth S
2016-03-01
Many domains of empirical research produce or analyze spatial paths as a measure of behavior. Previously, approaches for measuring the similarity or deviation between two paths have either required timing information or have used ad hoc or manual coding schemes. In this paper, we describe an optimization approach for robustly measuring the area-based deviation between two paths we call ALCAMP (Algorithm for finding the Least-Cost Areal Mapping between Paths). ALCAMP measures the deviation between two paths and produces a mapping between corresponding points on the two paths. The method is robust to a number of aspects in real path data, such as crossovers, self-intersections, differences in path segmentation, and partial or incomplete paths. Unlike similar algorithms that produce distance metrics between trajectories (i.e., paths that include timing information), this algorithm uses only the order of observed path segments to determine the mapping. We describe the algorithm and show its results on a number of sample problems and data sets, and demonstrate its effectiveness for assessing human memory for paths. We also describe available software code written in the R statistical computing language that implements the algorithm to enable data analysis.
A Multi-pipe Path Planning by Modified Ant Colony Optimization
Institute of Scientific and Technical Information of China (English)
QU Yan-feng; JIANG Dan; LIU Bin
2011-01-01
Path planning in 3D geometry space is used to find an optimal path in the restricted environment, according to a certain evaluation criteria. To solve the problem of long searching time and slow solving speed in 3D path planning, a modified ant colony optimization is proposed in this paper. Firstly, the grid method for environment modeling is adopted. Heuristic information is connected with the planning space. A semi-iterative global pheromone update mechanism is proposed. Secondly, the optimal ants mutate the paths to improve the diversity of the algorithm after a defined iterative number. Thirdly, co-evolutionary algorithm is used. Finally, the simulation result shows the effectiveness of the proposed algorithm in solving the problem of 3D pipe path planning.
Identification of Optimal Path in Power System Network Using Bellman Ford Algorithm
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S. Hemalatha
2012-01-01
Full Text Available Power system network can undergo outages during which there may be a partial or total blackout in the system. In that condition, transmission of power through the optimal path is an important problem in the process of reconfiguration of power system components. For a given set of generation, load pair, there could be many possible paths to transmit the power. The optimal path needs to consider the shortest path (minimum losses, capacity of the transmission line, voltage stability, priority of loads, and power balance between the generation and demand. In this paper, the Bellman Ford Algorithm (BFA is applied to find out the optimal path and also the several alternative paths by considering all the constraints. In order to demonstrate the capability of BFA, it has been applied to a practical 230 kV network. This restorative path search guidance tool is quite efficient in finding the optimal and also the alternate paths for transmitting the power from a generating station to demand.
In Search of the Optimal Path: How Learners at Task Use an Online Dictionary
Hamel, Marie-Josee
2012-01-01
We have analyzed circa 180 navigation paths followed by six learners while they performed three language encoding tasks at the computer using an online dictionary prototype. Our hypothesis was that learners who follow an "optimal path" while navigating within the dictionary, using its search and look-up functions, would have a high chance of…
Application of Artificial Intelligence Methods of Tool Path Optimization in CNC Machines: A Review
Directory of Open Access Journals (Sweden)
Khashayar Danesh Narooei
2014-08-01
Full Text Available Today, in most of metal machining process, Computer Numerical Control (CNC machine tools have been very popular due to their efficiencies and repeatability to achieve high accuracy positioning. One of the factors that govern the productivity is the tool path travel during cutting a work piece. It has been proved that determination of optimal cutting parameters can enhance the machining results to reach high efficiency and minimum the machining cost. In various publication and articles, scientist and researchers adapted several Artificial Intelligence (AI methods or hybrid method for tool path optimization such as Genetic Algorithms (GA, Artificial Neural Network (ANN, Artificial Immune Systems (AIS, Ant Colony Optimization (ACO and Particle Swarm Optimization (PSO. This study presents a review of researches in tool path optimization with different types of AI methods that show the capability of using different types of optimization methods in CNC machining process.
Optimal Routing with Failure-Independent Path Protection
DEFF Research Database (Denmark)
Stidsen, Thomas Riis; Petersen, Bjørn; Spoorendonk, Simon
2010-01-01
the associated routing problems become much harder. In this article we present a rigorous mathematical analysis of one of the most promising protection methods: Failure independent path protection. We present an LP model which is solved by column generation. The subproblem is proven to be strongly P...
An optimal antenna motion generation using shortest path planning
Jeon, Moon-Jin; Kwon, Dong-Soo
2017-03-01
This paper considers an angular velocity minimization method for a satellite antenna. For high speed transmission of science data, a directional antenna with a two-axis gimbal is generally used. When a satellite passes over a ground station while pointing directly at it, the angular velocity of the satellite antenna can increase rapidly due to the gimbal kinematics. The high angular velocity could exceed the dynamic constraint of the antenna. Furthermore, micro vibration induced by high speed antenna rotation during an imaging operation might cause jitter, which can degrade the satellite image quality. To solve this problem, a minimum-velocity antenna motion generation method is proposed. Boundaries of the azimuth and elevation angles of the antenna within an effective beam width are derived using antenna geometry. A minimum-velocity azimuth profile and elevation profile within the boundaries are generated sequentially using a shortest path planning method. For fast and correct generation of the shortest path, a new algorithm called a string nailing algorithm is proposed. A numerical simulation shows that the antenna profile generated by the shortest path planning has a much lower angular velocity than the profiles generated by previous methods. The proposed string nailing algorithm also spends much less computation time than a search-based shortest path planning algorithm to generate almost the same antenna profiles.
A Novel Global Path Planning Method for Mobile Robots Based on Teaching-Learning-Based Optimization
Directory of Open Access Journals (Sweden)
Zongsheng Wu
2016-07-01
Full Text Available The Teaching-Learning-Based Optimization (TLBO algorithm has been proposed in recent years. It is a new swarm intelligence optimization algorithm simulating the teaching-learning phenomenon of a classroom. In this paper, a novel global path planning method for mobile robots is presented, which is based on an improved TLBO algorithm called Nonlinear Inertia Weighted Teaching-Learning-Based Optimization (NIWTLBO algorithm in our previous work. Firstly, the NIWTLBO algorithm is introduced. Then, a new map model of the path between start-point and goal-point is built by coordinate system transformation. Lastly, utilizing the NIWTLBO algorithm, the objective function of the path is optimized; thus, a global optimal path is obtained. The simulation experiment results show that the proposed method has a faster convergence rate and higher accuracy in searching for the path than the basic TLBO and some other algorithms as well, and it can effectively solve the optimization problem for mobile robot global path planning.
Institute of Scientific and Technical Information of China (English)
De-tong Zhu
2001-01-01
In this paper we modify type approximate trust region methods via two curvilinear paths for unconstrained optimization. A mixed strategy using both trust region and line search techniques is adopted which switches to back tracking steps when a trial step produced by the trust region subproblem is unacceptable. We give a series of properties of both optimal path and modified gradient path. The global convergence and fast local convergence rate of the proposed algorithms are established under some reasonable conditions. A nonmonotonic criterion is used to speed up the convergence progress in some ill-conditioned cases.
Directory of Open Access Journals (Sweden)
L.Yang
2015-12-01
Full Text Available Three-dimensional path planning for underwater vehicles is an important problem that focuses on optimizing the route with consideration of various constraints in a complex underwater environment. In this paper, an improved ant colony optimization (IACO algorithm based on pheromone exclusion is proposed to solve the underwater vehicle 3D path planning problem. The IACO algorithm can balance the tasks of exploration and development in the ant search path, and enable the ants in the search process to explore initially and develop subsequently. Then, the underwater vehicle can find the safe path by connecting the chosen nodes of the 3D mesh while avoiding the threat area. This new approach can overcome common disadvantages of the basic ant colony algorithm, such as falling into local extremum, poor quality, and low accuracy. Experimental comparative results demonstrate that this proposed IACO method is more effective and feasible in underwater vehicle 3D path planning than the basic ACO model.
Dynamic path planning for mobile robot based on particle swarm optimization
Wang, Yong; Cai, Feng; Wang, Ying
2017-08-01
In the contemporary, robots are used in many fields, such as cleaning, medical treatment, space exploration, disaster relief and so on. The dynamic path planning of robot without collision is becoming more and more the focus of people's attention. A new method of path planning is proposed in this paper. Firstly, the motion space model of the robot is established by using the MAKLINK graph method. Then the A* algorithm is used to get the shortest path from the start point to the end point. Secondly, this paper proposes an effective method to detect and avoid obstacles. When an obstacle is detected on the shortest path, the robot will choose the nearest safety point to move. Moreover, calculate the next point which is nearest to the target. Finally, the particle swarm optimization algorithm is used to optimize the path. The experimental results can prove that the proposed method is more effective.
Optimal path for a quantum teleportation protocol in entangled networks
Di Franco, C.; Ballester, D.
2010-01-01
Bellman's optimality principle has been of enormous importance in the development of whole branches of applied mathematics, computer science, optimal control theory, economics, decision making, and classical physics. Examples are numerous: dynamic programming, Markov chains, stochastic dynamics, calculus of variations, and the brachistochrone problem. Here we show that Bellman's optimality principle is violated in a teleportation problem on a quantum network. This implies that finding the opt...
Planning Optimal Paths for Multi-agent Systems on Graphs
Yu, Jingjin
2012-01-01
For the problem of moving a set of agents on a connected graph with unit edge distance to agent-specific goal locations, free of collisions, we propose two multiflow based integer linear programming (ILP) models that find time optimal and distance optimal solutions, respectively. The resulting algorithms from our ILP models are complete and guaranteed to yield true optimal solutions. Focusing on the time optimal formulation, we evaluate its performance, both as a stand alone algorithm and as a generic heuristic for quickly solving large problem instances. The computational results confirm the effectiveness of our method.
Optimal path for a quantum teleportation protocol in entangled networks
di Franco, C.; Ballester, D.
2012-01-01
Bellman's optimality principle has been of enormous importance in the development of whole branches of applied mathematics, computer science, optimal control theory, economics, decision making, and classical physics. Examples are numerous: dynamic programming, Markov chains, stochastic dynamics, calculus of variations, and the brachistochrone problem. Here we show that Bellman's optimality principle is violated in a teleportation problem on a quantum network. This implies that finding the optimal fidelity route for teleporting a quantum state between two distant nodes on a quantum network with bipartite entanglement will be a tough problem and will require further investigation.
Optimization over polynomials: Selected topics
M. Laurent (Monique); S.Y. Jang; Y.R. Kim; D.-W. Lee; I. Yie
2014-01-01
htmlabstractMinimizing a polynomial function over a region defined by polynomial inequalities models broad classes of hard problems from combinatorics, geometry and optimization. New algorithmic approaches have emerged recently for computing the global minimum, by combining tools from real algebra
Determination of An Optimal Return-Path on Road Attributes for Mobile Robot Recharging
Directory of Open Access Journals (Sweden)
Fei Liu
2011-11-01
Full Text Available Optimal path-planning for mobile robot recharging is a very vital requirement in real applications. This paper proposes a strategy of determining an optimal return-path in consideration of road attributes which include length, surface roughness, road grade and the setting of speed-control hump. The road in the environment is partitioned into multiple segments, and for each one, a model of cost that the robot will pay for is established under the constraints of the attributes. The cost consists of energy consumption and the influence of vibration on mobile robot that is induced by motion. The return-path is constituted by multiple segments and its cost is defined to be the sum of the cost of each segment. The idle time, deduced from the cost, is firstly used as the decision factor for determining the optimal return-path. Finally, the simulation is given and the results prove the effectiveness and superiority of the strategy.
Research on the Optimization and Simulation of the Shortest Path Based on Algorithm of Dijkstra
Institute of Scientific and Technical Information of China (English)
2010-01-01
<正>Dijkstra algorithm is a theoretical basis to solve transportation network problems of the shortest path, which has a wide range of application in path optimization. Through analyzing traditional Dijkstra algorithm,on account of the insufficiency of this algorithm in path optimization,this paper uses adjacency list and circular linked list with combination to store date,and through the improved quick sorting algorithm for weight sorting, accomplish a quick search to the adjacent node,and so an improved Dijkstra algorithm is got.Then apply it to the optimal path search,and make simulation analysis for this algorithm through the example,also verify the effectiveness of the proposed algorithm.
Globally Optimal Path Planning with Anisotropic Running Costs
2013-03-01
Proceedings of the American Control Conference , pp...Jacques, D. R. & Pachter, M. (2002) Air vehicle optimal trajectories between two radars, in Proceedings of the American Control Conference . Pachter...M. & Hebert, J. (2001) Optimal aircraft trajectories for radar exposure mini- mization, in Proceedings of the American Control Conference .
Laser parameter influence on quantum path selection in a bichromatic field
Energy Technology Data Exchange (ETDEWEB)
Wang Shaoyi; Hong Weiyi; Lan Pengfei; Zhang Qingbin; Lu Peixiang [Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan 430074 (China)], E-mail: lupeixiang@mail.hust.edu.cn
2009-05-28
We theoretically investigate the laser parameter influence on quantum path selection in a two-colour laser pulse, of which both fundamental field and its controlling field are linearly polarized. The laser's parameters, namely, the relative intensity of the controlling field and its relative phase with respect to the fundamental field, determine the quantum path selection by affecting their ionization probabilities. In both cases of the {omega} + 3{omega} and {omega} + 2{omega} laser fields, it is shown that the quantum path selection in the multi-cycle pulse is more dependent on the parameters than that in the few-cycle pulse, and the selection of the quantum path in the multi-cycle {omega} + 3{omega} pulse shows stability to the phase and intensity variation. Our results are very beneficial to choosing appropriate parameters for quantum path selection in experiments.
Batzias, Dimitris F.; Pollalis, Yannis A.
2012-12-01
In several cases, a competitive market can be simulated by a game, where each company/opponent is referred to as a player. In order to accommodate the fact that each player (alone or with alliances) is working against some others' interest, the rather conservative maximin criterion is frequently used for selecting the strategy or the combination of strategies that yield the best of the worst possible outcomes for each one of the players. Under this criterion, an optimal solution is obtained when neither player finds it beneficial to alter his strategy, which means that an equilibrium has been achieved, giving also the value of the game. If conditions change as regards a player, e.g., because of either achieving an unexpected successful result in developing an innovative industrial product or obtaining higher liquidity permitting him to increase advertisement in order to acquire a larger market share, then a new equilibrium is reached. The identification of the path between the old and the new equilibrium points may prove to be valuable for investigating the robustness of the solution by means of sensitivity analysis, since uncertainty plays a critical role in this situation, where evaluation of the payoff matrix is usually based on experts' estimates. In this work, the development of a standard methodology (including 16 activity stages and 7 decision nodes) for tracing this path is presented while a numerical implementation follows to prove its functionality.
The optimal time path of clean energy R&D policy when patents have finite lifetime
Gerlagh, R.; Kverndokk, S.; Rosendahl, K.E.
We study the optimal time path for clean energy innovation policy. In a model with emission reduction through clean energy deployment, and with R&D increasing the overall productivity of clean energy, we describe optimal R&D policies jointly with emission pricing policies. We find that while
The optimal time path of clean energy R&D policy when patents have finite lifetime
Gerlagh, R.; Kverndokk, S.; Rosendahl, K.E.
2014-01-01
We study the optimal time path for clean energy innovation policy. In a model with emission reduction through clean energy deployment, and with R&D increasing the overall productivity of clean energy, we describe optimal R&D policies jointly with emission pricing policies. We find that while emissio
Path integrals and symmetry breaking for optimal control theory
Kappen, H J
2005-01-01
This paper considers linear-quadratic control of a non-linear dynamical system subject to arbitrary cost. I show that for this class of stochastic control problems the non-linear Hamilton-Jacobi-Bellman equation can be transformed into a linear equation. The transformation is similar to the transformation used to relate the classical Hamilton-Jacobi equation to the Schr\\"odinger equation. As a result of the linearity, the usual backward computation can be replaced by a forward diffusion process, that can be computed by stochastic integration or by the evaluation of a path integral. It is shown, how in the deterministic limit the PMP formalism is recovered. The significance of the path integral approach is that it forms the basis for a number of efficient computational methods, such as MC sampling, the Laplace approximation and the variational approximation. We show the effectiveness of the first two methods in number of examples. Examples are given that show the qualitative difference between stochastic and d...
Optimal selection for direct mail
Bult, [No Value; Wansbeek, T
1995-01-01
Direct marketing (mail) is a growing area of marketing practice, yet the academic journals contain very little research on this topic. The most important issue for direct marketers is how to sample targets from a population for a direct mail campaign. Although some selection methods are described in
Optimal selection for direct mail
Bult, [No Value; Wansbeek, T
1995-01-01
Direct marketing (mail) is a growing area of marketing practice, yet the academic journals contain very little research on this topic. The most important issue for direct marketers is how to sample targets from a population for a direct mail campaign. Although some selection methods are described in
Vector Broadcast Channels: Optimal Threshold Selection Problem
Samarasinghe, Tharaka; Evans, Jamie
2011-01-01
Threshold feedback policies are well known and provably rate-wise optimal selective feedback techniques for communication systems requiring partial channel state information (CSI). However, optimal selection of thresholds at mobile users to maximize information theoretic data rates subject to feedback constraints is an open problem. In this paper, we focus on the optimal threshold selection problem, and provide a solution for this problem for finite feedback systems. Rather surprisingly, we show that using the same threshold values at all mobile users is not always a rate-wise optimal feedback strategy, even for a system with identical users experiencing statistically the same channel conditions. By utilizing the theory of majorization, we identify an underlying Schur-concave structure in the rate function and obtain sufficient conditions for a homogenous threshold feedback policy to be optimal. Our results hold for most fading channel models, and we illustrate an application of our results to familiar Raylei...
Path Partition in Directed Graph – Modeling and Optimization
Directory of Open Access Journals (Sweden)
Issam Abdel Kader
2013-03-01
Full Text Available The concept of graph theory is therefore perfectly suitable to structure a problem in its initial analysis phases since a graph is the most general mathematical object. At the structural level, the nodes represent the objects, the variables… and the arc forms the binary relation of influence among them. Many real problems can be modeled as path partition in directed graph that played particular role in the operation of arranging a set of nodes especially in case of directed acyclic graph (DAG. We encounter such graph in schedule problems, the analysis of language structure, the probability theory, the game theory, compilers…. Moreover managerial problem can be modeled as acyclic graphs, also the potential problem has a suitable solution if and only if the graph is acyclic.
Path Optimization for Single and Multiple Searchers: Models and Algorithms
2008-09-01
in that cell during that time period given the target occupies cell c at time period t is described as 1− exp(−αc, tyc ,t) instead of 1− (1−g(c, t))yc,t...yc,t number of searchers in cell c in time period t Formulation min f(y) = ∑ ω∈Ω pω exp −∑ c,t αωc, tyc ,t s.t.∑ c′∈R(c) xc′,c,t−1 = ∑ c′∈F(c) xc...are ∂f(y) ∂yc,t = − ∑ ω∈Ω pωα ω c,t exp(− ∑ c,t αωc, tyc ,t). (IV.1) Since the formulation uses all possible target paths ω ∈ Ω, if the number of
Institute of Scientific and Technical Information of China (English)
胡云卿; 刘兴高; 薛安克
2014-01-01
This paper considers dealing with path constraints in the framework of the improved control vector it-eration (CVI) approach. Two available ways for enforcing equality path constraints are presented, which can be di-rectly incorporated into the improved CVI approach. Inequality path constraints are much more difficult to deal with, even for small scale problems, because the time intervals where the inequality path constraints are active are unknown in advance. To overcome the challenge, the l1 penalty function and a novel smoothing technique are in-troduced, leading to a new effective approach. Moreover, on the basis of the relevant theorems, a numerical algo-rithm is proposed for nonlinear dynamic optimization problems with inequality path constraints. Results obtained from the classic batch reactor operation problem are in agreement with the literature reports, and the computational efficiency is also high.
Single and multi–objective optimization of path placement for redundant robotic manipulators
Directory of Open Access Journals (Sweden)
J.A. Pamanes–García
2008-07-01
Full Text Available General formulations are presented in this paper to determine the best position and orientation of a desired path to be followed by a redundant manipulator. Two classes of problem are considered. In the first, a single manipulator's index of kinematic performance associated to one path point must be improved as much as possible. In the second case distinct indices of kinematic performance, corresponding to different points of the path, are to be optimized. Constraints are taken into account in order to guarantee the accessibility to the whole de sired task. Several case studies are presented to illustrate the effectiveness of the method for planar and spatial manipulators.
Location-based Mobile Relay Selection and Impact of Inaccurate Path Loss Model Parameters
DEFF Research Database (Denmark)
Nielsen, Jimmy Jessen; Madsen, Tatiana Kozlova; Schwefel, Hans-Peter
2010-01-01
In this paper we propose a relay selection scheme which uses collected location information together with a path loss model for relay selection, and analyze the performance impact of mobility and different error causes on this scheme. Performance is evaluated in terms of bit error rate...... in these situations. As the location-based scheme relies on a path loss model to estimate link qualities and select relays, the sensitivity with respect to inaccurate estimates of the unknown path loss model parameters is investigated. The parameter ranges that result in useful performance were found...
Optimized Flight Path for Localization Using Line of Bearing
2015-03-26
speed and angular velocity of heading angle will be considered. MATLAB / Simulink is used for solving the optimal control problem. Among the many...from real-world data can be compared to the result of the MATLAB / Simulink simulation predictions for future flight tests can be made. 6 1.6 Thesis...targeting. In addition, the specific process of the algorithm is explained using a sample result using MATLAB / Simulink . Sensitivity of the result and
Towards low power N-Path filters for flexible RF-Channel selection
Klumperink, Eric A.M.; Soer, Michiel C.M.; Struiksma, Remko E.; Vliet, van Frank E.; Nauta, Bram
2015-01-01
N-path filters can offer high-linearity high-Q channel selection filtering at a flexibly programmable RF center frequency, which is highly wanted for Software Defined Radio. Relying on capacitors and MOSFET switches, driven by digital non-overlapping clocks, N-path filters fit well to CMOS and benef
Optimizing Site Selection for HEDS
Marshall, J. R.
1999-01-01
MSP 2001 will be conducting environmental assessment for the Human exploration and Development of Space (HEDS) Program in order to safeguard future human exploration of the planet, in addition to geological studies being addressed by the APEX payload. In particular, the MECA experiment (see other abstracts, this volume), will address chemical toxicity of the soil, the presence of adhesive or abrasive soil dust components, and the geoelectrical-triboelectrical character of the surface environment. The attempt will be to quantify hazards to humans and machinery structures deriving from compounds that poison, corrode, abrade, invade (lungs or machinery), contaminate, or electrically interfere with the human presence. The DART experiment, will also address the size and electrical nature of airborne dust. Photo-imaging of the local scene with RAC and Pancam will be able to assess dust raising events such as local thermal vorticity-driven dust devils. The need to introduce discussion of HEDS landing site requirements stems from potential conflict, but also potential synergism with other '01 site requirements. In-situ Resource Utilization (ISRU) mission components desire as much solar radiation as possible, with some very limited amount of dust available; the planetary-astrobiology mission component desires sufficient rock abundance without inhibiting rover activities (and an interesting geological niche if available), the radiation component may again have special requirements, as will the engineers concerned with mission safety and mission longevity. The '01 mission affords an excellent opportunity to emphasize HEDS landing site requirements, given the constraint that both recent missions (Pathfinder, Mars '98) and future missions (MSP '03 & '05) have had or will have strong geological science drivers in the site selection process. What type of landing site best facilitates investigation of the physical, chemical, and behavioral properties of soil and dust? There are
Self-extinction through optimizing selection
Parvinen, Kalle; Dieckmann, Ulf
2013-01-01
Evolutionary suicide is a process in which selection drives a viable population to extinction. So far, such selection-driven self-extinction has been demonstrated in models with frequency-dependent selection. This is not surprising, since frequency-dependent selection can disconnect individual-level and population-level interests through environmental feedback. Hence it can lead to situations akin to the tragedy of the commons, with adaptations that serve the selfish interests of individuals ultimately ruining a population. For frequency-dependent selection to play such a role, it must not be optimizing. Together, all published studies of evolutionary suicide have created the impression that evolutionary suicide is not possible with optimizing selection. Here we disprove this misconception by presenting and analyzing an example in which optimizing selection causes self-extinction. We then take this line of argument one step further by showing, in a further example, that selection-driven self-extinction can occur even under frequency-independent selection. PMID:23583808
Urbanization Path Selection Toward Harmonious Urban-Rural Development
Institute of Scientific and Technical Information of China (English)
2010-01-01
Unbalanced urban-rural development is one of the most apparent issues in the process of Chinese urbanization.While harmonious urban-rural development is an objective of urbanization,urbanization is also necessary in realizing harmonious urban-rural development.Such development will be an emblem of the implementation of the Scientific View of Development,and may be realized through an urbanization path with integrated urban-rural system,win-win machine for urban and rural areas,integrated urban-rural market,and coordinated urban-rural industrial structures.
Reliable Path Selection Problem in Uncertain Traffic Network after Natural Disaster
Directory of Open Access Journals (Sweden)
Jing Wang
2013-01-01
Full Text Available After natural disaster, especially for large-scale disasters and affected areas, vast relief materials are often needed. In the meantime, the traffic networks are always of uncertainty because of the disaster. In this paper, we assume that the edges in the network are either connected or blocked, and the connection probability of each edge is known. In order to ensure the arrival of these supplies at the affected areas, it is important to select a reliable path. A reliable path selection model is formulated, and two algorithms for solving this model are presented. Then, adjustable reliable path selection model is proposed when the edge of the selected reliable path is broken. And the corresponding algorithms are shown to be efficient both theoretically and numerically.
A non-optimized follower load path may cause considerable intervertebral rotations.
Dreischarf, Marcel; Zander, Thomas; Bergmann, Georg; Rohlmann, Antonius
2010-09-17
Osseoligamentous spinal specimens buckle under even a small vertical compressive force. To allow higher axial forces, a compressive follower load (FL) was suggested previously that approximates the curvature of the spine without inducing intervertebral rotation in both the frontal and the sagittal planes. In in vitro experiments and finite element analyses, the location of the FL path is subjected to estimation by the investigator. Such non-optimized FLs may induce bending and so far it is still unknown how this affects the results of the study and their comparability. A symmetrical finite element model of the lumbar spine was employed to simulate upright standing while applying a follower load. In analogy to in vitro experiments, the path of this FL was estimated seven times by different members of our institute's spine group. Additionally, an optimized FL path was determined and additional moments of +/-7.5Nm were applied to simulate flexion and extension. Application of the optimized 500N compressive FL causes only a marginal alteration of the curvature (cardan angle L1-S1 in sagittal plane <0.25 degrees). An individual estimation of the FL path, however, results in flexions of up to 10.0 degrees or extensions of up to 12.3 degrees. The resulting angles for the different non-optimized FL paths depend on the magnitude of the bending moment applied and whether a differential or an absolute measurement is taken. A preceding optimization of the location of the FL path would increase the comparability of different studies.
Optimal Route Based Advanced Algorithm using Hot Link Split Multi-Path Routing Algorithm
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Akhilesh A. Waoo
2014-07-01
Full Text Available Present research work describes advancement in standard routing protocol AODV for mobile ad-hoc networks. Our mechanism sets up multiple optimal paths with the criteria of bandwidth and delay to store multiple optimal paths in the network. At time of link failure, it will switch to next available path. We have used the information that we get in the RREQ packet and also send RREP packet to more than one path, to set up multiple paths, It reduces overhead of local route discovery at the time of link failure and because of this End to End Delay and Drop Ratio decreases. The main feature of our mechanism is its simplicity and improved efficiency. This evaluates through simulations the performance of the AODV routing protocol including our scheme and we compare it with HLSMPRA (Hot Link Split Multi-Path Routing Algorithm Algorithm. Indeed, our scheme reduces routing load of network, end to end delay, packet drop ratio, and route error sent. The simulations have been performed using network simulator OPNET. The network simulator OPNET is discrete event simulation software for network simulations which means it simulates events not only sending and receiving packets but also forwarding and dropping packets. This modified algorithm has improved efficiency, with more reliability than Previous Algorithm.
UAV path planning using artificial potential field method updated by optimal control theory
Chen, Yong-bo; Luo, Guan-chen; Mei, Yue-song; Yu, Jian-qiao; Su, Xiao-long
2016-04-01
The unmanned aerial vehicle (UAV) path planning problem is an important assignment in the UAV mission planning. Based on the artificial potential field (APF) UAV path planning method, it is reconstructed into the constrained optimisation problem by introducing an additional control force. The constrained optimisation problem is translated into the unconstrained optimisation problem with the help of slack variables in this paper. The functional optimisation method is applied to reform this problem into an optimal control problem. The whole transformation process is deduced in detail, based on a discrete UAV dynamic model. Then, the path planning problem is solved with the help of the optimal control method. The path following process based on the six degrees of freedom simulation model of the quadrotor helicopters is introduced to verify the practicability of this method. Finally, the simulation results show that the improved method is more effective in planning path. In the planning space, the length of the calculated path is shorter and smoother than that using traditional APF method. In addition, the improved method can solve the dead point problem effectively.
Directory of Open Access Journals (Sweden)
Biwei Tang
2016-05-01
Full Text Available Global path planning is a challenging issue in the filed of mobile robotics due to its complexity and the nature of nondeterministic polynomial-time hard (NP-hard. Particle swarm optimization (PSO has gained increasing popularity in global path planning due to its simplicity and high convergence speed. However, since the basic PSO has difficulties balancing exploration and exploitation, and suffers from stagnation, its efficiency in solving global path planning may be restricted. Aiming at overcoming these drawbacks and solving the global path planning problem efficiently, this paper proposes a hybrid PSO algorithm that hybridizes PSO and differential evolution (DE algorithms. To dynamically adjust the exploration and exploitation abilities of the hybrid PSO, a novel PSO, the nonlinear time-varying PSO (NTVPSO, is proposed for updating the velocities and positions of particles in the hybrid PSO. In an attempt to avoid stagnation, a modified DE, the ranking-based self adaptive DE (RBSADE, is developed to evolve the personal best experience of particles in the hybrid PSO. The proposed algorithm is compared with four state-of-the-art evolutionary algorithms. Simulation results show that the proposed algorithm is highly competitive in terms of path optimality and can be considered as a vital alternative for solving global path planning.
Optimal Path Choice in Railway Passenger Travel Network Based on Residual Train Capacity
Directory of Open Access Journals (Sweden)
Fei Dou
2014-01-01
Full Text Available Passenger’s optimal path choice is one of the prominent research topics in the field of railway passenger transport organization. More and more different train types are available, increasing path choices from departure to destination for travelers are unstoppable. However, travelers cannot avoid being confused when they hope to choose a perfect travel plan based on various travel time and cost constraints before departure. In this study, railway passenger travel network is constructed based on train timetable. Both the generalized cost function we developed and the residual train capacity are considered to be the foundation of path searching procedure. The railway passenger travel network topology is analyzed based on residual train capacity. Considering the total travel time, the total travel cost, and the total number of passengers, we propose an optimal path searching algorithm based on residual train capacity in railway passenger travel network. Finally, the rationale of the railway passenger travel network and the optimal path generation algorithm are verified positively by case study.
Path planning for UAV based on quantum-behaved particle swarm optimization
Fu, Yangguang; Ding, Mingyue; Zhou, Chengping; Cai, Chao; Sun, Yangguang
2009-10-01
Based on quantum-behaved particle swarm optimization (QPSO), a novel path planner for unmanned aerial vehicle (UAV) is employed to generate a safe and flyable path. The standard particle swarm optimization (PSO) and quantum-behaved particle swarm optimization (QPSO) are presented and compared through a UAV path planning application. Every particle in swarm represents a potential path in search space. For the purpose of pruning the search space, constraints are incorporated into the pre-specified cost function, which is used to evaluate whether a particle is good or not. As the system iterated, each particle is pulled toward its local attractor, which is located between the personal best position (pbest) and the global best position (gbest) based on the interaction of particles' individual searches and group's public search. For the sake of simplicity, we only consider planning the projection of path on the plane and assume threats are static instead of moving. Simulation results demonstrated the effectiveness and feasibility of the proposed approach.
Optimal path choice in railway passenger travel network based on residual train capacity.
Dou, Fei; Yan, Kai; Huang, Yakun; Wang, Li; Jia, Limin
2014-01-01
Passenger's optimal path choice is one of the prominent research topics in the field of railway passenger transport organization. More and more different train types are available, increasing path choices from departure to destination for travelers are unstoppable. However, travelers cannot avoid being confused when they hope to choose a perfect travel plan based on various travel time and cost constraints before departure. In this study, railway passenger travel network is constructed based on train timetable. Both the generalized cost function we developed and the residual train capacity are considered to be the foundation of path searching procedure. The railway passenger travel network topology is analyzed based on residual train capacity. Considering the total travel time, the total travel cost, and the total number of passengers, we propose an optimal path searching algorithm based on residual train capacity in railway passenger travel network. Finally, the rationale of the railway passenger travel network and the optimal path generation algorithm are verified positively by case study.
Optimal path-finding through mental exploration based on neural energy field gradients.
Wang, Yihong; Wang, Rubin; Zhu, Yating
2017-02-01
Rodent animal can accomplish self-locating and path-finding task by forming a cognitive map in the hippocampus representing the environment. In the classical model of the cognitive map, the system (artificial animal) needs large amounts of physical exploration to study spatial environment to solve path-finding problems, which costs too much time and energy. Although Hopfield's mental exploration model makes up for the deficiency mentioned above, the path is still not efficient enough. Moreover, his model mainly focused on the artificial neural network, and clear physiological meanings has not been addressed. In this work, based on the concept of mental exploration, neural energy coding theory has been applied to the novel calculation model to solve the path-finding problem. Energy field is constructed on the basis of the firing power of place cell clusters, and the energy field gradient can be used in mental exploration to solve path-finding problems. The study shows that the new mental exploration model can efficiently find the optimal path, and present the learning process with biophysical meaning as well. We also analyzed the parameters of the model which affect the path efficiency. This new idea verifies the importance of place cell and synapse in spatial memory and proves that energy coding is effective to study cognitive activities. This may provide the theoretical basis for the neural dynamics mechanism of spatial memory.
Blondel, Arnaud
2004-05-01
Thermodynamic integration is a widely used method to calculate and analyze the effect of a chemical modification on the free energy of a chemical or biochemical process, for example, the impact of an amino acid substitution on protein association. Numerical fluctuations can introduce large uncertainties, limiting the domain of application of the method. The parametric energy function describing the chemical modification in the thermodynamic integration, the "Alchemical path," determines the amplitudes of the fluctuations. In the present work, I propose a measure of the fluctuations in the thermodynamic integration and an approach to search for a parametric energy path minimizing that measure. The optimal path derived with this approach is very close to the theoretical minimum of the measure, but produces nonergodic sampling. Nevertheless, this path is used to guide the design of a practical and efficient path producing correct sampling. The convergence with this practical path is evaluated on test cases, and compares favorably with that of other methods such as power or polynomial path, soft-core van der Waals, and some other approaches presented in the literature.
Calibration of neural networks using genetic algorithms, with application to optimal path planning
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.
Robust Video Stabilization Using Particle Keypoint Update and l₁-Optimized Camera Path.
Jeon, Semi; Yoon, Inhye; Jang, Jinbeum; Yang, Seungji; Kim, Jisung; Paik, Joonki
2017-02-10
Acquisition of stabilized video is an important issue for various type of digital cameras. This paper presents an adaptive camera path estimation method using robust feature detection to remove shaky artifacts in a video. The proposed algorithm consists of three steps: (i) robust feature detection using particle keypoints between adjacent frames; (ii) camera path estimation and smoothing; and (iii) rendering to reconstruct a stabilized video. As a result, the proposed algorithm can estimate the optimal homography by redefining important feature points in the flat region using particle keypoints. In addition, stabilized frames with less holes can be generated from the optimal, adaptive camera path that minimizes a temporal total variation (TV). The proposed video stabilization method is suitable for enhancing the visual quality for various portable cameras and can be applied to robot vision, driving assistant systems, and visual surveillance systems.
Design Optimization and the path towards a 2 MW Spallation Neutron Source
Energy Technology Data Exchange (ETDEWEB)
M. Blaskiewicz; N. Catalan-Lasheras; D. Davino; A. Fedotov; Y. Lee; N. Malitsky; Y. Papaphilippou; D. Raparia; A. Shishlo; N. Tsoupas; J. Wei; W. Weng; S. Zhang; J. Billen; S. Kurennoy; S. Nath; J. Stovall; H. Takeda; L. Young; R. Keller; J. Staples; A. Aleksandrov; Y. Cho; P. Chu; S. Cousineau; V. Danilov; M. Doleans; J. Galambos; J. Holmes; N. Holtkamp; D. Jeon; S. Kim; R. Kustom; E. Tanke; W. Wan; R. Sundelin
2001-08-01
The Spallation Neutron Source (SNS) is designed to ultimately reach an average proton beam power of 2 MW for pulsed neutron production. The SNS physics groups analyze the machine performance within the hardware constraints, optimize the accelerator design, and establish the best path towards a 2 MW and higher spallation neutron source.
Interactive Learning Environment for Bio-Inspired Optimization Algorithms for UAV Path Planning
Duan, Haibin; Li, Pei; Shi, Yuhui; Zhang, Xiangyin; Sun, Changhao
2015-01-01
This paper describes the development of BOLE, a MATLAB-based interactive learning environment, that facilitates the process of learning bio-inspired optimization algorithms, and that is dedicated exclusively to unmanned aerial vehicle path planning. As a complement to conventional teaching methods, BOLE is designed to help students consolidate the…
Directory of Open Access Journals (Sweden)
Zhou Feng
2013-09-01
Full Text Available A based on Rapidly-exploring Random Tree(RRT and Particle Swarm Optimizer (PSO for path planning of the robot is proposed.First the grid method is built to describe the working space of the mobile robot,then the Rapidly-exploring Random Tree algorithm is used to obtain the global navigation path,and the Particle Swarm Optimizer algorithm is adopted to get the better path.Computer experiment results demonstrate that this novel algorithm can plan an optimal path rapidly in a cluttered environment.The successful obstacle avoidance is achieved,and the model is robust and performs reliably.
Sequential Optimization of Paths in Directed Graphs Relative to Different Cost Functions
Mahayni, Malek A.
2011-07-01
Finding optimal paths in directed graphs is a wide area of research that has received much of attention in theoretical computer science due to its importance in many applications (e.g., computer networks and road maps). Many algorithms have been developed to solve the optimal paths problem with different kinds of graphs. An algorithm that solves the problem of paths’ optimization in directed graphs relative to different cost functions is described in [1]. It follows an approach extended from the dynamic programming approach as it solves the problem sequentially and works on directed graphs with positive weights and no loop edges. The aim of this thesis is to implement and evaluate that algorithm to find the optimal paths in directed graphs relative to two different cost functions ( , ). A possible interpretation of a directed graph is a network of roads so the weights for the function represent the length of roads, whereas the weights for the function represent a constraint of the width or weight of a vehicle. The optimization aim for those two functions is to minimize the cost relative to the function and maximize the constraint value associated with the function. This thesis also includes finding and proving the relation between the two different cost functions ( , ). When given a value of one function, we can find the best possible value for the other function. This relation is proven theoretically and also implemented and experimented using Matlab®[2].
Optimal Selective Feedback Policies for Opportunistic Beamforming
Samarasinghe, Tharaka; Evans, Jamie S
2011-01-01
This paper studies the structure of downlink sum-rate maximizing selective decentralized feedback policies for opportunistic beamforming under finite feedback constraints on the average number of mobile users feeding back. Firstly, it is shown that any sum-rate maximizing selective decentralized feedback policy must be a threshold feedback policy. This result holds for all fading channel models with continuous distribution functions. Secondly, the resulting optimum threshold selection problem is analyzed in detail. This is a non-convex optimization problem over finite dimensional Euclidean spaces. By utilizing the theory of majorization, an underlying Schur-concave structure in the sum-rate function is identified, and the sufficient conditions for the optimality of homogenous threshold feedback policies are obtained. Applications of these results are illustrated for well known fading channel models such as Rayleigh, Nakagami and Rician fading channels, along with various engineering and design insights. Rathe...
The Optimized data path ANN for Low power and Embedded applications.
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S N Prasad
2016-04-01
Full Text Available This present work is aimed at the optimization of ANN (artificial neural network for the low power & embedded applications. Due to rapid switching of the internal signals, power dissipation is very high in the modern VLSI systems. So the optimization is very much essential. This work explores the approaches to modify the existing building blocks of ANN in order to reduce the power (data path optimizations.by considering the 4:2 compressor architecture for the multiplier architecture of layered ANN. The design is modeled using Verilog HDL in the ASIC domain using the CMOS technological library of 65nm.The modified data path architecture consumes 15.91% of area and 26.09% of leakage power lesser when compared with existing architectures. This design provides the better speed up to 12.71%.
Global optimization of tool path for five-axis flank milling with a cylindrical cutter
Institute of Scientific and Technical Information of China (English)
无
2009-01-01
In this paper, optimum positioning of cylindrical cutter for five-axis flank milling of non-developable ruled surface is addressed from the perspective of surface approximation. Based on the developed interchangeability principle, global optimization of the five-axis tool path is modeled as approximation of the tool envelope surface to the data points on the design surface following the minimum zone criterion recommended by ANSI and ISO standards for tolerance evaluation. By using the signed point-to-surface distance function, tool path plannings for semi-finish and finish millings are formulated as two constrained optimization problems in a unified framework. Based on the second order Taylor approximation of the distance function, a sequential approximation algorithm along with a hierarchical algorithmic structure is developed for the optimization. Numerical examples are presented to confirm the validity of the proposed approach.
Global optimization of tool path for five-axis flank milling with a cylindrical cutter
Institute of Scientific and Technical Information of China (English)
DING Han; ZHU LiMin
2009-01-01
In this paper,optimum positioning of cylindrical cutter for five-axis flank milling of non-developable ruled surface is addressed from the perspective of surface approximation.Based on the developed interchangeability principle,global optimization of the five-axis tool path is modeled as approximation of the tool envelope surface to the data points on the design surface following the minimum zone criterion recommended by ANSI and ISO standards for tolerance evaluation.By using the signed point-to-surface distance function,tool path plannings for semi-finish and finish millings are formulated as two constrained optimization problems in a unified framework.Based on the second order Taylor approximation of the distance function,a sequential approximation algorithm along with a hierarchical algorithmic structure is developed for the optimization.Numerical examples are presented to confirm the validity of the proposed approach.
An optimal path planning problem for heterogeneous multi-vehicle systems
Directory of Open Access Journals (Sweden)
Klaučo Martin
2016-06-01
Full Text Available A path planning problem for a heterogeneous vehicle is considered. Such a vehicle consists of two parts which have the ability to move individually, but one of them has a shorter range and is therefore required to keep in a close distance to the main vehicle. The objective is to devise an optimal path of minimal length under the condition that at least one part of the heterogeneous system visits all desired waypoints exactly once. Two versions of the problem are considered. One assumes that the order in which the waypoints are visited is known a priori. In such a case we show that the optimal path can be found by solving a mixed-integer second-order cone problem. The second version assumes that the order in which the waypoints are visited is not known a priori, but can be optimized so as to shorten the length of the path. Two approaches to solve this problem are presented and evaluated with respect to computational complexity.
An Optimization-Driven Analysis Pipeline to Uncover Biomarkers and Signaling Paths: Cervix Cancer
Lorenzo, Enery; Camacho-Caceres, Katia; Ropelewski, Alexander J.; Rosas, Juan; Ortiz-Mojer, Michael; Perez-Marty, Lynn; Irizarry, Juan; Gonzalez, Valerie; Rodríguez, Jesús A.; Cabrera-Rios, Mauricio; Isaza, Clara
2015-01-01
Establishing how a series of potentially important genes might relate to each other is relevant to understand the origin and evolution of illnesses, such as cancer. High-throughput biological experiments have played a critical role in providing information in this regard. A special challenge, however, is that of trying to conciliate information from separate microarray experiments to build a potential genetic signaling path. This work proposes a two-step analysis pipeline, based on optimization, to approach meta-analysis aiming to build a proxy for a genetic signaling path. PMID:26388997
Optimization of beamforming and path planning for UAV-assisted wireless relay networks
Directory of Open Access Journals (Sweden)
Ouyang Jian
2014-04-01
Full Text Available Recently, unmanned aerial vehicles (UAVs acting as relay platforms have attracted considerable attention due to the advantages of extending coverage and improving connectivity for long-range communications. Specifically, in the scenario where the access point (AP is mobile, a UAV needs to find an efficient path to guarantee the connectivity of the relay link. Motivated by this fact, this paper proposes an optimal design for beamforming (BF and UAV path planning. First of all, we study a dual-hop amplify-and-forward (AF wireless relay network, in which a UAV is used as relay between a mobile AP and a fixed base station (BS. In the network, both of the AP and the BS are equipped with multiple antennas, whereas the UAV has a single antenna. Then, we obtain the output signal-to-noise ratio (SNR of the dual-hop relay network. Based on the criterion of maximizing the output SNR, we develop an optimal design to obtain the solution of the optimal BF weight vector and the UAV heading angle. Next, we derive the closed-form outage probability (OP expression to investigate the performance of the dual-hop relay network conveniently. Finally, computer simulations show that the proposed approach can obtain nearly optimal flying path and OP performance, indicating the effectiveness of the proposed algorithm. Furthermore, we find that increasing the antenna number at the BS or the maximal heading angle can significantly improve the performance of the considered relay network.
Service Priority based Reliable Routing Path Select Method in Smart Grid Communication Network
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Kaixuan Wang
2012-11-01
Full Text Available The new challenges and schemes for the Smart Grid require high reliable transmission technologiesto support various types of electrical services and applications. This paper concentrates the degree of importance of services and tries to allocate more important service to more reliable network routing path to deliver the key instructions in the Smart Grid communication networks. Pareto probability distribution is used to weight the reliability of IP-based router path. In order to definition the relationship of service and reliability of router path, we devise a mapping and optimal function is presented to access. An optimal method is used for adapting to find the appropriate value to match the objective function. Finally, we validate the proposed algorithms by experiments. The simulation results show that the proposed algorithm outperforms the random routing algorithms.
Behavioral optimization models for multicriteria portfolio selection
Directory of Open Access Journals (Sweden)
Mehlawat Mukesh Kumar
2013-01-01
Full Text Available In this paper, behavioral construct of suitability is used to develop a multicriteria decision making framework for portfolio selection. To achieve this purpose, we rely on multiple methodologies. Analytical hierarchy process technique is used to model the suitability considerations with a view to obtaining the suitability performance score in respect of each asset. A fuzzy multiple criteria decision making method is used to obtain the financial quality score of each asset based upon investor's rating on the financial criteria. Two optimization models are developed for optimal asset allocation considering simultaneously financial and suitability criteria. An empirical study is conducted on randomly selected assets from National Stock Exchange, Mumbai, India to demonstrate the effectiveness of the proposed methodology.
Optimization methods for activities selection problems
Mahad, Nor Faradilah; Alias, Suriana; Yaakop, Siti Zulaika; Arshad, Norul Amanina Mohd; Mazni, Elis Sofia
2017-08-01
Co-curriculum activities must be joined by every student in Malaysia and these activities bring a lot of benefits to the students. By joining these activities, the students can learn about the time management and they can developing many useful skills. This project focuses on the selection of co-curriculum activities in secondary school using the optimization methods which are the Analytic Hierarchy Process (AHP) and Zero-One Goal Programming (ZOGP). A secondary school in Negeri Sembilan, Malaysia was chosen as a case study. A set of questionnaires were distributed randomly to calculate the weighted for each activity based on the 3 chosen criteria which are soft skills, interesting activities and performances. The weighted was calculated by using AHP and the results showed that the most important criteria is soft skills. Then, the ZOGP model will be analyzed by using LINGO Software version 15.0. There are two priorities to be considered. The first priority which is to minimize the budget for the activities is achieved since the total budget can be reduced by RM233.00. Therefore, the total budget to implement the selected activities is RM11,195.00. The second priority which is to select the co-curriculum activities is also achieved. The results showed that 9 out of 15 activities were selected. Thus, it can concluded that AHP and ZOGP approach can be used as the optimization methods for activities selection problem.
Reuschel, Johanna; Rösler, Frank; Henriques, Denise Y P; Fiehler, Katja
2011-04-01
Many studies provide evidence that information from different modalities is integrated following the maximum likelihood estimation model (MLE). For instance, we recently found that visual and proprioceptive path trajectories are optimally combined (Reuschel et al. in Exp Brain Res 201:853-862, 2010). However, other studies have failed to reveal optimal integration of such dynamic information. In the present study, we aim to generalize our previous findings to different parts of the workspace (central, ipsilateral, or contralateral) and to different types of judgments (relative vs. absolute). Participants made relative judgments by judging whether an angular path was acute or obtuse, or they made absolute judgments by judging whether a one-segmented straight path was directed to left or right. Trajectories were presented in the visual, proprioceptive, or combined visual-proprioceptive modality. We measured the bias and the variance of these estimates and predicted both parameters using the MLE. In accordance with the MLE model, participants linearly combined and weighted the unimodal angular path information by their reliabilities irrespective of the side of workspace. However, the precision of bimodal estimates was not greater than that for unimodal estimates, which is inconsistent with the MLE. For the absolute judgment task, participants' estimates were highly accurate and did not differ across modalities. Thus, we were unable to test whether the bimodal percept resulted as a weighted average of the visual and proprioceptive input. Additionally, participants were not more precise in the bimodal compared with the unimodal conditions, which is inconsistent with the MLE. Current findings suggest that optimal integration of visual and proprioceptive information of path trajectory only applies in some conditions.
Deterministic Agent-Based Path Optimization by Mimicking the Spreading of Ripples.
Hu, Xiao-Bing; Wang, Ming; Leeson, Mark S; Di Paolo, Ezequiel A; Liu, Hao
2016-01-01
Inspirations from nature have contributed fundamentally to the development of evolutionary computation. Learning from the natural ripple-spreading phenomenon, this article proposes a novel ripple-spreading algorithm (RSA) for the path optimization problem (POP). In nature, a ripple spreads at a constant speed in all directions, and the node closest to the source is the first to be reached. This very simple principle forms the foundation of the proposed RSA. In contrast to most deterministic top-down centralized path optimization methods, such as Dijkstra's algorithm, the RSA is a bottom-up decentralized agent-based simulation model. Moreover, it is distinguished from other agent-based algorithms, such as genetic algorithms and ant colony optimization, by being a deterministic method that can always guarantee the global optimal solution with very good scalability. Here, the RSA is specifically applied to four different POPs. The comparative simulation results illustrate the advantages of the RSA in terms of effectiveness and efficiency. Thanks to the agent-based and deterministic features, the RSA opens new opportunities to attack some problems, such as calculating the exact complete Pareto front in multiobjective optimization and determining the kth shortest project time in project management, which are very difficult, if not impossible, for existing methods to resolve. The ripple-spreading optimization principle and the new distinguishing features and capacities of the RSA enrich the theoretical foundations of evolutionary computation.
Estimation Method of Path-Selecting Proportion for Urban Rail Transit Based on AFC Data
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Feng Zhou
2015-01-01
Full Text Available With the successful application of automatic fare collection (AFC system in urban rail transit (URT, the information of passengers’ travel time is recorded, which provides the possibility to analyze passengers’ path-selecting by AFC data. In this paper, the distribution characteristics of the components of travel time were analyzed, and an estimation method of path-selecting proportion was proposed. This method made use of single path ODs’ travel time data from AFC system to estimate the distribution parameters of the components of travel time, mainly including entry walking time (ewt, exit walking time (exwt, and transfer walking time (twt. Then, for multipath ODs, the distribution of each path’s travel time could be calculated under the condition of its components’ distributions known. After that, each path’s path-selecting proportion can be estimated. Finally, simulation experiments were designed to verify the estimation method, and the results show that the error rate is less than 2%. Compared with the traditional models of flow assignment, the estimation method can reduce the cost of artificial survey significantly and provide a new way to calculate the path-selecting proportion for URT.
Jafarizadeh, Saber
2010-01-01
Providing an analytical solution for the problem of finding Fastest Distributed Consensus (FDC) is one of the challenging problems in the field of sensor networks. Most of the methods proposed so far deal with the FDC averaging algorithm problem by numerical convex optimization methods and in general no closed-form solution for finding FDC has been offered up to now except in [3] where the conjectured answer for path has been proved. Here in this work we present an analytical solution for the problem of Fastest Distributed Consensus for the Path network using semidefinite programming particularly solving the slackness conditions, where the optimal weights are obtained by inductive comparing of the characteristic polynomials initiated by slackness conditions.
Lai, Xue-Cheng; Ge, Shuzhi Sam; Al Mamun, Abdullah
2007-12-01
This paper studies a hierarchical approach for incrementally driving a nonholonomic mobile robot to its destination in unknown environments. The A* algorithm is modified to handle a map containing unknown information. Based on it, optimal (discrete) paths are incrementally generated with a periodically updated map. Next, accelerations in varying velocities are taken into account in predicting the robot pose and the robot trajectory resulting from a motion command. Obstacle constraints are transformed to suitable velocity limits so that the robot can move as fast as possible while avoiding collisions when needed. Then, to trace the discrete path, the system searches for a waypoint-directed optimized motion in a reduced 1-D translation or rotation velocity space. Various situations of navigation are dealt with by using different strategies rather than a single objective function. Extensive simulations and experiments verified the efficacy of the proposed approach.
Inverse optimal control for speed-varying path following of marine vessels with actuator dynamics
Qu, Yang; Xu, Haixiang; Yu, Wenzhao; Feng, Hui; Han, Xin
2017-06-01
A controller which is locally optimal near the origin and globally inverse optimal for the nonlinear system is proposed for path following of over actuated marine crafts with actuator dynamics. The motivation is the existence of undesired signals sent to the actuators, which can result in bad behavior in path following. To attenuate the oscillation of the control signal and obtain smooth thrust outputs, the actuator dynamics are added into the ship maneuvering model. Instead of modifying the Line-of-Sight (LOS) guidance law, this proposed controller can easily adjust the vessel speed to minimize the large cross-track error caused by the high vessel speed when it is turning. Numerical simulations demonstrate the validity of this proposed controller.
Regression Test Case Selection for MultiObjective Optimization Using Metaheuristics
Directory of Open Access Journals (Sweden)
Rahul Chaudhary
2015-03-01
Full Text Available A new heuristic algorithm is proposed by this paper, on multi-objective optimization using metaheuristics and TSP (travelling salesman problems. Basic thinking behind this algorithm is minimizing the TSP path or tour by dividing the entire tour into blocks that are overlapped to each other and then improve each individual block separately. Although it is unproven that a good solution have small improvement chances if a node moved far way to a position compared to its original solution. By intensively searching each block, further improvement is possible in TSP path or tour that never be supported in various search methods and genetic algorithm. Proposed algorithm and computational experiment performance was tested, and these tests are carried out with support of already present instances of problem. According to the results represented by paper, the computation verifies that proposed algorithm can solve TSPs efficiently. Proposed algorithm is then used for selecting optimal test cases, thousands of those test cases which are selected after confirming that they identify bugs and they itself selected from a repository of test cases; these thousand test cases are those test cases which are selected from several thousand test cases because they detect bugs. Few test cases from repository act as milestones (nodes and having certain weight associated with each, proposed algorithm based on TSP implemented over selected result and select the optimal result or path or solution. These selected optimal test cases or selected path are further used to perform the regression testing, by applying those test cases selected by proposed algorithm in order to remove most of the faults or bugs effectively, i.e. take less time and identify almost all the bugs with few test cases. Hence this proposed algorithm assures most effective solution for regression testing test case selection.
Multi objective SNP selection using pareto optimality.
Gumus, Ergun; Gormez, Zeliha; Kursun, Olcay
2013-04-01
Biomarker discovery is a challenging task of bioinformatics especially when targeting high dimensional problems such as SNP (single nucleotide polymorphism) datasets. Various types of feature selection methods can be applied to accomplish this task. Typically, using features versus class labels of samples in the training dataset, these methods aim at selecting feature subsets with maximal classification accuracies. Although finding such class-discriminative features is crucial, selection of relevant SNPs for maximizing other properties that exist in the nature of population genetics such as the correlation between genetic diversity and geographical distance of ethnic groups can also be equally important. In this work, a methodology using a multi objective optimization technique called Pareto Optimal is utilized for selecting SNP subsets offering both high classification accuracy and correlation between genomic and geographical distances. In this method, discriminatory power of an SNP is determined using mutual information and its contribution to the genomic-geographical correlation is estimated using its loadings on principal components. Combining these objectives, the proposed method identifies SNP subsets that can better discriminate ethnic groups than those obtained with sole mutual information and yield higher correlation than those obtained with sole principal components on the Human Genome Diversity Project (HGDP) SNP dataset.
Time-optimal path planning in dynamic flows using level set equations: theory and schemes
Lolla, Tapovan; Lermusiaux, Pierre F. J.; Ueckermann, Mattheus P.; Haley, Patrick J.
2014-10-01
We develop an accurate partial differential equation-based methodology that predicts the time-optimal paths of autonomous vehicles navigating in any continuous, strong, and dynamic ocean currents, obviating the need for heuristics. The goal is to predict a sequence of steering directions so that vehicles can best utilize or avoid currents to minimize their travel time. Inspired by the level set method, we derive and demonstrate that a modified level set equation governs the time-optimal path in any continuous flow. We show that our algorithm is computationally efficient and apply it to a number of experiments. First, we validate our approach through a simple benchmark application in a Rankine vortex flow for which an analytical solution is available. Next, we apply our methodology to more complex, simulated flow fields such as unsteady double-gyre flows driven by wind stress and flows behind a circular island. These examples show that time-optimal paths for multiple vehicles can be planned even in the presence of complex flows in domains with obstacles. Finally, we present and support through illustrations several remarks that describe specific features of our methodology.
Institute of Scientific and Technical Information of China (English)
施虎; 龚国芳; 杨华勇; 梅雪松
2014-01-01
A motion parameter optimization method based on the objective of minimizing the total energy consumption in segment positioning was proposed for segment erector of shield tunneling machine. The segment positioning process was decomposed into rotation, lifting and sliding actions in deriving the energy calculation model of segment erection. The work of gravity was taken into account in the mathematical modeling of energy consumed by each actuator. In order to investigate the relationship between the work done by the actuator and the path moved along by the segment, the upward and downward directions as well as the operating quadrant of the segment erector were defined. Piecewise nonlinear function of energy was presented, of which the result is determined by closely coupled components as working parameters and some intermediate variables. Finally, the effectiveness of the optimization method was proved by conducting a case study with a segment erector for the tunnel with a diameter of 3 m and drawing comparisons between different assembling paths. The results show that the energy required by assembling a ring of segments along the optimized moving path can be reduced up to 5%. The method proposed in this work definitely provides an effective energy saving solution for shield tunneling machine.
Directory of Open Access Journals (Sweden)
Yang Liu
2016-01-01
Full Text Available This paper proposes a potential odor intensity grid based optimization approach for unmanned aerial vehicle (UAV path planning with particle swarm optimization (PSO technique. Odor intensity is created to color the area in the searching space with highest probability where candidate particles may locate. A potential grid construction operator is designed for standard PSO based on different levels of odor intensity. The potential grid construction operator generates two potential location grids with highest odor intensity. Then the middle point will be seen as the final position in current particle dimension. The global optimum solution will be solved as the average. In addition, solution boundaries of searching space in each particle dimension are restricted based on properties of threats in the flying field to avoid prematurity. Objective function is redesigned by taking minimum direction angle to destination into account and a sampling method is introduced. A paired samples t-test is made and an index called straight line rate (SLR is used to evaluate the length of planned path. Experiments are made with other three heuristic evolutionary algorithms. The results demonstrate that the proposed method is capable of generating higher quality paths efficiently for UAV than any other tested optimization techniques.
Liu, Taoming; Çavuşoğlu, M. Cenk
2015-01-01
This paper presents algorithms for optimal selection of needle grasp, for autonomous robotic execution of the minimally invasive surgical suturing task. In order to minimize the tissue trauma during the suturing motion, the best practices of needle path planning that are used by surgeons are applied for autonomous robotic surgical suturing tasks. Once an optimal needle trajectory in a well-defined suturing scenario is chosen, another critical issue for suturing is the choice of needle grasp f...
Liu, Haiyan; Lu, Zhenyu; Cisneros, G Andres; Yang, Weitao
2004-07-08
The determination of reaction paths for enzyme systems remains a great challenge for current computational methods. In this paper we present an efficient method for the determination of minimum energy reaction paths with the ab initio quantum mechanical/molecular mechanical approach. Our method is based on an adaptation of the path optimization procedure by Ayala and Schlegel for small molecules in gas phase, the iterative quantum mechanical/molecular mechanical (QM/MM) optimization method developed earlier in our laboratory and the introduction of a new metric defining the distance between different structures in the configuration space. In this method we represent the reaction path by a discrete set of structures. For each structure we partition the atoms into a core set that usually includes the QM subsystem and an environment set that usually includes the MM subsystem. These two sets are optimized iteratively: the core set is optimized to approximate the reaction path while the environment set is optimized to the corresponding energy minimum. In the optimization of the core set of atoms for the reaction path, we introduce a new metric to define the distances between the points on the reaction path, which excludes the soft degrees of freedom from the environment set and includes extra weights on coordinates describing chemical changes. Because the reaction path is represented by discrete structures and the optimization for each can be performed individually with very limited coupling, our method can be executed in a natural and efficient parallelization, with each processor handling one of the structures. We demonstrate the applicability and efficiency of our method by testing it on two systems previously studied by our group, triosephosphate isomerase and 4-oxalocrotonate tautomerase. In both cases the minimum energy paths for both enzymes agree with the previously reported paths.
Optimal Portfolio Selection Under Concave Price Impact
Energy Technology Data Exchange (ETDEWEB)
Ma Jin, E-mail: jinma@usc.edu [University of Southern California, Department of Mathematics (United States); Song Qingshuo, E-mail: songe.qingshuo@cityu.edu.hk [City University of Hong Kong, Department of Mathematics (Hong Kong); Xu Jing, E-mail: xujing8023@yahoo.com.cn [Chongqing University, School of Economics and Business Administration (China); Zhang Jianfeng, E-mail: jianfenz@usc.edu [University of Southern California, Department of Mathematics (United States)
2013-06-15
In this paper we study an optimal portfolio selection problem under instantaneous price impact. Based on some empirical analysis in the literature, we model such impact as a concave function of the trading size when the trading size is small. The price impact can be thought of as either a liquidity cost or a transaction cost, but the concavity nature of the cost leads to some fundamental difference from those in the existing literature. We show that the problem can be reduced to an impulse control problem, but without fixed cost, and that the value function is a viscosity solution to a special type of Quasi-Variational Inequality (QVI). We also prove directly (without using the solution to the QVI) that the optimal strategy exists and more importantly, despite the absence of a fixed cost, it is still in a 'piecewise constant' form, reflecting a more practical perspective.
Optimal Portfolio Selection under Concave Price Impact
Ma, Jin; Xu, Jing; Zhang, Jianfeng
2012-01-01
In this paper we study an optimal portfolio selection problem under instantaneous price compact. Based some empirical analysis in the literature, we model such impact as a concave function of the trading size when the trading size is small. The price impact can be thought of as either the liquidity cost or transaction cost, but the concavity nature of the cost leads to some fundamental difference from those in the existing literature. We show that the problem can be reduced to an impulse control problem, but without fixed cost, and that the value function is a viscosity solution to a special type of Quasi-Variational Inequality (QVI). We also prove directly (without using the solution to the QVI) that the optimal strategy exists and more importantly, despite the absence of a fixed cost, it is still in a "piecewise constant" form, reflecting a more practical perspective.
RSMDP-based Robust Q-learning for Optimal Path Planning in a Dynamic Environment
Directory of Open Access Journals (Sweden)
Yunfei Zhang
2014-07-01
Full Text Available This paper presents arobust Q-learning method for path planningin a dynamic environment. The method consists of three steps: first, a regime-switching Markov decision process (RSMDP is formed to present the dynamic environment; second a probabilistic roadmap (PRM is constructed, integrated with the RSMDP and stored as a graph whose nodes correspond to a collision-free world state for the robot; and third, an onlineQ-learning method with dynamic stepsize, which facilitates robust convergence of the Q-value iteration, is integrated with the PRM to determine an optimal path for reaching the goal. In this manner, the robot is able to use past experience for improving its performance in avoiding not only static obstacles but also moving obstacles, without knowing the nature of the obstacle motion. The use ofregime switching in the avoidance of obstacles with unknown motion is particularly innovative. The developed approach is applied to a homecare robot in computer simulation. The results show that the online path planner with Q-learning is able torapidly and successfully converge to the correct path.
Energy-aware path selection metric for IEEE 802.11s wireless mesh networking
CSIR Research Space (South Africa)
Mhlanga, MM
2009-01-01
Full Text Available The IEEE 802.11s working group has commenced activities, which would lead to the development of a standard for wireless mesh networks (WMNs). The draft of 802.11s introduces a new path selection metric called airtime link metric. However...
Optimal Path Design of Geared 5-bar mechanism using Differential Evolution Algorithm
Directory of Open Access Journals (Sweden)
Ali Aliniay Saghalaksari
2016-06-01
Full Text Available Five-bar linkage mechanisms with two degrees of freedom (DOF are more capable in generating coupler path than four-bar mechanisms with one DOF. The DOF of these mechanisms is reduced to one and they will have constant ratio of binary input when they are equipped by gear. Therefore, besides keeping the simple structure, it is possible to employ them to generate a more accurate path than that generated by four-bar mechanisms using only one input. In this study, using such mechanism for the considered paths, which are used for the comparison purpose, a singleobjective design is performed to optimize the length of mechanism links and revolution ratio of gears by considering the necessary constraints. The error function of square deviation of positions is considered as the objective function and the differential evolution algorithm is utilized in order to solve the considered optimization problems, which are Triangle Curve with 22 Discrete Points and Asteroid Curve with 41 Discrete Points. Compared with the main reference [9], the final results revealed a significant improvement.
Average Sample-path Optimality for Continuous-time Markov Decision Processes in Polish Spaces
Institute of Scientific and Technical Information of China (English)
Quan-xin ZHU
2011-01-01
In this paper we study the average sample-path cost (ASPC) problem for continuous-time Markov decision processes in Polish spaces.To the best of our knowledge,this paper is a first attempt to study the ASPC criterion on continuous-time MDPs with Polish state and action spaces.The corresponding transition rates are allowed to be unbounded,and the cost rates may have neither upper nor lower bounds.Under some mild hypotheses,we prove the existence of e (ε ≥ 0)-ASPC optimal stationary policies based on two different approaches:one is the “optimality equation” approach and the other is the “two optimality inequalities” approach.
Selectively-informed particle swarm optimization.
Gao, Yang; Du, Wenbo; Yan, Gang
2015-03-19
Particle swarm optimization (PSO) is a nature-inspired algorithm that has shown outstanding performance in solving many realistic problems. In the original PSO and most of its variants all particles are treated equally, overlooking the impact of structural heterogeneity on individual behavior. Here we employ complex networks to represent the population structure of swarms and propose a selectively-informed PSO (SIPSO), in which the particles choose different learning strategies based on their connections: a densely-connected hub particle gets full information from all of its neighbors while a non-hub particle with few connections can only follow a single yet best-performed neighbor. Extensive numerical experiments on widely-used benchmark functions show that our SIPSO algorithm remarkably outperforms the PSO and its existing variants in success rate, solution quality, and convergence speed. We also explore the evolution process from a microscopic point of view, leading to the discovery of different roles that the particles play in optimization. The hub particles guide the optimization process towards correct directions while the non-hub particles maintain the necessary population diversity, resulting in the optimum overall performance of SIPSO. These findings deepen our understanding of swarm intelligence and may shed light on the underlying mechanism of information exchange in natural swarm and flocking behaviors.
Time-optimal path planning in uncertain flow fields using ensemble method
Wang, Tong
2016-01-06
An ensemble-based approach is developed to conduct time-optimal path planning in unsteady ocean currents under uncertainty. We focus our attention on two-dimensional steady and unsteady uncertain flows, and adopt a sampling methodology that is well suited to operational forecasts, where a set deterministic predictions is used to model and quantify uncertainty in the predictions. In the operational setting, much about dynamics, topography and forcing of the ocean environment is uncertain, and as a result a single path produced by a model simulation has limited utility. To overcome this limitation, we rely on a finitesize ensemble of deterministic forecasts to quantify the impact of variability in the dynamics. The uncertainty of flow field is parametrized using a finite number of independent canonical random variables with known densities, and the ensemble is generated by sampling these variables. For each the resulting realizations of the uncertain current field, we predict the optimal path by solving a boundary value problem (BVP), based on the Pontryagin maximum principle. A family of backward-in-time trajectories starting at the end position is used to generate suitable initial values for the BVP solver. This allows us to examine and analyze the performance of sampling strategy, and develop insight into extensions dealing with regional or general circulation models. In particular, the ensemble method enables us to perform a statistical analysis of travel times, and consequently develop a path planning approach that accounts for these statistics. The proposed methodology is tested for a number of scenarios. We first validate our algorithms by reproducing simple canonical solutions, and then demonstrate our approach in more complex flow fields, including idealized, steady and unsteady double-gyre flows.
Study on an urban transportation optimal path algorithm%城市交通最优路径算法
Institute of Scientific and Technical Information of China (English)
陈亮; 何为; 韩力群
2012-01-01
城市智能交通系统中,最优路径算法及其优化是研究热点之一,是整个交通系统较为核心的部分.结合图论中最短路径算法,研究了城市交通可达路径算法,并对其进行了有效优化,通过图论中的路径代价函数,提出了城市最优路径算法,在此基础上,通过优化搜索区域、可达路径的搜索方向以及路网分层搜索等优化策略,达到了优化城市最优路径算法的目的,提出的城市最优路径及其优化算法能够给出行者提供多条参考的时间最优路线,方便出行者选择.通过算法的应用实例,验证了城市最优路径及其优化算法的有效性与实时性.%In urban intelligent transportation systems, the optimal path algorithm and its optimization are hot topic and the core of the whole transportation system. By introducing the shortest path algorithm in graph theory, this paper first researched the accessible paths for the urban transportation along with an optimization algorithm. Next, by using the path cost function, an optimal path algorithm for urban transportation was proposed. On this basis, by optimizing the search area, the search direction for accessible paths, and the road network hierarchical search optimization strategy, the goal of optimizing urban paths was attained. The proposed optimal urban path and its optimization algorithm were able to provide several time-optimal pedestrian paths for references. Through practical applications, the validity and real-time characteristics of the proposed urban optimal path and its optimization algorithm were verified.
Effect of Background Ions on the Selection of the Discharge Path
Institute of Scientific and Technical Information of China (English)
HE Zheng-Hao; LI Jin
2001-01-01
The effects of the background ions on the selection of the discharge path in an air gap have been studied with two different methods. The lightning impulse air discharge experiment is conducted using an independent ion generator, while the air discharge experiment uses a lightning impulse superimposed on a dc high voltage used to produce background ions. The influence of different background ions on the leader development, and thus on the discharge path, is observed. Consistent results have been obtained with the two methods. The probability for the discharge path passing through the negative ion space is much higher than that for the passing through the positive ion space. The mechanism of the effects of background ions is analysed based on the eleetron avalanche and the electric field.
Birkholz, Adam B; Schlegel, H Bernhard
2016-05-14
Reaction path optimization is being used more frequently as an alternative to the standard practice of locating a transition state and following the path downhill. The Variational Reaction Coordinate (VRC) method was proposed as an alternative to chain-of-states methods like nudged elastic band and string method. The VRC method represents the path using a linear expansion of continuous basis functions, allowing the path to be optimized variationally by updating the expansion coefficients to minimize the line integral of the potential energy gradient norm, referred to as the Variational Reaction Energy (VRE) of the path. When constraints are used to control the spacing of basis functions and to couple the minimization of the VRE with the optimization of one or more individual points along the path (representing transition states and intermediates), an approximate path as well as the converged geometries of transition states and intermediates along the path are determined in only a few iterations. This algorithmic efficiency comes at a high per-iteration cost due to numerical integration of the VRE derivatives. In the present work, methods for incorporating redundant internal coordinates and potential energy surface interpolation into the VRC method are described. With these methods, the per-iteration cost, in terms of the number of potential energy surface evaluations, of the VRC method is reduced while the high algorithmic efficiency is maintained.
Flying path optimization in UAV-assisted IoT sensor networks
Directory of Open Access Journals (Sweden)
Sang-Jo Yoo
2016-09-01
Full Text Available In this paper, we present an optimal flying path for unmanned aerial vehicle-assisted internet of things sensor networks using a location aware multi-layer information map considering different utility functions based on the sensor density, energy consumption, flight time, and flying risk level. The overall weighted sum of multi-objective utility functions is maximized using the genetic algorithm. The simulation results verify that the optimum solution points can be obtained by adjusting the weights while satisfying the required constraints.
Construction of Learning Path Using Ant Colony Optimization from a Frequent Pattern Graph
Directory of Open Access Journals (Sweden)
Souvik Sengupta
2011-11-01
Full Text Available In an e-Learning system a learner may come across multiple unknown terms, which are generally hyperlinked, while reading a text definition or theory on any topic. It becomes even harder when one tries to understand those unknown terms through further such links and they again find some new terms that have new links. As a consequence they get confused where to initiate from and what are the prerequisites. So it is very obvious for the learner to make a choice of what should be learnt before what. In this paper we have taken the data mining based frequent pattern graph model to define the association and sequencing between the words and then adopted the Ant Colony Optimization, an artificial intelligence approach, to derive a searching technique to obtain an efficient and optimized learning path to reach to a unknown term.
Shao, Wen-Yi; Xian, Hao
2016-11-01
When building an experimental platform for light propagation along an inhomogeneous turbulent path, it is very essential to set up the reasonable distribution of phase screen. Based on multi-layered model of phase screen, an iterative optimization algorithm of phase screen position is given in this paper. Thereafter, the optimal position of phase screens is calculated under the Hufnagel-Valley5/7 and Hefei-day turbulence profile. The results show that the positions of phase screen calculated by the iterative algorithm can fit well with the turbulence profile rather than mechanically placed phase screens at equal distance. Compared with the uniform distribution of phase screens position, the residual phase error of the iterative algorithm decreases very significantly. The similarity degree between them is minimal when number of layers is equal to two. Project supported by the National Natural Science Foundation of China (Grant No. 61308082).
Proposed optimal LSP selection method in MPLS networks
Kuribayashi, Shin-ichi
2012-01-01
Multi-Protocol Label Switching (MPLS) had been deployed by many data networking service providers, including the next-generation mobile backhaul networks, because of its undeniable potential in terms of virtual private network (VPN) management, traffic engineering, etc. In MPLS networks, IP packets are transmitted along a Label Switched Path (LSP) established between edge nodes. To improve the efficiency of resource use in MPLS networks, it is essential to utilize the LSPs efficiently. This paper proposes a method of selecting the optimal LSP pair from among multiple LSP pairs which are established between the same pair of edge nodes, on the assumption that both the upward and downward LSPs are established as a pair (both-way operation). It is supposed that both upward and downward bandwidths are allocated simultaneously in the selected LSP pair for each service request. It is demonstrated by simulation evaluations that the proposal method could reduce the total amount of the bandwidth required by up to 15% c...
Resource-efficient path-protection schemes and online selection of routes in reliable WDM networks
Monti, Paolo; Tacca, Marco; Fumagalli, Andrea
2004-04-01
The optimal choice of routing and wavelength assignment (RWA) for the working and protection path-pair of the newly generated demand request is often a complex problem in reliable wavelength-division-multiplexed (WDM) networks subject to dynamic traffic. The challenge is twofold: how to provide the required reliability level without over-reserving network resources and how to find a good solution of the RWA problem under constrained computational time. Two important contributions are made. First, the shared path protection (SPP) switching scheme is generalized to guarantee the required (differentiated) level of reliability to all arriving demands, while, at the same time, ensuring that they contain the required amount of reserved network resources. This generalization is referred to as SPP-DiR. Second, an approach for choosing the working and protection path-pair routing for the arriving demand is proposed. The approach is based on a matrix of preselected path-pairs: the disjoint path-pair matrix (DPM). Results show that, when the SPP-DiR scheme is applied, a small reduction in demand reliability corresponds to a significant reduction of the required network resources, when compared with the conventional SPP. In turn, the demand blocking probability may be reduced more than one order of magnitude. It is also shown that the DPM approach is suitable for obtaining satisfactory RWA solutions in both SPP-DiR and conventional SPP networks. The use of the DPM is most suited when the time for solving the RWA problem is constrained, e.g., when demand requests must be served swiftly.
A PATH INTEGRAL FORMULATION OF THE WRIGHT-FISHER PROCESS WITH GENIC SELECTION
SCHRAIBER, JOSHUA G.
2014-01-01
The Wright-Fisher process with selection is an important tool in population genetics theory. Traditional analysis of this process relies on the diffusion approximation. The diffusion approximation is usually studied in a partial differential equations framework. In this paper, I introduce a path integral formalism to study the Wright-Fisher process with selection and use that formalism to obtain a simple perturbation series to approximate the transition density. The perturbation series can be understood in terms of Feynman diagrams, which have a simple probabilistic interpretation in terms of selective events. The perturbation series proves to be an accurate approximation of the transition density for weak selection and is shown to be arbitrarily accurate for any selection coefficient. PMID:24269333
Path Planning of Mobile Elastic Robotic Arms by Indirect Approach of Optimal Control
Directory of Open Access Journals (Sweden)
Moharam Habibnejad Korayem
2011-03-01
Full Text Available Finding optimal trajectory is critical in several applications of robot manipulators. This paper is applied the open-loop optimal control approach for generating the optimal trajectory of the flexible mobile manipulators in point-to-point motion. This method is based on the Pontryagin-s minimum principle that by providing a two-point boundary value problem is solved the problem. This problem is known to be complex in particular when combined motion of the base and manipulator, non-holonomic constraint of the base and highly non-linear and complicated dynamic equations as a result of flexible nature of links are taken into account. The study emphasizes on modeling of the complete optimal control problem by remaining all nonlinear state and costate variables as well as control constraints. In this method, designer can compromise between different objectives by considering the proper penalty matrices and it yields to choose the proper trajectory among the various paths. The effectiveness and capability of the proposed approach are demonstrated through simulation studies. Finally, to verify the proposed method, the simulation results obtained from the model are compared with the results of those available in the literature.
MaNGA: Target selection and Optimization
Wake, David
2016-01-01
The 6-year SDSS-IV MaNGA survey will measure spatially resolved spectroscopy for 10,000 nearby galaxies using the Sloan 2.5m telescope and the BOSS spectrographs with a new fiber arrangement consisting of 17 individually deployable IFUs. We present the simultaneous design of the target selection and IFU size distribution to optimally meet our targeting requirements. The requirements for the main samples were to use simple cuts in redshift and magnitude to produce an approximately flat number density of targets as a function of stellar mass, ranging from 1x109 to 1x1011 M⊙, and radial coverage to either 1.5 (Primary sample) or 2.5 (Secondary sample) effective radii, while maximizing S/N and spatial resolution. In addition we constructed a "Color-Enhanced" sample where we required 25% of the targets to have an approximately flat number density in the color and mass plane. We show how these requirements are met using simple absolute magnitude (and color) dependent redshift cuts applied to an extended version of the NASA Sloan Atlas (NSA), how this determines the distribution of IFU sizes and the resulting properties of the MaNGA sample.
Optimal selection of biochars for remediating metals ...
Approximately 500,000 abandoned mines across the U.S. pose a considerable, pervasive risk to human health and the environment due to possible exposure to the residuals of heavy metal extraction. Historically, a variety of chemical and biological methods have been used to reduce the bioavailability of the metals at mine sites. Biochar with its potential to complex and immobilize heavy metals, is an emerging alternative for reducing bioavailability. Furthermore, biochar has been reported to improve soil conditions for plant growth and can be used for promoting the establishment of a soil-stabilizing native plant community to reduce offsite movement of metal-laden waste materials. Because biochar properties depend upon feedstock selection, pyrolysis production conditions, and activation procedures used, they can be designed to meet specific remediation needs. As a result biochar with specific properties can be produced to correspond to specific soil remediation situations. However, techniques are needed to optimally match biochar characteristics with metals contaminated soils to effectively reduce metal bioavailability. Here we present experimental results used to develop a generalized method for evaluating the ability of biochar to reduce metals in mine spoil soil from an abandoned Cu and Zn mine. Thirty-eight biochars were produced from approximately 20 different feedstocks and produced via slow pyrolysis or gasification, and were allowed to react with a f
Active Path Selection of Fluid Microcapsules in Artificial Blood Vessel by Acoustic Radiation Force
Masuda, Kohji; Muramatsu, Yusuke; Ueda, Sawami; Nakamoto, Ryusuke; Nakayashiki, Yusuke; Ishihara, Ken
2009-07-01
Micrometer-sized microcapsules collapse upon exposure to ultrasound. Use of this phenomenon for a drug delivery system (DDS), not only for local delivery of medication but also for gene therapy, should be possible. However, enhancing the efficiency of medication is limited because capsules in suspension diffuse in the human body after injection, since the motion of capsules in blood flow cannot be controlled. To control the behavior of microcapsules, acoustic radiation force was introduced. We detected local changes in microcapsule density by producing acoustic radiation force in an artificial blood vessel. Furthermore, we theoretically estimated the conditions required for active path selection of capsules at a bifurcation point in the artificial blood vessel. We observed the difference in capsule density at both in the bifurcation point and in alternative paths downstream of the bifurcation point for different acoustic radiation forces. Comparing the experimental results with those obtained theoretically, the conditions for active path selection were calculated from the acoustic radiation force and fluid resistance of the capsules. The possibility of controlling capsule flow towards a specific point in a blood vessel was demonstrated.
Wang, Liangbing; Zhang, Wenbo; Wang, Shenpeng; Gao, Zehua; Luo, Zhiheng; Wang, Xu; Zeng, Rui; Li, Aowen; Li, Hongliang; Wang, Menglin; Zheng, Xusheng; Zhu, Junfa; Zhang, Wenhua; Ma, Chao; Si, Rui; Zeng, Jie
2016-12-22
Rh-based heterogeneous catalysts generally have limited selectivity relative to their homogeneous counterparts in hydroformylation reactions despite of the convenience of catalyst separation in heterogeneous catalysis. Here, we develop CoO-supported Rh single-atom catalysts (Rh/CoO) with remarkable activity and selectivity towards propene hydroformylation. By increasing Rh mass loading, isolated Rh atoms switch to aggregated clusters of different atomicity. During the hydroformylation, Rh/CoO achieves the optimal selectivity of 94.4% for butyraldehyde and the highest turnover frequency number of 2,065 h(-1) among the obtained atomic-scale Rh-based catalysts. Mechanistic studies reveal that a structural reconstruction of Rh single atoms in Rh/CoO occurs during the catalytic process, facilitating the adsorption and activation of reactants. In kinetic view, linear products are determined as the dominating products by analysing reaction paths deriving from the two most stable co-adsorbed configurations. As a bridge of homogeneous and heterogeneous catalysis, single-atom catalysts can be potentially applied in other industrial reactions.
Wang, Liangbing; Zhang, Wenbo; Wang, Shenpeng; Gao, Zehua; Luo, Zhiheng; Wang, Xu; Zeng, Rui; Li, Aowen; Li, Hongliang; Wang, Menglin; Zheng, Xusheng; Zhu, Junfa; Zhang, Wenhua; Ma, Chao; Si, Rui; Zeng, Jie
2016-12-01
Rh-based heterogeneous catalysts generally have limited selectivity relative to their homogeneous counterparts in hydroformylation reactions despite of the convenience of catalyst separation in heterogeneous catalysis. Here, we develop CoO-supported Rh single-atom catalysts (Rh/CoO) with remarkable activity and selectivity towards propene hydroformylation. By increasing Rh mass loading, isolated Rh atoms switch to aggregated clusters of different atomicity. During the hydroformylation, Rh/CoO achieves the optimal selectivity of 94.4% for butyraldehyde and the highest turnover frequency number of 2,065 h-1 among the obtained atomic-scale Rh-based catalysts. Mechanistic studies reveal that a structural reconstruction of Rh single atoms in Rh/CoO occurs during the catalytic process, facilitating the adsorption and activation of reactants. In kinetic view, linear products are determined as the dominating products by analysing reaction paths deriving from the two most stable co-adsorbed configurations. As a bridge of homogeneous and heterogeneous catalysis, single-atom catalysts can be potentially applied in other industrial reactions.
Stationary phase optimized selectivity supercritical fluid chromatography (SOS-SFC)
Delahaye, Sander; Lynen, Frederic
2013-01-01
In stationary phase optimized selectivity liquid chromatography (SOS-LC) the stationary phase becomes a tunable parameter by connecting column segments with variable lengths of different stationary phases. An optimization procedure and algorithm based on the PRISMA model for optimization of the mobile phase in LC was developed to apply this strategy for isocratic and gradient separations. An optimized column segment combination, giving the highest separation selectivity for all compounds in a...
DEFF Research Database (Denmark)
Karlsson, Kenneth Bernard; Meibom, Peter
2008-01-01
that with an oil price at 100 $/barrel, a CO2 price at40 €/ton and the assumed penetration of hydrogen in the transport sector, it is economically optimal to cover more than 95% of the primary energy consumption for electricity and district heat by renewables in 2050. When the transport sector is converted......This paper investigates a possible long term investment path for the Nordic energy system focussing on renewable energy in the supply sector and on hydrogen as the main fuel for transportation, covering up to 70% of all transport in 2050. The optimisation model Balmorel [Ravn H, et al. Balmorel......: A model for analyses of the electricity and CHP markets in the Baltic Sea Region. 〈www.Balmorel.com〉; 2001. [1
Development of an optimal velocity selection method with velocity obstacle
Energy Technology Data Exchange (ETDEWEB)
Kim, Min Geuk; Oh, Jun Ho [KAIST, Daejeon (Korea, Republic of)
2015-08-15
The Velocity obstacle (VO) method is one of the most well-known methods for local path planning, allowing consideration of dynamic obstacles and unexpected obstacles. Typical VO methods separate a velocity map into a collision area and a collision-free area. A robot can avoid collisions by selecting its velocity from within the collision-free area. However, if there are numerous obstacles near a robot, the robot will have very few velocity candidates. In this paper, a method for choosing optimal velocity components using the concept of pass-time and vertical clearance is proposed for the efficient movement of a robot. The pass-time is the time required for a robot to pass by an obstacle. By generating a latticized available velocity map for a robot, each velocity component can be evaluated using a cost function that considers the pass-time and other aspects. From the output of the cost function, even a velocity component that will cause a collision in the future can be chosen as a final velocity if the pass-time is sufficiently long enough.
Optimizing antibiotic selection in treating COPD exacerbations
Directory of Open Access Journals (Sweden)
Attiya Siddiqi
2008-03-01
Full Text Available Attiya Siddiqi, Sanjay SethiDivision of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, Veterans Affairs Western New York Health Care System and University of Buffalo, State University of New York, Buffalo, New York, USAAbstract: Our understanding of the etiology, pathogenesis and consequences of acute exacerbations of chronic obstructive pulmonary disease (COPD has increased substantially in the last decade. Several new lines of evidence demonstrate that bacterial isolation from sputum during acute exacerbation in many instances reflects a cause-effect relationship. Placebo-controlled antibiotic trials in exacerbations of COPD demonstrate significant clinical benefits of antibiotic treatment in moderate and severe episodes. However, in the multitude of antibiotic comparison trials, the choice of antibiotics does not appear to affect the clinical outcome, which can be explained by several methodological limitations of these trials. Recently, comparison trials with nontraditional end-points have shown differences among antibiotics in the treatment of exacerbations of COPD. Observational studies that have examined clinical outcome of exacerbations have repeatedly demonstrated certain clinical characteristics to be associated with treatment failure or early relapse. Optimal antibiotic selection for exacerbations has therefore incorporated quantifying the risk for a poor outcome of the exacerbation and choosing antibiotics differently for low risk and high risk patients, reserving the broader spectrum drugs for the high risk patients. Though improved outcomes in exacerbations with antibiotic choice based on such risk stratification has not yet been demonstrated in prospective controlled trials, this approach takes into account concerns of disease heterogeneity, antibiotic resistance and judicious antibiotic use in exacerbations.Keywords: COPD, exacerbation, bronchitis, antibiotics
Institute of Scientific and Technical Information of China (English)
无
2010-01-01
Using constructal entransy dissipation rate minimization method based on discrete variable cross-section conducting path,constructal optimizations of elemental area with variable cross-section conducting path are performed,and the results are compared with the optimization results of elemental area with the constant cross-section conducting path.The comparison shows that the minimum mean temperature difference based on elemental area with variable cross-section conducting path increases and approaches a constant as the assembly’s order increases,but the minimum mean temperature difference based on elemental area with constant cross-section conducting path decreases and approaches a constant as the assembly’s order increases.The difference between them is caused by the different dimensionless mean temperature difference of the first order assembly.A universal constructal optimization method by self similar organization to improve heat transfer ability and its corresponding rule are proposed.With the constructal optimization method by self similar organization based on entransy dissipation rate minimization objective,the mean temperature difference approaches a constant as the assembly’s order increases.
Institute of Scientific and Technical Information of China (English)
Yunjuan WANG; Detong ZHU
2008-01-01
Based on a differentiable merit function proposed by Taji et al.in "Math.Prog. Stud.,58,1993,369-383",the authors propose an affine scaling interior trust region strategy via optimal path to modify Newton method for the strictly monotone variational inequality problem subject to linear equality and inequality constraints.By using the eigensystem decomposition and affine scaling mapping,the authors form an affine scaling optimal curvilinear path very easily in order to approximately solve the trust region subproblem.Theoretical analysis is given which shows that the proposed algorithm is globally convergent and has a local quadratic convergence rate under some reasonable conditions.
Optimalization of selected RFID systems Parameters
Directory of Open Access Journals (Sweden)
Peter Vestenicky
2004-01-01
Full Text Available This paper describes procedure for maximization of RFID transponder read range. This is done by optimalization of magnetics field intensity at transponder place and by optimalization of antenna and transponder coils coupling factor. Results of this paper can be used for RFID with inductive loop, i.e. system working in near electromagnetic field.
Kicken, Wendy; Brand-Gruwel, Saskia; Van Merriënboer, Jeroen
2008-01-01
Kicken, W., Brand-Gruwel, S., & Van Merrienboer, J. J. G. (2008). Scaffolding advice on task selection: A safe path toward self-directed learning in on-demand education. Journal of Vocational Education and Training, 60, 223-239.
Kicken, Wendy; Brand-Gruwel, Saskia; Van Merriënboer, Jeroen
2008-01-01
Kicken, W., Brand-Gruwel, S., & Van Merrienboer, J. J. G. (2008). Scaffolding advice on task selection: A safe path toward self-directed learning in on-demand education. Journal of Vocational Education and Training, 60, 223-239.
Data acquisition and path selection decision making for an autonomous roving vehicle
Frederick, D. K.; Shen, C. N.; Yerazunis, S. W.
1976-01-01
Problems related to the guidance of an autonomous rover for unmanned planetary exploration were investigated. Topics included in these studies were: simulation on an interactive graphics computer system of the Rapid Estimation Technique for detection of discrete obstacles; incorporation of a simultaneous Bayesian estimate of states and inputs in the Rapid Estimation Scheme; development of methods for estimating actual laser rangefinder errors and their application to date provided by Jet Propulsion Laboratory; and modification of a path selection system simulation computer code for evaluation of a hazard detection system based on laser rangefinder data.
A Path Select Algorithm with Error Control Schemes and Energy Efficient Wireless Sensor Networks
Directory of Open Access Journals (Sweden)
Sandeep Dahiya
2012-04-01
Full Text Available A wireless sensor network consists of a large number of sensor nodes that are spread densely to observe the phenomenon. The whole network lifetime relies on the lifetime of the each sensor node. If one node dies, it could lead to a separation of the sensor network. Also a multi hop structure and broadcast channel of wireless sensornecessitate error control scheme to achieve reliable data transmission. Automatic repeat request (ARQ and forward error correction (FEC are the key error control strategies in wire sensor network. In this paper we propose a path selection algorithm with error control schemes using energy efficient analysis.
MULTICRITERIА PROBLEM OF FINDING THE OPTIMAL PATHS FOR LARGE-SCALE TRANSPORT SYSTEM
Directory of Open Access Journals (Sweden)
Pavlov D. A.
2015-11-01
Full Text Available This article explores the multicriteria problems arise in the organization of routes in large-scale transport management system. As a mathematical tool for constructing a model, we were using the prefractal graphs. Prefractal graphs naturally reflect structure of the device of communications of transport system, reflecting its important features – locality and differentiation. Locality is provided with creation of internal routes (city, raionwide, etc.. Differentiation is understood as division of routes on intra regional, interregional and international. The objective is reduced to a covering of prefractal graphs by the simple paths which are crossed on edges and nodes. On the set of feasible solutions, vector criterion function with certain criteria is based. In concepts of transport system, the given criteria have concrete substantial interpretation, the transport routes allowing to design considering features of system. In this article, we construct polynomial algorithms for finding optimal according to certain criteria decision. By the criteria which aren't optimizing the allocated routes their estimates of the lower and upper bounds are given. On all given algorithms the estimates of computing complexity confirming advantage of use of methods of prefractal and fractal graphs before classical methods of the theory of graphs are constructed and proved
Neural Network-Based Solutions for Stochastic Optimal Control Using Path Integrals.
Rajagopal, Karthikeyan; Balakrishnan, Sivasubramanya Nadar; Busemeyer, Jerome R
2017-03-01
In this paper, an offline approximate dynamic programming approach using neural networks is proposed for solving a class of finite horizon stochastic optimal control problems. There are two approaches available in the literature, one based on stochastic maximum principle (SMP) formalism and the other based on solving the stochastic Hamilton-Jacobi-Bellman (HJB) equation. However, in the presence of noise, the SMP formalism becomes complex and results in having to solve a couple of backward stochastic differential equations. Hence, current solution methodologies typically ignore the noise effect. On the other hand, the inclusion of noise in the HJB framework is very straightforward. Furthermore, the stochastic HJB equation of a control-affine nonlinear stochastic system with a quadratic control cost function and an arbitrary state cost function can be formulated as a path integral (PI) problem. However, due to curse of dimensionality, it might not be possible to utilize the PI formulation for obtaining comprehensive solutions over the entire operating domain. A neural network structure called the adaptive critic design paradigm is used to effectively handle this difficulty. In this paper, a novel adaptive critic approach using the PI formulation is proposed for solving stochastic optimal control problems. The potential of the algorithm is demonstrated through simulation results from a couple of benchmark problems.
Smooth Optimization Approach for Sparse Covariance Selection
Lu, Zhaosong
2009-01-01
In this paper we first study a smooth optimization approach for solving a class of nonsmooth strictly concave maximization problems whose objective functions admit smooth convex minimization reformulations. In particular, we apply Nesterov's smooth optimization technique [Y.E. Nesterov, Dokl. Akad. Nauk SSSR, 269 (1983), pp. 543--547; Y. E. Nesterov, Math. Programming, 103 (2005), pp. 127--152] to their dual counterparts that are smooth convex problems. It is shown that the resulting approach...
Research on manufacturing grid resource service optimal-selection and composition framework
Tao, F.; Zhang, L.; Lu, K.; Zhao, D.
2012-05-01
In order to address the resource service optimal-selection (RSOS) and composition problem in manufacturing grid (MGrid) system and provide high-quality service to users, an MGrid RSOS and composition framework (MGrid-RSOSCF) is investigated in this study. The process of RSOS and composition is divided into the following five steps in MGrid-RSOSCF: (1) decomposing the submitted manufacturing task into several subtasks (i.e. single resource service requested task) if the submitted task is a multiple resource service requested task; (2) searching out the qualified resource service for each decomposed subtask and generating the corresponding candidate resource service set; (3) retrieving, evaluating and comparing the quality of service (QoS) for each candidate resource service, and provide data for service optimal-selection and composition -if the submitted task is a single resource service requested task; (4) evaluating synthetically the overall quality of each candidate resource service and ranking them, and selecting the optimal one for the task - if the submitted manufacturing task is an multiple resource service requested task; (5) selecting one candidate resource service from each candidate resource service set and constructing a new composite resource service according to the submitted task requirements, and collecting all the possible resource service composite execution paths (RSCEP) and selecting the optimal paths to execute the task. The proposed MGrid-RSOSCF consists of five layers and each layer provides the corresponding necessary services and algorithms to address one problem mentioned above. The five layers are: (1) T-layer, responsible for MGrid task decomposition; (2) S-layer, responsible for resource service match and search; (3) Q-layer, responsible for QoS processing; (4) O-layer, responsible for evaluating and ranking the candidate resource service and (5) C-layer is responsible for resource service composition and optimal-selection. The case
Optimal parallel algorithm for shortest-paths problem on interval graphs
Institute of Scientific and Technical Information of China (English)
MISHRA P.K.
2004-01-01
This paper presents an efficient parallel algorithm for the shortest-path problem in interval graph for computing shortest-paths in a weighted interval graph that runs in O(n) time with n intervals in a graph. A linear processor CRCW algorithm for determining the shortest-paths in an interval graphs is given.
OPTIMAL ALGORITHM FOR NO TOOlRETRACTIONS CONTOUR-PARALLEL OFFSET TOOL-PATH LINKING
Institute of Scientific and Technical Information of China (English)
HAO Yongtao; JIANG Lili
2007-01-01
A contour-parallel offset (CPO) tool-path linking algorithm is derived without toolretractions and with the largest practicability. The concept of "tool-path loop tree" (TPL-tree)providing the information on the parent/child relationships among the tool-path loops (TPLs) is presented. The direction, tool-path loop, leaf/branch, layer number, and the corresponding points of the TPL-tree are introduced. By defining TPL as a vector, and by traveling throughout the tree, a CPO tool-path without tool-retractions can be derived.
Water Quality Optimization through Selective Withdrawal.
1983-03-01
river. 16. Kaplan noted that Staha and Himmelblau compared the COMET al- gorithm to three nonlinear programming codes for 25 test problems. The...Mathematics, Vol 9. Staha, R. L. and Himmelblau , D. M. 1972. "Constrained Optimization Via Moving Exterior Truncations," presented at the Society for
The Optimal Selection for Restricted Linear Models with Average Estimator
Directory of Open Access Journals (Sweden)
Qichang Xie
2014-01-01
Full Text Available The essential task of risk investment is to select an optimal tracking portfolio among various portfolios. Statistically, this process can be achieved by choosing an optimal restricted linear model. This paper develops a statistical procedure to do this, based on selecting appropriate weights for averaging approximately restricted models. The method of weighted average least squares is adopted to estimate the approximately restricted models under dependent error setting. The optimal weights are selected by minimizing a k-class generalized information criterion (k-GIC, which is an estimate of the average squared error from the model average fit. This model selection procedure is shown to be asymptotically optimal in the sense of obtaining the lowest possible average squared error. Monte Carlo simulations illustrate that the suggested method has comparable efficiency to some alternative model selection techniques.
Directory of Open Access Journals (Sweden)
Subbaraj Potti
2011-01-01
Full Text Available Problem statement: A new multi-objective approach, Strength Pareto Evolutionary Algorithm (SPEA, is presented in this paper to solve the shortest path routing problem. The routing problem is formulated as a multi-objective mathematical programming problem which attempts to minimize both cost and delay objectives simultaneously. Approach: SPEA handles the shortest path routing problem as a true multi-objective optimization problem with competing and noncommensurable objectives. Results: SPEA combines several features of previous multi-objective evolutionary algorithms in a unique manner. SPEA stores nondominated solutions externally in another continuously-updated population and uses a hierarchical clustering algorithm to provide the decision maker with a manageable pareto-optimal set. SPEA is applied to a 20 node network as well as to large size networks ranging from 50-200 nodes. Conclusion: The results demonstrate the capabilities of the proposed approach to generate true and well distributed pareto-optimal nondominated solutions.
Pliutau, Denis; Prasad, Narasimha S.
2012-01-01
In this paper a modeling method based on data reductions is investigated which includes pre analyzed MERRA atmospheric fields for quantitative estimates of uncertainties introduced in the integrated path differential absorption methods for the sensing of various molecules including CO2. This approach represents the extension of our existing lidar modeling framework previously developed and allows effective on- and offline wavelength optimizations and weighting function analysis to minimize the interference effects such as those due to temperature sensitivity and water vapor absorption. The new simulation methodology is different from the previous implementation in that it allows analysis of atmospheric effects over annual spans and the entire Earth coverage which was achieved due to the data reduction methods employed. The effectiveness of the proposed simulation approach is demonstrated with application to the mixing ratio retrievals for the future ASCENDS mission. Independent analysis of multiple accuracy limiting factors including the temperature, water vapor interferences, and selected system parameters is further used to identify favorable spectral regions as well as wavelength combinations facilitating the reduction in total errors in the retrieved XCO2 values.
Sensor Optimization Selection Model Based on Testability Constraint
Institute of Scientific and Technical Information of China (English)
YANG Shuming; QIU Jing; LIU Guanjun
2012-01-01
Sensor selection and optimization is one of the important parts in design for testability.To address the problems that the traditional sensor optimization selection model does not take the requirements of prognostics and health management especially fault prognostics for testability into account and does not consider the impacts of sensor actual attributes on fault detectability,a novel sensor optimization selection model is proposed.Firstly,a universal architecture for sensor selection and optimization is provided.Secondly,a new testability index named fault predictable rate is defined to describe fault prognostics requirements for testability.Thirdly,a sensor selection and optimization model for prognostics and health management is constructed,which takes sensor cost as objective finction and the defined testability indexes as constraint conditions.Due to NP-hard property of the model,a generic algorithm is designed to obtain the optimal solution.At last,a case study is presented to demonstrate the sensor selection approach for a stable tracking servo platform.The application results and comparison analysis show the proposed model and algorithm are effective and feasible.This approach can be used to select sensors for prognostics and health management of any system.
Where do rivers grow? Path selection and growth in a harmonic field
Cohen, Yossi; Seybold, Hansjorg F; Yi, Robert S; Szymczak, Piotr; Rothman, Daniel H
2014-01-01
River networks exhibit a complex ramified structure that has inspired decades of studies. Yet, an understanding of the propagation of a single stream remains elusive. Here we invoke a criterion for path selection from fracture mechanics and apply it to the growth of streams in a diffusion field. We show that a stream will follow local symmetry in order to maximize the water flux and that its trajectory is defined by the local field in its vicinity. We also study the growth of a real network. We use this principle to construct the history of a network and to find a growth law associated with it. The results show that the deterministic growth of a single channel based on its local environment can be used to characterize the structure of river networks.
The Optimal Portfolio Selection Model under g -Expectation
National Research Council Canada - National Science Library
Li Li
2014-01-01
This paper solves the optimal portfolio selection model under the framework of the prospect theory proposed by Kahneman and Tversky in the 1970s with decision rule replaced by the g -expectation introduced by Peng...
On Optimal Input Design and Model Selection for Communication Channels
Energy Technology Data Exchange (ETDEWEB)
Li, Yanyan [ORNL; Djouadi, Seddik M [ORNL; Olama, Mohammed M [ORNL
2013-01-01
In this paper, the optimal model (structure) selection and input design which minimize the worst case identification error for communication systems are provided. The problem is formulated using metric complexity theory in a Hilbert space setting. It is pointed out that model selection and input design can be handled independently. Kolmogorov n-width is used to characterize the representation error introduced by model selection, while Gel fand and Time n-widths are used to represent the inherent error introduced by input design. After the model is selected, an optimal input which minimizes the worst case identification error is shown to exist. In particular, it is proven that the optimal model for reducing the representation error is a Finite Impulse Response (FIR) model, and the optimal input is an impulse at the start of the observation interval. FIR models are widely popular in communication systems, such as, in Orthogonal Frequency Division Multiplexing (OFDM) systems.
Optimal Route Selection Method Based on Vague Sets
Institute of Scientific and Technical Information of China (English)
GUO Rui; DU Li min; WANG Chun
2015-01-01
Optimal route selection is an important function of vehicle trac flow guidance system. Its core is to determine the index weight for measuring the route merits and to determine the evaluation method for selecting route. In this paper, subjective weighting method which relies on driver preference is used to determine the weight and the paper proposes the multi-criteria weighted decision method based on vague sets for selecting the optimal route. Examples show that, the usage of vague sets to describe route index value can provide more decision-making information for route selection.
Optimized tuner selection for engine performance estimation
Simon, Donald L. (Inventor); Garg, Sanjay (Inventor)
2013-01-01
A methodology for minimizing the error in on-line Kalman filter-based aircraft engine performance estimation applications is presented. This technique specifically addresses the underdetermined estimation problem, where there are more unknown parameters than available sensor measurements. A systematic approach is applied to produce a model tuning parameter vector of appropriate dimension to enable estimation by a Kalman filter, while minimizing the estimation error in the parameters of interest. Tuning parameter selection is performed using a multi-variable iterative search routine which seeks to minimize the theoretical mean-squared estimation error. Theoretical Kalman filter estimation error bias and variance values are derived at steady-state operating conditions, and the tuner selection routine is applied to minimize these values. The new methodology yields an improvement in on-line engine performance estimation accuracy.
Directory of Open Access Journals (Sweden)
Yong Ma
2013-01-01
Full Text Available We present one algorithm based on particle swarm optimization (PSO with penalty function to determine the conflict-free path for mobile objects in four-dimension (three spatial and one-time dimensions with obstacles. The shortest path of the mobile object is set as goal function, which is constrained by conflict-free criterion, path smoothness, and velocity and acceleration requirements. This problem is formulated as a calculus of variation problem (CVP. With parametrization method, the CVP is converted to a time-varying nonlinear programming problem (TNLPP. Constraints of TNLPP are transformed to general TNLPP without any constraints through penalty functions. Then, by using a little calculations and applying the algorithm PSO, the solution of the CVP is consequently obtained. Approach efficiency is confirmed by numerical examples.
Liu, Taoming; Çavuşoğlu, M Cenk
2015-05-01
This paper presents algorithms for optimal selection of needle grasp, for autonomous robotic execution of the minimally invasive surgical suturing task. In order to minimize the tissue trauma during the suturing motion, the best practices of needle path planning that are used by surgeons are applied for autonomous robotic surgical suturing tasks. Once an optimal needle trajectory in a well-defined suturing scenario is chosen, another critical issue for suturing is the choice of needle grasp for the robotic system. Inappropriate needle grasp increases operating time requiring multiple re-grasps to complete the desired task. The proposed methods use manipulability, dexterity and torque metrics for needle grasp selection. A simulation demonstrates the proposed methods and recommends a variety of grasps. Then a realistic demonstration compares the performances of the manipulator using different grasps.
Optimization of ultrasonic transducers for selective guided wave actuation
Miszczynski, Mateusz; Packo, Pawel; Zbyrad, Paulina; Stepinski, Tadeusz; Uhl, Tadeusz; Lis, Jerzy; Wiatr, Kazimierz
2016-04-01
The application of guided waves using surface-bonded piezoceramic transducers for nondestructive testing (NDT) and Structural Health Monitoring (SHM) have shown great potential. However, due to difficulty in identification of individual wave modes resulting from their dispersive and multi-modal nature, selective mode excitement methods are highly desired. The presented work focuses on an optimization-based approach to design of a piezoelectric transducer for selective guided waves generation. The concept of the presented framework involves a Finite Element Method (FEM) model in the optimization process. The material of the transducer is optimized in topological sense with the aim of tuning piezoelectric properties for actuation of specific guided wave modes.
Optimal Genetic View Selection Algorithm for Data Warehouse
Institute of Scientific and Technical Information of China (English)
Wang Ziqiang; Feng Boqin
2005-01-01
To efficiently solve the materialized view selection problem, an optimal genetic algorithm of how to select a set of views to be materialized is proposed so as to achieve both good query performance and low view maintenance cost under a storage space constraint. First, a pre-processing algorithm based on the maximum benefit per unit space is used to generate initial solutions. Then, the initial solutions are improved by the genetic algorithm having the mixture of optimal strategies. Furthermore, the generated infeasible solutions during the evolution process are repaired by loss function. The experimental results show that the proposed algorithm outperforms the heuristic algorithm and canonical genetic algorithm in finding optimal solutions.
The Structure of the Optimal Solution Set on the Shortest Paths for Networks%网络最短的最优解集结构
Institute of Scientific and Technical Information of China (English)
张振坤; 王斌
2007-01-01
The shortest path problem in a network G is to find shortest paths between some specified source vertices and terminal vertices when the lengths of edges are given.The structure of the optimal Solutions set on the shortest paths is studied in this Paper.First,the conditions of having unique shortest path between two distinguished vertices s and t in a network G are discussed;Second,the struetural properties of 2-transformation graph G on the shortest-paths for G are presented heavily.
Optimal Path Planning for Agricultural Machinery%田作业机械路径优化方法
Institute of Scientific and Technical Information of China (English)
孟志军; 刘卉; 王华; 付卫强
2012-01-01
提出了一种面向农田作业机械的地块全区域覆盖路径优化方法.基于农田地块几何形状、作业机具参数、地头转弯模式等先验信息,将田间作业划分为不同区域,根据选择不同的路径优化目标:转弯数最少、作业消耗最小、总作业路径最短或有效作业路径比最大,计算出最优作业方向,生成最优作业路径.基于地块全区域覆盖路径优化算法,设计开发了农田作业机械的路径规划软件,并选取了4块典型的凸四边形农田地块进行作业路径规划测试.测试结果表明,最优作业方向上的路径优化目标量比其他作业方向上有显著减少;对于上述4个地块,按照不同优化目标计算所得的最优作业方向均与地块某个边的方向角相同,对于长宽比较大的地块,最长边方向通常为最优作业方向.%A kind of optimal path planning method for complete field coverage in agricultural machinery farming was described. The given convex field was decomposed into sub regions according to typical faming pattern of complete field coverage. Optimal path planning should minimize agricultural machines turns, farming time, route distance or other optimization object. According to the different purpose of path planning above, optimal travel direction could be determined based on topological features of arable lands, turns pattern in the headland and other useful information. Fanning path planning software was developed by using this algorithm. The test results from four actual quadrangular fields showed that the optimization object for field operation in optimal travel direction decreased obviously compared with other directions. Each optimal travel direction in those quadrangular fields above was along any edge-Especially for a quadrangle having a larger edge ratio, the optimal travel direction was almost the longest edge.
Opposing selection and environmental variation modify optimal timing of breeding.
Tarwater, Corey E; Beissinger, Steven R
2013-09-17
Studies of evolution in wild populations often find that the heritable phenotypic traits of individuals producing the most offspring do not increase proportionally in the population. This paradox may arise when phenotypic traits influence both fecundity and viability and when there is a tradeoff between these fitness components, leading to opposing selection. Such tradeoffs are the foundation of life history theory, but they are rarely investigated in selection studies. Timing of breeding is a classic example of a heritable trait under directional selection that does not result in an evolutionary response. Using a 22-y study of a tropical parrot, we show that opposing viability and fecundity selection on the timing of breeding is common and affects optimal breeding date, defined by maximization of fitness. After accounting for sampling error, the directions of viability (positive) and fecundity (negative) selection were consistent, but the magnitude of selection fluctuated among years. Environmental conditions (rainfall and breeding density) primarily and breeding experience secondarily modified selection, shifting optimal timing among individuals and years. In contrast to other studies, viability selection was as strong as fecundity selection, late-born juveniles had greater survival than early-born juveniles, and breeding later in the year increased fitness under opposing selection. Our findings provide support for life history tradeoffs influencing selection on phenotypic traits, highlight the need to unify selection and life history theory, and illustrate the importance of monitoring survival as well as reproduction for understanding phenological responses to climate change.
Tortuous path chemical preconcentrator
Manginell, Ronald P.; Lewis, Patrick R.; Adkins, Douglas R.; Wheeler, David R.; Simonson, Robert J.
2010-09-21
A non-planar, tortuous path chemical preconcentrator has a high internal surface area having a heatable sorptive coating that can be used to selectively collect and concentrate one or more chemical species of interest from a fluid stream that can be rapidly released as a concentrated plug into an analytical or microanalytical chain for separation and detection. The non-planar chemical preconcentrator comprises a sorptive support structure having a tortuous flow path. The tortuosity provides repeated twists, turns, and bends to the flow, thereby increasing the interfacial contact between sample fluid stream and the sorptive material. The tortuous path also provides more opportunities for desorption and readsorption of volatile species. Further, the thermal efficiency of the tortuous path chemical preconcentrator is comparable or superior to the prior non-planar chemical preconcentrator. Finally, the tortuosity can be varied in different directions to optimize flow rates during the adsorption and desorption phases of operation of the preconcentrator.
Selection of Structures with Grid Optimization, in Multiagent Data Warehouse
Gorawski, Marcin; Bańkowski, Sławomir; Gorawski, Michał
The query optimization problem in data base and data warehouse management systems is quite similar. Changes to Joins sequences, projections and selections, usage of indexes, and aggregations are all decided during the analysis of an execution schedule. The main goal of these changes is to decrease the query response time. The optimization operation is often dedicated to a single node. This paper proposes optimization to grid or cluster data warehouses / databases. Tests were conducted in a multi-agent environment, and the optimization focused not only on a single node but on the whole system as well. A new idea is proposed here with multi-criteria optimization that is based on user-given parameters. Depending on query time, result admissible errors, and the level of system usage, task results were obtained along with grid optimization.
Optimal Selective Harmonic Control for Power Harmonics Mitigation
DEFF Research Database (Denmark)
Zhou, Keliang; Yang, Yongheng; Blaabjerg, Frede
2015-01-01
the cost, the complexity and the performance: high accuracy, fast transient response, easy-implementation, cost-effective, and also easy-to-design. The analysis and synthesis of the optimal SHC system are addressed. The proposed SHC offers power convert-ers a tailor-made optimal control solution......This paper proposes an Internal Model Principle (IMP) based optimal Selective Harmonic Controller (SHC) for power converters to mitigate power harmonics. According to the harmonics distribution caused by power converters, a universal recursive SHC module is developed to deal with a featured group...... of power harmonics. The proposed optimal SHC is of hybrid structure: all recursive SHC modules with weighted gains are connected in parallel. It bridges the real “nk+-m order RC” and the complex “parallel structure RC”. Compared to other IMP based control solutions, it offers an optimal trade-off among...
Global Optimization for Advertisement Selection in Sponsored Search
Institute of Scientific and Technical Information of China (English)
崔卿; 白峰杉; 高斌; 刘铁岩
2015-01-01
Advertisement (ad) selection plays an important role in sponsored search, since it is an upstream component and will heavily influence the effectiveness of the subsequent auction mechanism. However, most existing ad selection methods regard ad selection as a relatively independent module, and only consider the literal or semantic matching between queries and keywords during the ad selection process. In this paper, we argue that this approach is not globally optimal. Our proposal is to formulate ad selection as such an optimization problem that the selected ads can work together with downstream components (e.g., the auction mechanism) to achieve the maximization of user clicks, advertiser social welfare, and search engine revenue (we call the combination of these ob jective functions as the marketplace ob jective for ease of reference). To this end, we 1) extract a bunch of features to represent each pair of query and keyword, and 2) train a machine learning model that maps the features to a binary variable indicating whether the keyword is selected or not, by maximizing the aforementioned marketplace ob jective. This formalization seems quite natural; however, it is technically diﬃcult because the marketplace objective is non-convex, discontinuous, and indifferentiable regarding the model parameter due to the ranking and second-price rules in the auction mechanism. To tackle the challenge, we propose a probabilistic approximation of the marketplace objective, which is smooth and can be effectively optimized by conventional optimization techniques. We test the ad selection model learned with our proposed method using the sponsored search log from a commercial search engine. The experimental results show that our method can significantly outperform several ad selection algorithms on all the metrics under investigation.
Bevilacqua, R.; Romano, M.
2008-01-01
The article of record may be found at http://www.e-ndst.kiev.ua Autonomous close flight and docking of a chaser spacecraft to a target are still challenging problems. In this paper the Hill–Clohessy–Wiltshire equations are taken as dynamic model and inverted, after a variable change, in order to be used by a control algorithm to drive the chaser spacecraft along a specified path. The path parameterization is performed by using cubic B- splines and by having the curvilinear abscissa as para...
Institute of Scientific and Technical Information of China (English)
Peihua GUO; Detong ZHU
2008-01-01
The authors propose an affine scaling modified gradient path method in association with reduced projective Hessian and nonmonotonic interior backtracking line search techniques for solving the linear equality constrained optimization subject to bounds on variables. By employing the QR decomposition of the constraint matrix and the eigensystem decomposition of reduced projective Hes-sian matrix in the subproblem, the authors form affine scaling modified gradient curvilinear path very easily. By using interior backtracking line search technique, each iterate switches to trial step of strict interior feasibility. The global convergence and fast local superlinear/quadratical convergence rates of the proposed algorithm are established under some reasonable conditions. A nonmonotonic criterion should bring about speeding up the convergence progress in some ill-conditioned cases. The results of numerical experiments are reported to show the effectiveness of the proposed algorithm.
Immune secondary response and clonal selection inspired optimizers
Institute of Scientific and Technical Information of China (English)
Maoguo Gong; Licheng Jiao; Lining Zhang; Haifeng Du
2009-01-01
The immune system's ability to adapt its B cells to new types of antigen is powered by processes known as clonal selection and affinity maturation. When the body is exposed to the same antigen, immune system usually calls for a more rapid and larger response to the antigen, where B cells have the function of negative adjustment. Based on the clonal selection theory and the dynamic process of immune response, two novel artificial immune system algorithms, secondary response clonal programming algorithm (SRCPA) and secondary response clonal multi-objective algorithm (SRCMOA), are presented for solving single and multi-objective optimization problems, respectively. Clonal selection operator (CSO) and secondary response operator (SRO) are the main operators of SRCPA and SRCMOA. Inspired by the cional selection theory, CSO reproduces individuals and selects their improved maturated progenies after the affinity mat-uration process. SRO copies certain antibodies to a secondary pool, whose members do not participate in CSO, but these antibodies could be activated by some external stimulations. The update of the secondary pool pays more attention to maintain the population diversity. On the one hand, decimal-string representation makes SRCPA more suitable for solving high-dimensional function optimiza-tion problems. Special mutation and recombination methods are adopted in SRCPA to simulate the somatic mutation and receptor edit-ing process. Compared with some existing evolutionary algorithms, such as OGA/Q, IEA, IMCPA, BGA and AEA, SRCPA is shown to be able to solve complex optimization problems, such as high-dimensional function optimizations, with better performance. On the other hand, SRCMOA combines the Pareto-strength based fitness assignment strategy, CSO and SRO to solve multi-objective optimization problems. The performance comparison between SRCMOA, NSGA-Ⅱ, SPEA, and PAES based on eight well-known test problems shows that SRCMOA has better performance in
Optimal Predictive Control for Path Following of a Full Drive-by-Wire Vehicle at Varying Speeds
SONG, Pan; GAO, Bolin; XIE, Shugang; FANG, Rui
2017-05-01
The current research of the global chassis control problem for the full drive-by-wire vehicle focuses on the control allocation (CA) of the four-wheel-distributed traction/braking/steering systems. However, the path following performance and the handling stability of the vehicle can be enhanced a step further by automatically adjusting the vehicle speed to the optimal value. The optimal solution for the combined longitudinal and lateral motion control (MC) problem is given. First, a new variable step-size spatial transformation method is proposed and utilized in the prediction model to derive the dynamics of the vehicle with respect to the road, such that the tracking errors can be explicitly obtained over the prediction horizon at varying speeds. Second, a nonlinear model predictive control (NMPC) algorithm is introduced to handle the nonlinear coupling between any two directions of the vehicular planar motion and computes the sequence of the optimal motion states for following the desired path. Third, a hierarchical control structure is proposed to separate the motion controller into a NMPC based path planner and a terminal sliding mode control (TSMC) based path follower. As revealed through off-line simulations, the hierarchical methodology brings nearly 1700% improvement in computational efficiency without loss of control performance. Finally, the control algorithm is verified through a hardware in-the-loop simulation system. Double-lane-change (DLC) test results show that by using the optimal predictive controller, the root-mean-square (RMS) values of the lateral deviations and the orientation errors can be reduced by 41% and 30%, respectively, comparing to those by the optimal preview acceleration (OPA) driver model with the non-preview speed-tracking method. Additionally, the average vehicle speed is increased by 0.26 km/h with the peak sideslip angle suppressed to 1.9°. This research proposes a novel motion controller, which provides the full drive
Optimal Predictive Control for Path Following of a Full Drive-by-Wire Vehicle at Varying Speeds
SONG, Pan; GAO, Bolin; XIE, Shugang; FANG, Rui
2017-03-01
The current research of the global chassis control problem for the full drive-by-wire vehicle focuses on the control allocation (CA) of the four-wheel-distributed traction/braking/steering systems. However, the path following performance and the handling stability of the vehicle can be enhanced a step further by automatically adjusting the vehicle speed to the optimal value. The optimal solution for the combined longitudinal and lateral motion control (MC) problem is given. First, a new variable step-size spatial transformation method is proposed and utilized in the prediction model to derive the dynamics of the vehicle with respect to the road, such that the tracking errors can be explicitly obtained over the prediction horizon at varying speeds. Second, a nonlinear model predictive control (NMPC) algorithm is introduced to handle the nonlinear coupling between any two directions of the vehicular planar motion and computes the sequence of the optimal motion states for following the desired path. Third, a hierarchical control structure is proposed to separate the motion controller into a NMPC based path planner and a terminal sliding mode control (TSMC) based path follower. As revealed through off-line simulations, the hierarchical methodology brings nearly 1700% improvement in computational efficiency without loss of control performance. Finally, the control algorithm is verified through a hardware in-the-loop simulation system. Double-lane-change (DLC) test results show that by using the optimal predictive controller, the root-mean-square (RMS) values of the lateral deviations and the orientation errors can be reduced by 41% and 30%, respectively, comparing to those by the optimal preview acceleration (OPA) driver model with the non-preview speed-tracking method. Additionally, the average vehicle speed is increased by 0.26 km/h with the peak sideslip angle suppressed to 1.9°. This research proposes a novel motion controller, which provides the full drive
Runtime analysis of ant colony optimization on dynamic shortest path problems
DEFF Research Database (Denmark)
Lissovoi, Andrei; Witt, Carsten
2013-01-01
A simple ACO algorithm called λ-MMAS for dynamic variants of the single-destination shortest paths problem is studied by rigorous runtime analyses. Building upon previous results for the special case of 1-MMAS, it is studied to what extent an enlarged colony using $\\lambda$ ants per vertex helps...
Optimal Register Assignment with Minimum-Path Delay Compensation for Variation-Aware Datapaths
Inoue, Keisuke; Kaneko, Mineo; Iwagaki, Tsuyoshi
For recent and future nanometer-technology VLSIs, static and dynamic delay variations become a serious problem. In many cases, the hold constraint, as well as the setup constraint, becomes critical for latching a correct signal under delay variations. This paper treats the hold constraint in a datapath circuit, and discusses a register assignment in high level synthesis considering delay variations. Our approach to ensure the hold constraint under delay variations is to enlarge the minimum-path delay between registers, which is called minimum-path delay compensation (MDC) in this paper. MDC can be done by inserting delay elements mainly in non-critical paths of a functional unit (FU). One of our contributions is to show that the minimization of the number of minimum-path delay compensated FUs is NP-hard in general, and it is in the class P if the number of FUs is a constant. A polynomial time algorithm for the latter is also shown in this paper. In addition, an integer linear programming (ILP) formulation is also presented. The proposed method generates a datapath having (1) robustness against delay variations, which is ensured partly by MDC technique and partly by SRV-based register assignment, and (2) the minimum possible numbers of MDCs and registers.
Energy Technology Data Exchange (ETDEWEB)
Odette, G. Robert [Univ. of California, Santa Barbara, CA (United States)
2017-02-06
The objective of this work was to characterize the alloy 14YWT-PM2, which is an extruded and cross-rolled precursor alloy to a large heat of 14YWT being produced using an alternative processing path that incorporates Y during gas atomization process.
Ant-cuckoo colony optimization for feature selection in digital mammogram.
Jona, J B; Nagaveni, N
2014-01-15
Digital mammogram is the only effective screening method to detect the breast cancer. Gray Level Co-occurrence Matrix (GLCM) textural features are extracted from the mammogram. All the features are not essential to detect the mammogram. Therefore identifying the relevant feature is the aim of this work. Feature selection improves the classification rate and accuracy of any classifier. In this study, a new hybrid metaheuristic named Ant-Cuckoo Colony Optimization a hybrid of Ant Colony Optimization (ACO) and Cuckoo Search (CS) is proposed for feature selection in Digital Mammogram. ACO is a good metaheuristic optimization technique but the drawback of this algorithm is that the ant will walk through the path where the pheromone density is high which makes the whole process slow hence CS is employed to carry out the local search of ACO. Support Vector Machine (SVM) classifier with Radial Basis Kernal Function (RBF) is done along with the ACO to classify the normal mammogram from the abnormal mammogram. Experiments are conducted in miniMIAS database. The performance of the new hybrid algorithm is compared with the ACO and PSO algorithm. The results show that the hybrid Ant-Cuckoo Colony Optimization algorithm is more accurate than the other techniques.
An Approach of Profiling Inter-Procedural Selected Paths with Loops%过程间循环兴趣路径剖析方法
Institute of Scientific and Technical Information of China (English)
罗芳
2016-01-01
Proposes an approach called PISP to profile inter-procedural selected paths, which enables custom selection for inter-procedural paths. The method is a combination of PIP and PSP, in the process using PSP to generate the corresponding PCCG diagram, and then use the PIP method to profile the process. Theoretical analysis and experimental evaluation indicate that PISP performs effectively in optimization of selective profiling.%提出一种新的过程间循环路径选择性剖析方法PISP，可以精确地剖析带有循环的过程间兴趣路径。该方法是将PIP和PSP方法相结合，在过程间采用PSP生成相应的PCCG图，之后在过程内使用PIP方法来进行剖析。理论分析和实验评估表明PISP方法能够精确地剖析过程间循环兴趣路径并使用兴趣路径来提升剖析效率。
Optimal portfolio selection for general provisioning and terminal wealth problems
van Weert, K.; Dhaene, J.; Goovaerts, M.
2010-01-01
In Dhaene et al. (2005), multiperiod portfolio selection problems are discussed, using an analytical approach to find optimal constant mix investment strategies in a provisioning or a savings context. In this paper we extend some of these results, investigating some specific, real-life situations.
Optimal portfolio selection for general provisioning and terminal wealth problems
van Weert, K.; Dhaene, J.; Goovaerts, M.
2009-01-01
In Dhaene et al. (2005), multiperiod portfolio selection problems are discussed, using an analytical approach to find optimal constant mix investment strategies in a provisioning or savings context. In this paper we extend some of these results, investigating some specific, real-life situations. The
Immune clonal selection optimization method with combining mutation strategies
Institute of Scientific and Technical Information of China (English)
无
2007-01-01
In artificial immune optimization algorithm, the mutation of immune cells has been considered as the key operator that determines the algorithm performance. Traditional immune optimization algorithms have used a single mutation operator, typically a Gaussian. Using a variety of mutation operators that can be combined during evolution to generate different probability density function could hold the potential for producing better solutions with less computational effort. In view of this, a linear combination mutation operator of Gaussian and Cauchy mutation is presented in this paper, and a novel clonal selection optimization method based on clonal selection principle is proposed also. The simulation results show the combining mutation strategy can obtain the same performance as the best of pure strategies or even better in some cases.
Selection strategies for anti-cancer antibody discovery: searching off the beaten path
Sánchez-Martín, David; Sørensen, Morten Dræby; Lykkemark, Simon; Sanz, Laura; Kristensen, Peter; Ruoslahti, Erkki; Álvarez-Vallina, Luis
2017-01-01
Antibody based drugs represent one of the most successful and promising therapeutic approaches in oncology. Large combinatorial phage antibody libraries are available for identification of therapeutic antibodies, and a variety of technologies exist for their further conversion into multivalent and multispecific formats optimized for the desired pharmacokinetics and the pathological context. However, there is no technology for antigen profiling of intact tumors to identify tumor markers targetable with antibodies. Such constraints have led to a relative paucity of tumor-associated antigens for antibody targeting in oncology. Here we review novel approaches aimed at the identification of antibody-targetable, accessible antigens in intact tumors. We hope that such advanced selection approaches will be useful in the development of next-generation antibody therapeutics for cancer. PMID:25819764
Mathematical model for path selection by ants between nest and food source.
Bodnar, Marek; Okińczyc, Natalia; Vela-Pérez, M
2017-03-01
Several models have been proposed to describe the behavior of ants when moving from nest to food sources. Most of these studies where based on numerical simulations with no mathematical justification. In this paper, we propose a mechanism for the formation of paths of minimal length between two points by a collection of individuals undergoing reinforced random walks taking into account not only the lengths of the paths but also the angles (connected to the preference of ants to move along straight lines). Our model involves reinforcement (pheromone accumulation), persistence (tendency to preferably follow straight directions in absence of any external effect) and takes into account the bifurcation angles of each edge (represented by a probability of willingness of choosing the path with the smallest angle). We describe analytically the results for 2 ants and different path lengths and numerical simulations for several ants. Copyright © 2016 Elsevier Inc. All rights reserved.
Optimal selection of nodes to propagate influence on networks
Sun, Yifan
2016-11-01
How to optimize the spreading process on networks has been a hot issue in complex networks, marketing, epidemiology, finance, etc. In this paper, we investigate a problem of optimizing locally the spreading: identifying a fixed number of nodes as seeds which would maximize the propagation of influence to their direct neighbors. All the nodes except the selected seeds are assumed not to spread their influence to their neighbors. This problem can be mapped onto a spin glass model with a fixed magnetization. We provide a message-passing algorithm based on replica symmetrical mean-field theory in statistical physics, which can find the nearly optimal set of seeds. Extensive numerical results on computer-generated random networks and real-world networks demonstrate that this algorithm has a better performance than several other optimization algorithms.
Optimizing Event Selection with the Random Grid Search
Energy Technology Data Exchange (ETDEWEB)
Bhat, Pushpalatha C. [Fermilab; Prosper, Harrison B. [Florida State U.; Sekmen, Sezen [Kyungpook Natl. U.; Stewart, Chip [Broad Inst., Cambridge
2017-06-29
The random grid search (RGS) is a simple, but efficient, stochastic algorithm to find optimal cuts that was developed in the context of the search for the top quark at Fermilab in the mid-1990s. The algorithm, and associated code, have been enhanced recently with the introduction of two new cut types, one of which has been successfully used in searches for supersymmetry at the Large Hadron Collider. The RGS optimization algorithm is described along with the recent developments, which are illustrated with two examples from particle physics. One explores the optimization of the selection of vector boson fusion events in the four-lepton decay mode of the Higgs boson and the other optimizes SUSY searches using boosted objects and the razor variables.
Optimal tariff design under consumer self-selection
Energy Technology Data Exchange (ETDEWEB)
Raesaenen, M.; Ruusunen, J.; Haemaelaeinen, R.
1995-12-31
This report considers the design of electricity tariffs which guides an individual consumer to select the tariff designed for his consumption pattern. In the model the utility maximizes the weighted sum of individual consumers` benefits of electricity consumption subject to the utility`s revenue requirement constraints. The consumers` free choice of tariffs is ensured with the so-called self-selection constraints. The relationship between the consumers` optimal choice of tariffs and the weights in the aggregated consumers` benefit function is analyzed. If such weights exist, they will guarantee both the consumers` optimal choice of tariffs and the efficient consumption patterns. Also the welfare effects are analyzed by using demand parameters estimated from a Finnish dynamic pricing experiment. The results indicate that it is possible to design an efficient tariff menu with the welfare losses caused by the self-selection constraints being small compared with the costs created when some consumers choose tariffs other than assigned for them. (author)
Institute of Scientific and Technical Information of China (English)
汪华兵
2015-01-01
A path planning algorithm of fire suppression is proposed based on multiple binary tree Pareto optimal solution set , the fire scene environment map and fire evolvement trend is reconstructed, realize the optimization of path, using the Pareto optimal solution set, the construction of fire fighting path planning model of dynamic development trend of multi tree Pareto optimal solution set based on the fire. The experimental results show that, the model can quickly achieve the recogni⁃tion of fire hot, and it can effectively avoid the interference of path planning in complex building of obstacles, to achieve the optimal path selection for fire fighting. In dynamic unknown environment, the fire fighting path planning and selection can achieve the optimal segmentation, shortest path is obtained, it can effectively avoid the stop complex building, effectively save the fire fighting time.%提出一种基于多叉树Pareto最优解集的火灾扑救路径规划算法，对火灾现场的环境地图和火灾演化态势进行重构，实现对路径的优选，采用Pareto最优解集，构建基于多叉树Pareto最优解集的火源动态发展态势下的火灾扑救路径规划模型。实验结果表明，该模型能快速实现对火源热点的识别，并且规划路径能有效规避复杂建筑障碍物的干扰，实现对火灾扑救路径的最优选择。在动态未知环境中，对火灾扑救路径的规划和选择能达到最优，路径最短，分段较少，能有效地避免复杂建筑物的阻挡，有效节省了火灾扑救时间。
Strategy Developed for Selecting Optimal Sensors for Monitoring Engine Health
2004-01-01
Sensor indications during rocket engine operation are the primary means of assessing engine performance and health. Effective selection and location of sensors in the operating engine environment enables accurate real-time condition monitoring and rapid engine controller response to mitigate critical fault conditions. These capabilities are crucial to ensure crew safety and mission success. Effective sensor selection also facilitates postflight condition assessment, which contributes to efficient engine maintenance and reduced operating costs. Under the Next Generation Launch Technology program, the NASA Glenn Research Center, in partnership with Rocketdyne Propulsion and Power, has developed a model-based procedure for systematically selecting an optimal sensor suite for assessing rocket engine system health. This optimization process is termed the systematic sensor selection strategy. Engine health management (EHM) systems generally employ multiple diagnostic procedures including data validation, anomaly detection, fault-isolation, and information fusion. The effectiveness of each diagnostic component is affected by the quality, availability, and compatibility of sensor data. Therefore systematic sensor selection is an enabling technology for EHM. Information in three categories is required by the systematic sensor selection strategy. The first category consists of targeted engine fault information; including the description and estimated risk-reduction factor for each identified fault. Risk-reduction factors are used to define and rank the potential merit of timely fault diagnoses. The second category is composed of candidate sensor information; including type, location, and estimated variance in normal operation. The final category includes the definition of fault scenarios characteristic of each targeted engine fault. These scenarios are defined in terms of engine model hardware parameters. Values of these parameters define engine simulations that generate
Partner Selection Optimization Model of Agricultural Enterprises in Supply Chain
Directory of Open Access Journals (Sweden)
Feipeng Guo
2013-10-01
Full Text Available With more and more importance of correctly selecting partners in supply chain of agricultural enterprises, a large number of partner evaluation techniques are widely used in the field of agricultural science research. This study established a partner selection model to optimize the issue of agricultural supply chain partner selection. Firstly, it constructed a comprehensive evaluation index system after analyzing the real characteristics of agricultural supply chain. Secondly, a heuristic method for attributes reduction based on rough set theory and principal component analysis was proposed which can reduce multiple attributes into some principal components, yet retaining effective evaluation information. Finally, it used improved BP neural network which has self-learning function to select partners. The empirical analysis on an agricultural enterprise shows that this model is effective and feasible for practical partner selection.
Optimization of the transition path of the head hardening with using the genetic algorithms
Wróbel, Joanna; Kulawik, Adam
2016-06-01
An automated method of choice of the transition path of the head hardening in heat treatment process for the plane steel element is proposed in this communication. This method determines the points on the path of moving heat source using the genetic algorithms. The fitness function of the used algorithm is determined on the basis of effective stresses and yield point depending on the phase composition. The path of the hardening tool and also the area of the heat affected zone is determined on the basis of obtained points. A numerical model of thermal phenomena, phase transformations in the solid state and mechanical phenomena for the hardening process is implemented in order to verify the presented method. A finite element method (FEM) was used for solving the heat transfer equation and getting required temperature fields. The moving heat source is modeled with a Gaussian distribution and the water cooling is also included. The macroscopic model based on the analysis of the CCT and CHT diagrams of the medium-carbon steel is used to determine the phase transformations in the solid state. A finite element method is also used for solving the equilibrium equations giving us the stress field. The thermal and structural strains are taken into account in the constitutive relations.
Sungjoon Park,
2011-11-01
In this paper we present opportunistic relay communication strategies of decode and forward relaying. The channel that we are considering includes pathloss, shadowing, and fast fading effects. We find a simple outage probability formula for opportunistic relaying in the channel, and validate the results by comparing it with the exact outage probability. Also, we suggest a new relay selection algorithm that incorporates shadowing. We consider a protocol of broadcasting the channel gain of the previously selected relay. This saves resources in slow fading channel by reducing collisions in relay selection. We further investigate the optimal relay selection period to maximize the throughput while avoiding selection overhead. © 2011 IEEE.
Optimizing Ligand Efficiency of Selective Androgen Receptor Modulators (SARMs).
Handlon, Anthony L; Schaller, Lee T; Leesnitzer, Lisa M; Merrihew, Raymond V; Poole, Chuck; Ulrich, John C; Wilson, Joseph W; Cadilla, Rodolfo; Turnbull, Philip
2016-01-14
A series of selective androgen receptor modulators (SARMs) containing the 1-(trifluoromethyl)benzyl alcohol core have been optimized for androgen receptor (AR) potency and drug-like properties. We have taken advantage of the lipophilic ligand efficiency (LLE) parameter as a guide to interpret the effect of structural changes on AR activity. Over the course of optimization efforts the LLE increased over 3 log units leading to a SARM 43 with nanomolar potency, good aqueous kinetic solubility (>700 μM), and high oral bioavailability in rats (83%).
Tran, Huu-Khoa; Chiou, Juing -Shian; Peng, Shou-Tao
2016-01-01
In this paper, the feasibility of a Genetic Algorithm Optimization (GAO) education software based Fuzzy Logic Controller (GAO-FLC) for simulating the flight motion control of Unmanned Aerial Vehicles (UAVs) is designed. The generated flight trajectories integrate the optimized Scaling Factors (SF) fuzzy controller gains by using GAO algorithm. The…
Tran, Huu-Khoa; Chiou, Juing -Shian; Peng, Shou-Tao
2016-01-01
In this paper, the feasibility of a Genetic Algorithm Optimization (GAO) education software based Fuzzy Logic Controller (GAO-FLC) for simulating the flight motion control of Unmanned Aerial Vehicles (UAVs) is designed. The generated flight trajectories integrate the optimized Scaling Factors (SF) fuzzy controller gains by using GAO algorithm. The…
Energy Optimized Link Selection Algorithm for Mobile Cloud Computing
Directory of Open Access Journals (Sweden)
K.Ravindranath
2015-03-01
Full Text Available Mobile cloud computing is the revolutionary distributed computing research area which consists of three different domains: cloud computing, wireless networks and mobile computing targeting to improve the task computational capabilities of the mobile devices in order to minimize the energy consumption. Heavy computations can be offloaded to the cloud to decrease energy consumption for the mobile device. In some mobile cloud applications, it has been more energy inefficient to use the cloud compared to the conventional computing conducted in the local device. Despite mobile cloud computing being a reliable idea, still faces several problems for mobile phones such as storage, short battery life and so on. One of the most important concerns for mobile devices is low energy consumption. Different network links has different bandwidths to uplink and downlink task as well as data transmission from mobile to cloud or vice-versa. In this paper, a novel optimal link selection algorithm is proposed to minimize the mobile energy. In the first phase, all available networks are scanned and then signal strength is calculated. All the calculated signals along with network locations are given input to the optimal link selection algorithm. After the execution of link selection algorithm, an optimal network link is selected.
Optimal continuous-path control for manipulators with redundant degrees of freedom
Directory of Open Access Journals (Sweden)
Olav Egeland
1989-04-01
Full Text Available A control system for macro-mini manipulators is presented. A position transformation from the end-effector reference to joint coordinates is found using kinematic optimization. Decoupling and optimal control is used to coordinate the motion of the macro and micro part. The redundant manipulator will then have the speed of the micro manipulator and the large workspace of the macro manipulator. When optimal control is used, the redundant manipulator may be even faster than the micro manipulator provided that a suitable performance index is used. The performance of the manipulator is optimized over the whole reference, and this will give better results than the purely kinematic instantaneous optimization which is the dominating technique in research literature.
Directory of Open Access Journals (Sweden)
Indah Anita Sari
2013-12-01
Full Text Available Path coefficient analysis is frequently used for development of selection criteria on various type of plants. Path analysis on this research was conducted to find the selection criteria of yield component which directly affect bean weight. In addition to the value of path analysis coefficient, genetic variation coefficient, heritability and the value of genetic progress were also studied. The study was conducted at the Indonesian Coffee and Cocoa Research Institute. The research used randomized complete block design consisting of 14 accession numbers and each consisting of three replications. Pod girth, pod length, pod weight, wet beans weight per pod, number of normal beans per pod, number of abnormal beans per pod, dry weight per normal bean, and shell content were observed. The results showed that the pod weight character had an important role in determining the dry weight of normal bean. The character had a positive genotype correlation coefficient values which was high and significantly different (r=0.46 for dry weight per normal bean, considerable direct influence (P=0.479, moderate of the genotype variation coefficient (9.6%, and high genetic progress (95.23. Character of wet bean weight per pod could also be used indirectly for the selection criteria for dry weight per normal bean based on genetic variation coefficient value (11.88%, genetic progress value (82.48, and direct effect on dry weight per normal bean had positive value (P=0.006. Key words: Selection criteria, dry weight per bean, path analysis,Theobroma cacaoL.
Sahelgozin, M.; Sadeghi-Niaraki, A.; Dareshiri, S.
2015-12-01
A myriad of novel applications have emerged nowadays for different types of navigation systems. One of their most frequent applications is Wayfinding. Since there are significant differences between the nature of the pedestrian wayfinding problems and of those of the vehicles, navigation services which are designed for vehicles are not appropriate for pedestrian wayfinding purposes. In addition, diversity in environmental conditions of the users and in their preferences affects the process of pedestrian wayfinding with mobile devices. Therefore, a method is necessary that performs an intelligent pedestrian routing with regard to this diversity. This intelligence can be achieved by the help of a Ubiquitous service that is adapted to the Contexts. Such a service possesses both the Context-Awareness and the User-Awareness capabilities. These capabilities are the main features of the ubiquitous services that make them flexible in response to any user in any situation. In this paper, it is attempted to propose a multi-criteria path optimization method that provides a Ubiquitous Pedestrian Way Finding Service (UPWFS). The proposed method considers four criteria that are summarized in Length, Safety, Difficulty and Attraction of the path. A conceptual framework is proposed to show the influencing factors that have effects on the criteria. Then, a mathematical model is developed on which the proposed path optimization method is based. Finally, data of a local district in Tehran is chosen as the case study in order to evaluate performance of the proposed method in real situations. Results of the study shows that the proposed method was successful to understand effects of the contexts in the wayfinding procedure. This demonstrates efficiency of the proposed method in providing a ubiquitous pedestrian wayfinding service.
The optimal path of piston motion for Otto cycle with linear phenomenological heat transfer law
Institute of Scientific and Technical Information of China (English)
无
2009-01-01
An Otto cycle engine with internal and external irreversibilities of friction and heat leakage, in which the heat transfer between the working fluid and the environment obeys linear phenomenological heat transfer law [q ∝△(T -1)], is studied in this paper. The optimal piston motion trajectory for maximizing the work output per cycle is derived for the fixed total cycle time and fuel consumed per cycle. Optimal control theory is applied to determine the optimal piston trajectories for the cases of with and without piston acceleration constraint on each stroke and the optimal distribution of the total cycle time among the strokes. The optimal piston motion with acceleration constraint for each stroke consists of three segments, including initial maximum acceleration and final maximum deceleration boundary segments, respectively. Numerical examples for optimal configuration are provided, and the obtained results are compared with those obtained with Newton’s heat transfer law [q ∝△(T )]. The results also show that optimizing the piston motion can improve power and efficiency of the engine by more than 9%. This is primarily due to the decrease in heat leakage loss on the initial portion of the power stroke.
The optimal path of piston motion for Otto cycle with linear phenomenological heat transfer law
Institute of Scientific and Technical Information of China (English)
XIA ShaoJun; CHEN LinGen; SUN FengRui
2009-01-01
An Otto cycle engine with internal and external irreversibilities of friction and heat leakage, in which the heat transfer between the working fluid and the environment obeys linear phenomenological heat transfer law [q∝△(T-1)], is studied in this paper. The optimal piston motion trajectory for maximizing the work output per cycle is derived for the fixed total cycle time and fuel consumed per cycle. Optimal control theory is applied to determine the optimal piston trajectories for the cases of with and without piston acceleration constraint on each stroke and the optimal distribution of the total cycle time among the strokes. The optimal piston motion with acceleration constraint for each stroke consists of three segments, including initial maximum acceleration and final maximum deceleration boundary segments,respectively. Numerical examples for optimal configuration are provided, and the obtained results are compared with those obtained with Newton's heat transfer law [q∝△(T)]. The results also show that optimizing the piston motion can improve power and efficiency of the engine by more than 9%. This is primarily due to the decrease in heat leakage loss on the initial portion of the power stroke.
Optimal paths of piston motion of irreversible diesel cycle for minimum entropy generation
Directory of Open Access Journals (Sweden)
Ge Yanlin
2011-01-01
Full Text Available A Diesel cycle heat engine with internal and external irreversibility’s of heat transfer and friction, in which the finite rate of combustion is considered and the heat transfer between the working fluid and the environment obeys Newton’s heat transfer law [q≈ Δ(T], is studied in this paper. Optimal piston motion trajectories for minimizing entropy generation per cycle are derived for the fixed total cycle time and fuel consumed per cycle. Optimal control theory is applied to determine the optimal piston motion trajectories for the cases of with piston acceleration constraint on each stroke and the optimal distribution of the total cycle time among the strokes. The optimal piston motion with acceleration constraint for each stroke consists of three segments, including initial maximum acceleration and final maximum deceleration boundary segments, respectively. Numerical examples for optimal configurations are provided, and the results obtained are compared with those obtained when maximizing the work output with Newton’s heat transfer law. The results also show that optimizing the piston motion trajectories could reduce engine entropy generation by more than 20%. This is primarily due to the decrease in entropy generation caused by heat transfer loss on the initial portion of the power stroke.
Judgments in the selection of path generation techniques: a meta-analytic approach
DEFF Research Database (Denmark)
Prato, Carlo Giacomo
2012-01-01
objective judgments for effective route choice set generation. Initially, path generation techniques are implemented within a synthetic network to generate possible subjective choice sets considered by travelers. Next, “true model estimates” and “postulated predicted routes” are assumed from the simulation...... synthesizing the effect of judgments on the implementation of path generation techniques, since a large number of models generate a large amount of results that are otherwise difficult to summarize and to process. Meta-analysis estimates suggest that modelers should apply stochastic approaches...
Runtime analysis of ant colony optimization on dynamic shortest path problems
DEFF Research Database (Denmark)
Lissovoi, Andrei; Witt, Carsten
2015-01-01
A simple ACO algorithm called lambda-MMAS for dynamic variants of the single-destination shortest paths problem is studied by rigorous runtime analyses. Building upon previous results for the special case of 1-MMAS, it is studied to what extent an enlarged colony using lambda ants per vertex helps...... in tracking an oscillating optimum. It is shown that easy cases of oscillations can be tracked by a constant number of ants. However, the paper also identifies more involved oscillations that with overwhelming probability cannot be tracked with any polynomial-size colony. Finally, parameters of dynamic...
Particle swarm optimization algorithm for partner selection in virtual enterprise
Institute of Scientific and Technical Information of China (English)
Qiang Zhao; Xinhui Zhang; Renbin Xiao
2008-01-01
Partner selection is a fundamental problem in the formation and success of a virtual enterprise. The partner selection problem with precedence and due date constraint is the basis of the various extensions and is studied in this paper. A nonlinear integer program model for the partner selection problem is established. The problem is shown to be NP-complete by reduction to the knapsack problem, and therefore no polynomial time algorithm exists. To solve it efficiently, a particle swarm optimization (PSO) algorithm is adopted, and several mechanisms that include initialization expansion mechanism, variance mechanism and local searching mechanism have been developed to improve the performance of the proposed PSO algorithm. A set of experiments have been conducted using real examples and numerical simulation, and have shown that the PSO algorithm is an effective and efficient way to solve the partner selection problems with precedence and due date constraints.
Directory of Open Access Journals (Sweden)
A. A. Heidari
2013-09-01
Full Text Available This paper addresses an innovative evolutionary computation approach to 3D path planning of autonomous UAVs in real environment. To solve this Np-hard problem, Newtonian imperialist competitive algorithm (NICA was developed and extended for path planning problem. This paper is related to optimal trajectory-designing before UAV missions. NICA planner provides 3D optimal paths for UAV planning in real topography of north Tehran environment. To simulate UAV path planning, a real DTM is used to algorithm. For real-world applications, final generated paths should be smooth and also physical flyable that made the path planning problems complex and more constrained. The planner progressively presents a smooth 3D path from first position to mission target location. The objective function contains distinctive measures of the problem. Our main goal is minimization of the total mission time. For evaluating of NICA efficiency, it is compared with other three well-known methods, i.e. ICA, GA, and PSO. Then path planning of UAV will done. Finally simulations proved the high capabilities of proposed methodology.
Heidari, A. A.; Afghan-Toloee, A.; Abbaspour, R. A.
2013-09-01
This paper addresses an innovative evolutionary computation approach to 3D path planning of autonomous UAVs in real environment. To solve this Np-hard problem, Newtonian imperialist competitive algorithm (NICA) was developed and extended for path planning problem. This paper is related to optimal trajectory-designing before UAV missions. NICA planner provides 3D optimal paths for UAV planning in real topography of north Tehran environment. To simulate UAV path planning, a real DTM is used to algorithm. For real-world applications, final generated paths should be smooth and also physical flyable that made the path planning problems complex and more constrained. The planner progressively presents a smooth 3D path from first position to mission target location. The objective function contains distinctive measures of the problem. Our main goal is minimization of the total mission time. For evaluating of NICA efficiency, it is compared with other three well-known methods, i.e. ICA, GA, and PSO. Then path planning of UAV will done. Finally simulations proved the high capabilities of proposed methodology.
Phased array tuning for optimal ultrasonic guided wave mode selection
Bostron, J. H.; Rose, J. L.; Moose, C. A.
2014-02-01
Ultrasonic guided waves have become widely used in a variety of nondestructive evaluation applications due to their efficiency in defect detection, ability to inspect hidden areas, and other reasons. With a thorough understanding of guided wave mechanics, researchers can predict which guided wave modes will have a high probability of success in a particular nondestructive evaluation application. However, work continues to find optimal mode and frequency selection. An "optimal" mode could give the highest sensitivity to defects or the greatest penetration power, increasing inspection efficiency. In this work, we consider the use of guided interface waves for bond evaluation. A phased comb array transducer is used to sweep in the phase velocity - frequency space in an effort to determine optimal modes.
NEW ALGORITHM FOR OPTIMAL DIELECTRIC MATERIAL SELECTION IN MARINE ENVIRONMENT
Directory of Open Access Journals (Sweden)
Igor Vujović
2015-09-01
Full Text Available The materials’ selection demands knowledge from different disciplines, depending on application. Very important parameters that influence dielectric material's properties in, for example, marine applications are operating frequency, expected temperature in practice and moisture. The proposed optimal solution for the dielectrics choice is based on theory of sets. Instead of using function to find best value for the dielectric constant, the parameters’ dependences are used to produce sets of possible values, which are used to find the optimal material for the desired application. The intersection of the three sets of possible solution is the optimal solution if the material with such value exists. If not, maximum acceptable deviation is used to find the acceptable material.
Directory of Open Access Journals (Sweden)
Anna M. Manzoni
2016-03-01
Full Text Available The most commonly investigated high entropy alloy, AlCoCrCuFeNi, has been chosen for optimization of its microstructural and mechanical properties by means of compositional changes and heat treatments. Among the different available optimization paths, the decrease of segregating element Cu, the increase of oxidation protective elements Al and Cr and the approach towards a γ-γ′ microstructure like in Ni-based superalloys have been probed and compared. Microscopical observations have been made for every optimization step. Vickers microhardness measurements and/or tensile/compression test have been carried out when the alloy was appropriate. Five derived alloys AlCoCrFeNi, Al23Co15Cr23Cu8Fe15Ni16, Al8Co17Cr17Cu8Fe17Ni33, Al8Co17Cr14Cu8Fe17Ni34.8Mo0.1Ti1W0.1 and Al10Co25Cr8Fe15Ni36Ti6 (all at.% have been compared to the original AlCoCrCuFeNi and the most promising one has been selected for further investigation.
Evolutionary Optimization of State Selective Field Ionization for Quantum Computing
Jones, M L; Majeed, H O; Varcoe, B T H
2009-01-01
State selective field ionization detection techniques in physics require a specific progression through a complicated atomic state space to optimize state selectivity and overall efficiency. For large principle quantum number n, the theoretical models become computationally intractable and any results are often rendered irrelevant by small deviations from ideal experimental conditions, for example external electromagnetic fields. Several different proposals for quantum information processing rely heavily upon the quality of these detectors. In this paper, we show a proof of principle that it is possible to optimize experimental field profiles in situ by running a genetic algorithm to control aspects of the experiment itself. A simple experiment produced novel results that are consistent with analyses of existing results.
Test Cases Reduction and Selection Optimization in Testing Web Services
Directory of Open Access Journals (Sweden)
Izzat Alsmadi
2012-10-01
Full Text Available Software testing in web services environment faces different challenges in comparison with testing in traditional software environments. Regression testing activities are triggered based on software changes or evolutions. In web services, evolution is not a choice for service clients. They have always to use the current updated version of the software. In addition test execution or invocation is expensive in web services and hence providing algorithms to optimize test case generation and execution is vital. In this environment, we proposed several approach for test cases’ selection in web services’ regression testing. Testing in this new environment should evolve to be included part of the service contract. Service providers should provide data or usage sessions that can help service clients reduce testing expenses through optimizing the selected and executed test cases.
Monte Carlo optimization for site selection of new chemical plants.
Cai, Tianxing; Wang, Sujing; Xu, Qiang
2015-11-01
Geographic distribution of chemical manufacturing sites has significant impact on the business sustainability of industrial development and regional environmental sustainability as well. The common site selection rules have included the evaluation of the air quality impact of a newly constructed chemical manufacturing site to surrounding communities. In order to achieve this target, the simultaneous consideration should cover the regional background air-quality information, the emissions of new manufacturing site, and statistical pattern of local meteorological conditions. According to the above information, the risk assessment can be conducted for the potential air-quality impacts from candidate locations of a new chemical manufacturing site, and thus the optimization of the final site selection can be achieved by minimizing its air-quality impacts. This paper has provided a systematic methodology for the above purpose. There are total two stages of modeling and optimization work: i) Monte Carlo simulation for the purpose to identify background pollutant concentration based on currently existing emission sources and regional statistical meteorological conditions; and ii) multi-objective (simultaneous minimization of both peak pollutant concentration and standard deviation of pollutant concentration spatial distribution at air-quality concern regions) Monte Carlo optimization for optimal location selection of new chemical manufacturing sites according to their design data of potential emission. This study can be helpful to both determination of the potential air-quality impact for geographic distribution of multiple chemical plants with respect to regional statistical meteorological conditions, and the identification of an optimal site for each new chemical manufacturing site with the minimal environment impact to surrounding communities. The efficacy of the developed methodology has been demonstrated through the case studies.
Optimal paths of piston motion of irreversible diesel cycle for minimum entropy generation
Ge Yanlin; Chen Lingen; Sun Fengrui
2011-01-01
A Diesel cycle heat engine with internal and external irreversibility’s of heat transfer and friction, in which the finite rate of combustion is considered and the heat transfer between the working fluid and the environment obeys Newton’s heat transfer law [q≈ Δ(T)], is studied in this paper. Optimal piston motion trajectories for minimizing entropy generation per cycle are derived for the fixed total cycle time and fuel consumed per cycle. Optimal control theory is applied to determine...
Fuzzy Support Vector Machine-based Multi-agent Optimal Path
Directory of Open Access Journals (Sweden)
Gireesh Kumar T
2010-07-01
Full Text Available A mobile robot to navigate purposefully from a start location to a target location, needs three basic requirements: sensing, learning, and reasoning. In the existing system, the mobile robot navigates in a known environment on a predefined path. However, the pervasive presence of uncertainty in sensing and learning, makes the choice of a suitable tool of reasoning and decision-making that can deal with incomplete information, vital to ensure a robust control system. This problem can be overcome by the proposed navigation method using fuzzy support vector machine (FSVM. It proposes a fuzzy logic-based support vector machine (SVM approach to secure a collision-free path avoiding multiple dynamic obstacles. The navigator consists of an FSVM-based collision avoidance. The decisions are taken at each step for the mobile robot to attain the goal position without collision. Fuzzy-SVM rule bases are built, which require simple evaluation data rather than thousands of input-output training data. The effectiveness of the proposed method is verified by a series of simulations and implemented with a microcontroller for navigation.Defence Science Journal, 2010, 60(4, pp.387-391, DOI:http://dx.doi.org/10.14429/dsj.60.496
Optimal Licensing Contracts with Adverse Selection and Informational Rents
Directory of Open Access Journals (Sweden)
Daniela MARINESCU
2011-06-01
Full Text Available In the paper we analyse a model for determining the optimal licensing contract in both situations of symmetric and asymmetric information between the license’s owner and the potential buyer. Next we present another way of solving the corresponding adverse selection model, using the informational rents as variables. This approach is different from that of Macho-Stadler and Perez-Castrillo.
An Optimal SVM with Feature Selection Using Multiobjective PSO
Directory of Open Access Journals (Sweden)
Iman Behravan
2016-01-01
Full Text Available Support vector machine is a classifier, based on the structured risk minimization principle. The performance of the SVM depends on different parameters such as penalty factor, C, and the kernel factor, σ. Also choosing an appropriate kernel function can improve the recognition score and lower the amount of computation. Furthermore, selecting the useful features among several features in dataset not only increases the performance of the SVM, but also reduces the computational time and complexity. So this is an optimization problem which can be solved by heuristic algorithm. In some cases besides the recognition score, the reliability of the classifier’s output is important. So in such cases a multiobjective optimization algorithm is needed. In this paper we have got the MOPSO algorithm to optimize the parameters of the SVM, choose appropriate kernel function, and select the best feature subset simultaneously in order to optimize the recognition score and the reliability of the SVM concurrently. Nine different datasets, from UCI machine learning repository, are used to evaluate the power and the effectiveness of the proposed method (MOPSO-SVM. The results of the proposed method are compared to those which are achieved by single SVM, RBF, and MLP neural networks.
An Improved Particle Swarm Optimization for Feature Selection
Institute of Scientific and Technical Information of China (English)
Yuanning Liu; Gang Wang; Huiling Chen; Hao Dong; Xiaodong Zhu; Sujing Wang
2011-01-01
Particle Swarm Optimization (PSO) is a popular and bionic algorithm based on the social behavior associated with bird flocking for optimization problems. To maintain the diversity of swarms, a few studies of multi-swarm strategy have been reported. However, the competition among swarms, reservation or destruction of a swarm, has not been considered further. In this paper, we formulate four rules by introducing the mechanism for survival of the fittest, which simulates the competition among the swarms. Based on the mechanism, we design a modified Multi-Swarm PSO (MSPSO) to solve discrete problems,which consists of a number of sub-swarms and a multi-swarm scheduler that can monitor and control each sub-swarm using the rules. To further settle the feature selection problems, we propose an Improved Feature Selection (IFS) method by integrating MSPSO, Support Vector Machines (SVM) with F-score method. The IFS method aims to achieve higher generalization capability through performing kernel parameter optimization and feature selection simultaneously. The performance of the proposed method is compared with that of the standard PSO based, Genetic Algorithm (GA) based and the grid search based methods on 10 benchmark datasets, taken from UCI machine learning and StatLog databases. The numerical results and statistical analysis show that the proposed IFS method performs significantly better than the other three methods in terms of prediction accuracy with smaller subset of features.
Feature selection for optimized skin tumor recognition using genetic algorithms.
Handels, H; Ross, T; Kreusch, J; Wolff, H H; Pöppl, S J
1999-07-01
In this paper, a new approach to computer supported diagnosis of skin tumors in dermatology is presented. High resolution skin surface profiles are analyzed to recognize malignant melanomas and nevocytic nevi (moles), automatically. In the first step, several types of features are extracted by 2D image analysis methods characterizing the structure of skin surface profiles: texture features based on cooccurrence matrices, Fourier features and fractal features. Then, feature selection algorithms are applied to determine suitable feature subsets for the recognition process. Feature selection is described as an optimization problem and several approaches including heuristic strategies, greedy and genetic algorithms are compared. As quality measure for feature subsets, the classification rate of the nearest neighbor classifier computed with the leaving-one-out method is used. Genetic algorithms show the best results. Finally, neural networks with error back-propagation as learning paradigm are trained using the selected feature sets. Different network topologies, learning parameters and pruning algorithms are investigated to optimize the classification performance of the neural classifiers. With the optimized recognition system a classification performance of 97.7% is achieved.
OptimalCutpoints: An R Package for Selecting Optimal Cutpoints in Diagnostic Tests
Directory of Open Access Journals (Sweden)
Mónica López-Ratón
2014-11-01
Full Text Available Continuous diagnostic tests are often used for discriminating between healthy and diseased populations. For the clinical application of such tests, it is useful to select a cutpoint or discrimination value c that defines positive and negative test results. In general, individuals with a diagnostic test value of c or higher are classified as diseased. Several search strategies have been proposed for choosing optimal cutpoints in diagnostic tests, depending on the underlying reason for this choice. This paper introduces an R package, known as OptimalCutpoints, for selecting optimal cutpoints in diagnostic tests. It incorporates criteria that take the costs of the different diagnostic decisions into account, as well as the prevalence of the target disease and several methods based on measures of diagnostic test accuracy. Moreover, it enables optimal levels to be calculated according to levels of given (categorical covariates. While the numerical output includes the optimal cutpoint values and associated accuracy measures with their confidence intervals, the graphical output includes the receiver operating characteristic (ROC and predictive ROC curves. An illustration of the use of OptimalCutpoints is provided, using a real biomedical dataset.
Hyperopt: a Python library for model selection and hyperparameter optimization
Bergstra, James; Komer, Brent; Eliasmith, Chris; Yamins, Dan; Cox, David D.
2015-01-01
Sequential model-based optimization (also known as Bayesian optimization) is one of the most efficient methods (per function evaluation) of function minimization. This efficiency makes it appropriate for optimizing the hyperparameters of machine learning algorithms that are slow to train. The Hyperopt library provides algorithms and parallelization infrastructure for performing hyperparameter optimization (model selection) in Python. This paper presents an introductory tutorial on the usage of the Hyperopt library, including the description of search spaces, minimization (in serial and parallel), and the analysis of the results collected in the course of minimization. This paper also gives an overview of Hyperopt-Sklearn, a software project that provides automatic algorithm configuration of the Scikit-learn machine learning library. Following Auto-Weka, we take the view that the choice of classifier and even the choice of preprocessing module can be taken together to represent a single large hyperparameter optimization problem. We use Hyperopt to define a search space that encompasses many standard components (e.g. SVM, RF, KNN, PCA, TFIDF) and common patterns of composing them together. We demonstrate, using search algorithms in Hyperopt and standard benchmarking data sets (MNIST, 20-newsgroups, convex shapes), that searching this space is practical and effective. In particular, we improve on best-known scores for the model space for both MNIST and convex shapes. The paper closes with some discussion of ongoing and future work.
Institute of Scientific and Technical Information of China (English)
Chen Kaiyan; Si Junhong; Zhou Fubao; Zhang Renwei; Shao He; Zhao Hongmei
2015-01-01
In mine ventilation networks, the reasonable airflow distribution is very important for the production safety and economy. Three basic problems of the natural, full-controlled and semi-controlled splitting were reviewed in the paper. Aiming at the high difficulty semi-controlled splitting problem, the general nonlinear multi-objectives optimization mathematical model with constraints was established based on the theory of mine ventilation networks. A new algorithm, which combined the improved differential evaluation and the critical path method (CPM) based on the multivariable separate solution strategy, was put forward to search for the global optimal solution more efficiently. In each step of evolution, the feasible solutions of air quantity distribution are firstly produced by the improved differential evolu-tion algorithm, and then the optimal solutions of regulator pressure drop are obtained by the CPM. Through finite steps iterations, the optimal solution can be given. In this new algorithm, the population of feasible solutions were sorted and grouped for enhancing the global search ability and the individuals in general group were randomly initialized for keeping diversity. Meanwhile, the individual neighbor-hood in the fine group which may be closely to the optimal solutions were searched locally and slightly for achieving a balance between global searching and local searching, thus improving the convergence rate. The computer program was developed based on this method. Finally, the two ventilation networks with single-fan and multi-fans were solved. The results show that this algorithm has advantages of high effectiveness, fast convergence, good robustness and flexibility. This computer program could be used to solve large-scale generalized ventilation networks optimization problem in the future.
Directory of Open Access Journals (Sweden)
G. Srimathy
2012-04-01
Full Text Available In Wireless Ad hoc network, cooperation of nodes can be achieved by more interactions at higher protocol layers, particularly the MAC (Medium Access Control and network layers play vital role. MAC facilitates a routing protocol based on position location of nodes at network layer specially known as Beacon-less geographic routing (BLGR using Contention-based selection process. This paper proposes two levels of cross-layer framework -a MAC network cross-layer design for forwarder selection (or routing and a MAC-PHY for relay selection. CoopGeo; the proposed cross-layer protocol provides an efficient, distributed approach to select next hops and optimal relays to form a communication path. Wireless networks suffers huge number of communication at the same time leads to increase in collision and energy consumption; hence focused on new Contention access method that uses a dynamical change of channel access probability which can reduce the number of contention times and collisions. Simulation result demonstrates the best Relay selection and the comparative of direct mode with the cooperative networks. And Performance evaluation of contention probability with Collision avoidance.
Optimized Generation of Data-Path from C Codes for FPGAs
Guo, Zhi; Najjar, Walid; Vissers, Kees
2011-01-01
FPGAs, as computing devices, offer significant speedup over microprocessors. Furthermore, their configurability offers an advantage over traditional ASICs. However, they do not yet enjoy high-level language programmability, as microprocessors do. This has become the main obstacle for their wider acceptance by application designers. ROCCC is a compiler designed to generate circuits from C source code to execute on FPGAs, more specifically on CSoCs. It generates RTL level HDLs from frequently executing kernels in an application. In this paper, we describe ROCCC's system overview and focus on its data path generation. We compare the performance of ROCCC-generated VHDL code with that of Xilinx IPs. The synthesis result shows that ROCCC-generated circuit takes around 2x ~ 3x area and runs at comparable clock rate.
Institute of Scientific and Technical Information of China (English)
赵达维; 刘天琪; 唐健
2012-01-01
Despite the use of different indicators by existing methods for black-start path optimization to represent the importance of transmission lines and nodes in a power grid,they are not complete.A new method for black-start path optimization based on path and node weight factors is put forward,which are calculated to quantitatively judge the importance of path and node.This method considers each of the factors that influence the importance of path and node to increase the completeness of the indicators.Based on the calculation and comparison of relational weight factors,the identification of the restoration path can be made to realize the optimization of the black-start path.The restoration strategy is divided into four parts: system start-up,pre-formation of backbone network,backbone network restoration and all-direction restoration.Corresponding path optimization models are built to meet the demands of different phases.Finally,the new method combined with the restoration strategy is used to build black-start paths for an actual power grid.%现有黑启动路径寻优方法运用不同指标表征电网输电线路或节点重要性,但所采用的指标尚不完备。文中提出了基于路径和节点权重因子的黑启动路径寻优新方法,通过计算路径和节点权重因子等指标定量评判路径和节点的重要性,考虑了影响路径和节点重要性的各种因素以提高指标的完备性。基于关联权重因子的计算和比较,完成恢复路径的辨识,实现黑启动路径的优化。将电网恢复过程划分为系统启动、网架形成前、主网架恢复和辐射恢复等阶段,建立相应的路径寻优模型以满足不同阶段的恢复需求。最后,运用所提出的方法并结合电网恢复策略,为实际电网建立黑启动路径。
State-Selective Excitation of Quantum Systems via Geometrical Optimization.
Chang, Bo Y; Shin, Seokmin; Sola, Ignacio R
2015-09-08
We lay out the foundations of a general method of quantum control via geometrical optimization. We apply the method to state-selective population transfer using ultrashort transform-limited pulses between manifolds of levels that may represent, e.g., state-selective transitions in molecules. Assuming that certain states can be prepared, we develop three implementations: (i) preoptimization, which implies engineering the initial state within the ground manifold or electronic state before the pulse is applied; (ii) postoptimization, which implies engineering the final state within the excited manifold or target electronic state, after the pulse; and (iii) double-time optimization, which uses both types of time-ordered manipulations. We apply the schemes to two important dynamical problems: To prepare arbitrary vibrational superposition states on the target electronic state and to select weakly coupled vibrational states. Whereas full population inversion between the electronic states only requires control at initial time in all of the ground vibrational levels, only very specific superposition states can be prepared with high fidelity by either pre- or postoptimization mechanisms. Full state-selective population inversion requires manipulating the vibrational coherences in the ground electronic state before the optical pulse is applied and in the excited electronic state afterward, but not during all times.
Competition for shortest paths on sparse graphs.
Yeung, Chi Ho; Saad, David
2012-05-18
Optimal paths connecting randomly selected network nodes and fixed routers are studied analytically in the presence of a nonlinear overlap cost that penalizes congestion. Routing becomes more difficult as the number of selected nodes increases and exhibits ergodicity breaking in the case of multiple routers. The ground state of such systems reveals nonmonotonic complex behaviors in average path length and algorithmic convergence, depending on the network topology, and densities of communicating nodes and routers. A distributed linearly scalable routing algorithm is also devised.
Optimal paths for a light-driven engine with a linear phenomenological heat transfer law
Institute of Scientific and Technical Information of China (English)
无
2010-01-01
An irreversible light-driven engine is described in this paper, in which the heat transfer between the working fluid and the environment obeys a linear phenomenological heat transfer law [ q ∝Δ(T -1)], with a working fluid composed of the bimolecular reacting system 2SO 3 F■S 2 O 6 F2. Piston trajectories maximizing work output and minimizing entropy generation are determined for such an engine with rate-dependent loss mechanisms of friction and heat leakage. The optimal control theory is applied to determine the optimal configurations of the piston motion trajectory and the fluid temperature. Numerical examples for the optimal configuration are provided, and the obtained results are compared with those derived with Newtonian heat transfer law [ q ∝Δ(T )].
基于粒子群算法的学习路径推荐方法%Recommendation Method for Learning Path Based on Particle Swarm Optimization
Institute of Scientific and Technical Information of China (English)
肖会敏; 马彩娟
2013-01-01
智能训导系统（ITS）以提高学习者学习自主性，实现个性化的学习过程为目标。学习者的学习偏好根据学习者本身的属性，如学习目的，认知能力等变化。因此，为所有学生设计统一的学习路线已不能很好满足单个学习者的学习需要。首先将学习者进行特征聚类，然后将每个学习者作为一个粒子，将其在学习过程中的路径选择和评价值作为其空间代表值，使用粒子群算法进行个性化学习路径寻优，并通过实验证明其有效性。%Intelligent Tutor System(ITS) aims at improving the learner autonomy,and implementing personalized learning process. Learner’s preference changes with their learning target,cognitive ability and so on. We introduced a method which first organized the leaner through common character,then viewed the learner as a particle in a swarm,using their learn path selection and evaluation as a representative value of its space,used particle swarm optimization(PSO)to make personalized learning path optimization. At the end,we proved the effectiveness of the method through experiment.
Longitudinal Examination of Optimism, Personal Self-Efficacy and Student Well-Being: A Path Analysis
Phan, Huy P.
2016-01-01
The present longitudinal study, based on existing theoretical tenets, explored a conceptual model that depicted four major orientations: optimism, self-efficacy, and academic well-being. An important question for consideration, in this case, involved the testing of different untested trajectories that could explain and predict individuals'…
The Path Selections of the Resource-based Cities Transformation in China
Jinhuang Mao
2014-01-01
The transformation of resource-based cities, a worldwide problem, is a broad, cross-cutting and complex issue, which involves many fields of economics, sociology, geography, mining science and ecology. Therefore, the development of resource-based cities in China has been a top priority among researchers. In this paper, the author will work out the best pattern and path of resource-based cities by researching on transition modes, approaches and policies.
Capacity bounds for kth best path selection over generalized fading channels
Hanif, Muhammad Fainan
2014-02-01
Exact ergodic capacity calculation for fading wireless channels typically involves time-consuming numerical evaluation of infinite integrals. In this paper, lower and upper bounds on ergodic capacity for kth best path are presented. These bounds have simple analytic expressions which allow their fast evaluation. Numerical results show that the newly proposed bounds closely approximate the exact ergodic capacity for a large variety of system configurations. © 1997-2012 IEEE.
Multiobjective optimization using an immunodominance and clonal selection inspired algorithm
Institute of Scientific and Technical Information of China (English)
GONG MaoGuo; JIAO LiCheng; MA WenPing; DU HaiFeng
2008-01-01
Based on the mechanisms of immunodominance and clonal selection theory, we propose a new multiobjective optimization algorithm, immune dominance clonal multiobjective algorithm (IDCMA). IDCMA is unique in that its fitness values of current dominated individuals are assigned as the values of a custom distance measure, termed as Ab-Ab affinity, between the dominated individuals and one of the nondominated individuals found so far. According to the values of Ab-Ab affin-ity, all dominated individuals (antibodies) are divided into two kinds, subdominant antibodies and cryptic antibodies. Moreover, local search only applies to the sub-dominant antibodies, while the cryptic antibodies are redundant and have no func-tion during local search, but they can become subdominant (active) antibodies during the subsequent evolution. Furthermore, a new immune operation, clonal proliferation is provided to enhance local search. Using the clonal proliferation operation, IDCMA reproduces individuals and selects their improved maturated progenies after local search, so single individuals can exploit their surrounding space effectively and the newcomers yield a broader exploration of the search space. The performance comparison of IDCMA with MISA, NSGA-II, SPEA, PAES, NSGA, VEGA, NPGA, and HLGA in solving six well-known multiobjective function optimization problems and nine multiobjective 0/1 knapsack problems shows that IDCMA has a good performance in converging to approximate Pareto-optimal fronts with a good distribution.
Optimal Features Subset Selection and Classification for Iris Recognition
Directory of Open Access Journals (Sweden)
Roy Kaushik
2008-01-01
Full Text Available Abstract The selection of the optimal features subset and the classification have become an important issue in the field of iris recognition. We propose a feature selection scheme based on the multiobjectives genetic algorithm (MOGA to improve the recognition accuracy and asymmetrical support vector machine for the classification of iris patterns. We also suggest a segmentation scheme based on the collarette area localization. The deterministic feature sequence is extracted from the iris images using the 1D log-Gabor wavelet technique, and the extracted feature sequence is used to train the support vector machine (SVM. The MOGA is applied to optimize the features sequence and to increase the overall performance based on the matching accuracy of the SVM. The parameters of SVM are optimized to improve the overall generalization performance, and the traditional SVM is modified to an asymmetrical SVM to treat the false accept and false reject cases differently and to handle the unbalanced data of a specific class with respect to the other classes. Our experimental results indicate that the performance of SVM as a classifier is better than the performance of the classifiers based on the feedforward neural network, the k-nearest neighbor, and the Hamming and the Mahalanobis distances. The proposed technique is computationally effective with recognition rates of 99.81% and 96.43% on CASIA and ICE datasets, respectively.
Optimal Features Subset Selection and Classification for Iris Recognition
Directory of Open Access Journals (Sweden)
Prabir Bhattacharya
2008-06-01
Full Text Available The selection of the optimal features subset and the classification have become an important issue in the field of iris recognition. We propose a feature selection scheme based on the multiobjectives genetic algorithm (MOGA to improve the recognition accuracy and asymmetrical support vector machine for the classification of iris patterns. We also suggest a segmentation scheme based on the collarette area localization. The deterministic feature sequence is extracted from the iris images using the 1D log-Gabor wavelet technique, and the extracted feature sequence is used to train the support vector machine (SVM. The MOGA is applied to optimize the features sequence and to increase the overall performance based on the matching accuracy of the SVM. The parameters of SVM are optimized to improve the overall generalization performance, and the traditional SVM is modified to an asymmetrical SVM to treat the false accept and false reject cases differently and to handle the unbalanced data of a specific class with respect to the other classes. Our experimental results indicate that the performance of SVM as a classifier is better than the performance of the classifiers based on the feedforward neural network, the k-nearest neighbor, and the Hamming and the Mahalanobis distances. The proposed technique is computationally effective with recognition rates of 99.81% and 96.43% on CASIA and ICE datasets, respectively.
Field of view selection for optimal airborne imaging sensor performance
Goss, Tristan M.; Barnard, P. Werner; Fildis, Halidun; Erbudak, Mustafa; Senger, Tolga; Alpman, Mehmet E.
2014-05-01
The choice of the Field of View (FOV) of imaging sensors used in airborne targeting applications has major impact on the overall performance of the system. Conducting a market survey from published data on sensors used in stabilized airborne targeting systems shows a trend of ever narrowing FOVs housed in smaller and lighter volumes. This approach promotes the ever increasing geometric resolution provided by narrower FOVs, while it seemingly ignores the influences the FOV selection has on the sensor's sensitivity, the effects of diffraction, the influences of sight line jitter and collectively the overall system performance. This paper presents a trade-off methodology to select the optimal FOV for an imaging sensor that is limited in aperture diameter by mechanical constraints (such as space/volume available and window size) by balancing the influences FOV has on sensitivity and resolution and thereby optimizing the system's performance. The methodology may be applied to staring array based imaging sensors across all wavebands from visible/day cameras through to long wave infrared thermal imagers. Some examples of sensor analysis applying the trade-off methodology are given that highlights the performance advantages that can be gained by maximizing the aperture diameters and choosing the optimal FOV for an imaging sensor used in airborne targeting applications.
Beck, M.; Lowry, C.
2016-12-01
The exchange of surface water and groundwater in the hyporheic zone encourages biogeochemical reactions that naturally attenuate nutrients in streams. Stream restoration efforts often include instream, dam-like structures that increase hyporheic exchange, with the goal of enhancing natural attenuation. The effectiveness of these structures on improving stream quality has been widely researched, however the ideal installation location for these structures along a stream reach to achieve maximum hyporheic exchange must be optimized based on physical and temporal changes in bed forms and hydrologic drivers. Through the use of the finite difference model MODFLOW and particle tracking code MODPATH, the optimal location for emplacement of these stream barriers to maximize the spatial extent of the hyporheic zone was explored. In addition, impacts of seasonal changes in stream stage were also evaluated based on hyporheic zone path lengths. A total of sixteen realizations were created to vary the location of the stream barrier relative to a pool and riffle sequence. Once the ideal location of the barrier was determined, a region of variable groundwater discharge was prescribed to determine the effect of focused discharge. Using MODPATH, imaginary particles identify areas of maximized hyporheic exchange. The results show that the optimal location of the stream restoration structure changes based on stream stage and groundwater discharge zones. The spatial location of the instream barrier relative to zones of concentrated groundwater discharge as well as the location along a pool and riffle sequence has a significant effect on the extent of the hyporheic zone.
Institute of Scientific and Technical Information of China (English)
祝效华; 石昌帅; 童华
2015-01-01
In order to meet the high temperature environment requirement of deep and superdeep well exploitation, a technology of large length-to-diameter ratio metal stator screw lining meshing with rotor is presented. Based on the elastic-plasticity theory, and under the consideration of the effect of tube size, material mechanical parameters, friction coefficient and loading paths, the external pressure plastic forming mechanical model of metal stator screw lining is established, to study the optimal loading path of metal stator lining tube hydroforming process. The results show that wall thickness reduction of the external pressure tube hydroforming (THF) is about 4%, and three evaluation criteria of metal stator screw lining forming quality are presented: fillet stick mold coefficient, thickness relative error and forming quality coefficient. The smaller the three criteria are, the better the forming quality is. Each indicator has a trend of increase with the loading rate reducing, and the adjustment laws of die arc transition zone equidistance profile curve are acquired for improving tube forming quality. Hence, the research results prove the feasibility of external pressure THF used for processing high-accuracy large length-to-diameter ratio metal stator screw lining, and provide theoretical basis for designing new kind of stator structure which has better performance and longer service life.
Directory of Open Access Journals (Sweden)
Chao Zhang
2016-06-01
Full Text Available An improved ant colony optimization (ACO combined with immunosuppression and parameters switching strategy is proposed in this paper. In this algorithm, a novel judgment criterion for immunosuppression is introduced, that is, if the optimum solution has not changed for default iteration number, the immunosuppressive strategy is carried out. Moreover, two groups of parameters in ACO are switched back and forth according to the change of optimum solution as well. Therefore, the search space is expanded greatly and the problem of the traditional ACO such as falling into local minima easily is avoided effectively. The comparative simulation studies for path planning of landfill inspection robots in Asahikawa, Japan are executed, and the results show that the proposed algorithm has better performance characterized by higher search quality and faster search speed.
Frequency selective surface structure optimized by genetic algorithm
Institute of Scientific and Technical Information of China (English)
Lu Jun; Wang Jian-Bo; Sun Guan-Cheng
2009-01-01
Frequency selective surface(FSS)is a two-dimensional periodic structure which has prominent characteristics of bandpass or bandblock when interacting with electromagnetic waves.In this paper,the thickness,the dielectric constant,the element graph and the arrangement periodicity of an FSS medium are investigated by Genetic Algorithm(GA)when an electromagnetic wave is incident on the FSS at a wide angle,and an optimized FSS structure and transmission characteristics are obtained.The results show that the optimized structure has better stability in relation to incident angle of electromagnetic wave and preserves the stability of centre frequency even at an incident angle as large as 80°,thereby laying the foundation for the application of FSS to curved surfaces at wide angles.
Autonomous Control Modes and Optimized Path Guidance for Shipboard Landing in High Sea States
2015-07-30
performance factors that may oppose each other. Additionally, results indicate that the mathematical properties of the resulting optimization problem...Sea Based Aviation (SBA) initiative in Advanced Handling Qualities for Rotorcraft. Landing a rotorcraft on a moving ship deck and under the influence ...landing task begins to approach the limits of a human pilot’s capability. It is a similarly demanding task for shipboard launch and recovery of a
Hardware Genetic Algorithm Optimization by Critical Path Analysis using a Custom VLSI Architecture
Directory of Open Access Journals (Sweden)
Farouk Smith
2015-07-01
Full Text Available This paper propose a Virtual-Field Programmable Gate Array (V-FPGA architecture that allows direct access to its configuration bits to facilitate hardware evolution, thereby allowing any combinational or sequential digital circuit to be realized. By using the V-FPGA, this paper investigates two possible ways of making evolutionary hardware systems more scalable: by optimizing the system’s genetic algorithm (GA; and by decomposing the solution circuit into smaller, evolvable sub-circuits. GA optimization is done by: omitting a canonical GA’s crossover operator (i.e. by using a 1+λ algorithm; applying evolution constraints; and optimizing the fitness function. A noteworthy contribution this research has made is the in-depth analysis of the phenotypes’ CPs. Through analyzing the CPs, it has been shown that a great amount of insight can be gained into a phenotype’s fitness. We found that as the number of columns in the Cartesian Genetic Programming array increases, so the likelihood of an external output being placed in the column decreases. Furthermore, the number of used LEs per column also substantially decreases per added column. Finally, we demonstrated the evolution of a state-decomposed control circuit. It was shown that the evolution of each state’s sub-circuit was possible, and suggest that modular evolution can be a successful tool when dealing with scalability.
A Heuristic Algorithm for optimizing Page Selection Instructions
Li, Qing'an; Chen, Yong; Wu, Wei; Xu, Wenwen
2010-01-01
Page switching is a technique that increases the memory in microcontrollers without extending the address buses. This technique is widely used in the design of 8-bit MCUs. In this paper, we present an algorithm to reduce the overhead of page switching. To pursue small code size, we place the emphasis on the allocation of functions into suitable pages with a heuristic algorithm, thereby the cost-effective placement of page selection instructions. Our experimental results showed the optimization achieved a reduction in code size of 13.2 percent.
Optimal portfolio selection between different kinds of Renewable energy sources
Energy Technology Data Exchange (ETDEWEB)
Zakerinia, MohammadSaleh; Piltan, Mehdi; Ghaderi, Farid
2010-09-15
In this paper, selection of the optimal energy supply system in an industrial unit is taken into consideration. This study takes environmental, economical and social parameters into consideration in modeling along with technical factors. Several alternatives which include renewable energy sources, micro-CHP systems and conventional system has been compared by means of an integrated model of linear programming and three multi-criteria approaches (AHP, TOPSIS and ELECTRE III). New parameters like availability of sources, fuels' price volatility, besides traditional factors are considered in different scenarios. Results show with environmental preferences, renewable sources and micro-CHP are good alternatives for conventional systems.
NEAR OPTIMAL CLUSTER-HEAD SELECTION FOR WIRELESS SENSOR NETWORKS
Institute of Scientific and Technical Information of China (English)
无
2007-01-01
Clustering in wireless sensor networks is an effective way to save energy and reuse bandwidth. To our best knowledge, most of the clustering protocols proposed in literature are of a dynamic type, where cluster heads are selected in each period, followed by cluster formation. In this paper, a new static type clustering method called Hausdorff clustering, which is based on the location of sensor nodes as well as communication efficiency and network connectivity, is proposed. The cluster head, however,is rotated within the cluster by a fuzzy logic algorithm that optimizes the network lifetime. Simulation results show that this approach can significantly increase the lifetime of the sensor network.
Effect of Two Phase Mixtures on the Selection of the Discharge Path%混合两相体对放电路径选择的影响
Institute of Scientific and Technical Information of China (English)
邓鹤鸣; 何正浩; 王蕾
2008-01-01
This paper investigates the development, the breakdown process, and the discharge path selection of the lightning discharges in two-phase mixtures (TPMs). 13 kinds of solid-gas mixtures and 3 kinds of liquid-gas mixtures are employed to study effect of two phase mixtures on the selection of the discharge path under lightning impulses. Grain size effects are shown upon these experimental results. When the diameter of solid or liquid grains is less than about 10 μm, the discharge path does not select TPM but air. And the discharge path selects TPM when the diameter is greater than about 100 μm. And when the diameter is between about 10 μm and 100 μm, the discharge path selects TPM under negative lightning impulses, but it has a greater selection of air than TPMs under positive lightning impulses. Volume fraction and permittivity of solid/liquid can also influence the selection of the discharge path.
Directory of Open Access Journals (Sweden)
Wu Xing
2014-03-01
Full Text Available Vision recognition and RFID perception are used to develop a smart AGV travelling on fixed paths while retaining low-cost, simplicity and reliability. Visible landmarks can describe features of shapes and geometric dimensions of lines and intersections, and RFID tags can directly record global locations on pathways and the local topological relations of crossroads. A topological map is convenient for building and editing without the need for accurate poses when establishing a priori knowledge of a workplace. To obtain the flexibility of bidirectional movement along guide-paths, a camera placed in the centre of the AGV looks downward vertically at landmarks on the floor. A small visual field presents many difficulties for vision guidance, especially for real-time, correct and reliable recognition of multi-branch crossroads. First, the region projection and contour scanning methods are both used to extract the features of shapes. Then LDA is used to reduce the number of the features' dimensions. Third, a hierarchical SVM classifier is proposed to classify their multi-branch patterns once the features of the shapes are complete. Our experiments in landmark recognition and navigation show that low-cost vision systems are insusceptible to visual noises, image breakages and floor changes, and a vision-based AGV can locate itself precisely on its paths, recognize different crossroads intelligently by verifying the conformance of vision and RFID information, and select its next pathway efficiently in a bidirectional flow network.
Directory of Open Access Journals (Sweden)
Wu Xing
2014-03-01
Full Text Available Vision recognition and RFID perception are used to develop a smart AGV travelling on fixed paths while retaining low-cost, simplicity and reliability. Visible landmarks can describe features of shapes and geometric dimensions of lines and intersections, and RFID tags can directly record global locations on pathways and the local topological relations of crossroads. A topological map is convenient for building and editing without the need for accurate poses when establishing a priori knowledge of a workplace. To obtain the flexibility of bidirectional movement along guide-paths, a camera placed in the centre of the AGV looks downward vertically at landmarks on the floor. A small visual field presents many difficulties for vision guidance, especially for real- time, correct and reliable recognition of multi-branch crossroads. First, the region projection and contour scanning methods are both used to extract the features of shapes. Then LDA is used to reduce the number of the features’ dimensions. Third, a hierarchical SVM classifier is proposed to classify their multi-branch patterns once the features of the shapes are complete. Our experiments in landmark recognition and navigation show that low-cost vision systems are insusceptible to visual noises, image breakages and floor changes, and a vision-based AGV can locate itself precisely on its paths, recognize different crossroads intelligently by verifying the conformance of vision and RFID information, and select its next pathway efficiently in a bidirectional flow network.
Optimal Path Planning and Control of Quadrotor Unmanned Aerial Vehicle for Area Coverage
Fan, Jiankun
An Unmanned Aerial Vehicle (UAV) is an aircraft without a human pilot on board. Its flight is controlled either autonomously by computers onboard the vehicle, or remotely by a pilot on the ground, or by another vehicle. In recent years, UAVs have been used more commonly than prior years. The example includes areo-camera where a high speed camera was attached to a UAV which can be used as an airborne camera to obtain aerial video. It also could be used for detecting events on ground for tasks such as surveillance and monitoring which is a common task during wars. Similarly UAVs can be used for relaying communication signal during scenarios when regular communication infrastructure is destroyed. The objective of this thesis is motivated from such civilian operations such as search and rescue or wildfire detection and monitoring. One scenario is that of search and rescue where UAV's objective is to geo-locate a person in a given area. The task is carried out with the help of a camera whose live feed is provided to search and rescue personnel. For this objective, the UAV needs to carry out scanning of the entire area in the shortest time. The aim of this thesis to develop algorithms to enable a UAV to scan an area in optimal time, a problem referred to as "Coverage Control" in literature. The thesis focuses on a special kind of UAVs called "quadrotor" that is propelled with the help of four rotors. The overall objective of this thesis is achieved via solving two problems. The first problem is to develop a dynamic control model of quadrtor. In this thesis, a proportional-integral-derivative controller (PID) based feedback control system is developed and implemented on MATLAB's Simulink. The PID controller helps track any given trajectory. The second problem is to design a trajectory that will fulfill the mission. The planed trajectory should make sure the quadrotor will scan the whole area without missing any part to make sure that the quadrotor will find the lost
Optimized Image Steganalysis through Feature Selection using MBEGA
Geetha, S
2010-01-01
Feature based steganalysis, an emerging branch in information forensics, aims at identifying the presence of a covert communication by employing the statistical features of the cover and stego image as clues/evidences. Due to the large volumes of security audit data as well as complex and dynamic properties of steganogram behaviours, optimizing the performance of steganalysers becomes an important open problem. This paper is focussed at fine tuning the performance of six promising steganalysers in this field, through feature selection. We propose to employ Markov Blanket-Embedded Genetic Algorithm (MBEGA) for stego sensitive feature selection process. In particular, the embedded Markov blanket based memetic operators add or delete features (or genes) from a genetic algorithm (GA) solution so as to quickly improve the solution and fine-tune the search. Empirical results suggest that MBEGA is effective and efficient in eliminating irrelevant and redundant features based on both Markov blanket and predictive pow...
Selection, optimization, and compensation strategies : Interactive effects on daily work engagement
Zacher, Hannes; Chan, Felicia; Bakker, Arnold B.; Demerouti, Evangelia
2015-01-01
The theory of selective optimization with compensation (SOC) proposes that the "orchestrated" use of three distinct action regulation strategies (selection, optimization, and compensation) leads to positive employee outcomes. Previous research examined overall scores and additive models (i.e., main
Finite element model selection using Particle Swarm Optimization
Mthembu, Linda; Friswell, Michael I; Adhikari, Sondipon
2009-01-01
This paper proposes the application of particle swarm optimization (PSO) to the problem of finite element model (FEM) selection. This problem arises when a choice of the best model for a system has to be made from set of competing models, each developed a priori from engineering judgment. PSO is a population-based stochastic search algorithm inspired by the behaviour of biological entities in nature when they are foraging for resources. Each potentially correct model is represented as a particle that exhibits both individualistic and group behaviour. Each particle moves within the model search space looking for the best solution by updating the parameters values that define it. The most important step in the particle swarm algorithm is the method of representing models which should take into account the number, location and variables of parameters to be updated. One example structural system is used to show the applicability of PSO in finding an optimal FEM. An optimal model is defined as the model that has t...
Optimal Selection of Threshold Value 'r' for Refined Multiscale Entropy.
Marwaha, Puneeta; Sunkaria, Ramesh Kumar
2015-12-01
Refined multiscale entropy (RMSE) technique was introduced to evaluate complexity of a time series over multiple scale factors 't'. Here threshold value 'r' is updated as 0.15 times SD of filtered scaled time series. The use of fixed threshold value 'r' in RMSE sometimes assigns very close resembling entropy values to certain time series at certain temporal scale factors and is unable to distinguish different time series optimally. The present study aims to evaluate RMSE technique by varying threshold value 'r' from 0.05 to 0.25 times SD of filtered scaled time series and finding optimal 'r' values for each scale factor at which different time series can be distinguished more effectively. The proposed RMSE was used to evaluate over HRV time series of normal sinus rhythm subjects, patients suffering from sudden cardiac death, congestive heart failure, healthy adult male, healthy adult female and mid-aged female groups as well as over synthetic simulated database for different datalengths 'N' of 3000, 3500 and 4000. The proposed RMSE results in improved discrimination among different time series. To enhance the computational capability, empirical mathematical equations have been formulated for optimal selection of threshold values 'r' as a function of SD of filtered scaled time series and datalength 'N' for each scale factor 't'.
Optimal Subinterval Selection Approach for Power System Transient Stability Simulation
Directory of Open Access Journals (Sweden)
Soobae Kim
2015-10-01
Full Text Available Power system transient stability analysis requires an appropriate integration time step to avoid numerical instability as well as to reduce computational demands. For fast system dynamics, which vary more rapidly than what the time step covers, a fraction of the time step, called a subinterval, is used. However, the optimal value of this subinterval is not easily determined because the analysis of the system dynamics might be required. This selection is usually made from engineering experiences, and perhaps trial and error. This paper proposes an optimal subinterval selection approach for power system transient stability analysis, which is based on modal analysis using a single machine infinite bus (SMIB system. Fast system dynamics are identified with the modal analysis and the SMIB system is used focusing on fast local modes. An appropriate subinterval time step from the proposed approach can reduce computational burden and achieve accurate simulation responses as well. The performance of the proposed method is demonstrated with the GSO 37-bus system.
UPS Delivers Optimal Phase Diagram in High Dimensional Variable Selection
Ji, Pengsheng
2010-01-01
Consider linear regression in the so-called regime of p much larger than n. We propose the UPS as a new variable selection method. This is a Screen and Clean procedure [Wasserman and Roeder (2009)], in which we screen with the Univariate thresholding, and clean with the Penalized MLE. In many situations, the UPS possesses two important properties: Sure Screening and Separable After Screening (SAS). These properties enable us to reduce the original regression problem to many small-size regression problems that can be fitted separately. We measure the performance of variable selection procedure by the Hamming distance. In many situations, we find that the UPS achieves the optimal rate of convergence, and also yields an optimal partition of the so-called phase diagram. In the two-dimensional phase space calibrated by the signal sparsity and signal strength, there is a three-phase diagram shared by many choices of design matrices. In the first phase, it is possible to recover all signals. In the second phase, exa...
Lockie, Stewart; Lyons, Kristen; Lawrence, Geoffrey; Grice, Janet
2004-10-01
Path analysis of attitudinal, motivational, demographic and behavioural factors influencing food choice among Australian consumers who had consumed at least some organic food in the preceding 12 months showed that concern with the naturalness of food and the sensory and emotional experience of eating were the major determinants of increasing levels of organic consumption. Increasing consumption was also related to other 'green consumption' behaviours such as recycling and to lower levels of concern with convenience in the purchase and preparation of food. Most of these factors were, in turn, strongly affected by gender and the level of responsibility taken by respondents for food provisioning within their households, a responsibility dominated by women. Education had a slightly negative effect on the levels of concern for sensory and emotional appeal due to lower levels of education among women. Income, age, political and ecological values and willingness to pay a premium for safe and environmentally friendly foods all had extremely minor effects.
A stochastic analysis of terrain evaluation variables for path selection. [roving vehicle navigation
Donohue, J. G.; Shen, C. N.
1978-01-01
A stochastic analysis was performed on the variables associated with the characteristics of the terrain encountered by a roving system with an autonomous navigation system. A laser rangefinder is employed to detect terrain features at ranges up to 75 m. Analytic expressions and a numerical scheme were developed to calculate the variance of data on these four variables: (1) body clearance, (2) in-path slope, (3) tilt slope, and (4) wheel deviation. The variance is due to noise in the range data. It was found that the standard deviation of these terrain variables is large enough to warrant the use of a safety margin to aid the roving vehicle in avoiding high risk areas.
Institute of Scientific and Technical Information of China (English)
谭冠政; 贺欢; SLOMAN Aaron
2007-01-01
A novel method for the real-time globally optimal path planning of mobile robots is proposed based on the ant colony system (ACS) algorithm. This method includes three steps: the first step is utilizing the MAKLINK graph theory to establish the free space model of the mobile robot, the second step is utilizing the Dijkstra algorithm to find a sub-optimal collision-free path,and the third step is utilizing the ACS algorithm to optimize the location of the sub-optimal path so as to generate the globally optimal path. The result of computer simulation experiment shows that the proposed method is effective and can be used in the real-time path planning of mobile robots. It has been verified that the proposed method has better performance in convergence speed, solution variation, dynamic convergence behavior, and computational efficiency than the path planning method based on the genetic algorithm with elitist model.
Sun, Di; Guo, Chao; Zhang, Ziyang; Han, Tongshuai; Liu, Jin
2016-10-01
The blood hemoglobin concentration's (BHC) measurement using Photoplethysmography (PPG), which gets blood absorption to near infrared light from the instantaneous pulse of transmitted light intensity, has not been applied to the clinical use due to the non-enough precision. The main challenge might be caused of the non-enough stable pulse signal when it's very weak and it often varies in different human bodies or in the same body with different physiological states. We evaluated the detection limit of BHC using PPG as the measurement precision level, which can be considered as a best precision result because we got the relative stable subject's pulse signals recorded by using a spectrometer with high signal-to-noise ratio (SNR) level, which is about 30000:1 in short term. Moreover, we optimized the used pathlength using the theory based on optimum pathlength to get a better sensitivity to the absorption variation in blood. The best detection limit was evaluated as about 1 g/L for BHC, and the best SNR of pulse for in vivo measurement was about 2000:1 at 1130 and 1250 nm. Meanwhile, we conclude that the SNR of pulse signal should be better than 400:1 when the required detection limit is set to 5 g/L. Our result would be a good reference to the BHC measurement to get a desired BHC measurement precision of real application.
Xiao, Lin; Zhang, Yongsheng; Liao, Bolin; Zhang, Zhijun; Ding, Lei; Jin, Long
2017-01-01
A dual-robot system is a robotic device composed of two robot arms. To eliminate the joint-angle drift and prevent the occurrence of high joint velocity, a velocity-level bi-criteria optimization scheme, which includes two criteria (i.e., the minimum velocity norm and the repetitive motion), is proposed and investigated for coordinated path tracking of dual robot manipulators. Specifically, to realize the coordinated path tracking of dual robot manipulators, two subschemes are first presented for the left and right robot manipulators. After that, such two subschemes are reformulated as two general quadratic programs (QPs), which can be formulated as one unified QP. A recurrent neural network (RNN) is thus presented to solve effectively the unified QP problem. At last, computer simulation results based on a dual three-link planar manipulator further validate the feasibility and the efficacy of the velocity-level optimization scheme for coordinated path tracking using the recurrent neural network.
Portfolio optimization for seed selection in diverse weather scenarios.
Marko, Oskar; Brdar, Sanja; Panić, Marko; Šašić, Isidora; Despotović, Danica; Knežević, Milivoje; Crnojević, Vladimir
2017-01-01
The aim of this work was to develop a method for selection of optimal soybean varieties for the American Midwest using data analytics. We extracted the knowledge about 174 varieties from the dataset, which contained information about weather, soil, yield and regional statistical parameters. Next, we predicted the yield of each variety in each of 6,490 observed subregions of the Midwest. Furthermore, yield was predicted for all the possible weather scenarios approximated by 15 historical weather instances contained in the dataset. Using predicted yields and covariance between varieties through different weather scenarios, we performed portfolio optimisation. In this way, for each subregion, we obtained a selection of varieties, that proved superior to others in terms of the amount and stability of yield. According to the rules of Syngenta Crop Challenge, for which this research was conducted, we aggregated the results across all subregions and selected up to five soybean varieties that should be distributed across the network of seed retailers. The work presented in this paper was the winning solution for Syngenta Crop Challenge 2017.
Selection of an optimal treatment method for acute periodontitis disease.
Aliev, Rafik A; Aliyev, B F; Gardashova, Latafat A; Huseynov, Oleg H
2012-04-01
The present paper is devoted to selection of an optimal treatment method for acute periodontitis by using fuzzy Choquet integral-based approach. We consider application of different treatment methods depending on development stages and symptoms of the disease. The effectiveness of application of different treatment methods in each stage of the disease is linguistically evaluated by a dentist. The stages of the disease are also linguistically described by a dentist. Dentist's linguistic evaluations are represented by fuzzy sets. The total effectiveness of the each considered treatment method is calculated by using fuzzy Choquet integral with fuzzy number-valued integrand and fuzzy number-valued fuzzy measure. The most effective treatment method is determined by using fuzzy ranking method.
Criteria for Selecting Optimal Nitrogen Fertilizer Rates for Precision Agriculture
Directory of Open Access Journals (Sweden)
Bruno Basso
Full Text Available Yield rates vary spatially and maps produced by the yield monitor systems are evidence of the degree of withinfield variability. The magnitude of this variability is a good indication of the suitability of implementing a spatially variable management plan. Crop simulation models have the potential to integrate the effects of temporal and multiple stress interaction on crop growth under different environmental and management conditions. The strength of these models is their ability to account for stress by simulating the temporal interaction of stress on plant growth each day during the season. The objective of paper is to present a procedure that allows for the selection of optimal nitrogen fertilizer rates to be applied spatially on previously identified management zones through crop simulation modelling. The integration of yield maps, remote sensing imagery, ground truth measurements, electrical resistivity imaging allowed for the identifications of three distinct management zones based on their ability to produce yield and their stability over time (Basso et al., 2009. After validating the model, we simulated 7 N rates from 0 to 180 kg N/ha with a 30 kg N/ha increment. The model results illustrate the different N responses for each of the zone. The analysis allowed us to identify the optimal N rate for each of the zone based on agronomic, economic and environmental sustainability of N management.
Criteria for Selecting Optimal Nitrogen Fertilizer Rates for Precision Agriculture
Directory of Open Access Journals (Sweden)
Francesco Basso
2011-02-01
Full Text Available Yield rates vary spatially and maps produced by the yield monitor systems are evidence of the degree of withinfield variability. The magnitude of this variability is a good indication of the suitability of implementing a spatially variable management plan. Crop simulation models have the potential to integrate the effects of temporal and multiple stress interaction on crop growth under different environmental and management conditions. The strength of these models is their ability to account for stress by simulating the temporal interaction of stress on plant growth each day during the season. The objective of paper is to present a procedure that allows for the selection of optimal nitrogen fertilizer rates to be applied spatially on previously identified management zones through crop simulation modelling. The integration of yield maps, remote sensing imagery, ground truth measurements, electrical resistivity imaging allowed for the identifications of three distinct management zones based on their ability to produce yield and their stability over time (Basso et al., 2009. After validating the model, we simulated 7 N rates from 0 to 180 kg N/ha with a 30 kg N/ha increment. The model results illustrate the different N responses for each of the zone. The analysis allowed us to identify the optimal N rate for each of the zone based on agronomic, economic and environmental sustainability of N management.
Fournier-Level, Alexandre; Wilczek, Amity M; Cooper, Martha D; Roe, Judith L; Anderson, Jillian; Eaton, Deren; Moyers, Brook T; Petipas, Renee H; Schaeffer, Robert N; Pieper, Bjorn; Reymond, Matthieu; Koornneef, Maarten; Welch, Stephen M; Remington, David L; Schmitt, Johanna
2013-07-01
Selection on quantitative trait loci (QTL) may vary among natural environments due to differences in the genetic architecture of traits, environment-specific allelic effects or changes in the direction and magnitude of selection on specific traits. To dissect the environmental differences in selection on life history QTL across climatic regions, we grew a panel of interconnected recombinant inbred lines (RILs) of Arabidopsis thaliana in four field sites across its native European range. For each environment, we mapped QTL for growth, reproductive timing and development. Several QTL were pleiotropic across environments, three colocalizing with known functional polymorphisms in flowering time genes (CRY2, FRI and MAF2-5), but major QTL differed across field sites, showing conditional neutrality. We used structural equation models to trace selection paths from QTL to lifetime fitness in each environment. Only three QTL directly affected fruit number, measuring fitness. Most QTL had an indirect effect on fitness through their effect on bolting time or leaf length. Influence of life history traits on fitness differed dramatically across sites, resulting in different patterns of selection on reproductive timing and underlying QTL. In two oceanic field sites with high prereproductive mortality, QTL alleles contributing to early reproduction resulted in greater fruit production, conferring selective advantage, whereas alleles contributing to later reproduction resulted in larger size and higher fitness in a continental site. This demonstrates how environmental variation leads to change in both QTL effect sizes and direction of selection on traits, justifying the persistence of allelic polymorphism at life history QTL across the species range. © 2013 John Wiley & Sons Ltd.
The lead-lag relationship between stock index and stock index futures: A thermal optimal path method
Gong, Chen-Chen; Ji, Shen-Dan; Su, Li-Ling; Li, Sai-Ping; Ren, Fei
2016-02-01
The study of lead-lag relationship between stock index and stock index futures is of great importance for its wide application in hedging and portfolio investments. Previous works mainly use conventional methods like Granger causality test, GARCH model and error correction model, and focus on the causality relation between the index and futures in a certain period. By using a non-parametric approach-thermal optimal path (TOP) method, we study the lead-lag relationship between China Securities Index 300 (CSI 300), Hang Seng Index (HSI), Standard and Poor 500 (S&P 500) Index and their associated futures to reveal the variance of their relationship over time. Our finding shows evidence of pronounced futures leadership for well established index futures, namely HSI and S&P 500 index futures, while index of developing market like CSI 300 has pronounced leadership. We offer an explanation based on the measure of an indicator which quantifies the differences between spot and futures prices for the surge of lead-lag function. Our results provide new perspectives for the understanding of the dynamical evolution of lead-lag relationship between stock index and stock index futures, which is valuable for the study of market efficiency and its applications.
Energy Technology Data Exchange (ETDEWEB)
Chenel, A. [Laboratoire de Chimie Physique, UMR 8000 and CNRS, Université Paris-Sud, F-91405 Orsay (France); Meier, C. [Laboratoire Collisions, Agrégats, Réactivité, UMR 5589, IRSAMC, Université Paul Sabatier, F-31062 Toulouse (France); Dive, G. [Centre d’Ingéniérie des Protéines, Université de Liège, Sart Tilman, B6, B-4000 Liège (Belgium); Desouter-Lecomte, M. [Laboratoire de Chimie Physique, UMR 8000 and CNRS, Université Paris-Sud, F-91405 Orsay (France); Département de Chimie, Université de Liège, Bât B6c, Sart Tilman, B4000 Liège (Belgium)
2015-01-14
We compare the strategy found by the optimal control theory in a complex molecular system according to the active subspace coupled to the field. The model is the isomerization during a Cope rearrangement of Thiele’s ester that is the most stable dimer obtained by the dimerization of methyl-cyclopentadienenylcarboxylate. The crudest partitioning consists in retaining in the active space only the reaction coordinate, coupled to a dissipative bath of harmonic oscillators which are not coupled to the field. The control then fights against dissipation by accelerating the passage across the transition region which is very wide and flat in a Cope reaction. This mechanism has been observed in our previous simulations [Chenel et al., J. Phys. Chem. A 116, 11273 (2012)]. We compare here, the response of the control field when the reaction path is coupled to a second active mode. Constraints on the integrated intensity and on the maximum amplitude of the fields are imposed limiting the control landscape. Then, optimum field from one-dimensional simulation cannot provide a very high yield. Better guess fields based on the two-dimensional model allow the control to exploit different mechanisms providing a high control yield. By coupling the reaction surface to a bath, we confirm the link between the robustness of the field against dissipation and the time spent in the delocalized states above the transition barrier.
An Optimal Remanufacturing Centre Selection Algorithm for Reverse Logistics Alliance
Directory of Open Access Journals (Sweden)
Uzma Hameed
2013-07-01
Full Text Available Reverse logistics has been an emerging field both in academic as well as in applied research since last two decades because of increasing consumer awareness, legislative initiatives and profits associated with reuse of products or components. The costs associated with reverse logistics are usually high and these need to be minimized. The current study focuses on the formulation of alliance for cost reductions in reverse logistics. Remanufacturing, refurbishing, repair, cannibalization and reuse are the processes which add value to the reverse logistics system and are capable of converting it into a profitable venture. Used products contribute a cheaper source of components and spares required to remanufacture a product because of the less costs associated with the labor and material resources when compared with the manufacturing of new parts or products. When a defective part is removed from a product or assembly, it can be restored to its original state of functionality. Instead of purchasing a new, the same can be restored from repair/remanufacture centre just replacing defective part with a new part or spare. Furthermore, for manufacturers to reduce investments in reverse logistics, the formations of alliance and sharing of facilities for remanufacturing can lead to more profitability. In this study a focus has been made for the formation of remanufacturing alliance and an algorithm has been formulated for the selection of optimal remanufacturing center for the reverse logistics alliance. A case company has been selected from emerging Chinese electronic manufacturing industry. The case has been solved by using data set of the selected company with the help of formulated algorithm.
Uecker, Hannes
2015-01-01
p2pOC is an add-on toolbox to the Matlab package pde2path. It is aimed at the numerical solution of optimal control (OC) problems with an infinite time horizon for parabolic systems of PDE over 1D or 2D spatial domains. The basic idea is to treat the OC problem via the associated canonical system in two steps. First we use pde2path to find branches of stationary solutions of the canonical system, also called canonical steady states (CSS). In a second step we use the results and the spatial di...
GENETIC MUTATIONS AND NATURAL SELECTION – STEPS ON THE PATH OF EVOLUTION
Directory of Open Access Journals (Sweden)
Ioan Mihaela Balan
2007-08-01
Full Text Available It is nowadays considered that mutation is one o the most important sources of variety existent in nature. We may therefore consider that the diversity of the living world is the result of a long process of successive adaptations to the actions of the environment and that these adaptations were defined by natural selection as morphological and functional features of both animal and vegetal species.
River path selection in response to uplift and interaction with alluvial fans
Grimaud, J. L.; Paola, C.; Voller, V. R.
2015-12-01
River systems construct stratigraphic successions and build land by depositing and redistributing sediments as they migrate across the entire basin. This mobility arises from the intrinsic variability of a river system but can also be forced by external changes. It is particularly observable in tectonically active basins where the basement can be partly uplifted and where sediments can come from multiple sources. Theoretically, the ability of these perturbations to steer channels depends on their capacity to create lateral topographic gradients at a faster rate than the aggradation. Following these lines, we present an experimental study on the impacts of lateral tilting by tectonics and lateral alluvial fans on rivers path. The experiment was conducted in the eXperimental Earth Scape facility, also known as the Jurassic tank, where the basement tilting rate can be monitored by controlling individually gravel subsidence through 108 hexagonal cells. The basin was relatively uplifted on one side of the tank according to an anticline-shape and sediments were input through two sources: a main, axial one and a lateral, secondary one. We analyzed the differences in the topographic signature and flow occupation of rivers in response to the uplift or the lateral sediment source as well as the competition of these forcing in the late stages of the experiments. We found that both tectonic tilting and fan activity tend to decrease the basin-wide channel mobility. Indeed, the area at the convergence of the two interacting fans is a long-lasting topographic low that tends to channelize the flow while areas away from it are less visited. The position of this boundary is correlated with the relative flow contribution from both fans. This highlights the self-healing capacity of fans that are able to rapidly restore a graded shape. As opposed to fans, an uplifted area will not heal but force rivers to carve long-lasting valleys and increase the relief. When eroded, these uplifted
The Issues Facing the Sustainable Development of Rural Tourism and the Path Selection
Institute of Scientific and Technical Information of China (English)
Jianhong; ZHANG
2013-01-01
There is a long way to go for sustainable development of rural tourism.It is necessary to strengthen the planning for training rural tourism talents,and establish sustainable reserve tourism service personnel;innovate upon the promotion mode of rural tourism and open the tourist source market;strengthen the building of characteristic brand of rural tourism,and create sustainable development core of tourism;give play to the role of government in guiding rural tourism,strengthen the optimization of management of rural tourism market environment,and enhance the rural tourism safety;expand the rural tourism industry chain,and strengthen the management planning of sales market of rural tourism product.
Shape, sizing optimization and material selection based on mixed variables and genetic algorithm
Tang, X.; Bassir, D.H.; Zhang, W.
2010-01-01
In this work, we explore simultaneous designs of materials selection and structural optimization. As the material selection turns out to be a discrete process that finds the optimal distribution of materials over the design domain, it cannot be performed with common gradient-based optimization
GRASP with path-relinking for the selective pickup and delivery problem
DEFF Research Database (Denmark)
Ho, Sin C.; Szeto, W. Y.
2016-01-01
Bike sharing systems are very popular nowadays. One of the characteristics is that bikes are picked up from some surplus bike stations and transported to all deficit bike stations by a repositioning vehicle with limited capacity to satisfy the demand of deficit bike stations. Motivated by this real...... world bicycle repositioning problem, we study the selective pickup and delivery problem, where demand at every delivery node has to be satisfied by the supply collected from a subset of pickup nodes. The objective is to minimize the total travel cost incurred from visiting the nodes. We present a GRASP...
Applications of Optimal Building Energy System Selection and Operation
Energy Technology Data Exchange (ETDEWEB)
Marnay, Chris; Stadler, Michael; Siddiqui, Afzal; DeForest, Nicholas; Donadee, Jon; Bhattacharya, Prajesh; Lai, Judy
2011-04-01
Berkeley Lab has been developing the Distributed Energy Resources Customer Adoption Model (DER-CAM) for several years. Given load curves for energy services requirements in a building microgrid (u grid), fuel costs and other economic inputs, and a menu of available technologies, DER-CAM finds the optimum equipment fleet and its optimum operating schedule using a mixed integer linear programming approach. This capability is being applied using a software as a service (SaaS) model. Optimisation problems are set up on a Berkeley Lab server and clients can execute their jobs as needed, typically daily. The evolution of this approach is demonstrated by description of three ongoing projects. The first is a public access web site focused on solar photovoltaic generation and battery viability at large commercial and industrial customer sites. The second is a building CO2 emissions reduction operations problem for a University of California, Davis student dining hall for which potential investments are also considered. And the third, is both a battery selection problem and a rolling operating schedule problem for a large County Jail. Together these examples show that optimization of building u grid design and operation can be effectively achieved using SaaS.
巡线导航智能车的路径优化%Path Optimization of Line-tracking Navigation Smart Car
Institute of Scientific and Technical Information of China (English)
章登鹏; 谭彧
2011-01-01
Aiming at conventional smart car small visual range which affects path optimization, this paper proposes a control system of intelligent line-tracking vehicle with large vision based on servo driven sensor movement.It installs a servo driving motion sensor, increases the sensor range of the path identification.It uses a local path optimization, construction of a transitive relation, to create a mapping between sensor data and intelligent control of vehicle driving.Experimental results show that, installing a servo drive motion sensor and using a local path optimization, this control system increases path identification range, improves driving performance and the reliability of search lines, and increases the speed of transmission line.%针对现实应用中的巡线导航智能车道路检测范围小、路径规划困难的问题,提出增加传感器随动舵机的巡线导航智能车路径优化算法.安装随动摆头舵机带动传感器运动,增大传感器的路径识别范围.采用局部路径优化的厅法,构建一个导航传递关系,用以在传感器数据和智能车行驶控制之间建立映射.实验结果表明,增加传感器随动舵机的巡线导航智能车在采用路径优化算法之后视觉范围增大,能改善巡线导航智能车的行驶性能,提高了寻线的可靠性,增大了巡线速度.
Institute of Scientific and Technical Information of China (English)
DONG Zhao-yang; SUN Shu-dong
2006-01-01
The three levels optimizing strategy is put forward for the networked manufacturing resources optimizing configuration, namely, the optimizing of a logical manufacturing process,the optimizing of simulation-based integration of process planning and scheduling, and the optimizing of networked production scheduling. Then, the web services-based architecture of networked manufacturing resources optimizing configuration is brought forward. Finally, the key algorithm of the networked manufacturing resources optimizing configuration is discussed, namely, the two phases manufacturing partners selection method, which including the group technology-based manufacturing resources pre-configuration and the genetic algorithm-based executable manufacturing process optimizing.
Side transmission of radio waves on the Dushanbe-Leninabad radio-meteor path (Preliminary results)
Karpov, A. V.; Kodirov, A.; Mirdzhamolov, K.; Rubtsov, L. N.
Radio-wave propagation on the Dushanbe-Leninabad meteor path was studied experimentally in 1982, indicating that this path is suitable for ultrashort-wave communications. The selection of the optimal orientation of the antenna systems with respect to the path is examined, and it is shown that a maximum volume of data can be transmitted when the antennas are inclined at an angle of 70 deg from the path axis.
Directory of Open Access Journals (Sweden)
Michael Wolfson
2014-10-01
Full Text Available Background: It is widely known that that there is a significant dispersion in health status, as well as a strong correlation between health status and socio-economic status. But considerable uncertainty remains as to the quantitative importance of the various explanatory factors typically cited in this context. As health status is intrinsically a reflection of co-evolving dynamic processes, it is important to take a lifetime perspective when seeking to understand its determinants. The "bottom line" measure of overall population health is, though, health-adjusted life expectancy (HALE, which is an aggregation of individuals' health-adjusted life lengths (HALLs. Objective: In an exploratory mode, we intend to provide a realistic assessment of the relative importance of selected health determinants of HALE. Methods: This paper first draws on very detailed estimates of the covariates of vector-valued functional health trajectories, using the National Population Health Survey (Statistics Canada. We then use longitudinal microsimulation to draw out their implications by synthesising first a realistic base case -- specifically, a representative longitudinal population sample -- and then a series of exploratory counterfactual populations. Comparisons between and among counterfactuals and the base case are then used to estimate the quantitative importance of various factors in accounting for HALE. Results: Several surprising results emerged. Of the four risk factors explicitly examined, obesity had the smallest impacts on HALE: moving from the fifth to the 95th percentiles of BMI increased HALE 1.5 and 2.5 years for men and women, respectively. Eliminating smoking increased HALE by five and four years, while moving from the lowest to the highest levels of education had similar effects of about five years for both men and women. Perhaps surprisingly, moving from the fifth to the 95th percentiles of the psycho-social factor, Antonovsky's sense of coherence
Directory of Open Access Journals (Sweden)
Deng Lei Lei
2016-01-01
Full Text Available To realize the management and control of the water-saving irrigation of the path pipeline distribution in field plots, get the terrain information through remote sensing technology and analyze the path and the amount of the water in the field plots by the ant colony algorithm according to the matter of the low generality in most parts in China. The result shows that the rules were put forward with shorter path, smaller cost and the most utilization of water eventually. It can be widely used in most areas which is lack of water and scientific technology.
Wind selection and drift compensation optimize migratory pathways in a high-flying moth.
Chapman, Jason W; Reynolds, Don R; Mouritsen, Henrik; Hill, Jane K; Riley, Joe R; Sivell, Duncan; Smith, Alan D; Woiwod, Ian P
2008-04-01
Numerous insect species undertake regular seasonal migrations in order to exploit temporary breeding habitats [1]. These migrations are often achieved by high-altitude windborne movement at night [2-6], facilitating rapid long-distance transport, but seemingly at the cost of frequent displacement in highly disadvantageous directions (the so-called "pied piper" phenomenon [7]). This has lead to uncertainty about the mechanisms migrant insects use to control their migratory directions [8, 9]. Here we show that, far from being at the mercy of the wind, nocturnal moths have unexpectedly complex behavioral mechanisms that guide their migratory flight paths in seasonally-favorable directions. Using entomological radar, we demonstrate that free-flying individuals of the migratory noctuid moth Autographa gamma actively select fast, high-altitude airstreams moving in a direction that is highly beneficial for their autumn migration. They also exhibit common orientation close to the downwind direction, thus maximizing the rectilinear distance traveled. Most unexpectedly, we find that when winds are not closely aligned with the moth's preferred heading (toward the SSW), they compensate for cross-wind drift, thus increasing the probability of reaching their overwintering range. We conclude that nocturnally migrating moths use a compass and an inherited preferred direction to optimize their migratory track.
a Geographic Analysis of Optimal Signage Location Selection in Scenic Area
Ruan, Ling; Long, Ying; Zhang, Ling; Wu, Xiao Ling
2016-06-01
As an important part of the scenic area infrastructure services, signage guiding system plays an indispensable role in guiding the way and improving the quality of tourism experience. This paper proposes an optimal method in signage location selection and direction content design in a scenic area based on geographic analysis. The object of the research is to provide a best solution to arrange limited guiding boards in a tourism area to show ways arriving at any scenic spot from any entrance. There are four steps to achieve the research object. First, the spatial distribution of the junction of the scenic road, the passageway and the scenic spots is analyzed. Then, the count of scenic roads intersection on the shortest path between all entrances and all scenic spots is calculated. Next, combing with the grade of the scenic road and scenic spots, the importance of each road intersection is estimated quantitatively. Finally, according to the importance of all road intersections, the most suitable layout locations of signage guiding boards can be provided. In addition, the method is applied in the Ming Tomb scenic area in China and the result is compared with the existing signage guiding space layout.
A GEOGRAPHIC ANALYSIS OF OPTIMAL SIGNAGE LOCATION SELECTION IN SCENIC AREA
Directory of Open Access Journals (Sweden)
L. Ruan
2016-06-01
Full Text Available As an important part of the scenic area infrastructure services, signage guiding system plays an indispensable role in guiding the way and improving the quality of tourism experience. This paper proposes an optimal method in signage location selection and direction content design in a scenic area based on geographic analysis. The object of the research is to provide a best solution to arrange limited guiding boards in a tourism area to show ways arriving at any scenic spot from any entrance. There are four steps to achieve the research object. First, the spatial distribution of the junction of the scenic road, the passageway and the scenic spots is analyzed. Then, the count of scenic roads intersection on the shortest path between all entrances and all scenic spots is calculated. Next, combing with the grade of the scenic road and scenic spots, the importance of each road intersection is estimated quantitatively. Finally, according to the importance of all road intersections, the most suitable layout locations of signage guiding boards can be provided. In addition, the method is applied in the Ming Tomb scenic area in China and the result is compared with the existing signage guiding space layout.
The optimization of diffraction structures based on the principle selection of the main criterion
Kravets, O.; Beletskaja, S.; Lvovich, Ya; Lvovich, I.; Choporov, O.; Preobrazhenskiy, A.
2017-02-01
The possibilities of optimizing the characteristics of diffractive structures are analysed. A functional block diagram of a subsystem of diffractive structure optimization is shown. Next, a description of the method for the multicriterion optimization of diffractive structures is given. We then consider an algorithm for selecting the main criterion in the process of optimization. The algorithm efficiency is confirmed by an example of optimization of the diffractive structure.
Design and Implementation of Smart Car Based on Optimal Path%基于最优路径的智能车设计与实现
Institute of Scientific and Technical Information of China (English)
刘博; 张盛兵; 马志强
2012-01-01
For the intelligent driving of vehicles in city traffic, design and implement an optimal path control system based on monocular vision using MC9S12XS128 microcontroller as the core controller. In a simple simulation of road environment, collect road information using the CCD camera, first extract the path information in the binary image and then classify and identify the different intersection on the road. Construct special weighted directed graph of the road network, calculate the optimal path between points using the Dijkstra algorithm. Get the optimal path control strategy by analysing the relationship between the vertices on the path and carry out speed and special direction control. The experimental results show that the microcontroller can process data steadily and implement the optimal path autonomous driving between points for smart car in the 48MHz.%针对城市交通中车辆的智能行驶,以MC9S12XS128单片机为核心控制器,设计并实现了一种基于单目视觉的智能车最优路径控制系统；在简单模拟路况环境下,使用CCD摄像头采集道路信息,先提取二值化图像中的路径信息,再对道路中的不同路况进行分类与识别；构造特殊的路网带权有向图,使用Dijkstra算法计算两顶点间的最优路径；通过分析路径上顶点间的关系得到最优路径控制策略,进行速度和特殊转向控制;实验表明:单片机在48MH2下能够稳定地处理数据,实现了智能车点到点的最优路径自主行驶.
Energy Technology Data Exchange (ETDEWEB)
Martinello, Martina [Illinois Inst. of Technology, Chicago, IL (United States)
2016-12-01
causing temperature rising. The physics behind the magnetic flux expulsion is also analyzed, showing that during a fast cooldown the magnetic field structures, called vortices, tend to move in the same direction of the thermal gradient, from the Meissner state region to the mixed state region, minimizing the Gibbs free energy. On the other hand, during a slow cool down, not only the vortices movement is limited by the absence of thermal gradients, but, also, at the end of the superconducting transition, the magnetic field concentrates along randomly distributed normal-conducting region from which it cannot be expelled anymore. The systematic study of the surface resistance components performed for the different surface treatments, reveals that the BCS surface resistance and the trapped flux surface resistance have opposite trends as a function of the surface impurity content, defined by the mean free path. At medium field value, the BCS surface resistance is minimized for nitrogen-doped cavities and significantly larger for standard niobium cavities. On the other hand, Nitrogen-doped cavities show larger dissipation due to trapped flux. This is consequence of the bell-shaped trend of the trapped flux sensitivity as a function of the mean free path. Such experimental findings allow also a better understanding of the RF dissipation due to trapped flux. The best compromise between all the surface resistance components, taking into account the possibility of trapping some external magnetic field, is given by light nitrogen-doping treatments. However, the beneficial effects of the nitrogen-doping is completely lost when large amount of magnetic field is trapped during the cooldown, underlying the importance of both cooldown and magnetic field shielding optimization in high quality factors cryomodules.
Optimal Control of Reservoir Discharge Quality through Selective Withdrawal.
1982-02-01
this report is to present a procedure for the solution of the dynamic-optimal reservoir regulation problem . -- TTARGET a0 • o T MELEASII JAN STATIC...output state as a function of the input state and the associated decisions; that is, y = g(x,d) The dynamic-optimal reservoir regulation problem can be
A Polynomial Optimization Approach to Constant Rebalanced Portfolio Selection
Takano, Y.; Sotirov, R.
2010-01-01
We address the multi-period portfolio optimization problem with the constant rebalancing strategy. This problem is formulated as a polynomial optimization problem (POP) by using a mean-variance criterion. In order to solve the POPs of high degree, we develop a cutting-plane algorithm based on semide
Gong, Qian; Xu, Rong; Lin, Jintong
2004-04-01
Wavelength Division Multiplexed (WDM) networks that route optical connections using intelligent optical cross-connects (OXCs) is firmly established as the core constituent of next generation networks. Rapid failure recovery is fundamental to building reliable transport networks. Mesh restoration promises cost effective failure recovery compared with legacy ring networks, and is now seeing large-scale deployment. Many carriers are migrating away from SONET ring restoration for their core transport networks and replacing it with mesh restoration through "intelligent" O-E-O cross-connects (XC). The mesh restoration is typically provided via two fiber-disjoint paths: a service path and a restoration path. this scheme can restore any single link failure or node failure. And by used shared mesh restoration, although every service route is assigned a restoration route, no dedicated capacity needs to be reserved for the restoration route, resulting in capacity savings. The restoration approach we propose is Centralized Pre-computing, Local Distributed Optimization, and Shared Disjoint-backup Path. This approach combines the merits of centralized and distributed solutions. It avoids the scalability issues of centralized solutions by using a distributed control plane for disjoint service path computation and restoration path provisioning. Moreover, if the service routes of two demands are disjoint, no single failure will affect both demands simultaneously. This means that the restoration routes of these two demands can share link capacities, because these two routes will not be activated at the same time. So we can say, this restoration capacity sharing approach achieves low restoration capacity and fast restoration speed, while requiring few control plane changes.
Path optimization method based on GPS floating car%基于 GPS 浮动车的路径优化方法研究
Institute of Scientific and Technical Information of China (English)
陈冶灿; 王秀玲
2015-01-01
由于城市路径具有大规模路网等特点，传统的路径优化算法难以解决具有实际情况的城市交通路网问题。考虑城市实际道路路网信息，结合动态 GPS 浮动车数据，将实际道路长度和道路拟合成虚拟路径，提出了一种基于粒子群蚁群算法的混合算法。研究表明，混合算法在时间和精度上优于蚁群算法和粒子群算法，在提高高效性和准确性上具有较好的效果，为城市道路优化和城市出行提供可靠依据。%As the urban path has the characteristics of large-scale network,with the actual situation of the urban traffic road network problem is difficult to solved by traditional path optimization algorithm.The actual path length and the roads are compounded to synthesize virtual path by considering actual city road network information and combining with dynamic GPS floating car data,it is proposed a hybrid algorithm based on particle swarm of ant colony algorithm.Study shows that the hybrid algorithm in time and accuracy is better than the ant colony algorithm and particle swarm optimization,is effective in improving efficiency and accuracy,travel to provide reliable basis for the optimization of urban roads and cities.
Khehra, Baljit Singh; Pharwaha, Amar Partap Singh
2016-06-01
Ductal carcinoma in situ (DCIS) is one type of breast cancer. Clusters of microcalcifications (MCCs) are symptoms of DCIS that are recognized by mammography. Selection of robust features vector is the process of selecting an optimal subset of features from a large number of available features in a given problem domain after the feature extraction and before any classification scheme. Feature selection reduces the feature space that improves the performance of classifier and decreases the computational burden imposed by using many features on classifier. Selection of an optimal subset of features from a large number of available features in a given problem domain is a difficult search problem. For n features, the total numbers of possible subsets of features are 2n. Thus, selection of an optimal subset of features problem belongs to the category of NP-hard problems. In this paper, an attempt is made to find the optimal subset of MCCs features from all possible subsets of features using genetic algorithm (GA), particle swarm optimization (PSO) and biogeography-based optimization (BBO). For simulation, a total of 380 benign and malignant MCCs samples have been selected from mammogram images of DDSM database. A total of 50 features extracted from benign and malignant MCCs samples are used in this study. In these algorithms, fitness function is correct classification rate of classifier. Support vector machine is used as a classifier. From experimental results, it is also observed that the performance of PSO-based and BBO-based algorithms to select an optimal subset of features for classifying MCCs as benign or malignant is better as compared to GA-based algorithm.
Optimal Selection of the Sampling Interval for Estimation of Modal Parameters by an ARMA- Model
DEFF Research Database (Denmark)
Kirkegaard, Poul Henning
1993-01-01
Optimal selection of the sampling interval for estimation of the modal parameters by an ARMA-model for a white noise loaded structure modelled as a single degree of- freedom linear mechanical system is considered. An analytical solution for an optimal uniform sampling interval, which is optimal...
Institute of Scientific and Technical Information of China (English)
ChuFeixue; ChuYanfan; LiuXiumin
2005-01-01
Regarding the influencing factors in an optimal selection of pipeline design alternative as fuzzy variables with different weights, a fuzzy comprehensive assessment was applied to an optimal selection of the design alternative. Giving the Lanzhou-Chengdu pipeline as an example to explain the process, the result shows that this method is acceptable.
SELECT OF OPTIMAL SLEEP STATE IN ADAPTIVE SMAC USING DPM
National Research Council Canada - National Science Library
Elham Hajian; Kamal Jamshidi; Ali Bohlooli
2010-01-01
.... Therefore, optimal energy consumption for wsn protocols is a necessity. In a number of proposed protocols periodic sleep and wake is used for energy use reduction but these protocols result in increased end to end delay...
Immune clonal selection optimization method with combining mutation strategies
Institute of Scientific and Technical Information of China (English)
无
2007-01-01
In artificial immune optimization algorithm, the mutation of immune cells has been considered as the key operator that determines the algorithm performance. Traditional immune optimization algorithms have used a single mutation operator, typically a Gaussian. Using a variety of mutation operators that can be combined during evolution to generate different probability density function could hold the potential for producing better solutions with less computational effort. In view of this, a linear combination...
Optimal Bandwidth Selection in Observed-Score Kernel Equating
Häggström, Jenny; Wiberg, Marie
2014-01-01
The selection of bandwidth in kernel equating is important because it has a direct impact on the equated test scores. The aim of this article is to examine the use of double smoothing when selecting bandwidths in kernel equating and to compare double smoothing with the commonly used penalty method. This comparison was made using both an equivalent…
基于蚁群优化的AUV全局路径规划研究%Research on global path planning based on ant colony optimization for AUV
Institute of Scientific and Technical Information of China (English)
王宏健; 熊伟
2009-01-01
路径规划是自主式水下潜器(AUV)导航研究的重要课题,AUV可用于未知环境如海洋空间探测.在大范围海洋环境中,应用蚁群优化原理对自主式水下潜器的全局路径规划问题进行了研究.引入栅格建模方法建立了蚁群可视图模型,设计了蚁群信息素更新规则;给出了蚁群全局路径规划的操作步骤;针对蚁群规划路径不平滑问题,设计了切割算予和插点算子.仿真实验结果表明,蚁群全局规划算法非常适合于求解复杂环境中的规划问题,规划时间短、路径平滑,其原型系统可应用于非结构化无人环境监测.%Path planning is an important issue for autonomous underwater vehicles (AUVs) traversing an unknown environment such as a sea floor, a jungle, or the outer celestial planets. For this paper, global path planning using large-scale chart data was studied, and the principles of ant colony optimization (ACO) were applied. This paper introduced the idea of a visibility graph based on the grid workspace model. It also brought a series of pheromone updating rules for the ACO planning algorithm. The operational steps of the ACO algorithm are proposed as a model for a global path planning method for AUV. To mimic the process of smoothing a planned path, a cutting operator and an insertion-point operator were designed. Simulation results demonstrated that the ACO algorithm is suitable for global path planning. The system has many advantages, including that the operating path of the AUV can be quickly optimized, and it is shorter, safer, and smoother. The prototype system successfully demonstrated the feasibility of the concept, proving it can be applied to surveys of unstructured unmanned environments.
Directory of Open Access Journals (Sweden)
Wu Juan
2014-01-01
Full Text Available Military transport path selection directly affects the transport speed, efficiency, and safety. To a certain degree, the results of the path selection determine success or failure of the war situation. The purpose of this paper is to propose a model based on DEA (data envelopment analysis and multiobjective fuzzy decision-making for path selection. The path decision set is established according to a search algorithm based on overlapping section punishment. Considering the influence of various fuzzy factors, the model of optimal path is constructed based on DEA and multitarget fuzzy decision-making theory, where travel time, transport risk, quick response capability, and transport cost constitute the evaluation target set. A reasonable path set can be calculated and sorted according to the comprehensive scores of the paths. The numerical results show that the model and the related algorithms are effective for path selection of military transport.
Self-Selection, Optimal Income Taxation, and Redistribution
Amegashie, J. Atsu
2009-01-01
The author makes a pedagogical contribution to optimal income taxation. Using a very simple model adapted from George A. Akerlof (1978), he demonstrates a key result in the approach to public economics and welfare economics pioneered by Nobel laureate James Mirrlees. He shows how incomplete information, in addition to the need to preserve…
The Optimal Portfolio Selection Model under g-Expectation
Directory of Open Access Journals (Sweden)
Li Li
2014-01-01
complicated and sophisticated, the optimal solution turns out to be surprisingly simple, the payoff of a portfolio of two binary claims. Also I give the economic meaning of my model and the comparison with that one in the work of Jin and Zhou, 2008.
Optimal foraging in marine ecosystem models: selectivity, profitability and switching
DEFF Research Database (Denmark)
Visser, Andre W.; Fiksen, Ø.
2013-01-01
their diets towards the most profitable prey items. We present a simple algorithm for plankton feeding on a size-spectrum of prey with particular energetic content, handling times and capture probabilities. We show that the optimal diet breadth changes with relative densities, but in a different way...
Optimal design and selection of magneto-rheological brake types based on braking torque and mass
Nguyen, Q. H.; Lang, V. T.; Choi, S. B.
2015-06-01
In developing magnetorheological brakes (MRBs), it is well known that the braking torque and the mass of the MRBs are important factors that should be considered in the product’s design. This research focuses on the optimal design of different types of MRBs, from which we identify an optimal selection of MRB types, considering braking torque and mass. In the optimization, common types of MRBs such as disc-type, drum-type, hybrid-type, and T-shape types are considered. The optimization problem is to find an optimal MRB structure that can produce the required braking torque while minimizing its mass. After a brief description of the configuration of the MRBs, the MRBs’ braking torque is derived based on the Herschel-Bulkley rheological model of the magnetorheological fluid. Then, the optimal designs of the MRBs are analyzed. The optimization objective is to minimize the mass of the brake while the braking torque is constrained to be greater than a required value. In addition, the power consumption of the MRBs is also considered as a reference parameter in the optimization. A finite element analysis integrated with an optimization tool is used to obtain optimal solutions for the MRBs. Optimal solutions of MRBs with different required braking torque values are obtained based on the proposed optimization procedure. From the results, we discuss the optimal selection of MRB types, considering braking torque and mass.
Hodges, Kayleigh; Winstanley, Sue
2012-12-01
The psychological impact of a cancer diagnosis can extend through treatment, well into cancer survivorship and can be influenced by a range of psychosocial resources. At different stages in this trajectory, optimism is known to affect well-being directly. This study focusing upon the potential to flourish after cancer, investigates the relationship between optimism and positive affect during cancer survivorship together with four possible mediators: social support, fighting spirit, internal health locus of control and cancer worry, all of which have been shown to be important predictors of well-being in cancer patients. Participants (n = 102) from online cancer forums completed standardized questionnaires, and path analysis confirmed that optimism had a direct effect on positive affect in cancer survivors. Social support and fighting spirit were also shown to be significant mediators of this relationship, accounting collectively for 50% of the variance in positive affect. Whilst cancer worry and internal health locus of control could be predicted from levels of optimism, they did not mediate the optimism-positive affect relationship. Efforts to promote optimism and thus encourage fighting spirit at diagnosis through treatment may be worthwhile interventions, as would ensuring appropriate social support through the trajectory.
Optimizing drilling performance using a selected drilling fluid
Judzis, Arnis [Salt Lake City, UT; Black, Alan D [Coral Springs, FL; Green, Sidney J [Salt Lake City, UT; Robertson, Homer A [West Jordan, UT; Bland, Ronald G [Houston, TX; Curry, David Alexander [The Woodlands, TX; Ledgerwood, III, Leroy W.
2011-04-19
To improve drilling performance, a drilling fluid is selected based on one or more criteria and to have at least one target characteristic. Drilling equipment is used to drill a wellbore, and the selected drilling fluid is provided into the wellbore during drilling with the drilling equipment. The at least one target characteristic of the drilling fluid includes an ability of the drilling fluid to penetrate into formation cuttings during drilling to weaken the formation cuttings.
Optimal selection of on-site generation with combined heat andpower applications
Energy Technology Data Exchange (ETDEWEB)
Siddiqui, Afzal S.; Marnay, Chris; Bailey, Owen; HamachiLaCommare, Kristina
2004-11-30
While demand for electricity continues to grow, expansion of the traditional electricity supply system, or macrogrid, is constrained and is unlikely to keep pace with the growing thirst western economies have for electricity. Furthermore, no compelling case has been made that perpetual improvement in the overall power quality and reliability (PQR)delivered is technically possible or economically desirable. An alternative path to providing high PQR for sensitive loads would generate close to them in microgrids, such as the Consortium for Electricity Reliability Technology Solutions (CERTS) Microgrid. Distributed generation would alleviate the pressure for endless improvement in macrogrid PQR and might allow the establishment of a sounder economically based level of universal grid service. Energy conversion from available fuels to electricity close to loads can also provide combined heat and power (CHP) opportunities that can significantly improve the economics of small-scale on-site power generation, especially in hot climates when the waste heat serves absorption cycle cooling equipment that displaces expensive on-peak electricity. An optimization model, the Distributed Energy Resources Customer Adoption Model (DER-CAM), developed at Berkeley Lab identifies the energy bill minimizing combination of on-site generation and heat recovery equipment for sites, given their electricity and heat requirements, the tariffs they face, and a menu of available equipment. DER-CAM is used to conduct a systemic energy analysis of a southern California naval base building and demonstrates atypical current economic on-site power opportunity. Results achieve cost reductions of about 15 percent with DER, depending on the tariff.Furthermore, almost all of the energy is provided on-site, indicating that modest cost savings can be achieved when the microgrid is free to select distributed generation and heat recovery equipment in order to minimize its over all costs.
On the Optimal Selection of Electrical Machines Fans
Directory of Open Access Journals (Sweden)
Mădălin Costin
2014-09-01
Full Text Available In this paper an analytic relationship for electrical machine fan design has been developed. In the particularly case of salient poles synchronous machine (with salient poles – for electromagnetic field excitation or surface mounded permanent magnet, this approach allowed to express the fan power as a function of machine middle axe air gap. This analytic foundation developed may leads to different optimization criteria as specific active materials or costs. Numerical simulations confirm our approach.
ANFIS Approach for Optimal Selection of Reusable Components
Directory of Open Access Journals (Sweden)
K.S. Ravichandran
2012-12-01
Full Text Available In a growing world, the development of modern software system requires large-scale manpower, high development cost, larger completion time and high risk of maintaining the software quality. Component- Based Software Development (CBSD approach is based on the concept of developing modern software systems by selecting the appropriate reusable components or COTS (Commercial Off-The-Shelf components and then assembling them with well-defined software architecture. The proper selection of COTS components will really reduce the manpower, development cost, product completion time, risk, maintenance cost and also it addresses the high quality software product. In this paper, we develop an automated process of component selection by using Adaptive Neuro-Fuzzy Inference Systems (ANFIS based technique by using 14 reusable components’ parameters as a first time in this field. Again, for increasing the accuracy of a model, Fuzzy- Weighted-Relational-Coefficient (FWRC matrix is derived between the components and CBS development with the help of 14 component parameters, namely, Reliability, Stability, Portability, Consistency, Completeness, Interface & Structural Complexity, Understandability of Software Documents, Security, Usability, Accuracy, Compatibility, Performance, Serviceability and Customizable. In the recent literature studies reveals that almost all the researchers have been designed a general fuzzy-design rule for a component selection problem of all kinds of software architecture; but it leads to a poor selection of components and this paper suggests adoption of a specific fuzzy-design rule for every software architecture application for the selection of reusable components. Finally, it is concluded that the selection of reusable components through ANFIS performs better than the other models discussed so far.
A Hybrid Metaheuristic DE/CS Algorithm for UCAV Three-Dimension Path Planning
Gaige Wang; Lihong Guo; Hong Duan; Heqi Wang; Luo Liu; Mingzhen Shao
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 th...
A parallel optimization method for product configuration and supplier selection based on interval
Zheng, Jian; Zhang, Meng; Li, Guoxi
2017-06-01
In the process of design and manufacturing, product configuration is an important way of product development, and supplier selection is an essential component of supply chain management. To reduce the risk of procurement and maximize the profits of enterprises, this study proposes to combine the product configuration and supplier selection, and express the multiple uncertainties as interval numbers. An integrated optimization model of interval product configuration and supplier selection was established, and NSGA-II was put forward to locate the Pareto-optimal solutions to the interval multiobjective optimization model.
Egg-laying substrate selection for optimal camouflage by quail.
Lovell, P George; Ruxton, Graeme D; Langridge, Keri V; Spencer, Karen A
2013-02-04
Camouflage is conferred by background matching and disruption, which are both affected by microhabitat. However, microhabitat selection that enhances camouflage has only been demonstrated in species with discrete phenotypic morphs. For most animals, phenotypic variation is continuous; here we explore whether such individuals can select microhabitats to best exploit camouflage. We use substrate selection in a ground-nesting bird (Japanese quail, Coturnix japonica). For such species, threat from visual predators is high and egg appearance shows strong between-female variation. In quail, variation in appearance is particularly obvious in the amount of dark maculation on the light-colored shell. When given a choice, birds consistently selected laying substrates that made visual detection of their egg outline most challenging. However, the strategy for maximizing camouflage varied with the degree of egg maculation. Females laying heavily maculated eggs selected the substrate that more closely matched egg maculation color properties, leading to camouflage through disruptive coloration. For lightly maculated eggs, females chose a substrate that best matched their egg background coloration, suggesting background matching. Our results show that quail "know" their individual egg patterning and seek out a nest position that provides most effective camouflage for their individual phenotype.
Nguyen, Q. H.; Choi, S. B.
2012-01-01
This research focuses on optimal design of different types of magnetorheological brakes (MRBs), from which an optimal selection of MRB types is identified. In the optimization, common types of MRB such as disc-type, drum-type, hybrid-types, and T-shaped type are considered. The optimization problem is to find the optimal value of significant geometric dimensions of the MRB that can produce a maximum braking torque. The MRB is constrained in a cylindrical volume of a specific radius and length. After a brief description of the configuration of MRB types, the braking torques of the MRBs are derived based on the Herschel-Bulkley model of the MR fluid. The optimal design of MRBs constrained in a specific cylindrical volume is then analysed. The objective of the optimization is to maximize the braking torque while the torque ratio (the ratio of maximum braking torque and the zero-field friction torque) is constrained to be greater than a certain value. A finite element analysis integrated with an optimization tool is employed to obtain optimal solutions of the MRBs. Optimal solutions of MRBs constrained in different volumes are obtained based on the proposed optimization procedure. From the results, discussions on the optimal selection of MRB types depending on constrained volumes are given.
Mark Setterfield
2015-01-01
Path dependency is defined, and three different specific concepts of path dependency – cumulative causation, lock in, and hysteresis – are analyzed. The relationships between path dependency and equilibrium, and path dependency and fundamental uncertainty are also discussed. Finally, a typology of dynamical systems is developed to clarify these relationships.
Selecting Optimal Subset of Features for Student Performance Model
Directory of Open Access Journals (Sweden)
Hany M. Harb
2012-09-01
Full Text Available Educational data mining (EDM is a new growing research area and the essence of data mining concepts are used in the educational field for the purpose of extracting useful information on the student behavior in the learning process. Classification methods like decision trees, rule mining, and Bayesian network, can be applied on the educational data for predicting the student behavior like performance in an examination. This prediction may help in student evaluation. As the feature selection influences the predictive accuracy of any performance model, it is essential to study elaborately the effectiveness of student performance model in connection with feature selection techniques. The main objective of this work is to achieve high predictive performance by adopting various feature selection techniques to increase the predictive accuracy with least number of features. The outcomes show a reduction in computational time and constructional cost in both training and classification phases of the student performance model.
Optimization of Metamaterial Selective Emitters for Use in Thermophotovoltaic Applications
Pfiester, Nicole A.
The increasing costs of fossil fuels, both financial and environmental, has motivated many to look into sustainable energy sources. Thermophotovoltaics (TPVs), specialized photovoltaic cells focused on the infrared range, offer an opportunity to achieve both primary energy capture, similar to traditional photovoltaics, as well as secondary energy capture in the form of waste heat. However, to become a feasible energy source, TPV systems must become more efficient. One way to do this is through the development of selective emitters tailored to the bandgap of the TPV diode in question. This thesis proposes the use of metamaterial emitters as an engineerable, highly selective emitter that can withstand the temperatures required to collect waste heat. Metamaterial devices made of platinum and a dielectric such as alumina or silicon nitride were initially designed and tested as perfect absorbers. High temperature robustness testing demonstrates the device's ability to withstand the rigors of operating as a selective emitter.
Algorithms and theoretical topics on selected combinatorial optimization problems
Kaveh, Arman
2010-01-01
We study the Quadratic Assignment Problem (QAP), Three Dimensional Assignment Problem (3AP) and Quadratic Three Dimensional Assignment Problem (Q3AP), which combines aspects of both QAP and 3AP. The three problems are known to be NP-hard. We propose new algorithms for obtaining near optimal solutions of QAP and 3AP and present computational results. Our algorithms obtain improved solutions in some benchmark instances of QAP and 3AP. We also discuss theoretical results on 3AP and Q3AP such as ...
Response Time Optimization for Replica Selection Service in Data Grids
Directory of Open Access Journals (Sweden)
Husni H.E. AL-Mistarihi
2008-01-01
Full Text Available Problem Statement: Data Grid architecture provides a scalable infrastructure for grid services in order to manage data files and their corresponding replicas that were distributed across the globe. The grid services are designed to support a variety of data grid applications (jobs and projects. Replica selection is a high-level service that chooses a replica location from among many distributed replicas with the minimum response time for the users' jobs. Estimating the response time accurately in the grid environment is not an easy task. The current systems expose high response time in selecting the required replicas because the response time is estimated by considering the data transfer time only. Approach: We proposed a replica selection system that selects the best replica location for the users' running jobs in a minimum response time that can be estimated by considering new factors besides the data transfer time, namely, the storage access latency and the replica requests that waiting in the storage queue. Results: The performance of the proposed system was compared with a similar system that exists in the literature namely, SimpleOptimiser. The simulation results demonstrated that our system performed better than the SimpleOptimiser on an average of 6%. Conclusions: The proposed system can select the best replica location in a lesser response time than the SimpleOptimise. The efficiency of the proposed system is 6% higher than the SimpleOptimise. The efficiency level has a high impact on the quality of service that is perceived by grid users in a data grid environment where the data files are relatively big. For example, the data files produced from the scientific applications are of the size hundreds of Terabytes.
Selecting radiotherapy dose distributions by means of constrained optimization problems.
Alfonso, J C L; Buttazzo, G; García-Archilla, B; Herrero, M A; Núñez, L
2014-05-01
The main steps in planning radiotherapy consist in selecting for any patient diagnosed with a solid tumor (i) a prescribed radiation dose on the tumor, (ii) bounds on the radiation side effects on nearby organs at risk and (iii) a fractionation scheme specifying the number and frequency of therapeutic sessions during treatment. The goal of any radiotherapy treatment is to deliver on the tumor a radiation dose as close as possible to that selected in (i), while at the same time conforming to the constraints prescribed in (ii). To this day, considerable uncertainties remain concerning the best manner in which such issues should be addressed. In particular, the choice of a prescription radiation dose is mostly based on clinical experience accumulated on the particular type of tumor considered, without any direct reference to quantitative radiobiological assessment. Interestingly, mathematical models for the effect of radiation on biological matter have existed for quite some time, and are widely acknowledged by clinicians. However, the difficulty to obtain accurate in vivo measurements of the radiobiological parameters involved has severely restricted their direct application in current clinical practice.In this work, we first propose a mathematical model to select radiation dose distributions as solutions (minimizers) of suitable variational problems, under the assumption that key radiobiological parameters for tumors and organs at risk involved are known. Second, by analyzing the dependence of such solutions on the parameters involved, we then discuss the manner in which the use of those minimizers can improve current decision-making processes to select clinical dosimetries when (as is generally the case) only partial information on model radiosensitivity parameters is available. A comparison of the proposed radiation dose distributions with those actually delivered in a number of clinical cases strongly suggests that solutions of our mathematical model can be
Selection of optimal sensors for predicting performance of polymer electrolyte membrane fuel cell
Mao, Lei; Jackson, Lisa
2016-10-01
In this paper, sensor selection algorithms are investigated based on a sensitivity analysis, and the capability of optimal sensors in predicting PEM fuel cell performance is also studied using test data. The fuel cell model is developed for generating the sensitivity matrix relating sensor measurements and fuel cell health parameters. From the sensitivity matrix, two sensor selection approaches, including the largest gap method, and exhaustive brute force searching technique, are applied to find the optimal sensors providing reliable predictions. Based on the results, a sensor selection approach considering both sensor sensitivity and noise resistance is proposed to find the optimal sensor set with minimum size. Furthermore, the performance of the optimal sensor set is studied to predict fuel cell performance using test data from a PEM fuel cell system. Results demonstrate that with optimal sensors, the performance of PEM fuel cell can be predicted with good quality.
The Path Selection to Strengthen the Civil Service Ethics%加强公务员职业道德建设的路径选择
Institute of Scientific and Technical Information of China (English)
郑伟
2012-01-01
本文从分析当前我国公务员职业道德失范的主要现象及成因入手,试图有针对性地提出一些路径选择。%This paper starts from the analysis of China＇s civil service ethics anomie phenomenon,trying to have proposed some path selections.
Optimizing selection of decentralized stormwater management strategies in urbanized regions
Yu, Z.; Montalto, F.
2011-12-01
A variety of decentralized stormwater options are available for implementation in urbanized regions. These strategies, which include bio-retention, porous pavement, green roof etc., vary in terms of cost, ability to reduce runoff, and site applicability. This paper explores the tradeoffs between different types of stormwater control meastures that could be applied in a typical urban study area. A nested optimization strategy first identifies the most cost-effective (e.g. runoff reduction / life cycle cost invested ) options for individual land parcel typologies, and then scales up the results with detailed attention paid to uncertainty in adoption rates, life cycle costs, and hydrologic performance. The study is performed with a custom built stochastic rainfall-runoff model (Monte Carlo techniques are used to quantify uncertainties associated with phased implementation of different strategies and different land parcel typologies under synthetic precipitation ensembles). The results are presented as a comparison of cost-effectiveness over the time span of 30 years, and state an optimized strategy on the cumulative cost-effectiveness over the period.
PROBEmer: A web-based software tool for selecting optimal DNA oligos
National Research Council Canada - National Science Library
Emrich, Scott J; Lowe, Mary; Delcher, Arthur L
2003-01-01
PROBEmer (http://probemer.cs.loyola.edu) is a web-based software tool that enables a researcher to select optimal oligos for PCR applications and multiplex detection platforms including oligonucleotide microarrays and bead-based arrays...
Mobile sink-based path optimization strategy in wireless sensor networks%移动Sink的传感器网络路径优化策略
Institute of Scientific and Technical Information of China (English)
于志博; 孔祥雪; 裴金金
2016-01-01
Introducing mobile sink in wireless sensor networks(WSNs)can avoid network congestion and energy hole and reduce energy consumption of network,but it lead to large delay because of limitation of moving speed. Aiming at this problem,path optimization strategy of mobile sink under delay constrains is proposed. Adjustable node weight is designed according to relationship between delay and energy consumption of network. The optimal node weight is obtained through simulated annealing genetic algorithm. The sink nodes and the optimal moving path are acquired through iteration procedure based on the optimal node weight. Simulation results show that the strategy can reduce energy consumption network and have fast convergence under the premise of meeting delay constrains.%在无线传感器网络（WSNs）中引入移动 Sink 可以避免网络拥塞和能量空洞并降低网络能耗，但由于移动速度的限制导致时延较大。针对这一问题，提出了时延约束下的移动 Sink 路径优化策略，根据时延和网络能耗之间的关系设计了可调节的节点权重，通过模拟退火遗传算法得到最优节点权重，并依据此权重通过迭代得到汇聚节点和最佳移动路径。仿真结果表明：该策略能保证在满足时延约束的前提下降低网络能耗，且收敛速度快。
Selecting an optimal mixed products using grey relationship model
Directory of Open Access Journals (Sweden)
Farshad Faezy Razi
2013-06-01
Full Text Available This paper presents an integrated supplier selection and inventory management using grey relationship model (GRM as well as multi-objective decision making process. The proposed model of this paper first ranks different suppliers based on GRM technique and then determines the optimum level of inventory by considering different objectives. To show the implementation of the proposed model, we use some benchmark data presented by Talluri and Baker [Talluri, S., & Baker, R. C. (2002. A multi-phase mathematical programming approach for effective supply chain design. European Journal of Operational Research, 141(3, 544-558.]. The preliminary results indicate that the proposed model of this paper is capable of handling different criteria for supplier selection.
Energy Technology Data Exchange (ETDEWEB)
Castillo M, J. A.; Ortiz S, J. J.; Torres V, M.; Perusquia del Cueto, R., E-mail: alejandro.castillo@inin.gob.m [ININ, Carretera Mexico-Toluca s/n, 52750 Ocoyoacac, Estado de Mexico (Mexico)
2009-10-15
In this work are presented the obtained preliminary results to design nuclear fuel cells for boiling water reactors (BWR) using new strategies. To carry out the cells design some of the used rules in the fuel administration were discarded and other were implemented. The above-mentioned with the idea of making a comparative analysis between the used rules and those implemented here, under the hypothesis that it can be possible to design nuclear fuel cells without using all the used rules and executing the security restrictions that are imposed in these cases. To evaluate the quality of the obtained cells it was taken into account the power pick factor and the infinite multiplication factor, in the same sense, to evaluate the proposed configurations and to obtain the mentioned parameters was used the CASMO-4 code. To optimize the design it is uses the combinatorial optimization technique named Path Re linking and the Dispersed Search as local search method. The preliminary results show that it is possible to implement new strategies for the cells design of nuclear fuel following new rules. (Author)
DEFF Research Database (Denmark)
Zakrzewska, Anna; Ruepp, Sarah Renée; Berger, Michael Stübert
2013-01-01
The paper addresses the problem of optimal selection of Radio Access Technology (RAT) and assignment of resources in a multistandard wireless network scenario. It is shown how OPNET Modeler can be used to perform optimization studies using Integer Linear Programming (ILP) solvers. In this way...... an ongoing simulation is continuously fed with optimal assignment results. The setup allows the bounds of performance indicators to be defined and enables analysis and comparison with heuristic approaches and such results are given for the considered scenario....
Institute of Scientific and Technical Information of China (English)
罗义学
2011-01-01
基于智能Petri网构建了物流配送路径的优化模型.通过定义智能Petri网的运行规则,得到了基于智能Petri网的物流配送路径优化算法与计算流程,分析了车辆在交叉口的实际延误阻抗时配送分析的影响.该算法具有求解不需要对物流网络图作任何修改和容易实现配送过程的动态模拟的特点,利用该算法可以获取配送车辆从出发点到城市中任何一个节点的最优路径.将算例与改进的Dijkstra算法进行了对比分析,结果表明了该算法的可行性和有效性.%Based on the intelligent Petri net, a logistic delivery path optimization model is constructed. Through defining specific running rules for the intelligent Petri net, an optimization algorithm of logistic delivery path is put forward and calculation process is presented. The actual delay experienced by delivery vehicles at intersections is taken into account for analysis. The algorithm needn' t modify the logistic network and the dynamic version of the delivery process is simulated easily. The shortest path for a delivery vehicle travelling from the starting point to any point in the city is founded. By comparing with the Dijkstra algorithm in a case study, it is proven that our algorithm is both applicable and efficient.
Directory of Open Access Journals (Sweden)
P.K. Das
2016-03-01
Full Text Available Classical Q-learning takes huge computation to calculate the Q-value for all possible actions in a particular state and takes large space to store its Q-value for all actions, as a result of which its convergence rate is slow. This paper proposed a new methodology to determine the optimize trajectory of the path for multi-robots in clutter environment using hybridization of improving classical Q-learning based on four fundamental principles with improved particle swarm optimization (IPSO by modifying parameters and differentially perturbed velocity (DV algorithm for improving the convergence. The algorithms are used to minimize path length and arrival time of all the robots to their respective destination in the environment and reducing the turning angle of each robot to reduce the energy consumption of each robot. In this proposed scheme, the improve classical Q-learning stores the Q-value of the best action of the state and thus save the storage space, which is used to decide the Pbest and gbest of the improved PSO in each iteration, and the velocity of the IPSO is adjusted by the vector differential operator inherited from differential evolution (DE. The validation of the algorithm is studied in simulated and Khepera-II robot.
DEFF Research Database (Denmark)
Zakrzewska, Anna; Ruepp, Sarah Renée; Berger, Michael Stübert
2013-01-01
The paper addresses the problem of optimal selection of Radio Access Technology (RAT) and assignment of resources in a multistandard wireless network scenario. It is shown how OPNET Modeler can be used to perform optimization studies using Integer Linear Programming (ILP) solvers. In this way...
Optimal Selection of Floating Platform for Tidal Current Power Station
Directory of Open Access Journals (Sweden)
Fengmei Jing
2013-06-01
Full Text Available With continuous development of marine engineering, more and more new structures are used in the exploring of tidal current energy. Three are there different kinds of support structures for tidal current power station, which are sea-bed mounted/gravity based system, pile mounted system and floating moored platform. Comparison with them, the floating mooring system is suit for deep water and the application of which will be widely. In this study, catamaran and semi-submersible as floating platform of tidal current power station are studied. And they are compared with its economic, efficiency of turbine and stability of station. It is found that the catamaran is optimal choice. Based on basic ship theory and using software MOSES, the stability of Catamaran tidal current power station is also calculated. The research of this study is significant and it will be as the reference for the future study.
About the use of vector optimization for company's contractors selection
Medvedeva, M. A.; Medvedev, M. A.
2017-07-01
For effective functioning of an enterprise it is necessary to make a right choice of partners: suppliers of raw material, buyers of finished products, and others with which the company interacts in the course of their business. However, the presence on the market of big amount of enterprises makes the choice of the most appropriate among them very difficult and requires the ability to objectively assess of the possible partners, based on multilateral analysis of their activities. This analysis can be carried out based on the solution of multiobjective problem of mathematical programming by using the methods of vector optimization. The present work addresses the theoretical foundations of such approach and also describes an algorithm realizing proposed method on practical example.
Directory of Open Access Journals (Sweden)
Imen Châari
2014-07-01
Full Text Available Path planning is a fundamental optimization problem that is crucial for the navigation of a mobile robot. Among the vast array of optimization approaches, we focus in this paper on Ant Colony Optimization (ACO and Genetic Algorithms (GA for solving the global path planning problem in a static environment, considering their effectiveness in solving such a problem. Our objective is to design an efficient hybrid algorithm that takes profit of the advantages of both ACO and GA approaches for the sake of maximizing the chance to find the optimal path even under real-time constraints. In this paper, we present smartPATH, a new hybrid ACO-GA algorithm that relies on the combination of an improved ACO algorithm (IACO for efficient and fast path selection, and a modified crossover operator to reduce the risk of falling into a local minimum. We demonstrate through extensive simulations that smartPATH outperforms classical ACO (CACO, GA algorithms. It also outperforms the Dijkstra exact method in solving the path planning problem for large graph environments. It improves the solution quality up to 57% in comparison with CACO and reduces the execution time up to 83% as compared to Dijkstra for large and dense graphs. In addition, the experimental results on a real robot shows that smartPATH finds the optimal path with a probability up to 80% with a small gap not exceeding 1m in 98%.
A Hybrid Intelligent Algorithm for Optimal Birandom Portfolio Selection Problems
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Qi Li
2014-01-01
Full Text Available Birandom portfolio selection problems have been well developed and widely applied in recent years. To solve these problems better, this paper designs a new hybrid intelligent algorithm which combines the improved LGMS-FOA algorithm with birandom simulation. Since all the existing algorithms solving these problems are based on genetic algorithm and birandom simulation, some comparisons between the new hybrid intelligent algorithm and the existing algorithms are given in terms of numerical experiments, which demonstrate that the new hybrid intelligent algorithm is more effective and precise when the numbers of the objective function computations are the same.
Institute of Scientific and Technical Information of China (English)
蒋秀莲; 张亚楠; 宋祎宁; 韩莉; 嵇杰
2014-01-01
信息社会，企业间的竞争日渐激烈。随着网络购物的普及，物流配送日渐发展壮大，已成为第三方利润源泉，受到物流等相关企业的高度重视。合理科学的物流配送路径，可实现快速配送、提高配送质量、降低配送成本，提高经济效益。使用软件模拟物流配送中的路径规划问题，对于物流企业选择优化的配送路径和信息化具有一定的意义。%In the information society,the competition among enterprises is increasingly iferce.With the popularity of online shopping, logistics distribution gradually development and growth,has become the third profit source,subject to logistics and other related enterprises attach great importance.Reasonable and scientiifc logistics distribution path,which can realize fast delivery,improve the quality of distribution,reduce distribution costs,improve the economic beneift.Use software to simulate the path planning problem in logistics distribution,for logistics enterprises to select optimal distribution route and the information has certain signiifcance.
Yang, Jie; Zhang, Pengcheng; Zhang, Liyuan; Shu, Huazhong; Li, Baosheng; Gui, Zhiguo
2017-01-01
In inverse treatment planning of intensity-modulated radiation therapy (IMRT), the objective function is typically the sum of the weighted sub-scores, where the weights indicate the importance of the sub-scores. To obtain a high-quality treatment plan, the planner manually adjusts the objective weights using a trial-and-error procedure until an acceptable plan is reached. In this work, a new particle swarm optimization (PSO) method which can adjust the weighting factors automatically was investigated to overcome the requirement of manual adjustment, thereby reducing the workload of the human planner and contributing to the development of a fully automated planning process. The proposed optimization method consists of three steps. (i) First, a swarm of weighting factors (i.e., particles) is initialized randomly in the search space, where each particle corresponds to a global objective function. (ii) Then, a plan optimization solver is employed to obtain the optimal solution for each particle, and the values of the evaluation functions used to determine the particle's location and the population global location for the PSO are calculated based on these results. (iii) Next, the weighting factors are updated based on the particle's location and the population global location. Step (ii) is performed alternately with step (iii) until the termination condition is reached. In this method, the evaluation function is a combination of several key points on the dose volume histograms. Furthermore, a perturbation strategy - the crossover and mutation operator hybrid approach - is employed to enhance the population diversity, and two arguments are applied to the evaluation function to improve the flexibility of the algorithm. In this study, the proposed method was used to develop IMRT treatment plans involving five unequally spaced 6MV photon beams for 10 prostate cancer cases. The proposed optimization algorithm yielded high-quality plans for all of the cases, without human
He, Weili; Cao, Xiting; Xu, Lu
2012-02-28
The evaluation of clinical proof of concept, optimal dose selection, and phase III probability of success has traditionally been conducted by a subjective and qualitative assessment of the efficacy and safety data. This, in part, was responsible for the numerous failed phase III programs in the past. The need to utilize more quantitative approaches to assess efficacy and safety profiles has never been greater. In this paper, we propose a framework that incorporates efficacy and safety data simultaneously for the joint evaluation of clinical proof of concept, optimal dose selection, and phase III probability of success. Simulation studies were conducted to evaluate the properties of our proposed methods. The proposed approach was applied to two real clinical studies. On the basis of the true outcome of the two clinical studies, the assessment based on our proposed approach suggested a reasonable path forward for both clinical programs.
Multicriteria analysis in selecting the optimal variant of solar system
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Radziejowska Aleksandra
2016-01-01
Full Text Available Alternative energy sources are becoming more serious competition to traditional ways of generating energy. It becomes real integration of eco-energy with ecology, as well as the innovative technologies with low-energy construction. Apart from the cost an important issue are technical parameters of the equipment, durability, ease of installation, etc. The investor therefore is facing with the problem of decision-making to choose the best solution from the point of view of many criteria. In the article, the authors present the proposal to apply the methods of multi-criteria analysis to select the most beneficial variant of the solar system solutions. In this purpose will be use among other method: multivariate analysis of Saaty’s AHP, the taxonomic method of weighting factors and, belonging to a group of methods using outranking relations, the Promethee II method. Proposed comparative analysis can be used as a method for decision support during the selection of the most beneficial technological solution of solar installation and to evaluate operational efficiency existing buildings which will have implemented new systems.
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Jaber El Bouhdidi
2012-11-01
Full Text Available In this paper we present an intelligent architecture, oriented goals, to create individualized learning paths. The adaptation of learning paths to learner profiles is an area of research growing. More research in this field has shown that taking into account the preferences and learning styles of learners improve the quality of the teaching/learning; thus, the collection of information characterizing learners as, for instance, preferences, learning styles, goals ... etc, and those characterizing learning resources (annotation of resources are essential in order to make a matching between the query of learners and the profiles of hypermedia learning units. To recover their learning style, the learner is asked to take a test based on the model of Felder and Silverman. This test tells us about cognitive characteristics and affective behaviors and psychological which serve as relatively stable indicators of how learners perceive, interact and react with learning environments. Our contribution, therefore, consists of an adaptive approach based on semantic web, multi-agent systems and neural networks; thus, providing learners with personalized courses according to their profiles and their learning objectives.
Optimizing weight control in diabetes: antidiabetic drug selection
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S Kalra
2010-08-01
Full Text Available S Kalra1, B Kalra1, AG Unnikrishnan2, N Agrawal3, S Kumar41Bharti Hospital, Karnal; 2Amrita Institute of Medical Science, Kochi; 3Medical College, Gwalior; 4Excel Life Sciences, Noida, IndiaDate of preparation: 18th August 2010Conflict of interest: SK has received speaker fees from Novo Nordisk, sanofi-aventis, MSD, Eli Lilly, BMS, and AstraZeneca.Clinical question: Which antidiabetic drugs provide optimal weight control in patients with type 2 diabetes?Results: Metformin reduces weight gain, and may cause weight loss, when given alone or in combination with other drugs. Pioglitazone and rosiglitazone use is associated with weight gain. Use of the glucagon-like peptide-1 (GLP-1 analogs, liraglutide and exenatide, is associated with weight loss. Dipeptidyl peptidase-4 (DPP-4 inhibitors are considered weight-neutral. Results with insulin therapy are conflicting. Insulin detemir provides weight control along with glycemic control.Implementation: • Weight gain is considered an inevitable part of good glycemic control using conventional modalities of treatment such as sulfonylureas.• Use of metformin, weight-sparing insulin analogs such as insulin detemir, and liraglutide, should be encouraged as monotherapy, or in combination with other drugs.Keywords: weight control, diabetes
Optimal selection of epitopes for TXP-immunoaffinity mass spectrometry
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Joos Thomas
2010-06-01
Full Text Available Abstract Background Mass spectrometry (MS based protein profiling has become one of the key technologies in biomedical research and biomarker discovery. One bottleneck in MS-based protein analysis is sample preparation and an efficient fractionation step to reduce the complexity of the biological samples, which are too complex to be analyzed directly with MS. Sample preparation strategies that reduce the complexity of tryptic digests by using immunoaffinity based methods have shown to lead to a substantial increase in throughput and sensitivity in the proteomic mass spectrometry approach. The limitation of using such immunoaffinity-based approaches is the availability of the appropriate peptide specific capture antibodies. Recent developments in these approaches, where subsets of peptides with short identical terminal sequences can be enriched using antibodies directed against short terminal epitopes, promise a significant gain in efficiency. Results We show that the minimal set of terminal epitopes for the coverage of a target protein list can be found by the formulation as a set cover problem, preceded by a filtering pipeline for the exclusion of peptides and target epitopes with undesirable properties. Conclusions For small datasets (a few hundred proteins it is possible to solve the problem to optimality with moderate computational effort using commercial or free solvers. Larger datasets, like full proteomes require the use of heuristics.
Nyiredy, Sz; Szucs, Zoltán; Szepesy, L
2007-07-20
A new procedure (stationary phase optimized selectivity liquid chromatography: SOS-LC) is described for the optimization of the HPLC stationary phase, using serially connected columns and the principle of the "PRISMA" model. The retention factors (k) of the analytes were determined on three different stationary phases. By use of these data the k values were predicted applying theoretically combined stationary phases. These predictions resulted in numerous intermediate theoretical separations from among which only the optimal one was assembled and tested. The overall selectivity of this separation was better than that of any individual base stationary phase. SOS-LC is independent of the mechanism and the scale of separation.
A Global Path Planning Algorithm Based on Bidirectional SVGA
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Taizhi Lv
2017-01-01
Full Text Available For path planning algorithms based on visibility graph, constructing a visibility graph is very time-consuming. To reduce the computing time of visibility graph construction, this paper proposes a novel global path planning algorithm, bidirectional SVGA (simultaneous visibility graph construction and path optimization by A⁎. This algorithm does not construct a visibility graph before the path optimization. However it constructs a visibility graph and searches for an optimal path at the same time. At each step, a node with the lowest estimation cost is selected to be expanded. According to the status of this node, different through lines are drawn. If this line is free-collision, it is added to the visibility graph. If not, some vertices of obstacles which are passed through by this line are added to the OPEN list for expansion. In the SVGA process, only a few visible edges which are in relation to the optimal path are drawn and the most visible edges are ignored. For taking advantage of multicore processors, this algorithm performs SVGA in parallel from both directions. By SVGA and parallel performance, this algorithm reduces the computing time and space. Simulation experiment results in different environments show that the proposed algorithm improves the time and space efficiency of path planning.
Optimal Censoring Scheme Selection Based on Artificial Bee Colony Optimization (ABC Algorithm
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K. Kalaivani
2015-07-01
Full Text Available Life testing plans are more vital for carrying out researches on reliability and survival analysis. The inadequacy in the number of testing units or the timing limitations prevents the experiment from being continued until all the failures are detected. Hence, censoring grows to be an inheritably important and well-organized methodology for estimating the model parameters of underlying distributions. Type I and II censoring schemes are the most widely employed censoring schemes. The chief problem associated with the designing of life testing experiments practically is the determination of optimum censoring scheme. Hence, this study attempts to determine the optimum censoring through the minimization of total cost spent for the experiment, consuming less termination time and reasonable number of failures. The ABC algorithm is being employed in this study for obtaining the optimal censoring schemes. Entropy and variance serves as the optimal criterion. The proposed method utilizes Risk analysis to evaluate the efficiency or reliability of the optimal censoring scheme that is being determined. Optimum censoring scheme indicates the process of determining the best scheme from among the entire censoring schemes possible, in accordance to a specific optimality criterion.
Kyroudi, Archonteia; Petersson, Kristoffer; Ghandour, Sarah; Pachoud, Marc; Matzinger, Oscar; Ozsahin, Mahmut; Bourhis, Jean; Bochud, François; Moeckli, Raphaël
2016-08-01
Multi-criteria optimization provides decision makers with a range of clinical choices through Pareto plans that can be explored during real time navigation and then converted into deliverable plans. Our study shows that dosimetric differences can arise between the two steps, which could compromise the clinical choices made during navigation.
Optimization of event selections for tH-production
Tagay, Zhenisbek
2017-01-01
This summer student project studies the perspectives for the search of tH-production in di-photonic decay channel of Higgs-boson. Monte-Carlo simulation data of 3000 fb^{1} integrated luminosity corresponding to proton-proton collisions of center-of-mass energy 13 TeV at ATLAS detector are used in this analysis. The process being searched for, unlike other Higgs processes (e.g. ttH->γγ), is sensitive to the sign of Yukawa coupling between top-quark and Higgs-boson. However, tH-production has a very small cross section in SM, which complicates the searches. This works aims to find suitable selection criteria, which in its turn will suppress the background and provide enough statistics of tH signal for further searches.
Optimization of post combustion carbon capture process-solvent selection
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Udara S. P. R. Arachchige, Muhammad Mohsin, Morten C. Melaaen
2012-01-01
Full Text Available The reduction of the main energy requirements in the CO2 capture process that is re-boiler duty in stripper section is important. Present study was focused on selection of better solvent concentration and CO2 lean loading for CO2 capture process. Both coal and gas fired power plant flue gases were considered to develop the capture plant with different efficiencies. Solvent concentration was varied from 25 to 40 (w/w % and CO2 lean loading was varied from 0.15 to 0.30 (mol CO2/mol MEA for 70-95 (mol % CO2 removal efficiencies. The optimum specifications for coal and gas processes such as MEA concentration, CO2 lean loading, and solvent inlet flow rate were obtained.
Optimizing the Selection of General Surgery Residents: A National Consensus.
Louridas, Marisa; Szasz, Peter; Montbrun, Sandra de; Harris, Kenneth A; Grantcharov, Teodor P
Surgical programs strive to recruit trainees who will graduate as competent surgeons; however, selection processes vary between institutions. The purpose of the present study was to (1) solicit program directors' (PDs) opinions on the proportion of trainees who have difficulty achieving competence and (2) establish consensus on the desired attributes of general surgery (GS) candidates and the technical skills that would be most indicative of future performance. Delphi consensus methodology was used. An open-ended questionnaire, followed by a closed-ended questionnaire, formulated as a 5-point Likert scale, was administered. A Cronbach α ≥ 0.8 with 80% of responses in agreement (4-agree and 5-strongly agree) determined the threshold for consensus. The first and second rounds were completed by 14 and 11, of a potential 17, GS PDs, respectively. PDs felt that 5% or less of trainees have difficulty reaching competence in clinical knowledge, 5% to 10% in decision-making, and 5% to 15% in technical skill by the time of completion of training. Consensus was excellent (α = 0.92). The top attributes for success in GS included work ethic and passion for surgery. Technical skills that felt to be most appropriate were open tasks (one-handed tie and subcuticular suture) and laparoscopic tasks (coordination, grasping, and cutting). PDs indicate that of the 3 domains, the largest proportion of trainees had difficulty reaching competence in technical skill. Consensus among PDs suggests that top personal attributes include work ethic and passion for surgery. Consensus of technical tasks for inclusion into selection was basic open and laparoscopic skills. Copyright © 2016 Association of Program Directors in Surgery. Published by Elsevier Inc. All rights reserved.
Optimal Path and Design of Vegetable Distribution System%蔬菜配送系统的路径优化及设计开发
Institute of Scientific and Technical Information of China (English)
符志强; 罗锋杰
2014-01-01
Vegetable distribution has a very high demand for timeliness. Distribution path optimization becomes the key point of the enterprise profit. But optimization of path is NP hard problem and traditional algorithms which can't be given optimal solution in finite time. Uses Ant Colony Optimization to solve the optimization of vehicle routing problem. The optimization reduces the distribution cost and the operation cost of enterprises. Designs the system with Struts2, Hibernate, Spring. The intelligent distribution system has the function of positioning, tracking orders, price dynamic function, user management, order management, news management, customer service management and ser-vice of vegetables. The system has the advantages of clear structure and easy extension with adopting modular design.%蔬菜配送对时效性有很高的要求，配送路径优化成为企业赢利的关键点，而路径优化是NP难问题，传统的算法不能在有限的时间内给出最优解。使用蚁群算法（Ant Colony Optimization，ACO）并对其参数进行优化，解决车辆配送路径优化问题，使得配送路径实时最优化，降低配送成本和企业经营成本。本系统使用Struts2、Hibernate、Spring三大框架进行设计，前台具有智能定位、配送体系、订单跟踪、价格动态功能，后台集成用户管理、订单管理、新闻管理、蔬菜管理、客服服务功能。系统采用模块化设计，具有结构清晰，易于扩展的优点。
Parameter selection of support vector machine for function approximation based on chaos optimization
Institute of Scientific and Technical Information of China (English)
无
2008-01-01
The support vector machine (SVM) is a novel machine learning method,which has the ability to approximate nonlinear functions with arbitrary accuracy.Setting parameters well is very crucial for SVM learning results and generalization ability,and now there is no systematic,general method for parameter selection.In this article,the SVM parameter selection for function approximation is regarded as a compound optimization problem and a mutative scale chaos optimization algorithm is employed to search for optimal parameter values.The chaos optimization algorithm is an effective way for global optimal and the mutative scale chaos algorithm could improve the search efficiency and accuracy.Several simulation examples show the sensitivity of the SVM parameters and demonstrate the superiority of this proposed method for nonlinear function approximation.
Nguyen, Q. H.; Choi, S. B.; Lee, Y. S.; Han, M. S.
2013-11-01
This paper focuses on the optimal design of a compact and high damping force engine mount featuring magnetorheological fluid (MRF). In the mount, a MR valve structure with both annular and radial flows is employed to generate a high damping force. First, the configuration and working principle of the proposed MR mount is introduced. The MRF flows in the mount are then analyzed and the governing equations of the MR mount are derived based on the Bingham plastic behavior of the MRF. An optimal design of the MR mount is then performed to find the optimal structure of the MR valve to generate a maximum damping force with certain design constraints. In addition, the gap size of MRF ducts is empirically chosen considering the ‘lockup’ problem of the mount at high frequency. Performance of the optimized MR mount is then evaluated based on finite element analysis and discussions on performance results of the optimized MR mount are given. The effectiveness of the proposed MR engine mount is demonstrated via computer simulation by presenting damping force and power consumption.
Access Path Planning of Mobile Agent in Wireless Sensor Networks
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Chaoyu Yang
2014-02-01
Full Text Available Adopting the two-stage optimization model and hybrid optimized algorithm based on evolutionary computation, a new two-stage optimization model that more conforms to the actual demand is proposed on the basis of formal description of Mobile Agent access path planning. This new model divides the access path planning problem into two sub problems of integer linear programming --data integration sub paths and return sub paths, which can reduce search space and improve the efficiency of algorithm. Then a hybrid optimized method named GAPSO, combined with GA (Genetic Algorithm and PSO (Particle Swarm Optimization, is advanced to solve this model, which integrates discrete PSO into the interlace operation of GA to avoid infeasible solution and improve search quality. Meanwhile convergence can be accelerated by optimizing the GA population with PSO in search of return sub paths. By means of virtual connected topology graph, the high-quality to-be-accessed candidate node set is acquired, the number of to-be-selected nodes is reduced，and the complexity of solution space is decreased, making planning algorithm performance not rely on network scale directly any more. Simulation results show that the advantages of the optimization model is obvious as the node number increases, and GASPO has a better performance than GA and BPSO in the same model
Optimal multi-agent path planning for fast inverse modeling in UAV-based flood sensing applications
Abdelkader, Mohamed
2014-05-01
Floods are the most common natural disasters, causing thousands of casualties every year in the world. In particular, flash flood events are particularly deadly because of the short timescales on which they occur. Unmanned air vehicles equipped with mobile microsensors could be capable of sensing flash floods in real time, saving lives and greatly improving the efficiency of the emergency response. However, of the main issues arising with sensing floods is the difficulty of planning the path of the sensing agents in advance so as to obtain meaningful data as fast as possible. In this particle, we present a fast numerical scheme to quickly compute the trajectories of a set of UAVs in order to maximize the accuracy of model parameter estimation over a time horizon. Simulation results are presented, a preliminary testbed is briefly described, and future research directions and problems are discussed. © 2014 IEEE.
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Branka Marasović
2009-03-01
Full Text Available In this paper we select an optimal portfolio on the Croatian capital market by using the multicriterial programming. In accordance with the modern portfolio theory maximisation of returns at minimal risk should be the investment goal of any successful investor. However, contrary to the expectations of the modern portfolio theory, the tests carried out on a number of financial markets reveal the existence of other indicators important in portfolio selection. Considering the importance of variables other than return and risk, selection of the optimal portfolio becomes a multicriterial problem which should be solved by using the appropriate techniques.In order to select an optimal portfolio, absolute values of criteria, like return, risk, price to earning value ratio (P/E, price to book value ratio (P/B and price to sale value ratio (P/S are included in our multicriterial model. However the problem might occur as the mean values of some criteria are significantly different for different sectors and because financial managers emphasize that comparison of the same criteria for different sectors could lead us to wrong conclusions. In the second part of the paper, relative values of previously stated criteria (in relation to mean value of sector are included in model for selecting optimal portfolio. Furthermore, the paper shows that if relative values of criteria are included in multicriterial model for selecting optimal portfolio, return in subsequent period is considerably higher than if absolute values of the same criteria were used.
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Branka Marasović
2009-01-01
Full Text Available In this paper we select an optimal portfolio on the Croatian capital market by using the multicriterial programming. In accordance with the modern portfolio theory maximisation of returns at minimal risk should be the investment goal of any successful investor. However, contrary to the expectations of the modern portfolio theory, the tests carried out on a number of financial markets reveal the existence of other indicators important in portfolio selection. Considering the importance of variables other than return and risk, selection of the optimal portfolio becomes a multicriterial problem which should be solved by using the appropriate techniques.In order to select an optimal portfolio, absolute values of criteria, like return, risk, price to earning value ratio (P/E, price to book value ratio (P/B and price to sale value ratio (P/S are included in our multicriterial model. However the problem might occur as the mean values of some criteria are significantly different for different sectors and because financial managers emphasize that comparison of the same criteria for different sectors could lead us to wrong conclusions. In the second part of the paper, relative values of previously stated criteria (in relation to mean value of sector are included in model for selecting optimal portfolio. Furthermore, the paper shows that if relative values of criteria are included in multicriterial model for selecting optimal portfolio, the return in the subsequent period is considerably higher than if absolute values of the same criteria were used.
Optimality of Graphlet Screening in High Dimensional Variable Selection
Jin, Jiashun; Zhang, Qi
2012-01-01
Consider a linear regression model where the design matrix X has n rows and p columns. We assume (a) p is much large than n, (b) the coefficient vector beta is sparse in the sense that only a small fraction of its coordinates is nonzero, and (c) the Gram matrix G = X'X is sparse in the sense that each row has relatively few large coordinates (diagonals of G are normalized to 1). The sparsity in G naturally induces the sparsity of the so-called graph of strong dependence (GOSD). We find an interesting interplay between the signal sparsity and the graph sparsity, which ensures that in a broad context, the set of true signals decompose into many different small-size components of GOSD, where different components are disconnected. We propose Graphlet Screening (GS) as a new approach to variable selection, which is a two-stage Screen and Clean method. The key methodological innovation of GS is to use GOSD to guide both the screening and cleaning. Compared to m-variate brute-forth screening that has a computational...
Langberg, Tomer; Dashek, Ryan; Mulvey, Bernard; Miller, Kimberly A; Osting, Susan; Stafstrom, Carl E; Sutula, Thomas P
2016-01-01
Kindling is a phenomenon of activity-dependent neural circuit plasticity induced by repeated seizures that results in progressive permanent increases in susceptibility to epilepsy. As the permanent structural and functional modifications induced by kindling include a diverse range of molecular, cellular, and functional alterations in neural circuits, it is of interest to determine if genetic background associated with seizure-induced plasticity might also influence plasticity in neural circuitry underlying other behaviors. Outbred Sprague-Dawley (SD) rats were selected and bred for ~15 generations for "fast' or "slow" rates of kindling development in response to stimulation of the perforant path input to the hippocampus. After 7-8 generations of selection and breeding, consistent phenotypes of "fast" and "slow" kindling rates were observed. By the 15th generation "fast" kindling rats referred to as Perforant Path Kindling Susceptible (PPKS) rats demonstrated a kindling rate of 10.7 ± 1.1 afterdischarges (ADs) to the milestone of the first secondary generalized (Class V) seizure, which differed significantly from "slow" kindling Perforant Path Kindling Resistant (PPKR) rats requiring 25.5 ± 2.0 ADs, and outbred SD rats requiring 16.8 ± 2.5 ADs (pkindling) strain with increased susceptibility to seizure-induced plasticity demonstrated statistically significant increases in motor exploratory activity in the open field test and reduced spatial learning the Morris water maze, but demonstrated normal fear conditioned learning comparable to outbred SD rats and the "slow" kindling-resistant PPKR strain. These results confirm that selection and breeding on the basis of responses to repeated pathway activation by stimulation can produce enduring modification of genetic background influencing behavior. These observations also suggest that genetic background underlying susceptibility or resistance to seizure-induced plasticity in hippocampal circuitry also differentially
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Kirti Jain
2016-03-01
Full Text Available The diversity and applicability of swarm intelligence is increasing everyday in the fields of science and engineering. Swarm intelligence gives the features of the dynamic features optimization concept. We have used swarm intelligence for the process of feature optimization and feature selection for content-based image retrieval. The performance of content-based image retrieval faced the problem of precision and recall. The value of precision and recall depends on the retrieval capacity of the image. The basic raw image content has visual features such as color, texture, shape and size. The partial feature extraction technique is based on geometric invariant function. Three swarm intelligence algorithms were used for the optimization of features: ant colony optimization, particle swarm optimization (PSO, and glowworm optimization algorithm. Coral image dataset and MatLab software were used for evaluating performance.
Optimal Selection of the Sampling Interval for Estimation of Modal Parameters by an ARMA- Model
DEFF Research Database (Denmark)
Kirkegaard, Poul Henning
1993-01-01
Optimal selection of the sampling interval for estimation of the modal parameters by an ARMA-model for a white noise loaded structure modelled as a single degree of- freedom linear mechanical system is considered. An analytical solution for an optimal uniform sampling interval, which is optimal...... in a Fisherian sense, is given. The solution is investigated by a simulation study. It is shown that if the experimental length T1 is fixed it may be useful to sample the record at a high sampling rate, since more measurements from the system are then collected. No optimal sampling interval exists....... But if the total number of sample points N is fixed an optimal sampling interval exists. Then it is far worse to use a too large sampling interval than a too small one since the information losses increase rapidly when the sampling interval increases from the optimal value....
A convex optimization approach for path tracking of robot manipulators%基于凸规划的机械臂轨迹规划方法
Institute of Scientific and Technical Information of China (English)
赵建军; 魏毅; 朱登明; 夏时洪; 王兆其
2014-01-01
The problem of fast solving a robot manipulator’s path tracking with time constraints was studied, and a novel path tracking approach based on convex optimization was presented. To overcome the difficulties in dealing with the strong nonlinear dynamic constraints and time constraints in path tracking, the presented approach converts the nonlinear constraints into linear constraints by replacement of variables, and then adds new constraints to convert the original non convex optimization problem into a convex optimization problem, furthermore, converts it into a second order cone program (SOCP), and uses the optimization tools such as the SeDuMi to conduct the real time solving. This approach has several advantages. Firstly, SOCP problems can be solved in polynomial time by the interior point methods. Secondly, the convex optimization is globally stable and the solution is globally optimal. Besides, there is no need to provide initial values for the optimization. Thirdly, this approach has great flexibility and can be applied to the more complicated circumstances where some other types of constraints and objective functions can be taken into account, such as acceleration constraints, minimum energy objective function and minimum jerk objective function. The simulations on a six degrees of freedom robot manipulator show the better efficiency and effectiveness of the proposed approach.%研究了快速求解具有时间约束的机械臂轨迹规划问题，提出了一种基于凸规划的轨迹规划方法。该方法针对机械臂轨迹规划中动力学约束非线性强、时间约束不易处理的问题，首先通过变量替换，将非线性约束转化为线性约束，然后添加新的约束，将原始非凸优化问题转化为凸规划问题，在此基础上，将其写作二阶锥规划（SOCP）形式，使用SeDuMi等优化工具包近似实时求解。该方法具有以下优点：计算高效，凸规划问题能够在多项式时间内得到
Institute of Scientific and Technical Information of China (English)
LIU Ai-hua; DONG Lei; DONG Long-jun
2010-01-01
An optimization model of underground mining method selection was established on the basis of the unascertained measurement theory.Considering the geologic conditions,technology,economy and safety production,ten main factors influencing the selection of mining method were taken into account,and the comprehensive evaluation index system of mining method selection was constructed.The unascertained evaluation indices corresponding to the selected factors for the actual situation were solved both qualitatively and quantitatively.New measurement standards were constructed.Then,the unascertained measurement function of each evaluation index was established.The index weights of the factors were calculated by entropy theory,and credible degree recognition criteria were established according to the unascertained measurement theory.The results of mining method evaluation were obtained using the credible degree criteria,thus the best underground mining method was determined.Furthermore,this model was employed for the comprehensive evaluation and selection of the chosen standard mining methods in Xinli Gold Mine in Sanshandao of China.The results show that the relative superiority degrees of mining methods can be calculated using the unascertained measurement optimization model,so the optimal method can be easily determined.Meanwhile,the proposed method can take into account large amount of uncertain information in mining method selection,which can provide an effective way for selecting the optimal underground mining method.
Institute of Scientific and Technical Information of China (English)
吴宪祥; 郭宝龙; 王娟
2009-01-01
针对移动机器人路径规划问题,提出了一种基于粒了群三次样条优化的路径规划方法.借助三次样条连接描述路径,这样将路径规划问题转化为三次样条曲线的参数优化问题.借助粒了群优化算法快速收敛和全局寻优特性实现最优路径规划.实验结果表明:所提算法町以快速有效地实现障碍环境下机器人的无碰撞路径规划,规划路径平滑,利于机器人的运动控制.%A novel algorithm based on particle swarm optimization (PSO) of cubic splines is proposed for mobile robot path planning. The path is described by string of cubic splines, thus the path planning is equivalent to parameter optimization of particular cubic splines. PSO is introduced to get the optimal path for its fast convergence and global search character. Ex-perimental results show that a collision-avoidance path can be found fleetly and effectively among obstacles by the proposed algorithm. The planned path is smooth which is useful for robot motion control.
Open CNC Turret Typewriter Optimal Path Control System%开放式数控转塔打字机最优路径控制系统
Institute of Scientific and Technical Information of China (English)
赵立新; 张昆; 丁筱玲
2012-01-01
Based the existing software and hardware of CNC turret punch control system,this project combined the industrial control computer (IPC) and programmable controller (PLC),developed a set of open turret typewriter path optimization control system,and put forward a kind of IPC-PLC composition control model for a machine.In this control model,the IPC machine is an intelligent command center and the current operation information will be processed through a closed loop system,and the optimal path control instructions will be transferred to the down place machine; since the PLC is one of the core control components in this model,the ac servo system will drive actuating mechanism through a established procedure to complete the fast movement and accurate location of Xand Yaxis,realize the bi-directional rotation of knife tower,and at any position come near to change the tools function.Eventually the typing path of optimal control purpose could be accomplished.Field test shows that the PLC control servo motor could realize the numerical position control in the actual operation and can satisfy the control requirements,and the simulation of tower type path optimization control effect is remarkable.%通过对现有数控转塔冲床控制系统软、硬件的改进与设计,基于工控机(IPC)结合可编程控制器(PLC),开发出一套开放式转塔打字机路径优化控制系统,提出一种IPC与PLC组成的上、下位机的控制模式.闭环系统实时跟踪锁定当前作业信息,将最优路径控制指令传达给下位机；PLC作为运动控制核心部件,主要通过既定程序控制交流伺服系统驱动转塔执行机构,完成X轴、y轴等快速移动和精确定位,以实现刀塔双向旋转和任意位置就近换刀的功能,达到最优路径打字目的.现场试验表明,应用PLC控制伺服电机实现数控系统点位控制的方法在实际运行中是切实可行的,不仅能满足控制要求,而且对转塔打字路径优化控制效果显著.
Zhou, Chuan; Chan, Heang-Ping; Guo, Yanhui; Wei, Jun; Chughtai, Aamer; Hadjiiski, Lubomir M.; Sundaram, Baskaran; Patel, Smita; Kuriakose, Jean W.; Kazerooni, Ella A.
2013-03-01
The curved planar reformation (CPR) method re-samples the vascular structures along the vessel centerline to generate longitudinal cross-section views. The CPR technique has been commonly used in coronary CTA workstation to facilitate radiologists' visual assessment of coronary diseases, but has not yet been used for pulmonary vessel analysis in CTPA due to the complicated tree structures and the vast network of pulmonary vasculature. In this study, a new curved planar reformation and optimal path tracing (CROP) method was developed to facilitate feature extraction and false positive (FP) reduction and improve our PE detection system. PE candidates are first identified in the segmented pulmonary vessels at prescreening. Based on Dijkstra's algorithm, the optimal path (OP) is traced from the pulmonary trunk bifurcation point to each PE candidate. The traced vessel is then straightened and a reformatted volume is generated using CPR. Eleven new features that characterize the intensity, gradient, and topology are extracted from the PE candidate in the CPR volume and combined with the previously developed 9 features to form a new feature space for FP classification. With IRB approval, CTPA of 59 PE cases were retrospectively collected from our patient files (UM set) and 69 PE cases from the PIOPED II data set with access permission. 595 and 800 PEs were manually marked by experienced radiologists as reference standard for the UM and PIOPED set, respectively. At a test sensitivity of 80%, the average FP rate was improved from 18.9 to 11.9 FPs/case with the new method for the PIOPED set when the UM set was used for training. The FP rate was improved from 22.6 to 14.2 FPs/case for the UM set when the PIOPED set was used for training. The improvement in the free response receiver operating characteristic (FROC) curves was statistically significant (p<0.05) by JAFROC analysis, indicating that the new features extracted from the CROP method are useful for FP reduction.
Directory of Open Access Journals (Sweden)
P. C. Jha
2011-01-01
Full Text Available The cost associated with development of a large and complex software system is formidable. In today's customer driven market, improvement of quality aspects in terms of reliability of the product is also gaining increased importance. But the resources are limited and the manager has to maneuver within a tight schedule. In order to meet these challenges, many organizations are making use of Commercial Off-The-Shelf (COTS software. This paper develops a fuzzy multi objective optimization model approach for selecting the optimal COTS software product among alternatives for each module in the development of modular software system. The problem is formulated for consensus recovery block fault tolerant scheme. In today’s ever changing environment, it is arduous to estimate the precise cost and reliability of software. Therefore, we develop a fuzzy multi objective optimization models for selecting optimal COTS software products. Numerical illustrations are provided to demonstrate the models developed.
Walters, W
2002-01-01
The particular features of the r-process abundances with 100 < A < 150 have demonstrated the close connection between knowledge of nuclear structure and decay along the r-process path and the astrophysical environement in which these elements are produced. Key to this connection has been the measurement of data for nuclides (mostly even-N nuclides) that lie in the actual r-process path. Such data are of direct use in r-process calculations and they also serve to refine and test the predictive power of nuclear models where little or no data now exist. In this experiment we seek to use the newly developed ionization scheme for the Resonance Ionization Laser Ion Source (RILIS) to achieve selective ionization of neutron-rich antimony isotopes in order to measure the decay properties of r-process path nuclides $^{137,138,139}$Sb. These properties include the half-lives, delayed neutron branches, and daughter $\\gamma$-rays. The new nuclear structure data for the daughter Te nuclides is also of considerable in...
Optimizing selection of bridge raft based on fuzzy matter-element model and combination weighting
Institute of Scientific and Technical Information of China (English)
Li Feng; Shao Fei; Wang Jianping; Li Zhigang
2012-01-01
It＇ s a necessary selection to support the maneuver across Yangtze River by floating bridge constructed by portable steel bridge and civilian ships. It is a comprehensive index for the scheme of bridge raft, containing a variety of technical factors and uncertainties. The optimization is the selection in the constructing time, quantity of equipments and man power. Based on the calculation result of bridge rafts, an evaluating system is established, consisting of index of spacing between interior bays, raft length, truss numbers, operation difficulty and maximal bending stress. A fuzzy matter element model of optimizing selection of bridge rafts was built up by combining quantitative analysis with qualitative analysis. The method of combination weighting was used to calculate the value of weights index to reduce the subjective randomness. The sequence of schemes and the optimization resuh were gained finally based on euclid approach degree. The application result shows that it is simple and practical.
Institute of Scientific and Technical Information of China (English)
Xu Hongji; Liu Ju; Gu Bo
2007-01-01
An approach combining optimal antenna subset selection with blind detection scheme for Orthogonal Space-Time Block Coding (OSTBC) is proposed in this paper. The optimal antenna subset selection is taken into account at transmitter and/or receiver sides, which chooses the optimal antennas to increase the diversity order of OSTBC and improve further its performance. In order to enhance the robustness of the detection used in the conventional OSTBC scheme, a blind detection scheme based on Independent Component Analysis (ICA) is exploited which can directly extract transmitted signals without channel estimation. Performance analysis shows that the proposed approach can achieve the full diversity and the flexibility of system design by using the antenna selection and the ICA based blind detection schemes.
Optimization of meander line radiators for frequency selective surfaces by using genetic algorithm
Bucuci, Stefania C.; Dumitrascu, Ana; Danisor, Alin; Berescu, Serban; Tamas, Razvan D.
2015-02-01
In this paper we propose the use of frequency selective surfaces based on meander line radiators, as targets for monitoring slow displacements with synthetic aperture radars. The optimization of the radiators is performed by using genetic algorithms on only two parameters i.e., gain and size. As an example, we have optimized a single meander antenna, resonating in the X-band, at 9.65 GHz.
Gray comprehensive assessment and optimal selection of water consumption forecasting model
Institute of Scientific and Technical Information of China (English)
无
2006-01-01
A comprehensive assessing method based on the principle of the gray system theory and gray relational grade analysis was put forward to optimize water consumption forecasting models. The method provides a better accuracy for the assessment and the optimal selection of the water consumption forecasting models. The results show that the forecasting model built on this comprehensive assessing method presents better self-adaptability and accuracy in forecasting.
Directory of Open Access Journals (Sweden)
Ganghua Li
2014-08-01
Full Text Available Improvement of yield in rice (Oryza sativa L. is vital for ensuring food security in China. Both rice breeders and growers need an improved understanding of the relationship between yield and yield-related traits. New indica cultivars (53 in 2007 and 48 in 2008 were grown in Taoyuan, Yunnan province, to identify important components contributing to yield. Additionally, two standard indica rice cultivars with similar yield potentials, II You 107 (a large-panicle type and Xieyou 107 (a heavy-panicle type, were planted in Taoyuan, Yunnan province and Nanjing, Jiangsu province, from 2006 to 2008 to evaluate the stability of yield and yield-related attributes. Growth duration (GD, leaf area index (LAI, panicles per m2 (PN, and spikelets per m2 (SM were significantly and positively correlated with grain yield (GY over all years. Sequential path analysis identified PN and panicle weight (PW as important first-order traits that influenced grain yield. All direct effects were significant, as indicated by bootstrap analysis. Yield potential varied greatly across locations but not across years. Plant height (PH, days from heading to maturity (HM, and grain weight (GW were stable traits that showed little variation across sites or years, whereas GD (mainly the pre-heading period, PHP and PN varied significantly across locations. To achieve a yield of 15 t ha− 1, a cultivar should have a PH of 110–125 cm, a long GD with HM of approximately 40 days, a PN of 300–400 m− 2, and a GW of 29–31 mg.
Institute of Scientific and Technical Information of China (English)
Ganghua; Li; Jun; Zhang; Congdang; Yang; Yunpan; Song; Chengyan; Zheng; Shaohua; Wang; Zhenghui; Liu; Yanfeng; Ding
2014-01-01
Improvement of yield in rice(Oryza sativa L.) is vital for ensuring food security in China. Both rice breeders and growers need an improved understanding of the relationship between yield and yield-related traits. New indica cultivars(53 in 2007 and 48 in 2008) were grown in Taoyuan,Yunnan province, to identify important components contributing to yield. Additionally, two standard indica rice cultivars with similar yield potentials, II You 107(a large-panicle type) and Xieyou 107(a heavy-panicle type), were planted in Taoyuan, Yunnan province and Nanjing,Jiangsu province, from 2006 to 2008 to evaluate the stability of yield and yield-related attributes.Growth duration(GD), leaf area index(LAI), panicles per m2(PN), and spikelets per m2(SM) were significantly and positively correlated with grain yield(GY) over all years. Sequential path analysis identified PN and panicle weight(PW) as important first-order traits that influenced grain yield. All direct effects were significant, as indicated by bootstrap analysis. Yield potential varied greatly across locations but not across years. Plant height(PH), days from heading to maturity(HM), and grain weight(GW) were stable traits that showed little variation across sites or years, whereas GD(mainly the pre-heading period, PHP) and PN varied significantly across locations. To achieve a yield of 15 t ha-1, a cultivar should have a PH of 110–125 cm, a long GD with HM of approximately 40 days, a PN of 300–400 m-2, and a GW of 29–31 mg.
Welding Diagnostics by Means of Particle Swarm Optimization and Feature Selection
Directory of Open Access Journals (Sweden)
J. Mirapeix
2012-01-01
Full Text Available In a previous contribution, a welding diagnostics approach based on plasma optical spectroscopy was presented. It consisted of the employment of optimization algorithms and synthetic spectra to obtain the participation profiles of the species participating in the plasma. A modification of the model is discussed here: on the one hand the controlled random search algorithm has been substituted by a particle swarm optimization implementation. On the other hand a feature selection stage has been included to determine those spectral windows where the optimization process will take place. Both experimental and field tests will be shown to illustrate the performance of the solution that improves the results of the previous work.
Evaluation and Selection of the Optimal Scheme of Industrial Structure Adjustment Based on DEA
Institute of Scientific and Technical Information of China (English)
FU Lifang; GE Jiaqi; MENG Jun
2006-01-01
In the paper, the advanced assessment method of DEA (Date Envelopment Analysis) had been used to evaluate relative efficiency and select the optimal scheme of agricultural industrial structure adjustment. According to the results of DEA models, we analyzed scale benefits of every optional schemes, probed deeply the ultimate reason for not DEA efficient, which clarified the method and approach to improve these optional schemes. Finally, a new method had been proposed to rank and select the optimal scheme. The research is significant to direct the practice of the adjustment of agricultural industrial structure.
AHP-Based Optimal Selection of Garment Sizes for Online Shopping
Institute of Scientific and Technical Information of China (English)
无
2007-01-01
Garment online shopping has been accepted by more and more consumers in recent years. In online shopping, a buyer only chooses the garment size judged by his own experience without trying-on, so the selected garment may not be the fittest one for the buyer due to the variety of body's figures. Thus, we propose a method of optimal selection of garment sizes for online shopping based on Analytic Hierarchy Process (AHP). The hierarchical structure model for optimal selection of garment sizes is structured and the fittest garment for a buyer is found by calculating the matching degrees between individual's measurements and the corresponding key-part values of ready-to-wear clothing sizes. In order to demonstrate its feasibility, we provide an example of selecting the fittest sizes of men's bottom. The result shows that the proposed method is useful in online clothing sales application.
How to Achieve the Optimal DMT of Selective Fading MIMO Channels?
Mroueh, Lina
2010-01-01
In this paper, we consider a particular class of selective fading channel corresponding to a channel that is selective either in time or in frequency. For this class of channel, we propose a systematic way to achieve the optimal DMT derived in Coronel and B\\"olcskei, IEEE ISIT, 2007 by extending the non-vanishing determinant (NVD) criterion to the selective channel case. A new code construction based on split NVD parallel codes is then proposed to satisfy the NVD parallel criterion. This result is of significant interest not only in its own right, but also because it settles a long-standing debate in the literature related to the optimal DMT of selective fading channels.
Yang, Wei; Sun, Wanlu
2010-01-01
Energy-efficient communication is an important requirement for mobile relay networks due to the limited battery power of user terminals. This paper considers energy-efficient relaying schemes through selection of mobile relays in cooperative cellular systems with asymmetric traffic. The total energy consumption per information bit of the battery-powered terminals, i.e., the mobile station (MS) and the relay, is derived in theory. In the Joint Uplink and Downlink Relay Selection (JUDRS) scheme we proposed, the relay which minimizes the total energy consumption is selected. Additionally, the energy-efficient cooperation regions are investigated, and the optimal relay location is found for cooperative cellular systems with asymmetric traffic. The results reveal that the MS-relay and the relay-base station (BS) channels have different influence over relay selection decisions for optimal energy-efficiency. Information theoretic analysis of the diversity-multiplexing tradeoff (DMT) demonstrates that the proposed sc...
Selective waste collection optimization in Romania and its impact to urban climate
Mihai, Šercǎianu; Iacoboaea, Cristina; Petrescu, Florian; Aldea, Mihaela; Luca, Oana; Gaman, Florian; Parlow, Eberhard
2016-08-01
According to European Directives, transposed in national legislation, the Member States should organize separate collection systems at least for paper, metal, plastic, and glass until 2015. In Romania, since 2011 only 12% of municipal collected waste was recovered, the rest being stored in landfills, although storage is considered the last option in the waste hierarchy. At the same time there was selectively collected only 4% of the municipal waste. Surveys have shown that the Romanian people do not have selective collection bins close to their residencies. The article aims to analyze the current situation in Romania in the field of waste collection and management and to make a proposal for selective collection containers layout, using geographic information systems tools, for a case study in Romania. Route optimization is used based on remote sensing technologies and network analyst protocols. Optimizing selective collection system the greenhouse gases, particles and dust emissions can be reduced.
Institute of Scientific and Technical Information of China (English)
CHUNG Warn-ill; CHOI Jun-ho; BAE Hae-young
2004-01-01
Many commercial database systems maintain histograms to summarize the contents of relations and permit the efficient estimation of query result sizes and the access plan cost. In spatial database systems, most spatial query predicates are consisted of topological relationships between spatial objects, and it is very important to estimate the selectivity of those predicates for spatial query optimizer. In this paper, we propose a selectivity estimation scheme for spatial topological predicates based on the multidimensional histogram and the transformation scheme. Proposed scheme applies twopartition strategy on transformed object space to generate spatial histogram and estimates the selectivity of topological predicates based on the topological characteristics of the transformed space. Proposed scheme provides a way for estimating the selectivity without too much memory space usage and additional I/Os in most spatial query optimizers.
Directory of Open Access Journals (Sweden)
Ming-Yuan Cho
2017-01-01
Full Text Available Fast and accurate fault classification is essential to power system operations. In this paper, in order to classify electrical faults in radial distribution systems, a particle swarm optimization (PSO based support vector machine (SVM classifier has been proposed. The proposed PSO based SVM classifier is able to select appropriate input features and optimize SVM parameters to increase classification accuracy. Further, a time-domain reflectometry (TDR method with a pseudorandom binary sequence (PRBS stimulus has been used to generate a dataset for purposes of classification. The proposed technique has been tested on a typical radial distribution network to identify ten different types of faults considering 12 given input features generated by using Simulink software and MATLAB Toolbox. The success rate of the SVM classifier is over 97%, which demonstrates the effectiveness and high efficiency of the developed method.
Cho, Ming-Yuan; Hoang, Thi Thom
2017-01-01
Fast and accurate fault classification is essential to power system operations. In this paper, in order to classify electrical faults in radial distribution systems, a particle swarm optimization (PSO) based support vector machine (SVM) classifier has been proposed. The proposed PSO based SVM classifier is able to select appropriate input features and optimize SVM parameters to increase classification accuracy. Further, a time-domain reflectometry (TDR) method with a pseudorandom binary sequence (PRBS) stimulus has been used to generate a dataset for purposes of classification. The proposed technique has been tested on a typical radial distribution network to identify ten different types of faults considering 12 given input features generated by using Simulink software and MATLAB Toolbox. The success rate of the SVM classifier is over 97%, which demonstrates the effectiveness and high efficiency of the developed method.
Optimizing the selective flocculation of coal by means of a selective reagent
Energy Technology Data Exchange (ETDEWEB)
N.I. Nikitin; I.N. Nikitin [Khar' kov Polytechnic Institute, Kiev (Ukraine)
2008-04-15
The need to improve coal enrichment stems from the sharp deterioration in the coal being enriched (increasing ash and moisture content, content of small classes, rock content, etc.), which results from the widespread mechanization of mining operations, the development of high-ash deposits, and dust suppression. The most promising polymer reagents include selective flocculants made from synthetic latex, in the form of an aqueous colloidal dispersion of artificial rubber globules stabilized by various emulsifiers (most often ionogenic surfactants of anionic type). The basic experimental work on the selective flocculation of coal slurry by synthetic latex was undertaken at the Coal-Chemistry Institute (CCI), in collaboration with Voronezh synthetic-rubber plant and the Voronezh branch of the All-Russian Scientific-Research Institute of Synthetic Rubber. In all, 29 latex flocculants have been studied.
Institute of Scientific and Technical Information of China (English)
WANG Yunfeng; BIAN Jinian; HONG Xianlong; ZHOU Qiang; WU Qiang
2007-01-01
As the feature size of integrated circuits is reduced to the deep sub-micron level or the nanometer level, the interconnect delay is becoming more and more important in determining the total delay of a circuit. Re-synthesis after floorptan is expected to be very helpful for reducing the interconnect delay of a circuit. In this paper,a force-balance-based re-synthesis algorithm for interconnect delay o ptimization after floorplan is proposed. The algorithm optimizes the inter connect delay by changing the operation scheduling and the functional unit allocation andbinding. With this method the number and positions of all functional units are not changed, but some operations are allocated or bound to different units. Preliminary experimental results show that the interconnect wire delays are reduced efficiently without destroying the floorplan performance.
Ant colony optimization for the real-time train routing selection problem
SAMA, Marcella; Pellegrini, Paola; D'Ariano, Andrea; Rodriguez, Joaquin; Pacciarelli, Dario
2016-01-01
This paper deals with the real-time problem of scheduling and routing trains in a railway network. In the related literature, this problem is usually solved starting from a subset of routing alternatives and computing the near-optimal solution of the simplified routing problem. We study how to select the best subset of routing alternatives for each train among all possible alternatives. The real-time train routing selection problem is formulated as an integer linear programming formulation an...
Fournier-Level, A.; Wilczek, A.M.; Cooper, M.D.; Roe, J.L.; Anderson, J.; Eaton, D.; Moyers, B.T.; Petipas, R.H.; Schaeffer, R.N.; Pieper, B.; Reymond, M.; Koornneef, M.; Welch, S.M.; Remington, D.L.; Schmitt, J.
2013-01-01
Selection on quantitative trait loci (QTL) may vary among natural environments due to differences in the genetic architecture of traits, environment-specific allelic effects or changes in the direction and magnitude of selection on specific traits. To dissect the environmental differences in selecti
Fournier-Level, A.; Wilczek, A.M.; Cooper, M.D.; Roe, J.L.; Anderson, J.; Eaton, D.; Moyers, B.T.; Petipas, R.H.; Schaeffer, R.N.; Pieper, B.; Reymond, M.; Koornneef, M.; Welch, S.M.; Remington, D.L.; Schmitt, J.
2013-01-01
Selection on quantitative trait loci (QTL) may vary among natural environments due to differences in the genetic architecture of traits, environment-specific allelic effects or changes in the direction and magnitude of selection on specific traits. To dissect the environmental differences in selecti
Cui, Huanqing; Shu, Minglei; Song, Min; Wang, Yinglong
2017-03-01
Localization is a key technology in wireless sensor networks. Faced with the challenges of the sensors' memory, computational constraints, and limited energy, particle swarm optimization has been widely applied in the localization of wireless sensor networks, demonstrating better performance than other optimization methods. In particle swarm optimization-based localization algorithms, the variants and parameters should be chosen elaborately to achieve the best performance. However, there is a lack of guidance on how to choose these variants and parameters. Further, there is no comprehensive performance comparison among particle swarm optimization algorithms. The main contribution of this paper is three-fold. First, it surveys the popular particle swarm optimization variants and particle swarm optimization-based localization algorithms for wireless sensor networks. Secondly, it presents parameter selection of nine particle swarm optimization variants and six types of swarm topologies by extensive simulations. Thirdly, it comprehensively compares the performance of these algorithms. The results show that the particle swarm optimization with constriction coefficient using ring topology outperforms other variants and swarm topologies, and it performs better than the second-order cone programming algorithm.
Directory of Open Access Journals (Sweden)
Huanqing Cui
2017-03-01
Full Text Available Localization is a key technology in wireless sensor networks. Faced with the challenges of the sensors’ memory, computational constraints, and limited energy, particle swarm optimization has been widely applied in the localization of wireless sensor networks, demonstrating better performance than other optimization methods. In particle swarm optimization-based localization algorithms, the variants and parameters should be chosen elaborately to achieve the best performance. However, there is a lack of guidance on how to choose these variants and parameters. Further, there is no comprehensive performance comparison among particle swarm optimization algorithms. The main contribution of this paper is three-fold. First, it surveys the popular particle swarm optimization variants and particle swarm optimization-based localization algorithms for wireless sensor networks. Secondly, it presents parameter selection of nine particle swarm optimization variants and six types of swarm topologies by extensive simulations. Thirdly, it comprehensively compares the performance of these algorithms. The results show that the particle swarm optimization with constriction coefficient using ring topology outperforms other variants and swarm topologies, and it performs better than the second-order cone programming algorithm.
Bedford, J. L.; Webb, S.
2007-01-01
Direct-aperture optimization (DAO) was applied to iterative beam-orientation selection in intensity-modulated radiation therapy (IMRT), so as to ensure a realistic segmental treatment plan at each iteration. Nested optimization engines dealt separately with gantry angles, couch angles, collimator angles, segment shapes, segment weights and wedge angles. Each optimization engine performed a random search with successively narrowing step sizes. For optimization of segment shapes, the filtered backprojection (FBP) method was first used to determine desired fluence, the fluence map was segmented, and then constrained direct-aperture optimization was used thereafter. Segment shapes were fully optimized when a beam angle was perturbed, and minimally re-optimized otherwise. The algorithm was compared with a previously reported method using FBP alone at each orientation iteration. An example case consisting of a cylindrical phantom with a hemi-annular planning target volume (PTV) showed that for three-field plans, the method performed better than when using FBP alone, but for five or more fields, neither method provided much benefit over equally spaced beams. For a prostate case, improved bladder sparing was achieved through the use of the new algorithm. A plan for partial scalp treatment showed slightly improved PTV coverage and lower irradiated volume of brain with the new method compared to FBP alone. It is concluded that, although the method is computationally intensive and not suitable for searching large unconstrained regions of beam space, it can be used effectively in conjunction with prior class solutions to provide individually optimized IMRT treatment plans.
Cui, Huanqing; Shu, Minglei; Song, Min; Wang, Yinglong
2017-01-01
Localization is a key technology in wireless sensor networks. Faced with the challenges of the sensors’ memory, computational constraints, and limited energy, particle swarm optimization has been widely applied in the localization of wireless sensor networks, demonstrating better performance than other optimization methods. In particle swarm optimization-based localization algorithms, the variants and parameters should be chosen elaborately to achieve the best performance. However, there is a lack of guidance on how to choose these variants and parameters. Further, there is no comprehensive performance comparison among particle swarm optimization algorithms. The main contribution of this paper is three-fold. First, it surveys the popular particle swarm optimization variants and particle swarm optimization-based localization algorithms for wireless sensor networks. Secondly, it presents parameter selection of nine particle swarm optimization variants and six types of swarm topologies by extensive simulations. Thirdly, it comprehensively compares the performance of these algorithms. The results show that the particle swarm optimization with constriction coefficient using ring topology outperforms other variants and swarm topologies, and it performs better than the second-order cone programming algorithm. PMID:28257060
Optimal band selection for high dimensional remote sensing data using genetic algorithm
Zhang, Xianfeng; Sun, Quan; Li, Jonathan
2009-06-01
A 'fused' method may not be suitable for reducing the dimensionality of data and a band/feature selection method needs to be used for selecting an optimal subset of original data bands. This study examined the efficiency of GA in band selection for remote sensing classification. A GA-based algorithm for band selection was designed deliberately in which a Bhattacharyya distance index that indicates separability between classes of interest is used as fitness function. A binary string chromosome is designed in which each gene location has a value of 1 representing a feature being included or 0 representing a band being not included. The algorithm was implemented in MATLAB programming environment, and a band selection task for lithologic classification in the Chocolate Mountain area (California) was used to test the proposed algorithm. The proposed feature selection algorithm can be useful in multi-source remote sensing data preprocessing, especially in hyperspectral dimensionality reduction.
Sun, Jun; Fang, Wei; Wu, Xiaojun; Palade, Vasile; Xu, Wenbo
2012-01-01
Quantum-behaved particle swarm optimization (QPSO), motivated by concepts from quantum mechanics and particle swarm optimization (PSO), is a probabilistic optimization algorithm belonging to the bare-bones PSO family. Although it has been shown to perform well in finding the optimal solutions for many optimization problems, there has so far been little analysis on how it works in detail. This paper presents a comprehensive analysis of the QPSO algorithm. In the theoretical analysis, we analyze the behavior of a single particle in QPSO in terms of probability measure. Since the particle's behavior is influenced by the contraction-expansion (CE) coefficient, which is the most important parameter of the algorithm, the goal of the theoretical analysis is to find out the upper bound of the CE coefficient, within which the value of the CE coefficient selected can guarantee the convergence or boundedness of the particle's position. In the experimental analysis, the theoretical results are first validated by stochastic simulations for the particle's behavior. Then, based on the derived upper bound of the CE coefficient, we perform empirical studies on a suite of well-known benchmark functions to show how to control and select the value of the CE coefficient, in order to obtain generally good algorithmic performance in real world applications. Finally, a further performance comparison between QPSO and other variants of PSO on the benchmarks is made to show the efficiency of the QPSO algorithm with the proposed parameter control and selection methods.
Adaptive feature selection using v-shaped binary particle swarm optimization
Dong, Hongbin; Zhou, Xiurong
2017-01-01
Feature selection is an important preprocessing method in machine learning and data mining. This process can be used not only to reduce the amount of data to be analyzed but also to build models with stronger interpretability based on fewer features. Traditional feature selection methods evaluate the dependency and redundancy of features separately, which leads to a lack of measurement of their combined effect. Moreover, a greedy search considers only the optimization of the current round and thus cannot be a global search. To evaluate the combined effect of different subsets in the entire feature space, an adaptive feature selection method based on V-shaped binary particle swarm optimization is proposed. In this method, the fitness function is constructed using the correlation information entropy. Feature subsets are regarded as individuals in a population, and the feature space is searched using V-shaped binary particle swarm optimization. The above procedure overcomes the hard constraint on the number of features, enables the combined evaluation of each subset as a whole, and improves the search ability of conventional binary particle swarm optimization. The proposed algorithm is an adaptive method with respect to the number of feature subsets. The experimental results show the advantages of optimizing the feature subsets using the V-shaped transfer function and confirm the effectiveness and efficiency of the feature subsets obtained under different classifiers. PMID:28358850
Li, Xiangtao; Yin, Minghao
2013-12-01
Gene expression data play an important role in the development of efficient cancer diagnoses and classification. However, gene expression data are usually redundant and noisy, and only a subset of them present distinct profiles for different classes of samples. Thus, selecting high discriminative genes from gene expression data has become increasingly interesting in the field of bioinformatics. In this paper, a multi-objective biogeography based optimization method is proposed to select the small subset of informative gene relevant to the classification. In the proposed algorithm, firstly, the Fisher-Markov selector is used to choose the 60 top gene expression data. Secondly, to make biogeography based optimization suitable for the discrete problem, binary biogeography based optimization, as called BBBO, is proposed based on a binary migration model and a binary mutation model. Then, multi-objective binary biogeography based optimization, as we called MOBBBO, is proposed by integrating the non-dominated sorting method and the crowding distance method into the BBBO framework. Finally, the MOBBBO method is used for gene selection, and support vector machine is used as the classifier with the leave-one-out cross-validation method (LOOCV). In order to show the effective and efficiency of the algorithm, the proposed algorithm is tested on ten gene expression dataset benchmarks. Experimental results demonstrate that the proposed method is better or at least comparable with previous particle swarm optimization (PSO) algorithm and support vector machine (SVM) from literature when considering the quality of the solutions obtained.
Mode perturbation method for optimal guided wave mode and frequency selection.
Philtron, J H; Rose, J L
2014-09-01
With a thorough understanding of guided wave mechanics, researchers can predict which guided wave modes will have a high probability of success in a particular nondestructive evaluation application. However, work continues to find optimal mode and frequency selection for a given application. This "optimal" mode could give the highest sensitivity to defects or the greatest penetration power, increasing inspection efficiency. Since material properties used for modeling work may be estimates, in many cases guided wave mode and frequency selection can be adjusted for increased inspection efficiency in the field. In this paper, a novel mode and frequency perturbation method is described and used to identify optimal mode points based on quantifiable wave characteristics. The technique uses an ultrasonic phased array comb transducer to sweep in phase velocity and frequency space. It is demonstrated using guided interface waves for bond evaluation. After searching nearby mode points, an optimal mode and frequency can be selected which has the highest sensitivity to a defect, or gives the greatest penetration power. The optimal mode choice for a given application depends on the requirements of the inspection.
Energy Technology Data Exchange (ETDEWEB)
Ji, Aimin; Yin, Xu; Yuan, Minghai [Hohai University, Changzhou (China)
2015-09-15
There are two problems in Collaborative optimization (CO): (1) the local optima arising from the selection of an inappropriate initial point; (2) the low efficiency and accuracy root in inappropriate relaxation factors. To solve these problems, we first develop the Latin hypercube design (LHD) to determine an initial point of optimization, and then use the non-linear programming by quadratic Lagrangian (NLPQL) to search for the global solution. The effectiveness of the initial point selection strategy is verified by three benchmark functions with some dimensions and different complexities. Then we propose the Adaptive relaxation collaborative optimization (ARCO) algorithm to solve the inconsistency between the system level and the disciplines level, and in this method, the relaxation factors are determined according to the three separated stages of CO respectively. The performance of the ARCO algorithm is compared with the standard collaborative algorithm and the constant relaxation collaborative algorithm with a typical numerical example, which indicates that the ARCO algorithm is more efficient and accurate. Finally, we propose a Hybrid collaborative optimization (HCO) approach, which integrates the selection strategy of initial point with the ARCO algorithm. The results show that HCO can achieve the global optimal solution without the initial value and it also has advantages in convergence, accuracy and robustness. Therefore, the proposed HCO approach can solve the CO problems with applications in the spindle and the speed reducer.
Optimal processing pathway selection for microalgae-based biorefinery under uncertainty
DEFF Research Database (Denmark)
Rizwan, Muhammad; Zaman, Muhammad; Lee, Jay H.
2015-01-01
We propose a systematic framework for the selection of optimal processing pathways for a microalgaebased biorefinery under techno-economic uncertainty. The proposed framework promotes robust decision making by taking into account the uncertainties that arise due to inconsistencies among and short...
Comparison of optimal cutoff points for single and multiple tests in personnel selection
Ben-Yashar, Ruth; Nitzan, Shmuel; Vos, Hendrik J.
2006-01-01
This paper compares the determination of optimal cutoff points for single and multiple tests in the field of personnel selection. Decisional skills of predictor tests composing the multiple test are assumed to be endogenous variables that depend on the cutting points to be set. It is shown how the
van Weert, K.; Dhaene, J.; Goovaerts, M.
2011-01-01
In this paper we discuss multiperiod portfolio selection problems related to a specific provisioning problem. Our results are an extension of Dhaene et al. (2005) [14], where optimal constant mix investment strategies are obtained in a provisioning and savings context, using an analytical approach
Compensatory Analysis and Optimization for MADM for Heterogeneous Wireless Network Selection
Directory of Open Access Journals (Sweden)
Jian Zhou
2016-01-01
Full Text Available In the next-generation heterogeneous wireless networks, a mobile terminal with a multi-interface may have network access from different service providers using various technologies. In spite of this heterogeneity, seamless intersystem mobility is a mandatory requirement. One of the major challenges for seamless mobility is the creation of a network selection scheme, which is for users that select an optimal network with best comprehensive performance between different types of networks. However, the optimal network may be not the most reasonable one due to compensation of MADM (Multiple Attribute Decision Making, and the network is called pseudo-optimal network. This paper conducts a performance evaluation of a number of widely used MADM-based methods for network selection that aim to keep the mobile users always best connected anywhere and anytime, where subjective weight and objective weight are all considered. The performance analysis shows that the selection scheme based on MEW (weighted multiplicative method and combination weight can better avoid accessing pseudo-optimal network for balancing network load and reducing ping-pong effect in comparison with three other MADM solutions.
Robinson, Stephanie A.; Rickenbach, Elizabeth H.; Lachman, Margie E.
2016-01-01
The effective use of self-regulatory strategies, such as selection, optimization, and compensation (SOC) requires resources. However, it is theorized that SOC use is most advantageous for those experiencing losses and diminishing resources. The present study explored this seeming paradox within the context of limitations or constraints due to…
Selecting Optimal Parameters of Random Linear Network Coding for Wireless Sensor Networks
DEFF Research Database (Denmark)
Heide, Janus; Zhang, Qi; Fitzek, Frank
2013-01-01
This work studies how to select optimal code parameters of Random Linear Network Coding (RLNC) in Wireless Sensor Networks (WSNs). With Rateless Deluge [1] the authors proposed to apply Network Coding (NC) for Over-the-Air Programming (OAP) in WSNs, and demonstrated that with NC a significant...
Directory of Open Access Journals (Sweden)
Saleh LAshkari
2016-06-01
Full Text Available Selecting optimal features based on nature of the phenomenon and high discriminant ability is very important in the data classification problems. Since it doesn't require any assumption about stationary condition and size of the signal and the noise in Recurrent Quantification Analysis (RQA, it may be useful for epileptic seizure Detection. In this study, RQA was used to discriminate ictal EEG from the normal EEG where optimal features selected by combination of algorithm genetic and Bayesian Classifier. Recurrence plots of hundred samples in each two categories were obtained with five distance norms in this study: Euclidean, Maximum, Minimum, Normalized and Fixed Norm. In order to choose optimal threshold for each norm, ten threshold of ε was generated and then the best feature space was selected by genetic algorithm in combination with a bayesian classifier. The results shown that proposed method is capable of discriminating the ictal EEG from the normal EEG where for Minimum norm and 0.1˂ε˂1, accuracy was 100%. In addition, the sensitivity of proposed framework to the ε and the distance norm parameters was low. The optimal feature presented in this study is Trans which it was selected in most feature spaces with high accuracy.
Selective Segmentation for Global Optimization of Depth Estimation in Complex Scenes
Directory of Open Access Journals (Sweden)
Sheng Liu
2013-01-01
Full Text Available This paper proposes a segmentation-based global optimization method for depth estimation. Firstly, for obtaining accurate matching cost, the original local stereo matching approach based on self-adapting matching window is integrated with two matching cost optimization strategies aiming at handling both borders and occlusion regions. Secondly, we employ a comprehensive smooth term to satisfy diverse smoothness request in real scene. Thirdly, a selective segmentation term is used for enforcing the plane trend constraints selectively on the corresponding segments to further improve the accuracy of depth results from object level. Experiments on the Middlebury image pairs show that the proposed global optimization approach is considerably competitive with other state-of-the-art matching approaches.
Islam, Muhammad Nazmul; Kompella, Sastry
2011-01-01
In this paper, we investigate joint optimal relay selection and resource allocation that are fundamental to the understanding of bandwidth exchange (BE) and time exchange (TE) enabled incentivized cooperative forwarding in wireless networks. We consider a network where N nodes transmit data in the uplink to an access point (AP) or base station (BS). We first consider the scenario where each node gets an initial amount (equal, optimal or arbitrary) of resource in the form of bandwidth or time, and uses this resource as a flexible incentive for two hop relaying. We focus on ?-fair network utility maximization (NUM) and total power minimization in this environment. For both BE and TE, we show the concavity or convexity of the resource allocation problem for a fixed relay set. Defining the link weights of each relay pair as the utility gain due to cooperation (over noncooperation), we show that the optimal relay selection, often a combinatorially cumbersome problem, reduces to the maximum weighted matching (MWM) ...
Renaud, Marie Claude; Sebastianelli, Alexandra
2013-01-01
Epithelial ovarian cancer (EOC) is a deadly disease for which optimal cytoreduction to microscopic disease has shown the best correlation with survival. Electrically neutral argon plasma technology is a novel surgical tool to allow aggressive cytoreduction in selected patients with EOC, primary peritoneal cancer, and tubal cancer. We conducted a prospective feasibility study of the use of neutral argon plasma technology to complete cytoreductive surgery in order to assess its ability to obtain optimal cytoreduction. Six patients had their surgery completed with the neutral argon plasma device. None of the patients would have had optimal surgery unless the device had been available. All patients had cytoreduction to less than 5 mm to 10 mm without additional morbidity. One patient had complete cytoreduction, and two had residual disease of less than 2 mm. Electrically neutral plasma argon technology is a useful technology to maximize cytoreduction and to reduce tumour burden in selected cases of EOC.
The coalbed methane production potential method for optimization of wells location selection
Institute of Scientific and Technical Information of China (English)
Feng-Ke DOU; Yong-Shang KANG; Shao-Feng QIN; De-Lei MAO; Jun HAN
2013-01-01
A gas production potential method for optimization of gas wellsite locations selection is proposed in terms of the coalbed gas resources volume and the recoverability.The method uses the actual data about reservoirs in a coalbed gas field in central China to optimize wellsite locations in the studied area in combination with the dynamic data about actual production in the coalbed gas field,selects a favorable subarea for gas wells deployment.The method is established based on the basic properties of coal reservoirs,in combination with the coalbed thickness and the gas content to make an analysis of the gas storage potential of a coal reservoir,as well as resources volume and the permeability of a coal reservoir.This method can be popularized for optimization of wellsite locations in other methane gas development areas or blocks.
Optimal relay selection and power allocation for cognitive two-way relaying networks
Pandarakkottilil, Ubaidulla
2012-06-01
In this paper, we present an optimal scheme for power allocation and relay selection in a cognitive radio network where a pair of cognitive (or secondary) transceiver nodes communicate with each other assisted by a set of cognitive two-way relays. The secondary nodes share the spectrum with a licensed primary user (PU), and each node is assumed to be equipped with a single transmit/receive antenna. The interference to the PU resulting from the transmission from the cognitive nodes is kept below a specified limit. We propose joint relay selection and optimal power allocation among the secondary user (SU) nodes achieving maximum throughput under transmit power and PU interference constraints. A closed-form solution for optimal allocation of transmit power among the SU transceivers and the SU relay is presented. Furthermore, numerical simulations and comparisons are presented to illustrate the performance of the proposed scheme. © 2012 IEEE.
A Path Algorithm for Constrained Estimation.
Zhou, Hua; Lange, Kenneth
2013-01-01
Many least-square problems involve affine equality and inequality constraints. Although there are a variety of methods for solving such problems, most statisticians find constrained estimation challenging. The current article proposes a new path-following algorithm for quadratic programming that replaces hard constraints by what are called exact penalties. Similar penalties arise in l1 regularization in model selection. In the regularization setting, penalties encapsulate prior knowledge, and penalized parameter estimates represent a trade-off between the observed data and the prior knowledge. Classical penalty methods of optimization, such as the quadratic penalty method, solve a sequence of unconstrained problems that put greater and greater stress on meeting the constraints. In the limit as the penalty constant tends to ∞, one recovers the constrained solution. In the exact penalty method, squared penalties!are replaced by absolute value penalties, and the solution is recovered for a finite value of the penalty constant. The exact path-following method starts at the unconstrained solution and follows the solution path as the penalty constant increases. In the process, the solution path hits, slides along, and exits from the various constraints. Path following in Lasso penalized regression, in contrast, starts with a large value of the penalty constant and works its way downward. In both settings, inspection of the entire solution path is revealing. Just as with the Lasso and generalized Lasso, it is possible to plot the effective degrees of freedom along the solution path. For a strictly convex quadratic program, the exact penalty algorithm can be framed entirely in terms of the sweep operator of regression analysis. A few well-chosen examples illustrate the mechanics and potential of path following. This article has supplementary materials available online.
Institute of Scientific and Technical Information of China (English)
Hong; TAN
2014-01-01
The new curriculum,new ideas and new requirements concerning English teaching have made the rural English teaching face unprecedented challenges. There are many problems contributing to the poor effect of rural English teaching,such as outdated teaching equipment,unreasonable curriculum design,insufficient teaching staff,asymmetrical teaching content,family education and students’ personal problems. Based on the Chinese General Social Survey data,it is found that in terms of English reading,English speaking or English writing,the current English level of China’s rural residents is lagging behind. From the average,the reading level of rural residents is better than the speaking and writing level,but the paired T-test results show that there are no significant differences between them,suggesting that under the current system of rural English teaching,the English level of rural residents is constrained to a low level. To improve the rural English teaching in the future,it is necessary to pay close attention to the following aspects: stabilizing the investment in rural education; optimizing the English teaching content; converting the philosophy of education; increasing teacher training; establishing the new linkage system.
Partial order approach to compute shortest paths in multimodal networks
Ensor, Andrew
2011-01-01
Many networked systems involve multiple modes of transport. Such systems are called multimodal, and examples include logistic networks, biomedical phenomena, manufacturing process and telecommunication networks. Existing techniques for determining optimal paths in multimodal networks have either required heuristics or else application-specific constraints to obtain tractable problems, removing the multimodal traits of the network during analysis. In this paper weighted coloured--edge graphs are introduced to model multimodal networks, where colours represent the modes of transportation. Optimal paths are selected using a partial order that compares the weights in each colour, resulting in a Pareto optimal set of shortest paths. This approach is shown to be tractable through experimental analyses for random and real multimodal networks without the need to apply heuristics or constraints.
A MODEL SELECTION PROCEDURE IN MIXTURE-PROCESS EXPERIMENTS FOR INDUSTRIAL PROCESS OPTIMIZATION
Directory of Open Access Journals (Sweden)
Márcio Nascimento de Souza Leão
2015-08-01
Full Text Available We present a model selection procedure for use in Mixture and Mixture-Process Experiments. Certain combinations of restrictions on the proportions of the mixture components can result in a very constrained experimental region. This results in collinearity among the covariates of the model, which can make it difficult to fit the model using the traditional method based on the significance of the coefficients. For this reason, a model selection methodology based on information criteria will be proposed for process optimization. Two examples are presented to illustrate this model selection procedure.
Optimal selection of regularization parameter for l1-based image restoration based on SURE
Xue, Feng; Liu, Xin; Liu, Hongyan; Liu, Jiaqi
2016-10-01
To exploit the sparsity in transform domain (e.g. wavelets), the image deconvolution can be typically formulated as a l1-penalized minimization problem, which, however, generally requires proper selection of regularization parameter for desired reconstruction quality. The key contribution of this paper is to develop a novel data-driven scheme to optimize regularization parameter, such that the resultant restored image achieves minimum prediction error (p-error). First, we develop Stein's unbiased risk estimate (SURE), an unbiased estimate of p-error, for image degradation model. Then, we propose a recursive evaluation of SURE for the basic iterative shrinkage/thresholding (IST), which enables us to find the optimal value of regularization parameter by exhaustive search. The numerical experiments show that the proposed SURE-based optimization leads to nearly optimal deconvolution performance in terms of peak signal-to-noise ratio (PSNR).
Lin, Yi-Kuei; Yeh, Cheng-Ta
2013-05-01
From the perspective of supply chain management, the selected carrier plays an important role in freight delivery. This article proposes a new criterion of multi-commodity reliability and optimises the carrier selection based on such a criterion for logistics networks with routes and nodes, over which multiple commodities are delivered. Carrier selection concerns the selection of exactly one carrier to deliver freight on each route. The capacity of each carrier has several available values associated with a probability distribution, since some of a carrier's capacity may be reserved for various orders. Therefore, the logistics network, given any carrier selection, is a multi-commodity multi-state logistics network. Multi-commodity reliability is defined as a probability that the logistics network can satisfy a customer's demand for various commodities, and is a performance indicator for freight delivery. To solve this problem, this study proposes an optimisation algorithm that integrates genetic algorithm, minimal paths and Recursive Sum of Disjoint Products. A practical example in which multi-sized LCD monitors are delivered from China to Germany is considered to illustrate the solution procedure.
Institute of Scientific and Technical Information of China (English)
Haitao Zhao; Yuning Dong; Hui Zhang; Nanjie Liu; Hongbo Zhu
2013-01-01
This paper proposes an environment-aware best-retransmission count selected optimization control scheme over IEEE 802.11 multi-hop wireless networks. The proposed scheme predicts the wireless resources by using statistical channel state and provides maximum retransmission count optimization based on wireless channel environment state to improve the packet delivery success ratio. The media access control (MAC) layer selects the best-retransmission count by perceiving the types of packet loss in wireless link and using the wireless channel charac-teristics and environment information, and adjusts the packet for-warding adaptively aiming at improving the packet retransmission probability. Simulation results show that the best-retransmission count selected scheme achieves a higher packet successful de-livery percentage and a lower packet col ision probability than the corresponding traditional MAC transmission control protocols.
Dynamic nuclear polarization and optimal control spatial-selective 13C MRI and MRS
DEFF Research Database (Denmark)
Vinding, Mads Sloth; Laustsen, Christoffer; Maximov, Ivan I.
2013-01-01
Aimed at 13C metabolic magnetic resonance imaging (MRI) and spectroscopy (MRS) applications, we demonstrate that dynamic nuclear polarization (DNP) may be combined with optimal control 2D spatial selection to simultaneously obtain high sensitivity and well-defined spatial restriction. This is ach......Aimed at 13C metabolic magnetic resonance imaging (MRI) and spectroscopy (MRS) applications, we demonstrate that dynamic nuclear polarization (DNP) may be combined with optimal control 2D spatial selection to simultaneously obtain high sensitivity and well-defined spatial restriction....... This is achieved through the development of spatial-selective single-shot spiral-readout MRI and MRS experiments combined with dynamic nuclear polarization hyperpolarized [1-13C]pyruvate on a 4.7T pre-clinical MR scanner. The method stands out from related techniques by facilitating anatomic shaped region...
Shirangi, Mehrdad G.; Durlofsky, Louis J.
2016-11-01
The optimization of subsurface flow processes under geological uncertainty technically requires flow simulation to be performed over a large set of geological realizations for each function evaluation at every iteration of the optimizer. Because flow simulation over many permeability realizations (only permeability is considered to be uncertain in this study) may entail excessive computation, simulations are often performed for only a subset of 'representative' realizations. It is however challenging to identify a representative subset that provides flow statistics in close agreement with those from the full set, especially when the decision parameters (e.g., time-varying well pressures, well locations) are unknown a priori, as they are in optimization problems. In this work, we introduce a general framework, based on clustering, for selecting a representative subset of realizations for use in simulations involving 'new' sets of decision parameters. Prior to clustering, each realization is represented by a low-dimensional feature vector that contains a combination of permeability-based and flow-based quantities. Calculation of flow-based features requires the specification of a (base) flow problem and simulation over the full set of realizations. Permeability information is captured concisely through use of principal component analysis. By computing the difference between the flow response for the subset and the full set, we quantify the performance of various realization-selection methods. The impact of different weightings for flow and permeability information in the cluster-based selection procedure is assessed for a range of examples involving different types of decision parameters. These decision parameters are generated either randomly, in a manner that is consistent with the solutions proposed in global stochastic optimization procedures such as GA and PSO, or through perturbation around a base case, consistent with the solutions considered in pattern search
Directory of Open Access Journals (Sweden)
Mireille Bousquet-Mélou
2008-04-01
Full Text Available Let a and b be two positive integers. A culminating path is a path of ℤ 2 that starts from (0,0, consists of steps (1,a and (1,-b, stays above the x-axis and ends at the highest ordinate it ever reaches. These paths were first encountered in bioinformatics, in the analysis of similarity search algorithms. They are also related to certain models of Lorentzian gravity in theoretical physics. We first show that the language on a two letter alphabet that naturally encodes culminating paths is not context-free. Then, we focus on the enumeration of culminating paths. A step by step approach, combined with the kernel method, provides a closed form expression for the generating function of culminating paths ending at a (generic height k. In the case a = b, we derive from this expression the asymptotic behaviour of the number of culminating paths of length n. When a > b, we obtain the asymptotic behaviour by a simpler argument. When a < b, we only determine the exponential growth of the number of culminating paths. Finally, we study the uniform random generation of culminating paths via various methods. The rejection approach, coupled with a symmetry argument, gives an algorithm that is linear when a ≥ b, with no precomputation stage nor non-linear storage required. The choice of the best algorithm is not as clear when a < b. An elementary recursive approach yields a linear algorithm after a precomputation stage involving O (n 3 arithmetic operations, but we also present some alternatives that may be more efficient in practice.
Energy Technology Data Exchange (ETDEWEB)
Kim, Minsun, E-mail: mk688@uw.edu; Stewart, Robert D. [Department of Radiation Oncology, University of Washington, Seattle, Washington 98195-6043 (United States); Phillips, Mark H. [Departments of Radiation Oncology and Neurological Surgery, University of Washington, Seattle, Washington 98195-6043 (United States)
2015-11-15
Purpose: To investigate the impact of using spatiotemporal optimization, i.e., intensity-modulated spatial optimization followed by fractionation schedule optimization, to select the patient-specific fractionation schedule that maximizes the tumor biologically equivalent dose (BED) under dose constraints for multiple organs-at-risk (OARs). Methods: Spatiotemporal optimization was applied to a variety of lung tumors in a phantom geometry using a range of tumor sizes and locations. The optimal fractionation schedule for a patient using the linear-quadratic cell survival model depends on the tumor and OAR sensitivity to fraction size (α/β), the effective tumor doubling time (T{sub d}), and the size and location of tumor target relative to one or more OARs (dose distribution). The authors used a spatiotemporal optimization method to identify the optimal number of fractions N that maximizes the 3D tumor BED distribution for 16 lung phantom cases. The selection of the optimal fractionation schedule used equivalent (30-fraction) OAR constraints for the heart (D{sub mean} ≤ 45 Gy), lungs (D{sub mean} ≤ 20 Gy), cord (D{sub max} ≤ 45 Gy), esophagus (D{sub max} ≤ 63 Gy), and unspecified tissues (D{sub 05} ≤ 60 Gy). To assess plan quality, the authors compared the minimum, mean, maximum, and D{sub 95} of tumor BED, as well as the equivalent uniform dose (EUD) for optimized plans to conventional intensity-modulated radiation therapy plans prescribing 60 Gy in 30 fractions. A sensitivity analysis was performed to assess the effects of T{sub d} (3–100 days), tumor lag-time (T{sub k} = 0–10 days), and the size of tumors on optimal fractionation schedule. Results: Using an α/β ratio of 10 Gy, the average values of tumor max, min, mean BED, and D{sub 95} were up to 19%, 21%, 20%, and 19% larger than those from conventional prescription, depending on T{sub d} and T{sub k} used. Tumor EUD was up to 17% larger than the conventional prescription. For fast proliferating
Micheli, Fabrizio; Holmes, Ian; Arista, Luca; Bonanomi, Giorgio; Braggio, Simone; Cardullo, Francesca; Di Fabio, Romano; Donati, Daniele; Gentile, Gabriella; Hamprecht, Dieter; Terreni, Silvia; Heidbreder, Christian; Savoia, Chiara; Griffante, Cristiana; Worby, Angela
2009-08-01
The lead optimization process to identify new selective dopamine D(3) receptor antagonists is reported. DMPK parameters and binding data suggest that selective D(3) receptor antagonists as potential PET ligands might have been identified.
Fuzzy 2-partition entropy threshold selection based on Big Bang–Big Crunch Optimization algorithm
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Baljit Singh Khehra
2015-03-01
Full Text Available The fuzzy 2-partition entropy approach has been widely used to select threshold value for image segmenting. This approach used two parameterized fuzzy membership functions to form a fuzzy 2-partition of the image. The optimal threshold is selected by searching an optimal combination of parameters of the membership functions such that the entropy of fuzzy 2-partition is maximized. In this paper, a new fuzzy 2-partition entropy thresholding approach based on the technology of the Big Bang–Big Crunch Optimization (BBBCO is proposed. The new proposed thresholding approach is called the BBBCO-based fuzzy 2-partition entropy thresholding algorithm. BBBCO is used to search an optimal combination of parameters of the membership functions for maximizing the entropy of fuzzy 2-partition. BBBCO is inspired by the theory of the evolution of the universe; namely the Big Bang and Big Crunch Theory. The proposed algorithm is tested on a number of standard test images. For comparison, three different algorithms included Genetic Algorithm (GA-based, Biogeography-based Optimization (BBO-based and recursive approaches are also implemented. From experimental results, it is observed that the performance of the proposed algorithm is more effective than GA-based, BBO-based and recursion-based approaches.
High Performance Imaging Through Occlusion via Energy Minimization-Based Optimal Camera Selection
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Tao Yang
2013-11-01
Full Text Available Seeing an object in a cluttered scene with severe occlusion is a significantly challenging task for many computer vision applications. Although camera array synthetic aperture imaging has proven to be an effective way for occluded object imaging, its imaging quality is often significantly decreased by the shadows of the foreground occluder. To overcome this problem, some recent research has been presented to label the foreground occluder via object segmentation or 3D reconstruction. However, these methods usually fail in the case of complicated occluder or severe occlusion. In this paper, we present a novel optimal camera selection algorithm to handle the problem above. Firstly, in contrast to the traditional synthetic aperture photography methods, we formulate the occluded object imaging as a problem of visible light ray selection from the optimal camera view. To the best of our knowledge, this is the first time to "mosaic" a high quality occluded object image via selecting multi-view optimal visible light rays from a camera array or a single moving camera. Secondly, a greedy optimization framework is presented to propagate the visibility information among various depth focus planes. Thirdly, a multiple label energy minimization formulation is designed in each plane to select the optimal camera view. The energy is estimated in the 3D synthetic aperture image volume and integrates the multiple view intensity consistency, previous visibility property and camera view smoothness, which is minimized via graph cuts. Finally, we compare this approach with the traditional synthetic aperture imaging algorithms on UCSD light field datasets and our own datasets captured in indoor and outdoor environment, and extensive experimental results demonstrate the effectiveness and superiority of our approach.
On the Selection of Physical Layer Optimized Network Topologies for the Zigbee Network
Directory of Open Access Journals (Sweden)
Manpreet
2016-01-01
Full Text Available Zigbee standard has been designed for low data rate, low cost and limited power applications for short-range wireless communication. The successful implementation of Zigbee based network depends on the suitable selection of physical (PHY layer and medium access control (MAC layer parameters. In this work the PHY layer parameters have been optimized for star, tree and mesh topologies. The performance investigations have been carried out for different frequency band and data rate and different bandwidth (BW in each of standard topologies. Through extensive simulations, QoS parameters like throughput, network load and delay have been evaluated to achieve optimal performance of physical layer.
OPTIMAL SELECTION FOR THE WEIGHTED COEFFICIENTS OF THE CONSTRAINED VARIATIONAL PROBLEMS
Institute of Scientific and Technical Information of China (English)
魏鸣; 刘国庆; 王成刚; 葛文忠; 许秦
2003-01-01
The aim is to put forward the optimal selecting of weights in variational problem in which the linear advection equation is used as constraint. The selection of the functional weight coefficients (FWC) is one of the key problems for the relevant research. It was arbitrary and subjective to some extent presently. To overcome this difficulty, the reasonable assumptions were given for the observation field and analyz ed field, variational problems with "weak constraints" and "strong constraints" were considered separately. By solving Euler' s equation with the matrix theory and the finite difference method of partial differential equation, the objective weight coefficients were obtained in the minimum variance of the difference between the analyzed field and ideal field. Deduction results show that theoretically the optimal selection indeed exists in the weighting factors of the cost function in the means of the minimal variance between the analysis and ideal field in terms of the matrix theory and partial differential ( corresponding difference ) equation, if the reasonable assumption from the actual problem is valid and the differnece equation is stable.It may realize the coordination among the weight factors, numerical models and the observational data. With its theoretical basis as well as its prospects of applications, this objective selecting method is probably a way towards the finding of the optimal weighting factors in the variational problem.
Application of multilevel reduction algorithm to PCB path optimization%多级规约算法在PCB钻孔路径优化中的应用
Institute of Scientific and Technical Information of China (English)
程森林; 曾伟
2012-01-01
The PCB(Printed Circuit Board) path optimization is a large-scale TSP(Traveling Salesman Problem) problem. An Enhanced Multilevel Reduction algorithm (EMR) is proposed to solve this problem. The proposed algorithm redesigns the reduction operator and brings a control parameter to improve its flexibility. Meanwhile, in view of the difficulties of saving the results produced by reduction operator, a new data structure like human family tree is presented. Experimental results, compared with the other method, demonstrate that EMR outperforms in practicability and generality with higher quality and efficiency of optimization.%以求解PCB(Printed Circuit Board)钻孔路径优化这一大规模复杂的TSP(Traveling Salesman Problem)问题为背景,研究了一种改进的多级规约算法(EMR).该算法依据工程应用中实用性、通用性的特点重新设计了多级规约算法(MR)的规约和细化算子并增加了控制参数以提高算法的灵活性；针对算法中会产生大量部分解集且难以储存这一问题,设计了一种类似人类族谱的数据结构.实验结果以及与循环LK算法和蚁群算法的对比分析表明,EMR算法兼顾了实用性和通用性,且有较高的优化质量和优化效率.
Institute of Scientific and Technical Information of China (English)
赵著行; 闵应骅; 等
1997-01-01
For different delay models,the concept of sensitization can be very different.Traditonal concepts of sensitization cannot precisely describe circuit behavior when the input vectors change very fast.Using Boolean process aporoach,this paper presents a new definition of sensitization for arbitrary input waveforms.By this new concept it is found that if the inputs of a combinational circuit can change at any time,and each gate's delay varies within an interval (bounded gate delay model),then every path,which is not necessarily a single topological path,is sensitizable.From the experimental results it can be seen that,all nonsensitizable paths for traditional concepts actually can propagate transitions along them for some input waveforms.However,specified time between input transitions(STBIT) and minimum permissible pulse width(ε）are two major factors to make some paths non-sensitizable.
基于DXF文件的数控自动编程中的路径优化%Path Optimization in CNC Automatic Programming Based on DXF Files
Institute of Scientific and Technical Information of China (English)
梁艳青; 吴文江; 王品
2015-01-01
分析了 DXF 文件结构并在此基础上简要介绍了需要提取的信息。针对无序的加工路径使得空刀路径长度过大的情况，提出了一种基于遗传算法的路径优化算法。并比较了路径优化前后路径中空刀路径长度，可以得出经算法优化后得到的路径空刀路径明显减少，从而使得加工效率得到提高。%The DXF file structure is analyzed and on the basis of the analysis introduces the graphic meta information briefly which ought to be extracted from DXF files. In order to reduce the path length, there is a router optimization algorithm proposed based on genetic algorithms. By comparing the length of the routers, we can see the idle stroke is reduced efficiently and the machining efficiency is improved.
Directory of Open Access Journals (Sweden)
Roxanne M. Mitchell
2016-02-01
Full Text Available This study tested the effects of the principal’s professional orientation towards leadership/enabling school structure (ESS on two mediating variables, school academic optimism (SAO and professional teacher behavior (PTB, on the outcome variable school reading achievement (RA. Data were drawn from a sample of 54 schools (including 45 elementary schools and nine middle schools; the school was the unit of analysis. Data analysis supported a path to RA in which a structural variable, ESS was the immediate antecedent of SAO and PTB. Two control variables, school level and SES, were included in the model. SES had a significant effect on SAO but not on PTB. School level had a negative effect on both PTB and SAO suggesting that both variables were higher in elementary school and declined in middle school. SES paired with SAO in predicting RA. As expected, SAO had a greater effect on RA than SES. The significance of the findings lies in the confirmation of SAO as an important influence on RA and in demonstrating the importance of ESS in establishing a context in which AO and PTB can flourish.
In Vitro Selection of Optimal DNA Substrates for Ligation by a Water-Soluble Carbodiimide
Harada, Kazuo; Orgel, Leslie E.
1994-01-01
We have used in vitro selection to investigate the sequence requirements for efficient template-directed ligation of oligonucleotides at 0 deg C using a water-soluble carbodiimide as condensing agent. We find that only 2 bp at each side of the ligation junction are needed. We also studied chemical ligation of substrate ensembles that we have previously selected as optimal by RNA ligase or by DNA ligase. As anticipated, we find that substrates selected with DNA ligase ligate efficiently with a chemical ligating agent, and vice versa. Substrates selected using RNA ligase are not ligated by the chemical condensing agent and vice versa. The implications of these results for prebiotic chemistry are discussed.
Optimal Machine Tools Selection Using Interval-Valued Data FCM Clustering Algorithm
Directory of Open Access Journals (Sweden)
Yupeng Xin
2014-01-01
Full Text Available Machine tool selection directly affects production rates, accuracy, and flexibility. In order to quickly and accurately select the appropriate machine tools in machining process planning, this paper proposes an optimal machine tools selection method based on interval-valued data fuzzy C-means (FCM clustering algorithm. We define the machining capability meta (MAE as the smallest unit to describe machining capacity of machine tools and establish MAE library based on the MAE information model. According to the manufacturing process requirements, the MAEs can be queried from MAE library. Subsequently, interval-valued data FCM algorithm is used to select the appropriate machine tools for manufacturing process. Through computing matching degree between manufacturing process machining constraints and MAEs, we get the most appropriate MAEs and the corresponding machine tools. Finally, a case study of an exhaust duct part of the aeroengine is presented to demonstrate the applicability of the proposed method.
Optimal selection of individuals for repeated covariate measurements in follow-up studies.
Reinikainen, Jaakko; Karvanen, Juha; Tolonen, Hanna
2016-12-01
Repeated covariate measurements bring important information on the time-varying risk factors in long epidemiological follow-up studies. However, due to budget limitations, it may be possible to carry out the repeated measurements only for a subset of the cohort. We study cost-efficient alternatives for the simple random sampling in the selection of the individuals to be remeasured. The proposed selection criteria are based on forms of the D-optimality. The selection methods are compared with the simulation studies and illustrated with the data from the East-West study carried out in Finland from 1959 to 1999. The results indicate that cost savings can be achieved if the selection is focused on the individuals with high expected risk of the event and, on the other hand, on those with extreme covariate values in the previous measurements. © The Author(s) 2014.
A Scheme to Optimize Flow Routing and Polling Switch Selection of Software Defined Networks.
Directory of Open Access Journals (Sweden)
Huan Chen
Full Text Available This paper aims at minimizing the communication cost for collecting flow information in Software Defined Networks (SDN. Since flow-based information collecting method requires too much communication cost, and switch-based method proposed recently cannot benefit from controlling flow routing, jointly optimize flow routing and polling switch selection is proposed to reduce the communication cost. To this end, joint optimization problem is formulated as an Integer Linear Programming (ILP model firstly. Since the ILP model is intractable in large size network, we also design an optimal algorithm for the multi-rooted tree topology and an efficient heuristic algorithm for general topology. According to extensive simulations, it is found that our method can save up to 55.76% communication cost compared with the state-of-the-art switch-based scheme.